236 lines
7.9 KiB
Python
236 lines
7.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os.path as osp
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import sys
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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 mmpretrain.datasets.builder import build_dataset
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from mmpretrain.visualization import ClsVisualizer
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from mmpretrain.visualization.cls_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',
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'-o',
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default=None,
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type=str,
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help='If there is no display interface, you can save it.')
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parser.add_argument('--not-show', default=False, action='store_true')
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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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'--show-interval',
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'-i',
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type=float,
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default=2,
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help='the interval of show (s)')
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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='image 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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'--channel-order',
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'-c',
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default='BGR',
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choices=['BGR', 'RGB'],
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help='The channel order of the showing images, could be "BGR" '
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'or "RGB", Defaults to "BGR".')
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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(imgs, 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 = [img.shape[:2] for img in imgs]
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if rescale_factor is not None:
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imgs = [mmcv.imrescale(img, rescale_factor) for img in imgs]
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max_height = int(max(img.shape[0] for img in imgs) * 1.1)
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min_width = min(img.shape[1] for img in imgs)
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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, img in enumerate(imgs):
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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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# make border
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imgs[i] = cv2.copyMakeBorder(
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img,
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pad_height,
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max_height - img.shape[0] - pad_height + 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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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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return vis.get_image()
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class InspectCompose(Compose):
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"""Compose multiple transforms sequentially.
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And record "img" 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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if 'img' in data:
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self.intermediate_imgs.append({
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'name': 'original',
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'img': data['img'].copy()
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})
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for t in 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 'img' in data:
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self.intermediate_imgs.append({
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'name': t.__class__.__name__,
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'img': data['img'].copy()
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})
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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('mmpretrain') # Use mmpretrain as default scope.
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dataset_cfg = cfg.get(args.phase + '_dataloader').get('dataset')
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dataset = build_dataset(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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# init visualizer
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cfg.visualizer.pop('type')
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visualizer = ClsVisualizer(**cfg.visualizer)
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visualizer.dataset_meta = dataset.metainfo
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# init visualization image number
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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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image = intermediate_imgs[0]['img']
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elif args.mode == 'transformed':
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image = intermediate_imgs[-1]['img']
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elif args.mode == 'concat':
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ori_image = intermediate_imgs[0]['img']
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trans_image = intermediate_imgs[-1]['img']
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image = make_grid([ori_image, trans_image],
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['original', 'transformed'], rescale_factor)
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rescale_factor = None
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else:
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image = make_grid([result['img'] 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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# get filename from dataset or just use index as filename
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if hasattr(item['data_samples'], 'img_path'):
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filename = osp.basename(item['data_samples'].img_path)
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else:
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# some dataset have not image path
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filename = f'{i}.jpg'
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out_file = osp.join(args.output_dir,
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filename) if args.output_dir is not None else None
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visualizer.add_datasample(
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filename,
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image if args.channel_order == 'RGB' else image[..., ::-1],
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data_sample,
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rescale_factor=rescale_factor,
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show=not args.not_show,
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wait_time=args.show_interval,
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out_file=out_file)
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progress_bar.update()
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if __name__ == '__main__':
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main()
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