106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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from collections import Sequence
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from pathlib import Path
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import mmcv
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from mmcv import Config, DictAction
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from mmdet.core.utils import mask2ndarray
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from mmdet.core.visualization import imshow_det_bboxes
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from mmdet.datasets.builder import build_dataset
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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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'--skip-type',
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type=str,
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nargs='+',
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default=['DefaultFormatBundle', 'Normalize', 'Collect'],
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help='skip some useless pipeline')
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parser.add_argument(
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'--output-dir',
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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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'--show-interval',
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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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'--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 retrieve_data_cfg(config_path, skip_type, cfg_options):
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def skip_pipeline_steps(config):
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config['pipeline'] = [
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x for x in config.pipeline if x['type'] not in skip_type
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]
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cfg = Config.fromfile(config_path)
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if cfg_options is not None:
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cfg.merge_from_dict(cfg_options)
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train_data_cfg = cfg.data.train
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while 'dataset' in train_data_cfg and train_data_cfg[
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'type'] != 'MultiImageMixDataset':
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train_data_cfg = train_data_cfg['dataset']
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if isinstance(train_data_cfg, Sequence):
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[skip_pipeline_steps(c) for c in train_data_cfg]
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else:
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skip_pipeline_steps(train_data_cfg)
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return cfg
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def main():
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args = parse_args()
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cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options)
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dataset = build_dataset(cfg.data.train)
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progress_bar = mmcv.ProgressBar(len(dataset))
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for item in dataset:
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filename = os.path.join(args.output_dir,
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Path(item['filename']).name
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) if args.output_dir is not None else None
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gt_masks = item.get('gt_masks', None)
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if gt_masks is not None:
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gt_masks = mask2ndarray(gt_masks)
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imshow_det_bboxes(
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item['img'],
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item['gt_bboxes'],
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item['gt_labels'],
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gt_masks,
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class_names=dataset.CLASSES,
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show=not args.not_show,
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wait_time=args.show_interval,
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out_file=filename,
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bbox_color=(255, 102, 61),
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text_color=(255, 102, 61))
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progress_bar.update()
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if __name__ == '__main__':
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main()
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