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Repository: GREAT-WHU/DBA-Fusion
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Directory structure:
gitextract_ny3kcvri/
├── .gitignore
├── .gitmodules
├── LICENSE
├── README.md
├── batch_kitti360.py
├── batch_subt.py
├── batch_tumvi.py
├── batch_whu.py
├── calib/
│ ├── 0412.txt
│ ├── 0412new.txt
│ ├── 1012.txt
│ ├── barn.txt
│ ├── carla.txt
│ ├── eth.txt
│ ├── euroc.txt
│ ├── handheld.txt
│ ├── kitti_360.txt
│ ├── subt.txt
│ ├── tartan.txt
│ ├── tum3.txt
│ └── tumvi.txt
├── dataset/
│ ├── euroc_to_hdf5.py
│ ├── kitti360_to_hdf5.py
│ └── tumvi_to_hdf5.py
├── dbaf/
│ ├── covisible_graph.py
│ ├── data_readers/
│ │ ├── __init__.py
│ │ ├── augmentation.py
│ │ ├── base.py
│ │ ├── factory.py
│ │ ├── rgbd_utils.py
│ │ ├── stream.py
│ │ ├── tartan.py
│ │ └── tartan_test.txt
│ ├── dbaf.py
│ ├── dbaf_frontend.py
│ ├── depth_video.py
│ ├── droid_net.py
│ ├── geoFunc/
│ │ ├── __init__.py
│ │ ├── const_value.py
│ │ └── trans.py
│ ├── geom/
│ │ ├── __init__.py
│ │ ├── ba.py
│ │ ├── chol.py
│ │ ├── graph_utils.py
│ │ ├── losses.py
│ │ └── projective_ops.py
│ ├── modules/
│ │ ├── __init__.py
│ │ ├── clipping.py
│ │ ├── corr.py
│ │ ├── extractor.py
│ │ └── gru.py
│ ├── motion_filter.py
│ └── multi_sensor.py
├── demo_vio_kitti360.py
├── demo_vio_subt.py
├── demo_vio_tumvi.py
├── demo_vio_whu.py
├── evaluation_scripts/
│ ├── batch_tumvi.py
│ ├── evaluate_kitti.py
│ └── evaluate_tumvi.py
├── results/
│ └── PLACEHOLDER
├── setup.py
├── src/
│ ├── altcorr_kernel.cu
│ ├── bacore.h
│ ├── correlation_kernels.cu
│ ├── droid.cpp
│ └── droid_kernels.cu
└── visualization/
├── check_reconstruction_kitti.py
├── check_reconstruction_kitti_animation.py
├── check_reconstruction_tumvi.py
└── check_reconstruction_tumvi_animation.py
================================================
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================================================
FILE: .gitmodules
================================================
[submodule "thirdparty/lietorch"]
path = thirdparty/lietorch
url = https://github.com/princeton-vl/lietorch
[submodule "thirdparty/eigen"]
path = thirdparty/eigen
url = https://gitlab.com/libeigen/eigen.git
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# DBA-Fusion
>Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping
<div align=center>
<img alt="" src="./assets/abstract.png" width='500px' />
</div>
[[Paper](https://arxiv.org/abs/2403.13714)] [[Video](https://www.bilibili.com/video/BV1yeNEecEwR/?share_source=copy_web&vd_source=a659a573a520a1151e294d0c8b9c842a)]
## What is this?
**DBA-Fusion** is basically a VIO system which fuses DROID-SLAM-like dense bundle adjustment (DBA) with classic factor graph optimization. This work enables **realtime metric-scale localization and dense mapping** with excellent accuracy and robustness. Besides, this framework supports the **flexible fusion of multiple sensors** like GNSS or wheel speed sensors, extending its applicability to large-scale scenarios.
<br />
<div align=center>
<img alt="" src="./assets/Hv.svg" width='400px' />
</div>
<br />
<div align=center>
<img alt="" src="./assets/0005.gif" width='500px' />
</div>
<div align=center>
<img alt="" src="./assets/outdoors6.gif" width='500px' />
</div>
## Update log
- [x] Code Upload (2024/02/28)
- [x] Monocular VIO Examples (2024/02/28)
- [x] Multi-sensor data sequence (WUH1012) used in the manuscript is available [here](https://drive.google.com/file/d/1w7UsAwreou_9YRYHz13QIGu6jOJGpdg5/view?usp=sharing).
- [x] Multi-Sensor Fusion Examples
- [ ] Stereo/RGB-D VIO Support
## Installation
The pipeline of the work is based on python, and the computation part is mainly based on Pytorch (with CUDA) and GTSAM.
Use the following commands to set up the python environment.
```Bash
conda create -n dbaf python=3.10.11
conda activate dbaf
# Other CUDA versions should also be fine.
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install torch-scatter==2.0.9 -f https://data.pyg.org/whl/torch-1.11.0+cu113.html
pip install gdown tqdm numpy==1.25.0 numpy-quaternion==2022.4.3 opencv-python==4.7.0.72 scipy pyparsing matplotlib h5py
pip install evo --upgrade --no-binary evo
pip install open3d # optional for visualization
```
As for GTSAM, we make some modifications to it to extend the python wrapper APIs, clone it from the following repository and install it under your python environment.
```Bash
git clone https://github.com/yuxuanzhou97/gtsam
cd gtsam
mkdir build
cd build
cmake .. -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=3.10.11
make python-install
```
Finally, run the following command to build DBA-Fusion.
```Bash
git clone --recurse-submodules https://github.com/GREAT-WHU/DBA-Fusion.git
cd DBA-Fusion
python setup.py install
```
## Run DBA-Fusion
We don't modify the model of DROID-SLAM so you can directly employ the weight trained for DROID-SLAM. Here we use the [model](https://drive.google.com/file/d/1PpqVt1H4maBa_GbPJp4NwxRsd9jk-elh/view?usp=sharing) pre-trained on TartanAir (provided by [DROID-SLAM](https://github.com/princeton-vl/DROID-SLAM?tab=readme-ov-file)), which shows great zero-shot performance on real-world datasets.
**(Attention!!!)**
For the default configurations, around ~10GB GPU memory is needed. Lower the "max_factors" argument to 36 or lower could help reduce the memory usage to ~6GB.
### 1. TUM-VI
1.1 Download the [TUM-VI](https://cvg.cit.tum.de/data/datasets/visual-inertial-dataset) datasets (512*512).
**(Optional)**
For better speed performance, it is recommended to convert the PNG images to a single HDF5 file through
```Bash
python dataset/tumvi_to_hdf5.py --imagedir=${DATASET_DIR}/dataset-${SEQ}_512_16/mav0/cam0/data --imagestamp=${DATASET_DIR}/dataset-${SEQ}_512_16/mav0/cam0/data.csv --h5path=${SEQ}.h5 --calib=calib/tumvi.txt --stride 4
```
1.2 Specify the data path in [batch_tumvi.py](../batch_tumvi.py) (if you use HDF5 file, activate the "--enable_h5" and "--h5_path" arguments), run the following command
```Bash
python batch_tumvi.py # This would trigger demo_vio_tumvi.py automatically.
```
Look into [demo_vio_tumvi.py](../demo_vio_tumvi.py) to learn about the arguments. Data loading and almost all the parameters are specified in this **one** file.
1.3 The outputs of the program includes **a text file** which contains real-time navigation results and **a .pkl file** which contains all keyframe poses and point clouds.
To evaluate the realtime pose estimation performance, run the following command (notice to change the file paths in *evaluate_kitti.py*)
```Bash
python evaluation_scripts/evaluate_tumvi.py --seq ${SEQ}
```
or
```Bash
python evaluation_scripts/batch_evaluate_tumvi.py
```
For 3D visualization, currently we haven't handled the realtime visualization functionality. Run the scripts in the **"visualization"** folder for post-time visualization.
```Bash
python visualization/check_reconstruction_tumvi.py
```
### 2. KITTI-360
2.1 Download the [KITTI-360](https://www.cvlibs.net/datasets/kitti-360/index.php) datasets. Notice that we use the **unrectified perspective images** for the evaluation (named like "2013_05_28_drive_XXXX_sync/image_00/data_rgb").
For **IMU** data and IMU-centered **ground-truth poses**, we transform the axises to **Right-Forward-Up (RFU)** and check the synchronization. Besides, we use [OpenVINS](https://github.com/rpng/open_vins/) (in stereo VIO mode) to online refine the Camera-IMU extrinsics and time offsets (whose pre-calibrated values seem not very accurate) on the sequences. The refined parameters are used for for all the tests.
**To reproduce the results**, just download the our prepared IMU and ground-truth data from [here](https://drive.google.com/file/d/1BO8zGvoey7IdwbWXmAdlhGPr6hiCFJ6Y/view?usp=drive_link), then uncompress it to the data path.
**(Optional)**
Similar to the TUM-VI part, you can use the following script to generate a HDF5 file for best speed performance.
```Bash
python dataset/kitti360_to_hdf5.py --imagedir=${DATASET_DIR}/2013_05_28_drive_%s_sync/image_00/data_rgb --imagestamp=${DATASET_DIR}/2013_05_28_drive_%s_sync/camstamp.txt --h5path=${SEQ}.h5 --calib=calib/kitti360.txt --stride 2
```
2.2 Run the following command
```Bash
python batch_kitti360.py
```
Dataloading and parameters are specified in [demo_vio_kitti360.py](../demo_vio_kitti360.py).
2.3 For evaluation and visualization, run
```Bash
python evaluation_scripts/evaluate_kitti360.py --seq ${SEQ}
python visualization/check_reconstruction_kitti360.py
```
### 3. WUH1012
Download our self-collected data sequence from [here](https://drive.google.com/file/d/1w7UsAwreou_9YRYHz13QIGu6jOJGpdg5/view?usp=sharing).
See [batch_whu.py](../batch_whu.py) for multi-sensor fusion in different modes (VIO + wheel speed/GNSS), as described in the manuscript.
### 4. Run on Your Own Dataset
To run monocular VIO on your own dataset,
* Duplicate a script from [demo_vio_kitti360.py](../demo_vio_kitti360.py) or [demo_vio_tumvi.py](../demo_vio_tumvi.py).
* In the script, specify the data loading procedure of IMU data and images.
* Specify the camera intrinsics and camera-IMU extrinsics in the script.
* Try it!
## Some Results
- Visual point cloud map compared to accumulated LiDAR point clouds.
<div align=center>
<img alt="" src="./assets/lidar.jpg" width='500px' />
</div>
- Further processing on the visual point clouds. (P.S. For 3-D GS, the point positions and number are fixed. The training time is around 3 minutes on RTX4080 laptop. )
<div align=center>
<img alt="" src="./assets/postprocessing.png" width='750px' />
</div>
## Acknowledgement
DBA-Fusion is developed by [GREAT](http://igmas.users.sgg.whu.edu.cn/group) (GNSS+ REsearch, Application and Teaching) Group, School of Geodesy and Geomatics, Wuhan University.
<br/>
<div align=center>
<img alt="" src="./assets/GREAT.png" width='300px' />
</div>
<br/>
<div align=center>
<img alt="" src="./assets/whu.png" width='300px' />
</div>
<br/>
This work is based on [DROID-SLAM](https://github.com/princeton-vl/DROID-SLAM) and [GTSAM](https://github.com/borglab/gtsam). We use evaluation tools from [evo](https://github.com/MichaelGrupp/evo) and 3D visualization tools from [Open3d](https://github.com/MichaelGrupp/evo).
================================================
FILE: batch_kitti360.py
================================================
import os
import subprocess
for i in ['0000','0002','0003','0004','0005','0006','0009','0010']:
p = subprocess.Popen("python demo_vio_kitti360.py" +\
" --imagedir=/home/zhouyuxuan/data/2013_05_28_drive_%s_sync/image_00/data_rgb"%i +\
" --imagestamp=/home/zhouyuxuan/data/2013_05_28_drive_%s_sync/camstamp.txt"%i +\
" --imupath=/home/zhouyuxuan/data/2013_05_28_drive_%s_sync/imu.txt"%i +\
" --gtpath=/home/zhouyuxuan/data/2013_05_28_drive_%s_sync/gt_local.txt"%i +\
# " --enable_h5" +\
# " --h5path=/home/zhouyuxuan/DROID-SLAM/%s.h5"%i +\
" --resultpath=results/result_%s.txt"%i +\
" --calib=calib/kitti_360.txt" +\
" --stride=2" +\
" --max_factors=48" +\
" --active_window=12" +\
" --frontend_window=5" +\
" --frontend_radius=2" +\
" --frontend_nms=1" +\
" --inac_range=3" +\
" --visual_only=0" +\
" --far_threshold=-1" +\
" --translation_threshold=0.5" +\
" --mask_threshold=1.0" +\
" --skip_edge=[-4,-5,-6]" +\
" --save_pkl" +\
" --pklpath=results/%s.pkl"%i +\
" --show_plot",shell=True)
p.wait()
================================================
FILE: batch_subt.py
================================================
import os
import subprocess
for i in [\
# 'Handheld1_Folder',\
'Handheld2_Folder',\
]:
p = subprocess.Popen("python demo_vio_subt.py" +\
" --imagedir=/mnt/e/subt/%s/cam_0"%i +\
" --imagestamp=/mnt/e/subt/%s/cam_0/timestamps.txt"%i +\
" --imupath=/mnt/e/subt/%s/imu/imu_data.csv"%i +\
" --resultpath=results/result_%s.txt"%i +\
" --calib=calib/subt.txt" +\
" --stride=8" +\
" --max_factors=48" +\
" --active_window=12" +\
" --frontend_window=5" +\
" --frontend_radius=2" +\
" --frontend_nms=1" +\
" --far_threshold=0.02" +\
" --inac_range=3" +\
" --visual_only=0" +\
" --translation_threshold=0.2" +\
" --mask_threshold=-1.0" +\
" --skip_edge=[-4,-5,-6]" +\
" --save_pkl" +\
" --pklpath=results/%s.pkl"%i +\
" --show_plot",shell=True)
p.wait()
================================================
FILE: batch_tumvi.py
================================================
import os
import subprocess
for i in [\
'outdoors1',\
'outdoors2',\
'outdoors3',\
'outdoors4',\
'outdoors5',\
'outdoors6',\
'outdoors7',\
'outdoors8',\
'magistrale1',\
'magistrale2',\
'magistrale3',\
'magistrale4',\
'magistrale5',\
'magistrale6'
]:
p = subprocess.Popen("python demo_vio_tumvi.py" +\
" --imagedir=/mnt/z/tum-vi/dataset-%s_512_16/mav0/cam0/data"%i +\
" --imagestamp=/mnt/z/tum-vi/dataset-%s_512_16/mav0/cam0/data.csv"%i +\
" --imupath=/mnt/z/tum-vi/dataset-%s_512_16/mav0/imu0/data.csv"%i +\
" --gtpath=/mnt/z/tum-vi/dataset-%s_512_16/dso/gt_imu.csv"%i +\
# " --enable_h5" +\
# " --h5path=/home/zhouyuxuan/DROID-SLAM/%s.h5"%i +\
" --resultpath=results/result_%s.txt"%i +\
" --calib=calib/tumvi.txt" +\
" --stride=4" +\
" --max_factors=48" +\
" --active_window=12" +\
" --frontend_window=5" +\
" --frontend_radius=2" +\
" --frontend_nms=1" +\
" --far_threshold=0.02" +\
" --inac_range=3" +\
" --visual_only=0" +\
" --translation_threshold=0.2" +\
" --mask_threshold=-1.0" +\
" --skip_edge=[-4,-5,-6]" +\
" --save_pkl" +\
" --pklpath=results/%s.pkl"%i +\
" --show_plot",shell=True)
p.wait()
================================================
FILE: batch_whu.py
================================================
import os
import subprocess
# VIO
p = subprocess.Popen("python demo_vio_whu.py" +\
" --imagedir=/home/zhouyuxuan/data/WUH1012/cam0" +\
" --imagestamp=/home/zhouyuxuan/data/WUH1012/camstamp.txt" +\
" --imupath=/home/zhouyuxuan/data/WUH1012/imu.txt" +\
" --gtpath=/home/zhouyuxuan/data/WUH1012/IE.txt" +\
" --resultpath=results/result_whu_vio.txt" +\
" --calib=calib/1012.txt" +\
" --stride=2" +\
" --max_factors=48" +\
" --active_window=12" +\
" --frontend_window=5" +\
" --frontend_radius=2" +\
" --frontend_nms=1" +\
" --inac_range=3" +\
" --visual_only=0" +\
" --far_threshold=-1" +\
" --translation_threshold=0.25" +\
" --mask_threshold=0.0" +\
" --skip_edge=[]" +\
" --save_pkl" +\
" --use_zupt" +\
" --pklpath=results/whu.pkl" +\
" --show_plot",
shell=True)
p.wait()
# VIO + wheel
p = subprocess.Popen("python demo_vio_whu.py" +\
" --imagedir=/home/zhouyuxuan/data/WUH1012/cam0" +\
" --imagestamp=/home/zhouyuxuan/data/WUH1012/camstamp.txt" +\
" --imupath=/home/zhouyuxuan/data/WUH1012/imu.txt" +\
" --gtpath=/home/zhouyuxuan/data/WUH1012/IE.txt" +\
" --resultpath=results/result_whu_viow.txt" +\
" --calib=calib/1012.txt" +\
" --stride=2" +\
" --max_factors=48" +\
" --active_window=12" +\
" --frontend_window=5" +\
" --frontend_radius=2" +\
" --frontend_nms=1" +\
" --inac_range=3" +\
" --visual_only=0" +\
" --far_threshold=-1" +\
" --translation_threshold=0.25" +\
" --mask_threshold=0.0" +\
" --skip_edge=[]" +\
" --save_pkl" +\
" --use_odo" +\
" --odopath=/home/zhouyuxuan/data/WUH1012/odo_synthesis.txt" +\
" --pklpath=results/whu.pkl" +\
" --show_plot",
shell=True)
p.wait()
# VIO + GNSS
p = subprocess.Popen("python demo_vio_whu.py" +\
" --imagedir=/home/zhouyuxuan/data/WUH1012/cam0" +\
" --imagestamp=/home/zhouyuxuan/data/WUH1012/camstamp.txt" +\
" --imupath=/home/zhouyuxuan/data/WUH1012/imu.txt" +\
" --gtpath=/home/zhouyuxuan/data/WUH1012/IE.txt" +\
" --resultpath=results/result_whu_viog.txt" +\
" --calib=calib/1012.txt" +\
" --stride=2" +\
" --max_factors=48" +\
" --active_window=12" +\
" --frontend_window=5" +\
" --frontend_radius=2" +\
" --frontend_nms=1" +\
" --inac_range=3" +\
" --visual_only=0" +\
" --far_threshold=-1" +\
" --translation_threshold=0.25" +\
" --mask_threshold=0.0" +\
" --skip_edge=[]" +\
" --save_pkl" +\
" --use_gnss" +\
" --gnsspath=/home/zhouyuxuan/data/data_20221012103154/SEPT-PVT.flt" +\
" --pklpath=results/whu.pkl" +\
" --show_plot",
shell=True)
p.wait()
================================================
FILE: calib/0412.txt
================================================
889.32868436 889.32868436 515.73648834 202.43873596
================================================
FILE: calib/0412new.txt
================================================
885.839465 882.512623 505.509972 389.860117 -0.125551 0.065179 -0.000074 -0.000698
================================================
FILE: calib/1012.txt
================================================
890.21388839 889.56330572 512.88196119 381.38486858 -0.13095809 0.06640391 -0.00094794 0.0003389
================================================
FILE: calib/barn.txt
================================================
1161.545689 1161.545689 960.000000 540.000000 -0.025158 0.0 0.0 0.0
================================================
FILE: calib/carla.txt
================================================
886.8100 886.8100 512 256
================================================
FILE: calib/eth.txt
================================================
726.21081542969 726.21081542969 359.2048034668 202.47247314453
================================================
FILE: calib/euroc.txt
================================================
458.654 457.296 367.215 248.375 -0.28340811 0.07395907 0.00019359 1.76187114e-05
================================================
FILE: calib/handheld.txt
================================================
531.0895358407821 530.9183032386885 511.3708876141611 399.1276554093305 -0.3367182787437319 0.10679061024072911 0.0003055063102509499 0.0009756613499403765
================================================
FILE: calib/kitti_360.txt
================================================
788.629315 786.382230 687.158398 317.752196 -0.344441 0.141678 0.000414 -0.000222 -0.029608
================================================
FILE: calib/subt.txt
================================================
758.3153257832925 676.6492212772476 318.27111164892506 239.79816832491474 1.583106303248484 -0.059098218173967695 0.1793477408661115 0.0016819528105368057 -0.0005887999624264534
================================================
FILE: calib/tartan.txt
================================================
320.0 320.0 320.0 240.0
================================================
FILE: calib/tum3.txt
================================================
535.4 539.2 320.1 247.6
================================================
FILE: calib/tumvi.txt
================================================
190.97847715128717 190.9733070521226 254.93170605935475 256.8974428996504 0.0034823894022493434 0.0007150348452162257 -0.0020532361418706202 0.00020293673591811182
================================================
FILE: dataset/euroc_to_hdf5.py
================================================
from tqdm import tqdm
import numpy as np
import torch
import cv2
import os
import argparse
import h5py
import pickle
def show_image(image):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey(1)
def image_stream(imagedir, imagestamp, h5path, calib, stride):
""" image generator """
calib = np.loadtxt(calib, delimiter=" ")
fx, fy, cx, cy = calib[:4]
K = np.eye(3)
K[0,0] = fx
K[0,2] = cx
K[1,1] = fy
K[1,2] = cy
Kn = np.eye(3)
Kn[0,0] = fx
Kn[0,2] = cx
Kn[1,1] = fy
Kn[1,2] = cy
image_list = sorted(os.listdir(imagedir))[::stride]
image_stamps = np.loadtxt(imagestamp,str,delimiter=',')
image_dict = dict(zip(image_stamps[:,1],image_stamps[:,0]))
h5_f = h5py.File(h5path,'w')
for t, imfile in enumerate(image_list):
image = cv2.imread(os.path.join(imagedir, imfile))
if len(calib) > 4:
m1, m2 = cv2.initUndistortRectifyMap(K,calib[4:],np.eye(3),Kn,(image.shape[1],image.shape[0]),cv2.CV_32FC1)
image = cv2.remap(image, m1, m2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
tt = float(image_dict[imfile]) /1e9
h0, w0, _ = image.shape
h1 = int(h0 * np.sqrt((384 * 512) / (h0 * w0)))
w1 = int(w0 * np.sqrt((384 * 512) / (h0 * w0)))
image = cv2.resize(image, (w1, h1))
image = image[:h1-h1%8, :w1-w1%8]
image = torch.as_tensor(image).permute(2, 0, 1)
intrinsics = torch.as_tensor([fx, fy, cx, cy ])
intrinsics[0::2] *= (w1 / w0)
intrinsics[1::2] *= (h1 / h0)
h5_f.create_dataset('%.10f'%tt,data = np.fromstring(pickle.dumps((tt, image[None], intrinsics)),dtype='uint8'))
yield tt, image[None], intrinsics
h5_f.close()
if __name__ == '__main__':
