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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import os.path as osp | |
| from mmdet.models.utils import mask2ndarray | |
| from mmdet.structures.bbox import BaseBoxes | |
| from mmengine.config import Config, DictAction | |
| from mmengine.registry import init_default_scope | |
| from mmengine.utils import ProgressBar | |
| from mmyolo.registry import DATASETS, VISUALIZERS | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description='Browse a dataset') | |
| parser.add_argument('config', help='train config file path') | |
| parser.add_argument( | |
| '--output-dir', | |
| default=None, | |
| type=str, | |
| help='If there is no display interface, you can save it') | |
| parser.add_argument('--not-show', default=False, action='store_true') | |
| parser.add_argument( | |
| '--show-interval', | |
| type=float, | |
| default=0, | |
| help='the interval of show (s)') | |
| parser.add_argument( | |
| '--cfg-options', | |
| nargs='+', | |
| action=DictAction, | |
| help='override some settings in the used config, the key-value pair ' | |
| 'in xxx=yyy format will be merged into config file. If the value to ' | |
| 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' | |
| 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' | |
| 'Note that the quotation marks are necessary and that no white space ' | |
| 'is allowed.') | |
| args = parser.parse_args() | |
| return args | |
| def main(): | |
| args = parse_args() | |
| cfg = Config.fromfile(args.config) | |
| if args.cfg_options is not None: | |
| cfg.merge_from_dict(args.cfg_options) | |
| # register all modules in mmdet into the registries | |
| init_default_scope(cfg.get('default_scope', 'mmyolo')) | |
| dataset = DATASETS.build(cfg.train_dataloader.dataset) | |
| visualizer = VISUALIZERS.build(cfg.visualizer) | |
| visualizer.dataset_meta = dataset.metainfo | |
| progress_bar = ProgressBar(len(dataset)) | |
| for item in dataset: | |
| img = item['inputs'].permute(1, 2, 0).numpy() | |
| data_sample = item['data_samples'].numpy() | |
| gt_instances = data_sample.gt_instances | |
| img_path = osp.basename(item['data_samples'].img_path) | |
| out_file = osp.join( | |
| args.output_dir, | |
| osp.basename(img_path)) if args.output_dir is not None else None | |
| img = img[..., [2, 1, 0]] # bgr to rgb | |
| gt_bboxes = gt_instances.get('bboxes', None) | |
| if gt_bboxes is not None and isinstance(gt_bboxes, BaseBoxes): | |
| gt_instances.bboxes = gt_bboxes.tensor | |
| gt_masks = gt_instances.get('masks', None) | |
| if gt_masks is not None: | |
| masks = mask2ndarray(gt_masks) | |
| gt_instances.masks = masks.astype(bool) | |
| data_sample.gt_instances = gt_instances | |
| visualizer.add_datasample( | |
| osp.basename(img_path), | |
| img, | |
| data_sample, | |
| draw_pred=False, | |
| show=not args.not_show, | |
| wait_time=args.show_interval, | |
| out_file=out_file) | |
| progress_bar.update() | |
| if __name__ == '__main__': | |
| main() | |