Merge pull request #907 from irvingzhang0512/check-video-tool

[Improvement] Add a tool to find invalid videos.

docs/changelog.md

@@ -12,6 +12,8 @@
 
 **Improvements**
 
+- Add a tool to find invalid videos ([#907](https://github.com/open-mmlab/mmaction2/pull/907))
+
 **Bug and Typo Fixes**
 
 **ModelZoo**

docs/useful_tools.md

@@ -154,3 +154,11 @@ python tools/analysis/eval_metric.py ${CONFIG_FILE} ${RESULT_FILE} [--eval ${EVA
 ```shell
 python tools/print_config.py ${CONFIG} [-h] [--options ${OPTIONS [OPTIONS...]}]
 ```
+
+### Check videos
+
+`tools/check_videos.py` uses specified video encoder to iterate all samples that are specified by the input configuration file, looks for invalid videos (corrupted or missing), and saves the corresponding file path to the output file. Please note that after deleting invalid videos, users need to regenerate the video file list.
+
+```shell
+python tools/check_videos.py ${CONFIG} [-h] [--options OPTIONS [OPTIONS ...]] [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] [--output-file OUTPUT_FILE] [--split SPLIT] [--decoder DECODER] [--num-processes NUM_PROCESSES] [--remove-corrupted-videos]
+```

docs_zh_CN/useful_tools.md

@@ -151,3 +151,11 @@ python tools/analysis/eval_metric.py ${CONFIG_FILE} ${RESULT_FILE} [--eval ${EVA
 ```shell
 python tools/print_config.py ${CONFIG} [-h] [--options ${OPTIONS [OPTIONS...]}]
 ```
+
+### 检查视频
+
+`tools/check_videos.py` 脚本利用指定视频编码器，遍历指定配置文件视频数据集中所有样本，寻找无效视频文件（文件破损或者文件不存在），并将无效文件路径保存到输出文件中。请注意，删除无效视频文件后，需要重新生成视频文件列表。
+
+```shell
+python tools/check_videos.py ${CONFIG} [-h] [--options OPTIONS [OPTIONS ...]] [--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]] [--output-file OUTPUT_FILE] [--split SPLIT] [--decoder DECODER] [--num-processes NUM_PROCESSES] [--remove-corrupted-videos]
+```

tools/check_videos.py

@@ -0,0 +1,159 @@
+import argparse
+import os
+import warnings
+from functools import partial
+from multiprocessing import Manager, Pool
+
+import numpy as np
+from mmcv import Config, DictAction
+from tqdm import tqdm
+
+from mmaction.datasets import PIPELINES, build_dataset
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='MMAction2 check datasets')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument(
+        '--options',
+        nargs='+',
+        action=DictAction,
+        default={},
+        help='custom options for evaluation, the key-value pair in xxx=yyy '
+        'format will be kwargs for dataset.evaluate() function (deprecate), '
+        'change to --eval-options instead.')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        default={},
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. For example, '
+        "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
+    parser.add_argument(
+        '--output-file',
+        default='invalid-video.txt',
+        help='Output file path which keeps corrupted/missing video file paths')
+    parser.add_argument(
+        '--split',
+        default='train',
+        help='Dataset split, should be one of [train, val, test]')
+    parser.add_argument(
+        '--decoder',
+        default='decord',
+        help='Video decoder type, should be one of [decord, opencv, pyav]')
+    parser.add_argument(
+        '--num-processes',
+        type=int,
+        default=5,
+        help='Number of processes to check videos')
+    parser.add_argument(
+        '--remove-corrupted-videos',
+        action='store_true',
+        help='Whether to delete all corrupted videos')
+    args = parser.parse_args()
+
+    if args.options and args.eval_options:
+        raise ValueError(
+            '--options and --eval-options cannot be both '
+            'specified, --options is deprecated in favor of --eval-options')
+    if args.options:
+        warnings.warn('--options is deprecated in favor of --eval-options')
+        args.eval_options = args.options
+    return args
+
+
+@PIPELINES.register_module()
+class RandomSampleFrames:
+
+    def __call__(self, results):
+        """Select frames to verify.
+
+        Select the first, last and three random frames, Required key is
+        "total_frames", added or modified key is "frame_inds".
+        Args:
+            results (dict): The resulting dict to be modified and passed
+                to the next transform in pipeline.
+        """
+        assert results['total_frames'] > 0
+
+        # first and last frames
+        results['frame_inds'] = np.array([0, results['total_frames'] - 1])
+
+        # choose 3 random frames
+        if results['total_frames'] > 2:
+            results['frame_inds'] = np.concatenate([
+                results['frame_inds'],
+                np.random.randint(1, results['total_frames'] - 1, 3)
+            ])
+
+        return results
+
+
+def _do_check_videos(lock, dataset, output_file, idx):
+    try:
+        dataset[idx]
+    except:  # noqa
+        # save invalid video path to output file
+        lock.acquire()
+        with open(output_file, 'a') as f:
+            f.write(dataset.video_infos[idx]['filename'] + '\n')
+        lock.release()
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    assert args.split in ['train', 'val', 'test']
+
+    decoder_to_pipeline_prefix = dict(
+        decord='Decord',
+        opencv='OpenCV',
+        pyav='PyAV',
+    )
+    assert args.decoder in decoder_to_pipeline_prefix
+
+    # read config file
+    cfg = Config.fromfile(args.config)
+    cfg.merge_from_dict(args.cfg_options)
+
+    # build dataset
+    dataset_type = cfg.data[args.split].type
+    assert dataset_type == 'VideoDataset'
+    cfg.data[args.split].pipeline = [
+        dict(type=decoder_to_pipeline_prefix[args.decoder] + 'Init'),
+        dict(type='RandomSampleFrames'),
+        dict(type=decoder_to_pipeline_prefix[args.decoder] + 'Decode')
+    ]
+    dataset = build_dataset(cfg.data[args.split],
+                            dict(test_mode=(args.split != 'train')))
+
+    # prepare for checking
+    if os.path.exists(args.output_file):
+        # remove exsiting output file
+        os.remove(args.output_file)
+    pool = Pool(args.num_processes)
+    lock = Manager().Lock()
+    worker_fn = partial(_do_check_videos, lock, dataset, args.output_file)
+    ids = range(len(dataset))
+
+    # start checking
+    for _ in tqdm(pool.imap_unordered(worker_fn, ids), total=len(ids)):
+        pass
+    pool.join()
+
+    # print results and release resources
+    pool.close()
+    with open(args.output_file, 'r') as f:
+        print(f'Checked {len(dataset)} videos, '
+              f'{len(f)} is/are corrupted/missing.')
+
+    if args.remove_corrupted_videos:
+        print('Start deleting corrupted videos')
+        cnt = 0
+        with open(args.output_file, 'r') as f:
+            for line in f:
+                if os.path.exists(line.strip()):
+                    os.remove(line.strip())
+                    cnt += 1
+        print(f'Delete {cnt} corrupted videos.')

tools/data/kinetics/generate_videos_filelist.sh

@@ -14,4 +14,4 @@ echo "Train filelist for video generated."
 
 PYTHONPATH=. python tools/data/build_file_list.py ${DATASET} data/${DATASET}/videos_val/ --level 2 --format videos --num-split 1 --subset val --shuffle
 echo "Val filelist for video generated."
-cd tools/data/${DATASET}/
+cd tools/data/kinetics/