[ { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2021 Shaohui Liu\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "README.md", "content": "## Towards Better Generalization: Joint Depth-Pose Learning without PoseNet\nCreated by Wang Zhao, Shaohui Liu, Yezhi Shu, Yong-Jin Liu.\n \n## Introduction\nThis implementation is based on our CVPR'2020 paper \"Towards Better Generalization: Joint Depth-Pose Learning without PoseNet\". You can find the arXiv version of the paper here. In this repository we release code and pre-trained models for *TrianFlow* (our method) and a strong baseline *PoseNet-Flow* method.\n![img](./data/pipeline.png)\n\n## Installation\nThe code is based on Python3.6. You could use either virtualenv or conda to setup a specified environment. And then run:\n```bash\npip install -r requirements.txt\n```\n\n## Run a demo\nTo run a depth prediction demo, you may need to first download the pretrained model from here.\n\n```bash\npython test.py --config_file ./config/default_1scale.yaml --gpu 0 --mode depth --task demo --image_path ./data/demo/kitti.png --pretrained_model ./models/pretrained/depth_pretrained.pth --result_dir ./data/demo\n```\nThis will give you a predicted depth map for demo image.\n![img](./data/demo/kitti.png)\n![img](./data/demo/demo_depth.jpg)\n\n## Run experiments\n\n### Prepare training data:\n1. For KITTI depth and flow tasks, download KITTI raw dataset using the script provided on the official website. You also need to download KITTI 2015 dataset to evaluate the predicted optical flow. Run the following commands to generate groundtruth files for eigen test images. \n```\ncd ./data/eigen\npython export_gt_depth.py --data_path /path/to/your/kitti/root \n```\n2. For KITTI Odometry task, download KITTI Odometry dataset.\n3. For NYUv2 experiments, download NYUv2 raw sequences and labeled data mat, also the traing test split mat from here. Put the labeled data and splits file under the same directory. The data structure should be:\n```\nnyuv2\n | basements \n | cafe\n | ...\nnyuv2_test\n | nyu_depth_v2_labeled.mat \n | splits.mat\n```\n\n### Training:\n1. Modify the configuration file in the ./config directory to set up your path. The config file contains the important paths and default hyper-parameters used in the training process.\n2. For KITTI depth, we have the three-stage training schedule.\n```bash\n1. python train.py --config_file ./config/kitti.yaml --gpu [gpu_id] --mode flow --prepared_save_dir [name_of_your_prepared_dataset] --model_dir [your/directory/to/save/training/models]\n2. python train.py --config_file ./config/kitti.yaml --gpu [gpu_id] --mode depth --prepared_save_dir [name_of_your_prepared_dataset] --model_dir [your/directory/to/save/training/models] --flow_pretrained_model [path/to/your/stage1_flow_model]\n3. python train.py --config_file ./config/kitti_3stage.yaml --gpu [gpu_id] --mode depth_pose --prepared_save_dir [name_of_your_prepared_dataset] --model_dir [your/directory/to/save/training/models] --depth_pretrained_model [path/to/your/stage2_depth_model]\n```\nIf you are running experiments on the dataset for the first time, it would first process data and save in the [prepared_base_dir] path defined in your config file. \nFor other datasets like KITTI Odometry and NYUv2 dataset, you could run with the same commands using the appropriate config file.\n\nWe also implement and release codes for the strong baseline *PoseNet-Flow* method, you could run it by two-stage training:\n```bash\n1. python train.py --config_file [path/to/your/config/file] --gpu [gpu_id] --mode flow --prepared_save_dir [name_of_your_prepared_dataset] --model_dir [your/directory/to/save/training/models]\n2. python train.py --config_file [path/to/your/config/file] --gpu [gpu_id] --mode flowposenet --prepared_save_dir [name_of_your_prepared_dataset] --model_dir [your/directory/to/save/training/models] --flow_pretrained_model [path/to/your/stage1_flow_model]\n```\n\n### Evaluation:\nWe provide pretrained models here for different tasks. The performance could be slightly different with the paper due to randomness.\n\n1. To evaluate the monocular depth estimation on kitti eigen test split, run:\n```bash\npython test.py --config_file ./config/kitti.yaml --gpu [gpu_id] --mode depth --task kitti_depth --pretrained_model [path/to/your/model] --result_dir [path/to/save/results]\n```\n2. To evaluate the monocular depth estimation on nyuv2 test split, run:\n```bash\npython test.py --config_file ./config/nyu.yaml --gpu [gpu_id] --mode depth --task nyuv2 --pretrained_model [path/to/your/model] --result_dir [path/to/save/results]\n```\n3. To evaluate the optical flow estimation on KITTI 2015, run:\n```bash\npython test.py --config_file ./config/kitti.yaml --gpu [gpu_id] --mode flow_3stage --task kitti_flow --pretrained_model [path/to/your/model] --result_dir [path/to/save/results]\n```\n4. To evaluate the visual odometry task on KITTI Odometry dataset, first get predictions on a single sequence and then evaluate:\n```bash\npython infer_vo.py --config_file ./config/odo.yaml --gpu [gpu_id] --traj_save_dir_txt [where/to/save/the/prediction/file] --sequences_root_dir [the/root/dir/of/your/image/sequences] --sequence [the sequence id] ----pretrained_model [path/to/your/model]\npython ./core/evaluation/eval_odom.py --gt_txt [path/to/your/groundtruth/poses/txt] --result_txt [path/to/your/prediction/txt] --seq [sequence id to evaluate]\n```\nYou could evaluate on the sampled KITTI odometry dataset by simply sampling the raw image sequences and gt-pose txt. Then run *infer_vo.py* on the sampled image sequence and *eval_odom.py* with predicted txt and sampled gt txt to get results.\n\n### Citation\nIf you find our work useful in your research, please consider citing:\n```\n@inproceedings{zhao2020towards,\n title={Towards Better Generalization: Joint Depth-Pose Learning without PoseNet},\n author={Zhao, Wang and Liu, Shaohui and Shu, Yezhi and Liu, Yong-Jin},\n booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\n year={2020}\n}\n```\n\n### Related Projects\nDigging into Self-Supervised Monocular Depth Prediction.\n\nCompetitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation.\n\nVisual Odometry Revisited: What Should Be Learnt?\n\n\n\n" }, { "path": "config/kitti.yaml", "content": "cfg_name: 'default'\n\n# dataset\nraw_base_dir: '/home4/zhaow/data/kitti'\nprepared_base_dir: '/home5/zhaow/data/kitti_release'\ngt_2012_dir: '/home4/zhaow/data/kitti_stereo/kitti_2012/training'\ngt_2015_dir: '/home4/zhaow/data/kitti_stereo/kitti_2015/training'\nstatic_frames_txt: '/home5/zhaow/release/data/eigen/static_frames.txt'\ntest_scenes_txt: '/home5/zhaow/release/data/eigen/test_scenes.txt'\ndataset: 'kitti_depth'\nnum_scales: 3\n\n# training\nnum_iterations: 200000\n\n# loss hyperparameters\nw_ssim: 0.85 # w_pixel = 1 - w_ssim\nw_flow_smooth: 10.0\nw_flow_consis: 0.01\nw_geo: 1.0\nw_pt_depth: 1.0\nw_pj_depth: 0.1\nw_flow_error: 0.0\nw_depth_smooth: 0.001\n\n\nh_flow_consist_alpha: 3.0\nh_flow_consist_beta: 0.05\n\nransac_iters: 100\nransac_points: 6000\n\n# Depth Setting\ndepth_match_num: 6000\ndepth_sample_ratio: 0.20\ndepth_scale: 1\n\n# basic info\nimg_hw: [256, 832]\nuse_svd_gpu: False\n\n" }, { "path": "config/kitti_3stage.yaml", "content": "cfg_name: 'default'\n\n# dataset\nraw_base_dir: '/home/zhaow/data/kitti'\nprepared_base_dir: '/home/zhaow/data/kitti_seq'\ngt_2012_dir: '/home/zhaow/data/kitti_stereo/kitti_2012/training'\ngt_2015_dir: '/home/zhaow/data/kitti_stereo/kitti_2015/training'\nstatic_frames_txt: '/home/zhaow/data/kitti_seq/static_frames.txt'\ntest_scenes_txt: '/home5/zhaow/release/data/eigen/test_scenes.txt'\ndataset: 'kitti_depth'\nnum_scales: 3\n\n# training\nnum_iterations: 200000 # set -1 to use num_epochs\nnum_epochs: 0\n\n# loss hyperparameters\n\nw_ssim: 0.85 # w_pixel = 1 - w_ssim\nw_flow_smooth: 10.0\nw_flow_consis: 0.01\nw_geo: 0.002 #0.002\nw_pt_depth: 0.0\nw_pj_depth: 0.000\nw_flow_error: 0.01\nw_depth_smooth: 0.000\n\n\nh_flow_consist_alpha: 3.0\nh_flow_consist_beta: 0.05\n\n# dfe setting\ndfe_depth: 3\ndfe_depth_scales: 4\ndfe_points: 500\nransac_iters: 100\nransac_points: 6000\n\n# Depth Setting\ndepth_match_num: 6000\ndepth_sample_ratio: 0.20\ndepth_scale: 1\n\n# basic info\nimg_hw: [256, 832]\nuse_svd_gpu: False\n\n" }, { "path": "config/nyu.yaml", "content": "cfg_name: 'default'\n\n# dataset\nraw_base_dir: '/home5/zhaow/data/nyuv2'\nprepared_base_dir: '/home5/zhaow/data/nyu_seq_release'\nnyu_test_dir: '/home5/zhaow/data/nyuv2_test'\ndataset: 'nyuv2'\nnum_scales: 3\n\n# training\nnum_iterations: 400000\n\n# loss hyperparameters\nw_ssim: 0.85 # w_pixel = 1 - w_ssim\nw_flow_smooth: 10.0\nw_flow_consis: 0.01\nw_geo: 0.0\nw_pt_depth: 1.0\nw_pj_depth: 0.1\nw_flow_error: 0.00\nw_depth_smooth: 0.0001\n\n\nh_flow_consist_alpha: 3.0\nh_flow_consist_beta: 0.05\n\nransac_iters: 100\nransac_points: 6000\n\n# Depth Setting\ndepth_match_num: 6000\ndepth_sample_ratio: 0.20\ndepth_scale: 1\n\n# basic info\nimg_hw: [448, 576]\n#img_hw: [192, 256]\nblock_tri_grad: False\n\n" }, { "path": "config/nyu_192.yaml", "content": "cfg_name: 'default'\n\n# dataset\nraw_base_dir: '/home5/zhaow/data/nyuv2'\nprepared_base_dir: '/home5/zhaow/data/nyu_seq_release'\nnyu_test_dir: '/home5/zhaow/data/nyuv2_test'\ndataset: 'nyuv2'\nnum_scales: 3\n\n# training\nnum_iterations: 400000\n\n# loss hyperparameters\nw_ssim: 0.85 # w_pixel = 1 - w_ssim\nw_flow_smooth: 10.0\nw_flow_consis: 0.01\nw_geo: 0.0\nw_pt_depth: 1.0\nw_pj_depth: 0.1\nw_flow_error: 0.00\nw_depth_smooth: 0.0001\n\n\nh_flow_consist_alpha: 3.0\nh_flow_consist_beta: 0.05\n\nransac_iters: 100\nransac_points: 6000\n\n# Depth Setting\ndepth_match_num: 6000\ndepth_sample_ratio: 0.20\ndepth_scale: 1\n\n# basic info\n#img_hw: [448, 576]\nimg_hw: [192, 256]\nblock_tri_grad: False\n\n" }, { "path": "config/nyu_192_3stage.yaml", "content": "cfg_name: 'default'\n\n# dataset\nraw_base_dir: '/home5/zhaow/data/nyuv2'\nprepared_base_dir: '/home5/zhaow/data/nyu_seq_release'\nnyu_test_dir: '/home5/zhaow/data/nyuv2_test'\ndataset: 'nyuv2'\nnum_scales: 3\n\n# training\nnum_iterations: 400000 # set -1 to use num_epochs\n\n# loss hyperparameters\nw_ssim: 0.85 # w_pixel = 1 - w_ssim\nw_flow_smooth: 10.0\nw_flow_consis: 0.01\nw_geo: 0.1\nw_pt_depth: 0.1\nw_pj_depth: 0.01\nw_flow_error: 0.00\nw_depth_smooth: 0.0001\n\n\nh_flow_consist_alpha: 3.0\nh_flow_consist_beta: 0.05\n\nransac_iters: 100\nransac_points: 6000\n\n# Depth Setting\ndepth_match_num: 6000\ndepth_sample_ratio: 0.20\ndepth_scale: 1\n\n# basic info\n#img_hw: [448, 576]\nimg_hw: [192, 256]\nblock_tri_grad: False\n\n" }, { "path": "config/nyu_3stage.yaml", "content": "cfg_name: 'default'\n\n# dataset\nraw_base_dir: '/home5/zhaow/data/nyuv2'\nprepared_base_dir: '/home5/zhaow/data/nyu_seq_release'\nnyu_test_dir: '/home5/zhaow/data/nyuv2_test'\ndataset: 'nyuv2'\nnum_scales: 3\n\n# training\nnum_iterations: 400000 # set -1 to use num_epochs\n\n# loss hyperparameters\nw_ssim: 0.85 # w_pixel = 1 - w_ssim\nw_flow_smooth: 10.0\nw_flow_consis: 0.01\nw_geo: 0.1\nw_pt_depth: 0.1\nw_pj_depth: 0.01\nw_flow_error: 0.00\nw_depth_smooth: 0.0001\n\n\nh_flow_consist_alpha: 3.0\nh_flow_consist_beta: 0.05\n\nransac_iters: 100\nransac_points: 6000\n\n# Depth Setting\ndepth_match_num: 6000\ndepth_sample_ratio: 0.20\ndepth_scale: 1\n\n# basic info\nimg_hw: [448, 576]\n#img_hw: [192, 256]\nblock_tri_grad: False\n\n" }, { "path": "config/nyu_posenet_192.yaml", "content": "cfg_name: 'default'\n\n# dataset\nraw_base_dir: '/home5/zhaow/data/nyuv2_sub2'\nprepared_base_dir: '/home5/zhaow/data/nyu_seq_release'\nnyu_test_dir: '/home5/zhaow/data/nyuv2_test'\ndataset: 'nyuv2'\nnum_scales: 3\n\n# training\nnum_iterations: 500000 # set -1 to use num_epochs\nnum_epochs: 0\n\n# loss hyperparameters\nw_ssim: 0.85 # w_pixel = 1 - w_ssim\nw_flow_smooth: 10.0\nw_flow_consis: 0.01\nw_geo: 0.0\nw_pt_depth: 0.0\nw_pj_depth: 0.5\nw_flow_error: 10.0\nw_depth_smooth: 0.00001\n\n\nh_flow_consist_alpha: 3.0\nh_flow_consist_beta: 0.05\n\nransac_iters: 100\nransac_points: 6000\n\n# Depth Setting\ndepth_match_num: 6000\ndepth_scale: 1\n\n# basic info\nimg_hw: [192,256]\nblock_tri_grad: False\n\n" }, { "path": "config/odo.yaml", "content": "cfg_name: 'default'\n\n# dataset\nraw_base_dir: '/home4/zhaow/data/kitti_odometry/sequences'\nprepared_base_dir: '/home5/zhaow/data/kitti_odo_release/'\ngt_2012_dir: '/home4/zhaow/data/kitti_stereo/kitti_2012/training'\ngt_2015_dir: '/home4/zhaow/data/kitti_stereo/kitti_2015/training'\ndataset: 'kitti_odo'\nnum_scales: 3\n\n# training\nnum_iterations: 200000\n\n\nw_ssim: 0.85 # w_pixel = 1 - w_ssim\nw_flow_smooth: 10.0\nw_flow_consis: 0.01\nw_geo: 0.1\nw_pt_depth: 1.0\nw_pj_depth: 0.1\nw_flow_error: 0.0\nw_depth_smooth: 0.0001\n\n\nh_flow_consist_alpha: 3.0\nh_flow_consist_beta: 0.05\n\nransac_iters: 100\nransac_points: 6000\n\n# Depth Setting\ndepth_match_num: 6000\ndepth_sample_ratio: 0.20\ndepth_scale: 1\n\n# basic info\nimg_hw: [256, 832]\nblock_tri_grad: False\n\n" }, { "path": "config/odo_3stage.yaml", "content": "cfg_name: 'default'\n\n# dataset\nraw_base_dir: '/home4/zhaow/data/kitti_odometry/sequences'\nprepared_base_dir: '/home5/zhaow/data/kitti_odo_release/'\ngt_2012_dir: '/home4/zhaow/data/kitti_stereo/kitti_2012/training'\ngt_2015_dir: '/home4/zhaow/data/kitti_stereo/kitti_2015/training'\ndataset: 'kitti_odo'\nnum_scales: 3\n\n# training\nnum_iterations: 200000\n\n\nw_ssim: 0.85 # w_pixel = 1 - w_ssim\nw_flow_smooth: 10.0\nw_flow_consis: 0.01\nw_geo: 0.002 #0.002\nw_pt_depth: 0.0\nw_pj_depth: 0.000\nw_flow_error: 0.01\nw_depth_smooth: 0.000\n\n\nh_flow_consist_alpha: 3.0\nh_flow_consist_beta: 0.05\n\nransac_iters: 100\nransac_points: 6000\n\n# Depth Setting\ndepth_match_num: 6000\ndepth_sample_ratio: 0.20\ndepth_scale: 1\n\n# basic info\nimg_hw: [256, 832]\nblock_tri_grad: False\n\n" }, { "path": "config/odo_posenet.yaml", "content": "cfg_name: 'default'\n\n# dataset\nraw_base_dir: '/home4/zhaow/data/kitti_odometry/sequences'\nprepared_base_dir: '/home5/zhaow/data/kitti_odo/'\ngt_2012_dir: '/home4/zhaow/data/kitti_stereo/kitti_2012/training'\ngt_2015_dir: '/home4/zhaow/data/kitti_stereo/kitti_2015/training'\ndataset: 'kitti_odo'\nnum_scales: 3\n\n# training\nnum_iterations: 200000 # set -1 to use num_epochs\nnum_epochs: 0\n\n\nw_ssim: 0.85 # w_pixel = 1 - w_ssim\nw_flow_smooth: 10.0\nw_flow_consis: 0.01\nw_geo: 0.0\nw_pt_depth: 0.0\nw_pj_depth: 0.1\nw_flow_error: 0.5\nw_depth_smooth: 0.0001\n\n\nh_flow_consist_alpha: 3.0\nh_flow_consist_beta: 0.05\n\nransac_iters: 100\nransac_points: 6000\n\n# Depth Setting\ndepth_match_num: 6000\ndepth_scale: 1\n\n# basic info\nimg_hw: [256, 832]\nblock_tri_grad: False\n\n" }, { "path": "core/config/__init__.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom config_utils import generate_loss_weights_dict\n\n" }, { "path": "core/config/config_utils.py", "content": "import os, sys\n\ndef generate_loss_weights_dict(cfg):\n weight_dict = {}\n weight_dict['loss_pixel'] = 1 - cfg.w_ssim\n weight_dict['loss_ssim'] = cfg.w_ssim\n weight_dict['loss_flow_smooth'] = cfg.w_flow_smooth\n weight_dict['loss_flow_consis'] = cfg.w_flow_consis\n weight_dict['geo_loss'] = cfg.w_geo\n weight_dict['pt_depth_loss'] = cfg.w_pt_depth\n weight_dict['pj_depth_loss'] = cfg.w_pj_depth\n weight_dict['depth_smooth_loss'] = cfg.w_depth_smooth\n weight_dict['flow_error'] = cfg.w_flow_error\n return weight_dict\n\n" }, { "path": "core/dataset/__init__.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom kitti_raw import KITTI_RAW\nfrom kitti_prepared import KITTI_Prepared\nfrom kitti_2012 import KITTI_2012\nfrom kitti_2015 import KITTI_2015\nfrom nyu_v2 import NYU_Prepare, NYU_v2\nfrom kitti_odo import KITTI_Odo" }, { "path": "core/dataset/kitti_2012.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom kitti_prepared import KITTI_Prepared\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'evaluation'))\nfrom evaluate_flow import get_scaled_intrinsic_matrix, eval_flow_avg\nimport numpy as np\nimport cv2\nimport copy\n\nimport torch\nimport pdb\n\nclass KITTI_2012(KITTI_Prepared):\n def __init__(self, data_dir, img_hw=(256, 832), init=True):\n self.data_dir = data_dir\n self.img_hw = img_hw\n self.num_total = 194\n if init:\n self.data_list = self.get_data_list()\n\n def get_data_list(self):\n data_list = []\n for i in range(self.num_total):\n data = {}\n data['img1_dir'] = os.path.join(self.data_dir, 'image_2', str(i).zfill(6) + '_10.png')\n data['img2_dir'] = os.path.join(self.data_dir, 'image_2', str(i).zfill(6) + '_11.png')\n data['calib_file_dir'] = os.path.join(self.data_dir, 'calib_cam_to_cam', str(i).zfill(6) + '.txt')\n data_list.append(data)\n return data_list\n\n def __len__(self):\n return len(self.data_list)\n\n def read_cam_intrinsic(self, calib_file):\n input_intrinsic = get_scaled_intrinsic_matrix(calib_file, zoom_x=1.0, zoom_y=1.0)\n return input_intrinsic\n\n def __getitem__(self, idx):\n '''\n Returns:\n - img\t\ttorch.Tensor (N * H, W, 3)\n - K\ttorch.Tensor (num_scales, 3, 3)\n - K_inv\ttorch.Tensor (num_scales, 3, 3)\n '''\n data = self.data_list[idx]\n # load img\n img1 = cv2.imread(data['img1_dir'])\n img2 = cv2.imread(data['img2_dir'])\n img_hw_orig = (img1.shape[0], img1.shape[1])\n img = np.concatenate([img1, img2], 0)\n img = self.preprocess_img(img, self.img_hw, is_test=True)\n img = img.transpose(2,0,1)\n\n # load intrinsic\n cam_intrinsic = self.read_cam_intrinsic(data['calib_file_dir'])\n cam_intrinsic = self.rescale_intrinsics(cam_intrinsic, img_hw_orig, self.img_hw)\n K, K_inv = self.get_intrinsics_per_scale(cam_intrinsic, scale=0) # (3, 3), (3, 3)\n return torch.from_numpy(img).float(), torch.from_numpy(K).float(), torch.from_numpy(K_inv).float()\n\nif __name__ == '__main__':\n pass\n\n" }, { "path": "core/dataset/kitti_2015.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom kitti_2012 import KITTI_2012\n\nclass KITTI_2015(KITTI_2012):\n def __init__(self, data_dir, img_hw=(256, 832)):\n super(KITTI_2015, self).__init__(data_dir, img_hw, init=False)\n self.num_total = 200\n\n self.data_list = self.get_data_list()\n\nif __name__ == '__main__':\n pass\n\n" }, { "path": "core/dataset/kitti_odo.py", "content": "import os, sys\nimport numpy as np\nimport imageio\nfrom tqdm import tqdm\nimport torch.multiprocessing as mp\n\ndef process_folder(q, data_dir, output_dir, stride=1):\n while True:\n if q.empty():\n break\n folder = q.get()\n image_path = os.path.join(data_dir, folder, 'image_2/')\n dump_image_path = os.path.join(output_dir, folder)\n if not os.path.isdir(dump_image_path):\n os.makedirs(dump_image_path)\n f = open(os.path.join(dump_image_path, 'train.txt'), 'w')\n \n # Note. the os.listdir method returns arbitary order of list. We need correct order.\n numbers = len(os.listdir(image_path))\n for n in range(numbers - stride):\n s_idx = n\n e_idx = s_idx + stride\n curr_image = imageio.imread(os.path.join(image_path, '%.6d'%s_idx)+'.png')\n next_image = imageio.imread(os.path.join(image_path, '%.6d'%e_idx)+'.png')\n seq_images = np.concatenate([curr_image, next_image], axis=0)\n imageio.imsave(os.path.join(dump_image_path, '%.6d'%s_idx)+'.png', seq_images.astype('uint8'))\n\n # Write training files\n f.write('%s %s\\n' % (os.path.join(folder, '%.6d'%s_idx)+'.png', os.path.join(folder, 'calib.txt')))\n print(folder)\n\n\nclass KITTI_Odo(object):\n def __init__(self, data_dir):\n self.data_dir = data_dir\n self.train_seqs = ['00','01','02','03','04','05','06','07','08']\n\n def __len__(self):\n raise NotImplementedError\n\n def prepare_data_mp(self, output_dir, stride=1):\n num_processes = 16\n processes = []\n q = mp.Queue()\n if not os.path.isfile(os.path.join(output_dir, 'train.txt')):\n os.makedirs(output_dir)\n #f = open(os.path.join(output_dir, 'train.txt'), 'w')\n print('Preparing sequence data....')\n if not os.path.isdir(self.data_dir):\n raise\n dirlist = os.listdir(self.data_dir)\n total_dirlist = []\n # Get the different folders of images\n for d in dirlist:\n if d in self.train_seqs:\n q.put(d)\n # Process every folder\n for rank in range(num_processes):\n p = mp.Process(target=process_folder, args=(q, self.data_dir, output_dir, stride))\n p.start()\n processes.append(p)\n for p in processes:\n p.join()\n \n f = open(os.path.join(output_dir, 'train.txt'), 'w')\n for d in self.train_seqs:\n train_file = open(os.path.join(output_dir, d, 'train.txt'), 'r')\n for l in train_file.readlines():\n f.write(l)\n\n command = 'cp ' + os.path.join(self.data_dir, d, 'calib.txt') + ' ' + os.path.join(output_dir, d, 'calib.txt')\n os.system(command)\n \n print('Data Preparation Finished.')\n\n def __getitem__(self, idx):\n raise NotImplementedError\n\n\nif __name__ == '__main__':\n data_dir = '/home4/zhaow/data/kitti'\n dirlist = os.listdir('/home4/zhaow/data/kitti')\n output_dir = '/home4/zhaow/data/kitti_seq/data_generated_s2'\n total_dirlist = []\n # Get the different folders of images\n for d in dirlist:\n seclist = os.listdir(os.path.join(data_dir, d))\n for s in seclist:\n if os.path.isdir(os.path.join(data_dir, d, s)):\n total_dirlist.append(os.path.join(d, s))\n \n F = open(os.path.join(output_dir, 'train.txt'), 'w')\n for p in total_dirlist:\n traintxt = os.path.join(os.path.join(output_dir, p), 'train.txt')\n f = open(traintxt, 'r')\n for line in f.readlines():\n F.write(line)\n print(traintxt)" }, { "path": "core/dataset/kitti_prepared.py", "content": "import os, sys\nimport numpy as np\nimport cv2\nimport copy\n\nimport torch\nimport torch.utils.data\nimport pdb\n\nclass KITTI_Prepared(torch.utils.data.Dataset):\n def __init__(self, data_dir, num_scales=3, img_hw=(256, 832), num_iterations=None):\n super(KITTI_Prepared, self).__init__()\n self.data_dir = data_dir\n self.num_scales = num_scales\n self.img_hw = img_hw\n self.num_iterations = num_iterations\n\n info_file = os.path.join(self.data_dir, 'train.txt')\n #info_file = os.path.join(self.data_dir, 'train_flow.txt')\n self.data_list = self.get_data_list(info_file)\n\n def get_data_list(self, info_file):\n with open(info_file, 'r') as f:\n lines = f.readlines()\n data_list = []\n for line in lines:\n k = line.strip('\\n').split()\n data = {}\n data['image_file'] = os.path.join(self.data_dir, k[0])\n data['cam_intrinsic_file'] = os.path.join(self.data_dir, k[1])\n data_list.append(data)\n print('A total of {} image pairs found'.format(len(data_list)))\n return data_list\n\n def count(self):\n return len(self.data_list)\n\n def rand_num(self, idx):\n num_total = self.count()\n np.random.seed(idx)\n num = np.random.randint(num_total)\n return num\n\n def __len__(self):\n if self.num_iterations is None:\n return self.count()\n else:\n return self.num_iterations\n\n def resize_img(self, img, img_hw):\n '''\n Input size (N*H, W, 3)\n Output size (N*H', W', 3), where (H', W') == self.img_hw\n '''\n img_h, img_w = img.shape[0], img.shape[1]\n img_hw_orig = (int(img_h / 2), img_w) \n img1, img2 = img[:img_hw_orig[0], :, :], img[img_hw_orig[0]:, :, :]\n img1_new = cv2.resize(img1, (img_hw[1], img_hw[0]))\n img2_new = cv2.resize(img2, (img_hw[1], img_hw[0]))\n img_new = np.concatenate([img1_new, img2_new], 0)\n return img_new\n\n def random_flip_img(self, img):\n is_flip = (np.random.rand() > 0.5)\n if is_flip:\n img = cv2.flip(img, 1)\n return img\n\n def preprocess_img(self, img, img_hw=None, is_test=False):\n if img_hw is None:\n img_hw = self.img_hw\n img = self.resize_img(img, img_hw)\n if not is_test:\n img = self.random_flip_img(img)\n img = img / 255.0\n return img\n\n def read_cam_intrinsic(self, fname):\n with open(fname, 'r') as f:\n lines = f.readlines()\n data = lines[-1].strip('\\n').split(' ')[1:]\n data = [float(k) for k in data]\n data = np.array(data).reshape(3,4)\n cam_intrinsics = data[:3,:3]\n return cam_intrinsics\n\n def rescale_intrinsics(self, K, img_hw_orig, img_hw_new):\n K[0,:] = K[0,:] * img_hw_new[1] / img_hw_orig[1]\n K[1,:] = K[1,:] * img_hw_new[0] / img_hw_orig[0]\n return K\n\n def get_intrinsics_per_scale(self, K, scale):\n K_new = copy.deepcopy(K)\n K_new[0,:] = K_new[0,:] / (2**scale)\n K_new[1,:] = K_new[1,:] / (2**scale)\n K_new_inv = np.linalg.inv(K_new)\n return K_new, K_new_inv\n\n def get_multiscale_intrinsics(self, K, num_scales):\n K_ms, K_inv_ms = [], []\n for s in range(num_scales):\n K_new, K_new_inv = self.get_intrinsics_per_scale(K, s)\n K_ms.append(K_new[None,:,:])\n K_inv_ms.append(K_new_inv[None,:,:])\n K_ms = np.concatenate(K_ms, 0)\n K_inv_ms = np.concatenate(K_inv_ms, 0)\n return K_ms, K_inv_ms\n\n def __getitem__(self, idx):\n '''\n Returns:\n - img\t\ttorch.Tensor (N * H, W, 3)\n - K\ttorch.Tensor (num_scales, 3, 3)\n - K_inv\ttorch.Tensor (num_scales, 3, 3)\n '''\n if self.num_iterations is not None:\n idx = self.rand_num(idx)\n data = self.data_list[idx]\n # load img\n img = cv2.imread(data['image_file'])\n img_hw_orig = (int(img.shape[0] / 2), img.shape[1])\n img = self.preprocess_img(img, self.img_hw) # (img_h * 2, img_w, 3)\n img = img.transpose(2,0,1)\n\n # load intrinsic\n cam_intrinsic = self.read_cam_intrinsic(data['cam_intrinsic_file'])\n cam_intrinsic = self.rescale_intrinsics(cam_intrinsic, img_hw_orig, self.img_hw)\n K_ms, K_inv_ms = self.get_multiscale_intrinsics(cam_intrinsic, self.num_scales) # (num_scales, 3, 3), (num_scales, 3, 3)\n return torch.from_numpy(img).float(), torch.from_numpy(K_ms).float(), torch.from_numpy(K_inv_ms).float()\n\nif __name__ == '__main__':\n pass\n\n" }, { "path": "core/dataset/kitti_raw.py", "content": "import os, sys\nimport numpy as np\nimport imageio\nfrom tqdm import tqdm\nimport torch.multiprocessing as mp\nimport pdb\n\ndef process_folder(q, static_frames, test_scenes, data_dir, output_dir, stride=1):\n while True:\n if q.empty():\n break\n folder = q.get()\n if folder in static_frames.keys():\n static_ids = static_frames[folder]\n else:\n static_ids = []\n scene = folder.split('/')[1]\n if scene[:-5] in test_scenes:\n continue\n image_path = os.path.join(data_dir, folder, 'image_02/data')\n dump_image_path = os.path.join(output_dir, folder)\n if not os.path.isdir(dump_image_path):\n os.makedirs(dump_image_path)\n f = open(os.path.join(dump_image_path, 'train.txt'), 'w')\n \n # Note. the os.listdir method returns arbitary order of list. We need correct order.\n numbers = len(os.listdir(image_path))\n for n in range(numbers - stride):\n s_idx = n\n e_idx = s_idx + stride\n if '%.10d'%s_idx in static_ids or '%.10d'%e_idx in static_ids:\n #print('%.10d'%s_idx)\n continue\n curr_image = imageio.imread(os.path.join(image_path, '%.10d'%s_idx)+'.png')\n next_image = imageio.imread(os.path.join(image_path, '%.10d'%e_idx)+'.png')\n seq_images = np.concatenate([curr_image, next_image], axis=0)\n imageio.imsave(os.path.join(dump_image_path, '%.10d'%s_idx)+'.png', seq_images.astype('uint8'))\n\n # Write training files\n date = folder.split('/')[0]\n f.write('%s %s\\n' % (os.path.join(folder, '%.10d'%s_idx)+'.png', os.path.join(date, 'calib_cam_to_cam.txt')))\n print(folder)\n\n\nclass KITTI_RAW(object):\n def __init__(self, data_dir, static_frames_txt, test_scenes_txt):\n self.data_dir = data_dir\n self.static_frames_txt = static_frames_txt\n self.test_scenes_txt = test_scenes_txt\n\n def __len__(self):\n raise NotImplementedError\n\n def collect_static_frame(self):\n f = open(self.static_frames_txt)\n static_frames = {}\n for line in f.readlines():\n line = line.strip()\n date, drive, frame_id = line.split(' ')\n curr_fid = '%.10d' % (np.int(frame_id))\n if os.path.join(date, drive) not in static_frames.keys():\n static_frames[os.path.join(date, drive)] = []\n static_frames[os.path.join(date, drive)].append(curr_fid)\n return static_frames\n \n def collect_test_scenes(self):\n f = open(self.test_scenes_txt)\n test_scenes = []\n for line in f.readlines():\n line = line.strip()\n test_scenes.append(line)\n return test_scenes\n\n def prepare_data_mp(self, output_dir, stride=1):\n num_processes = 16\n processes = []\n q = mp.Queue()\n static_frames = self.collect_static_frame()\n test_scenes = self.collect_test_scenes()\n if not os.path.isfile(os.path.join(output_dir, 'train.txt')):\n os.makedirs(output_dir)\n #f = open(os.path.join(output_dir, 'train.txt'), 'w')\n print('Preparing sequence data....')