print(torch.cuda.device_count())
print(torch.cuda.is_available())
print(torch.cuda.current_device())
parser = argparse.ArgumentParser()
parser.add_argument("--imagedir", type=str, help="path to image directory")
parser.add_argument("--imagestamp", type=str, help="")
parser.add_argument("--h5path", type=str, help="")
parser.add_argument("--calib", type=str, help="path to calibration file")
parser.add_argument("--stride", default=4, type=int, help="frame stride")
parser.add_argument("--show_plot", action="store_true", help="")
args = parser.parse_args()
for (t, image, intrinsics) in tqdm(image_stream(args.imagedir, args.imagestamp,\
args.h5path, args.calib, args.stride)):
if args.show_plot:
show_image(image[0])
================================================
FILE: dataset/kitti360_to_hdf5.py
================================================
from tqdm import tqdm
import numpy as np
import torch
import cv2
import os
import argparse
import h5py
import pickle
def show_image(image):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey(1)
def image_stream(imagedir, imagestamp, h5path, calib, stride):
""" image generator """
calib = np.loadtxt(calib, delimiter=" ")
fx, fy, cx, cy = calib[:4]
K = np.eye(3)
K[0,0] = fx
K[0,2] = cx
K[1,1] = fy
K[1,2] = cy
Kn = np.eye(3)
Kn[0,0] = fx
Kn[0,2] = cx
Kn[1,1] = fy
Kn[1,2] = cy
image_list = sorted(os.listdir(imagedir))[::stride]
image_stamps = np.loadtxt(imagestamp,str)
image_dict = dict(zip(image_stamps[:,1],image_stamps[:,0]))
h5_f = h5py.File(h5path,'w')
for t, imfile in enumerate(image_list):
image = cv2.imread(os.path.join(imagedir, imfile))
if len(calib) > 4:
image = cv2.undistort(image, K, calib[4:])
tt = float(image_dict[imfile])
h0, w0, _ = image.shape
h1 = int(h0 * np.sqrt((384 * 512) / (h0 * w0)))
w1 = int(w0 * np.sqrt((384 * 512) / (h0 * w0)))
image = cv2.resize(image, (w1, h1))
image = image[:h1-h1%8, :w1-w1%8]
image = torch.as_tensor(image).permute(2, 0, 1)
intrinsics = torch.as_tensor([fx, fy, cx, cy])
intrinsics[0::2] *= (w1 / w0)
intrinsics[1::2] *= (h1 / h0)
h5_f.create_dataset('%.10f'%tt,data = np.fromstring(pickle.dumps((tt, image[None], intrinsics)),dtype='uint8'))
yield tt, image[None], intrinsics
h5_f.close()
if __name__ == '__main__':
print(torch.cuda.device_count())
print(torch.cuda.is_available())
print(torch.cuda.current_device())
parser = argparse.ArgumentParser()
parser.add_argument("--imagedir", type=str, help="path to image directory")
parser.add_argument("--imagestamp", type=str, help="")
parser.add_argument("--h5path", type=str, help="")
parser.add_argument("--calib", type=str, help="path to calibration file")
parser.add_argument("--stride", default=2, type=int, help="frame stride")
parser.add_argument("--show_plot", action="store_true", help="")
args = parser.parse_args()
for (t, image, intrinsics) in tqdm(image_stream(args.imagedir, args.imagestamp,\
args.h5path, args.calib, args.stride)):
if args.show_plot:
show_image(image[0])
================================================
FILE: dataset/tumvi_to_hdf5.py
================================================
from tqdm import tqdm
import numpy as np
import torch
import cv2
import os
import argparse
import h5py
import pickle
def show_image(image):
image = image.permute(1, 2, 0).cpu().numpy()
cv2.imshow('image', image / 255.0)
cv2.waitKey(1)
def image_stream(imagedir, imagestamp, h5path, calib, stride):
""" image generator """
calib = np.loadtxt(calib, delimiter=" ")
fx, fy, cx, cy = calib[:4]
K = np.eye(3)
K[0,0] = fx
K[0,2] = cx
K[1,1] = fy
K[1,2] = cy
Kn = np.eye(3)
Kn[0,0] = fx
Kn[0,2] = cx
Kn[1,1] = fy
Kn[1,2] = cy
image_list = sorted(os.listdir(imagedir))[::stride]
image_stamps = np.loadtxt(imagestamp,str,delimiter=',')
image_dict = dict(zip(image_stamps[:,1],image_stamps[:,0]))
h5_f = h5py.File(h5path,'w')
for t, imfile in enumerate(image_list):
image = cv2.imread(os.path.join(imagedir, imfile))
if len(calib) > 4:
m1, m2 = cv2.fisheye.initUndistortRectifyMap(K,calib[4:],np.eye(3),Kn,(512,512),cv2.CV_32FC1)
image = cv2.remap(image, m1, m2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
tt = float(image_dict[imfile]) /1e9
h0, w0, _ = image.shape
h1 = int(h0 * np.sqrt((384 * 512) / (h0 * w0)))
w1 = int(w0 * np.sqrt((384 * 512) / (h0 * w0)))
image = cv2.resize(image, (w1, h1))
image = image[:h1-h1%8, :w1-w1%8]
image = torch.as_tensor(image).permute(2, 0, 1)
intrinsics = torch.as_tensor([fx, fy, cx, cy ])
intrinsics[0::2] *= (w1 / w0)
intrinsics[1::2] *= (h1 / h0)
h5_f.create_dataset('%.10f'%tt,data = np.fromstring(pickle.dumps((tt, image[None], intrinsics)),dtype='uint8'))
yield tt, image[None], intrinsics
h5_f.close()
if __name__ == '__main__':
print(torch.cuda.device_count())
print(torch.cuda.is_available())
print(torch.cuda.current_device())
parser = argparse.ArgumentParser()
parser.add_argument("--imagedir", type=str, help="path to image directory")
parser.add_argument("--imagestamp", type=str, help="")
parser.add_argument("--h5path", type=str, help="")
parser.add_argument("--calib", type=str, help="path to calibration file")
parser.add_argument("--stride", default=4, type=int, help="frame stride")
parser.add_argument("--show_plot", action="store_true", help="")
args = parser.parse_args()
for (t, image, intrinsics) in tqdm(image_stream(args.imagedir, args.imagestamp,\
args.h5path, args.calib, args.stride)):
if args.show_plot:
show_image(image[0])
================================================
FILE: dbaf/covisible_graph.py
================================================
import torch
import lietorch
import numpy as np
import matplotlib.pyplot as plt
from lietorch import SE3
from modules.corr import CorrBlock, AltCorrBlock
import geom.projective_ops as pops
import matplotlib.pyplot as plt
import cv2
from depth_video import DepthVideo
import matplotlib.cm as cm
import matplotlib
class CovisibleGraph:
def __init__(self, video: DepthVideo, update_op, device="cuda:0", corr_impl="volume", args = None):
self.video = video
self.update_op = update_op
self.device = device
self.max_factors = args.max_factors
self.corr_impl = corr_impl
self.upsample = args.upsample
# operator at 1/8 resolution
self.ht = ht = video.ht // 8
self.wd = wd = video.wd // 8
self.coords0 = pops.coords_grid(ht, wd, device=device)
self.ii = torch.as_tensor([], dtype=torch.long, device=device)
self.jj = torch.as_tensor([], dtype=torch.long, device=device)
self.age = torch.as_tensor([], dtype=torch.long, device=device)
self.corr, self.net, self.inp = None, None, None
self.damping = 1e-6 * torch.ones_like(self.video.disps)
self.target = torch.zeros([1, 0, ht, wd, 2], device=device, dtype=torch.float)
self.weight = torch.zeros([1, 0, ht, wd, 2], device=device, dtype=torch.float)
# inactive factors
self.ii_inac = torch.as_tensor([], dtype=torch.long, device=device)
self.jj_inac = torch.as_tensor([], dtype=torch.long, device=device)
self.ii_bad = torch.as_tensor([], dtype=torch.long, device=device)
self.jj_bad = torch.as_tensor([], dtype=torch.long, device=device)
self.target_inac = torch.zeros([1, 0, ht, wd, 2], device=device, dtype=torch.float)
self.weight_inac = torch.zeros([1, 0, ht, wd, 2], device=device, dtype=torch.float)
self.far_threshold = args.far_threshold
self.inac_range = args.inac_range
self.mask_threshold = args.mask_threshold
self.img_count = 0
self.skip_edge = args.skip_edge
self.frontend_window = args.frontend_window
# simple online visualization
self.show_covisible_graph = False
self.show_oldest_disparity = False
self.show_flow_and_weight = False
def __filter_repeated_edges(self, ii, jj):
""" remove duplicate edges """
keep = torch.zeros(ii.shape[0], dtype=torch.bool, device=ii.device)
eset = set(
[(i.item(), j.item()) for i, j in zip(self.ii, self.jj)] +
[(i.item(), j.item()) for i, j in zip(self.ii_inac, self.jj_inac)])
for k, (i, j) in enumerate(zip(ii, jj)):
keep[k] = (i.item(), j.item()) not in eset
return ii[keep], jj[keep]
def print_edges(self):
ii = self.ii.cpu().numpy()
jj = self.jj.cpu().numpy()
ix = np.argsort(ii)
ii = ii[ix]
jj = jj[ix]
w = torch.mean(self.weight, dim=[0,2,3,4]).cpu().numpy()
w = w[ix]
for e in zip(ii, jj, w):
print(e)
print()
def filter_edges(self):
""" remove bad edges """
conf = torch.mean(self.weight, dim=[0,2,3,4])
mask = (torch.abs(self.ii-self.jj) > 2) & (conf < 0.001)
self.ii_bad = torch.cat([self.ii_bad, self.ii[mask]])
self.jj_bad = torch.cat([self.jj_bad, self.jj[mask]])
self.rm_factors(mask, store=False)
def clear_edges(self):
self.rm_factors(self.ii >= 0)
self.net = None
self.inp = None
@torch.cuda.amp.autocast(enabled=True)
def add_factors(self, ii, jj, remove=False):
""" add edges to factor graph """
if not isinstance(ii, torch.Tensor):
ii = torch.as_tensor(ii, dtype=torch.long, device=self.device)
if not isinstance(jj, torch.Tensor):
jj = torch.as_tensor(jj, dtype=torch.long, device=self.device)
# remove duplicate edges
ii, jj = self.__filter_repeated_edges(ii, jj)
if ii.shape[0] == 0:
return
# place limit on number of factors
if self.max_factors > 0 and self.ii.shape[0] + ii.shape[0] > self.max_factors \
and self.corr is not None and remove:
ix = torch.arange(len(self.age))[torch.argsort(self.age).cpu()]
self.rm_factors(ix >= self.max_factors - ii.shape[0], store=True)
net = self.video.nets[ii].to(self.device).unsqueeze(0)
# correlation volume for new edges
if self.corr_impl == "volume":
c = (ii == jj).long()
fmap1 = self.video.fmaps[ii,0].to(self.device).unsqueeze(0)
fmap2 = self.video.fmaps[jj,c].to(self.device).unsqueeze(0)
corr = CorrBlock(fmap1, fmap2)
self.corr = corr if self.corr is None else self.corr.cat(corr)
inp = self.video.inps[ii].to(self.device).unsqueeze(0)
self.inp = inp if self.inp is None else torch.cat([self.inp, inp], 1)
with torch.cuda.amp.autocast(enabled=False):
target, _ = self.video.reproject(ii, jj)
weight = torch.zeros_like(target)
self.ii = torch.cat([self.ii, ii], 0)
self.jj = torch.cat([self.jj, jj], 0)
self.age = torch.cat([self.age, torch.zeros_like(ii)], 0)
# reprojection factors
self.net = net if self.net is None else torch.cat([self.net, net], 1)
self.target = torch.cat([self.target, target], 1)
self.weight = torch.cat([self.weight, weight], 1)
@torch.cuda.amp.autocast(enabled=True)
def rm_factors(self, mask, store=False):
""" drop edges from factor graph """
# store estimated factors
if store:
self.ii_inac = torch.cat([self.ii_inac, self.ii[mask]], 0)
self.jj_inac = torch.cat([self.jj_inac, self.jj[mask]], 0)
self.target_inac = torch.cat([self.target_inac, self.target[:,mask]], 1)
self.weight_inac = torch.cat([self.weight_inac, self.weight[:,mask]], 1)
self.ii = self.ii[~mask]
self.jj = self.jj[~mask]
self.age = self.age[~mask]
if self.corr_impl == "volume":
self.corr = self.corr[~mask]
if self.net is not None:
self.net = self.net[:,~mask]
if self.inp is not None:
self.inp = self.inp[:,~mask]
self.target = self.target[:,~mask]
self.weight = self.weight[:,~mask]
@torch.cuda.amp.autocast(enabled=True)
def rm_keyframe(self, ix):
""" drop edges from factor graph """
with self.video.get_lock():
self.video.images[ix] = self.video.images[ix+1]
self.video.poses[ix] = self.video.poses[ix+1]
self.video.disps[ix] = self.video.disps[ix+1]
self.video.disps_sens[ix] = self.video.disps_sens[ix+1]
self.video.intrinsics[ix] = self.video.intrinsics[ix+1]
self.video.nets[ix] = self.video.nets[ix+1]
self.video.inps[ix] = self.video.inps[ix+1]
self.video.fmaps[ix] = self.video.fmaps[ix+1]
self.video.tstamp[ix] = self.video.tstamp[ix+1] # BUG fix
m = (self.ii_inac == ix) | (self.jj_inac == ix)
self.ii_inac[self.ii_inac >= ix] -= 1
self.jj_inac[self.jj_inac >= ix] -= 1
if torch.any(m):
self.ii_inac = self.ii_inac[~m]
self.jj_inac = self.jj_inac[~m]
self.target_inac = self.target_inac[:,~m]
self.weight_inac = self.weight_inac[:,~m]
m = (self.ii == ix) | (self.jj == ix)
self.ii[self.ii >= ix] -= 1
self.jj[self.jj >= ix] -= 1
self.rm_factors(m, store=False)
@torch.cuda.amp.autocast(enabled=True)
def update(self, t0=None, t1=None, itrs=2, use_inactive=False, EP=1e-7, motion_only=False, marg = False):
""" run update operator on factor graph """
self.video.logger.info('update')
with torch.cuda.amp.autocast(enabled=False):
coords1, mask = self.video.reproject(self.ii, self.jj)
motn = torch.cat([coords1 - self.coords0, self.target - coords1], dim=-1)
motn = motn.permute(0,1,4,2,3).clamp(-64.0, 64.0) # 1,2,4,48,96
corr = self.corr(coords1)
self.net, delta, weight, damping, upmask = \
self.update_op(self.net, self.inp, corr, motn, self.ii, self.jj, self.upsample)
if t0 is None:
t0 = max(1, self.ii.min().item()+1)
self.video.logger.info('ba')
with torch.cuda.amp.autocast(enabled=False):
self.target = coords1 + delta.to(dtype=torch.float)
self.weight = weight.to(dtype=torch.float)
ht, wd = self.coords0.shape[0:2]
if self.upsample:
self.damping[torch.unique(self.ii)] = damping
if use_inactive:
m = (self.ii_inac >= t0 - self.inac_range) & (self.jj_inac >= t0 - self.inac_range)
ii = torch.cat([self.ii_inac[m], self.ii], 0)
jj = torch.cat([self.jj_inac[m], self.jj], 0)
target = torch.cat([self.target_inac[:,m], self.target], 1)
weight = torch.cat([self.weight_inac[:,m], self.weight], 1)
else:
ii, jj, target, weight = self.ii, self.jj, self.target, self.weight
# Some real-time visualization for debugging
# 1) Disparity
if self.show_oldest_disparity:
disp_show_front = self.video.disps[self.ii[0]].cpu().numpy()
disp_show_front = cv2.resize(disp_show_front,[disp_show_front.shape[1]*8,disp_show_front.shape[0]*8],interpolation = cv2.INTER_NEAREST)
disp_show_front= disp_show_front.astype(np.float32)
normalizer = matplotlib.colors.Normalize(vmin=-0.2, vmax=1.0)
mapper = cm.ScalarMappable(norm=normalizer,cmap='magma')
colormapped_im = (mapper.to_rgba(disp_show_front)[:, :, :3] * 255).astype(np.uint8)
colormapped_im = cv2.cvtColor(colormapped_im,cv2.COLOR_RGB2BGR)
cv2.imshow('colormapped_im',colormapped_im)
cv2.waitKey(1)
# 2) Optical flow and weight
if self.show_flow_and_weight:
rgb = self.video.images[torch.max(self.ii)].cpu().numpy().transpose(1,2,0)
new_flow_id = torch.where(torch.logical_and(self.ii==torch.max(self.ii),self.jj==torch.max(self.ii)-5))[0][0].item()
weight_cpu = weight[0,new_flow_id].cpu().numpy().astype(np.float32)
weight_cpu = np.linalg.norm(weight_cpu,axis=2)
normalizer = matplotlib.colors.Normalize(vmin=-0.0, vmax=1.5)
mapper = cm.ScalarMappable(norm=normalizer,cmap='jet')
colormapped_im = (mapper.to_rgba(weight_cpu)[:, :, :3] * 255).astype(np.uint8)
colormapped_im = cv2.cvtColor(colormapped_im,cv2.COLOR_RGB2BGR)
colormapped_im = cv2.resize(colormapped_im,[rgb.shape[1],rgb.shape[0]])
colormapped_im = cv2.addWeighted(rgb,0.5,colormapped_im,0.5,0)
absflow = (self.target[0,new_flow_id] - self.coords0).cpu().numpy()
for iii in range(0,absflow.shape[0],4):
for jjj in range(0,absflow.shape[1],4):
colormapped_im = cv2.line(colormapped_im, (jjj * 8,iii * 8),(int(round((jjj-absflow[iii,jjj,0])* 8)) ,int(round((iii-absflow[iii,jjj,1]) * 8))),(255,255,255),1,cv2.LINE_AA)
cv2.imshow('weight_cpu',colormapped_im)
self.img_count += 1
# 3) Covisible graph
if self.show_covisible_graph:
i0 = min(ii)
i1 = max(ii)
ppp = SE3(self.video.poses[i0:(i1+1)]).inv().matrix()[:,0:3,3].cpu().numpy()
# [:,:3].cpu().numpy()
scale = max(max(ppp[:,0]) - min(ppp[:,0]),max(ppp[:,1]) - min(ppp[:,1]))
ppp[:,0] -= np.mean(ppp[:,0])
ppp[:,1] = -(ppp[:,1]- np.mean(ppp[:,1]))
ppp *= max(round(1/scale * 200 / 50)*50,50)
ppp += 500
mmm = np.zeros([1000,1000],dtype=np.uint8)
for iii in range(0,i1+1-i0):
mmm = cv2.circle(mmm,(int(round(ppp[iii,0])),int(round(ppp[iii,1]))),4,255,0)
for iii in range(self.ii_inac[m].shape[0]):
iiii = self.ii_inac[m][iii]-i0
jjjj = self.jj_inac[m][iii]-i0
mmm = cv2.line(mmm,(int(round(ppp[iiii,0])),int(round(ppp[iiii,1]))),(int(round(ppp[jjjj,0])),int(round(ppp[jjjj,1]))),128,1)
for iii in range(self.ii.shape[0]):
iiii = self.ii[iii]-i0
jjjj = self.jj[iii]-i0
mmm = cv2.line(mmm,(int(round(ppp[iiii,0])),int(round(ppp[iiii,1]))),(int(round(ppp[jjjj,0])),int(round(ppp[jjjj,1]))),255,1)
cv2.imshow('window',mmm)
## Tricks for better performance
# 1) downweight far points
if self.far_threshold > 0 and self.video.imu_enabled:
disp_mask = (self.video.disps < self.far_threshold)
mask = disp_mask[ii, :, :]
weight[:, mask] /= 1000.0
# 2) downweight far points
if self.mask_threshold > 0 and self.video.imu_enabled:
pose0 = SE3(self.video.poses[ii])
pose1 = SE3(self.video.poses[jj])
pose01 = pose0*pose1.inv()
mask = torch.norm(pose01.translation()[:,:3],dim=1) < self.mask_threshold
weight[:,mask,:,:,:] /= 1000.0
# 3) downweight edges related to the newest frame
downweight_newframe = True
if downweight_newframe:
weight[:,ii==max(ii)] /= 10.0
weight[:,jj==max(jj)] /= 4.0
damping = .2 * self.damping[torch.unique(ii)].contiguous() + EP
target = target.view(-1, ht, wd, 2).permute(0,3,1,2).contiguous()
weight = weight.view(-1, ht, wd, 2).permute(0,3,1,2).contiguous()
# Dense bundle adjustment
self.video.ba(target, weight, damping, ii, jj, t0, t1,
itrs=itrs, lm=1e-4, ep=0.1, motion_only=motion_only)
if self.upsample:
self.video.upsample(torch.unique(self.ii), upmask)
self.age += 1
def add_neighborhood_factors(self, t0, t1, r=3):
""" add edges between neighboring frames within radius r """
ii, jj = torch.meshgrid(torch.arange(t0,t1), torch.arange(t0,t1))
ii = ii.reshape(-1).to(dtype=torch.long, device=self.device)
jj = jj.reshape(-1).to(dtype=torch.long, device=self.device)
c = 1 if self.video.stereo else 0
keep = ((ii - jj).abs() > c) & ((ii - jj).abs() <= r)
self.add_factors(ii[keep], jj[keep])
def add_proximity_factors(self, t0=0, t1=0, rad=2, nms=2, beta=0.25, thresh=16.0, remove=False):
""" add edges to the factor graph based on distance """
t = self.video.counter.value
ix = torch.arange(t0, t)
jx = torch.arange(t1, t)
ii, jj = torch.meshgrid(ix, jx)
ii = ii.reshape(-1)
jj = jj.reshape(-1)
cc = ii.shape[0]
# Opportunistic "skip" edges in the graph
if self.skip_edge:
if torch.max(ii) - torch.min(ii) == self.frontend_window - 1:
jj_add = torch.min(ii) + torch.tensor(self.skip_edge)
jj_add = jj_add[jj_add>0]
ii_add = torch.zeros_like(jj_add) + torch.max(ii)
jj = torch.cat([jj,jj_add])
ii = torch.cat([ii,ii_add])
d = self.video.distance(ii, jj, beta=beta)
d[ii - rad < jj] = np.inf
d[d > 100] = np.inf
ii1 = torch.cat([self.ii, self.ii_bad, self.ii_inac], 0)
jj1 = torch.cat([self.jj, self.jj_bad, self.jj_inac], 0)
for i, j in zip(ii1.cpu().numpy(), jj1.cpu().numpy()):
for di in range(-nms, nms+1):
for dj in range(-nms, nms+1):
if abs(di) + abs(dj) <= max(min(abs(i-j)-2, nms), 0):
i1 = i + di
j1 = j + dj
if (t0 <= i1 < t) and (t1 <= j1 < t):
d[(i1-t0)*(t-t1) + (j1-t1)] = np.inf
es = []
for i in range(t0, t):
if self.video.stereo:
es.append((i, i))
d[(i-t0)*(t-t1) + (i-t1)] = np.inf
for j in range(max(i-rad-1,0), i):
es.append((i,j))
es.append((j,i))
if (i-t0)*(t-t1) + (j-t1) >=0:
d[(i-t0)*(t-t1) + (j-t1)] = np.inf
ix = torch.argsort(d)
for k in ix:
if k >= cc:
continue
if d[k].item() > thresh:
continue
if len(es) > self.max_factors:
break
i = ii[k]
j = jj[k]
# bidirectional
es.append((i, j))
es.append((j, i))
for di in range(-nms, nms+1):
for dj in range(-nms, nms+1):
if abs(di) + abs(dj) <= max(min(abs(i-j)-2, nms), 0):
i1 = i + di
j1 = j + dj
if (t0 <= i1 < t) and (t1 <= j1 < t):
d[(i1-t0)*(t-t1) + (j1-t1)] = np.inf
if ii.shape[0] > cc:
ix = torch.argsort(d[cc:ii.shape[0]])
if d[cc + ix[0]] < thresh and d[cc + ix[0]] > 0:
es.append((ii[cc+ix[0]],jj[cc+ix[0]]))
es.append((jj[cc+ix[0]],ii[cc+ix[0]]))
ii, jj = torch.as_tensor(es, device=self.device).unbind(dim=-1)
self.add_factors(ii, jj, remove)
================================================
FILE: dbaf/data_readers/__init__.py
================================================
================================================
FILE: dbaf/data_readers/augmentation.py
================================================
import torch
import torchvision.transforms as transforms
import numpy as np
import torch.nn.functional as F
class RGBDAugmentor:
""" perform augmentation on RGB-D video """
def __init__(self, crop_size):
self.crop_size = crop_size
self.augcolor = transforms.Compose([
transforms.ToPILImage(),
transforms.ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.4/3.14),
transforms.RandomGrayscale(p=0.1),
transforms.ToTensor()])
self.max_scale = 0.25
def spatial_transform(self, images, depths, poses, intrinsics):
""" cropping and resizing """
