| |
|
| | import argparse
|
| | import sys
|
| | import warnings
|
| | from copy import deepcopy
|
| |
|
| | import cv2
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| | import mmcv
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| | import numpy as np
|
| | from mmengine.config import Config, DictAction
|
| | from mmengine.dataset import Compose
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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 mmengine.visualization import Visualizer
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| |
|
| | from mmaction.registry import DATASETS
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| | from mmaction.visualization import ActionVisualizer
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| | from mmaction.visualization.action_visualizer import _get_adaptive_scale
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| |
|
| |
|
| | 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='output directory')
|
| | parser.add_argument('--label', default=None, type=str, help='label file')
|
| | parser.add_argument(
|
| | '--phase',
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| | '-p',
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| | default='train',
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| | type=str,
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| | choices=['train', 'test', 'val'],
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| | help='phase of dataset to visualize, accept "train" "test" and "val".'
|
| | ' Defaults to "train".')
|
| | parser.add_argument(
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| | '--show-number',
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| | '-n',
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| | type=int,
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| | default=sys.maxsize,
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| | help='number of images selected to visualize, must bigger than 0. if '
|
| | 'the number is bigger than length of dataset, show all the images in '
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| | 'dataset; default "sys.maxsize", show all images in dataset')
|
| | parser.add_argument(
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| | '--fps',
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| | default=5,
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| | type=int,
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| | help='specify fps value of the output video when using rawframes to '
|
| | 'generate file')
|
| | parser.add_argument(
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| | '--mode',
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| | '-m',
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| | default='transformed',
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| | type=str,
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| | choices=['original', 'transformed', 'concat', 'pipeline'],
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| | help='display mode; display original pictures or transformed pictures'
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| | ' or comparison pictures. "original" means show images load from disk'
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| | '; "transformed" means to show images after transformed; "concat" '
|
| | 'means show images stitched by "original" and "output" images. '
|
| | '"pipeline" means show all the intermediate images. '
|
| | 'Defaults to "transformed".')
|
| | parser.add_argument(
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| | '--rescale-factor',
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| | '-r',
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| | type=float,
|
| | help='video rescale factor, which is useful if the output is too '
|
| | 'large or too small.')
|
| | 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 '
|
| | '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 make_grid(videos, names, rescale_factor=None):
|
| | """Concat list of pictures into a single big picture, align height here."""
|
| | vis = Visualizer()
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| |
|
| | ori_shapes = [vid[0].shape[:2] for vid in videos]
|
| | if rescale_factor is not None:
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| | videos = [[mmcv.imrescale(img, rescale_factor) for img in video]
|
| | for video in videos]
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| |
|
| | max_height = int(max(vid[0].shape[0] for vid in videos) * 1.4)
|
| | min_width = min(vid[0].shape[1] for vid in videos)
|
| | horizontal_gap = min_width // 10
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| | img_scale = _get_adaptive_scale((max_height, min_width))
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| |
|
| | texts = []
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| | text_positions = []
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| | start_x = 0
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| | for i, vid in enumerate(videos):
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| | for j, img in enumerate(vid):
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| | pad_height = (max_height - img.shape[0]) // 2
|
| | pad_width = horizontal_gap // 2
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| |
|
| | videos[i][j] = cv2.copyMakeBorder(
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| | img,
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| | pad_height,
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| | max_height - img.shape[0] - pad_height +
|
| | int(img_scale * 30 * 2),
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| | pad_width,
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| | pad_width,
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| | cv2.BORDER_CONSTANT,
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| | value=(255, 255, 255))
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| |
|
| | texts.append(f'{names[i]}\n{ori_shapes[i]}')
|
| | text_positions.append(
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| | [start_x + img.shape[1] // 2 + pad_width, max_height])
|
| | start_x += img.shape[1] + horizontal_gap
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| |
|
| | out_frames = []
|
| | for i in range(len(videos[0])):
|
| | imgs = [vid[i] for vid in videos]
|
| | display_img = np.concatenate(imgs, axis=1)
|
| | vis.set_image(display_img)
|
| | img_scale = _get_adaptive_scale(display_img.shape[:2])
|
| | vis.draw_texts(
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| | texts,
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| | positions=np.array(text_positions),
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| | font_sizes=img_scale * 7,
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| | colors='black',
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| | horizontal_alignments='center',
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| | font_families='monospace')
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| | out_frames.append(vis.get_image())
|
| | return out_frames
|
| |
|
| |
|
| | class InspectCompose(Compose):
|
| | """Compose multiple transforms sequentially.
