Support skeleton (#155)
* pose * pose * pose * pose * 你的提交消息 * pose * pose * Delete train1.sh * pretreatment * configs * pose * reference * Update gaittr.py * naming * naming * Update transform.py * update for datasets * update README * update name and README * update * Update transform.py
This commit is contained in:
Dongyang Jin
GitHub
+11
-2
@@ -24,15 +24,24 @@ ls *.tgz | xargs -n1 tar xzvf
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After unpacking these compressed files, run this command:
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Step2 : To rearrange directory of GREW dataset, turning to id-type-view structure, Run
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Step2-1 : To rearrange directory of GREW dataset(for silhouette), turning to id-type-view structure, Run
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```
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python datasets/GREW/rearrange_GREW.py --input_path Path_of_GREW-raw --output_path Path_of_GREW-rearranged
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```
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Step2-2 : To rearrange directory of GREW dataset(for pose), turning to id-type-view structure, Run
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```
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python datasets/GREW/rearrange_GREW_pose.py --input_path Path_of_GREW-pose --output_path Path_of_GREW-pose-rearranged
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```
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Step3: Transforming images to pickle file, run
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Step3-1: Transforming images to pickle file, run
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```
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python datasets/pretreatment.py --input_path Path_of_GREW-rearranged --output_path Path_of_GREW-pkl --dataset GREW
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```
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Step3-2: Transforming pose txts to pickle file, run
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```
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python datasets/pretreatment.py --input_path Path_of_GREW-pose-rearranged --output_path Path_of_GREW-pose-pkl --pose --dataset GREW
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```
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Then you will see the structure like:
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- Processed
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@@ -0,0 +1,92 @@
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import argparse
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import os
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import shutil
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from pathlib import Path
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from tqdm import tqdm
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TOTAL_Test = 24000
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TOTAL_Train = 20000
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def rearrange_train(train_path: Path, output_path: Path) -> None:
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progress = tqdm(total=TOTAL_Train)
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for sid in train_path.iterdir():
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if not sid.is_dir():
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continue
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for sub_seq in sid.iterdir():
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if not sub_seq.is_dir():
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continue
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for subfile in os.listdir(sub_seq):
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src = os.path.join(train_path, sid.name, sub_seq.name)
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dst = os.path.join(output_path, sid.name+'train', '00', sub_seq.name)
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os.makedirs(dst,exist_ok=True)
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if subfile not in os.listdir(dst) and subfile.endswith('_2d_pose.txt'):
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pose_subfile = 'pose_'+subfile
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os.symlink(os.path.join(src, subfile),
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os.path.join(dst, pose_subfile))
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progress.update(1)
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def rearrange_test(test_path: Path, output_path: Path) -> None:
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# for gallery
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gallery = Path(os.path.join(test_path, 'gallery'))
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probe = Path(os.path.join(test_path, 'probe'))
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progress = tqdm(total=TOTAL_Test)
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for sid in gallery.iterdir():
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if not sid.is_dir():
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continue
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cnt = 1
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for sub_seq in sid.iterdir():
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if not sub_seq.is_dir():
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continue
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for subfile in sorted(os.listdir(sub_seq)):
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src = os.path.join(gallery, sid.name, sub_seq.name)
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dst = os.path.join(output_path, sid.name, '%02d'%cnt, sub_seq.name)
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os.makedirs(dst,exist_ok=True)
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if subfile not in os.listdir(dst) and subfile.endswith('_2d_pose.txt'):
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pose_subfile = 'pose_'+subfile
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os.symlink(os.path.join(src, subfile),
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os.path.join(dst, pose_subfile))
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cnt += 1
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progress.update(1)
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# for probe
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for sub_seq in probe.iterdir():
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if not sub_seq.is_dir():
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continue
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for subfile in os.listdir(sub_seq):
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src = os.path.join(probe, sub_seq.name)
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dst = os.path.join(output_path, 'probe', '03', sub_seq.name)
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os.makedirs(dst,exist_ok=True)
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if subfile not in os.listdir(dst) and subfile.endswith('_2d_pose.txt'):
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pose_subfile = 'pose_'+subfile
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os.symlink(os.path.join(src, subfile),
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os.path.join(dst, pose_subfile))
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progress.update(1)
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def rearrange_GREW(input_path: Path, output_path: Path) -> None:
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os.makedirs(output_path, exist_ok=True)
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for folder in input_path.iterdir():
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if not folder.is_dir():
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continue
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print(f'Rearranging {folder}')
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if folder.name == 'train':
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rearrange_train(folder,output_path)
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if folder.name == 'test':
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rearrange_test(folder, output_path)
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if folder.name == 'distractor':
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pass
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='GREW rearrange tool')
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parser.add_argument('-i', '--input_path', required=True, type=str,
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help='Root path of raw dataset.')
