mirror of https://github.com/open-mmlab/mmyolo.git
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
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# 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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from mmdet.models.utils import mask2ndarray
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from mmdet.structures.bbox import BaseBoxes
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from mmengine.config import Config, DictAction
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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 mmyolo.registry import DATASETS, VISUALIZERS
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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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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=0,
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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 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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# register all modules in mmdet into the registries
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init_default_scope(cfg.get('default_scope', 'mmyolo'))
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dataset = DATASETS.build(cfg.train_dataloader.dataset)
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visualizer = VISUALIZERS.build(cfg.visualizer)
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visualizer.dataset_meta = dataset.metainfo
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progress_bar = ProgressBar(len(dataset))
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for item in dataset:
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img = item['inputs'].permute(1, 2, 0).numpy()
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data_sample = item['data_samples'].numpy()
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gt_instances = data_sample.gt_instances
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img_path = osp.basename(item['data_samples'].img_path)
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out_file = osp.join(
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args.output_dir,
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osp.basename(img_path)) if args.output_dir is not None else None
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img = img[..., [2, 1, 0]] # bgr to rgb
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gt_bboxes = gt_instances.get('bboxes', None)
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if gt_bboxes is not None and isinstance(gt_bboxes, BaseBoxes):
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gt_instances.bboxes = gt_bboxes.tensor
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gt_masks = gt_instances.get('masks', None)
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if gt_masks is not None:
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masks = mask2ndarray(gt_masks)
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gt_instances.masks = masks.astype(bool)
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data_sample.gt_instances = gt_instances
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visualizer.add_datasample(
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osp.basename(img_path),
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img,
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data_sample,
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draw_pred=False,
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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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