|
| |
|
| | And record "imgs" field of all results in one list.
|
| | """
|
| |
|
| | def __init__(self, transforms, intermediate_imgs):
|
| | super().__init__(transforms=transforms)
|
| | self.intermediate_imgs = intermediate_imgs
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| |
|
| | def __call__(self, data):
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| |
|
| | for idx, t in enumerate(self.transforms):
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| | data = t(data)
|
| | if data is None:
|
| | return None
|
| | if 'imgs' in data:
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| | name = t.__class__.__name__
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| | imgs = deepcopy(data['imgs'])
|
| | if name == 'FormatShape':
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| | continue
|
| | if name == 'ThreeCrop':
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| | n_crops = 3
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| | clip_len = len(imgs) // n_crops
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| | crop_imgs = [
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| | imgs[idx * clip_len:(idx + 1) * clip_len]
|
| | for idx in range(n_crops)
|
| | ]
|
| | imgs = np.concatenate(crop_imgs, axis=1)
|
| | imgs = [img for img in imgs]
|
| | if name == 'TenCrop':
|
| | warnings.warn(
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| | 'TenCrop is not supported, only show one crop')
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| | self.intermediate_imgs.append({'name': name, 'imgs': imgs})
|
| | return data
|
| |
|
| |
|
| | 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', 'mmaction'))
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| |
|
| | dataset_cfg = cfg.get(args.phase + '_dataloader').get('dataset')
|
| | dataset = DATASETS.build(dataset_cfg)
|
| |
|
| | intermediate_imgs = []
|
| | dataset.pipeline = InspectCompose(dataset.pipeline.transforms,
|
| | intermediate_imgs)
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| |
|
| |
|
| | vis_backends = [dict(
|
| | type='LocalVisBackend',
|
| | save_dir=args.output_dir,
|
| | )]
|
| | visualizer = ActionVisualizer(
|
| | vis_backends=vis_backends, save_dir='place_holder')
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| |
|
| | if args.label:
|
| | labels = open(args.label).readlines()
|
| | labels = [x.strip() for x in labels]
|
| | visualizer.dataset_meta = dict(classes=labels)
|
| |
|
| |
|
| | display_number = min(args.show_number, len(dataset))
|
| | progress_bar = ProgressBar(display_number)
|
| |
|
| | for i, item in zip(range(display_number), dataset):
|
| | rescale_factor = args.rescale_factor
|
| | if args.mode == 'original':
|
| | video = intermediate_imgs[0]['imgs']
|
| | elif args.mode == 'transformed':
|
| | video = intermediate_imgs[-1]['imgs']
|
| | elif args.mode == 'concat':
|
| | ori_video = intermediate_imgs[0]['imgs']
|
| | trans_video = intermediate_imgs[-1]['imgs']
|
| | video = make_grid([ori_video, trans_video],
|
| | ['original', 'transformed'], rescale_factor)
|
| | rescale_factor = None
|
| | else:
|
| | video = make_grid([result['imgs'] for result in intermediate_imgs],
|
| | [result['name'] for result in intermediate_imgs],
|
| | rescale_factor)
|
| | rescale_factor = None
|
| |
|
| | intermediate_imgs.clear()
|
| |
|
| | data_sample = item['data_samples'].numpy()
|
| |
|
| | file_id = f'video_{i}'
|
| | video = [x[..., ::-1] for x in video]
|
| | visualizer.add_datasample(
|
| | file_id, video, data_sample, fps=args.fps, out_type='video')
|
| | progress_bar.update()
|
| |
|
| |
|
| | if __name__ == '__main__':
|
| | main()
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| |
|
