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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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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]] |
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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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