Merge branch 'master' of https://github.com/ShiqiYu/OpenGait

README.md

@@ -6,15 +6,16 @@
 
 OpenGait is a flexible and extensible gait recognition project provided by the [Shiqi Yu Group](https://faculty.sustech.edu.cn/yusq/) and supported in part by [WATRIX.AI](http://www.watrix.ai).
 
-**Highlighted features:**
+
+## What's New
+- [Mar 2022] [HID](http://hid2022.iapr-tc4.org/) support is ready in [misc/HID](./misc/HID).
+
+## Highlighted features
 - **Multiple Models Support**: We reproduced several SOTA methods, and reached the same or even the better performance. 
 - **DDP Support**: The officially recommended [`Distributed Data Parallel (DDP)`](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) mode is used during both the training and testing phases.
 - **AMP Support**: The [`Auto Mixed Precision (AMP)`](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html?highlight=amp) option is available.
 - **Nice log**: We use [`tensorboard`](https://pytorch.org/docs/stable/tensorboard.html) and `logging` to log everything, which looks pretty.
 
-## What's New
-- [Mar 2022] [HID](http://hid2022.iapr-tc4.org/) support is ready in [misc/HID](./misc/HID).
-
 ## Model Zoo
 
 ###  CASIA-B
@@ -57,15 +58,16 @@ The results in the parentheses are mentioned in the papers
     - tensorboard
     - opencv-python
     - tqdm
+    - py7zr
     
     Install dependenices by [Anaconda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html):
     ```
-    conda install tqdm pyyaml tensorboard opencv
+    conda install tqdm pyyaml tensorboard opencv py7zr
     conda install pytorch==1.6.0 torchvision -c pytorch
     ```    
     Or, Install dependenices by pip:
     ```
-    pip install tqdm pyyaml tensorboard opencv-python
+    pip install tqdm pyyaml tensorboard opencv-python py7zr
     pip install torch==1.6.0 torchvision==0.7.0
     ```
 ### Prepare dataset

docs/0.prepare_dataset.md

@@ -35,6 +35,9 @@ Download URL: http://www.cbsr.ia.ac.cn/GaitDatasetB-silh.zip
 Step1: Download URL: http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html
 
 Step2: Unzip the dataset, you will get a structure directory like:
+```
+python misc/extractor.py --input_path Path_of_OUMVLP-base --output_path Path_of_OUMVLP-raw --password Given_Password
+```  
 
 - Original
     ```
@@ -67,12 +70,12 @@ Step2: Unzip the dataset, you will get a structure directory like:
     ```
 Step3 : To rearrange directory of OUMVLP dataset, turning to id-type-view structure, Run 
 ```
-python misc/rearrange_OUMVLP.py --input_path OUMVLP-raw --output_path OUMVLP-rearrange
+python misc/rearrange_OUMVLP.py --input_path Path_of_OUMVLP-raw --output_path Path_of_OUMVLP-rearranged
 ```  
 
 Step4: Transforming images to pickle file, run 
 ```
-python misc/pretreatment.py --input_path OUMVLP-rearrange --output_path OUMVLP-pkl
+python misc/pretreatment.py --input_path Path_of_OUMVLP-rearranged --output_path Path_of_OUMVLP-pkl
 ```
 
 - Processed

misc/HID/baseline_hid.yaml

@@ -79,7 +79,7 @@ trainer_cfg:
   fix_BN: false
   log_iter: 100
   restore_ckpt_strict: true
-  restore_hint: 20000
+  restore_hint: 0
   save_iter: 10000
   save_name: Baseline
   sync_BN: true