\n if not os.path.isdir(self.data_dir):\n raise\n dirlist = os.listdir(self.data_dir)\n total_dirlist = []\n # Get the different folders of images\n for d in dirlist:\n seclist = os.listdir(os.path.join(self.data_dir, d))\n for s in seclist:\n if os.path.isdir(os.path.join(self.data_dir, d, s)):\n total_dirlist.append(os.path.join(d, s))\n q.put(os.path.join(d, s))\n # Process every folder\n for rank in range(num_processes):\n p = mp.Process(target=process_folder, args=(q, static_frames, test_scenes, self.data_dir, output_dir, stride))\n p.start()\n processes.append(p)\n for p in processes:\n p.join()\n \n # Collect the training frames.\n f = open(os.path.join(output_dir, 'train.txt'), 'w')\n for date in os.listdir(output_dir):\n if os.path.isdir(os.path.join(output_dir, date)):\n drives = os.listdir(os.path.join(output_dir, date))\n for d in drives:\n train_file = open(os.path.join(output_dir, date, d, 'train.txt'), 'r')\n for l in train_file.readlines():\n f.write(l)\n \n # Get calib files\n for date in os.listdir(self.data_dir):\n command = 'cp ' + os.path.join(self.data_dir, date, 'calib_cam_to_cam.txt') + ' ' + os.path.join(output_dir, date, 'calib_cam_to_cam.txt')\n os.system(command)\n \n print('Data Preparation Finished.')\n\n\n\n def prepare_data(self, output_dir):\n static_frames = self.collect_static_frame()\n test_scenes = self.collect_test_scenes()\n if not os.path.isfile(os.path.join(output_dir, 'train.txt')):\n os.makedirs(output_dir)\n f = open(os.path.join(output_dir, 'train.txt'), 'w')\n print('Preparing sequence data....')\n if not os.path.isdir(self.data_dir):\n raise\n dirlist = os.listdir(self.data_dir)\n total_dirlist = []\n # Get the different folders of images\n for d in dirlist:\n seclist = os.listdir(os.path.join(self.data_dir, d))\n for s in seclist:\n if os.path.isdir(os.path.join(self.data_dir, d, s)):\n total_dirlist.append(os.path.join(d, s))\n # Process every folder\n for folder in tqdm(total_dirlist):\n if folder in static_frames.keys():\n static_ids = static_frames[folder]\n else:\n static_ids = []\n scene = folder.split('/')[1]\n if scene in test_scenes:\n continue\n image_path = os.path.join(self.data_dir, folder, 'image_02/data')\n dump_image_path = os.path.join(output_dir, folder)\n if not os.path.isdir(dump_image_path):\n os.makedirs(dump_image_path)\n # Note. the os.listdir method returns arbitary order of list. We need correct order.\n numbers = len(os.listdir(image_path))\n for n in range(numbers - 1):\n s_idx = n\n e_idx = s_idx + 1\n if '%.10d'%s_idx in static_ids or '%.10d'%e_idx in static_ids:\n print('%.10d'%s_idx)\n continue\n curr_image = imageio.imread(os.path.join(image_path, '%.10d'%s_idx)+'.png')\n next_image = imageio.imread(os.path.join(image_path, '%.10d'%e_idx)+'.png')\n seq_images = np.concatenate([curr_image, next_image], axis=0)\n imageio.imsave(os.path.join(dump_image_path, '%.10d'%s_idx)+'.png', seq_images.astype('uint8'))\n \n # Write training files\n date = folder.split('/')[0]\n f.write('%s %s\\n' % (os.path.join(folder, '%.10d'%s_idx)+'.png', os.path.join(date, 'calib_cam_to_cam.txt')))\n print(folder)\n \n # Get calib files\n for date in os.listdir(self.data_dir):\n command = 'cp ' + os.path.join(self.data_dir, date, 'calib_cam_to_cam.txt') + ' ' + os.path.join(output_dir, date, 'calib_cam_to_cam.txt')\n os.system(command)\n \n return os.path.join(output_dir, 'train.txt')\n\n def __getitem__(self, idx):\n raise NotImplementedError\n\n\nif __name__ == '__main__':\n data_dir = '/home4/zhaow/data/kitti'\n dirlist = os.listdir('/home4/zhaow/data/kitti')\n output_dir = '/home4/zhaow/data/kitti_seq/data_generated_s2'\n total_dirlist = []\n # Get the different folders of images\n for d in dirlist:\n seclist = os.listdir(os.path.join(data_dir, d))\n for s in seclist:\n if os.path.isdir(os.path.join(data_dir, d, s)):\n total_dirlist.append(os.path.join(d, s))\n \n F = open(os.path.join(output_dir, 'train.txt'), 'w')\n for p in total_dirlist:\n traintxt = os.path.join(os.path.join(output_dir, p), 'train.txt')\n f = open(traintxt, 'r')\n for line in f.readlines():\n F.write(line)\n print(traintxt)\n\n\n\n\n\n" }, { "path": "core/dataset/nyu_v2.py", "content": "import os, sys\nimport numpy as np\nimport imageio\nimport cv2\nimport copy\nimport h5py\nimport scipy.io as sio\nimport torch\nimport torch.utils.data\nimport pdb\nfrom tqdm import tqdm\nimport torch.multiprocessing as mp\n\ndef collect_image_list(path):\n # Get ppm images list of a folder.\n files = os.listdir(path)\n sorted_file = sorted([f for f in files])\n image_list = []\n for l in sorted_file:\n if l.split('.')[-1] == 'ppm':\n image_list.append(l)\n return image_list\n\ndef process_folder(q, data_dir, output_dir, stride, train_scenes):\n # Directly process the original nyu v2 depth dataset.\n while True:\n if q.empty():\n break\n folder = q.get()\n scene_name = folder.split('/')[-1]\n s1,s2 = scene_name.split('_')[:-1], scene_name.split('_')[-1]\n scene_name_full = ''\n for j in s1:\n scene_name_full = scene_name_full + j + '_'\n scene_name_full = scene_name_full + s2[:4]\n \n if scene_name_full not in train_scenes:\n continue\n image_path = os.path.join(data_dir, folder)\n dump_image_path = os.path.join(output_dir, folder)\n if not os.path.isdir(dump_image_path):\n os.makedirs(dump_image_path)\n f = open(os.path.join(dump_image_path, 'train.txt'), 'w')\n \n # Note. the os.listdir method returns arbitary order of list. We need correct order.\n image_list = collect_image_list(image_path)\n #image_list = open(os.path.join(image_path, 'index.txt')).readlines()\n numbers = len(image_list) - 1 # The last ppm file seems truncated.\n for n in range(numbers - stride):\n s_idx = n\n e_idx = s_idx + stride\n s_name = image_list[s_idx].strip()\n e_name = image_list[e_idx].strip()\n \n curr_image = imageio.imread(os.path.join(image_path, s_name))\n next_image = imageio.imread(os.path.join(image_path, e_name))\n #curr_image = cv2.imread(os.path.join(image_path, s_name))\n #next_image = cv2.imread(os.path.join(image_path, e_name))\n seq_images = np.concatenate([curr_image, next_image], axis=0)\n imageio.imsave(os.path.join(dump_image_path, os.path.splitext(s_name)[0]+'.png'), seq_images.astype('uint8'))\n #cv2.imwrite(os.path.join(dump_image_path, os.path.splitext(s_name)[0]+'.png'), seq_images.astype('uint8'))\n\n # Write training files\n #date = folder.split('_')[2]\n f.write('%s %s\\n' % (os.path.join(folder, os.path.splitext(s_name)[0]+'.png'), 'calib_cam_to_cam.txt'))\n print(folder)\n\nclass NYU_Prepare(object):\n def __init__(self, data_dir, test_dir):\n self.data_dir = data_dir\n self.test_data = os.path.join(test_dir, 'nyu_depth_v2_labeled.mat')\n self.splits = os.path.join(test_dir, 'splits.mat')\n self.get_all_scenes()\n self.get_test_scenes()\n self.get_train_scenes()\n \n\n def __len__(self):\n raise NotImplementedError\n\n def get_all_scenes(self):\n self.all_scenes = []\n paths = os.listdir(self.data_dir)\n for p in paths:\n if os.path.isdir(os.path.join(self.data_dir, p)):\n pp = os.listdir(os.path.join(self.data_dir, p))\n for path in pp:\n self.all_scenes.append(path)\n\n def get_test_scenes(self):\n self.test_scenes = []\n test_data = h5py.File(self.test_data, 'r')\n test_split = sio.loadmat(self.splits)['testNdxs']\n test_split = np.array(test_split).squeeze(1)\n \n test_scenes = test_data['scenes'][0][test_split-1]\n for i in range(len(test_scenes)):\n obj = test_data[test_scenes[i]]\n name = \"\".join(chr(j) for j in obj[:])\n if name not in self.test_scenes:\n self.test_scenes.append(name)\n #pdb.set_trace()\n \n def get_train_scenes(self):\n self.train_scenes = []\n train_data = h5py.File(self.test_data, 'r')\n train_split = sio.loadmat(self.splits)['trainNdxs']\n train_split = np.array(train_split).squeeze(1)\n \n train_scenes = train_data['scenes'][0][train_split-1]\n for i in range(len(train_scenes)):\n obj = train_data[train_scenes[i]]\n name = \"\".join(chr(j) for j in obj[:])\n if name not in self.train_scenes:\n self.train_scenes.append(name)\n\n\n def prepare_data_mp(self, output_dir, stride=1):\n num_processes = 32\n processes = []\n q = mp.Queue()\n if not os.path.isfile(os.path.join(output_dir, 'train.txt')):\n os.makedirs(output_dir)\n #f = open(os.path.join(output_dir, 'train.txt'), 'w')\n print('Preparing sequence data....')\n if not os.path.isdir(self.data_dir):\n raise\n dirlist = os.listdir(self.data_dir)\n total_dirlist = []\n # Get the different folders of images\n for d in dirlist:\n if not os.path.isdir(os.path.join(self.data_dir, d)):\n continue\n seclist = os.listdir(os.path.join(self.data_dir, d))\n for s in seclist:\n if os.path.isdir(os.path.join(self.data_dir, d, s)):\n total_dirlist.append(os.path.join(d, s))\n q.put(os.path.join(d, s))\n # Process every folder\n for rank in range(num_processes):\n p = mp.Process(target=process_folder, args=(q, self.data_dir, output_dir, stride, self.train_scenes))\n p.start()\n processes.append(p)\n for p in processes:\n p.join()\n \n # Collect the training frames.\n f = open(os.path.join(output_dir, 'train.txt'), 'w')\n for dirlist in os.listdir(output_dir):\n if os.path.isdir(os.path.join(output_dir, dirlist)):\n seclists = os.listdir(os.path.join(output_dir, dirlist))\n for s in seclists:\n train_file = open(os.path.join(output_dir, dirlist, s, 'train.txt'), 'r')\n for l in train_file.readlines():\n f.write(l)\n f.close()\n \n f = open(os.path.join(output_dir, 'calib_cam_to_cam.txt'), 'w')\n f.write('P_rect: 5.1885790117450188e+02 0.0 3.2558244941119034e+02 0.0 0.0 5.1946961112127485e+02 2.5373616633400465e+02 0.0 0.0 0.0 1.0 0.0')\n f.close()\n print('Data Preparation Finished.')\n\n def __getitem__(self, idx):\n raise NotImplementedError\n\n\n\nclass NYU_v2(torch.utils.data.Dataset):\n def __init__(self, data_dir, num_scales=3, img_hw=(448, 576), num_iterations=None):\n super(NYU_v2, self).__init__()\n self.data_dir = data_dir\n self.num_scales = num_scales\n self.img_hw = img_hw\n self.num_iterations = num_iterations\n self.undist_coeff = np.array([2.07966153e-01, -5.8613825e-01, 7.223136313e-04, 1.047962719e-03, 4.98569866e-01])\n self.mapx, self.mapy = None, None\n self.roi = None\n\n info_file = os.path.join(self.data_dir, 'train.txt')\n self.data_list = self.get_data_list(info_file)\n\n def get_data_list(self, info_file):\n with open(info_file, 'r') as f:\n lines = f.readlines()\n data_list = []\n for line in lines:\n k = line.strip('\\n').split()\n data = {}\n data['image_file'] = os.path.join(self.data_dir, k[0])\n data['cam_intrinsic_file'] = os.path.join(self.data_dir, k[1])\n data_list.append(data)\n print('A total of {} image pairs found'.format(len(data_list)))\n return data_list\n\n def count(self):\n return len(self.data_list)\n\n def rand_num(self, idx):\n num_total = self.count()\n np.random.seed(idx)\n num = np.random.randint(num_total)\n return num\n\n def __len__(self):\n if self.num_iterations is None:\n return self.count()\n else:\n return self.num_iterations\n\n def resize_img(self, img, img_hw):\n '''\n Input size (N*H, W, 3)\n Output size (N*H', W', 3), where (H', W') == self.img_hw\n '''\n img_h, img_w = img.shape[0], img.shape[1]\n img_hw_orig = (int(img_h / 2), img_w) \n img1, img2 = img[:img_hw_orig[0], :, :], img[img_hw_orig[0]:, :, :]\n img1_new = cv2.resize(img1, (img_hw[1], img_hw[0]))\n img2_new = cv2.resize(img2, (img_hw[1], img_hw[0]))\n img_new = np.concatenate([img1_new, img2_new], 0)\n return img_new\n\n def random_flip_img(self, img):\n is_flip = (np.random.rand() > 0.5)\n if is_flip:\n img = cv2.flip(img, 1)\n return img\n \n def undistort_img(self, img, K):\n img_h, img_w = img.shape[0], img.shape[1]\n img_hw_orig = (int(img_h / 2), img_w) \n img1, img2 = img[:img_hw_orig[0], :, :], img[img_hw_orig[0]:, :, :]\n \n h, w = img_hw_orig\n if self.mapx is None:\n newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(K, self.undist_coeff, (w,h), 1, (w,h))\n self.mapx, self.mapy = cv2.initUndistortRectifyMap(K, self.undist_coeff, None, newcameramtx, (w,h), 5)\n \n img1_undist = cv2.remap(img1, self.mapx, self.mapy, cv2.INTER_LINEAR)\n img2_undist = cv2.remap(img2, self.mapx, self.mapy, cv2.INTER_LINEAR)\n x,y,w,h = self.roi\n img1_undist = img1_undist[y:y+h, x:x+w]\n img2_undist = img2_undist[y:y+h, x:x+w]\n img_undist = np.concatenate([img1_undist, img2_undist], 0)\n #cv2.imwrite('./test.png', img)\n #cv2.imwrite('./test_undist.png', img_undist)\n #pdb.set_trace()\n return img_undist\n\n def preprocess_img(self, img, K, img_hw=None, is_test=False):\n if img_hw is None:\n img_hw = self.img_hw\n if not is_test:\n #img = img\n img = self.undistort_img(img, K)\n #img = self.random_flip_img(img)\n \n img = self.resize_img(img, img_hw)\n img = img / 255.0\n return img\n\n def read_cam_intrinsic(self, fname):\n with open(fname, 'r') as f:\n lines = f.readlines()\n data = lines[-1].strip('\\n').split(' ')[1:]\n data = [float(k) for k in data]\n data = np.array(data).reshape(3,4)\n cam_intrinsics = data[:3,:3]\n return cam_intrinsics\n \n def rescale_intrinsics(self, K, img_hw_orig, img_hw_new):\n K_new = copy.deepcopy(K)\n K_new[0,:] = K_new[0,:] * img_hw_new[0] / img_hw_orig[0]\n K_new[1,:] = K_new[1,:] * img_hw_new[1] / img_hw_orig[1]\n return K_new\n\n def get_intrinsics_per_scale(self, K, scale):\n K_new = copy.deepcopy(K)\n K_new[0,:] = K_new[0,:] / (2**scale)\n K_new[1,:] = K_new[1,:] / (2**scale)\n K_new_inv = np.linalg.inv(K_new)\n return K_new, K_new_inv\n\n def get_multiscale_intrinsics(self, K, num_scales):\n K_ms, K_inv_ms = [], []\n for s in range(num_scales):\n K_new, K_new_inv = self.get_intrinsics_per_scale(K, s)\n K_ms.append(K_new[None,:,:])\n K_inv_ms.append(K_new_inv[None,:,:])\n K_ms = np.concatenate(K_ms, 0)\n K_inv_ms = np.concatenate(K_inv_ms, 0)\n return K_ms, K_inv_ms\n\n def __getitem__(self, idx):\n '''\n Returns:\n - img\t\ttorch.Tensor (N * H, W, 3)\n - K\ttorch.Tensor (num_scales, 3, 3)\n - K_inv\ttorch.Tensor (num_scales, 3, 3)\n '''\n if idx >= self.num_iterations:\n raise IndexError\n if self.num_iterations is not None:\n idx = self.rand_num(idx)\n data = self.data_list[idx]\n # load img\n img = cv2.imread(data['image_file'])\n img_hw_orig = (int(img.shape[0] / 2), img.shape[1])\n \n # load intrinsic\n cam_intrinsic_orig = self.read_cam_intrinsic(data['cam_intrinsic_file'])\n cam_intrinsic = self.rescale_intrinsics(cam_intrinsic_orig, img_hw_orig, self.img_hw)\n K_ms, K_inv_ms = self.get_multiscale_intrinsics(cam_intrinsic, self.num_scales) # (num_scales, 3, 3), (num_scales, 3, 3)\n \n # image preprocessing\n img = self.preprocess_img(img, cam_intrinsic_orig, self.img_hw) # (img_h * 2, img_w, 3)\n img = img.transpose(2,0,1)\n\n \n return torch.from_numpy(img).float(), torch.from_numpy(K_ms).float(), torch.from_numpy(K_inv_ms).float()\n\n\n\n\n\nif __name__ == '__main__':\n data_dir = '/home4/zhaow/data/kitti'\n dirlist = os.listdir('/home4/zhaow/data/kitti')\n output_dir = '/home4/zhaow/data/kitti_seq/data_generated_s2'\n total_dirlist = []\n # Get the different folders of images\n for d in dirlist:\n seclist = os.listdir(os.path.join(data_dir, d))\n for s in seclist:\n if os.path.isdir(os.path.join(data_dir, d, s)):\n total_dirlist.append(os.path.join(d, s))\n \n F = open(os.path.join(output_dir, 'train.txt'), 'w')\n for p in total_dirlist:\n traintxt = os.path.join(os.path.join(output_dir, p), 'train.txt')\n f = open(traintxt, 'r')\n for line in f.readlines():\n F.write(line)\n print(traintxt)\n\n\n\n\n\n" }, { "path": "core/evaluation/__init__.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom evaluate_flow import eval_flow_avg, load_gt_flow_kitti\nfrom evaluate_mask import load_gt_mask\nfrom evaluate_depth import eval_depth\n" }, { "path": "core/evaluation/eval_odom.py", "content": "import copy\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport os\nfrom glob import glob\nimport pdb\n\n\ndef scale_lse_solver(X, Y):\n \"\"\"Least-sqaure-error solver\n Compute optimal scaling factor so that s(X)-Y is minimum\n Args:\n X (KxN array): current data\n Y (KxN array): reference data\n Returns:\n scale (float): scaling factor\n \"\"\"\n scale = np.sum(X * Y)/np.sum(X ** 2)\n return scale\n\n\ndef umeyama_alignment(x, y, with_scale=False):\n \"\"\"\n Computes the least squares solution parameters of an Sim(m) matrix\n that minimizes the distance between a set of registered points.\n Umeyama, Shinji: Least-squares estimation of transformation parameters\n between two point patterns. IEEE PAMI, 1991\n :param x: mxn matrix of points, m = dimension, n = nr. of data points\n :param y: mxn matrix of points, m = dimension, n = nr. of data points\n :param with_scale: set to True to align also the scale (default: 1.0 scale)\n :return: r, t, c - rotation matrix, translation vector and scale factor\n \"\"\"\n if x.shape != y.shape:\n assert False, \"x.shape not equal to y.shape\"\n\n # m = dimension, n = nr. of data points\n m, n = x.shape\n\n # means, eq. 34 and 35\n mean_x = x.mean(axis=1)\n mean_y = y.mean(axis=1)\n\n # variance, eq. 36\n # \"transpose\" for column subtraction\n sigma_x = 1.0 / n * (np.linalg.norm(x - mean_x[:, np.newaxis])**2)\n\n # covariance matrix, eq. 38\n outer_sum = np.zeros((m, m))\n for i in range(n):\n outer_sum += np.outer((y[:, i] - mean_y), (x[:, i] - mean_x))\n cov_xy = np.multiply(1.0 / n, outer_sum)\n\n # SVD (text betw. eq. 38 and 39)\n u, d, v = np.linalg.svd(cov_xy)\n\n # S matrix, eq. 43\n s = np.eye(m)\n if np.linalg.det(u) * np.linalg.det(v) < 0.0:\n # Ensure a RHS coordinate system (Kabsch algorithm).\n s[m - 1, m - 1] = -1\n\n # rotation, eq. 40\n r = u.dot(s).dot(v)\n\n # scale & translation, eq. 42 and 41\n c = 1 / sigma_x * np.trace(np.diag(d).dot(s)) if with_scale else 1.0\n t = mean_y - np.multiply(c, r.dot(mean_x))\n\n return r, t, c\n\n\nclass KittiEvalOdom():\n # ----------------------------------------------------------------------\n\t# poses: N,4,4\n\t# pose: 4,4\n\t# ----------------------------------------------------------------------\n def __init__(self):\n self.lengths = [100, 200, 300, 400, 500, 600, 700, 800]\n self.num_lengths = len(self.lengths)\n\n def loadPoses(self, file_name):\n # ----------------------------------------------------------------------\n\t\t# Each line in the file should follow one of the following structures\n\t\t# (1) idx pose(3x4 matrix in terms of 12 numbers)\n\t\t# (2) pose(3x4 matrix in terms of 12 numbers)\n\t\t# ----------------------------------------------------------------------\n f = open(file_name, 'r')\n s = f.readlines()\n f.close()\n file_len = len(s)\n poses = {}\n for cnt, line in enumerate(s):\n P = np.eye(4)\n line_split = [float(i) for i in line.split(\" \")]\n withIdx = int(len(line_split) == 13)\n for row in range(3):\n for col in range(4):\n P[row, col] = line_split[row*4 + col + withIdx]\n if withIdx:\n frame_idx = line_split[0]\n else:\n frame_idx = cnt\n poses[frame_idx] = P\n return poses\n\n def trajectory_distances(self, poses):\n # ----------------------------------------------------------------------\n\t\t# poses: dictionary: [frame_idx: pose]\n\t\t# ----------------------------------------------------------------------\n dist = [0]\n sort_frame_idx = sorted(poses.keys())\n for i in range(len(sort_frame_idx)-1):\n cur_frame_idx = sort_frame_idx[i] \n next_frame_idx = sort_frame_idx[i+1]\n P1 = poses[cur_frame_idx]\n P2 = poses[next_frame_idx]\n dx = P1[0, 3] - P2[0, 3]\n dy = P1[1, 3] - P2[1, 3]\n dz = P1[2, 3] - P2[2, 3]\n dist.append(dist[i]+np.sqrt(dx**2+dy**2+dz**2))\n return dist\n\n def rotation_error(self, pose_error):\n a = pose_error[0, 0]\n b = pose_error[1, 1]\n c = pose_error[2, 2]\n d = 0.5*(a+b+c-1.0)\n rot_error = np.arccos(max(min(d, 1.0), -1.0))\n return rot_error\n\n def translation_error(self, pose_error):\n dx = pose_error[0, 3]\n dy = pose_error[1, 3]\n dz = pose_error[2, 3]\n return np.sqrt(dx**2+dy**2+dz**2)\n\n def last_frame_from_segment_length(self, dist, first_frame, len_):\n for i in range(first_frame, len(dist), 1):\n if dist[i] > (dist[first_frame] + len_):\n return i\n return -1\n\n def calc_sequence_errors(self, poses_gt, poses_result):\n err = []\n dist = self.trajectory_distances(poses_gt)\n self.step_size = 10\n\n for first_frame in range(0, len(poses_gt), self.step_size):\n for i in range(self.num_lengths):\n len_ = self.lengths[i]\n last_frame = self.last_frame_from_segment_length(dist, first_frame, len_)\n\n # ----------------------------------------------------------------------\n\t\t\t\t# Continue if sequence not long enough\n\t\t\t\t# ----------------------------------------------------------------------\n if last_frame == -1 or not(last_frame in poses_result.keys()) or not(first_frame in poses_result.keys()):\n continue\n\n # ----------------------------------------------------------------------\n\t\t\t\t# compute rotational and translational errors\n\t\t\t\t# ----------------------------------------------------------------------\n pose_delta_gt = np.dot(np.linalg.inv(poses_gt[first_frame]), poses_gt[last_frame])\n pose_delta_result = np.dot(np.linalg.inv(poses_result[first_frame]), poses_result[last_frame])\n pose_error = np.dot(np.linalg.inv(pose_delta_result), pose_delta_gt)\n\n r_err = self.rotation_error(pose_error)\n t_err = self.translation_error(pose_error)\n\n # ----------------------------------------------------------------------\n\t\t\t\t# compute speed \n\t\t\t\t# ----------------------------------------------------------------------\n num_frames = last_frame - first_frame + 1.0\n speed = len_/(0.1*num_frames)\n\n err.append([first_frame, r_err/len_, t_err/len_, len_, speed])\n return err\n \n def save_sequence_errors(self, err, file_name):\n fp = open(file_name, 'w')\n for i in err:\n line_to_write = \" \".join([str(j) for j in i])\n fp.writelines(line_to_write+\"\\n\")\n fp.close()\n\n def compute_overall_err(self, seq_err):\n t_err = 0\n r_err = 0\n\n seq_len = len(seq_err)\n\n for item in seq_err:\n r_err += item[1]\n t_err += item[2]\n ave_t_err = t_err / seq_len\n ave_r_err = r_err / seq_len\n return ave_t_err, ave_r_err\n\n def plotPath(self, seq, poses_gt, poses_result):\n plot_keys = [\"Ground Truth\", \"Ours\"]\n fontsize_ = 20\n plot_num =-1\n\n poses_dict = {}\n poses_dict[\"Ground Truth\"] = poses_gt\n poses_dict[\"Ours\"] = poses_result\n\n fig = plt.figure()\n ax = plt.gca()\n ax.set_aspect('equal')\n\n for key in plot_keys:\n pos_xz = []\n # for pose in poses_dict[key]:\n for frame_idx in sorted(poses_dict[key].keys()):\n pose = poses_dict[key][frame_idx]\n pos_xz.append([pose[0,3], pose[2,3]])\n pos_xz = np.asarray(pos_xz)\n plt.plot(pos_xz[:,0], pos_xz[:,1], label = key)\n\n plt.legend(loc=\"upper right\", prop={'size': fontsize_})\n plt.xticks(fontsize=fontsize_)\n plt.yticks(fontsize=fontsize_)\n plt.xlabel('x (m)', fontsize=fontsize_)\n plt.ylabel('z (m)', fontsize=fontsize_)\n fig.set_size_inches(10, 10)\n png_title = \"sequence_\"+(seq)\n plt.savefig(self.plot_path_dir + \"/\" + png_title + \".pdf\", bbox_inches='tight', pad_inches=0)\n # plt.show()\n\n def compute_segment_error(self, seq_errs):\n # ----------------------------------------------------------------------\n\t\t# This function calculates average errors for different segment.\n\t\t# ----------------------------------------------------------------------\n\n segment_errs = {}\n avg_segment_errs = {}\n for len_ in self.lengths:\n segment_errs[len_] = []\n # ----------------------------------------------------------------------\n\t\t# Get errors\n\t\t# ----------------------------------------------------------------------\n for err in seq_errs:\n len_ = err[3]\n t_err = err[2]\n r_err = err[1]\n segment_errs[len_].append([t_err, r_err])\n # ----------------------------------------------------------------------\n\t\t# Compute average\n\t\t# ----------------------------------------------------------------------\n for len_ in self.lengths:\n if segment_errs[len_] != []:\n avg_t_err = np.mean(np.asarray(segment_errs[len_])[:, 0])\n avg_r_err = np.mean(np.asarray(segment_errs[len_])[:, 1])\n avg_segment_errs[len_] = [avg_t_err, avg_r_err]\n else:\n avg_segment_errs[len_] = []\n return avg_segment_errs\n\n def scale_optimization(self, gt, pred):\n \"\"\" Optimize scaling factor\n Args:\n gt (4x4 array dict): ground-truth poses\n pred (4x4 array dict): predicted poses\n Returns:\n new_pred (4x4 array dict): predicted poses after optimization\n \"\"\"\n pred_updated = copy.deepcopy(pred)\n xyz_pred = []\n xyz_ref = []\n for i in pred:\n pose_pred = pred[i]\n pose_ref = gt[i]\n xyz_pred.append(pose_pred[:3, 3])\n xyz_ref.append(pose_ref[:3, 3])\n xyz_pred = np.asarray(xyz_pred)\n xyz_ref = np.asarray(xyz_ref)\n scale = scale_lse_solver(xyz_pred, xyz_ref)\n for i in pred_updated:\n pred_updated[i][:3, 3] *= scale\n return pred_updated\n\n def eval(self, gt_txt, result_txt, seq=None):\n # gt_dir: the directory of groundtruth poses txt\n # results_dir: the directory of predicted poses txt\n self.plot_path_dir = os.path.dirname(result_txt) + \"/plot_path\"\n if not os.path.exists(self.plot_path_dir):\n os.makedirs(self.plot_path_dir)\n \n self.gt_txt = gt_txt\n\n ave_t_errs = []\n ave_r_errs = []\n\n poses_result = self.loadPoses(result_txt)\n poses_gt = self.loadPoses(self.gt_txt)\n\n # Pose alignment to first frame\n idx_0 = sorted(list(poses_result.keys()))[0]\n pred_0 = poses_result[idx_0]\n gt_0 = poses_gt[idx_0]\n for cnt in poses_result:\n poses_result[cnt] = np.linalg.inv(pred_0) @ poses_result[cnt]\n poses_gt[cnt] = np.linalg.inv(gt_0) @ poses_gt[cnt]\n\n # get XYZ\n xyz_gt = []\n xyz_result = []\n for cnt in poses_result:\n xyz_gt.append([poses_gt[cnt][0, 3], poses_gt[cnt][1, 3], poses_gt[cnt][2, 3]])\n xyz_result.append([poses_result[cnt][0, 3], poses_result[cnt][1, 3], poses_result[cnt][2, 3]])\n xyz_gt = np.asarray(xyz_gt).transpose(1, 0)\n xyz_result = np.asarray(xyz_result).transpose(1, 0)\n\n r, t, scale = umeyama_alignment(xyz_result, xyz_gt, True)\n\n align_transformation = np.eye(4)\n align_transformation[:3:, :3] = r\n align_transformation[:3, 3] = t\n \n for cnt in poses_result:\n poses_result[cnt][:3, 3] *= scale\n poses_result[cnt] = align_transformation @ poses_result[cnt]\n\n # ----------------------------------------------------------------------\n # compute sequence errors\n # ----------------------------------------------------------------------\n seq_err = self.calc_sequence_errors(poses_gt, poses_result)\n\n # ----------------------------------------------------------------------\n # Compute segment errors\n # ----------------------------------------------------------------------\n avg_segment_errs = self.compute_segment_error(seq_err)\n\n # ----------------------------------------------------------------------\n # compute overall error\n # ----------------------------------------------------------------------\n ave_t_err, ave_r_err = self.compute_overall_err(seq_err)\n print(\"Sequence: \" + seq)\n print(\"Translational error (%): \", ave_t_err*100)\n print(\"Rotational error (deg/100m): \", ave_r_err/np.pi*180*100)\n ave_t_errs.append(ave_t_err)\n ave_r_errs.append(ave_r_err)\n\n # Plotting\n self.plotPath(seq, poses_gt, poses_result) \n\n print(\"-------------------- For Copying ------------------------------\")\n for i in range(len(ave_t_errs)):\n print(\"{0:.2f}\".format(ave_t_errs[i]*100))\n print(\"{0:.2f}\".format(ave_r_errs[i]/np.pi*180*100))\n\n\n\nif __name__ == '__main__':\n import argparse\n parser = argparse.ArgumentParser(description='KITTI evaluation')\n parser.add_argument('--gt_txt', type=str, required=True, help=\"Groundtruth directory\")\n parser.add_argument('--result_txt', type=str, required=True, help=\"Result directory\")\n parser.add_argument('--seq', type=str, help=\"sequences to be evaluated\", default='09')\n args = parser.parse_args()\n\n eval_tool = KittiEvalOdom()\n eval_tool.eval(args.gt_txt, args.result_txt, seq=args.seq)\n" }, { "path": "core/evaluation/evaluate_depth.py", "content": "from evaluation_utils import *\n\ndef process_depth(gt_depth, pred_depth, min_depth, max_depth):\n mask = gt_depth > 0\n pred_depth[pred_depth < min_depth] = min_depth\n pred_depth[pred_depth > max_depth] = max_depth\n gt_depth[gt_depth < min_depth] = min_depth\n gt_depth[gt_depth > max_depth] = max_depth\n\n return gt_depth, pred_depth, mask\n\n\ndef eval_depth(gt_depths,\n pred_depths,\n min_depth=1e-3,\n max_depth=80, nyu=False):\n num_samples = len(pred_depths)\n rms = np.zeros(num_samples, np.float32)\n log_rms = np.zeros(num_samples, np.float32)\n abs_rel = np.zeros(num_samples, np.float32)\n sq_rel = np.zeros(num_samples, np.float32)\n d1_all = np.zeros(num_samples, np.float32)\n a1 = np.zeros(num_samples, np.float32)\n a2 = np.zeros(num_samples, np.float32)\n a3 = np.zeros(num_samples, np.float32)\n\n for i in range(num_samples):\n gt_depth = gt_depths[i]\n pred_depth = pred_depths[i]\n mask = np.logical_and(gt_depth > min_depth, gt_depth < max_depth)\n \n if not nyu:\n gt_height, gt_width = gt_depth.shape\n crop = np.array([0.40810811 * gt_height, 0.99189189 * gt_height,\n 0.03594771 * gt_width, 0.96405229 * gt_width]).astype(np.int32)\n crop_mask = np.zeros(mask.shape)\n crop_mask[crop[0]:crop[1], crop[2]:crop[3]] = 1\n mask = np.logical_and(mask, crop_mask)\n \n gt_depth = gt_depth[mask]\n pred_depth = pred_depth[mask]\n scale = np.median(gt_depth) / np.median(pred_depth)\n pred_depth *= scale\n\n gt_depth, pred_depth, mask = process_depth(\n gt_depth, pred_depth, min_depth, max_depth)\n\n abs_rel[i], sq_rel[i], rms[i], log_rms[i], a1[i], a2[i], a3[\n i] = compute_errors(gt_depth, pred_depth, nyu=nyu)\n\n\n return [abs_rel.mean(), sq_rel.mean(), rms.mean(), log_rms.mean(), a1.mean(), a2.mean(), a3.mean()]\n\n" }, { "path": "core/evaluation/evaluate_flow.