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import argparse
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import sys
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import warnings
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from copy import deepcopy
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import cv2
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import mmcv
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import numpy as np
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from mmengine.config import Config, DictAction
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from mmengine.dataset import Compose
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from mmengine.registry import init_default_scope
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from mmengine.utils import ProgressBar
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from mmengine.visualization import Visualizer
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from mmaction.registry import DATASETS
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from mmaction.visualization import ActionVisualizer
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from mmaction.visualization.action_visualizer import _get_adaptive_scale
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def parse_args():
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parser = argparse.ArgumentParser(description='Browse a dataset')
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parser.add_argument('config', help='train config file path')
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parser.add_argument(
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'output_dir', default=None, type=str, help='output directory')
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parser.add_argument('--label', default=None, type=str, help='label file')
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parser.add_argument(
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'--phase',
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'-p',
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default='train',
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type=str,
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choices=['train', 'test', 'val'],
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help='phase of dataset to visualize, accept "train" "test" and "val".'
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' Defaults to "train".')
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parser.add_argument(
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'--show-number',
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'-n',
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type=int,
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default=sys.maxsize,
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help='number of images selected to visualize, must bigger than 0. if '
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'the number is bigger than length of dataset, show all the images in '
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'dataset; default "sys.maxsize", show all images in dataset')
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parser.add_argument(
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'--fps',
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default=5,
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type=int,
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help='specify fps value of the output video when using rawframes to '
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'generate file')
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parser.add_argument(
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'--mode',
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'-m',
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default='transformed',
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type=str,
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choices=['original', 'transformed', 'concat', 'pipeline'],
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help='display mode; display original pictures or transformed pictures'
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' or comparison pictures. "original" means show images load from disk'
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'; "transformed" means to show images after transformed; "concat" '
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'means show images stitched by "original" and "output" images. '
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'"pipeline" means show all the intermediate images. '
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'Defaults to "transformed".')
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parser.add_argument(
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'--rescale-factor',
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'-r',
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type=float,
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help='video rescale factor, which is useful if the output is too '
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'large or too small.')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. If the value to '
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
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'Note that the quotation marks are necessary and that no white space '
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'is allowed.')
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args = parser.parse_args()
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return args
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def make_grid(videos, names, rescale_factor=None):
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"""Concat list of pictures into a single big picture, align height here."""
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vis = Visualizer()
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ori_shapes = [vid[0].shape[:2] for vid in videos]
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if rescale_factor is not None:
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videos = [[mmcv.imrescale(img, rescale_factor) for img in video]
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for video in videos]
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max_height = int(max(vid[0].shape[0] for vid in videos) * 1.4)
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min_width = min(vid[0].shape[1] for vid in videos)
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horizontal_gap = min_width // 10
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img_scale = _get_adaptive_scale((max_height, min_width))
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texts = []
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text_positions = []
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start_x = 0
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for i, vid in enumerate(videos):
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for j, img in enumerate(vid):
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pad_height = (max_height - img.shape[0]) // 2
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pad_width = horizontal_gap // 2
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videos[i][j] = cv2.copyMakeBorder(
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img,
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pad_height,
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max_height - img.shape[0] - pad_height +
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int(img_scale * 30 * 2),
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pad_width,
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pad_width,
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cv2.BORDER_CONSTANT,
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value=(255, 255, 255))
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texts.append(f'{names[i]}\n{ori_shapes[i]}')
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text_positions.append(
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[start_x + img.shape[1] // 2 + pad_width, max_height])
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start_x += img.shape[1] + horizontal_gap
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out_frames = []
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for i in range(len(videos[0])):
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imgs = [vid[i] for vid in videos]
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display_img = np.concatenate(imgs, axis=1)
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vis.set_image(display_img)
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img_scale = _get_adaptive_scale(display_img.shape[:2])
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vis.draw_texts(
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texts,
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positions=np.array(text_positions),
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font_sizes=img_scale * 7,
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colors='black',
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horizontal_alignments='center',
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font_families='monospace')
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out_frames.append(vis.get_image())
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return out_frames
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class InspectCompose(Compose):
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"""Compose multiple transforms sequentially.
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And record "imgs" field of all results in one list.
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"""
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def __init__(self, transforms, intermediate_imgs):
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super().__init__(transforms=transforms)
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self.intermediate_imgs = intermediate_imgs
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def __call__(self, data):
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for idx, t in enumerate(self.transforms):
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data = t(data)
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if data is None:
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return None
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if 'imgs' in data:
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name = t.__class__.__name__
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imgs = deepcopy(data['imgs'])
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if name == 'FormatShape':
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continue
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if name == 'ThreeCrop':
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n_crops = 3
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clip_len = len(imgs) // n_crops
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crop_imgs = [
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imgs[idx * clip_len:(idx + 1) * clip_len]
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for idx in range(n_crops)
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]
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imgs = np.concatenate(crop_imgs, axis=1)
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imgs = [img for img in imgs]
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if name == 'TenCrop':
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warnings.warn(
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'TenCrop is not supported, only show one crop')
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self.intermediate_imgs.append({'name': name, 'imgs': imgs})
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return data
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def main():
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args = parse_args()
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cfg = Config.fromfile(args.config)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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init_default_scope(cfg.get('default_scope', 'mmaction'))
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dataset_cfg = cfg.get(args.phase + '_dataloader').get('dataset')
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dataset = DATASETS.build(dataset_cfg)
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intermediate_imgs = []
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dataset.pipeline = InspectCompose(dataset.pipeline.transforms,
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intermediate_imgs)
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vis_backends = [dict(
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type='LocalVisBackend',
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save_dir=args.output_dir,
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)]
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visualizer = ActionVisualizer(
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vis_backends=vis_backends, save_dir='place_holder')
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if args.label:
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labels = open(args.label).readlines()
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labels = [x.strip() for x in labels]
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visualizer.dataset_meta = dict(classes=labels)
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display_number = min(args.show_number, len(dataset))
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progress_bar = ProgressBar(display_number)
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for i, item in zip(range(display_number), dataset):
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rescale_factor = args.rescale_factor
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if args.mode == 'original':
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video = intermediate_imgs[0]['imgs']
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elif args.mode == 'transformed':
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video = intermediate_imgs[-1]['imgs']
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elif args.mode == 'concat':
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ori_video = intermediate_imgs[0]['imgs']
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trans_video = intermediate_imgs[-1]['imgs']
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video = make_grid([ori_video, trans_video],
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['original', 'transformed'], rescale_factor)
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rescale_factor = None
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else:
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video = make_grid([result['imgs'] for result in intermediate_imgs],
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[result['name'] for result in intermediate_imgs],
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rescale_factor)
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rescale_factor = None
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intermediate_imgs.clear()
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data_sample = item['data_samples'].numpy()
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file_id = f'video_{i}'
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video = [x[..., ::-1] for x in video]
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visualizer.add_datasample(
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file_id, video, data_sample, fps=args.fps, out_type='video')
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progress_bar.update()
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if __name__ == '__main__':
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main()
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