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parser.add_argument('-o', '--output_path', default='GREW_rearranged', type=str,
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help='Root path for output.')
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args = parser.parse_args()
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input_path = Path(args.input_path).resolve()
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output_path = Path(args.output_path).resolve()
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rearrange_GREW(input_path, output_path)
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@@ -0,0 +1,19 @@
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# Datasets for MSGG
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MSGG needs to convert the pose key format of other datasets(such as CASIA-B, GREW, Gait3D,) from coco17 to the input format of Pyramid keys.
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## Data Pretreatment
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```python
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python datasets/MSGG/pyramid_keypoints_msgg.py --input_path Path_of_pose_pkl --output_path Path_of_pose_pyramid_pkl
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```
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## Citation
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```
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@article{peng2023learning,
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title={Learning rich features for gait recognition by integrating skeletons and silhouettes},
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author={Peng, Yunjie and Ma, Kang and Zhang, Yang and He, Zhiqiang},
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journal={Multimedia Tools and Applications},
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pages={1--22},
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year={2023},
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publisher={Springer}
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}
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```
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@@ -0,0 +1,94 @@
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import os
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import os.path as osp
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import numpy as np
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import pdb
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import argparse
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import pickle
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ORG_KEYPOINTS = {
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'nose' :0,
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'left_eye' :1,
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'right_eye' :2,
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'left_ear' :3,
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'right_ear' :4,
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'left_shoulder' :5,
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'right_shoulder':6,
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'left_elbow' :7,
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'right_elbow' :8,
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'left_wrist' :9,
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'right_wrist' :10,
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'left_hip' :11,
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'right_hip' :12,
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'left_knee' :13,
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'right_knee' :14,
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'left_ankle' :15,
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'right_ankle' :16,
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}
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NEW_KEYPOINTS = {
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0: 'right_shoulder',
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1: 'right_elbow',
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2: 'right_knee',
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3: 'right_hip',
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4: 'left_elbow',
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5: 'left_knee',
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6: 'left_shoulder',
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7: 'right_wrist',
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8: 'right_ankle',
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9: 'left_hip',
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10: 'left_wrist',
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11: 'left_ankle',
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}
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def get_index_mapping():
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index_mapping = {}
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for _key in NEW_KEYPOINTS.keys():
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map_index = ORG_KEYPOINTS[NEW_KEYPOINTS[_key]]
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index_mapping[_key] = map_index
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return index_mapping
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='OpenGait dataset pretreatment module.')
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parser.add_argument('-i', '--input_path', default='', type=str, help='Root path of raw dataset.')
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parser.add_argument('-o', '--output_path', default='', type=str, help='Output path of pickled dataset.')
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args = parser.parse_args()
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index_mapping = get_index_mapping()
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data_path = args.input_path
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des_path = args.output_path
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id_list = sorted(os.listdir(data_path))
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for _id in id_list:
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type_list = sorted(os.listdir(osp.join(data_path, _id)))
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for _type in type_list:
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view_list = sorted(os.listdir(osp.join(data_path, _id, _type)))
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for _view in view_list:
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seq_info = [_id, _type, _view]
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seq_info_str = '-'.join(seq_info)
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seq_dir = osp.join(data_path, *seq_info)
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des_dir = osp.join(des_path, *seq_info)
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if osp.exists(des_dir) is False:
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os.makedirs(des_dir)
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keypoints_list = os.listdir(seq_dir)
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pkl_name = "{}.pkl".format(_view)
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seq_path = osp.join(seq_dir, pkl_name)
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save_path = osp.join(des_dir, pkl_name)
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seq_path_exists = osp.exists(seq_path)
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if seq_path_exists is False:
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print("seq:{} input:{}. ".format(seq_info_str, seq_path_exists))
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continue
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with open(seq_path, 'rb') as f:
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keypoints_data = pickle.load(f)
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to_pickle = []
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for keypoint in keypoints_data:
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mapped_keypoints = np.zeros((12, 3))
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for i in range(mapped_keypoints.shape[0]):
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mapped_keypoints[i] = keypoint[index_mapping[i]]
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to_pickle.append(mapped_keypoints)
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keypoints = np.stack(to_pickle)
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pickle.dump(keypoints, open(save_path, 'wb'))
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print("FINISHED: " + "-".join(seq_info))
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@@ -35,15 +35,27 @@ python datasets/OUMVLP/extractor.py --input_path Path_of_OUMVLP-base --output_pa
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......