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nimport numpy as np\nfrom flowlib import read_flow_png, flow_to_image\nimport cv2\nimport multiprocessing\nimport functools\n\ndef get_scaled_intrinsic_matrix(calib_file, zoom_x, zoom_y):\n intrinsics = load_intrinsics_raw(calib_file)\n intrinsics = scale_intrinsics(intrinsics, zoom_x, zoom_y)\n\n intrinsics[0, 1] = 0.0\n intrinsics[1, 0] = 0.0\n intrinsics[2, 0] = 0.0\n intrinsics[2, 1] = 0.0\n return intrinsics\n\ndef load_intrinsics_raw(calib_file):\n filedata = read_raw_calib_file(calib_file)\n if \"P_rect_02\" in filedata:\n P_rect = filedata['P_rect_02']\n else:\n P_rect = filedata['P2']\n P_rect = np.reshape(P_rect, (3, 4))\n intrinsics = P_rect[:3, :3]\n return intrinsics\n\ndef read_raw_calib_file(filepath):\n # From https://github.com/utiasSTARS/pykitti/blob/master/pykitti/utils.py\n \"\"\"Read in a calibration file and parse into a dictionary.\"\"\"\n data = {}\n\n with open(filepath, 'r') as f:\n for line in f.readlines():\n key, value = line.split(':', 1)\n # The only non-float values in these files are dates, which\n # we don't care about anyway\n try:\n data[key] = np.array([float(x) for x in value.split()])\n except ValueError:\n pass\n return data\n\ndef scale_intrinsics(mat, sx, sy):\n out = np.copy(mat)\n out[0, 0] *= sx\n out[0, 2] *= sx\n out[1, 1] *= sy\n out[1, 2] *= sy\n return out\n\ndef read_flow_gt_worker(dir_gt, i):\n flow_true = read_flow_png(\n os.path.join(dir_gt, \"flow_occ\", str(i).zfill(6) + \"_10.png\"))\n flow_noc_true = read_flow_png(\n os.path.join(dir_gt, \"flow_noc\", str(i).zfill(6) + \"_10.png\"))\n return flow_true, flow_noc_true[:, :, 2]\n\ndef load_gt_flow_kitti(gt_dataset_dir, mode):\n gt_flows = []\n noc_masks = []\n if mode == \"kitti_2012\":\n num_gt = 194\n dir_gt = gt_dataset_dir\n elif mode == \"kitti_2015\":\n num_gt = 200\n dir_gt = gt_dataset_dir\n else:\n num_gt = None\n dir_gt = None\n raise ValueError('Mode {} not found.'.format(mode))\n\n fun = functools.partial(read_flow_gt_worker, dir_gt)\n pool = multiprocessing.Pool(5)\n results = pool.imap(fun, range(num_gt), chunksize=10)\n pool.close()\n pool.join()\n\n for result in results:\n gt_flows.append(result[0])\n noc_masks.append(result[1])\n return gt_flows, noc_masks\n\ndef calculate_error_rate(epe_map, gt_flow, mask):\n bad_pixels = np.logical_and(\n epe_map * mask > 3,\n epe_map * mask / np.maximum(\n np.sqrt(np.sum(np.square(gt_flow), axis=2)), 1e-10) > 0.05)\n return bad_pixels.sum() / mask.sum()\n\n\ndef eval_flow_avg(gt_flows,\n noc_masks,\n pred_flows,\n cfg,\n moving_masks=None,\n write_img=False):\n error, error_noc, error_occ, error_move, error_static, error_rate = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0\n error_move_rate, error_static_rate = 0.0, 0.0\n\n num = len(gt_flows)\n for gt_flow, noc_mask, pred_flow, i in zip(gt_flows, noc_masks, pred_flows,\n range(len(gt_flows))):\n H, W = gt_flow.shape[0:2]\n\n pred_flow = np.copy(pred_flow)\n pred_flow[:, :, 0] = pred_flow[:, :, 0] / cfg.img_hw[1] * W\n pred_flow[:, :, 1] = pred_flow[:, :, 1] / cfg.img_hw[0] * H\n\n flo_pred = cv2.resize(\n pred_flow, (W, H), interpolation=cv2.INTER_LINEAR)\n\n if write_img:\n if not os.path.exists(os.path.join(cfg.model_dir, \"pred_flow\")):\n os.mkdir(os.path.join(cfg.model_dir, \"pred_flow\"))\n cv2.imwrite(\n os.path.join(cfg.model_dir, \"pred_flow\",\n str(i).zfill(6) + \"_10.png\"),\n flow_to_image(flo_pred))\n cv2.imwrite(\n os.path.join(cfg.model_dir, \"pred_flow\",\n str(i).zfill(6) + \"_10_gt.png\"),\n flow_to_image(gt_flow[:, :, 0:2]))\n cv2.imwrite(\n os.path.join(cfg.model_dir, \"pred_flow\",\n str(i).zfill(6) + \"_10_err.png\"),\n flow_to_image(\n (flo_pred - gt_flow[:, :, 0:2]) * gt_flow[:, :, 2:3]))\n\n epe_map = np.sqrt(\n np.sum(np.square(flo_pred[:, :, 0:2] - gt_flow[:, :, 0:2]),\n axis=2))\n error += np.sum(epe_map * gt_flow[:, :, 2]) / np.sum(gt_flow[:, :, 2])\n\n error_noc += np.sum(epe_map * noc_mask) / np.sum(noc_mask)\n\n error_occ += np.sum(epe_map * (gt_flow[:, :, 2] - noc_mask)) / max(\n np.sum(gt_flow[:, :, 2] - noc_mask), 1.0)\n\n error_rate += calculate_error_rate(epe_map, gt_flow[:, :, 0:2],\n gt_flow[:, :, 2])\n\n if moving_masks:\n move_mask = moving_masks[i]\n\n error_move_rate += calculate_error_rate(\n epe_map, gt_flow[:, :, 0:2], gt_flow[:, :, 2] * move_mask)\n error_static_rate += calculate_error_rate(\n epe_map, gt_flow[:, :, 0:2],\n gt_flow[:, :, 2] * (1.0 - move_mask))\n\n error_move += np.sum(epe_map * gt_flow[:, :, 2] *\n move_mask) / np.sum(gt_flow[:, :, 2] *\n move_mask)\n error_static += np.sum(epe_map * gt_flow[:, :, 2] * (\n 1.0 - move_mask)) / np.sum(gt_flow[:, :, 2] *\n (1.0 - move_mask))\n\n if moving_masks:\n result = \"{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10} \\n\".format(\n 'epe', 'epe_noc', 'epe_occ', 'epe_move', 'epe_static',\n 'move_err_rate', 'static_err_rate', 'err_rate')\n result += \"{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f} \\n\".format(\n error / num, error_noc / num, error_occ / num, error_move / num,\n error_static / num, error_move_rate / num, error_static_rate / num,\n error_rate / num)\n return result\n else:\n result = \"{:>10}, {:>10}, {:>10}, {:>10} \\n\".format(\n 'epe', 'epe_noc', 'epe_occ', 'err_rate')\n result += \"{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f} \\n\".format(\n error / num, error_noc / num, error_occ / num, error_rate / num)\n return result\n" }, { "path": "core/evaluation/evaluate_mask.py", "content": "import os\nimport numpy as np\nimport cv2\nimport functools\nimport matplotlib.pyplot as plt\nimport multiprocessing\n\n\"\"\"\nAdopted from https://github.com/martinkersner/py_img_seg_eval\n\"\"\"\n\nclass EvalSegErr(Exception):\n def __init__(self, value):\n self.value = value\n\n def __str__(self):\n return repr(self.value)\n\n\ndef pixel_accuracy(eval_segm, gt_segm):\n '''\n sum_i(n_ii) / sum_i(t_i)\n '''\n\n check_size(eval_segm, gt_segm)\n\n cl, n_cl = extract_classes(gt_segm)\n eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)\n\n sum_n_ii = 0\n sum_t_i = 0\n\n for i, c in enumerate(cl):\n curr_eval_mask = eval_mask[i, :, :]\n curr_gt_mask = gt_mask[i, :, :]\n\n sum_n_ii += np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))\n sum_t_i += np.sum(curr_gt_mask)\n\n if (sum_t_i == 0):\n pixel_accuracy_ = 0\n else:\n pixel_accuracy_ = sum_n_ii / sum_t_i\n\n return pixel_accuracy_\n\n\ndef mean_accuracy(eval_segm, gt_segm):\n '''\n (1/n_cl) sum_i(n_ii/t_i)\n '''\n\n check_size(eval_segm, gt_segm)\n\n cl, n_cl = extract_classes(gt_segm)\n eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)\n\n accuracy = list([0]) * n_cl\n\n for i, c in enumerate(cl):\n curr_eval_mask = eval_mask[i, :, :]\n curr_gt_mask = gt_mask[i, :, :]\n\n n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))\n t_i = np.sum(curr_gt_mask)\n\n if (t_i != 0):\n accuracy[i] = n_ii / t_i\n\n mean_accuracy_ = np.mean(accuracy)\n return mean_accuracy_\n\n\ndef mean_IU(eval_segm, gt_segm):\n '''\n (1/n_cl) * sum_i(n_ii / (t_i + sum_j(n_ji) - n_ii))\n '''\n\n check_size(eval_segm, gt_segm)\n\n cl, n_cl = union_classes(eval_segm, gt_segm)\n _, n_cl_gt = extract_classes(gt_segm)\n eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)\n\n IU = list([0]) * n_cl\n\n for i, c in enumerate(cl):\n curr_eval_mask = eval_mask[i, :, :]\n curr_gt_mask = gt_mask[i, :, :]\n\n if (np.sum(curr_eval_mask) == 0) or (np.sum(curr_gt_mask) == 0):\n continue\n\n n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))\n t_i = np.sum(curr_gt_mask)\n n_ij = np.sum(curr_eval_mask)\n\n IU[i] = n_ii / (t_i + n_ij - n_ii)\n\n mean_IU_ = np.sum(IU) / n_cl_gt\n return mean_IU_, np.array(IU)\n\n\ndef frequency_weighted_IU(eval_segm, gt_segm):\n '''\n sum_k(t_k)^(-1) * sum_i((t_i*n_ii)/(t_i + sum_j(n_ji) - n_ii))\n '''\n\n check_size(eval_segm, gt_segm)\n\n cl, n_cl = union_classes(eval_segm, gt_segm)\n eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)\n\n frequency_weighted_IU_ = list([0]) * n_cl\n\n for i, c in enumerate(cl):\n curr_eval_mask = eval_mask[i, :, :]\n curr_gt_mask = gt_mask[i, :, :]\n\n if (np.sum(curr_eval_mask) == 0) or (np.sum(curr_gt_mask) == 0):\n continue\n\n n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))\n t_i = np.sum(curr_gt_mask)\n n_ij = np.sum(curr_eval_mask)\n\n frequency_weighted_IU_[i] = (t_i * n_ii) / (t_i + n_ij - n_ii)\n\n sum_k_t_k = get_pixel_area(eval_segm)\n\n frequency_weighted_IU_ = np.sum(frequency_weighted_IU_) / sum_k_t_k\n return frequency_weighted_IU_\n\n\n'''\nAuxiliary functions used during evaluation.\n'''\n\n\ndef get_pixel_area(segm):\n return segm.shape[0] * segm.shape[1]\n\n\ndef extract_both_masks(eval_segm, gt_segm, cl, n_cl):\n eval_mask = extract_masks(eval_segm, cl, n_cl)\n gt_mask = extract_masks(gt_segm, cl, n_cl)\n\n return eval_mask, gt_mask\n\n\ndef extract_classes(segm):\n cl = np.unique(segm)\n n_cl = len(cl)\n\n return cl, n_cl\n\n\ndef union_classes(eval_segm, gt_segm):\n eval_cl, _ = extract_classes(eval_segm)\n gt_cl, _ = extract_classes(gt_segm)\n\n cl = np.union1d(eval_cl, gt_cl)\n n_cl = len(cl)\n\n return cl, n_cl\n\n\ndef extract_masks(segm, cl, n_cl):\n h, w = segm_size(segm)\n masks = np.zeros((n_cl, h, w))\n\n for i, c in enumerate(cl):\n masks[i, :, :] = segm == c\n\n return masks\n\n\ndef segm_size(segm):\n try:\n height = segm.shape[0]\n width = segm.shape[1]\n except IndexError:\n raise\n\n return height, width\n\n\ndef check_size(eval_segm, gt_segm):\n h_e, w_e = segm_size(eval_segm)\n h_g, w_g = segm_size(gt_segm)\n\n if (h_e != h_g) or (w_e != w_g):\n raise EvalSegErr(\"DiffDim: Different dimensions of matrices!\")\n\ndef read_mask_gt_worker(gt_dataset_dir, idx):\n return cv2.imread(\n gt_dataset_dir + \"/obj_map/\" + str(idx).zfill(6) + \"_10.png\", -1)\n\ndef load_gt_mask(gt_dataset_dir):\n num_gt = 200\n \n # the dataset dir should be the directory of kitti-2015.\n fun = functools.partial(read_mask_gt_worker, gt_dataset_dir)\n pool = multiprocessing.Pool(5)\n results = pool.imap(fun, range(num_gt), chunksize=10)\n pool.close()\n pool.join()\n\n gt_masks = []\n for m in results:\n m[m > 0.0] = 1.0\n gt_masks.append(m)\n return gt_masks\n\n\ndef eval_mask(pred_masks, gt_masks, opt):\n grey_cmap = plt.get_cmap(\"Greys\")\n if not os.path.exists(os.path.join(opt.trace, \"pred_mask\")):\n os.mkdir(os.path.join(opt.trace, \"pred_mask\"))\n\n pa_res, ma_res, mIU_res, fwIU_res = 0.0, 0.0, 0.0, 0.0\n IU_res = np.array([0.0, 0.0])\n\n num_total = len(gt_masks)\n for i in range(num_total):\n gt_mask = gt_masks[i]\n H, W = gt_mask.shape[0:2]\n\n pred_mask = cv2.resize(\n pred_masks[i], (W, H), interpolation=cv2.INTER_LINEAR)\n\n pred_mask[pred_mask >= 0.5] = 1.0\n pred_mask[pred_mask < 0.5] = 0.0\n\n cv2.imwrite(\n os.path.join(opt.trace, \"pred_mask\",\n str(i).zfill(6) + \"_10_plot.png\"),\n grey_cmap(pred_mask))\n cv2.imwrite(\n os.path.join(opt.trace, \"pred_mask\", str(i).zfill(6) + \"_10.png\"),\n pred_mask)\n\n pa_res += pixel_accuracy(pred_mask, gt_mask)\n ma_res += mean_accuracy(pred_mask, gt_mask)\n\n mIU, IU = mean_IU(pred_mask, gt_mask)\n mIU_res += mIU\n IU_res += IU\n\n fwIU_res += frequency_weighted_IU(pred_mask, gt_mask)\n\n return pa_res / 200., ma_res / 200., mIU_res / 200., fwIU_res / 200., IU_res / 200.\n" }, { "path": "core/evaluation/evaluation_utils.py", "content": "import numpy as np\nimport os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nimport cv2, skimage\nimport skimage.io\n#import scipy.misc as sm\nimport imageio as sm\n\n\n# Adopted from https://github.com/mrharicot/monodepth\ndef compute_errors(gt, pred, nyu=False):\n thresh = np.maximum((gt / pred), (pred / gt))\n a1 = (thresh < 1.25).mean()\n a2 = (thresh < 1.25**2).mean()\n a3 = (thresh < 1.25**3).mean()\n\n rmse = (gt - pred)**2\n rmse = np.sqrt(rmse.mean())\n\n rmse_log = (np.log(gt) - np.log(pred))**2\n rmse_log = np.sqrt(rmse_log.mean())\n\n log10 = np.mean(np.abs((np.log10(gt) - np.log10(pred))))\n\n abs_rel = np.mean(np.abs(gt - pred) / (gt))\n\n sq_rel = np.mean(((gt - pred)**2) / (gt))\n\n if nyu:\n return abs_rel, sq_rel, rmse, log10, a1, a2, a3\n else:\n return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3\n\n" }, { "path": "core/evaluation/flowlib.py", "content": "#!/usr/bin/python\n\"\"\"\nAdopted from https://github.com/liruoteng/OpticalFlowToolkit\n# ==============================\n# flowlib.py\n# library for optical flow processing\n# Author: Ruoteng Li\n# Date: 6th Aug 2016\n# ==============================\n\"\"\"\nimport png\nimport scipy\nimport numpy as np\nimport matplotlib.colors as cl\nimport matplotlib.pyplot as plt\nfrom PIL import Image\n#import cv2\n\nUNKNOWN_FLOW_THRESH = 1e7\nSMALLFLOW = 0.0\nLARGEFLOW = 1e8\n\"\"\"\n=============\nFlow Section\n=============\n\"\"\"\n\n\ndef show_flow(filename):\n \"\"\"\n visualize optical flow map using matplotlib\n :param filename: optical flow file\n :return: None\n \"\"\"\n flow = read_flow(filename)\n img = flow_to_image(flow)\n plt.imshow(img)\n plt.show()\n\n\ndef visualize_flow(flow, mode='Y'):\n \"\"\"\n this function visualize the input flow\n :param flow: input flow in array\n :param mode: choose which color mode to visualize the flow (Y: Ccbcr, RGB: RGB color)\n :return: None\n \"\"\"\n if mode == 'Y':\n # Ccbcr color wheel\n img = flow_to_image(flow)\n plt.imshow(img)\n plt.show()\n elif mode == 'RGB':\n (h, w) = flow.shape[0:2]\n du = flow[:, :, 0]\n dv = flow[:, :, 1]\n valid = flow[:, :, 2]\n max_flow = max(np.max(du), np.max(dv))\n img = np.zeros((h, w, 3), dtype=np.float64)\n # angle layer\n img[:, :, 0] = np.arctan2(dv, du) / (2 * np.pi)\n # magnitude layer, normalized to 1\n img[:, :, 1] = np.sqrt(du * du + dv * dv) * 8 / max_flow\n # phase layer\n img[:, :, 2] = 8 - img[:, :, 1]\n # clip to [0,1]\n small_idx = img[:, :, 0:3] < 0\n large_idx = img[:, :, 0:3] > 1\n img[small_idx] = 0\n img[large_idx] = 1\n # convert to rgb\n img = cl.hsv_to_rgb(img)\n # remove invalid point\n img[:, :, 0] = img[:, :, 0] * valid\n img[:, :, 1] = img[:, :, 1] * valid\n img[:, :, 2] = img[:, :, 2] * valid\n # show\n plt.imshow(img)\n plt.show()\n\n return None\n\n\ndef read_flow(filename):\n \"\"\"\n read optical flow from Middlebury .flo file\n :param filename: name of the flow file\n :return: optical flow data in matrix\n \"\"\"\n f = open(filename, 'rb')\n magic = np.fromfile(f, np.float32, count=1)\n data2d = None\n\n if 202021.25 != magic:\n print('Magic number incorrect. Invalid .flo file')\n else:\n w = np.fromfile(f, np.int32, count=1)[0]\n h = np.fromfile(f, np.int32, count=1)[0]\n print(\"Reading %d x %d flo file\" % (h, w))\n data2d = np.fromfile(f, np.float32, count=2 * w * h)\n # reshape data into 3D array (columns, rows, channels)\n data2d = np.resize(data2d, (h, w, 2))\n f.close()\n return data2d\n\n\ndef read_flow_png(flow_file):\n \"\"\"\n Read optical flow from KITTI .png file\n :param flow_file: name of the flow file\n :return: optical flow data in matrix\n \"\"\"\n flow_object = png.Reader(filename=flow_file)\n flow_direct = flow_object.asDirect()\n flow_data = list(flow_direct[2])\n (w, h) = flow_direct[3]['size']\n flow = np.zeros((h, w, 3), dtype=np.float64)\n for i in range(len(flow_data)):\n flow[i, :, 0] = flow_data[i][0::3]\n flow[i, :, 1] = flow_data[i][1::3]\n flow[i, :, 2] = flow_data[i][2::3]\n\n invalid_idx = (flow[:, :, 2] == 0)\n flow[:, :, 0:2] = (flow[:, :, 0:2] - 2**15) / 64.0\n flow[invalid_idx, 0] = 0\n flow[invalid_idx, 1] = 0\n return flow\n\n\ndef write_flow_png(flo, flow_file):\n h, w, _ = flo.shape\n out_flo = np.ones((h, w, 3), dtype=np.float32)\n out_flo[:, :, 0] = np.maximum(\n np.minimum(flo[:, :, 0] * 64.0 + 2**15, 2**16 - 1), 0)\n out_flo[:, :, 1] = np.maximum(\n np.minimum(flo[:, :, 1] * 64.0 + 2**15, 2**16 - 1), 0)\n out_flo = out_flo.astype(np.uint16)\n\n with open(flow_file, 'wb') as f:\n writer = png.Writer(width=w, height=h, bitdepth=16)\n # Convert z to the Python list of lists expected by\n # the png writer.\n z2list = out_flo.reshape(-1, w * 3).tolist()\n writer.write(f, z2list)\n\n\ndef write_flow(flow, filename):\n \"\"\"\n write optical flow in Middlebury .flo format\n :param flow: optical flow map\n :param filename: optical flow file path to be saved\n :return: None\n \"\"\"\n f = open(filename, 'wb')\n magic = np.array([202021.25], dtype=np.float32)\n (height, width) = flow.shape[0:2]\n w = np.array([width], dtype=np.int32)\n h = np.array([height], dtype=np.int32)\n magic.tofile(f)\n w.tofile(f)\n h.tofile(f)\n flow.tofile(f)\n f.close()\n\n\ndef segment_flow(flow):\n h = flow.shape[0]\n w = flow.shape[1]\n u = flow[:, :, 0]\n v = flow[:, :, 1]\n\n idx = ((abs(u) > LARGEFLOW) | (abs(v) > LARGEFLOW))\n idx2 = (abs(u) == SMALLFLOW)\n class0 = (v == 0) & (u == 0)\n u[idx2] = 0.00001\n tan_value = v / u\n\n class1 = (tan_value < 1) & (tan_value >= 0) & (u > 0) & (v >= 0)\n class2 = (tan_value >= 1) & (u >= 0) & (v >= 0)\n class3 = (tan_value < -1) & (u <= 0) & (v >= 0)\n class4 = (tan_value < 0) & (tan_value >= -1) & (u < 0) & (v >= 0)\n class8 = (tan_value >= -1) & (tan_value < 0) & (u > 0) & (v <= 0)\n class7 = (tan_value < -1) & (u >= 0) & (v <= 0)\n class6 = (tan_value >= 1) & (u <= 0) & (v <= 0)\n class5 = (tan_value >= 0) & (tan_value < 1) & (u < 0) & (v <= 0)\n\n seg = np.zeros((h, w))\n\n seg[class1] = 1\n seg[class2] = 2\n seg[class3] = 3\n seg[class4] = 4\n seg[class5] = 5\n seg[class6] = 6\n seg[class7] = 7\n seg[class8] = 8\n seg[class0] = 0\n seg[idx] = 0\n\n return seg\n\n\ndef flow_error(tu, tv, u, v):\n \"\"\"\n Calculate average end point error\n :param tu: ground-truth horizontal flow map\n :param tv: ground-truth vertical flow map\n :param u: estimated horizontal flow map\n :param v: estimated vertical flow map\n :return: End point error of the estimated flow\n \"\"\"\n smallflow = 0.0\n '''\n stu = tu[bord+1:end-bord,bord+1:end-bord]\n stv = tv[bord+1:end-bord,bord+1:end-bord]\n su = u[bord+1:end-bord,bord+1:end-bord]\n sv = v[bord+1:end-bord,bord+1:end-bord]\n '''\n stu = tu[:]\n stv = tv[:]\n su = u[:]\n sv = v[:]\n\n idxUnknow = (abs(stu) > UNKNOWN_FLOW_THRESH) | (\n abs(stv) > UNKNOWN_FLOW_THRESH)\n stu[idxUnknow] = 0\n stv[idxUnknow] = 0\n su[idxUnknow] = 0\n sv[idxUnknow] = 0\n\n ind2 = [(np.absolute(stu) > smallflow) | (np.absolute(stv) > smallflow)]\n index_su = su[ind2]\n index_sv = sv[ind2]\n an = 1.0 / np.sqrt(index_su**2 + index_sv**2 + 1)\n un = index_su * an\n vn = index_sv * an\n\n index_stu = stu[ind2]\n index_stv = stv[ind2]\n tn = 1.0 / np.sqrt(index_stu**2 + index_stv**2 + 1)\n tun = index_stu * tn\n tvn = index_stv * tn\n '''\n angle = un * tun + vn * tvn + (an * tn)\n index = [angle == 1.0]\n angle[index] = 0.999\n ang = np.arccos(angle)\n mang = np.mean(ang)\n mang = mang * 180 / np.pi\n '''\n\n epe = np.sqrt((stu - su)**2 + (stv - sv)**2)\n epe = epe[ind2]\n mepe = np.mean(epe)\n return mepe\n\n\ndef flow_to_image(flow):\n \"\"\"\n Convert flow into middlebury color code image\n :param flow: optical flow map\n :return: optical flow image in middlebury color\n \"\"\"\n u = flow[:, :, 0]\n v = flow[:, :, 1]\n\n maxu = -999.\n maxv = -999.\n minu = 999.\n minv = 999.\n\n idxUnknow = (abs(u) > UNKNOWN_FLOW_THRESH) | (abs(v) > UNKNOWN_FLOW_THRESH)\n u[idxUnknow] = 0\n v[idxUnknow] = 0\n\n maxu = max(maxu, np.max(u))\n minu = min(minu, np.min(u))\n\n maxv = max(maxv, np.max(v))\n minv = min(minv, np.min(v))\n\n rad = np.sqrt(u**2 + v**2)\n maxrad = max(-1, np.max(rad))\n\n print(\"max flow: %.4f\\nflow range:\\nu = %.3f .. %.3f\\nv = %.3f .. %.3f\" % (\n maxrad, minu, maxu, minv, maxv))\n\n u = u / (maxrad + np.finfo(float).eps)\n v = v / (maxrad + np.finfo(float).eps)\n\n img = compute_color(u, v)\n\n idx = np.repeat(idxUnknow[:, :, np.newaxis], 3, axis=2)\n img[idx] = 0\n\n return np.uint8(img)\n\n\ndef evaluate_flow_file(gt, pred):\n \"\"\"\n evaluate the estimated optical flow end point error according to ground truth provided\n :param gt: ground truth file path\n :param pred: estimated optical flow file path\n :return: end point error, float32\n \"\"\"\n # Read flow files and calculate the errors\n gt_flow = read_flow(gt) # ground truth flow\n eva_flow = read_flow(pred) # predicted flow\n # Calculate errors\n average_pe = flow_error(gt_flow[:, :, 0], gt_flow[:, :, 1],\n eva_flow[:, :, 0], eva_flow[:, :, 1])\n return average_pe\n\n\ndef evaluate_flow(gt_flow, pred_flow):\n \"\"\"\n gt: ground-truth flow\n pred: estimated flow\n \"\"\"\n average_pe = flow_error(gt_flow[:, :, 0], gt_flow[:, :, 1],\n pred_flow[:, :, 0], pred_flow[:, :, 1])\n return average_pe\n\n\n\"\"\"\n==============\nDisparity Section\n==============\n\"\"\"\n\n\ndef read_disp_png(file_name):\n \"\"\"\n Read optical flow from KITTI .png file\n :param file_name: name of the flow file\n :return: optical flow data in matrix\n \"\"\"\n image_object = png.Reader(filename=file_name)\n image_direct = image_object.asDirect()\n image_data = list(image_direct[2])\n (w, h) = image_direct[3]['size']\n channel = len(image_data[0]) / w\n flow = np.zeros((h, w, channel), dtype=np.uint16)\n for i in range(len(image_data)):\n for j in range(channel):\n flow[i, :, j] = image_data[i][j::channel]\n return flow[:, :, 0] / 256\n\n\ndef disp_to_flowfile(disp, filename):\n \"\"\"\n Read KITTI disparity file in png format\n :param disp: disparity matrix\n :param filename: the flow file name to save\n :return: None\n \"\"\"\n f = open(filename, 'wb')\n magic = np.array([202021.25], dtype=np.float32)\n (height, width) = disp.shape[0:2]\n w = np.array([width], dtype=np.int32)\n h = np.array([height], dtype=np.int32)\n empty_map = np.zeros((height, width), dtype=np.float32)\n data = np.dstack((disp, empty_map))\n magic.tofile(f)\n w.tofile(f)\n h.tofile(f)\n data.tofile(f)\n f.close()\n\n\n\"\"\"\n==============\nImage Section\n==============\n\"\"\"\n\n\ndef read_image(filename):\n \"\"\"\n Read normal image of any format\n :param filename: name of the image file\n :return: image data in matrix uint8 type\n \"\"\"\n img = Image.open(filename)\n im = np.array(img)\n return im\n\n\ndef warp_image(im, flow):\n \"\"\"\n Use optical flow to warp image to the next\n :param im: image to warp\n :param flow: optical flow\n :return: warped image\n \"\"\"\n image_height = im.shape[0]\n image_width = im.shape[1]\n flow_height = flow.shape[0]\n flow_width = flow.shape[1]\n n = image_height * image_width\n (iy, ix) = np.mgrid[0:image_height, 0:image_width]\n (fy, fx) = np.mgrid[0:flow_height, 0:flow_width]\n fx += flow[:, :, 0]\n fy += flow[:, :, 1]\n mask = fx < 0 | fx > flow_width | fy < 0 | fy > flow_height\n fx = np.min(np.max(fx, 0), flow_width)\n fy = np.min(np.max(fy, 0), flow_height)\n points = np.concatenate((ix.reshape(n, 1), iy.reshape(n, 1)), axis=1)\n xi = np.concatenate((fx.reshape(n, 1), fy.reshape(n, 1)), axis=1)\n warp = np.zeros((image_height, image_width, im.shape[2]))\n for i in range(im.shape[2]):\n channel = im[:, :, i]\n values = channel.reshape(n, 1)\n new_channel = scipy.interpolate.griddata(\n points, values, xi, method='cubic')\n new_channel[mask] = 1\n warp[:, :, i] = new_channel\n return warp\n\n\n\"\"\"\n==============\nOthers\n==============\n\"\"\"\n\n\ndef scale_image(image, new_range):\n \"\"\"\n Linearly scale the image into desired range\n :param image: input image\n :param new_range: the new range to be aligned\n :return: image normalized in new range\n \"\"\"\n min_val = np.min(image).astype(np.float32)\n max_val = np.max(image).astype(np.float32)\n min_val_new = np.array(min(new_range), dtype=np.float32)\n max_val_new = np.array(max(new_range), dtype=np.float32)\n scaled_image = (image - min_val) / (max_val - min_val) * (\n max_val_new - min_val_new) + min_val_new\n return scaled_image.astype(np.uint8)\n\n\ndef compute_color(u, v):\n \"\"\"\n compute optical flow color map\n :param u: optical flow horizontal map\n :param v: optical flow vertical map\n :return: optical flow in color code\n \"\"\"\n [h, w] = u.shape\n img = np.zeros([h, w, 3])\n nanIdx = np.isnan(u) | np.isnan(v)\n u[nanIdx] = 0\n v[nanIdx] = 0\n\n colorwheel = make_color_wheel()\n ncols = np.size(colorwheel, 0)\n\n rad = np.sqrt(u**2 + v**2)\n\n a = np.arctan2(-v, -u) / np.pi\n\n fk = (a + 1) / 2 * (ncols - 1) + 1\n\n k0 = np.floor(fk).astype(int)\n\n k1 = k0 + 1\n k1[k1 == ncols + 1] = 1\n f = fk - k0\n\n for i in range(0, np.size(colorwheel, 1)):\n tmp = colorwheel[:, i]\n col0 = tmp[k0 - 1] / 255\n col1 = tmp[k1 - 1] / 255\n col = (1 - f) * col0 + f * col1\n\n idx = rad <= 1\n col[idx] = 1 - rad[idx] * (1 - col[idx])\n notidx = np.logical_not(idx)\n\n col[notidx] *= 0.75\n img[:, :, i] = np.uint8(np.floor(255 * col * (1 - nanIdx)))\n\n return img\n\n\ndef make_color_wheel():\n \"\"\"\n Generate color wheel according Middlebury color code\n :return: Color wheel\n \"\"\"\n RY = 15\n YG = 6\n GC = 4\n CB = 11\n BM = 13\n MR = 6\n\n ncols = RY + YG + GC + CB + BM + MR\n\n colorwheel = np.zeros([ncols, 3])\n\n col = 0\n\n # RY\n colorwheel[0:RY, 0] = 255\n colorwheel[0:RY, 1] = np.transpose(np.floor(255 * np.arange(0, RY) / RY))\n col += RY\n\n # YG\n colorwheel[col:col + YG, 0] = 255 - np.transpose(\n np.floor(255 * np.arange(0, YG) / YG))\n colorwheel[col:col + YG, 1] = 255\n col += YG\n\n # GC\n colorwheel[col:col + GC, 1] = 255\n colorwheel[col:col + GC, 2] = np.transpose(\n np.floor(255 * np.arange(0, GC) / GC))\n col += GC\n\n # CB\n colorwheel[col:col + CB, 1] = 255 - np.transpose(\n np.floor(255 * np.arange(0, CB) / CB))\n colorwheel[col:col + CB, 2] = 255\n col += CB\n\n # BM\n colorwheel[col:col + BM, 2] = 255\n colorwheel[col:col + BM, 0] = np.transpose(\n np.floor(255 * np.arange(0, BM) / BM))\n col += +BM\n\n # MR\n colorwheel[col:col + MR, 2] = 255 - np.transpose(\n np.floor(255 * np.arange(0, MR) / MR))\n colorwheel[col:col + MR, 0] = 255\n\n return colorwheel\n" }, { "path": "core/networks/__init__.