ht, wd = images.shape[2:]
max_scale = self.max_scale
min_scale = np.log2(np.maximum(
(self.crop_size[0] + 1) / float(ht),
(self.crop_size[1] + 1) / float(wd)))
scale = 2 ** np.random.uniform(min_scale, max_scale)
intrinsics = scale * intrinsics
depths = depths.unsqueeze(dim=1)
images = F.interpolate(images, scale_factor=scale, mode='bilinear',
align_corners=False, recompute_scale_factor=True)
depths = F.interpolate(depths, scale_factor=scale, recompute_scale_factor=True)
# always perform center crop (TODO: try non-center crops)
y0 = (images.shape[2] - self.crop_size[0]) // 2
x0 = (images.shape[3] - self.crop_size[1]) // 2
intrinsics = intrinsics - torch.tensor([0.0, 0.0, x0, y0])
images = images[:, :, y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
depths = depths[:, :, y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
depths = depths.squeeze(dim=1)
return images, poses, depths, intrinsics
def color_transform(self, images):
""" color jittering """
num, ch, ht, wd = images.shape
images = images.permute(1, 2, 3, 0).reshape(ch, ht, wd*num)
images = 255 * self.augcolor(images[[2,1,0]] / 255.0)
return images[[2,1,0]].reshape(ch, ht, wd, num).permute(3,0,1,2).contiguous()
def __call__(self, images, poses, depths, intrinsics):
images = self.color_transform(images)
return self.spatial_transform(images, depths, poses, intrinsics)
================================================
FILE: dbaf/data_readers/base.py
================================================
import numpy as np
import torch
import torch.utils.data as data
import torch.nn.functional as F
import csv
import os
import cv2
import math
import random
import json
import pickle
import os.path as osp
from .augmentation import RGBDAugmentor
from .rgbd_utils import *
class RGBDDataset(data.Dataset):
def __init__(self, name, datapath, n_frames=4, crop_size=[384,512], fmin=8.0, fmax=75.0, do_aug=True):
""" Base class for RGBD dataset """
self.aug = None
self.root = datapath
self.name = name
self.n_frames = n_frames
self.fmin = fmin # exclude very easy examples
self.fmax = fmax # exclude very hard examples
if do_aug:
self.aug = RGBDAugmentor(crop_size=crop_size)
# building dataset is expensive, cache so only needs to be performed once
cur_path = osp.dirname(osp.abspath(__file__))
if not os.path.isdir(osp.join(cur_path, 'cache')):
os.mkdir(osp.join(cur_path, 'cache'))
cache_path = osp.join(cur_path, 'cache', '{}.pickle'.format(self.name))
if osp.isfile(cache_path):
scene_info = pickle.load(open(cache_path, 'rb'))[0]
else:
scene_info = self._build_dataset()
with open(cache_path, 'wb') as cachefile:
pickle.dump((scene_info,), cachefile)
self.scene_info = scene_info
self._build_dataset_index()
def _build_dataset_index(self):
self.dataset_index = []
for scene in self.scene_info:
if not self.__class__.is_test_scene(scene):
graph = self.scene_info[scene]['graph']
for i in graph:
if len(graph[i][0]) > self.n_frames:
self.dataset_index.append((scene, i))
else:
print("Reserving {} for validation".format(scene))
@staticmethod
def image_read(image_file):
return cv2.imread(image_file)
@staticmethod
def depth_read(depth_file):
return np.load(depth_file)
def build_frame_graph(self, poses, depths, intrinsics, f=16, max_flow=256):
""" compute optical flow distance between all pairs of frames """
def read_disp(fn):
depth = self.__class__.depth_read(fn)[f//2::f, f//2::f]
depth[depth < 0.01] = np.mean(depth)
return 1.0 / depth
poses = np.array(poses)
intrinsics = np.array(intrinsics) / f
disps = np.stack(list(map(read_disp, depths)), 0)
d = f * compute_distance_matrix_flow(poses, disps, intrinsics)
# uncomment for nice visualization
# import matplotlib.pyplot as plt
# plt.imshow(d)
# plt.show()
graph = {}
for i in range(d.shape[0]):
j, = np.where(d[i] < max_flow)
graph[i] = (j, d[i,j])
return graph
def __getitem__(self, index):
""" return training video """
index = index % len(self.dataset_index)
scene_id, ix = self.dataset_index[index]
frame_graph = self.scene_info[scene_id]['graph']
images_list = self.scene_info[scene_id]['images']
depths_list = self.scene_info[scene_id]['depths']
poses_list = self.scene_info[scene_id]['poses']
intrinsics_list = self.scene_info[scene_id]['intrinsics']
inds = [ ix ]
while len(inds) < self.n_frames:
# get other frames within flow threshold
k = (frame_graph[ix][1] > self.fmin) & (frame_graph[ix][1] < self.fmax)
frames = frame_graph[ix][0][k]
# prefer frames forward in time
if np.count_nonzero(frames[frames > ix]):
ix = np.random.choice(frames[frames > ix])
elif np.count_nonzero(frames):
ix = np.random.choice(frames)
inds += [ ix ]
images, depths, poses, intrinsics = [], [], [], []
for i in inds:
images.append(self.__class__.image_read(images_list[i]))
depths.append(self.__class__.depth_read(depths_list[i]))
poses.append(poses_list[i])
intrinsics.append(intrinsics_list[i])
images = np.stack(images).astype(np.float32)
depths = np.stack(depths).astype(np.float32)
poses = np.stack(poses).astype(np.float32)
intrinsics = np.stack(intrinsics).astype(np.float32)
images = torch.from_numpy(images).float()
images = images.permute(0, 3, 1, 2)
disps = torch.from_numpy(1.0 / depths)
poses = torch.from_numpy(poses)
intrinsics = torch.from_numpy(intrinsics)
if self.aug is not None:
images, poses, disps, intrinsics = \
self.aug(images, poses, disps, intrinsics)
# scale scene
if len(disps[disps>0.01]) > 0:
s = disps[disps>0.01].mean()
disps = disps / s
poses[...,:3] *= s
return images, poses, disps, intrinsics
def __len__(self):
return len(self.dataset_index)
def __imul__(self, x):
self.dataset_index *= x
return self
================================================
FILE: dbaf/data_readers/factory.py
================================================
import pickle
import os
import os.path as osp
# RGBD-Dataset
from .tartan import TartanAir
from .stream import ImageStream
from .stream import StereoStream
from .stream import RGBDStream
# streaming datasets for inference
from .tartan import TartanAirStream
from .tartan import TartanAirTestStream
def dataset_factory(dataset_list, **kwargs):
""" create a combined dataset """
from torch.utils.data import ConcatDataset
dataset_map = { 'tartan': (TartanAir, ) }
db_list = []
for key in dataset_list:
# cache datasets for faster future loading
db = dataset_map[key][0](**kwargs)
print("Dataset {} has {} images".format(key, len(db)))
db_list.append(db)
return ConcatDataset(db_list)
def create_datastream(dataset_path, **kwargs):
""" create data_loader to stream images 1 by 1 """
from torch.utils.data import DataLoader
if osp.isfile(osp.join(dataset_path, 'calibration.txt')):
db = ETH3DStream(dataset_path, **kwargs)
elif osp.isdir(osp.join(dataset_path, 'image_left')):
db = TartanAirStream(dataset_path, **kwargs)
elif osp.isfile(osp.join(dataset_path, 'rgb.txt')):
db = TUMStream(dataset_path, **kwargs)
elif osp.isdir(osp.join(dataset_path, 'mav0')):
db = EurocStream(dataset_path, **kwargs)
elif osp.isfile(osp.join(dataset_path, 'calib.txt')):
db = KITTIStream(dataset_path, **kwargs)
else:
# db = TartanAirStream(dataset_path, **kwargs)
db = TartanAirTestStream(dataset_path, **kwargs)
stream = DataLoader(db, shuffle=False, batch_size=1, num_workers=4)
return stream
def create_imagestream(dataset_path, **kwargs):
""" create data_loader to stream images 1 by 1 """
from torch.utils.data import DataLoader
db = ImageStream(dataset_path, **kwargs)
return DataLoader(db, shuffle=False, batch_size=1, num_workers=4)
def create_stereostream(dataset_path, **kwargs):
""" create data_loader to stream images 1 by 1 """
from torch.utils.data import DataLoader
db = StereoStream(dataset_path, **kwargs)
return DataLoader(db, shuffle=False, batch_size=1, num_workers=4)
def create_rgbdstream(dataset_path, **kwargs):
""" create data_loader to stream images 1 by 1 """
from torch.utils.data import DataLoader
db = RGBDStream(dataset_path, **kwargs)
return DataLoader(db, shuffle=False, batch_size=1, num_workers=4)
================================================
FILE: dbaf/data_readers/rgbd_utils.py
================================================
import numpy as np
import os.path as osp
import torch
from lietorch import SE3
import geom.projective_ops as pops
from scipy.spatial.transform import Rotation
def parse_list(filepath, skiprows=0):
""" read list data """
data = np.loadtxt(filepath, delimiter=' ', dtype=np.unicode_, skiprows=skiprows)
return data
def associate_frames(tstamp_image, tstamp_depth, tstamp_pose, max_dt=1.0):
""" pair images, depths, and poses """
associations = []
for i, t in enumerate(tstamp_image):
if tstamp_pose is None:
j = np.argmin(np.abs(tstamp_depth - t))
if (np.abs(tstamp_depth[j] - t) < max_dt):
associations.append((i, j))
else:
j = np.argmin(np.abs(tstamp_depth - t))
k = np.argmin(np.abs(tstamp_pose - t))
if (np.abs(tstamp_depth[j] - t) < max_dt) and \
(np.abs(tstamp_pose[k] - t) < max_dt):
associations.append((i, j, k))
return associations
def loadtum(datapath, frame_rate=-1):
""" read video data in tum-rgbd format """
if osp.isfile(osp.join(datapath, 'groundtruth.txt')):
pose_list = osp.join(datapath, 'groundtruth.txt')
elif osp.isfile(osp.join(datapath, 'pose.txt')):
pose_list = osp.join(datapath, 'pose.txt')
else:
return None, None, None, None
image_list = osp.join(datapath, 'rgb.txt')
depth_list = osp.join(datapath, 'depth.txt')
calib_path = osp.join(datapath, 'calibration.txt')
intrinsic = None
if osp.isfile(calib_path):
intrinsic = np.loadtxt(calib_path, delimiter=' ')
intrinsic = intrinsic.astype(np.float64)
image_data = parse_list(image_list)
depth_data = parse_list(depth_list)
pose_data = parse_list(pose_list, skiprows=1)
pose_vecs = pose_data[:,1:].astype(np.float64)
tstamp_image = image_data[:,0].astype(np.float64)
tstamp_depth = depth_data[:,0].astype(np.float64)
tstamp_pose = pose_data[:,0].astype(np.float64)
associations = associate_frames(tstamp_image, tstamp_depth, tstamp_pose)
# print(len(tstamp_image))
# print(len(associations))
indicies = range(len(associations))[::5]
# indicies = [ 0 ]
# for i in range(1, len(associations)):
# t0 = tstamp_image[associations[indicies[-1]][0]]
# t1 = tstamp_image[associations[i][0]]
# if t1 - t0 > 1.0 / frame_rate:
# indicies += [ i ]
images, poses, depths, intrinsics, tstamps = [], [], [], [], []
for ix in indicies:
(i, j, k) = associations[ix]
images += [ osp.join(datapath, image_data[i,1]) ]
depths += [ osp.join(datapath, depth_data[j,1]) ]
poses += [ pose_vecs[k] ]
tstamps += [ tstamp_image[i] ]
if intrinsic is not None:
intrinsics += [ intrinsic ]
return images, depths, poses, intrinsics, tstamps
def all_pairs_distance_matrix(poses, beta=2.5):
""" compute distance matrix between all pairs of poses """
poses = np.array(poses, dtype=np.float32)
poses[:,:3] *= beta # scale to balence rot + trans
poses = SE3(torch.from_numpy(poses))
r = (poses[:,None].inv() * poses[None,:]).log()
return r.norm(dim=-1).cpu().numpy()
def pose_matrix_to_quaternion(pose):
""" convert 4x4 pose matrix to (t, q) """
q = Rotation.from_matrix(pose[:3, :3]).as_quat()
return np.concatenate([pose[:3, 3], q], axis=0)
def compute_distance_matrix_flow(poses, disps, intrinsics):
""" compute flow magnitude between all pairs of frames """
if not isinstance(poses, SE3):
poses = torch.from_numpy(poses).float().cuda()[None]
poses = SE3(poses).inv()
disps = torch.from_numpy(disps).float().cuda()[None]
intrinsics = torch.from_numpy(intrinsics).float().cuda()[None]
N = poses.shape[1]
ii, jj = torch.meshgrid(torch.arange(N), torch.arange(N))
ii = ii.reshape(-1).cuda()
jj = jj.reshape(-1).cuda()
MAX_FLOW = 100.0
matrix = np.zeros((N, N), dtype=np.float32)
s = 2048
for i in range(0, ii.shape[0], s):
flow1, val1 = pops.induced_flow(poses, disps, intrinsics, ii[i:i+s], jj[i:i+s])
flow2, val2 = pops.induced_flow(poses, disps, intrinsics, jj[i:i+s], ii[i:i+s])
flow = torch.stack([flow1, flow2], dim=2)
val = torch.stack([val1, val2], dim=2)
mag = flow.norm(dim=-1).clamp(max=MAX_FLOW)
mag = mag.view(mag.shape[1], -1)
val = val.view(val.shape[1], -1)
mag = (mag * val).mean(-1) / val.mean(-1)
mag[val.mean(-1) < 0.7] = np.inf
i1 = ii[i:i+s].cpu().numpy()
j1 = jj[i:i+s].cpu().numpy()
matrix[i1, j1] = mag.cpu().numpy()
return matrix
def compute_distance_matrix_flow2(poses, disps, intrinsics, beta=0.4):
""" compute flow magnitude between all pairs of frames """
# if not isinstance(poses, SE3):
# poses = torch.from_numpy(poses).float().cuda()[None]
# poses = SE3(poses).inv()
# disps = torch.from_numpy(disps).float().cuda()[None]
# intrinsics = torch.from_numpy(intrinsics).float().cuda()[None]
N = poses.shape[1]
ii, jj = torch.meshgrid(torch.arange(N), torch.arange(N))
ii = ii.reshape(-1)
jj = jj.reshape(-1)
MAX_FLOW = 128.0
matrix = np.zeros((N, N), dtype=np.float32)
s = 2048
for i in range(0, ii.shape[0], s):
flow1a, val1a = pops.induced_flow(poses, disps, intrinsics, ii[i:i+s], jj[i:i+s], tonly=True)
flow1b, val1b = pops.induced_flow(poses, disps, intrinsics, ii[i:i+s], jj[i:i+s])
flow2a, val2a = pops.induced_flow(poses, disps, intrinsics, jj[i:i+s], ii[i:i+s], tonly=True)
flow2b, val2b = pops.induced_flow(poses, disps, intrinsics, ii[i:i+s], jj[i:i+s])
flow1 = flow1a + beta * flow1b
val1 = val1a * val2b
flow2 = flow2a + beta * flow2b
val2 = val2a * val2b
flow = torch.stack([flow1, flow2], dim=2)
val = torch.stack([val1, val2], dim=2)
mag = flow.norm(dim=-1).clamp(max=MAX_FLOW)
mag = mag.view(mag.shape[1], -1)
val = val.view(val.shape[1], -1)
mag = (mag * val).mean(-1) / val.mean(-1)
mag[val.mean(-1) < 0.8] = np.inf
i1 = ii[i:i+s].cpu().numpy()
j1 = jj[i:i+s].cpu().numpy()
matrix[i1, j1] = mag.cpu().numpy()
return matrix
================================================
FILE: dbaf/data_readers/stream.py
================================================
import numpy as np
import torch
import torch.utils.data as data
import torch.nn.functional as F
import csv
import os
import cv2
import math
import random
import json
import pickle
import os.path as osp
from .rgbd_utils import *
class RGBDStream(data.Dataset):
def __init__(self, datapath, frame_rate=-1, image_size=[384,512], crop_size=[0,0]):
self.datapath = datapath
self.frame_rate = frame_rate
self.image_size = image_size
self.crop_size = crop_size
self._build_dataset_index()
@staticmethod
def image_read(image_file):
return cv2.imread(image_file)
@staticmethod
def depth_read(depth_file):
return np.load(depth_file)
def __len__(self):
return len(self.images)
def __getitem__(self, index):
""" return training video """
image = self.__class__.image_read(self.images[index])
image = torch.from_numpy(image).float()
image = image.permute(2, 0, 1)
try:
tstamp = self.tstamps[index]
except:
tstamp = index
pose = torch.from_numpy(self.poses[index]).float()
intrinsic = torch.from_numpy(self.intrinsics[index]).float()
# resize image
sx = self.image_size[1] / image.shape[2]
sy = self.image_size[0] / image.shape[1]
image = F.interpolate(image[None], self.image_size, mode='bilinear', align_corners=False)[0]
fx, fy, cx, cy = intrinsic.unbind(dim=0)
fx, cx = sx * fx, sx * cx
fy, cy = sy * fy, sy * cy
# crop image
if self.crop_size[0] > 0:
cy = cy - self.crop_size[0]
image = image[:,self.crop_size[0]:-self.crop_size[0],:]
if self.crop_size[1] > 0:
cx = cx - self.crop_size[1]
image = image[:,:,self.crop_size[1]:-self.crop_size[1]]
intrinsic = torch.stack([fx, fy, cx, cy])
return tstamp, image, pose, intrinsic
class ImageStream(data.Dataset):
def __init__(self, datapath, intrinsics, rate=1, image_size=[384,512]):
rgb_list = osp.join(datapath, 'rgb.txt')
if os.path.isfile(rgb_list):
rgb_list = np.loadtxt(rgb_list, delimiter=' ', dtype=np.unicode_)
self.timestamps = rgb_list[:,0].astype(np.float)
self.images = [os.path.join(datapath, x) for x in rgb_list[:,1]]
self.images = self.images[::rate]
self.timestamps = self.timestamps[::rate]
else:
import glob
self.images = sorted(glob.glob(osp.join(datapath, '*.jpg'))) + sorted(glob.glob(osp.join(datapath, '*.png')))
self.images = self.images[::rate]
self.intrinsics = intrinsics
self.image_size = image_size
def __len__(self):
return len(self.images)
@staticmethod
def image_read(imfile):
return cv2.imread(imfile)
def __getitem__(self, index):
""" return training video """
image = self.__class__.image_read(self.images[index])
try:
tstamp = self.timestamps[index]
except:
tstamp = index
ht0, wd0 = image.shape[:2]
ht1, wd1 = self.image_size
intrinsics = torch.as_tensor(self.intrinsics)
intrinsics[0] *= wd1 / wd0
intrinsics[1] *= ht1 / ht0
intrinsics[2] *= wd1 / wd0
intrinsics[3] *= ht1 / ht0
# resize image
ikwargs = {'mode': 'bilinear', 'align_corners': True}
image = torch.from_numpy(image).float().permute(2, 0, 1)
image = F.interpolate(image[None], self.image_size, **ikwargs)[0]
return tstamp, image, intrinsics
class StereoStream(data.Dataset):
def __init__(self, datapath, intrinsics, rate=1, image_size=[384,512],
map_left=None, map_right=None, left_root='image_left', right_root='image_right'):
import glob
self.intrinsics = intrinsics
self.image_size = image_size
imgs = sorted(glob.glob(osp.join(datapath, left_root, '*.png')))[::rate]
self.images_l = []
self.images_r = []
self.tstamps = []
for img_l in imgs:
img_r = img_l.replace(left_root, right_root)
if os.path.isfile(img_r):
t = np.float(img_l.split('/')[-1].replace('.png', ''))
self.tstamps.append(t)
self.images_l += [ img_l ]
self.images_r += [ img_r ]
self.map_left = map_left
self.map_right = map_right
def __len__(self):
return len(self.images_l)
@staticmethod
def image_read(imfile, imap=None):
image = cv2.imread(imfile)
if imap is not None:
image = cv2.remap(image, imap[0], imap[1], interpolation=cv2.INTER_LINEAR)
return image
def __getitem__(self, index):
""" return training video """
tstamp = self.tstamps[index]
image_l = self.__class__.image_read(self.images_l[index], self.map_left)
image_r = self.__class__.image_read(self.images_r[index], self.map_right)
ht0, wd0 = image_l.shape[:2]
ht1, wd1 = self.image_size
intrinsics = torch.as_tensor(self.intrinsics)
intrinsics[0] *= wd1 / wd0
intrinsics[1] *= ht1 / ht0
intrinsics[2] *= wd1 / wd0
intrinsics[3] *= ht1 / ht0
image_l = torch.from_numpy(image_l).float().permute(2, 0, 1)
image_r = torch.from_numpy(image_r).float().permute(2, 0, 1)
# resize image
ikwargs = {'mode': 'bilinear', 'align_corners': True}
image_l = F.interpolate(image_l[None], self.image_size, **ikwargs)[0]
image_r = F.interpolate(image_r[None], self.image_size, **ikwargs)[0]
return tstamp, image_l, image_r, intrinsics
# class RGBDStream(data.Dataset):
# def __init__(self, datapath, intrinsics=None, rate=1, image_size=[384,512]):
# assoc_file = osp.join(datapath, 'associated.txt')
# assoc_list = np.loadtxt(assoc_file, delimiter=' ', dtype=np.unicode_)
# self.intrinsics = intrinsics
# self.image_size = image_size
# self.timestamps = assoc_list[:,0].astype(np.float)[::rate]
# self.images = [os.path.join(datapath, x) for x in assoc_list[:,1]][::rate]
# self.depths = [os.path.join(datapath, x) for x in assoc_list[:,3]][::rate]
# def __len__(self):
# return len(self.images)
# @staticmethod
# def image_read(imfile):
# return cv2.imread(imfile)
# @staticmethod
# def depth_read(depth_file):
# depth = cv2.imread(depth_file, cv2.IMREAD_ANYDEPTH)
# return depth.astype(np.float32) / 5000.0
# def __getitem__(self, index):
# """ return training video """
# tstamp = self.timestamps[index]
# image = self.__class__.image_read(self.images[index])
# depth = self.__class__.depth_read(self.depths[index])
# ht0, wd0 = image.shape[:2]
# ht1, wd1 = self.image_size
# intrinsics = torch.as_tensor(self.intrinsics)
# intrinsics[0] *= wd1 / wd0
# intrinsics[1] *= ht1 / ht0
# intrinsics[2] *= wd1 / wd0
# intrinsics[3] *= ht1 / ht0
# # resize image
# ikwargs = {'mode': 'bilinear', 'align_corners': True}
# image = torch.from_numpy(image).float().permute(2, 0, 1)
# image = F.interpolate(image[None], self.image_size, **ikwargs)[0]
# depth = torch.from_numpy(depth).float()[None,None]
# depth = F.interpolate(depth, self.image_size, mode='nearest').squeeze()
# return tstamp, image, depth, intrinsics
================================================
FILE: dbaf/data_readers/tartan.py
================================================
import numpy as np
import torch
import glob
import cv2
import os
import os.path as osp
from lietorch import SE3
from .base import RGBDDataset
from .stream import RGBDStream
cur_path = osp.dirname(osp.abspath(__file__))