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......
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```
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Step3 : To rearrange directory of OUMVLP dataset, turning to id-type-view structure, Run
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Step3-1 : To rearrange directory of OUMVLP dataset(for silhouette), turning to id-type-view structure, Run
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```
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python datasets/OUMVLP/rearrange_OUMVLP.py --input_path Path_of_OUMVLP-raw --output_path Path_of_OUMVLP-rearranged
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```
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Step3-2 : To rearrange directory of OUMVLP dataset(for pose), turning to id-type-view structure, Run
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```
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python datasets/OUMVLP/rearrange_OUMVLP_pose.py --input_path Path_of_OUMVLP-pose --output_path Path_of_OUMVLP-pose-rearranged
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```
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Step4: Transforming images to pickle file, run
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Step4-1: Transforming images to pickle file, run
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```
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python datasets/pretreatment.py --input_path Path_of_OUMVLP-rearranged --output_path Path_of_OUMVLP-pkl
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```
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Step4-2: Transforming pose txts to pickle file, run
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```
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python datasets/pretreatment.py --input_path Path_of_GREW-pose-rearranged --output_path Path_of_GREW-pose-pkl --pose --dataset GREW
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```
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gernerate the 17 Number of Pose Points Format from 18 Number of Pose Points
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```
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python datasets/OUMVLP/rearrange_OUMVLP_pose.py --input_path Path_of_OUMVLP-pose18 --output_path Path_of_OUMVLP-pose17
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```
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- Processed
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```
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@@ -0,0 +1,85 @@
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import pickle
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from tqdm import tqdm
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from pathlib import Path
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import os
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import os.path as osp
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import argparse
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import logging
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'''
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gernerate the 17 Number of Pose Points Format from 18 Number of Pose Points
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OUMVLP 17
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# keypoints = {
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# 0: "nose",
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# 1: "left_eye",
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# 2: "right_eye",
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# 3: "left_ear",
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# 4: "right_ear",
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# 5: "left_shoulder",
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# 6: "right_shoulder",
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# 7: "left_elbow",
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# 8: "right_elbow",
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# 9: "left_wrist",
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# 10: "right_wrist",
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# 11: "left_hip",
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# 12: "right_hip",
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# 13: "left_knee",
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# 14: "right_knee",
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# 15: "left_ankle",
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# 16: "right_ankle"
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# }
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OUMVLP 18
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mask=[0,15,14,17,16,5,2,6,3,7,4,11,8,12,9,13,10]
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# keypoints = {
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# 0: "nose",
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# 1: "neck",
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# 2: "Rshoulder",
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# 3: "Relbow",
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# 4: "Rwrist",
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# 5: "Lshoudler",
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# 6: "Lelbow",
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# 7: "Lwrist",
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# 8: "Rhip",
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# 9: "Rknee",
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# 10: "Rankle",
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# 11: "Lhip",
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# 12: "Lknee",
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# 13: "Lankle",
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# 14: "Reye",
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# 15: "Leye",
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# 16: "Rear",
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# 17: "Lear"
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# }
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'''
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def ToOUMVLP17(input_path: Path, output_path: Path):
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mask=[0,15,14,17,16,5,2,6,3,7,4,11,8,12,9,13,10]
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TOTAL_SUBJECTS = 10307
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progress = tqdm(total=TOTAL_SUBJECTS)
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for subject in input_path.iterdir():
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output_subject = subject.name
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for seq in subject.iterdir():
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output_seq = seq.name
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for view in seq.iterdir():
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src = os.path.join(view, f"{view.name}.pkl")
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dst = os.path.join(output_path, output_subject, output_seq, view.name)
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os.makedirs(dst, exist_ok=True)
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with open(src,'rb') as f:
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srcdata = pickle.load(f)
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#[T,18,3]
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data = srcdata[...,mask,:].copy()
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# #[T,17,3]
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pkl_path = os.path.join(dst,f'{view.name}.pkl')
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pickle.dump(data,open(pkl_path,'wb'))
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progress.update(1)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='OpenGait dataset pretreatment module.')
