| |
| import argparse |
| import os.path as osp |
|
|
| from mmengine.config import Config, DictAction |
| from mmengine.registry import init_default_scope |
| from mmengine.utils import ProgressBar |
|
|
| from mmdet.models.utils import mask2ndarray |
| from mmdet.registry import DATASETS, VISUALIZERS |
| from mmdet.structures.bbox import BaseBoxes |
|
|
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser(description='Browse a dataset') |
| parser.add_argument('--config',default='/media/ubuntu/lzx/HSI_OD/mmdetection/configs/1_htd/faster-rcnn_r50_fpn_1x_hsic.py', help='train config file path') |
| parser.add_argument( |
| '--output-dir', |
| default='../show', |
| 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=2, |
| 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) |
|
|
| |
| init_default_scope(cfg.get('default_scope', 'mmdet')) |
|
|
| 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]] |
| 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() |
|
|