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom model_flow import Model_flow\nfrom model_triangulate_pose import Model_triangulate_pose\nfrom model_depth_pose import Model_depth_pose\nfrom model_flowposenet import Model_flowposenet\n\ndef get_model(mode):\n if mode == 'flow':\n return Model_flow\n elif mode == 'pose' or mode == 'pose_flow':\n return Model_triangulate_pose\n elif mode == 'depth' or mode == 'depth_pose':\n return Model_depth_pose\n elif mode == 'flowposenet':\n return Model_flowposenet\n else:\n raise ValueError('Mode {} not found.'.format(mode))\n" }, { "path": "core/networks/model_depth_pose.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom structures import *\nfrom model_triangulate_pose import Model_triangulate_pose\nfrom pytorch_ssim import SSIM\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'visualize'))\nfrom visualizer import *\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport pdb\n\nclass Model_depth_pose(nn.Module):\n def __init__(self, cfg):\n super(Model_depth_pose, self).__init__()\n self.depth_match_num = cfg.depth_match_num\n self.depth_sample_ratio = cfg.depth_sample_ratio\n self.depth_scale = cfg.depth_scale\n self.w_flow_error = cfg.w_flow_error\n self.dataset = cfg.dataset\n\n self.depth_net = Depth_Model(cfg.depth_scale)\n self.model_pose = Model_triangulate_pose(cfg)\n\n def meshgrid(self, h, w):\n xx, yy = np.meshgrid(np.arange(0,w), np.arange(0,h))\n meshgrid = np.transpose(np.stack([xx,yy], axis=-1), [2,0,1]) # [2,h,w]\n meshgrid = torch.from_numpy(meshgrid)\n return meshgrid\n \n def robust_rand_sample(self, match, mask, num):\n # match: [b, 4, -1] mask: [b, 1, -1]\n b, n = match.shape[0], match.shape[2]\n nonzeros_num = torch.min(torch.sum(mask > 0, dim=-1)) # []\n if nonzeros_num.detach().cpu().numpy() == n:\n rand_int = torch.randint(0, n, [num])\n select_match = match[:,:,rand_int]\n else:\n # If there is zero score in match, sample the non-zero matches.\n num = np.minimum(nonzeros_num.detach().cpu().numpy(), num)\n select_idxs = []\n for i in range(b):\n nonzero_idx = torch.nonzero(mask[i,0,:]) # [nonzero_num,1]\n rand_int = torch.randint(0, nonzero_idx.shape[0], [int(num)])\n select_idx = nonzero_idx[rand_int, :] # [num, 1]\n select_idxs.append(select_idx)\n select_idxs = torch.stack(select_idxs, 0) # [b,num,1]\n select_match = torch.gather(match.transpose(1,2), index=select_idxs.repeat(1,1,4), dim=1).transpose(1,2) # [b, 4, num]\n return select_match, num\n\n def top_ratio_sample(self, match, mask, ratio):\n # match: [b, 4, -1] mask: [b, 1, -1]\n b, total_num = match.shape[0], match.shape[-1]\n scores, indices = torch.topk(mask, int(ratio*total_num), dim=-1) # [B, 1, ratio*tnum]\n select_match = torch.gather(match.transpose(1,2), index=indices.squeeze(1).unsqueeze(-1).repeat(1,1,4), dim=1).transpose(1,2) # [b, 4, ratio*tnum]\n return select_match, scores\n \n def rand_sample(self, match, num):\n b, c, n = match.shape[0], match.shape[1], match.shape[2]\n rand_int = torch.randint(0, match.shape[-1], size=[num])\n select_pts = match[:,:,rand_int]\n return select_pts\n \n def filt_negative_depth(self, point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord):\n # Filter out the negative projection depth.\n # point2d_1_depth: [b, n, 1]\n b, n = point2d_1_depth.shape[0], point2d_1_depth.shape[1]\n mask = (point2d_1_depth > 0.01).float() * (point2d_2_depth > 0.01).float()\n select_idxs = []\n flag = 0\n for i in range(b):\n if torch.sum(mask[i,:,0]) == n:\n idx = torch.arange(n).to(mask.get_device())\n else:\n nonzero_idx = torch.nonzero(mask[i,:,0]).squeeze(1) # [k]\n if nonzero_idx.shape[0] < 0.1*n:\n idx = torch.arange(n).to(mask.get_device())\n flag = 1\n else:\n res = torch.randint(0, nonzero_idx.shape[0], size=[n-nonzero_idx.shape[0]]).to(mask.get_device()) # [n-nz]\n idx = torch.cat([nonzero_idx, nonzero_idx[res]], 0)\n select_idxs.append(idx)\n select_idxs = torch.stack(select_idxs, dim=0) # [b,n]\n point2d_1_depth = torch.gather(point2d_1_depth, index=select_idxs.unsqueeze(-1), dim=1) # [b,n,1]\n point2d_2_depth = torch.gather(point2d_2_depth, index=select_idxs.unsqueeze(-1), dim=1) # [b,n,1]\n point2d_1_coord = torch.gather(point2d_1_coord, index=select_idxs.unsqueeze(-1).repeat(1,1,2), dim=1) # [b,n,2]\n point2d_2_coord = torch.gather(point2d_2_coord, index=select_idxs.unsqueeze(-1).repeat(1,1,2), dim=1) # [b,n,2]\n return point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag\n \n def filt_invalid_coord(self, point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, max_h, max_w):\n # Filter out the negative projection depth.\n # point2d_1_depth: [b, n, 1]\n b, n = point2d_1_coord.shape[0], point2d_1_coord.shape[1]\n max_coord = torch.Tensor([max_w, max_h]).to(point2d_1_coord.get_device())\n mask = (point2d_1_coord > 0).all(dim=-1, keepdim=True).float() * (point2d_2_coord > 0).all(dim=-1, keepdim=True).float() * \\\n (point2d_1_coord < max_coord).all(dim=-1, keepdim=True).float() * (point2d_2_coord < max_coord).all(dim=-1, keepdim=True).float()\n \n flag = 0\n if torch.sum(1.0-mask) == 0:\n return point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag\n\n select_idxs = []\n for i in range(b):\n if torch.sum(mask[i,:,0]) == n:\n idx = torch.arange(n).to(mask.get_device())\n else:\n nonzero_idx = torch.nonzero(mask[i,:,0]).squeeze(1) # [k]\n if nonzero_idx.shape[0] < 0.1*n:\n idx = torch.arange(n).to(mask.get_device())\n flag = 1\n else:\n res = torch.randint(0, nonzero_idx.shape[0], size=[n-nonzero_idx.shape[0]]).to(mask.get_device())\n idx = torch.cat([nonzero_idx, nonzero_idx[res]], 0)\n select_idxs.append(idx)\n select_idxs = torch.stack(select_idxs, dim=0) # [b,n]\n point2d_1_depth = torch.gather(point2d_1_depth, index=select_idxs.unsqueeze(-1), dim=1) # [b,n,1]\n point2d_2_depth = torch.gather(point2d_2_depth, index=select_idxs.unsqueeze(-1), dim=1) # [b,n,1]\n point2d_1_coord = torch.gather(point2d_1_coord, index=select_idxs.unsqueeze(-1).repeat(1,1,2), dim=1) # [b,n,2]\n point2d_2_coord = torch.gather(point2d_2_coord, index=select_idxs.unsqueeze(-1).repeat(1,1,2), dim=1) # [b,n,2]\n return point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag\n \n def ray_angle_filter(self, match, P1, P2, return_angle=False):\n # match: [b, 4, n] P: [B, 3, 4]\n b, n = match.shape[0], match.shape[2]\n K = P1[:,:,:3] # P1 with identity rotation and zero translation\n K_inv = torch.inverse(K)\n RT1 = K_inv.bmm(P1) # [b, 3, 4]\n RT2 = K_inv.bmm(P2)\n ones = torch.ones([b,1,n]).to(match.get_device())\n pts1 = torch.cat([match[:,:2,:], ones], 1)\n pts2 = torch.cat([match[:,2:,:], ones], 1)\n \n ray1_dir = (RT1[:,:,:3].transpose(1,2)).bmm(K_inv).bmm(pts1)# [b,3,n]\n ray1_dir = ray1_dir / (torch.norm(ray1_dir, dim=1, keepdim=True, p=2) + 1e-12)\n ray1_origin = (-1) * RT1[:,:,:3].transpose(1,2).bmm(RT1[:,:,3].unsqueeze(-1)) # [b, 3, 1]\n ray2_dir = (RT2[:,:,:3].transpose(1,2)).bmm(K_inv).bmm(pts2) # [b,3,n]\n ray2_dir = ray2_dir / (torch.norm(ray2_dir, dim=1, keepdim=True, p=2) + 1e-12)\n ray2_origin = (-1) * RT2[:,:,:3].transpose(1,2).bmm(RT2[:,:,3].unsqueeze(-1)) # [b, 3, 1]\n\n # We compute the angle betwwen vertical line from ray1 origin to ray2 and ray1.\n p1p2 = (ray1_origin - ray2_origin).repeat(1,1,n)\n verline = ray2_origin.repeat(1,1,n) + torch.sum(p1p2 * ray2_dir, dim=1, keepdim=True) * ray2_dir - ray1_origin.repeat(1,1,n) # [b,3,n]\n cosvalue = torch.sum(ray1_dir * verline, dim=1, keepdim=True) / \\\n ((torch.norm(ray1_dir, dim=1, keepdim=True, p=2) + 1e-12) * (torch.norm(verline, dim=1, keepdim=True, p=2) + 1e-12))# [b,1,n]\n\n mask = (cosvalue > 0.001).float() # we drop out angles less than 1' [b,1,n]\n flag = 0\n num = torch.min(torch.sum(mask, -1)).int()\n if num.cpu().detach().numpy() == 0:\n flag = 1\n filt_match = match[:,:,:100]\n if return_angle:\n return filt_match, flag, torch.zeros_like(mask).to(filt_match.get_device())\n else:\n return filt_match, flag\n nonzero_idx = []\n for i in range(b):\n idx = torch.nonzero(mask[i,0,:])[:num] # [num,1]\n nonzero_idx.append(idx)\n nonzero_idx = torch.stack(nonzero_idx, 0) # [b,num,1]\n filt_match = torch.gather(match.transpose(1,2), index=nonzero_idx.repeat(1,1,4), dim=1).transpose(1,2) # [b,4,num]\n if return_angle:\n return filt_match, flag, mask\n else:\n return filt_match, flag\n \n def midpoint_triangulate(self, match, K_inv, P1, P2):\n # match: [b, 4, num] P1: [b, 3, 4]\n # Match is in the image coordinates. P1, P2 is camera parameters. [B, 3, 4] match: [B, M, 4]\n b, n = match.shape[0], match.shape[2]\n RT1 = K_inv.bmm(P1) # [b, 3, 4]\n RT2 = K_inv.bmm(P2)\n ones = torch.ones([b,1,n]).to(match.get_device())\n pts1 = torch.cat([match[:,:2,:], ones], 1)\n pts2 = torch.cat([match[:,2:,:], ones], 1)\n \n ray1_dir = (RT1[:,:,:3].transpose(1,2)).bmm(K_inv).bmm(pts1)# [b,3,n]\n ray1_dir = ray1_dir / (torch.norm(ray1_dir, dim=1, keepdim=True, p=2) + 1e-12)\n ray1_origin = (-1) * RT1[:,:,:3].transpose(1,2).bmm(RT1[:,:,3].unsqueeze(-1)) # [b, 3, 1]\n ray2_dir = (RT2[:,:,:3].transpose(1,2)).bmm(K_inv).bmm(pts2) # [b,3,n]\n ray2_dir = ray2_dir / (torch.norm(ray2_dir, dim=1, keepdim=True, p=2) + 1e-12)\n ray2_origin = (-1) * RT2[:,:,:3].transpose(1,2).bmm(RT2[:,:,3].unsqueeze(-1)) # [b, 3, 1]\n \n dir_cross = torch.cross(ray1_dir, ray2_dir, dim=1) # [b,3,n]\n denom = 1.0 / (torch.sum(dir_cross * dir_cross, dim=1, keepdim=True)+1e-12) # [b,1,n]\n origin_vec = (ray2_origin - ray1_origin).repeat(1,1,n) # [b,3,n]\n a1 = origin_vec.cross(ray2_dir, dim=1) # [b,3,n]\n a1 = torch.sum(a1 * dir_cross, dim=1, keepdim=True) * denom # [b,1,n]\n a2 = origin_vec.cross(ray1_dir, dim=1) # [b,3,n]\n a2 = torch.sum(a2 * dir_cross, dim=1, keepdim=True) * denom # [b,1,n]\n p1 = ray1_origin + a1 * ray1_dir\n p2 = ray2_origin + a2 * ray2_dir\n point = (p1 + p2) / 2.0 # [b,3,n]\n # Convert to homo coord to get consistent with other functions.\n point_homo = torch.cat([point, ones], dim=1).transpose(1,2) # [b,n,4]\n return point_homo\n \n def rt_from_fundamental_mat_nyu(self, fmat, K, depth_match):\n # F: [b, 3, 3] K: [b, 3, 3] depth_match: [b ,4, n]\n #verify_match = self.rand_sample(depth_match, 5000) # [b,4,100]\n verify_match = depth_match.transpose(1,2).cpu().detach().numpy()\n K_inv = torch.inverse(K)\n b = fmat.shape[0]\n fmat_ = K.transpose(1,2).bmm(fmat)\n essential_mat = fmat_.bmm(K)\n iden = torch.cat([torch.eye(3), torch.zeros([3,1])], -1).unsqueeze(0).repeat(b,1,1).to(K.get_device()) # [b,3,4]\n P1 = K.bmm(iden)\n flags = []\n number_inliers = []\n P2 = []\n for i in range(b):\n cnum, R, t, _ = cv2.recoverPose(essential_mat[i].cpu().detach().numpy().astype('float64'), verify_match[i,:,:2].astype('float64'), \\\n verify_match[i,:,2:].astype('float64'), cameraMatrix=K[i,:,:].cpu().detach().numpy().astype('float64'))\n p2 = torch.from_numpy(np.concatenate([R, t], axis=-1)).float().to(K.get_device())\n P2.append(p2)\n if cnum > depth_match.shape[-1] / 7.0:\n flags.append(1)\n else:\n flags.append(0)\n number_inliers.append(cnum)\n \n P2 = K.bmm(torch.stack(P2, axis=0))\n #pdb.set_trace()\n \n return P1, P2, flags\n \n def verifyRT(self, match, K_inv, P1, P2):\n # match: [b, 4, n] P1: [b,3,4] P2: [b,3,4]\n b, n = match.shape[0], match.shape[2]\n point3d = self.midpoint_triangulate(match, K_inv, P1, P2).reshape([-1,4]).unsqueeze(-1) # [b*n, 4, 1]\n P1_ = P1.repeat(n,1,1)\n P2_ = P2.repeat(n,1,1)\n depth1 = P1_.bmm(point3d)[:,-1,:] / point3d[:,-1,:] # [b*n, 1]\n depth2 = P2_.bmm(point3d)[:,-1,:] / point3d[:,-1,:]\n inlier_num = torch.sum((depth1.view([b,n]) > 0).float() * (depth2.view([b,n]) > 0).float(), 1) # [b]\n return inlier_num\n \n def rt_from_fundamental_mat(self, fmat, K, depth_match):\n # F: [b, 3, 3] K: [b, 3, 3] depth_match: [b ,4, n]\n verify_match = self.rand_sample(depth_match, 200) # [b,4,100]\n K_inv = torch.inverse(K)\n b = fmat.shape[0]\n fmat_ = K.transpose(1,2).bmm(fmat)\n essential_mat = fmat_.bmm(K)\n essential_mat_cpu = essential_mat.cpu()\n U, S, V = torch.svd(essential_mat_cpu)\n U, S, V = U.to(K.get_device()), S.to(K.get_device()), V.to(K.get_device())\n W = torch.from_numpy(np.array([[[0., -1., 0.],[1., 0., 0.],[0., 0., 1.]]])).float().repeat(b,1,1).to(K.get_device())\n # R = UWV^t or UW^tV^t t = U[:,2] the third column of U\n R1 = U.bmm(W).bmm(V.transpose(1,2)) # Do we need matrix determinant sign?\n R1 = torch.sign(torch.det(R1)).unsqueeze(-1).unsqueeze(-1) * R1\n R2 = U.bmm(W.transpose(1,2)).bmm(V.transpose(1,2))\n R2 = torch.sign(torch.det(R2)).unsqueeze(-1).unsqueeze(-1) * R2\n t1 = U[:,:,2].unsqueeze(-1) # The third column\n t2 = -U[:,:,2].unsqueeze(-1) # Inverse direction\n \n iden = torch.cat([torch.eye(3), torch.zeros([3,1])], -1).unsqueeze(0).repeat(b,1,1).to(K.get_device()) # [b,3,4]\n P1 = K.bmm(iden)\n P2_1 = K.bmm(torch.cat([R1, t1], -1))\n P2_2 = K.bmm(torch.cat([R2, t1], -1))\n P2_3 = K.bmm(torch.cat([R1, t2], -1))\n P2_4 = K.bmm(torch.cat([R2, t2], -1))\n P2_c = [P2_1, P2_2, P2_3, P2_4]\n flags = []\n for i in range(4):\n with torch.no_grad():\n inlier_num = self.verifyRT(verify_match, K_inv, P1, P2_c[i])\n flags.append(inlier_num)\n P2_c = torch.stack(P2_c, dim=1) # [B, 4, 3, 4]\n flags = torch.stack(flags, dim=1) # [B, 4]\n idx = torch.argmax(flags, dim=-1, keepdim=True) # [b,1]\n P2 = torch.gather(P2_c, index=idx.unsqueeze(-1).unsqueeze(-1).repeat(1,1,3,4), dim=1).squeeze(1) # [b,3,4]\n #pdb.set_trace()\n return P1, P2\n \n def reproject(self, P, point3d):\n # P: [b,3,4] point3d: [b,n,4]\n point2d = P.bmm(point3d.transpose(1,2)) # [b,4,n]\n point2d_coord = (point2d[:,:2,:] / (point2d[:,2,:].unsqueeze(1) + 1e-12)).transpose(1,2) # [b,n,2]\n point2d_depth = point2d[:,2,:].unsqueeze(1).transpose(1,2) # [b,n,1]\n return point2d_coord, point2d_depth\n\n def scale_adapt(self, depth1, depth2, eps=1e-12):\n with torch.no_grad():\n A = torch.sum((depth1 ** 2) / (depth2 ** 2 + eps), dim=1) # [b,1]\n C = torch.sum(depth1 / (depth2 + eps), dim=1) # [b,1]\n a = C / (A + eps)\n return a\n\n def affine_adapt(self, depth1, depth2, use_translation=True, eps=1e-12):\n a_scale = self.scale_adapt(depth1, depth2, eps=eps)\n if not use_translation: # only fit the scale parameter\n return a_scale, torch.zeros_like(a_scale)\n else:\n with torch.no_grad():\n A = torch.sum((depth1 ** 2) / (depth2 ** 2 + eps), dim=1) # [b,1]\n B = torch.sum(depth1 / (depth2 ** 2 + eps), dim=1) # [b,1]\n C = torch.sum(depth1 / (depth2 + eps), dim=1) # [b,1]\n D = torch.sum(1.0 / (depth2 ** 2 + eps), dim=1) # [b,1]\n E = torch.sum(1.0 / (depth2 + eps), dim=1) # [b,1]\n a = (B*E - D*C) / (B*B - A*D + 1e-12)\n b = (B*C - A*E) / (B*B - A*D + 1e-12)\n\n # check ill condition\n cond = (B*B - A*D)\n valid = (torch.abs(cond) > 1e-4).float()\n a = a * valid + a_scale * (1 - valid)\n b = b * valid\n return a, b\n\n def register_depth(self, depth_pred, coord_tri, depth_tri):\n # depth_pred: [b, 1, h, w] coord_tri: [b,n,2] depth_tri: [b,n,1]\n batch, _, h, w = depth_pred.shape[0], depth_pred.shape[1], depth_pred.shape[2], depth_pred.shape[3]\n n = depth_tri.shape[1]\n coord_tri_nor = torch.stack([2.0*coord_tri[:,:,0] / (w-1.0) - 1.0, 2.0*coord_tri[:,:,1] / (h-1.0) - 1.0], -1)\n depth_inter = F.grid_sample(depth_pred, coord_tri_nor.view([batch,n,1,2]), padding_mode='reflection').squeeze(-1).transpose(1,2) # [b,n,1]\n\n # Normalize\n scale = torch.median(depth_inter, 1)[0] / (torch.median(depth_tri, 1)[0] + 1e-12)\n scale = scale.detach() # [b,1]\n scale_depth_inter = depth_inter / (scale.unsqueeze(-1) + 1e-12)\n scale_depth_pred = depth_pred / (scale.unsqueeze(-1).unsqueeze(-1) + 1e-12)\n \n # affine adapt\n a, b = self.affine_adapt(scale_depth_inter, depth_tri, use_translation=False)\n affine_depth_inter = a.unsqueeze(1) * scale_depth_inter + b.unsqueeze(1) # [b,n,1]\n affine_depth_pred = a.unsqueeze(-1).unsqueeze(-1) * scale_depth_pred + b.unsqueeze(-1).unsqueeze(-1) # [b,1,h,w]\n return affine_depth_pred, affine_depth_inter\n \n def get_trian_loss(self, tri_depth, pred_tri_depth):\n # depth: [b,n,1]\n loss = torch.pow(1.0 - pred_tri_depth / (tri_depth + 1e-12), 2).mean((1,2))\n return loss\n \n def get_reproj_fdp_loss(self, pred1, pred2, P2, K, K_inv, valid_mask, rigid_mask, flow, visualizer=None):\n # pred: [b,1,h,w] Rt: [b,3,4] K: [b,3,3] mask: [b,1,h,w] flow: [b,2,h,w]\n b, h, w = pred1.shape[0], pred1.shape[2], pred1.shape[3]\n xy = self.meshgrid(h,w).unsqueeze(0).repeat(b,1,1,1).float().to(flow.get_device()) # [b,2,h,w]\n ones = torch.ones([b,1,h,w]).float().to(flow.get_device())\n pts1_3d = K_inv.bmm(torch.cat([xy, ones], 1).view([b,3,-1])) * pred1.view([b,1,-1]) # [b,3,h*w]\n pts2_coord, pts2_depth = self.reproject(P2, torch.cat([pts1_3d, ones.view([b,1,-1])], 1).transpose(1,2)) # [b,h*w, 2]\n # TODO Here some of the reprojection coordinates are invalid. (<0 or >max)\n reproj_valid_mask = (pts2_coord > torch.Tensor([0,0]).to(pred1.get_device())).all(-1, True).float() * \\\n (pts2_coord < torch.Tensor([w-1,h-1]).to(pred1.get_device())).all(-1, True).float() # [b,h*w, 1]\n reproj_valid_mask = (valid_mask * reproj_valid_mask.view([b,h,w,1]).permute([0,3,1,2])).detach()\n rigid_mask = rigid_mask.detach()\n pts2_depth = pts2_depth.transpose(1,2).view([b,1,h,w])\n\n # Get the interpolated depth prediction2\n pts2_coord_nor = torch.cat([2.0 * pts2_coord[:,:,0].unsqueeze(-1) / (w - 1.0) - 1.0, 2.0 * pts2_coord[:,:,1].unsqueeze(-1) / (h - 1.0) - 1.0], -1)\n inter_depth2 = F.grid_sample(pred2, pts2_coord_nor.view([b, h, w, 2]), padding_mode='reflection') # [b,1,h,w]\n pj_loss_map = (torch.abs(1.0 - pts2_depth / (inter_depth2 + 1e-12)) * rigid_mask * reproj_valid_mask)\n pj_loss = pj_loss_map.mean((1,2,3)) / ((reproj_valid_mask * rigid_mask).mean((1,2,3))+1e-12)\n #pj_loss = (valid_mask * mask * torch.abs(pts2_depth - inter_depth2) / (torch.abs(pts2_depth + inter_depth2)+1e-12)).mean((1,2,3)) / ((valid_mask * mask).mean((1,2,3))+1e-12) # [b]\n flow_loss = (rigid_mask * torch.abs(flow + xy - pts2_coord.detach().permute(0,2,1).view([b,2,h,w]))).mean((1,2,3)) / (rigid_mask.mean((1,2,3)) + 1e-12)\n return pj_loss, flow_loss\n \n def disp2depth(self, disp, min_depth=0.1, max_depth=100.0):\n min_disp = 1 / max_depth\n max_disp = 1 / min_depth\n scaled_disp = min_disp + (max_disp - min_disp) * disp\n depth = 1 / scaled_disp\n return scaled_disp, depth\n \n def get_smooth_loss(self, img, disp):\n # img: [b,3,h,w] depth: [b,1,h,w]\n \"\"\"Computes the smoothness loss for a disparity image\n The color image is used for edge-aware smoothness\n \"\"\"\n grad_disp_x = torch.abs(disp[:, :, :, :-1] - disp[:, :, :, 1:])\n grad_disp_y = torch.abs(disp[:, :, :-1, :] - disp[:, :, 1:, :])\n\n grad_img_x = torch.mean(torch.abs(img[:, :, :, :-1] - img[:, :, :, 1:]), 1, keepdim=True)\n grad_img_y = torch.mean(torch.abs(img[:, :, :-1, :] - img[:, :, 1:, :]), 1, keepdim=True)\n\n grad_disp_x *= torch.exp(-grad_img_x)\n grad_disp_y *= torch.exp(-grad_img_y)\n\n return grad_disp_x.mean((1,2,3)) + grad_disp_y.mean((1,2,3))\n\n def infer_depth(self, img):\n disp_list = self.depth_net(img)\n disp, depth = self.disp2depth(disp_list[0])\n return disp_list[0]\n \n def infer_vo(self, img1, img2, K, K_inv, match_num=6000):\n b, img_h, img_w = img1.shape[0], img1.shape[2], img1.shape[3]\n F_final, img1_valid_mask, img1_rigid_mask, fwd_flow, fwd_match = self.model_pose.inference(img1, img2, K, K_inv)\n # infer depth\n disp1_list = self.depth_net(img1) # Nscales * [B, 1, H, W]\n disp2_list = self.depth_net(img2)\n disp1, depth1 = self.disp2depth(disp1_list[0])\n disp2, depth2 = self.disp2depth(disp2_list[0])\n\n img1_depth_mask = img1_rigid_mask * img1_valid_mask\n # [b, 4, match_num]\n top_ratio_match, top_ratio_mask = self.top_ratio_sample(fwd_match.view([b,4,-1]), img1_depth_mask.view([b,1,-1]), ratio=0.30) # [b, 4, ratio*h*w]\n depth_match, depth_match_num = self.robust_rand_sample(top_ratio_match, top_ratio_mask, num=match_num)\n return depth_match, depth1, depth2\n \n def check_rt(self, img1, img2, K, K_inv):\n # initialization\n b = img1.shape[0]\n flag1, flag2, flag3 = 0, 0, 0\n images = torch.cat([img1, img2], dim=2)\n inputs = [images, K.unsqueeze(1), K_inv.unsqueeze(1)]\n\n # Pose Network\n #self.profiler.reset()\n loss_pack, F_final, img1_valid_mask, img1_rigid_score, img1_inlier_mask, fwd_flow, fwd_match = self.model_pose(inputs, output_F=True)\n # Get masks\n img1_depth_mask = img1_rigid_score * img1_valid_mask\n\n # Select top score matches to triangulate depth.\n top_ratio_match, top_ratio_mask = self.top_ratio_sample(fwd_match.view([b,4,-1]), img1_depth_mask.view([b,1,-1]), ratio=0.20) # [b, 4, ratio*h*w]\n depth_match, depth_match_num = self.robust_rand_sample(top_ratio_match, top_ratio_mask, num=self.depth_match_num)\n\n P1, P2, flags = self.rt_from_fundamental_mat_nyu(F_final.detach(), K, depth_match)\n P1 = P1.detach()\n P2 = P2.detach()\n flags = torch.from_numpy(np.stack(flags, axis=0)).float().to(K.get_device())\n\n return flags\n\n def inference(self, img1, img2, K, K_inv):\n b, img_h, img_w = img1.shape[0], img1.shape[2], img1.shape[3]\n visualizer = Visualizer_debug('./vis/', np.transpose(255*img1.detach().cpu().numpy(), [0,2,3,1]), \\\n np.transpose(255*img2.detach().cpu().numpy(), [0,2,3,1]))\n\n F_final, img1_valid_mask, img1_rigid_mask, fwd_flow, fwd_match = self.model_pose.inference(img1, img2, K, K_inv)\n # infer depth\n disp1_list = self.depth_net(img1) # Nscales * [B, 1, H, W]\n disp2_list = self.depth_net(img2)\n disp1, _ = self.disp2depth(disp1_list[0])\n disp2, _ = self.disp2depth(disp2_list[0])\n\n # Get Camera Matrix\n img1_depth_mask = img1_rigid_mask * img1_valid_mask\n # [b, 4, match_num]\n top_ratio_match, top_ratio_mask = self.top_ratio_sample(fwd_match.view([b,4,-1]), img1_depth_mask.view([b,1,-1]), ratio=0.20) # [b, 4, ratio*h*w]\n depth_match, depth_match_num = self.robust_rand_sample(top_ratio_match, top_ratio_mask, num=self.depth_match_num)\n if self.dataset == 'nyuv2':\n P1, P2, _ = self.rt_from_fundamental_mat_nyu(F_final, K, depth_match)\n else:\n P1, P2 = self.rt_from_fundamental_mat(F_final, K, depth_match)\n Rt = K_inv.bmm(P2)\n \n filt_depth_match, flag1 = self.ray_angle_filter(depth_match, P1, P2) # [b, 4, filt_num]\n point3d_1 = self.midpoint_triangulate(filt_depth_match, K_inv, P1, P2)\n point2d_1_coord, point2d_1_depth = self.reproject(P1, point3d_1) # [b,n,2], [b,n,1]\n point2d_2_coord, point2d_2_depth = self.reproject(P2, point3d_1)\n \n # Filter out some invalid triangulation results to stablize training.\n point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag2 = self.filt_negative_depth(point2d_1_depth, \\\n point2d_2_depth, point2d_1_coord, point2d_2_coord)\n point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag3 = self.filt_invalid_coord(point2d_1_depth, \\\n point2d_2_depth, point2d_1_coord, point2d_2_coord, max_h=img_h, max_w=img_w)\n \n return fwd_flow, disp1, disp2, Rt, point2d_1_coord, point2d_1_depth\n \n\n def forward(self, inputs):\n # initialization\n images, K_ms, K_inv_ms = inputs\n K, K_inv = K_ms[:,0,:,:], K_inv_ms[:,0,:,:]\n assert (images.shape[1] == 3)\n img_h, img_w = int(images.shape[2] / 2), images.shape[3] \n img1, img2 = images[:,:,:img_h,:], images[:,:,img_h:,:]\n b = img1.shape[0]\n flag1, flag2, flag3 = 0, 0, 0\n visualizer = Visualizer_debug('./vis/', img1=255*img1.permute([0,2,3,1]).detach().cpu().numpy(), \\\n img2=255*img2.permute([0,2,3,1]).detach().cpu().numpy())\n \n # Pose Network\n loss_pack, F_final, img1_valid_mask, img1_rigid_mask, fwd_flow, fwd_match = self.model_pose(inputs, output_F=True, visualizer=visualizer)\n # infer depth\n disp1_list = self.depth_net(img1) # Nscales * [B, 1, H, W]\n disp2_list = self.depth_net(img2)\n\n # Get masks\n img1_depth_mask = img1_rigid_mask * img1_valid_mask\n \n # Select top score matches to triangulate depth.\n top_ratio_match, top_ratio_mask = self.top_ratio_sample(fwd_match.view([b,4,-1]), img1_depth_mask.view([b,1,-1]), ratio=self.depth_sample_ratio) # [b, 4, ratio*h*w]\n depth_match, depth_match_num = self.robust_rand_sample(top_ratio_match, top_ratio_mask, num=self.depth_match_num)\n \n if self.dataset == 'nyuv2':\n P1, P2, flags = self.rt_from_fundamental_mat_nyu(F_final.detach(), K, depth_match)\n flags = torch.from_numpy(np.stack(flags, axis=0)).float().to(K.get_device())\n else:\n P1, P2 = self.rt_from_fundamental_mat(F_final.detach(), K, depth_match)\n P1 = P1.detach()\n P2 = P2.detach()\n\n # Get triangulated points\n filt_depth_match, flag1 = self.ray_angle_filter(depth_match, P1, P2, return_angle=False) # [b, 4, filt_num]\n \n point3d_1 = self.midpoint_triangulate(filt_depth_match, K_inv, P1, P2)\n point2d_1_coord, point2d_1_depth = self.reproject(P1, point3d_1) # [b,n,2], [b,n,1]\n point2d_2_coord, point2d_2_depth = self.reproject(P2, point3d_1)\n \n # Filter out some invalid triangulation results to stablize training.\n point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag2 = self.filt_negative_depth(point2d_1_depth, \\\n point2d_2_depth, point2d_1_coord, point2d_2_coord)\n point2d_1_depth, point2d_2_depth, point2d_1_coord, point2d_2_coord, flag3 = self.filt_invalid_coord(point2d_1_depth, \\\n point2d_2_depth, point2d_1_coord, point2d_2_coord, max_h=img_h, max_w=img_w)\n\n \n if flag1 + flag2 + flag3 > 0:\n loss_pack['pt_depth_loss'] = torch.zeros([2]).to(point3d_1.get_device()).requires_grad_()\n loss_pack['pj_depth_loss'] = torch.zeros([2]).to(point3d_1.get_device()).requires_grad_()\n loss_pack['flow_error'] = torch.zeros([2]).to(point3d_1.get_device()).requires_grad_()\n loss_pack['depth_smooth_loss'] = torch.zeros([2]).to(point3d_1.get_device()).requires_grad_()\n return loss_pack\n\n pt_depth_loss = 0\n pj_depth_loss = 0\n flow_error = 0\n depth_smooth_loss = 0\n for s in range(self.depth_scale):\n disp_pred1 = F.interpolate(disp1_list[s], size=(img_h, img_w), mode='bilinear') # [b,1,h,w]\n disp_pred2 = F.interpolate(disp2_list[s], size=(img_h, img_w), mode='bilinear')\n scaled_disp1, depth_pred1 = self.disp2depth(disp_pred1)\n scaled_disp2, depth_pred2 = self.disp2depth(disp_pred2)\n # Rescale predicted depth according to triangulated depth\n # [b,1,h,w], [b,n,1]\n rescaled_pred1, inter_pred1 = self.register_depth(depth_pred1, point2d_1_coord, point2d_1_depth)\n rescaled_pred2, inter_pred2 = self.register_depth(depth_pred2, point2d_2_coord, point2d_2_depth)\n # Get Losses\n \n pt_depth_loss += self.get_trian_loss(point2d_1_depth, inter_pred1) + self.get_trian_loss(point2d_2_depth, inter_pred2)\n pj_depth, flow_loss = self.get_reproj_fdp_loss(rescaled_pred1, rescaled_pred2, P2, K, K_inv, img1_valid_mask, img1_rigid_mask, fwd_flow, visualizer=visualizer)\n depth_smooth_loss += self.get_smooth_loss(img1, disp_pred1 / (disp_pred1.mean((2,3), True) + 1e-12)) + \\\n self.get_smooth_loss(img2, disp_pred2 / (disp_pred2.mean((2,3), True) + 1e-12))\n pj_depth_loss += pj_depth\n flow_error += flow_loss\n\n if self.dataset == 'nyuv2':\n loss_pack['pt_depth_loss'] = pt_depth_loss * flags\n loss_pack['pj_depth_loss'], loss_pack['flow_error'] = pj_depth_loss * flags, flow_error * flags\n loss_pack['depth_smooth_loss'] = depth_smooth_loss * flags\n else:\n loss_pack['pt_depth_loss'] = pt_depth_loss\n loss_pack['pj_depth_loss'], loss_pack['flow_error'] = pj_depth_loss, flow_error\n loss_pack['depth_smooth_loss'] = depth_smooth_loss\n return loss_pack\n\n\n" }, { "path": "core/networks/model_flow.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom structures import *\nfrom pytorch_ssim import SSIM\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport pdb\nimport cv2\n\ndef transformerFwd(U,\n flo,\n out_size,\n name='SpatialTransformerFwd'):\n \"\"\"Forward Warping Layer described in \n 'Occlusion Aware Unsupervised Learning of Optical Flow by Yang Wang et al'\n\n Parameters\n ----------\n U : float\n The output of a convolutional net should have the\n shape [num_batch, height, width, num_channels].