# Copyright (c) OpenMMLab. All rights reserved. import argparse import os.path as osp import sys 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 mmpretrain.datasets.builder import build_dataset from mmpretrain.visualization import ClsVisualizer from mmpretrain.visualization.cls_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', '-o', 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', '-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( '--show-interval', '-i', type=float, default=2, help='the interval of show (s)') 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='image rescale factor, which is useful if the output is too ' 'large or too small.') parser.add_argument( '--channel-order', '-c', default='BGR', choices=['BGR', 'RGB'], help='The channel order of the showing images, could be "BGR" ' 'or "RGB", Defaults to "BGR".') 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(imgs, names, rescale_factor=None): """Concat list of pictures into a single big picture, align height here.""" vis = Visualizer() ori_shapes = [img.shape[:2] for img in imgs] if rescale_factor is not None: imgs = [mmcv.imrescale(img, rescale_factor) for img in imgs] max_height = int(max(img.shape[0] for img in imgs) * 1.1) min_width = min(img.shape[1] for img in imgs) horizontal_gap = min_width // 10 img_scale = _get_adaptive_scale((max_height, min_width)) texts = [] text_positions = [] start_x = 0 for i, img in enumerate(imgs): pad_height = (max_height - img.shape[0]) // 2 pad_width = horizontal_gap // 2 # make border imgs[i] = 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 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') return vis.get_image() class InspectCompose(Compose): """Compose multiple transforms sequentially. And record "img" 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): if 'img' in data: self.intermediate_imgs.append({ 'name': 'original', 'img': data['img'].copy() }) for t in self.transforms: data = t(data) if data is None: return None if 'img' in data: self.intermediate_imgs.append({ 'name': t.__class__.__name__, 'img': data['img'].copy() }) 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('mmpretrain') # Use mmpretrain as default scope. dataset_cfg = cfg.get(args.phase + '_dataloader').get('dataset') dataset = build_dataset(dataset_cfg) intermediate_imgs = [] dataset.pipeline = InspectCompose(dataset.pipeline.transforms, intermediate_imgs) # init visualizer cfg.visualizer.pop('type') visualizer = ClsVisualizer(**cfg.visualizer) visualizer.dataset_meta = dataset.metainfo # init visualization image 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': image = intermediate_imgs[0]['img'] elif args.mode == 'transformed': image = intermediate_imgs[-1]['img'] elif args.mode == 'concat': ori_image = intermediate_imgs[0]['img'] trans_image = intermediate_imgs[-1]['img'] image = make_grid([ori_image, trans_image], ['original', 'transformed'], rescale_factor) rescale_factor = None else: image = make_grid([result['img'] 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() # get filename from dataset or just use index as filename if hasattr(item['data_samples'], 'img_path'): filename = osp.basename(item['data_samples'].img_path) else: # some dataset have not image path filename = f'{i}.jpg' out_file = osp.join(args.output_dir, filename) if args.output_dir is not None else None visualizer.add_datasample( filename, image if args.channel_order == 'RGB' else image[..., ::-1], data_sample, rescale_factor=rescale_factor, show=not args.not_show, wait_time=args.show_interval, out_file=out_file) progress_bar.update() if __name__ == '__main__': main()