misc/extractor.py

@@ -0,0 +1,44 @@
+import argparse
+import os
+from pathlib import Path
+
+import py7zr
+from tqdm import tqdm
+
+
+def extractall(base_path: Path, output_path: Path, passwords) -> None:
+    """Extract all archives in base_path to output_path.
+
+    Args:
+        base_path (Path): Path to the directory containing the archives.
+        output_path (Path): Path to the directory to extract the archives to.
+    """
+
+    os.makedirs(output_path, exist_ok=True)
+    for file_path in tqdm(list(base_path.rglob('Silhouette_*.7z'))):
+        if output_path.joinpath(file_path.stem).exists():
+            continue
+        with py7zr.SevenZipFile(file_path, password=passwords) as archive:
+            total_items = len(
+                [f for f in archive.getnames() if f.endswith('.png')]
+            )
+            archive.extractall(output_path)
+
+        extracted_files = len(
+            list(output_path.joinpath(file_path.stem).rglob('*.png')))
+
+        assert extracted_files == total_items, f'{extracted_files} != {total_items}'
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='OUMVLP extractor')
+    parser.add_argument('-b', '--input_path', type=str,
+                        required=True, help='Base path to OUMVLP .7z files')
+    parser.add_argument('-o', '--output_path', type=str,
+                        required=True, help='Output path for extracted files')
+    parser.add_argument('-p', '--password', type=str,
+                        required=True, help='password for extracted files')
+
+    args = parser.parse_args()
+
+    extractall(Path(args.input_path), Path(args.output_path), args.password)

misc/pretreatment.py

@@ -1,201 +1,141 @@
-# modified from https://github.com/AbnerHqC/GaitSet/blob/master/pretreatment.py
+# This source is based on https://github.com/AbnerHqC/GaitSet/blob/master/pretreatment.py
-
+import argparse
+import logging
+import multiprocessing as mp
 import os
+import pickle
+from collections import defaultdict
+from functools import partial
+from pathlib import Path
+from typing import Tuple
+
 import cv2
 import numpy as np
-from warnings import warn
+from tqdm import tqdm
-from time import sleep
-import argparse
-import pickle
-
-from multiprocessing import Pool
-from multiprocessing import TimeoutError as MP_TimeoutError
-
-START = "START"
-FINISH = "FINISH"
-WARNING = "WARNING"
-FAIL = "FAIL"
 
 
-def boolean_string(s):
+def imgs2pickle(img_groups: Tuple, output_path: Path, img_size: int = 64, verbose: bool = False) -> None:
-    if s.upper() not in {'FALSE', 'TRUE'}:
+    """Reads a group of images and saves the data in pickle format.
-        raise ValueError('Not a valid boolean string')
+
-    return s.upper() == 'TRUE'
+    Args:
+        img_groups (Tuple): Tuple of (sid, seq, view) and list of image paths.
+        output_path (Path): Output path.
+        img_size (int, optional): Image resizing size. Defaults to 64.
+        verbose (bool, optional): Display debug info. Defaults to False.
+    """    
+    sinfo = img_groups[0]
+    img_paths = img_groups[1]
+    to_pickle = []
+    for img_file in sorted(img_paths):
+        if verbose:
+            logging.debug(f'Reading sid {sinfo[0]}, seq {sinfo[1]}, view {sinfo[2]} from {img_file}')
+
+        img = cv2.imread(str(img_file), cv2.IMREAD_GRAYSCALE)
+
+        if img.sum() <= 10000:
+            if verbose:
+                logging.debug(f'Image sum: {img.sum()}')
+            logging.warning(f'{img_file} has no data.')
+            continue
+
+        # Get the upper and lower points
+        y_sum = img.sum(axis=1)
+        y_top = (y_sum != 0).argmax(axis=0)
+        y_btm = (y_sum != 0).cumsum(axis=0).argmax(axis=0)
+        img = img[y_top: y_btm + 1, :]
+
+        # As the height of a person is larger than the width,
+        # use the height to calculate resize ratio.
+        ratio = img.shape[1] / img.shape[0]
+        img = cv2.resize(img, (int(img_size * ratio), img_size), interpolation=cv2.INTER_CUBIC)
+
+        # Get the median of the x-axis and take it as the person's x-center.
+        x_csum = img.sum(axis=0).cumsum()
+        x_center = None
+        for idx, csum in enumerate(x_csum):
+            if csum > img.sum() / 2:
+                x_center = idx
+                break
+
+        if not x_center:
+            logging.warning(f'{img_file} has no center.')
+            continue
+
+        # Get the left and right points
+        half_width = img_size // 2
+        left = x_center - half_width
+        right = x_center + half_width
+        if left <= 0 or right >= img.shape[1]:
+            left += half_width
+            right += half_width
+            _ = np.zeros((img.shape[0], half_width))
+            img = np.concatenate([_, img, _], axis=1)
+
+        to_pickle.append(img[:, left: right].astype('uint8'))
+
+    if to_pickle:
+        to_pickle = np.asarray(to_pickle)
+        dst_path = os.path.join(output_path, *sinfo)
+        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(to_pickle, open(pkl_path, 'wb'))   
+        logging.info(f'Saved {len(to_pickle)} valid frames to {pkl_path}.')
 