\n flo: float\n The optical flow used for forward warping \n having the shape of [num_batch, height, width, 2].\n backprop: boolean\n Indicates whether to back-propagate through forward warping layer\n out_size: tuple of two ints\n The size of the output of the network (height, width)\n \"\"\"\n\n def _repeat(x, n_repeats):\n rep = torch.ones(size=[n_repeats], dtype=torch.long).unsqueeze(1).transpose(1,0)\n x = x.view([-1,1]).mm(rep)\n return x.view([-1]).int()\n\n def _interpolate(im, x, y, out_size):\n # constants\n num_batch, height, width, channels = im.shape[0], im.shape[1], im.shape[2], im.shape[3]\n out_height = out_size[0]\n out_width = out_size[1]\n max_y = int(height - 1)\n max_x = int(width - 1)\n\n # scale indices from [-1, 1] to [0, width/height]\n x = (x + 1.0) * (width - 1.0) / 2.0\n y = (y + 1.0) * (height - 1.0) / 2.0\n\n # do sampling\n x0 = (torch.floor(x)).int()\n x1 = x0 + 1\n y0 = (torch.floor(y)).int()\n y1 = y0 + 1\n\n x0_c = torch.clamp(x0, 0, max_x)\n x1_c = torch.clamp(x1, 0, max_x)\n y0_c = torch.clamp(y0, 0, max_y)\n y1_c = torch.clamp(y1, 0, max_y)\n\n dim2 = width\n dim1 = width * height\n base = _repeat(torch.arange(0, num_batch) * dim1, out_height * out_width).to(im.get_device())\n\n base_y0 = base + y0_c * dim2\n base_y1 = base + y1_c * dim2\n idx_a = base_y0 + x0_c\n idx_b = base_y1 + x0_c\n idx_c = base_y0 + x1_c\n idx_d = base_y1 + x1_c\n\n # use indices to lookup pixels in the flat image and restore\n # channels dim\n im_flat = im.view([-1, channels])\n im_flat = im_flat.float()\n\n # and finally calculate interpolated values\n x0_f = x0.float()\n x1_f = x1.float()\n y0_f = y0.float()\n y1_f = y1.float()\n wa = ((x1_f - x) * (y1_f - y)).unsqueeze(1)\n wb = ((x1_f - x) * (y - y0_f)).unsqueeze(1)\n wc = ((x - x0_f) * (y1_f - y)).unsqueeze(1)\n wd = ((x - x0_f) * (y - y0_f)).unsqueeze(1)\n\n zerof = torch.zeros_like(wa)\n wa = torch.where(\n (torch.eq(x0_c, x0) & torch.eq(y0_c, y0)).unsqueeze(1), wa, zerof)\n wb = torch.where(\n (torch.eq(x0_c, x0) & torch.eq(y1_c, y1)).unsqueeze(1), wb, zerof)\n wc = torch.where(\n (torch.eq(x1_c, x1) & torch.eq(y0_c, y0)).unsqueeze(1), wc, zerof)\n wd = torch.where(\n (torch.eq(x1_c, x1) & torch.eq(y1_c, y1)).unsqueeze(1), wd, zerof)\n\n zeros = torch.zeros(\n size=[\n int(num_batch) * int(height) *\n int(width), int(channels)\n ],\n dtype=torch.float)\n output = zeros.to(im.get_device())\n output = output.scatter_add(dim=0, index=idx_a.long().unsqueeze(1).repeat(1,channels), src=im_flat * wa)\n output = output.scatter_add(dim=0, index=idx_b.long().unsqueeze(1).repeat(1,channels), src=im_flat * wb)\n output = output.scatter_add(dim=0, index=idx_c.long().unsqueeze(1).repeat(1,channels), src=im_flat * wc)\n output = output.scatter_add(dim=0, index=idx_d.long().unsqueeze(1).repeat(1,channels), src=im_flat * wd)\n\n return output\n\n def _meshgrid(height, width):\n # This should be equivalent to:\n x_t, y_t = np.meshgrid(np.linspace(-1, 1, width),\n np.linspace(-1, 1, height))\n # ones = np.ones(np.prod(x_t.shape))\n # grid = np.vstack([x_t.flatten(), y_t.flatten(), ones])\n return torch.from_numpy(x_t).float(), torch.from_numpy(y_t).float()\n\n def _transform(flo, input_dim, out_size):\n num_batch, height, width, num_channels = input_dim.shape[0:4]\n\n # grid of (x_t, y_t, 1), eq (1) in ref [1]\n height_f = float(height)\n width_f = float(width)\n out_height = out_size[0]\n out_width = out_size[1]\n x_s, y_s = _meshgrid(out_height, out_width)\n x_s = x_s.to(flo.get_device()).unsqueeze(0)\n x_s = x_s.repeat([num_batch, 1, 1])\n\n y_s = y_s.to(flo.get_device()).unsqueeze(0)\n y_s =y_s.repeat([num_batch, 1, 1])\n\n x_t = x_s + flo[:, :, :, 0] / ((out_width - 1.0) / 2.0)\n y_t = y_s + flo[:, :, :, 1] / ((out_height - 1.0) / 2.0)\n\n x_t_flat = x_t.view([-1])\n y_t_flat = y_t.view([-1])\n\n input_transformed = _interpolate(input_dim, x_t_flat, y_t_flat,\n out_size)\n\n output = input_transformed.view([num_batch, out_height, out_width, num_channels])\n return output\n\n #out_size = int(out_size)\n output = _transform(flo, U, out_size)\n return output\n\n\nclass Model_flow(nn.Module):\n def __init__(self, cfg):\n super(Model_flow, self).__init__()\n self.fpyramid = FeaturePyramid()\n self.pwc_model = PWC_tf()\n if cfg.mode == 'depth' or cfg.mode == 'flowposenet':\n # Stage 2 training\n for param in self.fpyramid.parameters():\n param.requires_grad = False\n for param in self.pwc_model.parameters():\n param.requires_grad = False\n \n # hyperparameters\n self.dataset = cfg.dataset\n self.num_scales = cfg.num_scales\n self.flow_consist_alpha = cfg.h_flow_consist_alpha\n self.flow_consist_beta = cfg.h_flow_consist_beta\n\n def get_occlusion_mask_from_flow(self, tensor_size, flow):\n mask = torch.ones(tensor_size).to(flow.get_device())\n h, w = mask.shape[2], mask.shape[3]\n occ_mask = transformerFwd(mask.permute(0,2,3,1), flow.permute(0,2,3,1), out_size=[h,w]).permute(0,3,1,2)\n with torch.no_grad():\n occ_mask = torch.clamp(occ_mask, 0.0, 1.0)\n return occ_mask\n\n def get_flow_norm(self, flow, p=2):\n '''\n Inputs:\n flow (bs, 2, H, W)\n '''\n flow_norm = torch.norm(flow, p=p, dim=1).unsqueeze(1) + 1e-12\n return flow_norm\n\n def get_visible_masks(self, optical_flows, optical_flows_rev):\n # get occlusion masks\n batch_size, _, img_h, img_w = optical_flows[0].shape\n img2_visible_masks, img1_visible_masks = [], []\n for s, (optical_flow, optical_flow_rev) in enumerate(zip(optical_flows, optical_flows_rev)):\n shape = [batch_size, 1, int(img_h / (2**s)), int(img_w / (2**s))]\n img2_visible_masks.append(self.get_occlusion_mask_from_flow(shape, optical_flow))\n img1_visible_masks.append(self.get_occlusion_mask_from_flow(shape, optical_flow_rev))\n return img2_visible_masks, img1_visible_masks\n\n def get_consistent_masks(self, optical_flows, optical_flows_rev):\n # get consist masks\n batch_size, _, img_h, img_w = optical_flows[0].shape\n img2_consis_masks, img1_consis_masks, fwd_flow_diff_pyramid, bwd_flow_diff_pyramid = [], [], [], []\n for s, (optical_flow, optical_flow_rev) in enumerate(zip(optical_flows, optical_flows_rev)):\n \n bwd2fwd_flow = warp_flow(optical_flow_rev, optical_flow)\n fwd2bwd_flow = warp_flow(optical_flow, optical_flow_rev)\n\n fwd_flow_diff = torch.abs(bwd2fwd_flow + optical_flow)\n fwd_flow_diff_pyramid.append(fwd_flow_diff)\n bwd_flow_diff = torch.abs(fwd2bwd_flow + optical_flow_rev)\n bwd_flow_diff_pyramid.append(bwd_flow_diff)\n\n # flow consistency condition\n bwd_consist_bound = torch.max(self.flow_consist_beta * self.get_flow_norm(optical_flow_rev), torch.from_numpy(np.array([self.flow_consist_alpha])).float().to(optical_flow_rev.get_device()))\n fwd_consist_bound = torch.max(self.flow_consist_beta * self.get_flow_norm(optical_flow), torch.from_numpy(np.array([self.flow_consist_alpha])).float().to(optical_flow.get_device()))\n with torch.no_grad():\n noc_masks_img2 = (self.get_flow_norm(bwd_flow_diff) < bwd_consist_bound).float()\n noc_masks_img1 = (self.get_flow_norm(fwd_flow_diff) < fwd_consist_bound).float()\n img2_consis_masks.append(noc_masks_img2)\n img1_consis_masks.append(noc_masks_img1)\n return img2_consis_masks, img1_consis_masks, fwd_flow_diff_pyramid, bwd_flow_diff_pyramid\n\n def generate_img_pyramid(self, img, num_pyramid):\n img_h, img_w = img.shape[2], img.shape[3]\n img_pyramid = []\n for s in range(num_pyramid):\n img_new = F.adaptive_avg_pool2d(img, [int(img_h / (2**s)), int(img_w / (2**s))]).data\n img_pyramid.append(img_new)\n return img_pyramid\n\n def warp_flow_pyramid(self, img_pyramid, flow_pyramid):\n img_warped_pyramid = []\n for img, flow in zip(img_pyramid, flow_pyramid):\n img_warped_pyramid.append(warp_flow(img, flow))\n return img_warped_pyramid\n\n def compute_loss_pixel(self, img_pyramid, img_warped_pyramid, occ_mask_list):\n loss_list = []\n for scale in range(self.num_scales):\n img, img_warped, occ_mask = img_pyramid[scale], img_warped_pyramid[scale], occ_mask_list[scale]\n divider = occ_mask.mean((1,2,3))\n img_diff = torch.abs((img - img_warped)) * occ_mask.repeat(1,3,1,1)\n loss_pixel = img_diff.mean((1,2,3)) / (divider + 1e-12) # (B)\n loss_list.append(loss_pixel[:,None])\n loss = torch.cat(loss_list, 1).sum(1) # (B)\n return loss\n\n def compute_loss_ssim(self, img_pyramid, img_warped_pyramid, occ_mask_list):\n loss_list = []\n for scale in range(self.num_scales):\n img, img_warped, occ_mask = img_pyramid[scale], img_warped_pyramid[scale], occ_mask_list[scale]\n divider = occ_mask.mean((1,2,3))\n occ_mask_pad = occ_mask.repeat(1,3,1,1)\n ssim = SSIM(img * occ_mask_pad, img_warped * occ_mask_pad)\n loss_ssim = torch.clamp((1.0 - ssim) / 2.0, 0, 1).mean((1,2,3))\n loss_ssim = loss_ssim / (divider + 1e-12)\n loss_list.append(loss_ssim[:,None])\n loss = torch.cat(loss_list, 1).sum(1)\n return loss\n\n def gradients(self, img):\n dy = img[:,:,1:,:] - img[:,:,:-1,:]\n dx = img[:,:,:,1:] - img[:,:,:,:-1]\n return dx, dy\n\n def cal_grad2_error(self, flow, img):\n img_grad_x, img_grad_y = self.gradients(img)\n w_x = torch.exp(-10.0 * torch.abs(img_grad_x).mean(1).unsqueeze(1))\n w_y = torch.exp(-10.0 * torch.abs(img_grad_y).mean(1).unsqueeze(1))\n\n dx, dy = self.gradients(flow)\n dx2, _ = self.gradients(dx)\n _, dy2 = self.gradients(dy)\n error = (w_x[:,:,:,1:] * torch.abs(dx2)).mean((1,2,3)) + (w_y[:,:,1:,:] * torch.abs(dy2)).mean((1,2,3))\n return error / 2.0\n\n def compute_loss_flow_smooth(self, optical_flows, img_pyramid):\n loss_list = []\n for scale in range(self.num_scales):\n flow, img = optical_flows[scale], img_pyramid[scale]\n error = self.cal_grad2_error(flow/20.0, img)\n loss_list.append(error[:,None])\n loss = torch.cat(loss_list, 1).sum(1)\n return loss\n\n def compute_loss_flow_consis(self, fwd_flow_diff_pyramid, occ_mask_list):\n loss_list = []\n for scale in range(self.num_scales):\n fwd_flow_diff, occ_mask = fwd_flow_diff_pyramid[scale], occ_mask_list[scale]\n divider = occ_mask.mean((1,2,3))\n loss_consis = (fwd_flow_diff * occ_mask).mean((1,2,3))\n loss_consis = loss_consis / (divider + 1e-12)\n loss_list.append(loss_consis[:,None])\n loss = torch.cat(loss_list, 1).sum(1)\n return loss\n\n def inference_flow(self, img1, img2):\n img_hw = [img1.shape[2], img1.shape[3]]\n feature_list_1, feature_list_2 = self.fpyramid(img1), self.fpyramid(img2)\n optical_flow = self.pwc_model(feature_list_1, feature_list_2, img_hw)[0]\n return optical_flow\n \n def inference_corres(self, img1, img2):\n batch_size, img_h, img_w = img1.shape[0], img1.shape[2], img1.shape[3]\n \n # get the optical flows and reverse optical flows for each pair of adjacent images\n feature_list_1, feature_list_2 = self.fpyramid(img1), self.fpyramid(img2)\n optical_flows = self.pwc_model(feature_list_1, feature_list_2, [img_h, img_w])\n optical_flows_rev = self.pwc_model(feature_list_2, feature_list_1, [img_h, img_w])\n\n # get occlusion masks\n img2_visible_masks, img1_visible_masks = self.get_visible_masks(optical_flows, optical_flows_rev)\n # get consistent masks\n img2_consis_masks, img1_consis_masks, fwd_flow_diff_pyramid, bwd_flow_diff_pyramid = self.get_consistent_masks(optical_flows, optical_flows_rev)\n # get final valid masks \n img2_valid_masks, img1_valid_masks = [], []\n for i, (img2_visible_mask, img1_visible_mask, img2_consis_mask, img1_consis_mask) in enumerate(zip(img2_visible_masks, img1_visible_masks, img2_consis_masks, img1_consis_masks)):\n img2_valid_masks.append(img2_visible_mask * img2_consis_mask)\n img1_valid_masks.append(img1_visible_mask * img1_consis_mask)\n \n return optical_flows[0], optical_flows_rev[0], img1_valid_masks[0], img2_valid_masks[0], fwd_flow_diff_pyramid[0], bwd_flow_diff_pyramid[0]\n\n def forward(self, inputs, output_flow=False, use_flow_loss=True):\n images, K_ms, K_inv_ms = inputs\n assert (images.shape[1] == 3)\n img_h, img_w = int(images.shape[2] / 2), images.shape[3] \n img1, img2 = images[:,:,:img_h,:], images[:,:,img_h:,:]\n batch_size = img1.shape[0]\n\n #cv2.imwrite('./test1.png', np.transpose(255*img1[0].cpu().detach().numpy(), [1,2,0]).astype(np.uint8))\n #cv2.imwrite('./test2.png', np.transpose(255*img2[0].cpu().detach().numpy(), [1,2,0]).astype(np.uint8))\n #pdb.set_trace()\n # get the optical flows and reverse optical flows for each pair of adjacent images\n feature_list_1, feature_list_2 = self.fpyramid(img1), self.fpyramid(img2)\n optical_flows = self.pwc_model(feature_list_1, feature_list_2, [img_h, img_w])\n optical_flows_rev = self.pwc_model(feature_list_2, feature_list_1, [img_h, img_w])\n \n # get occlusion masks\n img2_visible_masks, img1_visible_masks = self.get_visible_masks(optical_flows, optical_flows_rev)\n # get consistent masks\n img2_consis_masks, img1_consis_masks, fwd_flow_diff_pyramid, bwd_flow_diff_pyramid = self.get_consistent_masks(optical_flows, optical_flows_rev)\n # get final valid masks \n img2_valid_masks, img1_valid_masks = [], []\n \n for i, (img2_visible_mask, img1_visible_mask, img2_consis_mask, img1_consis_mask) in enumerate(zip(img2_visible_masks, img1_visible_masks, img2_consis_masks, img1_consis_masks)):\n if self.dataset == 'nyuv2':\n img2_valid_masks.append(img2_visible_mask)\n img1_valid_masks.append(img1_visible_mask)\n else:\n img2_valid_masks.append(img2_visible_mask * img2_consis_mask)\n img1_valid_masks.append(img1_visible_mask * img1_consis_mask)\n \n loss_pack = {}\n if not use_flow_loss:\n loss_pack['loss_pixel'] = torch.zeros([2]).to(img1.get_device()).requires_grad_()\n loss_pack['loss_ssim'] = torch.zeros([2]).to(img1.get_device()).requires_grad_()\n loss_pack['loss_flow_smooth'] = torch.zeros([2]).to(img1.get_device()).requires_grad_()\n loss_pack['loss_flow_consis'] = torch.zeros([2]).to(img1.get_device()).requires_grad_()\n return loss_pack, optical_flows[0], optical_flows_rev[0], img1_valid_masks[0], img2_valid_masks[0], fwd_flow_diff_pyramid[0], bwd_flow_diff_pyramid[0]\n\n # warp images\n img1_pyramid = self.generate_img_pyramid(img1, len(optical_flows_rev))\n img2_pyramid = self.generate_img_pyramid(img2, len(optical_flows))\n\n img1_warped_pyramid = self.warp_flow_pyramid(img2_pyramid, optical_flows)\n img2_warped_pyramid = self.warp_flow_pyramid(img1_pyramid, optical_flows_rev)\n \n # compute loss\n loss_pack['loss_pixel'] = self.compute_loss_pixel(img1_pyramid, img1_warped_pyramid, img1_valid_masks) + \\\n self.compute_loss_pixel(img2_pyramid, img2_warped_pyramid, img2_valid_masks)\n loss_pack['loss_ssim'] = self.compute_loss_ssim(img1_pyramid, img1_warped_pyramid, img1_valid_masks) + \\\n self.compute_loss_ssim(img2_pyramid, img2_warped_pyramid, img2_valid_masks)\n loss_pack['loss_flow_smooth'] = self.compute_loss_flow_smooth(optical_flows, img1_pyramid) + \\\n self.compute_loss_flow_smooth(optical_flows_rev, img2_pyramid)\n \n #loss_pack['loss_flow_consis'] = self.compute_loss_flow_consis(fwd_flow_diff_pyramid, img1_valid_masks) + \\\n # self.compute_loss_flow_consis(bwd_flow_diff_pyramid, img2_valid_masks)\n loss_pack['loss_flow_consis'] = torch.zeros([2]).to(img1.get_device()).requires_grad_()\n if output_flow:\n return loss_pack, optical_flows[0], optical_flows_rev[0], img1_valid_masks[0], img2_valid_masks[0], fwd_flow_diff_pyramid[0], bwd_flow_diff_pyramid[0]\n else:\n return loss_pack\n\n" }, { "path": "core/networks/model_flowposenet.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom structures import *\nfrom pytorch_ssim import SSIM\nfrom model_flow import Model_flow\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'visualize'))\nfrom visualizer import *\nfrom profiler import Profiler\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport pdb\nimport cv2\n\ndef mean_on_mask(diff, valid_mask):\n mask = valid_mask.expand_as(diff)\n mean_value = (diff * mask).sum() / mask.sum()\n return mean_value\n\n\ndef edge_aware_smoothness_loss(pred_disp, img, max_scales):\n def gradient_x(img):\n gx = img[:, :, :-1, :] - img[:, :, 1:, :]\n return gx\n\n def gradient_y(img):\n gy = img[:, :, :, :-1] - img[:, :, :, 1:]\n return gy\n\n def get_edge_smoothness(img, pred):\n pred_gradients_x = gradient_x(pred)\n pred_gradients_y = gradient_y(pred)\n\n image_gradients_x = gradient_x(img)\n image_gradients_y = gradient_y(img)\n\n weights_x = torch.exp(-torch.mean(torch.abs(image_gradients_x),\n 1, keepdim=True))\n weights_y = torch.exp(-torch.mean(torch.abs(image_gradients_y),\n 1, keepdim=True))\n\n smoothness_x = torch.abs(pred_gradients_x) * weights_x\n smoothness_y = torch.abs(pred_gradients_y) * weights_y\n return torch.mean(smoothness_x) + torch.mean(smoothness_y)\n\n loss = 0\n weight = 1.\n\n s = 0\n for scaled_disp in pred_disp:\n s += 1\n if s > max_scales:\n break\n\n b, _, h, w = scaled_disp.size()\n scaled_img = nn.functional.adaptive_avg_pool2d(img, (h, w))\n loss += get_edge_smoothness(scaled_img, scaled_disp) * weight\n weight /= 4.0\n\n return loss\n\n\ndef compute_smooth_loss(tgt_depth, tgt_img, ref_depth, ref_img, max_scales=1):\n loss = edge_aware_smoothness_loss(tgt_depth, tgt_img, max_scales)\n loss = edge_aware_smoothness_loss(ref_depth, ref_img, max_scales)\n\n return loss\n\n\nclass Model_flowposenet(nn.Module):\n def __init__(self, cfg):\n super(Model_flowposenet, self).__init__()\n assert cfg.depth_scale == 1\n self.pose_net = FlowPoseNet()\n self.model_flow = Model_flow(cfg)\n self.depth_net = Depth_Model(cfg.depth_scale)\n \n def compute_pairwise_loss(self, tgt_img, ref_img, tgt_depth, ref_depth, pose, intrinsic):\n ref_img_warped, valid_mask, projected_depth, computed_depth = inverse_warp2(ref_img, tgt_depth, ref_depth,\n pose, intrinsic, 'zeros')\n\n diff_img = (tgt_img - ref_img_warped).abs()\n\n diff_depth = ((computed_depth - projected_depth).abs() /\n (computed_depth + projected_depth).abs()).clamp(0, 1)\n\n \n ssim_map = (0.5*(1 - SSIM(tgt_img, ref_img_warped))).clamp(0, 1)\n diff_img = (0.15 * diff_img + 0.85 * ssim_map)\n\n # Modified in 01.19.2020\n #weight_mask = (1 - diff_depth)\n #diff_img = diff_img * weight_mask\n \n\n # compute loss\n reconstruction_loss = diff_img.mean()\n geometry_consistency_loss = diff_depth.mean()\n #reconstruction_loss = mean_on_mask(diff_img, valid_mask)\n #geometry_consistency_loss = mean_on_mask(diff_depth, valid_mask)\n\n return reconstruction_loss, geometry_consistency_loss\n\n \n def disp2depth(self, disp, min_depth=0.01, max_depth=80.0):\n min_disp = 1 / max_depth\n max_disp = 1 / min_depth\n scaled_disp = min_disp + (max_disp - min_disp) * disp\n depth = 1 / scaled_disp\n return scaled_disp, depth\n\n def infer_depth(self, img):\n b, img_h, img_w = img.shape[0], img.shape[2], img.shape[3]\n disp_list = self.depth_net(img)\n disp, depth = self.disp2depth(disp_list[0])\n return disp_list[0]\n \n def inference(self, img1, img2, K, K_inv):\n flow = self.model_flow.inference_flow(img1, img2)\n return flow, None, None, None, None, None\n \n def inference_flow(self, img1, img2):\n flow = self.model_flow.inference_flow(img1, img2)\n return flow\n \n def infer_pose(self, img1, img2, K, K_inv):\n img_h, img_w = img1.shape[2], img1.shape[3]\n flow = self.model_flow.inference_flow(img1, img2)\n flow[:,0,:,:] /= img_w\n flow[:,1,:,:] /= img_h\n pose = self.pose_net(flow)\n return pose\n\n def forward(self, inputs):\n # initialization\n images, K_ms, K_inv_ms = inputs\n K, K_inv = K_ms[:,0,:,:], K_inv_ms[:,0,:,:]\n assert (images.shape[1] == 3)\n img_h, img_w = int(images.shape[2] / 2), images.shape[3] \n img1, img2 = images[:,:,:img_h,:], images[:,:,img_h:,:]\n b = img1.shape[0]\n visualizer = Visualizer_debug('./vis/', img1=255*img1.permute([0,2,3,1]).detach().cpu().numpy(), \\\n img2=255*img2.permute([0,2,3,1]).detach().cpu().numpy())\n \n # Flow Network\n loss_pack, fwd_flow, bwd_flow, img1_valid_mask, img2_valid_mask, img1_flow_diff_mask, img2_flow_diff_mask = self.model_flow(inputs, output_flow=True, use_flow_loss=False)\n fwd_flow[:,0,:,:] /= img_w\n fwd_flow[:,1,:,:] /= img_h\n bwd_flow[:,0,:,:] /= img_w\n bwd_flow[:,1,:,:] /= img_h\n\n # Pose Network\n pose = self.pose_net(fwd_flow)\n pose_inv = self.pose_net(bwd_flow)\n disp1_list = self.depth_net(img1) # Nscales * [B, 1, H, W]\n disp2_list = self.depth_net(img2)\n disp1, depth1 = self.disp2depth(disp1_list[0])\n disp2, depth2 = self.disp2depth(disp2_list[0])\n #pdb.set_trace()\n\n loss_1, loss_3 = self.compute_pairwise_loss(img1, img2, depth1, depth2, pose, K)\n loss_1_2, loss_3_2 = self.compute_pairwise_loss(img2, img1, depth2, depth1, pose_inv, K)\n loss_ph = loss_1 + loss_1_2\n loss_pj = loss_3 + loss_3_2\n\n loss_2 = compute_smooth_loss([depth1], img1, [depth2], img2)\n\n loss_pack['pt_depth_loss'] = torch.zeros([2]).to(loss_2.get_device()).requires_grad_()\n loss_pack['pj_depth_loss'], loss_pack['flow_error'] = loss_pj, loss_ph\n loss_pack['depth_smooth_loss'] = loss_2\n #loss_pack['depth_smooth_loss'] = torch.zeros([2]).to(loss_2.get_device()).requires_grad_()\n loss_pack['geo_loss'] = torch.zeros([2]).to(loss_2.get_device()).requires_grad_()\n return loss_pack\n\n\n" }, { "path": "core/networks/model_triangulate_pose.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom structures import *\nfrom model_flow import Model_flow\nimport pdb\nimport cv2\n\nclass Model_triangulate_pose(nn.Module):\n def __init__(self, cfg):\n super(Model_triangulate_pose, self).__init__()\n self.model_flow = Model_flow(cfg)\n self.mode = cfg.mode\n if cfg.dataset == 'nyuv2':\n self.inlier_thres = 0.1\n self.rigid_thres = 1.0\n else:\n self.inlier_thres = 0.1\n self.rigid_thres = 0.5\n self.filter = reduced_ransac(check_num=cfg.ransac_points, thres=self.inlier_thres, dataset=cfg.dataset)\n \n def meshgrid(self, h, w):\n xx, yy = np.meshgrid(np.arange(0,w), np.arange(0,h))\n meshgrid = np.transpose(np.stack([xx,yy], axis=-1), [2,0,1]) # [2,h,w]\n meshgrid = torch.from_numpy(meshgrid)\n return meshgrid\n \n def compute_epipolar_loss(self, fmat, match, mask):\n # fmat: [b, 3, 3] match: [b, 4, h*w] mask: [b,1,h*w]\n num_batch = match.shape[0]\n match_num = match.shape[-1]\n\n points1 = match[:,:2,:]\n points2 = match[:,2:,:]\n ones = torch.ones(num_batch, 1, match_num).to(points1.get_device())\n points1 = torch.cat([points1, ones], 1) # [b,3,n]\n points2 = torch.cat([points2, ones], 1).transpose(1,2) # [b,n,3]\n\n # compute fundamental matrix loss\n fmat = fmat.unsqueeze(1)\n fmat_tiles = fmat.view([-1,3,3])\n epi_lines = fmat_tiles.bmm(points1) #[b,3,n] [b*n, 3, 1]\n dist_p2l = torch.abs((epi_lines.permute([0, 2, 1]) * points2).sum(-1, keepdim=True)) # [b,n,1]\n a = epi_lines[:,0,:].unsqueeze(1).transpose(1,2) # [b,n,1]\n b = epi_lines[:,1,:].unsqueeze(1).transpose(1,2) # [b,n,1]\n dist_div = torch.sqrt(a*a + b*b) + 1e-6\n dist_map = dist_p2l / dist_div # [B, n, 1]\n loss = (dist_map * mask.transpose(1,2)).mean([1,2]) / mask.mean([1,2])\n return loss, dist_map\n\n def get_rigid_mask(self, dist_map):\n rigid_mask = (dist_map < self.rigid_thres).float()\n inlier_mask = (dist_map < self.inlier_thres).float()\n rigid_score = rigid_mask * 1.0 / (1.0 + dist_map)\n return rigid_mask, inlier_mask, rigid_score\n \n def inference(self, img1, img2, K, K_inv):\n batch_size, img_h, img_w = img1.shape[0], img1.shape[2], img1.shape[3]\n \n fwd_flow, bwd_flow, img1_valid_mask, img2_valid_mask, img1_flow_diff_mask, img2_flow_diff_mask = self.model_flow.inference_corres(img1, img2)\n \n grid = self.meshgrid(img_h, img_w).float().to(img1.get_device()).unsqueeze(0).repeat(batch_size,1,1,1) #[b,2,h,w]\n corres = torch.cat([(grid[:,0,:,:] + fwd_flow[:,0,:,:]).clamp(0,img_w-1.0).unsqueeze(1), \\\n (grid[:,1,:,:] + fwd_flow[:,1,:,:]).clamp(0,img_h-1.0).unsqueeze(1)], 1)\n match = torch.cat([grid, corres], 1) # [b,4,h,w]\n\n img1_score_mask = img1_valid_mask * 1.0 / (0.1 + img1_flow_diff_mask.mean(1).unsqueeze(1))\n F_final = self.filter(match, img1_score_mask)\n geo_loss, rigid_mask = self.compute_epipolar_loss(F_final, match.view([batch_size,4,-1]), img1_valid_mask.view([batch_size,1,-1]))\n img1_rigid_mask = (rigid_mask.view([batch_size,img_h,img_w,1]) < self.inlier_thres).float()\n \n return F_final, img1_valid_mask, img1_rigid_mask.permute(0,3,1,2), fwd_flow, match\n\n def forward(self, inputs, output_F=False, visualizer=None):\n images, K_ms, K_inv_ms = inputs\n K, K_inv = K_ms[:,0,:,:], K_inv_ms[:,0,:,:]\n assert (images.shape[1] == 3)\n img_h, img_w = int(images.shape[2] / 2), images.shape[3] \n img1, img2 = images[:,:,:img_h,:], images[:,:,img_h:,:]\n batch_size = img1.shape[0]\n\n if self.mode == 'depth':\n loss_pack, fwd_flow, bwd_flow, img1_valid_mask, img2_valid_mask, img1_flow_diff_mask, img2_flow_diff_mask = self.model_flow(inputs, output_flow=True, use_flow_loss=False)\n else:\n loss_pack, fwd_flow, bwd_flow, img1_valid_mask, img2_valid_mask, img1_flow_diff_mask, img2_flow_diff_mask = self.model_flow(inputs, output_flow=True)\n \n grid = self.meshgrid(img_h, img_w).float().to(img1.get_device()).unsqueeze(0).repeat(batch_size,1,1,1) #[b,2,h,w]\n \n fwd_corres = torch.cat([(grid[:,0,:,:] + fwd_flow[:,0,:,:]).unsqueeze(1), (grid[:,1,:,:] + fwd_flow[:,1,:,:]).unsqueeze(1)], 1)\n fwd_match = torch.cat([grid, fwd_corres], 1) # [b,4,h,w]\n\n bwd_corres = torch.cat([(grid[:,0,:,:] + bwd_flow[:,0,:,:]).unsqueeze(1), (grid[:,1,:,:] + bwd_flow[:,1,:,:]).unsqueeze(1)], 1)\n bwd_match = torch.cat([grid, bwd_corres], 1) # [b,4,h,w]\n\n # Use fwd-bwd consistency map for filter\n img1_score_mask = img1_valid_mask * 1.0 / (0.1+img1_flow_diff_mask.mean(1).unsqueeze(1))\n img2_score_mask = img2_valid_mask * 1.0 / (0.1+img2_flow_diff_mask.mean(1).unsqueeze(1))\n # img1_score_mask = img1_valid_mask\n \n F_final_1 = self.filter(fwd_match, img1_score_mask, visualizer=visualizer)\n _, dist_map_1 = self.compute_epipolar_loss(F_final_1, fwd_match.view([batch_size,4,-1]), img1_valid_mask.view([batch_size,1,-1]))\n dist_map_1 = dist_map_1.view([batch_size, img_h, img_w, 1])\n\n # Compute geo loss for regularize correspondence.\n rigid_mask_1, inlier_mask_1, rigid_score_1 = self.get_rigid_mask(dist_map_1)\n \n # We only use rigid mask to filter out the moving objects for computing geo loss.\n geo_loss = (dist_map_1 * (rigid_mask_1 - inlier_mask_1)).mean((1,2,3)) / \\\n (rigid_mask_1 - inlier_mask_1).mean((1,2,3))\n\n loss_pack['geo_loss'] = geo_loss\n \n if output_F:\n return loss_pack, F_final_1, img1_score_mask, rigid_score_1.permute(0,3,1,2), fwd_flow, fwd_match\n else:\n return loss_pack\n\n\n\n\n" }, { "path": "core/networks/pytorch_ssim/__init__.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom ssim import SSIM\n\n" }, { "path": "core/networks/pytorch_ssim/ssim.py", "content": "import torch\nimport torch.nn as nn\n\ndef SSIM(x, y):\n C1 = 0.01 ** 2\n C2 = 0.03 ** 2\n\n mu_x = nn.AvgPool2d(3, 1, padding=1)(x)\n mu_y = nn.AvgPool2d(3, 1, padding=1)(y)\n\n sigma_x = nn.AvgPool2d(3, 1, padding=1)(x**2) - mu_x**2\n sigma_y = nn.AvgPool2d(3, 1, padding=1)(y**2) - mu_y**2\n sigma_xy = nn.AvgPool2d(3, 1, padding=1)(x * y) - mu_x * mu_y\n\n SSIM_n = (2 * mu_x * mu_y + C1) * (2 * sigma_xy + C2)\n SSIM_d = (mu_x**2 + mu_y**2 + C1) * (sigma_x + sigma_y + C2)\n\n SSIM = SSIM_n / SSIM_d\n return SSIM\n\n" }, { "path": "core/networks/structures/__init__.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom feature_pyramid import FeaturePyramid\nfrom pwc_tf import PWC_tf\nfrom ransac import reduced_ransac\nfrom depth_model import Depth_Model\nfrom net_utils import conv, deconv, warp_flow\nfrom flowposenet import FlowPoseNet\nfrom inverse_warp import inverse_warp2\n" }, { "path": "core/networks/structures/depth_model.py", "content": "'''\nThis code was ported from existing repos\n[LINK] https://github.com/nianticlabs/monodepth2\n'''\nfrom __future__ import absolute_import, division, print_function\nimport os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.models as models\nimport torch.utils.model_zoo as model_zoo\nfrom collections import OrderedDict\nimport pdb\n\nclass ResNetMultiImageInput(models.ResNet):\n \"\"\"Constructs a resnet model with varying number of input images.