test_split = osp.join(cur_path, 'tartan_test.txt')
test_split = open(test_split).read().split()
class TartanAir(RGBDDataset):
# scale depths to balance rot & trans
DEPTH_SCALE = 5.0
def __init__(self, mode='training', **kwargs):
self.mode = mode
self.n_frames = 2
super(TartanAir, self).__init__(name='TartanAir', **kwargs)
@staticmethod
def is_test_scene(scene):
# print(scene, any(x in scene for x in test_split))
return any(x in scene for x in test_split)
def _build_dataset(self):
from tqdm import tqdm
print("Building TartanAir dataset")
scene_info = {}
scenes = glob.glob(osp.join(self.root, '*/*/*/*'))
for scene in tqdm(sorted(scenes)):
images = sorted(glob.glob(osp.join(scene, 'image_left/*.png')))
depths = sorted(glob.glob(osp.join(scene, 'depth_left/*.npy')))
poses = np.loadtxt(osp.join(scene, 'pose_left.txt'), delimiter=' ')
poses = poses[:, [1, 2, 0, 4, 5, 3, 6]]
poses[:,:3] /= TartanAir.DEPTH_SCALE
intrinsics = [TartanAir.calib_read()] * len(images)
# graph of co-visible frames based on flow
graph = self.build_frame_graph(poses, depths, intrinsics)
scene = '/'.join(scene.split('/'))
scene_info[scene] = {'images': images, 'depths': depths,
'poses': poses, 'intrinsics': intrinsics, 'graph': graph}
return scene_info
@staticmethod
def calib_read():
return np.array([320.0, 320.0, 320.0, 240.0])
@staticmethod
def image_read(image_file):
return cv2.imread(image_file)
@staticmethod
def depth_read(depth_file):
depth = np.load(depth_file) / TartanAir.DEPTH_SCALE
depth[depth==np.nan] = 1.0
depth[depth==np.inf] = 1.0
return depth
class TartanAirStream(RGBDStream):
def __init__(self, datapath, **kwargs):
super(TartanAirStream, self).__init__(datapath=datapath, **kwargs)
def _build_dataset_index(self):
""" build list of images, poses, depths, and intrinsics """
self.root = 'datasets/TartanAir'
scene = osp.join(self.root, self.datapath)
image_glob = osp.join(scene, 'image_left/*.png')
images = sorted(glob.glob(image_glob))
poses = np.loadtxt(osp.join(scene, 'pose_left.txt'), delimiter=' ')
poses = poses[:, [1, 2, 0, 4, 5, 3, 6]]
poses = SE3(torch.as_tensor(poses))
poses = poses[[0]].inv() * poses
poses = poses.data.cpu().numpy()
intrinsic = self.calib_read(self.datapath)
intrinsics = np.tile(intrinsic[None], (len(images), 1))
self.images = images[::int(self.frame_rate)]
self.poses = poses[::int(self.frame_rate)]
self.intrinsics = intrinsics[::int(self.frame_rate)]
@staticmethod
def calib_read(datapath):
return np.array([320.0, 320.0, 320.0, 240.0])
@staticmethod
def image_read(image_file):
return cv2.imread(image_file)
class TartanAirTestStream(RGBDStream):
def __init__(self, datapath, **kwargs):
super(TartanAirTestStream, self).__init__(datapath=datapath, **kwargs)
def _build_dataset_index(self):
""" build list of images, poses, depths, and intrinsics """
self.root = 'datasets/mono'
image_glob = osp.join(self.root, self.datapath, '*.png')
images = sorted(glob.glob(image_glob))
poses = np.loadtxt(osp.join(self.root, 'mono_gt', self.datapath + '.txt'), delimiter=' ')
poses = poses[:, [1, 2, 0, 4, 5, 3, 6]]
poses = SE3(torch.as_tensor(poses))
poses = poses[[0]].inv() * poses
poses = poses.data.cpu().numpy()
intrinsic = self.calib_read(self.datapath)
intrinsics = np.tile(intrinsic[None], (len(images), 1))
self.images = images[::int(self.frame_rate)]
self.poses = poses[::int(self.frame_rate)]
self.intrinsics = intrinsics[::int(self.frame_rate)]
@staticmethod
def calib_read(datapath):
return np.array([320.0, 320.0, 320.0, 240.0])
@staticmethod
def image_read(image_file):
return cv2.imread(image_file)
================================================
FILE: dbaf/data_readers/tartan_test.txt
================================================
abandonedfactory/abandonedfactory/Easy/P011
abandonedfactory/abandonedfactory/Hard/P011
abandonedfactory_night/abandonedfactory_night/Easy/P013
abandonedfactory_night/abandonedfactory_night/Hard/P014
amusement/amusement/Easy/P008
amusement/amusement/Hard/P007
carwelding/carwelding/Easy/P007
endofworld/endofworld/Easy/P009
gascola/gascola/Easy/P008
gascola/gascola/Hard/P009
hospital/hospital/Easy/P036
hospital/hospital/Hard/P049
japanesealley/japanesealley/Easy/P007
japanesealley/japanesealley/Hard/P005
neighborhood/neighborhood/Easy/P021
neighborhood/neighborhood/Hard/P017
ocean/ocean/Easy/P013
ocean/ocean/Hard/P009
office2/office2/Easy/P011
office2/office2/Hard/P010
office/office/Hard/P007
oldtown/oldtown/Easy/P007
oldtown/oldtown/Hard/P008
seasidetown/seasidetown/Easy/P009
seasonsforest/seasonsforest/Easy/P011
seasonsforest/seasonsforest/Hard/P006
seasonsforest_winter/seasonsforest_winter/Easy/P009
seasonsforest_winter/seasonsforest_winter/Hard/P018
soulcity/soulcity/Easy/P012
soulcity/soulcity/Hard/P009
westerndesert/westerndesert/Easy/P013
westerndesert/westerndesert/Hard/P007
================================================
FILE: dbaf/dbaf.py
================================================
import torch
import lietorch
import numpy as np
from droid_net import DroidNet
from depth_video import DepthVideo
from motion_filter import MotionFilter
from dbaf_frontend import DBAFusionFrontend
from collections import OrderedDict
from torch.multiprocessing import Process
from lietorch import SE3
import geom.projective_ops as pops
import droid_backends
import pickle
class DBAFusion:
def __init__(self, args):
super(DBAFusion, self).__init__()
self.load_weights(args.weights) # load DroidNet weights
self.args = args
# store images, depth, poses, intrinsics (shared between processes)
self.video = DepthVideo(args.image_size, args.buffer, save_pkl = args.save_pkl, stereo=args.stereo, upsample=args.upsample)
# filter incoming frames so that there is enough motion
self.filterx = MotionFilter(self.net, self.video, thresh=args.filter_thresh)
# frontend process
self.frontend = DBAFusionFrontend(self.net, self.video, self.args)
self.pklpath = args.pklpath
self.upsample = args.upsample
def load_weights(self, weights):
""" load trained model weights """
print(weights)
self.net = DroidNet()
state_dict = OrderedDict([
(k.replace("module.", ""), v) for (k, v) in torch.load(weights).items()])
state_dict["update.weight.2.weight"] = state_dict["update.weight.2.weight"][:2]
state_dict["update.weight.2.bias"] = state_dict["update.weight.2.bias"][:2]
state_dict["update.delta.2.weight"] = state_dict["update.delta.2.weight"][:2]
state_dict["update.delta.2.bias"] = state_dict["update.delta.2.bias"][:2]
self.net.load_state_dict(state_dict)
self.net.to("cuda:0").eval()
def track(self, tstamp, image, depth=None, intrinsics=None):
""" main thread - update map """
with torch.no_grad():
# check there is enough motion
self.filterx.track(tstamp, image, depth, intrinsics)
# local bundle adjustment
self.frontend()
def terminate(self, stream=None):
""" terminate the visualization process, return poses [t, q] """
del self.frontend
def save_vis_easy(self):
mcameras = {}
mpoints = {}
mstamps = {}
with torch.no_grad():
dirty_index = torch.arange(0,self.video.count_save,device='cuda')
stamps= torch.index_select(self.video.tstamp_save, 0 ,dirty_index)
poses= torch.index_select( self.video.poses_save, 0 ,dirty_index)
disps= torch.index_select( self.video.disps_save, 0 ,dirty_index)
images = torch.index_select( self.video.images_save, 0 ,dirty_index)
Ps = SE3(poses).inv().matrix().cpu().numpy()
points = droid_backends.iproj(SE3(poses).inv().data, disps, self.video.intrinsics[0]).cpu()
thresh = 0.4 * torch.ones_like(disps.mean(dim=[1,2])) / 4.0 * (1.0 / torch.median(disps.mean(dim=[1,2])))
# thresh = 0.4 * torch.ones_like(disps.mean(dim=[1,2]))
count = droid_backends.depth_filter(
self.video.poses_save, self.video.disps_save, self.video.intrinsics[0], dirty_index, thresh)
count = count.cpu()
disps = disps.cpu()
if self.upsample:
disps_up= torch.index_select( self.video.disps_up_save, 0 ,dirty_index)
disps_up = disps_up.cpu()
masks = ((count >= 1) & (disps > .5*disps.mean(dim=[1,2], keepdim=True)))
for i in range(len(dirty_index)):
pose = Ps[i]
ix = dirty_index[i].item()
mcameras[ix] = pose
mask = masks[i].reshape(-1)
pts = points[i].reshape(-1, 3)[mask].cpu().numpy()
clr = images[i].reshape(-1, 3)[mask].cpu().numpy()
stamp = stamps[i].cpu()
if self.upsample:
mpoints[ix] = {'pts':pts,'clr':clr,'disp':disps[i].cpu().numpy(),'disps_up':disps_up[i].cpu().numpy(),'rgb':images[i].cpu().numpy()}
else:
mpoints[ix] = {'pts':pts,'clr':clr,'disp':disps[i].cpu().numpy(),'rgb':images[i].cpu().numpy()}
mstamps[ix] = stamp
ddict = {'points':mpoints,'cameras':mcameras,'stamps':mstamps}
f_save = open(self.pklpath, 'wb')
pickle.dump(ddict,f_save)
mcameras = {}
mpoints = {}
mstamps = {}
with torch.no_grad():
dirty_index = torch.arange(0,self.video.count_save,device='cuda')
stamps= torch.index_select(self.video.tstamp_save, 0 ,dirty_index)
poses= torch.index_select( self.video.poses_save, 0 ,dirty_index)
disps= torch.index_select( self.video.disps_save, 0 ,dirty_index)
images = torch.index_select( self.video.images_save, 0 ,dirty_index)
Ps = SE3(poses).inv().matrix().cpu().numpy()
points = droid_backends.iproj(SE3(poses).inv().data, disps, self.video.intrinsics[0]).cpu()
thresh = 0.4 * torch.ones_like(disps.mean(dim=[1,2]))
count = droid_backends.depth_filter(
self.video.poses_save, self.video.disps_save, self.video.intrinsics[0], dirty_index, thresh)
count = count.cpu()
disps = disps.cpu()
masks = ((count >= 0) & (disps > .5*disps.mean(dim=[1,2], keepdim=True)))
for i in range(len(dirty_index)):
pose = Ps[i]
ix = dirty_index[i].item()
mcameras[ix] = pose
mask = masks[i].reshape(-1)
pts = points[i].reshape(-1, 3)[mask].cpu().numpy()
clr = images[i].reshape(-1, 3)[mask].cpu().numpy()
stamp = stamps[i].cpu()
mpoints[ix] = {'pts':pts,'clr':clr,'disp':disps[i].cpu().numpy(),'rgb':images[i].cpu().numpy()}
mstamps[ix] = stamp
ddict = {'points':mpoints,'cameras':mcameras,'stamps':mstamps}
f_save = open(self.pklpath.split('.')[0] + '_raw.pkl', 'wb')
pickle.dump(ddict,f_save)
================================================
FILE: dbaf/dbaf_frontend.py
================================================
import torch
import torchvision
import numpy as np
from lietorch import SE3, SO3
from covisible_graph import CovisibleGraph
import matplotlib.pyplot as plt
import gtsam
import math
import bisect
from math import atan2, cos, sin
import geoFunc.trans as trans
from scipy.spatial.transform import Rotation
class DBAFusionFrontend:
def __init__(self, net, video, args):
self.video = video
self.update_op = net.update
self.graph = CovisibleGraph(video, net.update, args=args)
# local optimization window
self.t0 = 0
self.t1 = 0
# frontend variables
self.is_initialized = False
self.count = 0
self.warmup = args.warmup
self.vi_warmup = 12
if 'vi_warmup' in args: self.vi_warmup = args.vi_warmup
self.beta = args.beta
self.frontend_nms = args.frontend_nms
self.keyframe_thresh = args.keyframe_thresh
self.frontend_window = args.frontend_window
self.frontend_thresh = args.frontend_thresh
self.frontend_radius = args.frontend_radius
### DBAFusion
self.all_imu = None
self.cur_imu_ii = 0
self.is_init = False
self.all_gnss = None
self.all_odo = None
self.all_gt = None
self.all_gt_keys = None
self.all_stamp = None
self.cur_stamp_ii = 0
self.visual_only = args.visual_only
self.visual_only_init = False
self.translation_threshold = 0.0
self.active_window = args.active_window
self.high_freq_output = True
self.zupt = ('use_zupt' in args and args.use_zupt)
if not self.visual_only:
self.max_age = 25
self.iters1 = 2
self.iters2 = 1
else:
self.max_age = 25
self.iters1 = 4
self.iters2 = 2
# visualization/output
self.show_plot = args.show_plot
self.result_file = open(args.resultpath,'wt')
self.plt_pos = [[],[]] # X, Y
self.plt_pos_ref = [[],[]] # X, Y
self.plt_att = [[],[],[]] # pitch, roll, yaw
self.plt_bg = [[],[],[]] # X, Y, Z
self.plt_t = []
self.refTw = np.eye(4,4)
if self.show_plot:
plt.figure('monitor',figsize=[13,4])
plt.subplot(1,3,1); plt.gca().set_title('Trajectory')
plt.gca().set_aspect(1)
plt.subplot(1,3,2); plt.gca().set_title('Attitude Error/Attitude')
plt.subplot(1,3,3); plt.gca().set_title('Gyroscope Bias')
plt.ion()
plt.pause(0.1)
def get_pose_ref(self, tt:float):
tt_found = self.all_gt_keys[bisect.bisect(self.all_gt_keys,tt)]
return tt_found, self.all_gt[tt_found]
def __rollup(self, roll):
""" roll up window states to save memory """
self.t1 -= roll
self.count -= roll
self.video.counter.value -= roll
self.video.tstamp = torch.roll(self.video.tstamp ,-roll,0)
self.video.images = torch.roll(self.video.images ,-roll,0)
self.video.dirty = torch.roll(self.video.dirty ,-roll,0)
self.video.red = torch.roll(self.video.red ,-roll,0)
self.video.poses = torch.roll(self.video.poses ,-roll,0)
self.video.disps = torch.roll(self.video.disps ,-roll,0)
self.video.disps_sens = torch.roll(self.video.disps_sens,-roll,0)
self.video.disps_up = torch.roll(self.video.disps_up ,-roll,0)
self.video.intrinsics = torch.roll(self.video.intrinsics,-roll,0)
self.video.fmaps = torch.roll(self.video.fmaps ,-roll,0)
self.video.nets = torch.roll(self.video.nets ,-roll,0)
self.video.inps = torch.roll(self.video.inps ,-roll,0)
self.graph.ii -= roll
self.graph.jj -= roll
self.graph.ii_inac -= roll
self.graph.jj_inac -= roll
rm_inac_index = torch.logical_and(torch.greater_equal(self.graph.ii_inac,0),torch.greater_equal(self.graph.jj_inac,0))
self.graph.ii_inac = self.graph.ii_inac[rm_inac_index]
self.graph.jj_inac = self.graph.jj_inac[rm_inac_index]
self.graph.target_inac = self.graph.target_inac[:,rm_inac_index,:,:,:]
self.graph.weight_inac = self.graph.weight_inac[:,rm_inac_index,:,:,:] # need test
self.graph.ii_bad -= roll
self.graph.jj_bad -= roll
self.video.last_t0 -= roll
self.video.last_t1 -= roll
self.video.cur_ii -= roll
self.video.cur_jj -= roll
if self.video.imu_enabled:
graph_temp = gtsam.NonlinearFactorGraph()
for i in range(self.video.cur_graph.size()):
f = self.video.cur_graph.at(i)
graph_temp.push_back(f.rekey((np.array(f.keys())-roll).tolist()))
self.video.cur_graph = graph_temp
result_temp = gtsam.Values()
for i in self.video.cur_result.keys():
if gtsam.Symbol(i).chr() == ord('b'):
result_temp.insert(i-roll,self.video.cur_result.atConstantBias(i))
elif gtsam.Symbol(i).chr() == ord('v'):
result_temp.insert(i-roll,self.video.cur_result.atVector(i))
elif gtsam.Symbol(i).chr() == ord('x'):
result_temp.insert(i-roll,self.video.cur_result.atPose3(i))
else:
raise Exception()
self.video.cur_result = result_temp
self.video.marg_factor = self.video.marg_factor.rekey((np.array(self.video.marg_factor.keys())-roll).tolist())
self.video.state.timestamps = self.video.state.timestamps [roll:]
self.video.state.wTbs = self.video.state.wTbs [roll:]
self.video.state.vs = self.video.state.vs [roll:]
self.video.state.bs = self.video.state.bs [roll:]
self.video.state.preintegrations = self.video.state.preintegrations [roll:]
self.video.state.preintegrations_meas = self.video.state.preintegrations_meas [roll:]
self.video.state.gnss_valid = self.video.state.gnss_valid [roll:]
self.video.state.gnss_position = self.video.state.gnss_position [roll:]
self.video.state.odo_valid = self.video.state.odo_valid [roll:]
self.video.state.odo_vel = self.video.state.odo_vel [roll:]
def __update(self):
""" add edges, perform update """
self.count += 1
self.t1 += 1
if self.video.imu_enabled and (self.video.tstamp[self.t1-1] - self.video.vi_init_time > 5.0):
self.video.reinit = True
self.video.vi_init_time = 1e9
## new frame comes, append IMU
cur_t = float(self.video.tstamp[self.t1-1].detach().cpu())
self.video.logger.info('predict %f' %cur_t)
while self.all_imu[self.cur_imu_ii][0] < cur_t:
## high-frequency output
# predict the pose of skipped frames through IMU preintegration
if self.high_freq_output and self.video.imu_enabled:
if self.all_imu[self.cur_imu_ii][0] > float(self.all_stamp[self.cur_stamp_ii][0]):
self.video.state.append_imu_temp(float(self.all_stamp[self.cur_stamp_ii][0]),\
self.all_imu[self.cur_imu_ii][4:7],\
self.all_imu[self.cur_imu_ii][1:4]/180*math.pi,True)
if float(self.all_stamp[self.cur_stamp_ii][0]) > self.video.state.timestamps[-1] and\
math.fabs(cur_t - float(self.all_stamp[self.cur_stamp_ii][0]))>1e-3:
pose_temp = self.video.state.pose_temp
ppp = pose_temp.pose().translation()
qqq = Rotation.from_matrix(pose_temp.pose().rotation().matrix()).as_quat()
line = '%.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f'%(float(self.all_stamp[self.cur_stamp_ii][0]),ppp[0],ppp[1],ppp[2]\
,qqq[0],qqq[1],qqq[2],qqq[3])
if self.video.gnss_init_t1>0:
p = self.video.ten0 + np.matmul(trans.Cen(self.video.ten0), ppp)
line += ' %.6f %.6f %.6f'% (p[0],p[1],p[2])
self.result_file.writelines(line+'\n')
# self.result_file.flush()
self.cur_stamp_ii += 1
self.video.state.append_imu_temp(self.all_imu[self.cur_imu_ii][0],\
self.all_imu[self.cur_imu_ii][4:7],\
self.all_imu[self.cur_imu_ii][1:4]/180*math.pi)
self.video.state.append_imu(self.all_imu[self.cur_imu_ii][0],\
self.all_imu[self.cur_imu_ii][4:7],\
self.all_imu[self.cur_imu_ii][1:4]/180*math.pi)
self.cur_imu_ii += 1
self.video.state.append_imu(cur_t,\
self.all_imu[self.cur_imu_ii][4:7],\
self.all_imu[self.cur_imu_ii][1:4]/180*math.pi)
self.video.state.append_img(cur_t)
## append GNSS
if len(self.all_gnss) > 0: gnss_found = bisect.bisect(self.all_gnss[:,0],cur_t - 1e-6)
else: gnss_found = -1
if gnss_found > 0 and self.all_gnss[gnss_found,0] - cur_t < 0.01 :
self.video.state.append_gnss(cur_t,self.all_gnss[gnss_found,1:4])
## append ZUPT
if self.zupt and self.video.state.preintegrations[self.t1-3].deltaTij() > 3.0:
if np.linalg.norm(self.video.state.vs[self.t1-2]) < 0.025:
self.video.state.append_odo(cur_t,np.array([.0,.0,.0]))
## append ODO
if len(self.all_odo) > 0: odo_found = bisect.bisect(self.all_odo[:,0],cur_t - 1e-6)
else: odo_found = -1
if odo_found > 0 and self.all_odo[odo_found,0] - cur_t < 0.01 :
self.video.state.append_odo(cur_t,self.all_odo[odo_found,1:4])
self.video.state.append_imu(self.all_imu[self.cur_imu_ii][0],\
self.all_imu[self.cur_imu_ii][4:7],\
self.all_imu[self.cur_imu_ii][1:4]/180*math.pi)
self.cur_imu_ii += 1
## predict pose (<5 ms)
if self.video.imu_enabled:
Twc = (self.video.state.wTbs[-1] * self.video.Tbc).matrix()
TTT = torch.tensor(np.linalg.inv(Twc))
q = torch.tensor(Rotation.from_matrix(TTT[:3, :3]).as_quat())
t = TTT[:3,3]
self.video.poses[self.t1-1] = torch.cat([t,q])
self.video.logger.info('manage edges')
## manage edges (60 ms)
if self.graph.corr is not None:
if self.visual_only:
self.graph.rm_factors(torch.logical_and(self.graph.age > self.max_age,\
torch.logical_or(self.graph.ii < self.t1-self.active_window,self.graph.jj < self.t1-self.active_window)), store=True)
else:
self.graph.rm_factors(torch.logical_or(self.graph.age > self.max_age,\
torch.logical_or(self.graph.ii < self.t1-self.active_window,self.graph.jj < self.t1-self.active_window)), store=True)
self.graph.add_proximity_factors(self.t1-5, max(self.t1-self.frontend_window, 0),
rad=self.frontend_radius, nms=self.frontend_nms, thresh=self.frontend_thresh, beta=self.beta, remove=True)
self.video.logger.info('non-keyframes %d' % self.graph.ii.shape[0])
## non-keyframe update
self.video.disps[self.t1-1] = torch.where(self.video.disps_sens[self.t1-1] > 0,
self.video.disps_sens[self.t1-1], self.video.disps[self.t1-1])
for itr in range(self.iters1):
self.graph.update(None, None, use_inactive=True)
self.rollup = False
if self.t1 > 65:
self.__rollup(30)
print('rollup ',self.graph.ii)
self.rollup = True
self.video.logger.info('output')
## visualization/output
poses = SE3(self.video.poses)
d = self.video.distance([self.t1-3], [self.t1-2], beta=self.beta, bidirectional=True)
TTT = np.matmul(poses[self.t1-1].cpu().inv().matrix(),np.linalg.inv(self.video.Ti1c))
if self.video.imu_enabled or (self.visual_only and self.visual_only_init):
ppp = TTT[0:3,3]
qqq = Rotation.from_matrix(TTT[:3, :3]).as_quat()
line = '%.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f'%(cur_t,ppp[0],ppp[1],ppp[2]\
,qqq[0],qqq[1],qqq[2],qqq[3])
if self.video.gnss_init_t1>0:
p = self.video.ten0 + np.matmul(trans.Cen(self.video.ten0), ppp.numpy())
line += ' %.6f %.6f %.6f'% (p[0],p[1],p[2])
self.result_file.writelines(line+'\n')
self.result_file.flush()
TTTref = np.matmul(self.refTw,TTT)
ppp = TTTref[0:3,3]
if self.show_plot:
# if math.fabs(tt_found - cur_t) < 0.1: # for kitti and whu
self.plt_pos[0].append(ppp[0])