 
-parser = argparse.ArgumentParser(description='Test')
+    if len(to_pickle) < 5:
-parser.add_argument('--input_path', default='', type=str,
+        logging.warning(f'{sinfo} has less than 5 valid data.')
-                    help='Root path of raw dataset.')
-parser.add_argument('--output_path', default='', type=str,
-                    help='Root path for output.')
-parser.add_argument('--log_file', default='./pretreatment.log', type=str,
-                    help='Log file path. Default: ./pretreatment.log')
-parser.add_argument('--log', default=False, type=boolean_string,
-                    help='If set as True, all logs will be saved. '
-                         'Otherwise, only warnings and errors will be saved.'
-                         'Default: False')
-parser.add_argument('--worker_num', default=1, type=int,
-                    help='How many subprocesses to use for data pretreatment. '
-                         'Default: 1')
-parser.add_argument('--img_size', default=64, type=int,
-                    help='image size')
-opt = parser.parse_args()
-
-INPUT_PATH = opt.input_path
-OUTPUT_PATH = opt.output_path
-IF_LOG = opt.log
-LOG_PATH = opt.log_file
-WORKERS = opt.worker_num
-
-T_H = opt.img_size
-T_W = opt.img_size
-
-def log2str(pid, comment, logs):
-    str_log = ''
-    if type(logs) is str:
-        logs = [logs]
-    for log in logs:
-        str_log += "# JOB %d : --%s-- %s\n" % (
-            pid, comment, log)
-    return str_log
-
-def log_print(pid, comment, logs):
-    str_log = log2str(pid, comment, logs)
-    if comment in [WARNING, FAIL]:
-        with open(LOG_PATH, 'a') as log_f:
-            log_f.write(str_log)
-    if comment in [START, FINISH]:
-        if pid % 500 != 0:
-            return
-    print(str_log, end='')
-
-def cut_img(img, seq_info, frame_name, pid):
-    # A silhouette contains too little white pixels
-    # might be not valid for identification.
-    if img.sum() <= 10000:
-        message = 'seq:%s, frame:%s, no data, %d.' % (
-            '-'.join(seq_info), frame_name, img.sum())
-        warn(message)
-        log_print(pid, WARNING, message)
-        return None
-    # Get the top and bottom point
-    y = img.sum(axis=1)
-    y_top = (y != 0).argmax(axis=0)
-    y_btm = (y != 0).cumsum(axis=0).argmax(axis=0)
-    img = img[y_top:y_btm + 1, :]
-    # As the height of a person is larger than the width,
-    # use the height to calculate resize ratio.
-    _r = img.shape[1] / img.shape[0]
-    _t_w = int(T_H * _r)
-    img = cv2.resize(img, (_t_w, T_H), interpolation=cv2.INTER_CUBIC)
-    # Get the median of x axis and regard it as the x center of the person.
-    sum_point = img.sum()
-    sum_column = img.sum(axis=0).cumsum()
-    x_center = -1
-    for i in range(sum_column.size):
-        if sum_column[i] > sum_point / 2:
-            x_center = i
-            break
-    if x_center < 0:
-        message = 'seq:%s, frame:%s, no center.' % (
-            '-'.join(seq_info), frame_name)
-        warn(message)
-        log_print(pid, WARNING, message)
-        return None
-    h_T_W = int(T_W / 2)
-    left = x_center - h_T_W
-    right = x_center + h_T_W
-    if left <= 0 or right >= img.shape[1]:
-        left += h_T_W
-        right += h_T_W
-        _ = np.zeros((img.shape[0], h_T_W))
-        img = np.concatenate([_, img, _], axis=1)
-    img = img[:, left:right]
-    return img.astype('uint8')
 
 
-def cut_pickle(seq_info, pid):
-    seq_name = '-'.join(seq_info)
-    log_print(pid, START, seq_name)
-    seq_path = os.path.join(INPUT_PATH, *seq_info)
-    out_dir = os.path.join(OUTPUT_PATH, *seq_info)
-    frame_list = os.listdir(seq_path)
-    frame_list.sort()
-    count_frame = 0
-    all_imgs = []
-    view = seq_info[-1]
-    for _frame_name in frame_list:
-        frame_path = os.path.join(seq_path, _frame_name)
-        img = cv2.imread(frame_path)[:, :, 0]
-        img = cut_img(img, seq_info, _frame_name, pid)
-        if img is not None:
-            # Save the cut img
-            all_imgs.append(img)
-            count_frame += 1
 
-    all_imgs = np.asarray(all_imgs)
+def pretreat(input_path: Path, output_path: Path, img_size: int = 64, workers: int = 4, verbose: bool = False) -> None:
+    """Reads a dataset and saves the data in pickle format.
 