\n Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py\n \"\"\"\n def __init__(self, block, layers, num_classes=1000, num_input_images=1):\n super(ResNetMultiImageInput, self).__init__(block, layers)\n self.inplanes = 64\n self.conv1 = nn.Conv2d(\n num_input_images * 3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n self.bn1 = nn.BatchNorm2d(64)\n self.relu = nn.ReLU(inplace=True)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n self.layer1 = self._make_layer(block, 64, layers[0])\n self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n \n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n\ndef resnet_multiimage_input(num_layers, pretrained=False, num_input_images=1):\n \"\"\"Constructs a ResNet model.\n Args:\n num_layers (int): Number of resnet layers. Must be 18 or 50\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n num_input_images (int): Number of frames stacked as input\n \"\"\"\n assert num_layers in [18, 50], \"Can only run with 18 or 50 layer resnet\"\n blocks = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers]\n block_type = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers]\n model = ResNetMultiImageInput(block_type, blocks, num_input_images=num_input_images)\n\n if pretrained:\n loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)])\n loaded['conv1.weight'] = torch.cat(\n [loaded['conv1.weight']] * num_input_images, 1) / num_input_images\n model.load_state_dict(loaded)\n return model\n\nclass ResnetEncoder(nn.Module):\n \"\"\"Pytorch module for a resnet encoder\n \"\"\"\n def __init__(self, num_layers, pretrained, num_input_images=1):\n super(ResnetEncoder, self).__init__()\n\n self.num_ch_enc = np.array([64, 64, 128, 256, 512])\n\n resnets = {18: models.resnet18,\n 34: models.resnet34,\n 50: models.resnet50,\n 101: models.resnet101,\n 152: models.resnet152}\n\n if num_layers not in resnets:\n raise ValueError(\"{} is not a valid number of resnet layers\".format(num_layers))\n\n if num_input_images > 1:\n self.encoder = resnet_multiimage_input(num_layers, pretrained, num_input_images)\n else:\n self.encoder = resnets[num_layers](pretrained)\n\n if num_layers > 34:\n self.num_ch_enc[1:] *= 4\n\n def forward(self, input_image):\n self.features = []\n x = (input_image - 0.45) / 0.225\n x = self.encoder.conv1(x)\n x = self.encoder.bn1(x)\n self.features.append(self.encoder.relu(x))\n self.features.append(self.encoder.layer1(self.encoder.maxpool(self.features[-1])))\n self.features.append(self.encoder.layer2(self.features[-1]))\n self.features.append(self.encoder.layer3(self.features[-1]))\n self.features.append(self.encoder.layer4(self.features[-1]))\n return self.features\n\nclass ConvBlock(nn.Module):\n \"\"\"Layer to perform a convolution followed by ELU\n \"\"\"\n def __init__(self, in_channels, out_channels):\n super(ConvBlock, self).__init__()\n\n self.conv = Conv3x3(in_channels, out_channels)\n self.nonlin = nn.ELU(inplace=True)\n\n def forward(self, x):\n out = self.conv(x)\n out = self.nonlin(out)\n return out\n\nclass Conv3x3(nn.Module):\n \"\"\"Layer to pad and convolve input\n \"\"\"\n def __init__(self, in_channels, out_channels, use_refl=True):\n super(Conv3x3, self).__init__()\n\n if use_refl:\n self.pad = nn.ReflectionPad2d(1)\n else:\n self.pad = nn.ZeroPad2d(1)\n self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3)\n\n def forward(self, x):\n out = self.pad(x)\n out = self.conv(out)\n return out\n\ndef upsample(x):\n \"\"\"Upsample input tensor by a factor of 2\n \"\"\"\n #return F.interpolate(x, scale_factor=2, mode=\"nearest\")\n # TODO\n return F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)\n\nclass DepthDecoder(nn.Module):\n def __init__(self, num_ch_enc, scales=range(4), num_output_channels=1, use_skips=True):\n super(DepthDecoder, self).__init__()\n\n self.num_output_channels = num_output_channels\n self.use_skips = use_skips\n self.scales = scales\n\n self.num_ch_enc = num_ch_enc\n self.num_ch_dec = np.array([16, 32, 64, 128, 256])\n\n # decoder\n self.init_decoder()\n\n def init_decoder(self):\n self.upconvs = nn.ModuleList()\n for i in range(4, -1, -1):\n upconvs_now = nn.ModuleList()\n # upconv_0\n num_ch_in = self.num_ch_enc[-1] if i == 4 else self.num_ch_dec[i + 1]\n num_ch_out = self.num_ch_dec[i]\n upconvs_now.append(ConvBlock(num_ch_in, num_ch_out))\n\n # upconv_1\n num_ch_in = self.num_ch_dec[i]\n if self.use_skips and i > 0:\n num_ch_in += self.num_ch_enc[i - 1]\n num_ch_out = self.num_ch_dec[i]\n upconvs_now.append(ConvBlock(num_ch_in, num_ch_out))\n self.upconvs.append(upconvs_now)\n\n self.dispconvs = nn.ModuleList()\n for s in self.scales:\n self.dispconvs.append(Conv3x3(self.num_ch_dec[s], self.num_output_channels))\n\n # self.decoder = nn.ModuleList(list(self.convs.values()))\n self.sigmoid = nn.Sigmoid()\n\n def forward(self, input_features):\n self.outputs = {}\n\n # decoder\n x = input_features[-1]\n for scale in range(4, -1, -1): # [4, 3, 2, 1, 0]\n idx = 4 - scale\n x = self.upconvs[idx][0](x)\n x = [upsample(x)]\n if self.use_skips and scale > 0:\n x += [input_features[scale - 1]]\n x = torch.cat(x, 1)\n x = self.upconvs[idx][1](x)\n\n # get disp\n if scale in self.scales:\n scale_idx = self.scales.index(scale)\n self.outputs[(\"disp\", scale)] = self.sigmoid(self.dispconvs[scale_idx](x))\n return self.outputs\n\nclass Depth_Model(nn.Module):\n def __init__(self, depth_scale, num_layers=18):\n super(Depth_Model, self).__init__()\n self.depth_scale = depth_scale\n self.encoder = ResnetEncoder(num_layers=num_layers, pretrained=False)\n self.decoder = DepthDecoder(self.encoder.num_ch_enc, scales=range(depth_scale))\n\n def forward(self, img):\n features = self.encoder(img)\n outputs = self.decoder(features)\n depth_list = []\n disp_list = []\n for i in range(self.depth_scale):\n disp = outputs['disp', i]\n #s_disp, depth = self.disp2depth(disp, self.min_depth, self.max_depth)\n #depth_list.append(depth)\n #disp_list.append(s_disp)\n disp_list.append(disp)\n return disp_list\n\n" }, { "path": "core/networks/structures/feature_pyramid.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom net_utils import conv\nimport torch\nimport torch.nn as nn\n\nclass FeaturePyramid(nn.Module):\n def __init__(self):\n super(FeaturePyramid, self).__init__()\n self.conv1 = conv(3, 16, kernel_size=3, stride=2)\n self.conv2 = conv(16, 16, kernel_size=3, stride=1)\n self.conv3 = conv(16, 32, kernel_size=3, stride=2)\n self.conv4 = conv(32, 32, kernel_size=3, stride=1)\n self.conv5 = conv(32, 64, kernel_size=3, stride=2)\n self.conv6 = conv(64, 64, kernel_size=3, stride=1)\n self.conv7 = conv(64, 96, kernel_size=3, stride=2)\n self.conv8 = conv(96, 96, kernel_size=3, stride=1)\n self.conv9 = conv(96, 128, kernel_size=3, stride=2)\n self.conv10 = conv(128, 128, kernel_size=3, stride=1)\n self.conv11 = conv(128, 196, kernel_size=3, stride=2)\n self.conv12 = conv(196, 196, kernel_size=3, stride=1)\n '''\n for m in self.modules():\n if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\n nn.init.constant_(m.weight.data, 0.0)\n if m.bias is not None:\n m.bias.data.zero_()\n '''\n def forward(self, img):\n cnv2 = self.conv2(self.conv1(img))\n cnv4 = self.conv4(self.conv3(cnv2))\n cnv6 = self.conv6(self.conv5(cnv4))\n cnv8 = self.conv8(self.conv7(cnv6))\n cnv10 = self.conv10(self.conv9(cnv8))\n cnv12 = self.conv12(self.conv11(cnv10))\n return cnv2, cnv4, cnv6, cnv8, cnv10, cnv12\n\n" }, { "path": "core/networks/structures/flowposenet.py", "content": "import torch\nimport torch.nn as nn\nfrom torch import sigmoid\nfrom torch.nn.init import xavier_uniform_, zeros_\n\n\ndef conv(in_planes, out_planes, kernel_size=3):\n return nn.Sequential(\n nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, padding=(kernel_size-1)//2, stride=2),\n nn.ReLU(inplace=True)\n )\n\ndef upconv(in_planes, out_planes):\n return nn.Sequential(\n nn.ConvTranspose2d(in_planes, out_planes, kernel_size=4, stride=2, padding=1),\n nn.ReLU(inplace=True)\n )\n\nclass FlowPoseNet(nn.Module):\n\n def __init__(self):\n super(FlowPoseNet, self).__init__()\n\n conv_planes = [16, 32, 64, 128, 256, 256, 256]\n self.conv1 = conv(2, conv_planes[0], kernel_size=7)\n self.conv2 = conv(conv_planes[0], conv_planes[1], kernel_size=5)\n self.conv3 = conv(conv_planes[1], conv_planes[2])\n self.conv4 = conv(conv_planes[2], conv_planes[3])\n self.conv5 = conv(conv_planes[3], conv_planes[4])\n self.conv6 = conv(conv_planes[4], conv_planes[5])\n self.conv7 = conv(conv_planes[5], conv_planes[6])\n\n self.pose_pred = nn.Conv2d(conv_planes[6], 6, kernel_size=1, padding=0)\n\n def init_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\n xavier_uniform_(m.weight.data)\n if m.bias is not None:\n zeros_(m.bias)\n\n def forward(self, flow):\n # [B, 2, H, W]\n input = flow\n out_conv1 = self.conv1(input)\n out_conv2 = self.conv2(out_conv1)\n out_conv3 = self.conv3(out_conv2)\n out_conv4 = self.conv4(out_conv3)\n out_conv5 = self.conv5(out_conv4)\n out_conv6 = self.conv6(out_conv5)\n out_conv7 = self.conv7(out_conv6)\n\n pose = self.pose_pred(out_conv7)\n pose = pose.mean(3).mean(2)\n pose = 0.01 * pose.view(pose.size(0), 6)\n \n return pose\n\n\n\n\n" }, { "path": "core/networks/structures/inverse_warp.py", "content": "from __future__ import division\nimport torch\nimport torch.nn.functional as F\n\npixel_coords = None\n\n\ndef set_id_grid(depth):\n global pixel_coords\n b, h, w = depth.size()\n i_range = torch.arange(0, h).view(1, h, 1).expand(\n 1, h, w).type_as(depth) # [1, H, W]\n j_range = torch.arange(0, w).view(1, 1, w).expand(\n 1, h, w).type_as(depth) # [1, H, W]\n ones = torch.ones(1, h, w).type_as(depth)\n\n pixel_coords = torch.stack((j_range, i_range, ones), dim=1) # [1, 3, H, W]\n\n\ndef check_sizes(input, input_name, expected):\n condition = [input.ndimension() == len(expected)]\n for i, size in enumerate(expected):\n if size.isdigit():\n condition.append(input.size(i) == int(size))\n assert(all(condition)), \"wrong size for {}, expected {}, got {}\".format(\n input_name, 'x'.join(expected), list(input.size()))\n\n\ndef pixel2cam(depth, intrinsics_inv):\n global pixel_coords\n \"\"\"Transform coordinates in the pixel frame to the camera frame.\n Args:\n depth: depth maps -- [B, H, W]\n intrinsics_inv: intrinsics_inv matrix for each element of batch -- [B, 3, 3]\n Returns:\n array of (u,v,1) cam coordinates -- [B, 3, H, W]\n \"\"\"\n b, h, w = depth.size()\n if (pixel_coords is None) or pixel_coords.size(2) < h:\n set_id_grid(depth)\n current_pixel_coords = pixel_coords[:, :, :h, :w].expand(\n b, 3, h, w).reshape(b, 3, -1) # [B, 3, H*W]\n cam_coords = (intrinsics_inv @ current_pixel_coords).reshape(b, 3, h, w)\n return cam_coords * depth.unsqueeze(1)\n\n\ndef cam2pixel(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):\n \"\"\"Transform coordinates in the camera frame to the pixel frame.\n Args:\n cam_coords: pixel coordinates defined in the first camera coordinates system -- [B, 4, H, W]\n proj_c2p_rot: rotation matrix of cameras -- [B, 3, 4]\n proj_c2p_tr: translation vectors of cameras -- [B, 3, 1]\n Returns:\n array of [-1,1] coordinates -- [B, 2, H, W]\n \"\"\"\n b, _, h, w = cam_coords.size()\n cam_coords_flat = cam_coords.reshape(b, 3, -1) # [B, 3, H*W]\n if proj_c2p_rot is not None:\n pcoords = proj_c2p_rot @ cam_coords_flat\n else:\n pcoords = cam_coords_flat\n\n if proj_c2p_tr is not None:\n pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]\n X = pcoords[:, 0]\n Y = pcoords[:, 1]\n Z = pcoords[:, 2].clamp(min=1e-3)\n\n # Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]\n X_norm = 2*(X / Z)/(w-1) - 1\n Y_norm = 2*(Y / Z)/(h-1) - 1 # Idem [B, H*W]\n\n pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]\n return pixel_coords.reshape(b, h, w, 2)\n\n\ndef euler2mat(angle):\n \"\"\"Convert euler angles to rotation matrix.\n Reference: https://github.com/pulkitag/pycaffe-utils/blob/master/rot_utils.py#L174\n Args:\n angle: rotation angle along 3 axis (in radians) -- size = [B, 3]\n Returns:\n Rotation matrix corresponding to the euler angles -- size = [B, 3, 3]\n \"\"\"\n B = angle.size(0)\n x, y, z = angle[:, 0], angle[:, 1], angle[:, 2]\n\n cosz = torch.cos(z)\n sinz = torch.sin(z)\n\n zeros = z.detach()*0\n ones = zeros.detach()+1\n zmat = torch.stack([cosz, -sinz, zeros,\n sinz, cosz, zeros,\n zeros, zeros, ones], dim=1).reshape(B, 3, 3)\n\n cosy = torch.cos(y)\n siny = torch.sin(y)\n\n ymat = torch.stack([cosy, zeros, siny,\n zeros, ones, zeros,\n -siny, zeros, cosy], dim=1).reshape(B, 3, 3)\n\n cosx = torch.cos(x)\n sinx = torch.sin(x)\n\n xmat = torch.stack([ones, zeros, zeros,\n zeros, cosx, -sinx,\n zeros, sinx, cosx], dim=1).reshape(B, 3, 3)\n\n rotMat = xmat @ ymat @ zmat\n return rotMat\n\n\ndef quat2mat(quat):\n \"\"\"Convert quaternion coefficients to rotation matrix.\n Args:\n quat: first three coeff of quaternion of rotation. fourht is then computed to have a norm of 1 -- size = [B, 3]\n Returns:\n Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]\n \"\"\"\n norm_quat = torch.cat([quat[:, :1].detach()*0 + 1, quat], dim=1)\n norm_quat = norm_quat/norm_quat.norm(p=2, dim=1, keepdim=True)\n w, x, y, z = norm_quat[:, 0], norm_quat[:,\n 1], norm_quat[:, 2], norm_quat[:, 3]\n\n B = quat.size(0)\n\n w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)\n wx, wy, wz = w*x, w*y, w*z\n xy, xz, yz = x*y, x*z, y*z\n\n rotMat = torch.stack([w2 + x2 - y2 - z2, 2*xy - 2*wz, 2*wy + 2*xz,\n 2*wz + 2*xy, w2 - x2 + y2 - z2, 2*yz - 2*wx,\n 2*xz - 2*wy, 2*wx + 2*yz, w2 - x2 - y2 + z2], dim=1).reshape(B, 3, 3)\n return rotMat\n\n\ndef pose_vec2mat(vec, rotation_mode='euler'):\n \"\"\"\n Convert 6DoF parameters to transformation matrix.\n Args:s\n vec: 6DoF parameters in the order of tx, ty, tz, rx, ry, rz -- [B, 6]\n Returns:\n A transformation matrix -- [B, 3, 4]\n \"\"\"\n translation = vec[:, :3].unsqueeze(-1) # [B, 3, 1]\n rot = vec[:, 3:]\n if rotation_mode == 'euler':\n rot_mat = euler2mat(rot) # [B, 3, 3]\n elif rotation_mode == 'quat':\n rot_mat = quat2mat(rot) # [B, 3, 3]\n transform_mat = torch.cat([rot_mat, translation], dim=2) # [B, 3, 4]\n return transform_mat\n\n\ndef inverse_warp(img, depth, pose, intrinsics, rotation_mode='euler', padding_mode='zeros'):\n \"\"\"\n Inverse warp a source image to the target image plane.\n Args:\n img: the source image (where to sample pixels) -- [B, 3, H, W]\n depth: depth map of the target image -- [B, H, W]\n pose: 6DoF pose parameters from target to source -- [B, 6]\n intrinsics: camera intrinsic matrix -- [B, 3, 3]\n Returns:\n projected_img: Source image warped to the target image plane\n valid_points: Boolean array indicating point validity\n \"\"\"\n check_sizes(img, 'img', 'B3HW')\n check_sizes(depth, 'depth', 'BHW')\n check_sizes(pose, 'pose', 'B6')\n check_sizes(intrinsics, 'intrinsics', 'B33')\n\n batch_size, _, img_height, img_width = img.size()\n\n cam_coords = pixel2cam(depth, intrinsics.inverse()) # [B,3,H,W]\n\n pose_mat = pose_vec2mat(pose, rotation_mode) # [B,3,4]\n\n # Get projection matrix for tgt camera frame to source pixel frame\n proj_cam_to_src_pixel = intrinsics @ pose_mat # [B, 3, 4]\n\n rot, tr = proj_cam_to_src_pixel[:, :, :3], proj_cam_to_src_pixel[:, :, -1:]\n src_pixel_coords = cam2pixel(\n cam_coords, rot, tr, padding_mode) # [B,H,W,2]\n projected_img = F.grid_sample(\n img, src_pixel_coords, padding_mode=padding_mode)\n\n valid_points = src_pixel_coords.abs().max(dim=-1)[0] <= 1\n\n return projected_img, valid_points\n\n\ndef cam2pixel2(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):\n \"\"\"Transform coordinates in the camera frame to the pixel frame.\n Args:\n cam_coords: pixel coordinates defined in the first camera coordinates system -- [B, 4, H, W]\n proj_c2p_rot: rotation matrix of cameras -- [B, 3, 4]\n proj_c2p_tr: translation vectors of cameras -- [B, 3, 1]\n Returns:\n array of [-1,1] coordinates -- [B, 2, H, W]\n \"\"\"\n b, _, h, w = cam_coords.size()\n cam_coords_flat = cam_coords.reshape(b, 3, -1) # [B, 3, H*W]\n if proj_c2p_rot is not None:\n pcoords = proj_c2p_rot @ cam_coords_flat\n else:\n pcoords = cam_coords_flat\n\n if proj_c2p_tr is not None:\n pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]\n X = pcoords[:, 0]\n Y = pcoords[:, 1]\n Z = pcoords[:, 2].clamp(min=1e-3)\n\n # Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]\n X_norm = 2*(X / Z)/(w-1) - 1\n Y_norm = 2*(Y / Z)/(h-1) - 1 # Idem [B, H*W]\n if padding_mode == 'zeros':\n X_mask = ((X_norm > 1)+(X_norm < -1)).detach()\n # make sure that no point in warped image is a combinaison of im and gray\n X_norm[X_mask] = 2\n Y_mask = ((Y_norm > 1)+(Y_norm < -1)).detach()\n Y_norm[Y_mask] = 2\n\n pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]\n return pixel_coords.reshape(b, h, w, 2), Z.reshape(b, 1, h, w)\n\n\ndef inverse_warp2(img, depth, ref_depth, pose, intrinsics, padding_mode='zeros'):\n \"\"\"\n Inverse warp a source image to the target image plane.\n Args:\n img: the source image (where to sample pixels) -- [B, 3, H, W]\n depth: depth map of the target image -- [B, 1, H, W]\n ref_depth: the source depth map (where to sample depth) -- [B, 1, H, W] \n pose: 6DoF pose parameters from target to source -- [B, 6]\n intrinsics: camera intrinsic matrix -- [B, 3, 3]\n Returns:\n projected_img: Source image warped to the target image plane\n valid_mask: Float array indicating point validity\n \"\"\"\n check_sizes(img, 'img', 'B3HW')\n check_sizes(depth, 'depth', 'B1HW')\n check_sizes(ref_depth, 'ref_depth', 'B1HW')\n check_sizes(pose, 'pose', 'B6')\n check_sizes(intrinsics, 'intrinsics', 'B33')\n\n batch_size, _, img_height, img_width = img.size()\n\n cam_coords = pixel2cam(depth.squeeze(1), intrinsics.inverse()) # [B,3,H,W]\n\n pose_mat = pose_vec2mat(pose) # [B,3,4]\n\n # Get projection matrix for tgt camera frame to source pixel frame\n proj_cam_to_src_pixel = intrinsics @ pose_mat # [B, 3, 4]\n\n rot, tr = proj_cam_to_src_pixel[:, :, :3], proj_cam_to_src_pixel[:, :, -1:]\n src_pixel_coords, computed_depth = cam2pixel2(\n cam_coords, rot, tr, padding_mode) # [B,H,W,2]\n projected_img = F.grid_sample(\n img, src_pixel_coords, padding_mode=padding_mode)\n\n valid_points = src_pixel_coords.abs().max(dim=-1)[0] <= 1\n valid_mask = valid_points.unsqueeze(1).float()\n\n projected_depth = F.grid_sample(\n ref_depth, src_pixel_coords, padding_mode=padding_mode).clamp(min=1e-3)\n\n return projected_img, valid_mask, projected_depth, computed_depth" }, { "path": "core/networks/structures/net_utils.py", "content": "import torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport pdb\nimport numpy as np\n\ndef conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):\n return nn.Sequential(\n nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,\n padding=padding, dilation=dilation, bias=True),\n nn.LeakyReLU(0.1))\n\ndef deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):\n return nn.ConvTranspose2d(in_planes, out_planes, kernel_size, stride, padding, bias=True)\n\ndef warp_flow(x, flow, use_mask=False):\n \"\"\"\n warp an image/tensor (im2) back to im1, according to the optical flow\n\n Inputs:\n x: [B, C, H, W] (im2)\n flow: [B, 2, H, W] flow\n\n Returns:\n ouptut: [B, C, H, W]\n \"\"\"\n B, C, H, W = x.size()\n # mesh grid \n xx = torch.arange(0, W).view(1,-1).repeat(H,1)\n yy = torch.arange(0, H).view(-1,1).repeat(1,W)\n xx = xx.view(1,1,H,W).repeat(B,1,1,1)\n yy = yy.view(1,1,H,W).repeat(B,1,1,1)\n grid = torch.cat((xx,yy),1).float()\n\n if grid.shape != flow.shape:\n raise ValueError('the shape of grid {0} is not equal to the shape of flow {1}.'.format(grid.shape, flow.shape))\n if x.is_cuda:\n grid = grid.to(x.get_device())\n vgrid = grid + flow\n\n # scale grid to [-1,1] \n vgrid[:,0,:,:] = 2.0*vgrid[:,0,:,:].clone() / max(W-1,1)-1.0\n vgrid[:,1,:,:] = 2.0*vgrid[:,1,:,:].clone() / max(H-1,1)-1.0\n\n vgrid = vgrid.permute(0,2,3,1) \n output = nn.functional.grid_sample(x, vgrid)\n if use_mask:\n mask = torch.autograd.Variable(torch.ones(x.size())).to(x.get_device())\n mask = nn.functional.grid_sample(mask, vgrid)\n mask[mask < 0.9999] = 0\n mask[mask > 0] = 1\n return output * mask\n else:\n return output\n\nif __name__ == '__main__':\n x = np.ones([1,1,10,10])\n flow = np.stack([np.ones([1,10,10])*3.0, np.zeros([1,10,10])], axis=1)\n y = warp_flow(torch.from_numpy(x).cuda().float(),torch.from_numpy(flow).cuda().float()).cpu().detach().numpy()\n print(y)\n\n" }, { "path": "core/networks/structures/pwc_tf.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom net_utils import conv, deconv, warp_flow\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'external'))\n# from correlation_package.correlation import Correlation\n# from spatial_correlation_sampler import SpatialCorrelationSampler as Correlation\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport numpy as np\nimport pdb\nimport torch.nn.functional as F\n#from spatial_correlation_sampler import spatial_correlation_sample\n\nclass PWC_tf(nn.Module):\n def __init__(self, md=4):\n super(PWC_tf, self).__init__()\n self.corr = self.corr_naive\n # self.corr = self.correlate\n self.leakyRELU = nn.LeakyReLU(0.1)\n \n nd = (2*md+1)**2\n #dd = np.cumsum([128,128,96,64,32])\n dd = np.array([128,128,96,64,32])\n\n od = nd\n self.conv6_0 = conv(od, 128, kernel_size=3, stride=1)\n self.conv6_1 = conv(dd[0], 128, kernel_size=3, stride=1)\n self.conv6_2 = conv(dd[0]+dd[1],96, kernel_size=3, stride=1)\n self.conv6_3 = conv(dd[1]+dd[2],64, kernel_size=3, stride=1)\n self.conv6_4 = conv(dd[2]+dd[3],32, kernel_size=3, stride=1) \n self.predict_flow6 = self.predict_flow(dd[3]+dd[4])\n #self.deconv6 = deconv(2, 2, kernel_size=4, stride=2, padding=1) \n #self.upfeat6 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) \n \n od = nd+128+2\n self.conv5_0 = conv(od, 128, kernel_size=3, stride=1)\n self.conv5_1 = conv(dd[0], 128, kernel_size=3, stride=1)\n self.conv5_2 = conv(dd[0]+dd[1],96, kernel_size=3, stride=1)\n self.conv5_3 = conv(dd[1]+dd[2],64, kernel_size=3, stride=1)\n self.conv5_4 = conv(dd[2]+dd[3],32, kernel_size=3, stride=1)\n self.predict_flow5 = self.predict_flow(dd[3]+dd[4]) \n #self.deconv5 = deconv(2, 2, kernel_size=4, stride=2, padding=1) \n #self.upfeat5 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) \n \n od = nd+96+2\n self.conv4_0 = conv(od, 128, kernel_size=3, stride=1)\n self.conv4_1 = conv(dd[0], 128, kernel_size=3, stride=1)\n self.conv4_2 = conv(dd[0]+dd[1],96, kernel_size=3, stride=1)\n self.conv4_3 = conv(dd[1]+dd[2],64, kernel_size=3, stride=1)\n self.conv4_4 = conv(dd[2]+dd[3],32, kernel_size=3, stride=1)\n self.predict_flow4 = self.predict_flow(dd[3]+dd[4]) \n #self.deconv4 = deconv(2, 2, kernel_size=4, stride=2, padding=1) \n #self.upfeat4 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) \n \n od = nd+64+2\n self.conv3_0 = conv(od, 128, kernel_size=3, stride=1)\n self.conv3_1 = conv(dd[0], 128, kernel_size=3, stride=1)\n self.conv3_2 = conv(dd[0]+dd[1],96, kernel_size=3, stride=1)\n self.conv3_3 = conv(dd[1]+dd[2],64, kernel_size=3, stride=1)\n self.conv3_4 = conv(dd[2]+dd[3],32, kernel_size=3, stride=1)\n self.predict_flow3 = self.predict_flow(dd[3]+dd[4])\n #self.deconv3 = deconv(2, 2, kernel_size=4, stride=2, padding=1) \n #self.upfeat3 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) \n \n od = nd+32+2\n self.conv2_0 = conv(od, 128, kernel_size=3, stride=1)\n self.conv2_1 = conv(dd[0], 128, kernel_size=3, stride=1)\n self.conv2_2 = conv(dd[0]+dd[1],96, kernel_size=3, stride=1)\n self.conv2_3 = conv(dd[1]+dd[2],64, kernel_size=3, stride=1)\n self.conv2_4 = conv(dd[2]+dd[3],32, kernel_size=3, stride=1)\n self.predict_flow2 = self.predict_flow(dd[3]+dd[4]) \n #self.deconv2 = deconv(2, 2, kernel_size=4, stride=2, padding=1) \n \n self.dc_conv1 = conv(dd[4]+2, 128, kernel_size=3, stride=1, padding=1, dilation=1)\n self.dc_conv2 = conv(128, 128, kernel_size=3, stride=1, padding=2, dilation=2)\n self.dc_conv3 = conv(128, 128, kernel_size=3, stride=1, padding=4, dilation=4)\n self.dc_conv4 = conv(128, 96, kernel_size=3, stride=1, padding=8, dilation=8)\n self.dc_conv5 = conv(96, 64, kernel_size=3, stride=1, padding=16, dilation=16)\n self.dc_conv6 = conv(64, 32, kernel_size=3, stride=1, padding=1, dilation=1)\n self.dc_conv7 = self.predict_flow(32)\n\n '''\n for m in self.modules():\n if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\n nn.init.kaiming_zeros_(m.weight.data)\n if m.bias is not None:\n m.bias.data.zero_()\n '''\n def predict_flow(self, in_planes):\n return nn.Conv2d(in_planes,2,kernel_size=3,stride=1,padding=1,bias=True)\n\n def warp(self, x, flow):\n return warp_flow(x, flow, use_mask=False)\n\n def corr_naive(self, input1, input2, d=4):\n # naive pytorch implementation of the correlation layer.\n assert (input1.shape == input2.shape)\n batch_size, feature_num, H, W = input1.shape[0:4]\n input2 = F.pad(input2, (d,d,d,d), value=0)\n cv = []\n for i in range(2 * d + 1):\n for j in range(2 * d + 1):\n cv.append((input1 * input2[:, :, i:(i + H), j:(j + W)]).mean(1).unsqueeze(1))\n return torch.cat(cv, 1)\n \n def forward(self, feature_list_1, feature_list_2, img_hw):\n c11, c12, c13, c14, c15, c16 = feature_list_1\n c21, c22, c23, c24, c25, c26 = feature_list_2\n\n corr6 = self.corr(c16, c26) \n x0 = self.conv6_0(corr6)\n x1 = self.conv6_1(x0)\n x2 = self.conv6_2(torch.cat((x0,x1),1))\n x3 = self.conv6_3(torch.cat((x1,x2),1))\n x4 = self.conv6_4(torch.cat((x2,x3),1))\n flow6 = self.predict_flow6(torch.cat((x3,x4),1))\n up_flow6 = F.interpolate(flow6, scale_factor=2.0, mode='bilinear')*2.0\n\n warp5 = self.warp(c25, up_flow6)\n corr5 = self.corr(c15, warp5) \n x = torch.cat((corr5, c15, up_flow6), 1)\n x0 = self.conv5_0(x)\n x1 = self.conv5_1(x0)\n x2 = self.conv5_2(torch.cat((x0,x1),1))\n x3 = self.conv5_3(torch.cat((x1,x2),1))\n x4 = self.conv5_4(torch.cat((x2,x3),1))\n flow5 = self.predict_flow5(torch.cat((x3,x4),1))\n flow5 = flow5 + up_flow6\n up_flow5 = F.interpolate(flow5, scale_factor=2.0, mode='bilinear')*2.0\n\n \n warp4 = self.warp(c24, up_flow5)\n corr4 = self.corr(c14, warp4) \n x = torch.cat((corr4, c14, up_flow5), 1)\n x0 = self.conv4_0(x)\n x1 = self.conv4_1(x0)\n x2 = self.conv4_2(torch.cat((x0,x1),1))\n x3 = self.conv4_3(torch.cat((x1,x2),1))\n x4 = self.conv4_4(torch.cat((x2,x3),1))\n flow4 = self.predict_flow4(torch.cat((x3,x4),1))\n flow4 = flow4 + up_flow5\n up_flow4 = F.interpolate(flow4, scale_factor=2.0, mode='bilinear')*2.0\n\n warp3 = self.warp(c23, up_flow4)\n corr3 = self.corr(c13, warp3) \n x = torch.cat((corr3, c13, up_flow4), 1)\n x0 = self.conv3_0(x)\n x1 = self.conv3_1(x0)\n x2 = self.conv3_2(torch.cat((x0,x1),1))\n x3 = self.conv3_3(torch.cat((x1,x2),1))\n x4 = self.conv3_4(torch.cat((x2,x3),1))\n flow3 = self.predict_flow3(torch.cat((x3,x4),1))\n flow3 = flow3 + up_flow4\n up_flow3 = F.interpolate(flow3, scale_factor=2.0, mode='bilinear')*2.0\n\n\n warp2 = self.warp(c22, up_flow3) \n corr2 = self.corr(c12, warp2)\n x = torch.cat((corr2, c12, up_flow3), 1)\n x0 = self.conv2_0(x)\n x1 = self.conv2_1(x0)\n x2 = self.conv2_2(torch.cat((x0,x1),1))\n x3 = self.conv2_3(torch.cat((x1,x2),1))\n x4 = self.conv2_4(torch.cat((x2,x3),1))\n flow2 = self.predict_flow2(torch.cat((x3,x4),1))\n flow2 = flow2 + up_flow3\n \n x = self.dc_conv4(self.dc_conv3(self.dc_conv2(self.dc_conv1(torch.cat([flow2, x4], 1)))))\n flow2 = flow2 + self.dc_conv7(self.dc_conv6(self.dc_conv5(x)))\n\n img_h, img_w = img_hw[0], img_hw[1]\n flow2 = F.interpolate(flow2 * 4.0, [img_h, img_w], mode='bilinear')\n flow3 = F.interpolate(flow3 * 4.0, [img_h // 2, img_w // 2], mode='bilinear')\n flow4 = F.interpolate(flow4 * 4.0, [img_h // 4, img_w // 4], mode='bilinear')\n flow5 = F.interpolate(flow5 * 4.0, [img_h // 8, img_w // 8], mode='bilinear')\n \n return [flow2, flow3, flow4, flow5]\n\n" }, { "path": "core/networks/structures/ransac.py", "content": "import torch\nimport numpy as np\nimport os, sys\nimport torch.nn as nn\nimport pdb\nimport cv2\n\nclass reduced_ransac(nn.Module):\n def __init__(self, check_num, thres, dataset):\n super(reduced_ransac, self).