# Copyright (c) OpenMMLab. All rights reserved. import argparse import sys import warnings from copy import deepcopy import cv2 import mmcv import numpy as np from mmengine.config import Config, DictAction from mmengine.dataset import Compose from mmengine.registry import init_default_scope from mmengine.utils import ProgressBar from mmengine.visualization import Visualizer from mmaction.registry import DATASETS from mmaction.visualization import ActionVisualizer from mmaction.visualization.action_visualizer import _get_adaptive_scale 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', '-p', 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-number', '-n', type=int, default=sys.maxsize, 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 ' 'dataset; default "sys.maxsize", show all images in dataset') parser.add_argument( '--fps', default=5, type=int, help='specify fps value of the output video when using rawframes to ' 'generate file') parser.add_argument( '--mode', '-m', default='transformed', type=str, choices=['original', 'transformed', 'concat', 'pipeline'], help='display mode; display original pictures or transformed pictures' ' or comparison pictures. "original" means show images load from disk' '; "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( '--rescale-factor', '-r', type=float, help='video rescale factor, which is useful if the output is too ' 'large or too small.') 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 make_grid(videos, names, rescale_factor=None): """Concat list of pictures into a single big picture, align height here.""" vis = Visualizer() ori_shapes = [vid[0].shape[:2] for vid in videos] if rescale_factor is not None: videos = [[mmcv.imrescale(img, rescale_factor) for img in video] for video in videos] 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 img_scale = _get_adaptive_scale((max_height, min_width)) texts = [] text_positions = [] start_x = 0 for i, vid in enumerate(videos): for j, img in enumerate(vid): pad_height = (max_height - img.shape[0]) // 2 pad_width = horizontal_gap // 2 # make border videos[i][j] = cv2.copyMakeBorder( img, pad_height, max_height - img.shape[0] - pad_height + int(img_scale * 30 * 2), pad_width, pad_width, cv2.BORDER_CONSTANT, value=(255, 255, 255)) texts.append(f'{names[i]}\n{ori_shapes[i]}') text_positions.append( [start_x + img.shape[1] // 2 + pad_width, max_height]) start_x += img.shape[1] + horizontal_gap 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( texts, positions=np.array(text_positions), font_sizes=img_scale * 7, colors='black', horizontal_alignments='center', font_families='monospace') 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 def __call__(self, data): for idx, t in enumerate(self.transforms): data = t(data) if data is None: return None if 'imgs' in data: name = t.__class__.__name__ imgs = deepcopy(data['imgs']) if name == 'FormatShape': continue if name == 'ThreeCrop': n_crops = 3 clip_len = len(imgs) // n_crops crop_imgs = [ 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( 'TenCrop is not supported, only show one crop') 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')) dataset_cfg = cfg.get(args.phase + '_dataloader').get('dataset') dataset = DATASETS.build(dataset_cfg) intermediate_imgs = [] dataset.pipeline = InspectCompose(dataset.pipeline.transforms, intermediate_imgs) # init visualizer vis_backends = [dict( type='LocalVisBackend', save_dir=args.output_dir, )] visualizer = ActionVisualizer( vis_backends=vis_backends, save_dir='place_holder') if args.label: labels = open(args.label).readlines() labels = [x.strip() for x in labels] visualizer.dataset_meta = dict(classes=labels) # init visualization video number 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()