-    if count_frame > 0:
+    Args:
-        os.makedirs(out_dir)
+        input_path (Path): Dataset root path.
-        all_imgs_pkl = os.path.join(out_dir, '{}.pkl'.format(view))
+        output_path (Path): Output path.
-        pickle.dump(all_imgs, open(all_imgs_pkl, 'wb'))    
+        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.
+    """
+    img_groups = defaultdict(list)
+    logging.info(f'Listing {input_path}')
+    total_files = 0
+    for img_path in input_path.rglob('*.png'):
+        if verbose:
+            logging.debug(f'Adding {img_path}')
+        *_, sid, seq, view, _ = img_path.as_posix().split('/')
+        img_groups[(sid, seq, view)].append(img_path)
+        total_files += 1
 
-    # Warn if the sequence contains less than 5 frames
+    logging.info(f'Total files listed: {total_files}')
-    if count_frame < 5:
-        message = 'seq:%s, less than 5 valid data.' % (
-            '-'.join(seq_info))
-        warn(message)
-        log_print(pid, WARNING, message)
 
-    log_print(pid, FINISH,
+    progress = tqdm(total=len(img_groups), desc='Pretreating', unit='folder')
-              'Contain %d valid frames. Saved to %s.'
+
-              % (count_frame, out_dir))
+    with mp.Pool(workers) as pool:
+        logging.info(f'Start pretreating {input_path}')
+        for _ in pool.imap_unordered(partial(imgs2pickle, output_path=output_path, img_size=img_size, verbose=verbose), img_groups.items()):
+            progress.update(1)
+    logging.info('Done')
 
 
-pool = Pool(WORKERS)
+if __name__ == '__main__':
-results = list()
+    parser = argparse.ArgumentParser(description='OpenGait dataset pretreatment module.')
-pid = 0
+    parser.add_argument('-r', '--input_path', default='', type=str, help='Root path of raw dataset.')
+    parser.add_argument('-o', '--output_path', default='', type=str, help='Output path of pickled dataset.')
+    parser.add_argument('-l', '--log_file', default='./pretreatment.log', type=str, help='Log file path. Default: ./pretreatment.log')
+    parser.add_argument('-n', '--n_workers', default=4, type=int, help='Number of thread workers. Default: 4')
+    parser.add_argument('-i', '--img_size', default=64, type=int, help='Image resizing size. Default 64')
+    parser.add_argument('-v', '--verbose', default=False, action='store_true', help='Display debug info.')
+    args = parser.parse_args()
 
-print('Pretreatment Start.\n'
+    logging.basicConfig(level=logging.INFO, filename=args.log_file, filemode='w', format='[%(asctime)s - %(levelname)s]: %(message)s')
-      'Input path: %s\n'
+    
-      'Output path: %s\n'
+    if args.verbose:
-      'Log file: %s\n'
+        logging.getLogger().setLevel(logging.DEBUG)
-      'Worker num: %d' % (
+        logging.info('Verbose mode is on.')
-          INPUT_PATH, OUTPUT_PATH, LOG_PATH, WORKERS))
+        for k, v in args.__dict__.items():
+            logging.debug(f'{k}: {v}')
 