self.plt_pos[1].append(ppp[1])
a1 = np.array(trans.m2att(TTTref[0:3,0:3]) )* 57.3
if self.all_gt is not None:
tt_found,dd = self.get_pose_ref(cur_t -1e-3)
self.plt_pos_ref[0].append(dd['T'][0,3])
self.plt_pos_ref[1].append(dd['T'][1,3])
a2 = np.array(trans.m2att(dd['T'][0:3,0:3]) )* 57.3
a1 -= a2
self.plt_att[0].append(a1[0])
self.plt_att[1].append(a1[1])
self.plt_att[2].append(a1[2])
bg = self.video.state.bs[self.t1-1].gyroscope()
self.plt_bg[0].append(bg[0])
self.plt_bg[1].append(bg[1])
self.plt_bg[2].append(bg[2])
self.plt_t.append(cur_t)
if self.rollup:
plt.subplot(1,3,1)
plt.cla(); plt.gca().set_title('Trajectory')
plt.plot(self.plt_pos[0],self.plt_pos[1],marker='^')
plt.plot(self.plt_pos_ref[0],self.plt_pos_ref[1],marker='^')
plt.subplot(1,3,2)
plt.cla(); plt.gca().set_title('Attitude Error/Attitude')
plt.plot(self.plt_t,self.plt_att[0],c='r')
plt.plot(self.plt_t,self.plt_att[1],c='g')
plt.plot(self.plt_t,self.plt_att[2],c='b')
plt.ylim([-10,10])
plt.subplot(1,3,3)
plt.cla(); plt.gca().set_title('Gyroscope Bias')
plt.plot(self.plt_t,self.plt_bg[0],c='r')
plt.plot(self.plt_t,self.plt_bg[1],c='g')
plt.plot(self.plt_t,self.plt_bg[2],c='b')
plt.pause(0.1)
## keyframe update
self.video.logger.info('keyframes %d' % self.graph.ii.shape[0])
if self.t1 > 10:
cam_translation = torch.norm((poses[(self.t1-10):(self.t1-3)] * poses[self.t1-2].inv()[None]).translation()[:,0:3],dim=1)
else:
cam_translation = torch.norm((poses[(self.t1-6):(self.t1-3)] * poses[self.t1-2].inv()[None]).translation()[:,0:3],dim=1)
if (d.item() < self.keyframe_thresh or (self.video.imu_enabled and torch.sum(cam_translation < self.translation_threshold)>0)): # gnss
self.video.logger.info('remove new frame!!!!!!!!!!!!1')
self.graph.rm_keyframe(self.t1 - 2)
# merge preintegration[self.t1-2] and preintegration[self.t1-3]
for iii in range(len(self.video.state.preintegrations_meas[self.t1-2])):
dd = self.video.state.preintegrations_meas[self.t1-2][iii]
if dd[2] > 0:
self.video.state.preintegrations[self.t1-3].integrateMeasurement(dd[0],\
dd[1],\
dd[2])
self.video.state.preintegrations_meas[self.t1-3].append(dd)
self.video.state.preintegrations[self.t1-2] = self.video.state.preintegrations[self.t1-1]
self.video.state.preintegrations_meas[self.t1-2] = self.video.state.preintegrations_meas[self.t1-1]
self.video.state.preintegrations.pop()
self.video.state.preintegrations_meas.pop()
self.video.rm_new_gnss(self.t1-2)
self.video.state.wTbs[self.t1-2] = self.video.state.wTbs[self.t1-1]; self.video.state.wTbs.pop()
self.video.state.bs [self.t1-2] = self.video.state.bs [self.t1-1]; self.video.state.bs.pop()
self.video.state.vs [self.t1-2] = self.video.state.vs [self.t1-1]; self.video.state.vs .pop()
self.video.state.gnss_valid [self.t1-2] = self.video.state.gnss_valid [self.t1-1]; self.video.state.gnss_valid .pop()
self.video.state.gnss_position [self.t1-2] = self.video.state.gnss_position [self.t1-1]; self.video.state.gnss_position .pop()
self.video.state.odo_valid [self.t1-2] = self.video.state.odo_valid [self.t1-1]; self.video.state.odo_valid .pop()
self.video.state.odo_vel [self.t1-2] = self.video.state.odo_vel [self.t1-1]; self.video.state.odo_vel .pop()
with self.video.get_lock():
self.video.counter.value -= 1
self.t1 -= 1
else:
for itr in range(self.iters2):
# print('b%d' % itr)
self.graph.update(None, None, use_inactive=True)
## try initializing VI/GNSS
if self.t1 > self.vi_warmup and self.video.vi_init_t1 < 0:
self.init_VI()
if not self.visual_only:
for i in range(len(self.all_stamp)): # skip to next image
if float(self.all_stamp[i][0]) < cur_t + 1e-6: continue
else:
self.cur_stamp_ii = i
break
if self.video.imu_enabled and self.video.gnss_init_time <= 0.0 and len(self.all_gnss)>0:
self.init_GNSS()
## set pose for next itration
self.video.poses[self.t1] = self.video.poses[self.t1-1]
self.video.disps[self.t1] = self.video.disps[self.t1-1].mean() * 1.0
self.video.dirty[self.graph.ii.min():self.t1] = True
def init_IMU(self):
""" initialize IMU states """
cur_t = float(self.video.tstamp[self.t0].detach().cpu())
for i in range(len(self.all_imu)):
if self.all_imu[i][0] < cur_t - 1e-6: continue
else:
self.cur_imu_ii = i
break
for i in range(self.t0,self.t1):
tt = self.video.tstamp[i]
if i == self.t0:
self.video.state.init_first_state(cur_t,np.zeros(3),\
np.eye(3),\
np.zeros(3))
self.video.state.append_imu(self.all_imu[self.cur_imu_ii][0],\
self.all_imu[self.cur_imu_ii][4:7],\
self.all_imu[self.cur_imu_ii][1:4]/180*math.pi)
self.cur_imu_ii += 1
self.is_init = True
else:
cur_t = float(self.video.tstamp[i].detach().cpu())
while self.all_imu[self.cur_imu_ii][0] < cur_t:
self.video.state.append_imu(self.all_imu[self.cur_imu_ii][0],\
self.all_imu[self.cur_imu_ii][4:7],\
self.all_imu[self.cur_imu_ii][1:4]/180*math.pi)
self.cur_imu_ii += 1
self.video.state.append_imu(cur_t,\
self.all_imu[self.cur_imu_ii][4:7],\
self.all_imu[self.cur_imu_ii][1:4]/180*math.pi)
self.video.state.append_img(cur_t)
if len(self.all_gnss) > 0: gnss_found = bisect.bisect(self.all_gnss[:,0],cur_t - 1e-6)
else: gnss_found = -1
if gnss_found > 0 and self.all_gnss[gnss_found,0] - cur_t < 0.01:
self.video.state.append_gnss(cur_t,self.all_gnss[gnss_found,1:4])
if len(self.all_odo) > 0: odo_found = bisect.bisect(self.all_odo[:,0],cur_t - 1e-6)
else: odo_found = -1
if odo_found > 0 and self.all_odo[odo_found,0] - cur_t < 0.01 :
self.video.state.append_odo(cur_t,self.all_odo[odo_found,1:4])
self.video.state.append_imu(self.all_imu[self.cur_imu_ii][0],\
self.all_imu[self.cur_imu_ii][4:7],\
self.all_imu[self.cur_imu_ii][1:4]/180*math.pi)
self.cur_imu_ii += 1
Twc = np.matmul(np.array([[1,0,0,0],\
[0,1,0,0],\
[0,0,1,0.02*i],\
[0,0,0,1]]),self.video.Ti1c) # perturb the camera poses, which benefits the robustness of initial BA
TTT = torch.tensor(np.linalg.inv(Twc))
q = torch.tensor(Rotation.from_matrix(TTT[:3, :3]).as_quat())
t = TTT[:3,3]
if not self.video.imu_enabled:
self.video.poses[i] = torch.cat([t,q])
def init_VI(self):
""" initialize the V-I system, referring to VIN-Fusion """
sum_g = np.zeros(3,dtype = np.float64)
ccount = 0
for i in range(self.t1 - 8 ,self.t1-1):
dt = self.video.state.preintegrations[i].deltaTij()
tmp_g = self.video.state.preintegrations[i].deltaVij()/dt
sum_g += tmp_g
ccount += 1
aver_g = sum_g * 1.0 / ccount
var_g = 0.0
for i in range(self.t1 - 8 ,self.t1-1):
dt = self.video.state.preintegrations[i].deltaTij()
tmp_g = self.video.state.preintegrations[i].deltaVij()/dt
var_g += np.linalg.norm(tmp_g - aver_g)**2
var_g =math.sqrt(var_g/ccount)
if var_g < 0.25:
print("IMU excitation not enough!",var_g)
else:
poses = SE3(self.video.poses)
self.plt_pos = [[],[]]
self.plt_pos_ref = [[],[]]
for i in range(0,self.t1):
ppp = np.matmul(poses[i].cpu().inv().matrix(),np.linalg.inv(self.video.Ti1c))[0:3,3]
self.plt_pos[0].append(ppp[0])
self.plt_pos[1].append(ppp[1])
if self.all_gt is not None:
tt_found,dd = self.get_pose_ref(self.video.tstamp[i]-1e-3)
self.plt_pos_ref[0].append(dd['T'][0,3])
self.plt_pos_ref[1].append(dd['T'][1,3])
if self.show_plot:
plt.subplot(1,3,1)
plt.cla(); plt.gca().set_title('Trajectory')
plt.plot(self.plt_pos[0],self.plt_pos[1],marker='^')
plt.plot(self.plt_pos_ref[0],self.plt_pos_ref[1],marker='^')
plt.pause(0.1)
if not self.visual_only:
self.VisualIMUAlignment(self.t1 - 8 ,self.t1, ignore_lever= True)
self.graph.update(None, None, use_inactive=True)
self.VisualIMUAlignment(self.t1 - 8 ,self.t1, ignore_lever= False)
self.graph.update(None, None, use_inactive=True)
self.VisualIMUAlignment(self.t1 - 8 ,self.t1, ignore_lever= False)
self.video.imu_enabled = True
else:
self.VisualIMUAlignment(self.t1 - 8 ,self.t1, ignore_lever= True)
self.graph.update(None, None, use_inactive=True)
self.VisualIMUAlignment(self.t1 - 8 ,self.t1, ignore_lever= False)
self.graph.update(None, None, use_inactive=True)
self.VisualIMUAlignment(self.t1 - 8 ,self.t1, ignore_lever= False)
self.visual_only_init = True
self.video.set_prior(self.video.last_t0,self.t1)
self.plt_pos = [[],[]]
self.plt_pos_ref = [[],[]]
for i in range(0,self.t1):
TTT = self.video.state.wTbs[i].matrix()
ppp = TTT[0:3,3]
qqq = Rotation.from_matrix(TTT[:3, :3]).as_quat()
self.result_file.writelines('%.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f\n'%(self.video.tstamp[i],ppp[0],ppp[1],ppp[2]\
,qqq[0],qqq[1],qqq[2],qqq[3]))
TTTref = np.matmul(self.refTw,TTT) # for visualization
ppp = TTTref[0:3,3]
qqq = Rotation.from_matrix(TTTref[:3, :3]).as_quat()
self.plt_pos[0].append(ppp[0])
self.plt_pos[1].append(ppp[1])
if self.all_gt is not None:
tt_found,dd = self.get_pose_ref(self.video.tstamp[i]-1e-3)
self.plt_pos_ref[0].append(dd['T'][0,3])
self.plt_pos_ref[1].append(dd['T'][1,3])
if self.show_plot:
plt.subplot(1,3,1)
plt.cla(); plt.gca().set_title('Trajectory')
plt.plot(self.plt_pos[0],self.plt_pos[1],marker='^')
plt.plot(self.plt_pos_ref[0],self.plt_pos_ref[1],marker='^')
plt.pause(0.1)
for itr in range(1):
self.graph.update(None, None, use_inactive=True)
def init_GNSS(self):
""" initialize the GNSS for geo-referencing fusion """
ten0 = np.array([self.all_gt[self.all_gt_keys[0]]['X0'],\
self.all_gt[self.all_gt_keys[0]]['Y0'],\
self.all_gt[self.all_gt_keys[0]]['Z0']])
self.video.ten0 = ten0
tn0 = []; tw =[]
for i in range(len(self.video.state.wTbs) - 10,len(self.video.state.wTbs)):
if self.video.state.gnss_valid[i]:
# if not is_ref_set:
# ten0 = self.video.sgraph.gnss_position[i]
# is_ref_set = True
teg = self.video.state.gnss_position[i]
print(self.video.ten0)
print(self.video.state.gnss_position[i])
tn0g = np.matmul(trans.Cen(self.video.ten0).T,(self.video.state.gnss_position[i] - self.video.ten0))
twb = self.video.state.wTbs[i].translation()
tn0.append(tn0g)
tw.append(twb)
if len(tn0) > 1:
tn0 = np.array(tn0)
tw = np.array(tw)
bl = np.linalg.norm(tn0[-1] - tn0[0])
print('GNSS Alignment Baseline: %.5f' % bl)
if bl < 10.0:
print('Baseline too short!!')
return
heading_w = math.atan2(tw[-1,1]-tw[0,1],tw[-1,0]-tw[0,0])
heading_n0 = math.atan2(tn0[-1,1]-tn0[0,1],tn0[-1,0]-tn0[0,0])
s_w = np.linalg.norm(tw[-1] - tw[0])
s_n0 = np.linalg.norm(tn0[-1] - tn0[0])
s = s_n0 / s_w
Rn0w = trans.att2m(np.array([.0,.0,-heading_w + heading_n0]))
tn0w = tn0 - np.matmul(Rn0w,tw.T * s).T
poses = SE3(self.video.poses)
wTcs = poses.inv().matrix().cpu().numpy()
wTbs = np.matmul(wTcs,self.video.Tbc.inverse().matrix())
wTbs[:,0:3,3] = np.matmul(Rn0w,(wTbs[:,0:3,3]*s).T).T + tn0w[0]
wTbs[:,0:3,0:3] = np.matmul(Rn0w, (wTbs[:,0:3,0:3]).T).T
self.refTw = np.eye(4,4)
for i in range(0,self.t1):
self.video.state.wTbs[i] = gtsam.Pose3(wTbs[i])
self.video.state.vs[i] *= s
wTcs = np.matmul(wTbs,self.video.Tbc.matrix())
for i in range(0,self.t1):
TTT = np.linalg.inv(wTcs[i])
q = torch.tensor(Rotation.from_matrix(TTT[:3, :3]).as_quat())
t = torch.tensor(TTT[:3,3])
self.video.poses[i] = torch.cat([t,q])
self.video.disps[i] /= s
self.video.gnss_init_t1 = self.t1
self.video.gnss_init_time = self.video.tstamp[self.t1-1]
self.video.set_prior(self.video.last_t0,self.t1)
self.plt_pos = [[],[]]
self.plt_pos_ref = [[],[]]
for i in range(0,self.t1):
TTT = self.video.state.wTbs[i].matrix()
ppp = TTT[0:3,3]
qqq = Rotation.from_matrix(TTT[:3, :3]).as_quat()
self.result_file.writelines('%.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f\n'%(self.video.tstamp[i],ppp[0],ppp[1],ppp[2]\
,qqq[0],qqq[1],qqq[2],qqq[3]))
TTTref = np.matmul(self.refTw,TTT) # for visualization
ppp = TTTref[0:3,3]
qqq = Rotation.from_matrix(TTTref[:3, :3]).as_quat()
self.plt_pos[0].append(ppp[0])
self.plt_pos[1].append(ppp[1])
if self.all_gt is not None:
tt_found,dd = self.get_pose_ref(self.video.tstamp[i]-1e-3)
self.plt_pos_ref[0].append(dd['T'][0,3])
self.plt_pos_ref[1].append(dd['T'][1,3])
if self.show_plot:
plt.subplot(1,3,1)
plt.cla(); plt.gca().set_title('Trajectory')
plt.plot(self.plt_pos[0],self.plt_pos[1],marker='^')
plt.plot(self.plt_pos_ref[0],self.plt_pos_ref[1],marker='^')
plt.pause(0.1)
for itr in range(1):
self.graph.update(None, None, use_inactive=True)
print('GNSS initialized!!!!')
def VisualIMUAlignment(self, t0, t1, ignore_lever, disable_scale = False):
poses = SE3(self.video.poses)
wTcs = poses.inv().matrix().cpu().numpy()
if not ignore_lever:
wTbs = np.matmul(wTcs,self.video.Tbc.inverse().matrix())
else:
T_tmp = self.video.Tbc.inverse().matrix()
T_tmp[0:3,3] = 0.0
wTbs = np.matmul(wTcs,T_tmp)
cost = 0.0
# solveGyroscopeBias
A = np.zeros([3,3])
b = np.zeros(3)
H1 =np.zeros([15,6], order='F', dtype=np.float64)
H2 =np.zeros([15,3], order='F', dtype=np.float64)
H3 =np.zeros([15,6], order='F', dtype=np.float64)
H4 =np.zeros([15,3], order='F', dtype=np.float64)
H5 =np.zeros([15,6], order='F', dtype=np.float64) # navstate wrt. bias
H6 =np.zeros([15,6], order='F', dtype=np.float64)
for i in range(t0,t1-1):
pose_i = gtsam.Pose3(wTbs[i])
pose_j = gtsam.Pose3(wTbs[i+1])
Rij = np.matmul(pose_i.rotation().matrix().T,pose_j.rotation().matrix())
imu_factor = gtsam.gtsam.CombinedImuFactor(0,1,2,3,4,5,self.video.state.preintegrations[i])
err = imu_factor.evaluateErrorCustom(pose_i,self.video.state.vs[i],\
pose_j,self.video.state.vs[i+1],\
self.video.state.bs[i],self.video.state.bs[i+1],\
H1,H2,H3,H4,H5,H6)
tmp_A = H5[0:3,3:6]
tmp_b = err[0:3]
cost += np.dot(tmp_b,tmp_b)
A += np.matmul(tmp_A.T,tmp_A)
b += np.matmul(tmp_A.T,tmp_b)
bg = -np.matmul(np.linalg.inv(A),b)
for i in range(0,t1-1):
pim = gtsam.PreintegratedCombinedMeasurements(self.video.state.params,\
gtsam.imuBias.ConstantBias(np.array([.0,.0,.0]),bg))
for iii in range(len(self.video.state.preintegrations_meas[i])):
dd = self.video.state.preintegrations_meas[i][iii]
if dd[2] > 0: pim.integrateMeasurement(dd[0],dd[1],dd[2])
self.video.state.preintegrations[i] = pim
self.video.state.bs[i] = gtsam.imuBias.ConstantBias(np.array([.0,.0,.0]),bg)
print('bg: ',bg)
# linearAlignment
all_frame_count = t1 - t0
n_state = all_frame_count * 3 + 3 + 1
A = np.zeros([n_state,n_state])
b = np.zeros(n_state)
i_count = 0
for i in range(t0,t1-1):
pose_i = gtsam.Pose3(wTbs[i])
pose_j = gtsam.Pose3(wTbs[i+1])
R_i = pose_i.rotation().matrix()
t_i = pose_i.translation()
R_j = pose_j.rotation().matrix()
t_j = pose_j.translation()
pim = self.video.state.preintegrations[i]
tic = self.video.Tbc.translation()
tmp_A = np.zeros([6,10])
tmp_b = np.zeros(6)
dt = pim.deltaTij()
tmp_A[0:3,0:3] = -dt * np.eye(3,3)
tmp_A[0:3,6:9] = R_i.T * dt * dt / 2
tmp_A[0:3,9] = np.matmul(R_i.T, t_j-t_i) / 100.0
tmp_b[0:3] = pim.deltaPij()
tmp_A[3:6,0:3] = -np.eye(3,3)
tmp_A[3:6,3:6] = np.matmul(R_i.T, R_j)
tmp_A[3:6,6:9] = R_i.T * dt
tmp_b[3:6] = pim.deltaVij()
r_A = np.matmul(tmp_A.T,tmp_A)
r_b = np.matmul(tmp_A.T,tmp_b)
A[i_count*3:i_count*3+6,i_count*3:i_count*3+6] += r_A[0:6,0:6]
b[i_count*3:i_count*3+6] += r_b[0:6]
A[-4:,-4:] += r_A[-4:,-4:]
b[-4:] += r_b[-4:]
A[i_count*3:i_count*3+6,n_state-4:] += r_A[0:6,-4:]
A[n_state-4:,i_count*3:i_count*3+6] += r_A[-4:,0:6]
i_count += 1
A = A * 1000.0
b = b * 1000.0
x = np.matmul(np.linalg.inv(A),b)
s = x[n_state-1] / 100.0
g = x[-4:-1]
# RefineGravity
g0 = g / np.linalg.norm(g) * 9.81
lx = np.zeros(3)
ly = np.zeros(3)
n_state = all_frame_count * 3 + 2 + 1
A = np.zeros([n_state,n_state])
b = np.zeros(n_state)
for k in range(4):
aa = g / np.linalg.norm(g)
tmp = np.array([.0,.0,1.0])
bb = (tmp - np.dot(aa,tmp) * aa)
bb /= np.linalg.norm(bb)
cc = np.cross(aa,bb)
bc = np.zeros([3,2])
bc[0:3,0] = bb
bc[0:3,1] = cc
lxly = bc
i_count = 0
for i in range(t0,t1-1):
pose_i = gtsam.Pose3(wTbs[i])
pose_j = gtsam.Pose3(wTbs[i+1])
R_i = pose_i.rotation().matrix()
t_i = pose_i.translation()
R_j = pose_j.rotation().matrix()
t_j = pose_j.translation()
tmp_A = np.zeros([6,9])
tmp_b = np.zeros(6)
pim = self.video.state.preintegrations[i]
dt = pim.deltaTij()
tmp_A[0:3,0:3] = -dt *np.eye(3,3)
tmp_A[0:3,6:8] = np.matmul(R_i.T,lxly) * dt * dt /2
tmp_A[0:3,8] = np.matmul(R_i.T,t_j - t_i) / 100.0
tmp_b[0:3] = pim.deltaPij() - np.matmul(R_i.T,g0) * dt * dt / 2
tmp_A[3:6,0:3] = -np.eye(3)
tmp_A[3:6,3:6] = np.matmul(R_i.T,R_j)
tmp_A[3:6,6:8] = np.matmul(R_i.T,lxly) * dt
tmp_b[3:6] = pim.deltaVij() - np.matmul(R_i.T,g0) * dt
r_A = np.matmul(tmp_A.T,tmp_A)
r_b = np.matmul(tmp_A.T,tmp_b)
A[i_count*3:i_count*3+6,i_count*3:i_count*3+6] += r_A[0:6,0:6]
b[i_count*3:i_count*3+6] += r_b[0:6]
A[-3:,-3:] += r_A[-3:,-3:]
b[-3:] += r_b[-3:]
A[i_count*3:i_count*3+6,n_state-3:] += r_A[0:6,-3:]
A[n_state-3:,i_count*3:i_count*3+6] += r_A[-3:,0:6]
i_count += 1
A = A * 1000.0
b = b * 1000.0
x = np.matmul(np.linalg.inv(A),b)
dg = x[-3:-1]
g0 = g0 + np.matmul(lxly,dg)
g0 = g0 / np.linalg.norm(g0) * 9.81
s = x[-1] / 100.0
print(s,g0,x)
if disable_scale:
s = 1.0
print('g,s:',g,s)
if math.fabs(np.linalg.norm(g) - 9.81) < 0.5 and s > 0:
print('V-I successfully initialized!')
# visualInitialAlign
wTbs[:,0:3,3] *= s # !!!!!!!!!!!!!!!!!!!!!!!!
for i in range(0, t1-t0):
self.video.state.vs[i+t0] = np.matmul(wTbs[i+t0,0:3,0:3],x[i*3:i*3+3])
# g2R
ng1 = g0/ np.linalg.norm(g0)
ng2 = np.array([0,0,1.0])
R0 = trans.FromTwoVectors(ng1,ng2)
yaw = trans.R2ypr(R0)[0]
R0 = np.matmul(trans.ypr2R(np.array([-yaw,0,0])),R0)
# align for visualization
ppp = np.matmul(R0,wTbs[t1-1,0:3,3])
RRR = np.matmul(R0,wTbs[t1-1,0:3,0:3])
if self.all_gt is not None: # align the initial poses for visualization
tt_found,dd = self.get_pose_ref(self.video.tstamp[t1-1]-1e-3)
self.refTw = np.matmul(dd['T'],np.linalg.inv(wTbs[t1-1]))
self.refTw[0:3,0:3] = trans.att2m([0,0,trans.m2att(self.refTw[0:3,0:3])[2]])
g = np.matmul(R0,g0)
for i in range(0,t1):
wTbs[i,0:3,3] = np.matmul(R0,wTbs[i,0:3,3])
wTbs[i,0:3,0:3] = np.matmul(R0,wTbs[i,0:3,0:3])
self.video.state.vs[i] = np.matmul(R0, self.video.state.vs[i])
self.video.state.wTbs[i] = gtsam.Pose3(wTbs[i])
self.video.vi_init_t1 = t1
self.video.vi_init_time = self.video.tstamp[t1-1]
if not ignore_lever:
wTcs = np.matmul(wTbs,self.video.Tbc.matrix())
else:
T_tmp = self.video.Tbc.matrix()
T_tmp[0:3,3] = 0.0
wTcs = np.matmul(wTbs,T_tmp)
for i in range(0,t1):
TTT = np.linalg.inv(wTcs[i])
q = torch.tensor(Rotation.from_matrix(TTT[:3, :3]).as_quat())
t = torch.tensor(TTT[:3,3])
self.video.poses[i] = torch.cat([t,q])
self.video.disps[i] /= s
def __initialize(self):
""" initialize the SLAM system """
self.t0 = 0
self.t1 = self.video.counter.value
self.graph.add_neighborhood_factors(self.t0, self.t1, r=3)
self.init_IMU()
self.graph.video.imu_enabled = False
for itr in range(8):
self.graph.update(1, use_inactive=True)
self.graph.add_proximity_factors(0, 0, rad=2, nms=2, thresh=self.frontend_thresh, remove=False)
for itr in range(8):
self.graph.update(1, use_inactive=True)
self.graph.video.imu_enabled = False
for itr in range(8):
self.graph.update(1, use_inactive=True)
# torch.concat([self.graph.ii[None],self.graph.jj[None]]).T
# self.video.normalize()
self.video.poses[self.t1] = self.video.poses[self.t1-1].clone()
self.video.disps[self.t1] = self.video.disps[self.t1-4:self.t1].mean()
# initialization complete
self.is_initialized = True
with self.video.get_lock():
self.video.ready.value = 1
self.video.dirty[:self.t1] = True
self.graph.rm_factors(self.graph.ii < self.warmup-4, store=True)
def __call__(self):
""" main update """
# do initialization
if not self.is_initialized and self.video.counter.value == self.warmup:
self.__initialize()
# do update
elif self.is_initialized and self.t1 < self.video.counter.value:
self.__update()
================================================
FILE: dbaf/depth_video.py
================================================
import numpy as np
import torch
import lietorch
import droid_backends
from torch.multiprocessing import Process, Queue, Lock, Value
from droid_net import cvx_upsample
import geom.projective_ops as pops
from multi_sensor import MultiSensorState
import gtsam
from gtsam.symbol_shorthand import B, V, X
from scipy.spatial.transform import Rotation
import copy
import logging
import geoFunc.trans as trans
from lietorch import SE3
def BA2GTSAM(H: np.ndarray, v: np.ndarray, Tbc: gtsam.Pose3):
A = -Tbc.inverse().AdjointMap()
# A = -np.eye(6,6)
A = np.concatenate([A[3:6,:],A[0:3,:]],axis=0)
ss = H.shape[0]//6
J = np.zeros_like(H)
for i in range(ss):
J[(i*6):(i*6+6),(i*6):(i*6+6)] = A
JT = J.T
return np.matmul(np.matmul(JT,H),J),np.matmul(JT,v)
def CustomHessianFactor(values: gtsam.Values, H: np.ndarray, v: np.ndarray):
info_expand = np.zeros([H.shape[0]+1,H.shape[1]+1])
info_expand[0:-1,0:-1] = H
info_expand[0:-1,-1] = v
info_expand[-1,-1] = 0.0 # This is meaningless.