__init__()\n self.check_num = check_num\n self.thres = thres\n self.dataset = dataset\n\n def robust_rand_sample(self, match, mask, num, robust=True):\n # match: [b, 4, -1] mask: [b, 1, -1]\n b, n = match.shape[0], match.shape[2]\n nonzeros_num = torch.min(torch.sum(mask > 0, dim=-1)) # []\n if nonzeros_num.detach().cpu().numpy() == n:\n rand_int = torch.randint(0, n, [num])\n select_match = match[:,:,rand_int]\n else:\n # If there is zero score in match, sample the non-zero matches.\n select_idxs = []\n if robust:\n num = np.minimum(nonzeros_num.detach().cpu().numpy(), num)\n for i in range(b):\n nonzero_idx = torch.nonzero(mask[i,0,:]) # [nonzero_num,1]\n rand_int = torch.randint(0, nonzero_idx.shape[0], [int(num)])\n select_idx = nonzero_idx[rand_int, :] # [num, 1]\n select_idxs.append(select_idx)\n select_idxs = torch.stack(select_idxs, 0) # [b,num,1]\n select_match = torch.gather(match.transpose(1,2), index=select_idxs.repeat(1,1,4), dim=1).transpose(1,2) # [b, 4, num]\n return select_match, num\n\n def top_ratio_sample(self, match, mask, ratio):\n # match: [b, 4, -1] mask: [b, 1, -1]\n b, total_num = match.shape[0], match.shape[-1]\n scores, indices = torch.topk(mask, int(ratio*total_num), dim=-1) # [B, 1, ratio*tnum]\n select_match = torch.gather(match.transpose(1,2), index=indices.squeeze(1).unsqueeze(-1).repeat(1,1,4), dim=1).transpose(1,2) # [b, 4, ratio*tnum]\n return select_match, scores\n\n\n def forward(self, match, mask, visualizer=None):\n # match: [B, 4, H, W] mask: [B, 1, H, W]\n b, h, w = match.shape[0], match.shape[2], match.shape[3]\n match = match.view([b, 4, -1]).contiguous()\n mask = mask.view([b, 1, -1]).contiguous()\n \n # Sample matches for RANSAC 8-point and best F selection\n top_ratio_match, top_ratio_mask = self.top_ratio_sample(match, mask, ratio=0.20) # [b, 4, ratio*H*W] \n check_match, check_num = self.robust_rand_sample(top_ratio_match, top_ratio_mask, num=self.check_num) # [b, 4, check_num]\n check_match = check_match.contiguous()\n\n cv_f = []\n for i in range(b):\n if self.dataset == 'nyuv2':\n f, m = cv2.findFundamentalMat(check_match[i,:2,:].transpose(0,1).detach().cpu().numpy(), check_match[i,2:,:].transpose(0,1).detach().cpu().numpy(), cv2.FM_LMEDS, 0.99)\n else:\n f, m = cv2.findFundamentalMat(check_match[i,:2,:].transpose(0,1).detach().cpu().numpy(), check_match[i,2:,:].transpose(0,1).detach().cpu().numpy(), cv2.FM_RANSAC, 0.1, 0.99)\n cv_f.append(f)\n cv_f = np.stack(cv_f, axis=0)\n cv_f = torch.from_numpy(cv_f).float().to(match.get_device())\n \n return cv_f\n\n" }, { "path": "core/visualize/__init__.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom visualizer import Visualizer\nfrom visualizer import Visualizer_debug\nfrom profiler import Profiler\n" }, { "path": "core/visualize/profiler.py", "content": "import os\nimport time\nimport torch\nimport pdb\n\nclass Profiler(object):\n def __init__(self, silent=False):\n self.silent = silent\n torch.cuda.synchronize()\n self.start = time.time()\n self.cache_time = self.start\n\n def reset(self, silent=None):\n if silent is None:\n silent = self.silent\n self.__init__(silent=silent)\n\n def report_process(self, process_name):\n if self.silent:\n return None\n torch.cuda.synchronize()\n now = time.time()\n print('{0}\\t: {1:.4f}'.format(process_name, now - self.cache_time))\n self.cache_time = now\n\n def report_all(self, whole_process_name):\n if self.silent:\n return None\n torch.cuda.synchronize()\n now = time.time()\n print('{0}\\t: {1:.4f}'.format(whole_process_name, now - self.start))\n pdb.set_trace()\n\n" }, { "path": "core/visualize/visualizer.py", "content": "import os, sys\nimport numpy as np\nimport cv2\nimport pdb\nimport pickle\nfrom mpl_toolkits import mplot3d\nimport matplotlib.pyplot as plt\nimport PIL.Image as pil\nimport matplotlib as mpl\nimport matplotlib.cm as cm\n\n\ncolorlib = [(0,0,255),(255,0,0),(0,255,0),(255,255,0),(0,255,255),(255,0,255),(0,0,0),(255,255,255)]\n\nclass Visualizer(object):\n def __init__(self, loss_weights_dict, dump_dir=None):\n self.loss_weights_dict = loss_weights_dict\n self.use_flow_error = (self.loss_weights_dict['flow_error'] > 0)\n self.dump_dir = dump_dir\n\n self.log_list = []\n\n def add_log_pack(self, log_pack):\n self.log_list.append(log_pack)\n\n def dump_log(self, fname=None):\n if fname is None:\n fname = self.dump_dir\n with open(fname, 'wb') as f:\n pickle.dump(self.log_list, f)\n\n def print_loss(self, loss_pack, iter_=None):\n loss_pixel = loss_pack['loss_pixel'].mean().detach().cpu().numpy()\n loss_ssim = loss_pack['loss_ssim'].mean().detach().cpu().numpy()\n loss_flow_smooth = loss_pack['loss_flow_smooth'].mean().detach().cpu().numpy()\n loss_flow_consis = loss_pack['loss_flow_consis'].mean().detach().cpu().numpy()\n if 'pt_depth_loss' in loss_pack.keys():\n loss_pt_depth = loss_pack['pt_depth_loss'].mean().detach().cpu().numpy()\n loss_pj_depth = loss_pack['pj_depth_loss'].mean().detach().cpu().numpy()\n loss_depth_smooth = loss_pack['depth_smooth_loss'].mean().detach().cpu().numpy()\n str_= ('iter: {0}, loss_pixel: {1:.6f}, loss_ssim: {2:.6f}, loss_pt_depth: {3:.6f}, loss_pj_depth: {4:.6f}, loss_depth_smooth: {5:.6f}'.format(\\\n iter_, loss_pixel, loss_ssim, loss_pt_depth, loss_pj_depth, loss_depth_smooth))\n if self.use_flow_error:\n loss_flow_error = loss_pack['flow_error'].mean().detach().cpu().numpy()\n str_ = str_ + ', loss_flow_error: {0:.6f}'.format(loss_flow_error)\n print(str_)\n else:\n print('iter: {4}, loss_pixel: {0:.6f}, loss_ssim: {1:.6f}, loss_flow_smooth: {2:.6f}, loss_flow_consis: {3:.6f}'.format(loss_pixel, loss_ssim, loss_flow_smooth, loss_flow_consis, iter_))\n\nclass Visualizer_debug():\n def __init__(self, dump_dir=None, img1=None, img2=None):\n self.dump_dir = dump_dir\n self.img1 = img1\n self.img2 = img2\n \n def draw_point_corres(self, batch_idx, match, name):\n img1 = self.img1[batch_idx]\n img2 = self.img2[batch_idx]\n self.show_corres(img1, img2, match, name)\n print(\"Correspondence Saved in \" + self.dump_dir + '/' + name)\n\n def draw_invalid_corres_ray(self, img1, img2, depth_match, point2d_1_coord, point2d_2_coord, point2d_1_depth, point2d_2_depth, P1, P2):\n # img: [H, W, 3] match: [4, n] point2d_coord: [n, 2] P: [3, 4]\n idx = np.where(point2d_1_depth < 0)[0]\n select_match = depth_match[:, idx]\n self.show_corres(img1, img2, select_match)\n pdb.set_trace()\n \n def draw_epipolar_line(self, batch_idx, match, F, name):\n # img: [H, W, 3] match: [4,n] F: [3,3]\n img1 = self.img1[batch_idx]\n img2 = self.img2[batch_idx]\n self.show_epipolar_line(img1, img2, match, F, name)\n print(\"Epipolar Lines Saved in \" + self.dump_dir + '/' + name)\n\n def show_corres(self, img1, img2, match, name):\n # img: [H, W, 3] match: [4, n]\n cv2.imwrite(os.path.join(self.dump_dir, name+'_img1_cor.png'), img1)\n cv2.imwrite(os.path.join(self.dump_dir, name+'_img2_cor.png'), img2)\n img1 = cv2.imread(os.path.join(self.dump_dir, name+'_img1_cor.png'))\n img2 = cv2.imread(os.path.join(self.dump_dir, name+'_img2_cor.png'))\n n = np.shape(match)[1]\n for i in range(n):\n x1,y1 = match[:2,i]\n x2,y2 = match[2:,i]\n #print((x1, y1))\n #print((x2, y2))\n cv2.circle(img1, (x1,y1), radius=1, color=colorlib[i%len(colorlib)], thickness=2)\n cv2.circle(img2, (x2,y2), radius=1, color=colorlib[i%len(colorlib)], thickness=2)\n cv2.imwrite(os.path.join(self.dump_dir, name+'_img1_cor.png'), img1)\n cv2.imwrite(os.path.join(self.dump_dir, name+'_img2_cor.png'), img2)\n \n def show_mask(self, mask, name):\n # mask: [H, W, 1]\n mask = mask / np.max(mask) * 255.0\n cv2.imwrite(os.path.join(self.dump_dir, name+'.png'), mask)\n \n def save_img(self, img, name):\n cv2.imwrite(os.path.join(self.dump_dir, name+'.png'), img)\n \n def save_depth_img(self, depth, name):\n # depth: [h,w,1]\n minddepth = np.min(depth)\n maxdepth = np.max(depth)\n depth_nor = (depth-minddepth) / (maxdepth-minddepth) * 255.0\n depth_nor = depth_nor.astype(np.uint8)\n cv2.imwrite(os.path.join(self.dump_dir, name+'_depth.png'), depth_nor)\n \n def save_disp_color_img(self, disp, name):\n vmax = np.percentile(disp, 95)\n normalizer = mpl.colors.Normalize(vmin=disp.min(), vmax=vmax)\n mapper = cm.ScalarMappable(norm=normalizer, cmap='magma')\n colormapped_im = (mapper.to_rgba(disp)[:,:,:3] * 255).astype(np.uint8)\n im = pil.fromarray(colormapped_im)\n \n name_dest_im = os.path.join(self.dump_dir, name + '_depth.jpg')\n im.save(name_dest_im)\n\n\n def drawlines(self, img1, img2, lines, pts1, pts2):\n ''' img1 - image on which we draw the epilines for the points in img2\n lines - corresponding epilines '''\n r,c, _ = img1.shape\n for r,pt1,pt2 in zip(lines,pts1,pts2):\n color = tuple(np.random.randint(0,255,3).tolist())\n x0,y0 = map(int, [0, -r[2]/r[1] ])\n x1,y1 = map(int, [c, -(r[2]+r[0]*c)/r[1] ])\n img1 = cv2.line(img1, (x0,y0), (x1,y1), color,1)\n img1 = cv2.circle(img1,tuple(pt1),3,color,-1)\n img2 = cv2.circle(img2,tuple(pt2),3,color,-1)\n return img1,img2\n \n def show_epipolar_line(self, img1, img2, match, F, name):\n # img: [H,W,3] match: [4,n] F: [3,3]\n pts1 = np.transpose(match[:2,:], [1,0])\n pts2 = np.transpose(match[2:,:], [1,0])\n lines1 = cv2.computeCorrespondEpilines(pts2.reshape(-1,1,2), 2,F)\n lines1 = lines1.reshape(-1,3)\n img5,img6 = self.drawlines(img1,img2,lines1,pts1,pts2)\n\n # Find epilines corresponding to points in left image (first image) and\n # drawing its lines on right image\n lines2 = cv2.computeCorrespondEpilines(pts1.reshape(-1,1,2), 1,F)\n lines2 = lines2.reshape(-1,3)\n img3,img4 = self.drawlines(img2,img1,lines2,pts2,pts1)\n\n cv2.imwrite(os.path.join(self.dump_dir, name+'_1eline.png'), img5)\n cv2.imwrite(os.path.join(self.dump_dir, name+'_2eline.png'), img3)\n\n return None\n\n \n def show_ray(self, ax, K, RT, point2d, cmap='Greens'):\n K_inv = np.linalg.inv(K)\n R, T = RT[:,:3], RT[:,3]\n ray_direction = np.matmul(np.matmul(R.T, K_inv), np.array([point2d[0], point2d[1], 1]))\n ray_direction = ray_direction / (np.linalg.norm(ray_direction, ord=2) + 1e-12)\n ray_origin = (-1) * np.matmul(R.T, T)\n\n scatters = [ray_origin + t * ray_direction for t in np.linspace(0.0, 100.0, 1000)]\n scatters = np.stack(scatters, axis=0)\n self.visualize_points(ax, scatters, cmap=cmap)\n self.scatter_3d(ax, scatters[0], scatter_color='r')\n return ray_direction\n\n def visualize_points(self, ax, points, cmap=None):\n # ax.plot3D(points[:,0], points[:,1], points[:,2], c=points[:,2], cmap=cmap)\n # ax.plot3D(points[:,0], points[:,1], points[:,2], c=points[:,2])\n ax.plot3D(points[:,0], points[:,1], points[:,2])\n\n def scatter_3d(self, ax, point, scatter_color='r'):\n ax.scatter(point[0], point[1], point[2], c=scatter_color)\n\n def visualize_two_rays(self, ax, match, P1, P2):\n # match: [4] P: [3,4]\n K = P1[:,:3] # the first P1 has identity rotation matrix and zero translation.\n K_inv = np.linalg.inv(K)\n RT1, RT2 = np.matmul(K_inv, P1), np.matmul(K_inv, P2) \n x1, y1, x2, y2 = match\n d1 = self.show_ray(ax, K, RT1, [x1, y1], cmap='Greens')\n d2 = self.show_ray(ax, K, RT2, [x2, y2], cmap='Reds')\n print(np.dot(d1.squeeze(), d2.squeeze()))\n \nif __name__ == '__main__':\n img1 = cv2.imread('./vis/ga.png')\n img2 = cv2.imread('./vis/gb.png')\n match = np.load('./vis/gmatch.npy')\n print(np.shape(img1))\n match = np.reshape(match, [4,-1])\n select_match = match[:,np.random.randint(200000, size=100)]\n show_corres(img1, img2, select_match)\n" }, { "path": "data/eigen/export_gt_depth.py", "content": "# Copyright Niantic 2019. Patent Pending. All rights reserved.\n#\n# This software is licensed under the terms of the Monodepth2 licence\n# which allows for non-commercial use only, the full terms of which are made\n# available in the LICENSE file.\n\nfrom __future__ import absolute_import, division, print_function\n\nimport os\n\nimport argparse\nimport numpy as np\nimport PIL.Image as pil\nimport os\nimport numpy as np\nfrom collections import Counter\n\n\ndef load_velodyne_points(filename):\n \"\"\"Load 3D point cloud from KITTI file format\n (adapted from https://github.com/hunse/kitti)\n \"\"\"\n points = np.fromfile(filename, dtype=np.float32).reshape(-1, 4)\n points[:, 3] = 1.0 # homogeneous\n return points\n\n\ndef read_calib_file(path):\n \"\"\"Read KITTI calibration file\n (from https://github.com/hunse/kitti)\n \"\"\"\n float_chars = set(\"0123456789.e+- \")\n data = {}\n with open(path, 'r') as f:\n for line in f.readlines():\n key, value = line.split(':', 1)\n value = value.strip()\n data[key] = value\n if float_chars.issuperset(value):\n # try to cast to float array\n try:\n data[key] = np.array(list(map(float, value.split(' '))))\n except ValueError:\n # casting error: data[key] already eq. value, so pass\n pass\n\n return data\n\n\ndef sub2ind(matrixSize, rowSub, colSub):\n \"\"\"Convert row, col matrix subscripts to linear indices\n \"\"\"\n m, n = matrixSize\n return rowSub * (n-1) + colSub - 1\n\n\ndef generate_depth_map(calib_dir, velo_filename, cam=2, vel_depth=False):\n \"\"\"Generate a depth map from velodyne data\n \"\"\"\n # load calibration files\n cam2cam = read_calib_file(os.path.join(calib_dir, 'calib_cam_to_cam.txt'))\n velo2cam = read_calib_file(os.path.join(calib_dir, 'calib_velo_to_cam.txt'))\n velo2cam = np.hstack((velo2cam['R'].reshape(3, 3), velo2cam['T'][..., np.newaxis]))\n velo2cam = np.vstack((velo2cam, np.array([0, 0, 0, 1.0])))\n\n # get image shape\n im_shape = cam2cam[\"S_rect_02\"][::-1].astype(np.int32)\n\n # compute projection matrix velodyne->image plane\n R_cam2rect = np.eye(4)\n R_cam2rect[:3, :3] = cam2cam['R_rect_00'].reshape(3, 3)\n P_rect = cam2cam['P_rect_0'+str(cam)].reshape(3, 4)\n P_velo2im = np.dot(np.dot(P_rect, R_cam2rect), velo2cam)\n\n # load velodyne points and remove all behind image plane (approximation)\n # each row of the velodyne data is forward, left, up, reflectance\n velo = load_velodyne_points(velo_filename)\n velo = velo[velo[:, 0] >= 0, :]\n\n # project the points to the camera\n velo_pts_im = np.dot(P_velo2im, velo.T).T\n velo_pts_im[:, :2] = velo_pts_im[:, :2] / velo_pts_im[:, 2][..., np.newaxis]\n\n if vel_depth:\n velo_pts_im[:, 2] = velo[:, 0]\n\n # check if in bounds\n # use minus 1 to get the exact same value as KITTI matlab code\n velo_pts_im[:, 0] = np.round(velo_pts_im[:, 0]) - 1\n velo_pts_im[:, 1] = np.round(velo_pts_im[:, 1]) - 1\n val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0)\n val_inds = val_inds & (velo_pts_im[:, 0] < im_shape[1]) & (velo_pts_im[:, 1] < im_shape[0])\n velo_pts_im = velo_pts_im[val_inds, :]\n\n # project to image\n depth = np.zeros((im_shape[:2]))\n depth[velo_pts_im[:, 1].astype(np.int), velo_pts_im[:, 0].astype(np.int)] = velo_pts_im[:, 2]\n\n # find the duplicate points and choose the closest depth\n inds = sub2ind(depth.shape, velo_pts_im[:, 1], velo_pts_im[:, 0])\n dupe_inds = [item for item, count in Counter(inds).items() if count > 1]\n for dd in dupe_inds:\n pts = np.where(inds == dd)[0]\n x_loc = int(velo_pts_im[pts[0], 0])\n y_loc = int(velo_pts_im[pts[0], 1])\n depth[y_loc, x_loc] = velo_pts_im[pts, 2].min()\n depth[depth < 0] = 0\n\n return depth\n\n\ndef export_gt_depths_kitti():\n\n parser = argparse.ArgumentParser(description='export_gt_depth')\n\n parser.add_argument('--data_path',\n type=str,\n help='path to the root of the KITTI data',\n required=True)\n opt = parser.parse_args()\n\n f = open(\"test_files.txt\", 'r')\n lines = f.read().splitlines()\n\n print(\"Exporting ground truth depths for eigen\")\n\n gt_depths = []\n for line in lines:\n\n folder, frame_id, _ = line.split()\n frame_id = int(frame_id)\n\n calib_dir = os.path.join(opt.data_path, folder.split(\"/\")[0])\n velo_filename = os.path.join(opt.data_path, folder,\n \"velodyne_points/data\", \"{:010d}.bin\".format(frame_id))\n gt_depth = generate_depth_map(calib_dir, velo_filename, 2, True)\n\n gt_depths.append(gt_depth.astype(np.float32))\n\n output_path = \"gt_depths.npz\"\n\n print(\"Saving to eigen\")\n\n np.savez_compressed(output_path, data=np.array(gt_depths))\n\n\nif __name__ == \"__main__\":\n export_gt_depths_kitti()\n" }, { "path": "data/eigen/static_frames.txt", "content": "2011_09_26 2011_09_26_drive_0009_sync 0000000386\n2011_09_26 2011_09_26_drive_0009_sync 0000000387\n2011_09_26 2011_09_26_drive_0009_sync 0000000390\n2011_09_26 2011_09_26_drive_0009_sync 0000000391\n2011_09_26 2011_09_26_drive_0009_sync 0000000392\n2011_09_26 2011_09_26_drive_0009_sync 0000000394\n2011_09_26 2011_09_26_drive_0009_sync 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0000000416\n2011_09_26 2011_09_26_drive_0009_sync 0000000417\n2011_09_26 2011_09_26_drive_0009_sync 0000000418\n2011_09_26 2011_09_26_drive_0009_sync 0000000419\n2011_09_26 2011_09_26_drive_0009_sync 0000000420\n2011_09_26 2011_09_26_drive_0009_sync 0000000421\n2011_09_26 2011_09_26_drive_0009_sync 0000000422\n2011_09_26 2011_09_26_drive_0009_sync 0000000423\n2011_09_26 2011_09_26_drive_0009_sync 0000000424\n2011_09_26 2011_09_26_drive_0009_sync 0000000425\n2011_09_26 2011_09_26_drive_0009_sync 0000000426\n2011_09_26 2011_09_26_drive_0009_sync 0000000427\n2011_09_26 2011_09_26_drive_0009_sync 0000000428\n2011_09_26 2011_09_26_drive_0009_sync 0000000429\n2011_09_26 2011_09_26_drive_0009_sync 0000000430\n2011_09_26 2011_09_26_drive_0009_sync 0000000431\n2011_09_26 2011_09_26_drive_0009_sync 0000000432\n2011_09_26 2011_09_26_drive_0009_sync 0000000433\n2011_09_26 2011_09_26_drive_0009_sync 0000000434\n2011_09_26 2011_09_26_drive_0009_sync 0000000435\n2011_09_26 2011_09_26_drive_0009_sync 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l\n2011_09_30/2011_09_30_drive_0016_sync 0000000066 l\n2011_09_30/2011_09_30_drive_0016_sync 0000000000 l\n2011_09_30/2011_09_30_drive_0016_sync 0000000165 l\n2011_09_30/2011_09_30_drive_0016_sync 0000000264 l\n2011_09_30/2011_09_30_drive_0016_sync 0000000253 l\n2011_09_30/2011_09_30_drive_0016_sync 0000000209 l\n2011_09_30/2011_09_30_drive_0016_sync 0000000121 l\n2011_09_30/2011_09_30_drive_0018_sync 0000000107 l\n2011_09_30/2011_09_30_drive_0018_sync 0000002247 l\n2011_09_30/2011_09_30_drive_0018_sync 0000001391 l\n2011_09_30/2011_09_30_drive_0018_sync 0000000535 l\n2011_09_30/2011_09_30_drive_0018_sync 0000001819 l\n2011_09_30/2011_09_30_drive_0018_sync 0000001177 l\n2011_09_30/2011_09_30_drive_0018_sync 0000000428 l\n2011_09_30/2011_09_30_drive_0018_sync 0000001926 l\n2011_09_30/2011_09_30_drive_0018_sync 0000000749 l\n2011_09_30/2011_09_30_drive_0018_sync 0000001284 l\n2011_09_30/2011_09_30_drive_0018_sync 0000002140 l\n2011_09_30/2011_09_30_drive_0018_sync 0000001605 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l\n2011_09_30/2011_09_30_drive_0027_sync 0000000164 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000794 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000328 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000615 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000917 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000369 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000287 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000123 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000876 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000410 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000492 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000958 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000656 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000000 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000753 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000574 l\n2011_09_30/2011_09_30_drive_0027_sync 0000001081 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000041 l\n2011_09_30/2011_09_30_drive_0027_sync 0000000246 l\n2011_10_03/2011_10_03_drive_0027_sync 0000002906 l\n2011_10_03/2011_10_03_drive_0027_sync 0000002544 l\n2011_10_03/2011_10_03_drive_0027_sync 0000000362 l\n2011_10_03/2011_10_03_drive_0027_sync 0000004535 l\n2011_10_03/2011_10_03_drive_0027_sync 0000000734 l\n2011_10_03/2011_10_03_drive_0027_sync 0000001096 l\n2011_10_03/2011_10_03_drive_0027_sync 0000004173 l\n2011_10_03/2011_10_03_drive_0027_sync 0000000543 l\n2011_10_03/2011_10_03_drive_0027_sync 0000001277 l\n2011_10_03/2011_10_03_drive_0027_sync 0000004354 l\n2011_10_03/2011_10_03_drive_0027_sync 0000001458 l\n2011_10_03/2011_10_03_drive_0027_sync 0000001820 l\n2011_10_03/2011_10_03_drive_0027_sync 0000003449 l\n2011_10_03/2011_10_03_drive_0027_sync 0000003268 l\n2011_10_03/2011_10_03_drive_0027_sync 0000000915 l\n2011_10_03/2011_10_03_drive_0027_sync 0000002363 l\n2011_10_03/2011_10_03_drive_0027_sync 0000002725 l\n2011_10_03/2011_10_03_drive_0027_sync 0000000181 l\n2011_10_03/2011_10_03_drive_0027_sync 0000001639 l\n2011_10_03/2011_10_03_drive_0027_sync 0000003992 l\n2011_10_03/2011_10_03_drive_0027_sync 0000003087 l\n2011_10_03/2011_10_03_drive_0027_sync 0000002001 l\n2011_10_03/2011_10_03_drive_0027_sync 0000003811 l\n2011_10_03/2011_10_03_drive_0027_sync 0000003630 l\n2011_10_03/2011_10_03_drive_0027_sync 0000000000 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000096 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000800 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000320 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000576 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000000 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000480 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000640 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000032 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000384 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000160 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000704 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000736 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000672 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000064 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000288 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000352 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000512 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000544 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000608 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000128 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000224 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000416 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000192 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000448 l\n2011_10_03/2011_10_03_drive_0047_sync 0000000768 l\n" }, { "path": "data/eigen/test_scenes.txt", "content": "2011_09_26_drive_0117\n2011_09_28_drive_0002\n2011_09_26_drive_0052\n2011_09_30_drive_0016\n2011_09_26_drive_0059\n2011_09_26_drive_0027\n2011_09_26_drive_0020\n2011_09_26_drive_0009\n2011_09_26_drive_0013\n2011_09_26_drive_0101\n2011_09_26_drive_0046\n2011_09_26_drive_0029\n2011_09_26_drive_0064\n2011_09_26_drive_0048\n2011_10_03_drive_0027\n2011_09_26_drive_0002\n2011_09_26_drive_0036\n2011_09_29_drive_0071\n2011_10_03_drive_0047\n2011_09_30_drive_0027\n2011_09_26_drive_0086\n2011_09_26_drive_0084\n2011_09_26_drive_0096\n2011_09_30_drive_0018\n2011_09_26_drive_0106\n2011_09_26_drive_0056\n2011_09_26_drive_0023\n2011_09_26_drive_0093" }, { "path": "infer_vo.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom core.networks.model_depth_pose import Model_depth_pose\nfrom core.networks.model_flow import Model_flow\nfrom visualizer import *\nfrom profiler import Profiler\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport pdb\nfrom sklearn import linear_model\nimport yaml\nimport warnings\nimport copy\nfrom collections import OrderedDict\nwarnings.filterwarnings(\"ignore\")\n\ndef save_traj(path, poses):\n \"\"\"\n path: file path of saved poses\n poses: list of global poses\n \"\"\"\n f = open(path, 'w')\n for i in range(len(poses)):\n pose = poses[i].flatten()[:12] # [3x4]\n line = \" \".join([str(j) for j in pose])\n f.write(line + '\\n')\n print('Trajectory Saved.')\n\ndef projection(xy, points, h_max, w_max):\n # Project the triangulation points to depth map. Directly correspondence mapping rather than projection.\n # xy: [N, 2] points: [3, N]\n depth = np.zeros((h_max, w_max))\n xy_int = np.around(xy).astype('int')\n\n # Ensure all the correspondences are inside the image.\n y_idx = (xy_int[:, 0] >= 0) * (xy_int[:, 0] < w_max)\n x_idx = (xy_int[:, 1] >= 0) * (xy_int[:, 1] < h_max)\n idx = y_idx * x_idx\n xy_int = xy_int[idx]\n points_valid = points[:, idx]\n\n depth[xy_int[:, 1], xy_int[:, 0]] = points_valid[2]\n return depth\n\ndef unprojection(xy, depth, K):\n # xy: [N, 2] image coordinates of match points\n # depth: [N] depth value of match points\n N = xy.shape[0]\n # initialize regular grid\n ones = np.ones((N, 1))\n xy_h = np.concatenate([xy, ones], axis=1)\n xy_h = np.transpose(xy_h, (1,0)) # [3, N]\n #depth = np.transpose(depth, (1,0)) # [1, N]\n \n K_inv = np.linalg.inv(K)\n points = np.matmul(K_inv, xy_h) * depth\n points = np.transpose(points) # [N, 3]\n return points\n\ndef cv_triangulation(matches, pose):\n # matches: [N, 4], the correspondence xy coordinates\n # pose: [4, 4], the relative pose trans from 1 to 2\n xy1 = matches[:, :2].transpose()\n xy2 = matches[:, 2:].transpose() # [2, N]\n pose1 = np.eye(4)\n pose2 = pose1 @ pose\n points = cv2.triangulatePoints(pose1[:3], pose2[:3], xy1, xy2)\n points /= points[3]\n\n points1 = pose1[:3] @ points\n points2 = pose2[:3] @ points\n return points1, points2\n\nclass infer_vo():\n def __init__(self, seq_id, sequences_root_dir):\n self.img_dir = sequences_root_dir\n #self.img_dir = '/home4/zhaow/data/kitti_odometry/sampled_s4_sequences/'\n self.seq_id = seq_id\n self.raw_img_h = 370.0#320\n self.raw_img_w = 1226.0#1024\n self.new_img_h = 256#320\n self.new_img_w = 832#1024\n self.max_depth = 50.0\n self.min_depth = 0.0\n self.cam_intrinsics = self.read_rescale_camera_intrinsics(os.path.join(self.img_dir, seq_id) + '/calib.txt')\n self.flow_pose_ransac_thre = 0.1 #0.2\n self.flow_pose_ransac_times = 10 #5\n self.flow_pose_min_flow = 5\n self.align_ransac_min_samples = 3\n self.align_ransac_max_trials = 100\n self.align_ransac_stop_prob = 0.99\n self.align_ransac_thre = 1.0\n self.PnP_ransac_iter = 1000\n self.PnP_ransac_thre = 1\n self.PnP_ransac_times = 5\n \n def read_rescale_camera_intrinsics(self, path):\n raw_img_h = self.raw_img_h\n raw_img_w = self.raw_img_w\n new_img_h = self.new_img_h\n new_img_w = self.new_img_w\n with open(path, 'r') as f:\n lines = f.readlines()\n data = lines[-1].strip('\\n').split(' ')[1:]\n data = [float(k) for k in data]\n data = np.array(data).reshape(3,4)\n cam_intrinsics = data[:3,:3]\n cam_intrinsics[0,:] = cam_intrinsics[0,:] * new_img_w / raw_img_w\n cam_intrinsics[1,:] = cam_intrinsics[1,:] * new_img_h / raw_img_h\n return cam_intrinsics\n \n def load_images(self):\n path = self.img_dir\n seq = self.seq_id\n new_img_h = self.new_img_h\n new_img_w = self.new_img_w\n seq_dir = os.path.join(path, seq)\n image_dir = os.path.join(seq_dir, 'image_2')\n num = len(os.listdir(image_dir))\n images = []\n for i in range(num):\n image = cv2.imread(os.path.join(image_dir, '%.6d'%i)+'.png')\n image = cv2.resize(image, (new_img_w, new_img_h))\n images.append(image)\n return images\n \n def get_prediction(self, img1, img2, model, K, K_inv, match_num):\n # img1: [3,H,W] K: [3,3]\n #visualizer = Visualizer_debug('/home3/zhaow/TrianFlow-pytorch/vis/')\n img1_t = torch.from_numpy(np.transpose(img1 / 255.0, [2,0,1])).cuda().float().unsqueeze(0)\n img2_t = torch.from_numpy(np.transpose(img2 / 255.0, [2,0,1])).cuda().float().unsqueeze(0)\n K = torch.from_numpy(K).cuda().float().unsqueeze(0)\n K_inv = torch.from_numpy(K_inv).cuda().float().unsqueeze(0)\n\n filt_depth_match, depth1, depth2 = model.infer_vo(img1_t, img2_t, K, K_inv, match_num)\n return filt_depth_match[0].transpose(0,1).cpu().detach().numpy(), depth1[0].squeeze(0).cpu().detach().numpy(), depth2[0].squeeze(0).cpu().detach().numpy()\n\n \n def process_video(self, images, model):\n '''Process a sequence to get scale consistent trajectory results. \n Register according to depth net predictions. Here we assume depth predictions have consistent scale.