-id_list = os.listdir(INPUT_PATH)
+    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)
-id_list.sort()
-# Walk the input path
-for _id in id_list:
-    seq_type = os.listdir(os.path.join(INPUT_PATH, _id))
-    seq_type.sort()
-    for _seq_type in seq_type:
-        view = os.listdir(os.path.join(INPUT_PATH, _id, _seq_type))
-        view.sort()
-        for _view in view:
-            seq_info = [_id, _seq_type, _view]
-            out_dir = os.path.join(OUTPUT_PATH, *seq_info)
-            # os.makedirs(out_dir)
-            results.append(
-                pool.apply_async(
-                    cut_pickle,
-                    args=(seq_info, pid)))
-            sleep(0.02)
-            pid += 1
-
-pool.close()
-unfinish = 1
-while unfinish > 0:
-    unfinish = 0
-    for i, res in enumerate(results):
-        try:
-            res.get(timeout=0.1)
-        except Exception as e:
-            if type(e) == MP_TimeoutError:
-                unfinish += 1
-                continue
-            else:
-                print('\n\n\nERROR OCCUR: PID ##%d##, ERRORTYPE: %s\n\n\n',
-                      i, type(e))
-                raise e
-pool.join()

misc/rearrange_OUMVLP.py

@@ -1,59 +1,47 @@
+import argparse
 import os
 import shutil
+from pathlib import Path
+
 from tqdm import tqdm
-import argparse
 
 
-parser = argparse.ArgumentParser(description='Test')
+TOTAL_SUBJECTS = 10307
-parser.add_argument('--input_path', default='/home1/data/OUMVLP_raw', type=str,
-                    help='Root path of raw dataset.')
-parser.add_argument('--output_path', default='/home1/data/OUMVLP_rearranged', type=str,
-                    help='Root path for output.')
 
 
-opt = parser.parse_args()
+def sanitize(name: str) -> (str, str):
-
+    return name.split('_')[1].split('-')
-INPUT_PATH = opt.input_path
-OUTPUT_PATH = opt.output_path
 
 
-def mv_dir(src, dst):
+def rearrange(input_path: Path, output_path: Path) -> None:
-    shutil.copytree(src, dst)
+    os.makedirs(output_path, exist_ok=True)
-    print(src, dst)
+
+    for folder in input_path.iterdir():
+        print(f'Rearranging {folder}')
+        view, seq = sanitize(folder.name)
+        progress = tqdm(total=TOTAL_SUBJECTS)
+        for sid in folder.iterdir():
+            src = os.path.join(input_path, f'Silhouette_{view}-{seq}', sid.name)
+            dst = os.path.join(output_path, sid.name, seq, view)
+            os.makedirs(dst, exist_ok=True)
+            for subfile in os.listdir(src):
+                if subfile not in os.listdir(dst) and subfile.endswith('.png'):
+                    os.symlink(os.path.join(src, subfile),
+                               os.path.join(dst, subfile))
+                # else:
+                #     os.remove(os.path.join(src, subfile))
+            progress.update(1)
 
 
-sils_name_list = os.listdir(INPUT_PATH)
+if __name__ == '__main__':
-name_space = 'Silhouette_'
+    parser = argparse.ArgumentParser(description='OUMVLP rearrange tool')
-views = sorted(list(
+    parser.add_argument('-i', '--input_path', required=True, type=str,
-    set([each.replace(name_space, '').split('-')[0] for each in sils_name_list])))
+                        help='Root path of raw dataset.')
-seqs = sorted(list(
+    parser.add_argument('-o', '--output_path', default='OUMVLP_rearranged', type=str,
-    set([each.replace(name_space, '').split('-')[1] for each in sils_name_list])))
+                        help='Root path for output.')
-ids = list()
-for each in sils_name_list:
-    ids.extend(os.listdir(os.path.join(INPUT_PATH, each)))
 
+    args = parser.parse_args()
 
-progress = tqdm(total=len(set(ids)))
+    input_path = Path(args.input_path).resolve()
-
+    output_path = Path(args.output_path).resolve()
-
+    rearrange(input_path, output_path)
-results = list()
-pid = 0
-for _id in sorted(set(ids)):
-    progress.update(1)
-    for _view in views:
-        for _seq in seqs:
-            seq_info = [_id, _seq, _view]
-            name = name_space + _view + '-' + _seq + '/' + _id
-            src = os.path.join(INPUT_PATH, name)
-            dst = os.path.join(OUTPUT_PATH, *seq_info)
-            if os.path.exists(src):
-                try:
-                    if os.path.exists(dst):
-                        pass
-                    else:
-                        os.makedirs(dst)
-                    for subfile in os.listdir(src):
-                        os.symlink(os.path.join(src, subfile),
-                                   os.path.join(dst, subfile))
-                except OSError as err:
-                    print(err)