h_f = gtsam.HessianFactor(values.keys(),[6]*len(values.keys()),info_expand)
l_c = gtsam.LinearContainerFactor(h_f,values)
return l_c
class DepthVideo:
def __init__(self, image_size=[480, 640], buffer=1024, save_pkl = False, stereo=False, upsample = False, device="cuda:0"):
# current keyframe count
self.counter = Value('i', 0)
self.ready = Value('i', 0)
self.ht = ht = image_size[0]
self.wd = wd = image_size[1]
### state attributes ###
self.tstamp = torch.zeros(buffer, device="cuda", dtype=torch.float64).share_memory_()
self.images = torch.zeros(buffer, 3, ht, wd, device="cuda", dtype=torch.uint8)
self.dirty = torch.zeros(buffer, device="cuda", dtype=torch.bool).share_memory_()
self.red = torch.zeros(buffer, device="cuda", dtype=torch.bool).share_memory_()
self.poses = torch.zeros(buffer, 7, device="cuda", dtype=torch.float).share_memory_()
self.disps = torch.ones(buffer, ht//8, wd//8, device="cuda", dtype=torch.float).share_memory_()
self.disps_sens = torch.zeros(buffer, ht//8, wd//8, device="cuda", dtype=torch.float).share_memory_()
self.disps_up = torch.zeros(buffer, ht, wd, device="cuda", dtype=torch.float).share_memory_()
self.intrinsics = torch.zeros(buffer, 4, device="cuda", dtype=torch.float).share_memory_()
self.stereo = stereo
c = 1 if not self.stereo else 2
### feature attributes ###
self.fmaps = torch.zeros(buffer, c, 128, ht//8, wd//8, dtype=torch.half, device="cuda").share_memory_()
self.nets = torch.zeros(buffer, 128, ht//8, wd//8, dtype=torch.half, device="cuda").share_memory_()
self.inps = torch.zeros(buffer, 128, ht//8, wd//8, dtype=torch.half, device="cuda").share_memory_()
# initialize poses to identity transformation
self.poses[:] = torch.as_tensor([0, 0, 0, 0, 0, 0, 1], dtype=torch.float, device="cuda")
### DBAFusion
# for .pkl saving
self.disps_save = torch.ones(5000, ht//8, wd//8, device="cuda", dtype=torch.float)
self.poses_save = torch.ones(5000, 7, device="cuda", dtype=torch.float)
self.tstamp_save = torch.zeros(5000, device="cuda", dtype=torch.float64)
self.images_save = torch.zeros(5000, ht//8, wd//8, 3, device="cuda", dtype=torch.float)
if upsample:
self.disps_up_save = torch.zeros(5000, ht, wd, device="cuda", dtype=torch.float).share_memory_()
self.count_save = 0
self.save_pkl = save_pkl
self.upsample_flag = upsample
self.state = MultiSensorState()
self.last_t0 = 0
self.last_t1 = 0
self.cur_graph = None
self.cur_result = None
self.marg_factor = None
self.prior_factor = []
self.prior_factor_map = {}
self.cur_ii = None
self.cur_jj = None
self.cur_target = None
self.cur_weight = None
self.cur_eta = None
self.imu_enabled = False
self.ignore_imu = False
self.xyz_ref = []
# extrinsics, need to be set in the main .py
self.Ti1c = None # shape = (4,4)
self.Tbc = None # gtsam.Pose3
self.tbg = None # shape = (3)
self.reinit = False
self.vi_init_t1 = -1
self.vi_init_time = 0.0
self.gnss_init_t1 = -1
self.gnss_init_time = 0.0
self.ten0 = None
self.init_pose_sigma =np.array([0.1, 0.1, 0.0001, 0.0001,0.0001,0.0001])
self.init_bias_sigma =np.array([1.0,1.0,1.0, 0.1, 0.1, 0.1])
self.logger = logging.getLogger('dba_fusion')
self.logger.setLevel(logging.DEBUG)
fh = logging.FileHandler('dba_fusion.log')
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
# add the handlers to the logger
self.logger.addHandler(fh)
self.logger.info('Start logging!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
def get_lock(self):
return self.counter.get_lock()
def __item_setter(self, index, item):
if isinstance(index, int) and index >= self.counter.value:
self.counter.value = index + 1
elif isinstance(index, torch.Tensor) and index.max().item() > self.counter.value:
self.counter.value = index.max().item() + 1
self.tstamp[index] = item[0]
self.images[index] = item[1]
if item[2] is not None:
self.poses[index] = item[2]
if item[3] is not None:
self.disps[index] = item[3]
if item[4] is not None:
depth = item[4][3::8,3::8]
self.disps_sens[index] = torch.where(depth>0, 1.0/depth, depth)
if item[5] is not None:
self.intrinsics[index] = item[5]
if len(item) > 6:
self.fmaps[index] = item[6]
if len(item) > 7:
self.nets[index] = item[7]
if len(item) > 8:
self.inps[index] = item[8]
def __setitem__(self, index, item):
with self.get_lock():
self.__item_setter(index, item)
def __getitem__(self, index):
""" index the depth video """
with self.get_lock():
# support negative indexing
if isinstance(index, int) and index < 0:
index = self.counter.value + index
item = (
self.poses[index],
self.disps[index],
self.intrinsics[index],
self.fmaps[index],
self.nets[index],
self.inps[index])
return item
def append(self, *item):
with self.get_lock():
self.__item_setter(self.counter.value, item)
### geometric operations ###
@staticmethod
def format_indicies(ii, jj):
""" to device, long, {-1} """
if not isinstance(ii, torch.Tensor):
ii = torch.as_tensor(ii)
if not isinstance(jj, torch.Tensor):
jj = torch.as_tensor(jj)
ii = ii.to(device="cuda", dtype=torch.long).reshape(-1)
jj = jj.to(device="cuda", dtype=torch.long).reshape(-1)
return ii, jj
def upsample(self, ix, mask):
""" upsample disparity """
disps_up = cvx_upsample(self.disps[ix].unsqueeze(-1), mask)
self.disps_up[ix] = disps_up.squeeze()
def normalize(self):
""" normalize depth and poses """
with self.get_lock():
s = self.disps[:self.counter.value].mean()
self.disps[:self.counter.value] /= s
self.poses[:self.counter.value,:3] *= s
self.dirty[:self.counter.value] = True
def reproject(self, ii, jj):
""" project points from ii -> jj """
ii, jj = DepthVideo.format_indicies(ii, jj)
Gs = lietorch.SE3(self.poses[None])
coords, valid_mask = \
pops.projective_transform(Gs, self.disps[None], self.intrinsics[None], ii, jj)
return coords, valid_mask
def reproject_comp(self, ii, jj, xyz_comp):
ii, jj = DepthVideo.format_indicies(ii,jj)
Gs = lietorch.SE3(self.poses[None])
coords, valid_mask = \
pops.projective_transform_comp(Gs, self.disps[None], self.intrinsics[None], ii, jj, xyz_comp)
return coords, valid_mask
def distance(self, ii=None, jj=None, beta=0.3, bidirectional=True):
""" frame distance metric """
return_matrix = False
if ii is None:
return_matrix = True
N = self.counter.value
ii, jj = torch.meshgrid(torch.arange(N), torch.arange(N))
ii, jj = DepthVideo.format_indicies(ii, jj)
if bidirectional:
poses = self.poses[:self.counter.value].clone()
d1 = droid_backends.frame_distance(
poses, self.disps, self.intrinsics[0], ii, jj, beta)
d2 = droid_backends.frame_distance(
poses, self.disps, self.intrinsics[0], jj, ii, beta)
d = .5 * (d1 + d2)
else:
d = droid_backends.frame_distance(
self.poses, self.disps, self.intrinsics[0], ii, jj, beta)
if return_matrix:
return d.reshape(N, N)
return d
def rm_new_gnss(self, t1):
if (self.gnss_init_t1> 0 and self.state.gnss_valid[t1]) or self.state.odo_valid[t1]:
graph_temp = gtsam.NonlinearFactorGraph()
linear_point = self.marg_factor.linearizationPoint()
graph_temp.push_back(self.marg_factor)
if self.state.gnss_valid[t1]:
T1 = self.state.wTbs[t1]
T0 = self.state.wTbs[t1-1]
p = np.matmul(trans.Cen(self.ten0).T, self.state.gnss_position[t1] - self.ten0)
n0pbg = self.state.wTbs[t1].rotation().rotate(self.tbg)
p = p - n0pbg
p = p - T1.translation() + T0.translation()
if not linear_point.exists(X(t1-1)):
linear_point.insert(X(t1-1), self.cur_result.atPose3(X(t1-1)))
gnss_factor = gtsam.GPSFactor(X(t1-1), p,\
gtsam.noiseModel.Robust.Create(\
gtsam.noiseModel.mEstimator.Cauchy(0.08),\
gtsam.noiseModel.Diagonal.Sigmas(np.array([1.0,1.0,5.0]))))
graph_temp.push_back(gnss_factor)
if self.state.odo_valid[t1]:
v1 = np.matmul(self.state.wTbs[t1].rotation().matrix().T, self.state.vs[t1])
v0 = np.matmul(self.state.wTbs[t1-1].rotation().matrix().T, self.state.vs[t1-1])
v = self.state.odo_vel[t1] - v1 + v0
if not linear_point.exists(X(t1-1)):
linear_point.insert(X(t1-1), self.cur_result.atPose3(X(t1-1)))
if not linear_point.exists(V(t1-1)):
linear_point.insert(V(t1-1), self.cur_result.atVector(V(t1-1)))
odo_factor = gtsam.VelFactor(X(t1-1),V(t1-1),v,gtsam.noiseModel.Diagonal.Sigmas(np.array([2.0,2.0,2.0])))
graph_temp.push_back(odo_factor)
h_factor = graph_temp.linearizeToHessianFactor(linear_point)
self.marg_factor = gtsam.LinearContainerFactor(h_factor,linear_point)
def set_prior(self, t0, t1):
for i in range(t0,t0+2):
self.prior_factor_map[i] = []
init_pose_sigma = self.init_pose_sigma
if len(self.init_pose_sigma.shape) > 1:
init_pose_sigma = self.init_pose_sigma[i-t0]
self.prior_factor_map[i].append(gtsam.PriorFactorPose3(X(i),\
self.state.wTbs[i], \
gtsam.noiseModel.Diagonal.Sigmas(init_pose_sigma)))
if not self.ignore_imu:
self.prior_factor_map[i].append(gtsam.PriorFactorConstantBias(B(i),\
self.state.bs[i], \
gtsam.noiseModel.Diagonal.Sigmas(self.init_bias_sigma)))
self.last_t0 = t0
self.last_t1 = t1
def ba(self, target, weight, eta, ii, jj, t0=1, t1=None, itrs=2, lm=1e-4, ep=0.1, motion_only=False):
""" dense bundle adjustment (DBA) """
with self.get_lock():
if t1 is None:
t1 = max(ii.max().item(), jj.max().item()) + 1
# 1) visual-only BA
# 2) multi-sensor BA
if not self.imu_enabled:
droid_backends.ba(self.poses, self.disps, self.intrinsics[0], self.disps_sens,
target, weight, eta, ii, jj, t0, t1, itrs, lm, ep, motion_only)
for i in range(self.last_t0, min(ii.min().item(), jj.min().item())):
if self.save_pkl:
# save marginalized results
self.tstamp_save[self.count_save] = self.tstamp[i].clone()
self.disps_save[self.count_save] = self.disps[i].clone()
self.poses_save[self.count_save] = self.poses[i].clone()
if self.upsample_flag:
self.disps_up_save[self.count_save] = self.disps_up[i].clone()
self.images_save[self.count_save] = self.images[i,[2,1,0],::8,::8].permute(1,2,0) / 255.0 # might be "3::8, 3::8"?
self.count_save += 1
self.last_t0 = min(ii.min().item(), jj.min().item())
self.last_t1 = t1
else:
t0 = min(ii.min().item(), jj.min().item())
""" marginalization """
if self.last_t1!=t1 or self.last_t0 != t0:
if self.last_t0 > t0:
t0 = self.last_t0
elif self.last_t0 == t0:
t0 = self.last_t0
else:
marg_paras = []
# Construct a temporary factor graph (related to the old states) to obtain the marginalization information
graph = gtsam.NonlinearFactorGraph()
marg_idx = torch.logical_and(torch.greater_equal(self.cur_ii,self.last_t0),\
torch.less(self.cur_ii,t0))
marg_idx2 = torch.logical_and(torch.less(self.cur_ii,self.last_t1-2),\
torch.less(self.cur_jj,self.last_t1-2))
marg_idx = torch.logical_and(marg_idx,marg_idx2)
marg_ii = self.cur_ii[marg_idx]
marg_jj = self.cur_jj[marg_idx]
marg_t0 = self.last_t0
marg_t1 = t0 + 1
if len(marg_ii) > 0:
marg_t0 = self.last_t0
marg_t1 = torch.max(marg_jj).item()+1
marg_result = gtsam.Values()
for i in range(self.last_t0,marg_t1):
if i < t0:
marg_paras.append(X(i))
if self.save_pkl:
# save marginalized results
self.tstamp_save[self.count_save] = self.tstamp[i].clone()
self.disps_save[self.count_save] = self.disps[i].clone()
self.poses_save[self.count_save] = self.poses[i].clone()
if self.upsample_flag:
self.disps_up_save[self.count_save] = self.disps_up[i].clone()
self.images_save[self.count_save] = self.images[i,[2,1,0],::8,::8].permute(1,2,0) / 255.0 # might be "3::8, 3::8"?
self.count_save += 1
marg_result.insert(X(i), self.cur_result.atPose3(X(i)))
marg_target = self.cur_target[marg_idx]
marg_weight = self.cur_weight[marg_idx]
marg_eta = self.cur_eta[0:marg_t1-marg_t0]
bacore = droid_backends.BACore()
bacore.init(self.poses, self.disps, self.intrinsics[0], torch.zeros_like(self.disps_sens),
marg_target, marg_weight, marg_eta, marg_ii, marg_jj, marg_t0, marg_t1, itrs, lm, ep, motion_only)
H = torch.zeros([(marg_t1-marg_t0)*6,(marg_t1-marg_t0)*6],dtype=torch.float64,device='cpu')
v = torch.zeros([(marg_t1-marg_t0)*6],dtype=torch.float64,device='cpu')
bacore.hessian(H,v)
for i in range(6): H[i,i] += 0.00025 # for stability
# Hg,vg = BA2GTSAM(H,v,self.Tbc)
Hgg = gtsam.BA2GTSAM(H,v,self.Tbc)
Hg = Hgg[0:(marg_t1-marg_t0)*6,0:(marg_t1-marg_t0)*6]
vg = Hgg[0:(marg_t1-marg_t0)*6, (marg_t1-marg_t0)*6]
vis_factor = CustomHessianFactor(marg_result,Hg,vg)
graph.push_back(vis_factor)
for i in range(self.last_t0,marg_t1):
if i < t0:
if X(i) not in marg_paras:
marg_paras.append(X(i))
if not self.ignore_imu:
marg_paras.append(V(i))
marg_paras.append(B(i))
graph.push_back(gtsam.gtsam.CombinedImuFactor(\
X(i),V(i),X(i+1),V(i+1),B(i),B(i+1),\
self.state.preintegrations[i]))
if self.gnss_init_t1 > 0:
if self.state.gnss_valid[i]:
p = np.matmul(trans.Cen(self.ten0).T, self.state.gnss_position[i] - self.ten0)
n0pbg = self.state.wTbs[i].rotation().rotate(self.tbg)
p = p - n0pbg
gnss_factor = gtsam.GPSFactor(X(i), p,\
gtsam.noiseModel.Robust.Create(\
gtsam.noiseModel.mEstimator.Cauchy(0.08),\
gtsam.noiseModel.Diagonal.Sigmas(np.array([1.0,1.0,5.0]))))
graph.push_back(gnss_factor)
if self.state.odo_valid[i]:
vb = self.state.odo_vel[i]
odo_factor = gtsam.VelFactor(X(i),V(i),vb,gtsam.noiseModel.Diagonal.Sigmas(np.array([2.0,2.0,2.0])))
graph.push_back(odo_factor)
keys = self.prior_factor_map.keys()
for i in sorted(keys):
if i < t0:
for iii in range(len(self.prior_factor_map[i])):
graph.push_back(self.prior_factor_map[i][iii])
del self.prior_factor_map[i]
if not self.marg_factor == None:
graph.push_back(self.marg_factor)
self.marg_factor = gtsam.marginalizeOut(graph,self.cur_result,marg_paras)
# covariance inflation of IMU biases
if self.reinit == True:
all_keys = self.marg_factor.keys()
for i in range(len(all_keys)):
if all_keys[i] == B(t0):
all_keys[i] = B(0)
graph = gtsam.NonlinearFactorGraph()
graph.push_back(self.marg_factor.rekey(all_keys))
b_l = gtsam.BetweenFactorConstantBias(B(0),B(t0),gtsam.imuBias.ConstantBias(np.array([.0,.0,.0]),np.array([.0,.0,.0])),\
gtsam.noiseModel.Diagonal.Sigmas(self.init_bias_sigma))
graph.push_back(b_l)
result_tmp = self.marg_factor.linearizationPoint()
result_tmp.insert(B(0),result_tmp.atConstantBias(B(t0)))
self.marg_factor = gtsam.marginalizeOut(graph,result_tmp,[B(0)])
self.reinit = False
self.last_t0 = t0
self.last_t1 = t1
""" optimization """
H = torch.zeros([(t1-t0)*6,(t1-t0)*6],dtype=torch.float64,device='cpu')
v = torch.zeros([(t1-t0)*6],dtype=torch.float64,device='cpu')
dx = torch.zeros([(t1-t0)*6],dtype=torch.float64,device='cpu')
bacore = droid_backends.BACore()
active_index = torch.logical_and(ii>=t0,jj>=t0)
self.cur_ii = ii[active_index]
self.cur_jj = jj[active_index]
self.cur_target = target[active_index]
self.cur_weight = weight[active_index]
self.cur_eta = eta[(t0-ii.min().item()):]
bacore.init(self.poses, self.disps, self.intrinsics[0], self.disps_sens,
self.cur_target, self.cur_weight, self.cur_eta, self.cur_ii, self.cur_jj, t0, t1, itrs, lm, ep, motion_only)
self.cur_graph = gtsam.NonlinearFactorGraph()
params = gtsam.LevenbergMarquardtParams()#;params.setMaxIterations(1)
# imu factor
if not self.ignore_imu:
for i in range(t0,t1):
if i > t0:
imu_factor = gtsam.gtsam.CombinedImuFactor(\
X(i-1),V(i-1),X(i),V(i),B(i-1),B(i),\
self.state.preintegrations[i-1])
self.cur_graph.add(imu_factor)
# prior factor
keys = self.prior_factor_map.keys()
for i in sorted(keys):
if i >= t0 and i < t1:
for iii in range(len(self.prior_factor_map[i])):
self.cur_graph.push_back(self.prior_factor_map[i][iii])
# marginalization factor
if self.marg_factor is not None:
self.cur_graph.push_back(self.marg_factor)
# GNSS factor
if self.gnss_init_t1 > 0:
for i in range(t0,t1):
if self.state.gnss_valid[i]:
p = np.matmul(trans.Cen(self.ten0).T, self.state.gnss_position[i] - self.ten0)
n0pbg = self.state.wTbs[i].rotation().rotate(self.tbg)
p = p - n0pbg
gnss_factor = gtsam.GPSFactor(X(i), p,\
gtsam.noiseModel.Robust.Create(\
gtsam.noiseModel.mEstimator.Cauchy(0.08),\
gtsam.noiseModel.Diagonal.Sigmas(np.array([1.0,1.0,5.0]))))
self.cur_graph.push_back(gnss_factor)
# Odo factor
for i in range(t0,t1):
if self.state.odo_valid[i]:
vb = self.state.odo_vel[i]
odo_factor = gtsam.VelFactor(X(i),V(i),vb,gtsam.noiseModel.Diagonal.Sigmas(np.array([2.0,2.0,2.0])))
self.cur_graph.push_back(odo_factor)
""" multi-sensor DBA iterations """
for iter in range(2):
if iter > 0:
self.cur_graph.resize(self.cur_graph.size()-1)
bacore.hessian(H,v) # camera frame
Hgg = gtsam.BA2GTSAM(H,v,self.Tbc)
Hg = Hgg[0:(t1-t0)*6,0:(t1-t0)*6]
vg = Hgg[0:(t1-t0)*6,(t1-t0)*6]
initial = gtsam.Values()
for i in range(t0,t1):
initial.insert(X(i), self.state.wTbs[i]) # the indice need to be handled
initial_vis = copy.deepcopy(initial)
vis_factor = CustomHessianFactor(initial_vis,Hg,vg)
self.cur_graph.push_back(vis_factor)
if not self.ignore_imu:
for i in range(t0,t1):
initial.insert(B(i),self.state.bs[i])
initial.insert(V(i),self.state.vs[i])
optimizer = gtsam.LevenbergMarquardtOptimizer(self.cur_graph, initial, params)
self.cur_result = optimizer.optimize()
# retraction and depth update
for i in range(t0,t1):
p0 = initial.atPose3(X(i))
p1 = self.cur_result.atPose3(X(i))
xi = gtsam.Pose3.Logmap(p0.inverse()*p1)
dx[(i-t0)*6:(i-t0)*6+6] = torch.tensor(xi)
if not self.ignore_imu:
self.state.bs[i] = self.cur_result.atConstantBias(B(i))
self.state.vs[i] = self.cur_result.atVector(V(i))
self.state.wTbs[i] = self.cur_result.atPose3(X(i))
dx = torch.tensor(gtsam.GTSAM2BA(dx,self.Tbc))
dx_dz = bacore.retract(dx)
del bacore
self.disps.clamp_(min=0.001)
================================================
FILE: dbaf/droid_net.py
================================================
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from modules.extractor import BasicEncoder
from modules.corr import CorrBlock
from modules.gru import ConvGRU
from modules.clipping import GradientClip
from lietorch import SE3
from geom.ba import BA
import geom.projective_ops as pops
from geom.graph_utils import graph_to_edge_list, keyframe_indicies
from torch_scatter import scatter_mean
import time
def cvx_upsample(data, mask):
""" upsample pixel-wise transformation field """
batch, ht, wd, dim = data.shape
data = data.permute(0, 3, 1, 2)
mask = mask.view(batch, 1, 9, 8, 8, ht, wd)
mask = torch.softmax(mask, dim=2)
up_data = F.unfold(data, [3,3], padding=1)
up_data = up_data.view(batch, dim, 9, 1, 1, ht, wd)
up_data = torch.sum(mask * up_data, dim=2)
up_data = up_data.permute(0, 4, 2, 5, 3, 1)
up_data = up_data.reshape(batch, 8*ht, 8*wd, dim)
return up_data
def upsample_disp(disp, mask):
batch, num, ht, wd = disp.shape
disp = disp.view(batch*num, ht, wd, 1)
mask = mask.view(batch*num, -1, ht, wd)
return cvx_upsample(disp, mask).view(batch, num, 8*ht, 8*wd)
class GraphAgg(nn.Module):
def __init__(self):
super(GraphAgg, self).__init__()
self.conv1 = nn.Conv2d(128, 128, 3, padding=1)
self.conv2 = nn.Conv2d(128, 128, 3, padding=1)
self.relu = nn.ReLU(inplace=True)
self.eta = nn.Sequential(
nn.Conv2d(128, 1, 3, padding=1),
GradientClip(),
nn.Softplus())
self.upmask = nn.Sequential(
nn.Conv2d(128, 8*8*9, 1, padding=0))
def forward(self, net, ii):
batch, num, ch, ht, wd = net.shape
net = net.view(batch*num, ch, ht, wd)
_, ix = torch.unique(ii, return_inverse=True)
net = self.relu(self.conv1(net))
net = net.view(batch, num, 128, ht, wd)
net = scatter_mean(net, ix, dim=1)
net = net.view(-1, 128, ht, wd)
net = self.relu(self.conv2(net))
eta = self.eta(net).view(batch, -1, ht, wd)
upmask = self.upmask(net).view(batch, -1, 8*8*9, ht, wd)
return .01 * eta, upmask
class UpdateModule(nn.Module):
def __init__(self):
super(UpdateModule, self).__init__()
cor_planes = 4 * (2*3 + 1)**2
self.corr_encoder = nn.Sequential(
nn.Conv2d(cor_planes, 128, 1, padding=0),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, padding=1),
nn.ReLU(inplace=True))
self.flow_encoder = nn.Sequential(
nn.Conv2d(4, 128, 7, padding=3),
nn.ReLU(inplace=True),
nn.Conv2d(128, 64, 3, padding=1),
nn.ReLU(inplace=True))
self.weight = nn.Sequential(
nn.Conv2d(128, 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 2, 3, padding=1),
GradientClip(),
nn.Sigmoid())
self.delta = nn.Sequential(
nn.Conv2d(128, 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 2, 3, padding=1),
GradientClip())
self.gru = ConvGRU(128, 128+128+64)
self.agg = GraphAgg()
def forward(self, net, inp, corr, flow=None, ii=None, jj=None, upsample = False):
""" RaftSLAM update operator """
batch, num, ch, ht, wd = net.shape
if flow is None:
flow = torch.zeros(batch, num, 4, ht, wd, device=net.device)
output_dim = (batch, num, -1, ht, wd)
net = net.view(batch*num, -1, ht, wd)
inp = inp.view(batch*num, -1, ht, wd)
corr = corr.view(batch*num, -1, ht, wd)
flow = flow.view(batch*num, -1, ht, wd)
corr = self.corr_encoder(corr)
flow = self.flow_encoder(flow)
net = self.gru(net, inp, corr, flow)
### update variables ###
delta = self.delta(net).view(*output_dim)
weight = self.weight(net).view(*output_dim)
delta = delta.permute(0,1,3,4,2)[...,:2].contiguous()
weight = weight.permute(0,1,3,4,2)[...,:2].contiguous()
net = net.view(*output_dim)
if ii is not None:
### ATTENTION!!!! ###
# We found this useless for VIO performance, thus disable it to save computation.
# Feel free to re-enable it.
if upsample:
eta, upmask = self.agg(net, ii.to(net.device))
return net, delta, weight, eta, upmask
else:
return net, delta, weight, None, None
else:
return net, delta, weight
class DroidNet(nn.Module):
def __init__(self):
super(DroidNet, self).__init__()
self.fnet = BasicEncoder(output_dim=128, norm_fn='instance')
self.cnet = BasicEncoder(output_dim=256, norm_fn='none')
self.update = UpdateModule()
def extract_features(self, images):
""" run feeature extraction networks """
# normalize images
images = images[:, :, [2,1,0]] / 255.0
mean = torch.as_tensor([0.485, 0.456, 0.406], device=images.device)
std = torch.as_tensor([0.229, 0.224, 0.225], device=images.device)
images = images.sub_(mean[:, None, None]).div_(std[:, None, None])
fmaps = self.fnet(images)
net = self.cnet(images)
net, inp = net.split([128,128], dim=2)
net = torch.tanh(net)
inp = torch.relu(inp)
return fmaps, net, inp
def forward(self, Gs, images, disps, intrinsics, graph=None, num_steps=12, fixedp=2):
""" Estimates SE3 or Sim3 between pair of frames """
u = keyframe_indicies(graph)
ii, jj, kk = graph_to_edge_list(graph)
ii = ii.to(device=images.device, dtype=torch.long)
jj = jj.to(device=images.device, dtype=torch.long)
fmaps, net, inp = self.extract_features(images)
net, inp = net[:,ii], inp[:,ii]
corr_fn = CorrBlock(fmaps[:,ii], fmaps[:,jj], num_levels=4, radius=3)
ht, wd = images.shape[-2:]
coords0 = pops.coords_grid(ht//8, wd//8, device=images.device)
coords1, _ = pops.projective_transform(Gs, disps, intrinsics, ii, jj)
target = coords1.clone()
Gs_list, disp_list, residual_list = [], [], []
for step in range(num_steps):
Gs = Gs.detach()
disps = disps.detach()
coords1 = coords1.detach()
target = target.detach()
# extract motion features
corr = corr_fn(coords1)
resd = target - coords1
flow = coords1 - coords0
motion = torch.cat([flow, resd], dim=-1)
motion = motion.permute(0,1,4,2,3).clamp(-64.0, 64.0)
net, delta, weight, eta, upmask = \
self.update(net, inp, corr, motion, ii, jj)
target = coords1 + delta
for i in range(2):
Gs, disps = BA(target, weight, eta, Gs, disps, intrinsics, ii, jj, fixedp=2)
coords1, valid_mask = pops.projective_transform(Gs, disps, intrinsics, ii, jj)
residual = (target - coords1)
Gs_list.append(Gs)
disp_list.append(upsample_disp(disps, upmask))
residual_list.append(valid_mask * residual)
return Gs_list, disp_list, residual_list
================================================
FILE: dbaf/geoFunc/__init__.py
================================================
================================================
FILE: dbaf/geoFunc/const_value.py
================================================
import math
pi=math.pi
a = 6378137
finv = 298.257223563
================================================
FILE: dbaf/geoFunc/trans.py
================================================
import math
from math import atan2, sin, cos
from . import const_value
import numpy as np
from scipy.spatial.transform import Rotation
def cart2geod(Xinput):
X=Xinput[0]
Y=Xinput[1]
Z=Xinput[2]
tolsq = 1e-10
maxit = 10
rtd = 180/const_value.pi
esq = (2-1/const_value.finv) / const_value.finv
oneesq = 1-esq
P=math.sqrt(X*X+Y*Y)
if P > 1e-20:
dlambda = math.atan2(Y,X) *rtd
else:
dlambda = 0
if dlambda <0:
dlambda = dlambda +360
r=math.sqrt(P*P+Z*Z)
if r>1e-20:
sinphi=Z/r
else :
sinphi=0
dphi = math.asin(sinphi)
if r<1e-20:
h=0
return
h = r-const_value.a*(1-sinphi*sinphi/const_value.finv)
for i in range(maxit):
sinphi = math.sin(dphi)
cosphi = math.cos(dphi)
N_phi = const_value.a/math.sqrt(1-esq*sinphi*sinphi)
dP =P -(N_phi+h)*cosphi
dZ=Z-(N_phi*oneesq+h)*sinphi
h=h+(sinphi*dZ+cosphi*dP)
dphi =dphi+(cosphi*dZ - sinphi*dP)/(N_phi+h)
if dP*dP + dZ*dZ<tolsq:
break
if i==maxit-1:
print('sth. wrong in cart2geod.')
dphi=dphi*rtd
geod=[]
geod.append(dphi)
geod.append(dlambda)
geod.append(h)
# print(geod)
return geod
def cart2enu(X, dx):
dtr=const_value.pi/180
geod = cart2geod(X)
# print(geod)
cl = math.cos(geod[1]*dtr)
sl = math.sin(geod[1]*dtr)
cb = math.cos(geod[0]*dtr)
sb = math.sin(geod[0]*dtr)
east = -sl* dx[0] +cl* dx[1]+0
north= -sb*cl*dx[0] -sb*sl*dx[1]+cb*dx[2]
up = cb*cl*dx[0] +cb*sl*dx[1]+sb*dx[2]
enu=[]
enu.append(east)
enu.append(north)
enu.append(up)
return enu
def enu2cart(X, enu):
dtr=const_value.pi/180
geod = cart2geod(X)
# print(geod)
cl = math.cos(geod[1]*dtr)
sl = math.sin(geod[1]*dtr)
cb = math.cos(geod[0]*dtr)
sb = math.sin(geod[0]*dtr)
#east = -sl* dx[0] +cl* dx[1]+0
#north= -sb*cl*dx[0] -sb*sl*dx[1]+cb*dx[2]
#up = cb*cl*dx[0] +cb*sl*dx[1]+sb*dx[2]
dx0 = -sl*enu[0]-sb*cl*enu[1]+cb*cl*enu[2]
dx1 = cl*enu[0]-sb*sl*enu[1]+cb*sl*enu[2]
dx2 = 0+ cb*enu[1]+ sb*enu[2]
dx=[]
dx.append(dx0)
dx.append(dx1)
dx.append(dx2)
return dx
def hhmmss2sec(hhmmss):
elem = hhmmss.split(':')
sec = float(elem[0])*3600+float(elem[1])*60+float(elem[2])
return sec
def Cen(X):
dtr=const_value.pi/180
geod = cart2geod(X)
# print(geod)
cl = math.cos(geod[1]*dtr)
sl = math.sin(geod[1]*dtr)
cb = math.cos(geod[0]*dtr)
sb = math.sin(geod[0]*dtr)
M = np.array([[-sl,cl,0],[-sb*cl,-sb*sl,cb],[cb*cl,cb*sl,sb]]).T
return M
def rad2deg(l):
ll = []
for i in range(len(l)):
ll.append(l[i]*180/math.pi)
return ll
def deg2rad(l):
ll = []
for i in range(len(l)):
ll.append(l[i]/180*math.pi)
return ll
def m2att(R):
att=[0,0,0]
att[0] = math.asin(R[2, 1])
att[1] = math.atan2(-R[2, 0], R[2, 2])
att[2] = math.atan2(-R[0, 1], R[1, 1])
return att
def att2m(att):
sp = math.sin(att[0])
cp=math.cos(att[0])
sr = math.sin(att[1])
cr =math.cos(att[1])
sy=math.sin(att[2])
cy= math.cos(att[2])
R=np.array([[cy*cr - sy*sp*sr, -sy*cp, cy*sr + sy*sp*cr],\
[sy*cr + cy*sp*sr, cy*cp, sy*sr - cy*sp*cr],\
[-cp*sr, sp, cp*cr]])
return R
def q2att(qnb):
q0 = qnb[0]
q1 = qnb[1]
q2 = qnb[2]
q3 = qnb[3]
q11 = q0*q0
q12 = q0*q1
q13 = q0*q2
q14 = q0*q3
q22 = q1*q1
q23 = q1*q2
q24 = q1*q3
q33 = q2*q2
q34 = q2*q3
q44 = q3*q3
att=[0,0,0]
att[0] = math.asin(2 * (q34 + q12))
att[1] = math.atan2(-2 * (q24 - q13), q11 - q22 - q33 + q44)
att[2] = math.atan2(-2 * (q23 - q14), q11 - q22 + q33 - q44)
return att
def q2R(qnb):
return att2m(q2att(qnb))
def alignRt(xyz0,xyz1):
if len(xyz0)!=len(xyz1):
raise Exception()
N = len(xyz0)
p1 = np.array([0.0,0.0,0.0])
p2 = np.array([0.0,0.0,0.0])
for i in range(N):
p1 += np.array(xyz0[i])
p2 += np.array(xyz1[i])
p1 /= N
p2 /= N
W = np.zeros([3,3])
for j in range(N):
q1 = np.array(xyz0[j]) - p1
q2 = np.array(xyz1[j]) - p2
W += np.matmul(q1.reshape(3,1),q2.reshape(1,3))
U, sigma, VT = np.linalg.svd(W)
R= np.matmul(U,VT)
t=p1-np.matmul(R,p2)
return R,t
def R2ypr(R):
n = R[0]
o = R[1]
a = R[2]
y = atan2(n[1], n[0])
p = atan2(-n[2], n[0] * cos(y) + n[1] * sin(y))
r = atan2(a[0] * sin(y) - a[1] * cos(y), -o[0] * sin(y) + o[1] * cos(y))
return np.array([y,p,r])
def ypr2R(ypr):
y = ypr[0]
p = ypr[1]
r = ypr[2]
Rz = np.array([[cos(y),-sin(y),0],[sin(y),cos(y),0],[0,0,1]])
Ry = np.array([[cos(p),0,sin(p)],[0,1,0],[-sin(p),0,cos(p)]])
Rx = np.array([[1,0,0],[0,cos(r),-sin(r)],[0,sin(r),cos(r)]])
return np.matmul(np.matmul(Rz,Ry),Rx)
def FromTwoVectors(a,b):
v0 = a/np.linalg.norm(a)
v1 = b/np.linalg.norm(b)
c = np.dot(v1,v0)
axis = np.cross(v0,v1)
s = math.sqrt((1+c)*2)
invs = 1/s
vec = axis*invs
w = s* 0.5
return Rotation.from_quat(np.array([vec[0],vec[1],vec[2],w])).as_matrix()
================================================
FILE: dbaf/geom/__init__.py
================================================
================================================
FILE: dbaf/geom/ba.py
================================================
import lietorch
import torch
import torch.nn.functional as F
from .chol import block_solve, schur_solve
import geom.projective_ops as pops
from torch_scatter import scatter_sum
# utility functions for scattering ops
def safe_scatter_add_mat(A, ii, jj, n, m):
v = (ii >= 0) & (jj >= 0) & (ii < n) & (jj < m)
return scatter_sum(A[:,v], ii[v]*m + jj[v], dim=1, dim_size=n*m)
def safe_scatter_add_vec(b, ii, n):
v = (ii >= 0) & (ii < n)
return scatter_sum(b[:,v], ii[v], dim=1, dim_size=n)
# apply retraction operator to inv-depth maps
def disp_retr(disps, dz, ii):
ii = ii.to(device=dz.device)
return disps + scatter_sum(dz, ii, dim=1, dim_size=disps.shape[1])
# apply retraction operator to poses
def pose_retr(poses, dx, ii):
ii = ii.to(device=dx.device)
return poses.retr(scatter_sum(dx, ii, dim=1, dim_size=poses.shape[1]))
def BA(target, weight, eta, poses, disps, intrinsics, ii, jj, fixedp=1, rig=1):
""" Full Bundle Adjustment """
B, P, ht, wd = disps.shape
N = ii.shape[0]
D = poses.manifold_dim
### 1: commput
gitextract_ny3kcvri/
├── .gitignore
├── .gitmodules
├── LICENSE
├── README.md
├── batch_kitti360.py
├── batch_subt.py
├── batch_tumvi.py
├── batch_whu.py
├── calib/
│ ├── 0412.txt
│ ├── 0412new.txt
│ ├── 1012.txt
│ ├── barn.txt
│ ├── carla.txt
│ ├── eth.txt
│ ├── euroc.txt
│ ├── handheld.txt
│ ├── kitti_360.txt
│ ├── subt.txt
│ ├── tartan.txt
│ ├── tum3.txt
│ └── tumvi.txt
├── dataset/
│ ├── euroc_to_hdf5.py
│ ├── kitti360_to_hdf5.py
│ └── tumvi_to_hdf5.py
├── dbaf/
│ ├── covisible_graph.py
│ ├── data_readers/
│ │ ├── __init__.py
│ │ ├── augmentation.py
│ │ ├── base.py
│ │ ├── factory.py
│ │ ├── rgbd_utils.py
│ │ ├── stream.py
│ │ ├── tartan.py
│ │ └── tartan_test.txt
│ ├── dbaf.py
│ ├── dbaf_frontend.py
│ ├── depth_video.py
│ ├── droid_net.py
│ ├── geoFunc/
│ │ ├── __init__.py
│ │ ├── const_value.py
│ │ └── trans.py
│ ├── geom/
│ │ ├── __init__.py
│ │ ├── ba.py
│ │ ├── chol.py
│ │ ├── graph_utils.py
│ │ ├── losses.py
│ │ └── projective_ops.py
│ ├── modules/
│ │ ├── __init__.py
│ │ ├── clipping.py
│ │ ├── corr.py
│ │ ├── extractor.py
│ │ └── gru.py
│ ├── motion_filter.py
│ └── multi_sensor.py
├── demo_vio_kitti360.py
├── demo_vio_subt.py
├── demo_vio_tumvi.py
├── demo_vio_whu.py
├── evaluation_scripts/
│ ├── batch_tumvi.py
│ ├── evaluate_kitti.py
│ └── evaluate_tumvi.py
├── results/
│ └── PLACEHOLDER
├── setup.py
├── src/
│ ├── altcorr_kernel.cu
│ ├── bacore.h
│ ├── correlation_kernels.cu
│ ├── droid.cpp
│ └── droid_kernels.cu
└── visualization/
├── check_reconstruction_kitti.py
├── check_reconstruction_kitti_animation.py
├── check_reconstruction_tumvi.py
└── check_reconstruction_tumvi_animation.py
SYMBOL INDEX (260 symbols across 38 files)
FILE: dataset/euroc_to_hdf5.py
function show_image (line 11) | def show_image(image):
function image_stream (line 16) | def image_stream(imagedir, imagestamp, h5path, calib, stride):
FILE: dataset/kitti360_to_hdf5.py
function show_image (line 11) | def show_image(image):
function image_stream (line 16) | def image_stream(imagedir, imagestamp, h5path, calib, stride):
FILE: dataset/tumvi_to_hdf5.py
function show_image (line 11) | def show_image(image):
function image_stream (line 16) | def image_stream(imagedir, imagestamp, h5path, calib, stride):
FILE: dbaf/covisible_graph.py
class CovisibleGraph (line 15) | class CovisibleGraph:
method __init__ (line 16) | def __init__(self, video: DepthVideo, update_op, device="cuda:0", corr...
method __filter_repeated_edges (line 61) | def __filter_repeated_edges(self, ii, jj):
method print_edges (line 74) | def print_edges(self):
method filter_edges (line 88) | def filter_edges(self):
method clear_edges (line 97) | def clear_edges(self):
method add_factors (line 103) | def add_factors(self, ii, jj, remove=False):
method rm_factors (line 152) | def rm_factors(self, mask, store=False):
method rm_keyframe (line 180) | def rm_keyframe(self, ix):
method update (line 214) | def update(self, t0=None, t1=None, itrs=2, use_inactive=False, EP=1e-7...
method add_neighborhood_factors (line 344) | def add_neighborhood_factors(self, t0, t1, r=3):
method add_proximity_factors (line 357) | def add_proximity_factors(self, t0=0, t1=0, rad=2, nms=2, beta=0.25, t...
FILE: dbaf/data_readers/augmentation.py
class RGBDAugmentor (line 7) | class RGBDAugmentor:
method __init__ (line 10) | def __init__(self, crop_size):
method spatial_transform (line 20) | def spatial_transform(self, images, depths, poses, intrinsics):
method color_transform (line 49) | def color_transform(self, images):
method __call__ (line 56) | def __call__(self, images, poses, depths, intrinsics):
FILE: dbaf/data_readers/base.py
class RGBDDataset (line 19) | class RGBDDataset(data.Dataset):
method __init__ (line 20) | def __init__(self, name, datapath, n_frames=4, crop_size=[384,512], fm...
method _build_dataset_index (line 50) | def _build_dataset_index(self):
method image_read (line 62) | def image_read(image_file):
method depth_read (line 66) | def depth_read(depth_file):
method build_frame_graph (line 69) | def build_frame_graph(self, poses, depths, intrinsics, f=16, max_flow=...
method __getitem__ (line 94) | def __getitem__(self, index):
method __len__ (line 152) | def __len__(self):
method __imul__ (line 155) | def __imul__(self, x):
FILE: dbaf/data_readers/factory.py
function dataset_factory (line 17) | def dataset_factory(dataset_list, **kwargs):
function create_datastream (line 34) | def create_datastream(dataset_path, **kwargs):
function create_imagestream (line 62) | def create_imagestream(dataset_path, **kwargs):
function create_stereostream (line 69) | def create_stereostream(dataset_path, **kwargs):
function create_rgbdstream (line 76) | def create_rgbdstream(dataset_path, **kwargs):
FILE: dbaf/data_readers/rgbd_utils.py
function parse_list (line 11) | def parse_list(filepath, skiprows=0):
function associate_frames (line 16) | def associate_frames(tstamp_image, tstamp_depth, tstamp_pose, max_dt=1.0):
function loadtum (line 35) | def loadtum(datapath, frame_rate=-1):
function all_pairs_distance_matrix (line 91) | def all_pairs_distance_matrix(poses, beta=2.5):
function pose_matrix_to_quaternion (line 100) | def pose_matrix_to_quaternion(pose):
function compute_distance_matrix_flow (line 105) | def compute_distance_matrix_flow(poses, disps, intrinsics):
function compute_distance_matrix_flow2 (line 145) | def compute_distance_matrix_flow2(poses, disps, intrinsics, beta=0.4):
FILE: dbaf/data_readers/stream.py
class RGBDStream (line 18) | class RGBDStream(data.Dataset):
method __init__ (line 19) | def __init__(self, datapath, frame_rate=-1, image_size=[384,512], crop...
method image_read (line 27) | def image_read(image_file):
method depth_read (line 31) | def depth_read(depth_file):
method __len__ (line 34) | def __len__(self):
method __getitem__ (line 37) | def __getitem__(self, index):
class ImageStream (line 75) | class ImageStream(data.Dataset):
method __init__ (line 76) | def __init__(self, datapath, intrinsics, rate=1, image_size=[384,512]):
method __len__ (line 93) | def __len__(self):
method image_read (line 97) | def image_read(imfile):
method __getitem__ (line 100) | def __getitem__(self, index):
class StereoStream (line 127) | class StereoStream(data.Dataset):
method __init__ (line 128) | def __init__(self, datapath, intrinsics, rate=1, image_size=[384,512],
method __len__ (line 150) | def __len__(self):
method image_read (line 154) | def image_read(imfile, imap=None):
method __getitem__ (line 160) | def __getitem__(self, index):
FILE: dbaf/data_readers/tartan.py
class TartanAir (line 18) | class TartanAir(RGBDDataset):
method __init__ (line 23) | def __init__(self, mode='training', **kwargs):
method is_test_scene (line 29) | def is_test_scene(scene):
method _build_dataset (line 33) | def _build_dataset(self):
method calib_read (line 58) | def calib_read():
method image_read (line 62) | def image_read(image_file):
method depth_read (line 66) | def depth_read(depth_file):
class TartanAirStream (line 73) | class TartanAirStream(RGBDStream):
method __init__ (line 74) | def __init__(self, datapath, **kwargs):
method _build_dataset_index (line 77) | def _build_dataset_index(self):
method calib_read (line 100) | def calib_read(datapath):
method image_read (line 104) | def image_read(image_file):
class TartanAirTestStream (line 108) | class TartanAirTestStream(RGBDStream):
method __init__ (line 109) | def __init__(self, datapath, **kwargs):
method _build_dataset_index (line 112) | def _build_dataset_index(self):
method calib_read (line 133) | def calib_read(datapath):
method image_read (line 137) | def image_read(image_file):
FILE: dbaf/dbaf.py
class DBAFusion (line 16) | class DBAFusion:
method __init__ (line 17) | def __init__(self, args):
method load_weights (line 34) | def load_weights(self, weights):
method track (line 50) | def track(self, tstamp, image, depth=None, intrinsics=None):
method terminate (line 60) | def terminate(self, stream=None):
method save_vis_easy (line 64) | def save_vis_easy(self):
FILE: dbaf/dbaf_frontend.py
class DBAFusionFrontend (line 16) | class DBAFusionFrontend:
method __init__ (line 17) | def __init__(self, net, video, args):
method get_pose_ref (line 85) | def get_pose_ref(self, tt:float):
method __rollup (line 89) | def __rollup(self, roll):
method __update (line 153) | def __update(self):
method init_IMU (line 377) | def init_IMU(self):
method init_VI (line 434) | def init_VI(self):
method init_GNSS (line 517) | def init_GNSS(self):
method VisualIMUAlignment (line 606) | def VisualIMUAlignment(self, t0, t1, ignore_lever, disable_scale = Fal...
method __initialize (line 816) | def __initialize(self):
method __call__ (line 853) | def __call__(self):
FILE: dbaf/depth_video.py
function BA2GTSAM (line 20) | def BA2GTSAM(H: np.ndarray, v: np.ndarray, Tbc: gtsam.Pose3):
function CustomHessianFactor (line 31) | def CustomHessianFactor(values: gtsam.Values, H: np.ndarray, v: np.ndarr...
class DepthVideo (line 40) | class DepthVideo:
method __init__ (line 41) | def __init__(self, image_size=[480, 640], buffer=1024, save_pkl = Fals...
method get_lock (line 126) | def get_lock(self):
method __item_setter (line 129) | def __item_setter(self, index, item):
method __setitem__ (line 161) | def __setitem__(self, index, item):
method __getitem__ (line 165) | def __getitem__(self, index):
method append (line 183) | def append(self, *item):
method format_indicies (line 191) | def format_indicies(ii, jj):
method upsample (line 205) | def upsample(self, ix, mask):
method normalize (line 211) | def normalize(self):
method reproject (line 221) | def reproject(self, ii, jj):
method reproject_comp (line 231) | def reproject_comp(self, ii, jj, xyz_comp):
method distance (line 240) | def distance(self, ii=None, jj=None, beta=0.3, bidirectional=True):
method rm_new_gnss (line 272) | def rm_new_gnss(self, t1):
method set_prior (line 307) | def set_prior(self, t0, t1):
method ba (line 323) | def ba(self, target, weight, eta, ii, jj, t0=1, t1=None, itrs=2, lm=1e...
FILE: dbaf/droid_net.py
function cvx_upsample (line 17) | def cvx_upsample(data, mask):
function upsample_disp (line 33) | def upsample_disp(disp, mask):
class GraphAgg (line 40) | class GraphAgg(nn.Module):
method __init__ (line 41) | def __init__(self):
method forward (line 55) | def forward(self, net, ii):
class UpdateModule (line 74) | class UpdateModule(nn.Module):
method __init__ (line 75) | def __init__(self):
method forward (line 107) | def forward(self, net, inp, corr, flow=None, ii=None, jj=None, upsampl...
class DroidNet (line 145) | class DroidNet(nn.Module):
method __init__ (line 146) | def __init__(self):
method extract_features (line 153) | def extract_features(self, images):
method forward (line 171) | def forward(self, Gs, images, disps, intrinsics, graph=None, num_steps...