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parser.add_argument('-i', '--input_path', default='', type=str, help='Root path of raw dataset.')
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parser.add_argument('-o', '--output_path', default='', type=str, help='Output path of pickled dataset.')
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parser.add_argument('-l', '--log_to_file', default='./pretreatment.log', type=str, help='Log file path. Default: ./pretreatment.log')
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args = parser.parse_args()
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logging.info('Begin')
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ToOUMVLP17(input_path=Path(args.input_path), output_path=Path(args.output_path))
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logging.info('Done')
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@@ -0,0 +1,44 @@
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import argparse
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import os
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import shutil
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from pathlib import Path
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from typing import Tuple
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from tqdm import tqdm
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TOTAL_SUBJECTS = 10307
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def sanitize(name: str) -> Tuple[str, str]:
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return name.split('_')
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def rearrange(input_path: Path, output_path: Path) -> None:
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os.makedirs(output_path, exist_ok=True)
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progress = tqdm(total=TOTAL_SUBJECTS)
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for folder in input_path.iterdir():
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subject = folder.name
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for sid in folder.iterdir():
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view, seq = sanitize(sid.name)
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src = os.path.join(input_path, subject,sid.name)
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dst = os.path.join(output_path, subject, seq, view)
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os.makedirs(dst, exist_ok=True)
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for subfile in os.listdir(src):
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if subfile not in os.listdir(dst) and subfile.endswith('.json'):
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os.symlink(os.path.join(src, subfile),
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os.path.join(dst, subfile))
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progress.update(1)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='OUMVLP rearrange tool')
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parser.add_argument('-i', '--input_path', required=True, type=str,
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help='Root path of raw dataset.')
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parser.add_argument('-o', '--output_path', default='OUMVLP_rearranged', type=str,
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help='Root path for output.')
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args = parser.parse_args()
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input_path = Path(args.input_path).resolve()
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output_path = Path(args.output_path).resolve()
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rearrange(input_path, output_path)
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@@ -12,7 +12,7 @@ from typing import Tuple
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import cv2
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import numpy as np
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from tqdm import tqdm
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import json
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def imgs2pickle(img_groups: Tuple, output_path: Path, img_size: int = 64, verbose: bool = False, dataset='CASIAB') -> None:
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"""Reads a group of images and saves the data in pickle format.
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@@ -127,6 +127,92 @@ def pretreat(input_path: Path, output_path: Path, img_size: int = 64, workers: i
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progress.update(1)
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logging.info('Done')
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def txts2pickle(txt_groups: Tuple, output_path: Path, verbose: bool = False, dataset='CASIAB') -> None:
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"""
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Reads a group of images and saves the data in pickle format.
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Args:
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img_groups (Tuple): Tuple of (sid, seq, view) and list of image paths.
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output_path (Path): Output path.
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img_size (int, optional): Image resizing size. Defaults to 64.
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verbose (bool, optional): Display debug info. Defaults to False.
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"""
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sinfo = txt_groups[0]
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txt_paths = txt_groups[1]
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to_pickle = []
|
||||
if dataset == 'OUMVLP':
|
||||
for txt_file in sorted(txt_paths):
|
||||
try:
|
||||
with open(txt_file) as f:
|
||||
jsondata = json.load(f)
|
||||
if len(jsondata['people'])==0:
|
||||
continue
|
||||
data = np.array(jsondata["people"][0]["pose_keypoints_2d"]).reshape(-1,3)
|
||||
to_pickle.append(data)
|
||||
except:
|
||||
print(txt_file)
|
||||
else:
|
||||
for txt_file in sorted(txt_paths):
|
||||
if verbose:
|
||||
logging.debug(f'Reading sid {sinfo[0]}, seq {sinfo[1]}, view {sinfo[2]} from {txt_file}')
|
||||
data = np.genfromtxt(txt_file, delimiter=',')[2:].reshape(-1,3)
|
||||
to_pickle.append(data)
|
||||
|
||||
if to_pickle:
|
||||
dst_path = os.path.join(output_path, *sinfo)
|
||||
keypoints = np.stack(to_pickle)
|
||||
os.makedirs(dst_path, exist_ok=True)
|
||||
pkl_path = os.path.join(dst_path, f'{sinfo[2]}.pkl')
|
||||
if verbose:
|
||||
logging.debug(f'Saving {pkl_path}...')