\n If not, pleas use process_video_tri which only use triangulated depth to get self-consistent scaled pose.\n '''\n poses = []\n global_pose = np.eye(4)\n # The first one global pose is origin.\n poses.append(copy.deepcopy(global_pose))\n seq_len = len(images)\n K = self.cam_intrinsics\n K_inv = np.linalg.inv(self.cam_intrinsics)\n for i in range(seq_len-1):\n img1, img2 = images[i], images[i+1]\n depth_match, depth1, depth2 = self.get_prediction(img1, img2, model, K, K_inv, match_num=5000)\n \n rel_pose = np.eye(4)\n flow_pose = self.solve_pose_flow(depth_match[:,:2], depth_match[:,2:])\n rel_pose[:3,:3] = copy.deepcopy(flow_pose[:3,:3])\n if np.linalg.norm(flow_pose[:3,3:]) != 0:\n scale = self.align_to_depth(depth_match[:,:2], depth_match[:,2:], flow_pose, depth2)\n rel_pose[:3,3:] = flow_pose[:3,3:] * scale\n \n if np.linalg.norm(flow_pose[:3,3:]) == 0 or scale == -1:\n print('PnP '+str(i))\n pnp_pose = self.solve_pose_pnp(depth_match[:,:2], depth_match[:,2:], depth1)\n rel_pose = pnp_pose\n\n global_pose[:3,3:] = np.matmul(global_pose[:3,:3], rel_pose[:3,3:]) + global_pose[:3,3:]\n global_pose[:3,:3] = np.matmul(global_pose[:3,:3], rel_pose[:3,:3])\n poses.append(copy.deepcopy(global_pose))\n print(i)\n return poses\n \n def normalize_coord(self, xy, K):\n xy_norm = copy.deepcopy(xy)\n xy_norm[:,0] = (xy[:,0] - K[0,2]) / K[0,0]\n xy_norm[:,1] = (xy[:,1] - K[1,2]) / K[1,1]\n\n return xy_norm\n \n def align_to_depth(self, xy1, xy2, pose, depth2):\n # Align the translation scale according to triangulation depth\n # xy1, xy2: [N, 2] pose: [4, 4] depth2: [H, W]\n \n # Triangulation\n img_h, img_w = np.shape(depth2)[0], np.shape(depth2)[1]\n pose_inv = np.linalg.inv(pose)\n\n xy1_norm = self.normalize_coord(xy1, self.cam_intrinsics)\n xy2_norm = self.normalize_coord(xy2, self.cam_intrinsics)\n\n points1_tri, points2_tri = cv_triangulation(np.concatenate([xy1_norm, xy2_norm], axis=1), pose_inv)\n \n depth2_tri = projection(xy2, points2_tri, img_h, img_w)\n depth2_tri[depth2_tri < 0] = 0\n \n # Remove negative depths\n valid_mask = (depth2 > 0) * (depth2_tri > 0)\n depth_pred_valid = depth2[valid_mask]\n depth_tri_valid = depth2_tri[valid_mask]\n \n if np.sum(valid_mask) > 100:\n scale_reg = linear_model.RANSACRegressor(base_estimator=linear_model.LinearRegression(fit_intercept=False), min_samples=self.align_ransac_min_samples, \\\n max_trials=self.align_ransac_max_trials, stop_probability=self.align_ransac_stop_prob, residual_threshold=self.align_ransac_thre)\n scale_reg.fit(depth_tri_valid.reshape(-1, 1), depth_pred_valid.reshape(-1, 1))\n scale = scale_reg.estimator_.coef_[0, 0]\n else:\n scale = -1\n\n return scale\n \n def solve_pose_pnp(self, xy1, xy2, depth1):\n # Use pnp to solve relative poses.\n # xy1, xy2: [N, 2] depth1: [H, W]\n\n img_h, img_w = np.shape(depth1)[0], np.shape(depth1)[1]\n \n # Ensure all the correspondences are inside the image.\n x_idx = (xy2[:, 0] >= 0) * (xy2[:, 0] < img_w)\n y_idx = (xy2[:, 1] >= 0) * (xy2[:, 1] < img_h)\n idx = y_idx * x_idx\n xy1 = xy1[idx]\n xy2 = xy2[idx]\n\n xy1_int = xy1.astype(np.int)\n sample_depth = depth1[xy1_int[:,1], xy1_int[:,0]]\n valid_depth_mask = (sample_depth < self.max_depth) * (sample_depth > self.min_depth)\n\n xy1 = xy1[valid_depth_mask]\n xy2 = xy2[valid_depth_mask]\n\n # Unproject to 3d space\n points1 = unprojection(xy1, sample_depth[valid_depth_mask], self.cam_intrinsics)\n\n # ransac\n best_rt = []\n max_inlier_num = 0\n max_ransac_iter = self.PnP_ransac_times\n \n for i in range(max_ransac_iter):\n if xy2.shape[0] > 4:\n flag, r, t, inlier = cv2.solvePnPRansac(objectPoints=points1, imagePoints=xy2, cameraMatrix=self.cam_intrinsics, distCoeffs=None, iterationsCount=self.PnP_ransac_iter, reprojectionError=self.PnP_ransac_thre)\n if flag and inlier.shape[0] > max_inlier_num:\n best_rt = [r, t]\n max_inlier_num = inlier.shape[0]\n pose = np.eye(4)\n if len(best_rt) != 0:\n r, t = best_rt\n pose[:3,:3] = cv2.Rodrigues(r)[0]\n pose[:3,3:] = t\n pose = np.linalg.inv(pose)\n return pose\n \n def solve_pose_flow(self, xy1, xy2):\n # Solve essential matrix to find relative pose from flow.\n\n # ransac\n best_rt = []\n max_inlier_num = 0\n max_ransac_iter = self.flow_pose_ransac_times\n best_inliers = np.ones((xy1.shape[0])) == 1\n pp = (self.cam_intrinsics[0,2], self.cam_intrinsics[1,2])\n \n # flow magnitude\n avg_flow = np.mean(np.linalg.norm(xy1 - xy2, axis=1))\n if avg_flow > self.flow_pose_min_flow:\n for i in range(max_ransac_iter):\n E, inliers = cv2.findEssentialMat(xy2, xy1, focal=self.cam_intrinsics[0,0], pp=pp, method=cv2.RANSAC, prob=0.99, threshold=self.flow_pose_ransac_thre)\n cheirality_cnt, R, t, _ = cv2.recoverPose(E, xy2, xy1, focal=self.cam_intrinsics[0,0], pp=pp)\n if inliers.sum() > max_inlier_num and cheirality_cnt > 50:\n best_rt = [R, t]\n max_inlier_num = inliers.sum()\n best_inliers = inliers\n if len(best_rt) == 0:\n R = np.eye(3)\n t = np.zeros((3,1))\n best_rt = [R, t]\n else:\n R = np.eye(3)\n t = np.zeros((3,1))\n best_rt = [R, t]\n R, t = best_rt\n pose = np.eye(4)\n pose[:3,:3] = R\n pose[:3,3:] = t\n return pose\n\n\nif __name__ == '__main__':\n import argparse\n arg_parser = argparse.ArgumentParser(\n description=\"TrianFlow training pipeline.\"\n )\n arg_parser.add_argument('-c', '--config_file', default=None, help='config file.')\n arg_parser.add_argument('-g', '--gpu', type=str, default=0, help='gpu id.')\n arg_parser.add_argument('--mode', type=str, default='flow', help='training mode.')\n arg_parser.add_argument('--traj_save_dir_txt', type=str, default=None, help='directory for saving results')\n arg_parser.add_argument('--sequences_root_dir', type=str, default=None, help='directory for test sequences')\n arg_parser.add_argument('--sequence', type=str, default='09', help='Test sequence id.')\n arg_parser.add_argument('--pretrained_model', type=str, default=None, help='directory for loading pretrained models')\n args = arg_parser.parse_args()\n\n with open(args.config_file, 'r') as f:\n cfg = yaml.safe_load(f)\n cfg['dataset'] = 'kitti_odo'\n # copy attr into cfg\n for attr in dir(args):\n if attr[:2] != '__':\n cfg[attr] = getattr(args, attr)\n class pObject(object):\n def __init__(self):\n pass\n cfg_new = pObject()\n for attr in list(cfg.keys()):\n setattr(cfg_new, attr, cfg[attr])\n \n os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)\n\n model = Model_depth_pose(cfg_new)\n model.cuda()\n weights = torch.load(args.pretrained_model)\n model.load_state_dict(weights['model_state_dict'])\n \n model.eval()\n print('Model Loaded.')\n\n print('Testing VO.')\n vo_test = infer_vo(args.sequence, args.sequences_root_dir)\n images = vo_test.load_images()\n print('Images Loaded. Total ' + str(len(images)) + ' images found.')\n poses = vo_test.process_video(images, model)\n print('Test completed.')\n\n traj_txt = args.traj_save_dir_txt\n save_traj(traj_txt, poses)\n" }, { "path": "requirements.txt", "content": "cffi==1.12.3\ncycler==0.10.0\nCython==0.29.13\ndecorator==4.4.0\neasydict==1.9\nh5py==2.10.0\nimageio==2.5.0\njoblib==0.14.0\nkiwisolver==1.1.0\nmatplotlib==3.1.1\nnetworkx==2.3\nnumpy==1.17.2\nopencv-python==4.2.0.32\nPillow==9.0.1\nprotobuf==3.15.0\npycparser==2.19\npyparsing==2.4.2\npypng==0.0.20\npython-dateutil==2.8.0\nPyWavelets==1.0.3\nPyYAML==5.4\nscikit-image==0.15.0\nscikit-learn==0.21.3\nscipy==1.3.1\nsix==1.12.0\nsklearn\ntensorboardX==2.0\ntorch==1.2.0\ntorchvision==0.4.0\ntqdm==4.36.1\n" }, { "path": "test.py", "content": "import os, sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom core.dataset import KITTI_2012, KITTI_2015\nfrom core.evaluation import eval_flow_avg, load_gt_flow_kitti\nfrom core.evaluation import eval_depth\nfrom core.visualize import Visualizer_debug\nfrom core.networks import Model_depth_pose, Model_flow, Model_flowposenet\nfrom core.evaluation import load_gt_flow_kitti, load_gt_mask\nimport torch\nfrom tqdm import tqdm\nimport pdb\nimport cv2\nimport numpy as np\nimport yaml\n\ndef test_kitti_2012(cfg, model, gt_flows, noc_masks):\n dataset = KITTI_2012(cfg.gt_2012_dir)\n flow_list = []\n for idx, inputs in enumerate(tqdm(dataset)):\n img, K, K_inv = inputs\n img = img[None,:,:,:]\n K = K[None,:,:]\n K_inv = K_inv[None,:,:]\n img_h = int(img.shape[2] / 2)\n img1, img2 = img[:,:,:img_h,:], img[:,:,img_h:,:]\n img1, img2, K, K_inv = img1.cuda(), img2.cuda(), K.cuda(), K_inv.cuda()\n if cfg.mode == 'flow' or cfg.mode == 'flowposenet':\n flow = model.inference_flow(img1, img2)\n else:\n flow, _, _, _, _, _ = model.inference(img1, img2, K, K_inv)\n #pdb.set_trace()\n flow = flow[0].detach().cpu().numpy()\n flow = flow.transpose(1,2,0)\n flow_list.append(flow)\n \n eval_flow_res = eval_flow_avg(gt_flows, noc_masks, flow_list, cfg, write_img=False)\n \n print('CONFIG: {0}, mode: {1}'.format(cfg.config_file, cfg.mode))\n print('[EVAL] [KITTI 2012]')\n print(eval_flow_res)\n return eval_flow_res\n\ndef test_kitti_2015(cfg, model, gt_flows, noc_masks, gt_masks, depth_save_dir=None):\n dataset = KITTI_2015(cfg.gt_2015_dir)\n visualizer = Visualizer_debug(depth_save_dir)\n pred_flow_list = []\n pred_disp_list = []\n img_list = []\n for idx, inputs in enumerate(tqdm(dataset)):\n img, K, K_inv = inputs\n img = img[None,:,:,:]\n K = K[None,:,:]\n K_inv = K_inv[None,:,:]\n img_h = int(img.shape[2] / 2)\n img1, img2 = img[:,:,:img_h,:], img[:,:,img_h:,:]\n img_list.append(img1)\n img1, img2, K, K_inv = img1.cuda(), img2.cuda(), K.cuda(), K_inv.cuda()\n if cfg.mode == 'flow' or cfg.mode == 'flowposenet':\n flow = model.inference_flow(img1, img2)\n else:\n flow, disp1, disp2, Rt, _, _ = model.inference(img1, img2, K, K_inv)\n disp = disp1[0].detach().cpu().numpy()\n disp = disp.transpose(1,2,0)\n pred_disp_list.append(disp)\n\n flow = flow[0].detach().cpu().numpy()\n flow = flow.transpose(1,2,0)\n pred_flow_list.append(flow)\n \n #pdb.set_trace()\n eval_flow_res = eval_flow_avg(gt_flows, noc_masks, pred_flow_list, cfg, moving_masks=gt_masks, write_img=False)\n print('CONFIG: {0}, mode: {1}'.format(cfg.config_file, cfg.mode))\n print('[EVAL] [KITTI 2015]')\n print(eval_flow_res)\n ## depth evaluation\n return eval_flow_res\n\ndef disp2depth(disp, min_depth=0.001, max_depth=80.0):\n min_disp = 1 / max_depth\n max_disp = 1 / min_depth\n scaled_disp = min_disp + (max_disp - min_disp) * disp\n depth = 1 / scaled_disp\n return scaled_disp, depth\n\ndef resize_depths(gt_depth_list, pred_disp_list):\n gt_disp_list = []\n pred_depth_list = []\n pred_disp_resized = []\n for i in range(len(pred_disp_list)):\n h, w = gt_depth_list[i].shape\n pred_disp = cv2.resize(pred_disp_list[i], (w,h))\n pred_depth = 1.0 / (pred_disp + 1e-4)\n pred_depth_list.append(pred_depth)\n pred_disp_resized.append(pred_disp)\n \n return pred_depth_list, pred_disp_resized\n\n\ndef test_eigen_depth(cfg, model):\n print('Evaluate depth using eigen split. Using model in ' + cfg.model_dir)\n filenames = open('./data/eigen/test_files.txt').readlines()\n pred_disp_list = []\n for i in range(len(filenames)):\n path1, idx, _ = filenames[i].strip().split(' ')\n img = cv2.imread(os.path.join(os.path.join(cfg.raw_base_dir, path1), 'image_02/data/'+str(idx)+'.png'))\n #img_resize = cv2.resize(img, (832,256))\n img_resize = cv2.resize(img, (cfg.img_hw[1], cfg.img_hw[0]))\n img_input = torch.from_numpy(img_resize / 255.0).float().cuda().unsqueeze(0).permute(0,3,1,2)\n disp = model.infer_depth(img_input)\n disp = disp[0].detach().cpu().numpy()\n disp = disp.transpose(1,2,0)\n pred_disp_list.append(disp)\n #print(i)\n \n gt_depths = np.load('./data/eigen/gt_depths.npz', allow_pickle=True)['data']\n pred_depths, pred_disp_resized = resize_depths(gt_depths, pred_disp_list)\n eval_depth_res = eval_depth(gt_depths, pred_depths)\n abs_rel, sq_rel, rms, log_rms, a1, a2, a3 = eval_depth_res\n sys.stderr.write(\n \"{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10} \\n\".\n format('abs_rel', 'sq_rel', 'rms', 'log_rms',\n 'a1', 'a2', 'a3'))\n sys.stderr.write(\n \"{:10.4f}, {:10.4f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f} \\n\".\n format(abs_rel, sq_rel, rms, log_rms, a1, a2, a3))\n\n return eval_depth_res\n\n\ndef resize_disp(pred_disp_list, gt_depths):\n pred_depths = []\n h, w = gt_depths[0].shape[0], gt_depths[0].shape[1]\n for i in range(len(pred_disp_list)):\n disp = pred_disp_list[i]\n resize_disp = cv2.resize(disp, (w,h))\n depth = 1.0 / resize_disp\n pred_depths.append(depth)\n \n return pred_depths\n\nimport h5py\nimport scipy.io as sio\ndef load_nyu_test_data(data_dir):\n data = h5py.File(os.path.join(data_dir, 'nyu_depth_v2_labeled.mat'), 'r')\n splits = sio.loadmat(os.path.join(data_dir, 'splits.mat'))\n test = np.array(splits['testNdxs']).squeeze(1)\n images = np.transpose(data['images'], [0,1,3,2])\n depths = np.transpose(data['depths'], [0,2,1])\n images = images[test-1]\n depths = depths[test-1]\n return images, depths\n\ndef test_nyu(cfg, model, test_images, test_gt_depths):\n leng = test_images.shape[0]\n print('Test nyu depth on '+str(leng)+' images. Using depth model in '+cfg.model_dir)\n pred_disp_list = []\n crop_imgs = []\n crop_gt_depths = []\n for i in range(leng):\n img = test_images[i]\n img_crop = img[:,45:472,41:602]\n crop_imgs.append(img_crop)\n gt_depth_crop = test_gt_depths[i][45:472,41:602]\n crop_gt_depths.append(gt_depth_crop)\n #img = np.transpose(cv2.resize(np.transpose(img_crop, [1,2,0]), (576,448)), [2,0,1])\n img = np.transpose(cv2.resize(np.transpose(img_crop, [1,2,0]), (cfg.img_hw[1],cfg.img_hw[0])), [2,0,1])\n img_t = torch.from_numpy(img).float().cuda().unsqueeze(0) / 255.0\n disp = model.infer_depth(img_t)\n disp = np.transpose(disp[0].cpu().detach().numpy(), [1,2,0])\n pred_disp_list.append(disp)\n \n pred_depths = resize_disp(pred_disp_list, crop_gt_depths)\n eval_depth_res = eval_depth(crop_gt_depths, pred_depths, nyu=True)\n abs_rel, sq_rel, rms, log_rms, a1, a2, a3 = eval_depth_res\n sys.stderr.write(\n \"{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10} \\n\".\n format('abs_rel', 'sq_rel', 'rms', 'log10', \n 'a1', 'a2', 'a3'))\n sys.stderr.write(\n \"{:10.4f}, {:10.4f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f} \\n\".\n format(abs_rel, sq_rel, rms, log_rms, a1, a2, a3))\n \n return eval_depth_res\n\ndef test_single_image(img_path, model, training_hw, save_dir='./'):\n img = cv2.imread(img_path)\n h, w = img.shape[0:2]\n img_resized = cv2.resize(img, (training_hw[1], training_hw[0]))\n img_t = torch.from_numpy(np.transpose(img_resized, [2,0,1])).float().cuda().unsqueeze(0) / 255.0\n disp = model.infer_depth(img_t)\n disp = np.transpose(disp[0].cpu().detach().numpy(), [1,2,0])\n disp_resized = cv2.resize(disp, (w,h))\n depth = 1.0 / (1e-6 + disp_resized)\n\n visualizer = Visualizer_debug(dump_dir=save_dir)\n visualizer.save_disp_color_img(disp_resized, name='demo')\n print('Depth prediction saved in ' + save_dir)\n\n\nif __name__ == '__main__':\n import argparse\n arg_parser = argparse.ArgumentParser(\n description=\"TrianFlow testing.\"\n )\n arg_parser.add_argument('-c', '--config_file', default=None, help='config file.')\n arg_parser.add_argument('-g', '--gpu', type=str, default=0, help='gpu id.')\n arg_parser.add_argument('--mode', type=str, default='depth', help='mode for testing.')\n arg_parser.add_argument('--task', type=str, default='kitti_depth', help='To test on which task, kitti_depth or kitti_flow or nyuv2 or demo')\n arg_parser.add_argument('--image_path', type=str, default=None, help='Set this only when task==demo. Depth demo for single image.')\n arg_parser.add_argument('--pretrained_model', type=str, default=None, help='directory for loading flow pretrained models')\n arg_parser.add_argument('--result_dir', type=str, default=None, help='directory for saving predictions')\n\n args = arg_parser.parse_args()\n if not os.path.exists(args.config_file):\n raise ValueError('config file not found.')\n with open(args.config_file, 'r') as f:\n cfg = yaml.safe_load(f)\n cfg['img_hw'] = (cfg['img_hw'][0], cfg['img_hw'][1])\n #cfg['log_dump_dir'] = os.path.join(args.model_dir, 'log.pkl')\n cfg['model_dir'] = args.result_dir\n\n # copy attr into cfg\n for attr in dir(args):\n if attr[:2] != '__':\n cfg[attr] = getattr(args, attr)\n \n class pObject(object):\n def __init__(self):\n pass\n cfg_new = pObject()\n for attr in list(cfg.keys()):\n setattr(cfg_new, attr, cfg[attr])\n\n if args.mode == 'flow':\n model = Model_flow(cfg_new)\n elif args.mode == 'depth' or args.mode == 'flow_3stage':\n model = Model_depth_pose(cfg_new)\n elif args.mode == 'flowposenet':\n model = Model_flowposenet(cfg_new)\n \n if args.task == 'demo':\n model = Model_depth_pose(cfg_new)\n\n model.cuda()\n weights = torch.load(args.pretrained_model)\n model.load_state_dict(weights['model_state_dict'])\n model.eval()\n print('Model Loaded.')\n\n if args.task == 'kitti_depth':\n depth_res = test_eigen_depth(cfg_new, model)\n elif args.task == 'kitti_flow':\n gt_flows_2015, noc_masks_2015 = load_gt_flow_kitti(cfg_new.gt_2015_dir, 'kitti_2015')\n gt_masks_2015 = load_gt_mask(cfg_new.gt_2015_dir)\n flow_res = test_kitti_2015(cfg_new, model, gt_flows_2015, noc_masks_2015, gt_masks_2015)\n elif args.task == 'nyuv2':\n test_images, test_gt_depths = load_nyu_test_data(cfg_new.nyu_test_dir)\n depth_res = test_nyu(cfg_new, model, test_images, test_gt_depths)\n elif args.task == 'demo':\n test_single_image(args.image_path, model, training_hw=cfg['img_hw'], save_dir=args.result_dir)\n\n" }, { "path": "train.py", "content": "import os, sys\nimport yaml\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom core.dataset import KITTI_RAW, KITTI_Prepared, NYU_Prepare, NYU_v2, KITTI_Odo\nfrom core.networks import get_model\nfrom core.config import generate_loss_weights_dict\nfrom core.visualize import Visualizer\nfrom core.evaluation import load_gt_flow_kitti, load_gt_mask\nfrom test import test_kitti_2012, test_kitti_2015, test_eigen_depth, test_nyu, load_nyu_test_data\n\nfrom collections import OrderedDict\nimport torch\nimport torch.utils.data\nfrom tqdm import tqdm\nimport shutil\nimport pickle\nimport pdb\n\ndef save_model(iter_, model_dir, filename, model, optimizer):\n torch.save({\"iteration\": iter_, \"model_state_dict\": model.state_dict(), 'optimizer_state_dict': optimizer.state_dict()}, os.path.join(model_dir, filename))\n\ndef load_model(model_dir, filename, model, optimizer):\n data = torch.load(os.path.join(model_dir, filename))\n iter_ = data['iteration']\n model.load_state_dict(data['model_state_dict'])\n optimizer.load_state_dict(data['optimizer_state_dict'])\n return iter_, model, optimizer\n\ndef train(cfg):\n # load model and optimizer\n model = get_model(cfg.mode)(cfg)\n if cfg.multi_gpu:\n model = torch.nn.DataParallel(model)\n model = model.cuda()\n optimizer = torch.optim.Adam([{'params': filter(lambda p: p.requires_grad, model.parameters()), 'lr': cfg.lr}])\n\n # Load Pretrained Models\n if cfg.resume:\n if cfg.iter_start > 0:\n cfg.iter_start, model, optimizer = load_model(cfg.model_dir, 'iter_{}.pth'.format(cfg.iter_start), model, optimizer)\n else:\n cfg.iter_start, model, optimizer = load_model(cfg.model_dir, 'last.pth', model, optimizer)\n elif cfg.flow_pretrained_model:\n data = torch.load(cfg.flow_pretrained_model)['model_state_dict']\n renamed_dict = OrderedDict()\n for k, v in data.items():\n if cfg.multi_gpu:\n name = 'module.model_flow.' + k\n elif cfg.mode == 'flowposenet':\n name = 'model_flow.' + k\n else:\n name = 'model_pose.model_flow.' + k\n renamed_dict[name] = v\n missing_keys, unexp_keys = model.load_state_dict(renamed_dict, strict=False)\n print(missing_keys)\n print(unexp_keys)\n print('Load Flow Pretrained Model from ' + cfg.flow_pretrained_model)\n if cfg.depth_pretrained_model and not cfg.resume:\n data = torch.load(cfg.depth_pretrained_model)['model_state_dict']\n if cfg.multi_gpu:\n renamed_dict = OrderedDict()\n for k, v in data.items():\n name = 'module.' + k\n renamed_dict[name] = v\n missing_keys, unexp_keys = model.load_state_dict(renamed_dict, strict=False)\n else:\n missing_keys, unexp_keys = model.load_state_dict(data, strict=False)\n print(missing_keys)\n print('##############')\n print(unexp_keys)\n print('Load Depth Pretrained Model from ' + cfg.depth_pretrained_model)\n \n loss_weights_dict = generate_loss_weights_dict(cfg)\n visualizer = Visualizer(loss_weights_dict, cfg.log_dump_dir)\n\n # load dataset\n data_dir = os.path.join(cfg.prepared_base_dir, cfg.prepared_save_dir)\n if not os.path.exists(os.path.join(data_dir, 'train.txt')):\n if cfg.dataset == 'kitti_depth':\n kitti_raw_dataset = KITTI_RAW(cfg.raw_base_dir, cfg.static_frames_txt, cfg.test_scenes_txt)\n kitti_raw_dataset.prepare_data_mp(data_dir, stride=1)\n elif cfg.dataset == 'kitti_odo':\n kitti_raw_dataset = KITTI_Odo(cfg.raw_base_dir)\n kitti_raw_dataset.prepare_data_mp(data_dir, stride=1)\n elif cfg.dataset == 'nyuv2':\n nyu_raw_dataset = NYU_Prepare(cfg.raw_base_dir, cfg.nyu_test_dir)\n nyu_raw_dataset.prepare_data_mp(data_dir, stride=10)\n else:\n raise NotImplementedError\n \n if cfg.dataset == 'kitti_depth':\n dataset = KITTI_Prepared(data_dir, num_scales=cfg.num_scales, img_hw=cfg.img_hw, num_iterations=(cfg.num_iterations - cfg.iter_start) * cfg.batch_size)\n elif cfg.dataset == 'kitti_odo':\n dataset = KITTI_Prepared(data_dir, num_scales=cfg.num_scales, img_hw=cfg.img_hw, num_iterations=(cfg.num_iterations - cfg.iter_start) * cfg.batch_size)\n elif cfg.dataset == 'nyuv2':\n dataset = NYU_v2(data_dir, num_scales=cfg.num_scales, img_hw=cfg.img_hw, num_iterations=(cfg.num_iterations - cfg.iter_start) * cfg.batch_size)\n else:\n raise NotImplementedError\n \n dataloader = torch.utils.data.DataLoader(dataset, batch_size=cfg.batch_size, shuffle=True, num_workers=cfg.num_workers, drop_last=False)\n if cfg.dataset == 'kitti_depth' or cfg.dataset == 'kitti_odo':\n gt_flows_2012, noc_masks_2012 = load_gt_flow_kitti(cfg.gt_2012_dir, 'kitti_2012')\n gt_flows_2015, noc_masks_2015 = load_gt_flow_kitti(cfg.gt_2015_dir, 'kitti_2015')\n gt_masks_2015 = load_gt_mask(cfg.gt_2015_dir)\n elif cfg.dataset == 'nyuv2':\n test_images, test_gt_depths = load_nyu_test_data(cfg.nyu_test_dir)\n\n # training\n print('starting iteration: {}.'.format(cfg.iter_start))\n for iter_, inputs in enumerate(tqdm(dataloader)):\n if (iter_ + 1) % cfg.test_interval == 0 and (not cfg.no_test):\n model.eval()\n if args.multi_gpu:\n model_eval = model.module\n else:\n model_eval = model\n if cfg.dataset == 'kitti_depth' or cfg.dataset == 'kitti_odo':\n if not (cfg.mode == 'depth' or cfg.mode == 'flowposenet'):\n eval_2012_res = test_kitti_2012(cfg, model_eval, gt_flows_2012, noc_masks_2012)\n eval_2015_res = test_kitti_2015(cfg, model_eval, gt_flows_2015, noc_masks_2015, gt_masks_2015, depth_save_dir=os.path.join(cfg.model_dir, 'results'))\n visualizer.add_log_pack({'eval_2012_res': eval_2012_res, 'eval_2015_res': eval_2015_res})\n elif cfg.dataset == 'nyuv2':\n if not cfg.mode == 'flow':\n eval_nyu_res = test_nyu(cfg, model_eval, test_images, test_gt_depths)\n visualizer.add_log_pack({'eval_nyu_res': eval_nyu_res})\n visualizer.dump_log(os.path.join(cfg.model_dir, 'log.pkl'))\n model.train()\n iter_ = iter_ + cfg.iter_start\n optimizer.zero_grad()\n inputs = [k.cuda() for k in inputs]\n loss_pack = model(inputs)\n if iter_ % cfg.log_interval == 0:\n visualizer.print_loss(loss_pack, iter_=iter_)\n\n loss_list = []\n for key in list(loss_pack.keys()):\n loss_list.append((loss_weights_dict[key] * loss_pack[key].mean()).unsqueeze(0))\n loss = torch.cat(loss_list, 0).sum()\n loss.backward()\n optimizer.step()\n if (iter_ + 1) % cfg.save_interval == 0:\n save_model(iter_, cfg.model_dir, 'iter_{}.pth'.format(iter_), model, optimizer)\n save_model(iter_, cfg.model_dir, 'last.pth'.format(iter_), model, optimizer)\n \n if cfg.dataset == 'kitti_depth':\n if cfg.mode == 'depth' or cfg.mode == 'depth_pose':\n eval_depth_res = test_eigen_depth(cfg, model_eval)\n\nif __name__ == '__main__':\n import argparse\n arg_parser = argparse.ArgumentParser(\n description=\"TrianFlow training pipeline.\"\n )\n arg_parser.add_argument('-c', '--config_file', default=None, help='config file.')\n arg_parser.add_argument('-g', '--gpu', type=str, default=0, help='gpu id.')\n arg_parser.add_argument('--batch_size', type=int, default=8, help='batch size.')\n arg_parser.add_argument('--iter_start', type=int, default=0, help='starting iteration.')\n arg_parser.add_argument('--lr', type=float, default=0.0001, help='learning rate')\n arg_parser.add_argument('--num_workers', type=int, default=6, help='number of workers.')\n arg_parser.add_argument('--log_interval', type=int, default=100, help='interval for printing loss.')\n arg_parser.add_argument('--test_interval', type=int, default=2000, help='interval for evaluation.')\n arg_parser.add_argument('--save_interval', type=int, default=2000, help='interval for saving models.')\n arg_parser.add_argument('--mode', type=str, default='flow', help='training mode.')\n arg_parser.add_argument('--model_dir', type=str, default=None, help='directory for saving models')\n arg_parser.add_argument('--prepared_save_dir', type=str, default='data_s1', help='directory name for generated training dataset')\n arg_parser.add_argument('--flow_pretrained_model', type=str, default=None, help='directory for loading flow pretrained models')\n arg_parser.add_argument('--depth_pretrained_model', type=str, default=None, help='directory for loading depth pretrained models')\n arg_parser.add_argument('--resume', action='store_true', help='to resume training.')\n arg_parser.add_argument('--multi_gpu', action='store_true', help='to use multiple gpu for training.')\n arg_parser.add_argument('--no_test', action='store_true', help='without evaluation.')\n args = arg_parser.parse_args()\n #args.config_file = 'config/debug.yaml'\n if args.config_file is None:\n raise ValueError('config file needed. -c --config_file.')\n\n # set model\n if args.model_dir is None:\n args.model_dir = os.path.join('models', os.path.splitext(os.path.split(args.config_file)[1])[0])\n args.model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), args.model_dir, args.mode)\n if not os.path.exists(args.model_dir):\n os.makedirs(args.model_dir)\n if not os.path.exists(args.config_file):\n raise ValueError('config file not found.')\n with open(args.config_file, 'r') as f:\n cfg = yaml.safe_load(f)\n cfg['img_hw'] = (cfg['img_hw'][0], cfg['img_hw'][1])\n cfg['log_dump_dir'] = os.path.join(args.model_dir, 'log.pkl')\n shutil.copy(args.config_file, args.model_dir)\n\n # copy attr into cfg\n for attr in dir(args):\n if attr[:2] != '__':\n cfg[attr] = getattr(args, attr)\n\n # set gpu\n num_gpus = len(args.gpu.split(','))\n if (args.multi_gpu and num_gpus <= 1) or ((not args.multi_gpu) and num_gpus > 1):\n raise ValueError('Error! the number of gpus used in the --gpu argument does not match the argument --multi_gpu.')\n if args.multi_gpu:\n cfg['batch_size'] = cfg['batch_size'] * num_gpus\n cfg['num_iterations'] = int(cfg['num_iterations'] / num_gpus)\n os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)\n\n class pObject(object):\n def __init__(self):\n pass\n cfg_new = pObject()\n for attr in list(cfg.keys()):\n setattr(cfg_new, attr, cfg[attr])\n with open(os.path.join(args.model_dir, 'config.pkl'), 'wb') as f:\n pickle.dump(cfg_new, f)\n\n # main function \n train(cfg_new)\n\n" } ]