FILE: dbaf/geoFunc/trans.py
function cart2geod (line 7) | def cart2geod(Xinput):
function cart2enu (line 71) | def cart2enu(X, dx):
function enu2cart (line 92) | def enu2cart(X, enu):
function hhmmss2sec (line 117) | def hhmmss2sec(hhmmss):
function Cen (line 122) | def Cen(X):
function rad2deg (line 135) | def rad2deg(l):
function deg2rad (line 141) | def deg2rad(l):
function m2att (line 147) | def m2att(R):
function att2m (line 156) | def att2m(att):
function q2att (line 168) | def q2att(qnb):
function q2R (line 190) | def q2R(qnb):
function alignRt (line 193) | def alignRt(xyz0,xyz1):
function R2ypr (line 215) | def R2ypr(R):
function ypr2R (line 225) | def ypr2R(ypr):
function FromTwoVectors (line 236) | def FromTwoVectors(a,b):
FILE: dbaf/geom/ba.py
function safe_scatter_add_mat (line 10) | def safe_scatter_add_mat(A, ii, jj, n, m):
function safe_scatter_add_vec (line 14) | def safe_scatter_add_vec(b, ii, n):
function disp_retr (line 19) | def disp_retr(disps, dz, ii):
function pose_retr (line 24) | def pose_retr(poses, dx, ii):
function BA (line 29) | def BA(target, weight, eta, poses, disps, intrinsics, ii, jj, fixedp=1, ...
function MoBA (line 107) | def MoBA(target, weight, eta, poses, disps, intrinsics, ii, jj, fixedp=1...
FILE: dbaf/geom/chol.py
class CholeskySolver (line 5) | class CholeskySolver(torch.autograd.Function):
method forward (line 7) | def forward(ctx, H, b):
method backward (line 22) | def backward(ctx, grad_x):
function block_solve (line 32) | def block_solve(H, b, ep=0.1, lm=0.0001):
function schur_solve (line 46) | def schur_solve(H, E, C, v, w, ep=0.1, lm=0.0001, sless=False):
FILE: dbaf/geom/graph_utils.py
function graph_to_edge_list (line 10) | def graph_to_edge_list(graph):
function keyframe_indicies (line 23) | def keyframe_indicies(graph):
function meshgrid (line 26) | def meshgrid(m, n, device='cuda'):
function neighbourhood_graph (line 30) | def neighbourhood_graph(n, r):
function build_frame_graph (line 37) | def build_frame_graph(poses, disps, intrinsics, num=16, thresh=24.0, r=2):
function build_frame_graph_v2 (line 72) | def build_frame_graph_v2(poses, disps, intrinsics, num=16, thresh=24.0, ...
FILE: dbaf/geom/losses.py
function pose_metrics (line 9) | def pose_metrics(dE):
function fit_scale (line 21) | def fit_scale(Ps, Gs):
function geodesic_loss (line 30) | def geodesic_loss(Ps, Gs, graph, gamma=0.9, do_scale=True):
function residual_loss (line 77) | def residual_loss(residuals, gamma=0.9):
function flow_loss (line 89) | def flow_loss(Ps, disps, poses_est, disps_est, intrinsics, graph, gamma=...
FILE: dbaf/geom/projective_ops.py
function extract_intrinsics (line 8) | def extract_intrinsics(intrinsics):
function coords_grid (line 11) | def coords_grid(ht, wd, **kwargs):
function iproj (line 18) | def iproj(disps, intrinsics, jacobian=False):
function proj (line 39) | def proj(Xs, intrinsics, jacobian=False, return_depth=False):
function actp (line 67) | def actp(Gij, X0, jacobian=False):
function projective_transform (line 96) | def projective_transform(poses, depths, intrinsics, ii, jj, jacobian=Fal...
function projective_transform_comp (line 127) | def projective_transform_comp(poses, depths, intrinsics, ii, jj, xyz_com...
function induced_flow (line 160) | def induced_flow(poses, disps, intrinsics, ii, jj):
FILE: dbaf/modules/clipping.py
class GradClip (line 7) | class GradClip(torch.autograd.Function):
method forward (line 9) | def forward(ctx, x):
method backward (line 13) | def backward(ctx, grad_x):
class GradientClip (line 19) | class GradientClip(nn.Module):
method __init__ (line 20) | def __init__(self):
method forward (line 23) | def forward(self, x):
FILE: dbaf/modules/corr.py
class CorrSampler (line 6) | class CorrSampler(torch.autograd.Function):
method forward (line 9) | def forward(ctx, volume, coords, radius):
method backward (line 16) | def backward(ctx, grad_output):
class CorrBlock (line 23) | class CorrBlock:
method __init__ (line 24) | def __init__(self, fmap1, fmap2, num_levels=4, radius=3):
method __call__ (line 40) | def __call__(self, coords):
method cat (line 52) | def cat(self, other):
method __getitem__ (line 57) | def __getitem__(self, index):
method corr (line 64) | def corr(fmap1, fmap2):
class CorrLayer (line 74) | class CorrLayer(torch.autograd.Function):
method forward (line 76) | def forward(ctx, fmap1, fmap2, coords, r):
method backward (line 83) | def backward(ctx, grad_corr):
class AltCorrBlock (line 91) | class AltCorrBlock:
method __init__ (line 92) | def __init__(self, fmaps, num_levels=4, radius=3):
method corr_fn (line 106) | def corr_fn(self, coords, ii, jj):
method __call__ (line 128) | def __call__(self, coords, ii, jj):
FILE: dbaf/modules/extractor.py
class ResidualBlock (line 6) | class ResidualBlock(nn.Module):
method __init__ (line 7) | def __init__(self, in_planes, planes, norm_fn='group', stride=1):
method forward (line 47) | def forward(self, x):
class BottleneckBlock (line 58) | class BottleneckBlock(nn.Module):
method __init__ (line 59) | def __init__(self, in_planes, planes, norm_fn='group', stride=1):
method forward (line 104) | def forward(self, x):
class BasicEncoder (line 118) | class BasicEncoder(nn.Module):
method __init__ (line 119) | def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0, multi...
method _make_layer (line 175) | def _make_layer(self, dim, stride=1):
method forward (line 183) | def forward(self, x):
FILE: dbaf/modules/gru.py
class ConvGRU (line 5) | class ConvGRU(nn.Module):
method __init__ (line 6) | def __init__(self, h_planes=128, i_planes=128):
method forward (line 19) | def forward(self, net, *inputs):
FILE: dbaf/motion_filter.py
class MotionFilter (line 12) | class MotionFilter:
method __init__ (line 15) | def __init__(self, net, video, thresh=2.5, device="cuda:0"):
method __context_encoder (line 33) | def __context_encoder(self, image):
method context_encoder (line 39) | def context_encoder(self, image):
method __feature_encoder (line 45) | def __feature_encoder(self, image):
method feature_encoder (line 50) | def feature_encoder(self, image):
method track (line 56) | def track(self, tstamp, image, depth=None, intrinsics=None):
FILE: dbaf/multi_sensor.py
class MultiSensorState (line 7) | class MultiSensorState:
method __init__ (line 8) | def __init__(self):
method set_imu_params (line 32) | def set_imu_params(self, noise = None):
method init_first_state (line 71) | def init_first_state(self,t,pos,R,vel):
method append_imu (line 86) | def append_imu(self, t, measuredAcc, measuredOmega):
method append_imu_temp (line 105) | def append_imu_temp(self, t, measuredAcc, measuredOmega, predict_pose ...
method append_img (line 114) | def append_img(self, t):
method append_gnss (line 138) | def append_gnss(self,t,pos):
method append_odo (line 145) | def append_odo(self,t,vel):
method predict (line 152) | def predict(self):
FILE: demo_vio_kitti360.py
function show_image (line 20) | def show_image(image):
function image_stream (line 25) | def image_stream(imagedir, imagestamp, enable_h5, h5path, calib, stride):
FILE: demo_vio_subt.py
function show_image (line 19) | def show_image(image):
function image_stream (line 24) | def image_stream(imagedir, imagestamp, enable_h5, h5path, calib, stride):
FILE: demo_vio_tumvi.py
function show_image (line 19) | def show_image(image):
function image_stream (line 24) | def image_stream(imagedir, imagestamp, enable_h5, h5path, calib, stride):
FILE: demo_vio_whu.py
function show_image (line 19) | def show_image(image):
function image_stream (line 24) | def image_stream(imagedir, imagestamp, enable_h5, h5path, calib, stride):
FILE: evaluation_scripts/evaluate_kitti.py
function ape (line 25) | def ape(traj_ref: PosePath3D, traj_est: PosePath3D,
FILE: evaluation_scripts/evaluate_tumvi.py
function ape (line 43) | def ape(traj_ref: PosePath3D, traj_est: PosePath3D,
FILE: src/bacore.h
function class (line 4) | class BACore
FILE: src/droid.cpp
function ba (line 109) | std::vector<torch::Tensor> ba(
function ba_extend (line 140) | std::vector<torch::Tensor> ba_extend(
function frame_distance (line 181) | torch::Tensor frame_distance(
function projmap (line 200) | std::vector<torch::Tensor> projmap(
function iproj (line 218) | torch::Tensor iproj(
function corr_index_forward (line 231) | std::vector<torch::Tensor> corr_index_forward(
function corr_index_backward (line 241) | std::vector<torch::Tensor> corr_index_backward(
function altcorr_forward (line 254) | std::vector<torch::Tensor> altcorr_forward(
function altcorr_backward (line 266) | std::vector<torch::Tensor> altcorr_backward(
function depth_filter (line 281) | torch::Tensor depth_filter(
function PYBIND11_MODULE (line 297) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
FILE: visualization/check_reconstruction_kitti.py
function create_camera_actor (line 24) | def create_camera_actor(g, scale=0.05):
function create_point_actor (line 34) | def create_point_actor(points, colors):
function str2array (line 44) | def str2array(ss):
function key_action_callback (line 62) | def key_action_callback(vis, action, mods):
function save_callback (line 70) | def save_callback(vis, action, mods):
FILE: visualization/check_reconstruction_kitti_animation.py
function create_camera_actor (line 25) | def create_camera_actor(g, scale=0.05):
function create_point_actor (line 35) | def create_point_actor(points, colors):
function str2array (line 45) | def str2array(ss):
function key_action_callback (line 63) | def key_action_callback(vis, action, mods):
FILE: visualization/check_reconstruction_tumvi.py
function create_camera_actor (line 24) | def create_camera_actor(g, scale=0.05):
function create_point_actor (line 34) | def create_point_actor(points, colors):
function str2array (line 44) | def str2array(ss):
function key_action_callback (line 63) | def key_action_callback(vis, action, mods):
FILE: visualization/check_reconstruction_tumvi_animation.py
function create_camera_actor (line 25) | def create_camera_actor(g, scale=0.05):
function create_point_actor (line 35) | def create_point_actor(points, colors):
function str2array (line 45) | def str2array(ss):
function key_action_callback (line 63) | def key_action_callback(vis, action, mods):
Condensed preview — 71 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (404K chars).
[
{
"path": ".gitignore",
"chars": 2158,
"preview": "a# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packag"
},
{
"path": ".gitmodules",
"chars": 211,
"preview": "[submodule \"thirdparty/lietorch\"]\n\tpath = thirdparty/lietorch\n\turl = https://github.com/princeton-vl/lietorch\n[submodule"
},
{
"path": "LICENSE",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 8162,
"preview": "# DBA-Fusion\n\n>Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localizati"
},
{
"path": "batch_kitti360.py",
"chars": 1123,
"preview": "import os\nimport subprocess\n\nfor i in ['0000','0002','0003','0004','0005','0006','0009','0010']:\n p = subprocess.Pope"
},
{
"path": "batch_subt.py",
"chars": 846,
"preview": "import os\nimport subprocess\n\nfor i in [\\\n # 'Handheld1_Folder',\\\n 'Handheld2_Folder',\\\n ]:\n p = subprocess"
},
{
"path": "batch_tumvi.py",
"chars": 1267,
"preview": "import os\nimport subprocess\n\nfor i in [\\\n 'outdoors1',\\\n 'outdoors2',\\\n 'outdoors3',\\\n 'outdoors4',\\\n 'ou"
},
{
"path": "batch_whu.py",
"chars": 2782,
"preview": "import os\nimport subprocess\n\n# VIO\np = subprocess.Popen(\"python demo_vio_whu.py\" +\\\n \" --imagedir=/home/zhouyuxuan/da"
},
{
"path": "calib/0412.txt",
"chars": 51,
"preview": "889.32868436 889.32868436 515.73648834 202.43873596"
},
{
"path": "calib/0412new.txt",
"chars": 83,
"preview": "885.839465 882.512623 505.509972 389.860117 -0.125551 0.065179 -0.000074 -0.000698\n"
},
{
"path": "calib/1012.txt",
"chars": 97,
"preview": "890.21388839 889.56330572 512.88196119 381.38486858 -0.13095809 0.06640391 -0.00094794 0.0003389\n"
},
{
"path": "calib/barn.txt",
"chars": 67,
"preview": "1161.545689 1161.545689 960.000000 540.000000 -0.025158 0.0 0.0 0.0"
},
{
"path": "calib/carla.txt",
"chars": 25,
"preview": "886.8100 886.8100 512 256"
},
{
"path": "calib/eth.txt",
"chars": 62,
"preview": "726.21081542969 726.21081542969 359.2048034668 202.47247314453"
},
{
"path": "calib/euroc.txt",
"chars": 80,
"preview": "458.654 457.296 367.215 248.375 -0.28340811 0.07395907 0.00019359 1.76187114e-05"
},
{
"path": "calib/handheld.txt",
"chars": 155,
"preview": "531.0895358407821 530.9183032386885 511.3708876141611 399.1276554093305 -0.3367182787437319 0.10679061024072911 0.000305"
},
{
"path": "calib/kitti_360.txt",
"chars": 91,
"preview": "788.629315 786.382230 687.158398 317.752196 -0.344441 0.141678 0.000414 -0.000222 -0.029608"
},
{
"path": "calib/subt.txt",
"chars": 177,
"preview": "758.3153257832925 676.6492212772476 318.27111164892506 239.79816832491474 1.583106303248484 -0.059098218173967695 0.1793"
},
{
"path": "calib/tartan.txt",
"chars": 23,
"preview": "320.0 320.0 320.0 240.0"
},
{
"path": "calib/tum3.txt",
"chars": 23,
"preview": "535.4 539.2 320.1 247.6"
},
{
"path": "calib/tumvi.txt",
"chars": 163,
"preview": "190.97847715128717 190.9733070521226 254.93170605935475 256.8974428996504 0.0034823894022493434 0.0007150348452162257 -0"
},
{
"path": "dataset/euroc_to_hdf5.py",
"chars": 2685,
"preview": "from tqdm import tqdm\nimport numpy as np\nimport torch\nimport cv2\nimport os\nimport argparse\n\nimport h5py\nimport pickle\n\nd"
},
{
"path": "dataset/kitti360_to_hdf5.py",
"chars": 2489,
"preview": "from tqdm import tqdm\nimport numpy as np\nimport torch\nimport cv2\nimport os\nimport argparse\n\nimport h5py\nimport pickle\n\nd"
},
{
"path": "dataset/tumvi_to_hdf5.py",
"chars": 2671,
"preview": "from tqdm import tqdm\nimport numpy as np\nimport torch\nimport cv2\nimport os\nimport argparse\n\nimport h5py\nimport pickle\n\nd"
},
{
"path": "dbaf/covisible_graph.py",
"chars": 18019,
"preview": "import torch\nimport lietorch\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nfrom lietorch import SE3\nfrom modules.c"
},
{
"path": "dbaf/data_readers/__init__.py",
"chars": 1,
"preview": "\n"
},
{
"path": "dbaf/data_readers/augmentation.py",
"chars": 2243,
"preview": "import torch\nimport torchvision.transforms as transforms\nimport numpy as np\nimport torch.nn.functional as F\n\n\nclass RGBD"
},
{
"path": "dbaf/data_readers/base.py",
"chars": 5181,
"preview": "\nimport numpy as np\nimport torch\nimport torch.utils.data as data\nimport torch.nn.functional as F\n\nimport csv\nimport os\ni"
},
{
"path": "dbaf/data_readers/factory.py",
"chars": 2458,
"preview": "\nimport pickle\nimport os\nimport os.path as osp\n\n# RGBD-Dataset\nfrom .tartan import TartanAir\n\nfrom .stream import ImageS"
},
{
"path": "dbaf/data_readers/rgbd_utils.py",
"chars": 6453,
"preview": "import numpy as np\nimport os.path as osp\n\nimport torch\nfrom lietorch import SE3\n\nimport geom.projective_ops as pops\nfrom"
},
{
"path": "dbaf/data_readers/stream.py",
"chars": 7658,
"preview": "\nimport numpy as np\nimport torch\nimport torch.utils.data as data\nimport torch.nn.functional as F\n\nimport csv\nimport os\ni"
},
{
"path": "dbaf/data_readers/tartan.py",
"chars": 4468,
"preview": "\nimport numpy as np\nimport torch\nimport glob\nimport cv2\nimport os\nimport os.path as osp\n\nfrom lietorch import SE3\nfrom ."
},
{
"path": "dbaf/data_readers/tartan_test.txt",
"chars": 1098,
"preview": "abandonedfactory/abandonedfactory/Easy/P011\nabandonedfactory/abandonedfactory/Hard/P011\nabandonedfactory_night/abandoned"
},
{
"path": "dbaf/dbaf.py",
"chars": 6167,
"preview": "import torch\nimport lietorch\nimport numpy as np\nfrom droid_net import DroidNet\nfrom depth_video import DepthVideo\nfrom m"
},
{
"path": "dbaf/dbaf_frontend.py",
"chars": 39895,
"preview": "import torch\nimport torchvision\nimport numpy as np\n\nfrom lietorch import SE3, SO3\nfrom covisible_graph import CovisibleG"
},
{
"path": "dbaf/depth_video.py",
"chars": 26297,
"preview": "import numpy as np\nimport torch\nimport lietorch\nimport droid_backends\n\nfrom torch.multiprocessing import Process, Queue,"
},
{
"path": "dbaf/droid_net.py",
"chars": 7399,
"preview": "import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom collections import OrderedDic"
},
{
"path": "dbaf/geoFunc/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "dbaf/geoFunc/const_value.py",
"chars": 60,
"preview": "import math\r\n\r\npi=math.pi\r\na = 6378137\r\nfinv = 298.257223563"
},
{
"path": "dbaf/geoFunc/trans.py",
"chars": 5595,
"preview": "import math\r\nfrom math import atan2, sin, cos\r\nfrom . import const_value\r\nimport numpy as np\r\nfrom scipy.spatial.transfo"
},
{
"path": "dbaf/geom/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "dbaf/geom/ba.py",
"chars": 4687,
"preview": "import lietorch\nimport torch\nimport torch.nn.functional as F\n\nfrom .chol import block_solve, schur_solve\nimport geom.pro"
},
{
"path": "dbaf/geom/chol.py",
"chars": 1827,
"preview": "import torch\nimport torch.nn.functional as F\nimport geom.projective_ops as pops\n\nclass CholeskySolver(torch.autograd.Fun"
},
{
"path": "dbaf/geom/graph_utils.py",
"chars": 2860,
"preview": "\nimport torch\nimport numpy as np\nfrom collections import OrderedDict\n\nimport lietorch\nfrom data_readers.rgbd_utils impor"
},
{
"path": "dbaf/geom/losses.py",
"chars": 3262,
"preview": "from collections import OrderedDict\nimport numpy as np\nimport torch\nfrom lietorch import SO3, SE3, Sim3\nfrom .graph_util"
},
{
"path": "dbaf/geom/projective_ops.py",
"chars": 5069,
"preview": "import torch\nimport torch.nn.functional as F\n\nfrom lietorch import SE3, Sim3\n\nMIN_DEPTH = 0.2\n\ndef extract_intrinsics(in"
},
{
"path": "dbaf/modules/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "dbaf/modules/clipping.py",
"chars": 580,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nGRAD_CLIP = .01\n\nclass GradClip(torch.autograd.Funct"
},
{
"path": "dbaf/modules/corr.py",
"chars": 4671,
"preview": "import torch\nimport torch.nn.functional as F\n\nimport droid_backends\n\nclass CorrSampler(torch.autograd.Function):\n\n @s"
},
{
"path": "dbaf/modules/extractor.py",
"chars": 6798,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ResidualBlock(nn.Module):\n def __init__(se"
},
{
"path": "dbaf/modules/gru.py",
"chars": 1276,
"preview": "import torch\nimport torch.nn as nn\n\n\nclass ConvGRU(nn.Module):\n def __init__(self, h_planes=128, i_planes=128):\n "
},
{
"path": "dbaf/motion_filter.py",
"chars": 3549,
"preview": "import cv2\nimport torch\nimport lietorch\n\nfrom collections import OrderedDict\nfrom droid_net import DroidNet\n\nimport geom"
},
{
"path": "dbaf/multi_sensor.py",
"chars": 6752,
"preview": "import numpy as np\nimport gtsam\nimport math\n\nGRAVITY = 9.807\n\nclass MultiSensorState:\n def __init__(self):\n se"
},
{
"path": "demo_vio_kitti360.py",
"chars": 8837,
"preview": "import sys\nsys.path.append('dbaf')\nsys.path.append('dbaf/geoFunc')\n\nfrom tqdm import tqdm\nimport numpy as np\nimport torc"
},
{
"path": "demo_vio_subt.py",
"chars": 9320,
"preview": "import sys\nsys.path.append('dbaf')\n\nfrom tqdm import tqdm\nimport numpy as np\nimport torch\nimport cv2\nimport os\nimport ar"
},
{
"path": "demo_vio_tumvi.py",
"chars": 9190,
"preview": "import sys\nsys.path.append('dbaf')\n\nfrom tqdm import tqdm\nimport numpy as np\nimport torch\nimport cv2\nimport os\nimport ar"
},
{
"path": "demo_vio_whu.py",
"chars": 10060,
"preview": "import sys\nsys.path.append('dbaf')\nsys.path.append('dbaf/geoFunc')\nfrom tqdm import tqdm\nimport numpy as np\nimport torch"
},
{
"path": "evaluation_scripts/batch_tumvi.py",
"chars": 362,
"preview": "import subprocess\n\nfor seq in ['magistrale1','magistrale2','magistrale3','magistrale4','magistrale5','magistrale6',\\\n "
},
{
"path": "evaluation_scripts/evaluate_kitti.py",
"chars": 8766,
"preview": "import argparse\nimport logging\nimport typing\n\nimport numpy as np\n\nimport evo.common_ape_rpe as common\nfrom evo.core impo"
},
{
"path": "evaluation_scripts/evaluate_tumvi.py",
"chars": 7923,
"preview": "import argparse\nimport logging\nimport typing\n\nimport numpy as np\n\nimport evo.common_ape_rpe as common\nfrom evo.core impo"
},
{
"path": "results/PLACEHOLDER",
"chars": 0,
"preview": ""
},
{
"path": "setup.py",
"chars": 2220,
"preview": "from setuptools import setup\nfrom torch.utils.cpp_extension import BuildExtension, CUDAExtension\n\nimport os.path as osp\n"
},
{
"path": "src/altcorr_kernel.cu",
"chars": 11307,
"preview": "#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <vector>\n#include <cuda_fp16.h>\n#inclu"
},
{
"path": "src/bacore.h",
"chars": 1513,
"preview": "#include <torch/extension.h>\n#include <vector>\n\nclass BACore\n{\npublic:\n BACore(){}\n ~BACore(){}\npublic:\n void init(to"
},
{
"path": "src/correlation_kernels.cu",
"chars": 5216,
"preview": "#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <vector>\n#include <cuda_fp16.h>\n#inclu"
},
{
"path": "src/droid.cpp",
"chars": 7503,
"preview": "#include <torch/extension.h>\n#include <vector>\n\n#include \"bacore.h\"\n\n// CUDA forward declarations\nstd::vector<torch::Ten"
},
{
"path": "src/droid_kernels.cu",
"chars": 56217,
"preview": "#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cuda_runtime.h>\n\n#include <vector>\n#i"
},
{
"path": "visualization/check_reconstruction_kitti.py",
"chars": 3051,
"preview": "import numpy as np\nimport cv2\nimport open3d as o3d\nimport matplotlib.pyplot as plt\nfrom lietorch import SO3, SE3, Sim3\nf"
},
{
"path": "visualization/check_reconstruction_kitti_animation.py",
"chars": 3399,
"preview": "import numpy as np\nimport cv2\nimport open3d as o3d\nimport matplotlib.pyplot as plt\nfrom lietorch import SO3, SE3, Sim3\nf"
},
{
"path": "visualization/check_reconstruction_tumvi.py",
"chars": 3024,
"preview": "import numpy as np\nimport cv2\nimport open3d as o3d\nimport matplotlib.pyplot as plt\nfrom lietorch import SO3, SE3, Sim3\nf"
},
{
"path": "visualization/check_reconstruction_tumvi_animation.py",
"chars": 3915,
"preview": "import numpy as np\nimport cv2\nimport open3d as o3d\nimport matplotlib.pyplot as plt\nfrom lietorch import SO3, SE3, Sim3\nf"
}
]
About this extraction
This page contains the full source code of the GREAT-WHU/DBA-Fusion GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 71 files (377.7 KB), approximately 112.5k tokens, and a symbol index with 260 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.