|
||||
pickle.dump(keypoints, open(pkl_path, 'wb'))
|
||||
logging.info(f'Saved {len(to_pickle)} valid frames\' keypoints to {pkl_path}.')
|
||||
|
||||
if len(to_pickle) < 5:
|
||||
logging.warning(f'{sinfo} has less than 5 valid data.')
|
||||
|
||||
|
||||
|
||||
def pretreat_pose(input_path: Path, output_path: Path, workers: int = 4, verbose: bool = False, dataset='CASIAB') -> None:
|
||||
"""Reads a dataset and saves the data in pickle format.
|
||||
|
||||
Args:
|
||||
input_path (Path): Dataset root path.
|
||||
output_path (Path): Output path.
|
||||
img_size (int, optional): Image resizing size. Defaults to 64.
|
||||
workers (int, optional): Number of thread workers. Defaults to 4.
|
||||
verbose (bool, optional): Display debug info. Defaults to False.
|
||||
"""
|
||||
txt_groups = defaultdict(list)
|
||||
logging.info(f'Listing {input_path}')
|
||||
total_files = 0
|
||||
if dataset == 'OUMVLP':
|
||||
for json_path in input_path.rglob('*.json'):
|
||||
if verbose:
|
||||
logging.debug(f'Adding {json_path}')
|
||||
*_, sid, seq, view, _ = json_path.as_posix().split('/')
|
||||
txt_groups[(sid, seq, view)].append(json_path)
|
||||
total_files += 1
|
||||
else:
|
||||
for txt_path in input_path.rglob('*.txt'):
|
||||
if verbose:
|
||||
logging.debug(f'Adding {txt_path}')
|
||||
*_, sid, seq, view, _ = txt_path.as_posix().split('/')
|
||||
txt_groups[(sid, seq, view)].append(txt_path)
|
||||
total_files += 1
|
||||
|
||||
logging.info(f'Total files listed: {total_files}')
|
||||
|
||||
progress = tqdm(total=len(txt_groups), desc='Pretreating', unit='folder')
|
||||
|
||||
with mp.Pool(workers) as pool:
|
||||
logging.info(f'Start pretreating {input_path}')
|
||||
for _ in pool.imap_unordered(partial(txts2pickle, output_path=output_path, verbose=verbose, dataset=args.dataset), txt_groups.items()):
|
||||
progress.update(1)
|
||||
logging.info('Done')
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='OpenGait dataset pretreatment module.')
|
||||
@@ -137,6 +223,7 @@ if __name__ == '__main__':
|
||||
parser.add_argument('-r', '--img_size', default=64, type=int, help='Image resizing size. Default 64')
|
||||
parser.add_argument('-d', '--dataset', default='CASIAB', type=str, help='Dataset for pretreatment.')
|
||||
parser.add_argument('-v', '--verbose', default=False, action='store_true', help='Display debug info.')
|
||||
parser.add_argument('-p', '--pose', default=False, action='store_true', help='Processing pose.')
|
||||
args = parser.parse_args()
|
||||
|
||||
logging.basicConfig(level=logging.INFO, filename=args.log_file, filemode='w', format='[%(asctime)s - %(levelname)s]: %(message)s')
|
||||
@@ -146,5 +233,7 @@ if __name__ == '__main__':
|
||||
logging.info('Verbose mode is on.')
|
||||
for k, v in args.__dict__.items():
|
||||
logging.debug(f'{k}: {v}')
|
||||
|
||||
pretreat(input_path=Path(args.input_path), output_path=Path(args.output_path), img_size=args.img_size, workers=args.n_workers, verbose=args.verbose, dataset=args.dataset)
|
||||
if args.pose:
|
||||
pretreat_pose(input_path=Path(args.input_path), output_path=Path(args.output_path), workers=args.n_workers, verbose=args.verbose, dataset=args.dataset)
|
||||
else:
|
||||
pretreat(input_path=Path(args.input_path), output_path=Path(args.output_path), img_size=args.img_size, workers=args.n_workers, verbose=args.verbose, dataset=args.dataset)
|
||||
|
||||
Reference in New Issue
Block a user