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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import os | |
| import os.path as osp | |
| import mmcv | |
| import mmengine | |
| import mmengine.fileio as fileio | |
| import numpy as np | |
| from mmengine import Config, DictAction | |
| from mmengine.registry import build_from_cfg, init_default_scope | |
| from mmengine.structures import InstanceData | |
| from mmpose.registry import DATASETS, VISUALIZERS | |
| from mmpose.structures import PoseDataSample | |
| 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( | |
| '--phase', | |
| default='train', | |
| type=str, | |
| choices=['train', 'test', 'val'], | |
| help='phase of dataset to visualize, accept "train" "test" and "val".' | |
| ' Defaults to "train".') | |
| parser.add_argument( | |
| '--show-interval', | |
| type=float, | |
| default=2, | |
| help='the interval of show (s)') | |
| parser.add_argument( | |
| '--mode', | |
| default='transformed', | |
| type=str, | |
| choices=['original', 'transformed'], | |
| help='display mode; display original pictures or transformed ' | |
| 'pictures. "original" means to show images load from disk' | |
| '; "transformed" means to show images after transformed;' | |
| 'Defaults to "transformed".') | |
| 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 generate_dup_file_name(out_file): | |
| """Automatically rename out_file when duplicated file exists. | |
| This case occurs when there is multiple instances on one image. | |
| """ | |
| if out_file and osp.exists(out_file): | |
| img_name, postfix = osp.basename(out_file).rsplit('.', 1) | |
| exist_files = tuple( | |
| filter(lambda f: f.startswith(img_name), | |
| os.listdir(osp.dirname(out_file)))) | |
| if len(exist_files) > 0: | |
| img_path = f'{img_name}({len(exist_files)}).{postfix}' | |
| out_file = osp.join(osp.dirname(out_file), img_path) | |
| return out_file | |
| def main(): | |
| args = parse_args() | |
| cfg = Config.fromfile(args.config) | |
| if args.cfg_options is not None: | |
| cfg.merge_from_dict(args.cfg_options) | |
| backend_args = cfg.get('backend_args', dict(backend='local')) | |
| # register all modules in mmpose into the registries | |
| scope = cfg.get('default_scope', 'mmpose') | |
| if scope is not None: | |
| init_default_scope(scope) | |
| if args.mode == 'original': | |
| cfg[f'{args.phase}_dataloader'].dataset.pipeline = [] | |
| else: | |
| # pack transformed keypoints for visualization | |
| cfg[f'{args.phase}_dataloader'].dataset.pipeline[ | |
| -1].pack_transformed = True | |
| dataset = build_from_cfg(cfg[f'{args.phase}_dataloader'].dataset, DATASETS) | |
| visualizer = VISUALIZERS.build(cfg.visualizer) | |
| visualizer.set_dataset_meta(dataset.metainfo) | |
| progress_bar = mmengine.ProgressBar(len(dataset)) | |
| idx = 0 | |
| item = dataset[0] | |
| while idx < len(dataset): | |
| idx += 1 | |
| next_item = None if idx >= len(dataset) else dataset[idx] | |
| if args.mode == 'original': | |
| if next_item is not None and item['img_path'] == next_item[ | |
| 'img_path']: | |
| # merge annotations for one image | |
| item['keypoints'] = np.concatenate( | |
| (item['keypoints'], next_item['keypoints'])) | |
| item['keypoints_visible'] = np.concatenate( | |
| (item['keypoints_visible'], | |
| next_item['keypoints_visible'])) | |
| item['bbox'] = np.concatenate( | |
| (item['bbox'], next_item['bbox'])) | |
| progress_bar.update() | |
| continue | |
| else: | |
| img_path = item['img_path'] | |
| img_bytes = fileio.get(img_path, backend_args=backend_args) | |
| img = mmcv.imfrombytes(img_bytes, channel_order='bgr') | |
| # forge pseudo data_sample | |
| gt_instances = InstanceData() | |
| gt_instances.keypoints = item['keypoints'] | |
| gt_instances.keypoints_visible = item['keypoints_visible'] | |
| gt_instances.bboxes = item['bbox'] | |
| data_sample = PoseDataSample() | |
| data_sample.gt_instances = gt_instances | |
| item = next_item | |
| else: | |
| img = item['inputs'].permute(1, 2, 0).numpy() | |
| data_sample = item['data_samples'] | |
| img_path = data_sample.img_path | |
| item = next_item | |
| out_file = osp.join( | |
| args.output_dir, | |
| osp.basename(img_path)) if args.output_dir is not None else None | |
| out_file = generate_dup_file_name(out_file) | |
| img = mmcv.bgr2rgb(img) | |
| visualizer.add_datasample( | |
| osp.basename(img_path), | |
| img, | |
| data_sample, | |
| draw_pred=False, | |
| draw_bbox=(args.mode == 'original'), | |
| draw_heatmap=True, | |
| show=not args.not_show, | |
| wait_time=args.show_interval, | |
| out_file=out_file) | |
| progress_bar.update() | |
| if __name__ == '__main__': | |
| main() | |