# Copyright (c) OpenMMLab. All rights reserved. import argparse import os.path as osp import sys from typing import Tuple import cv2 import mmcv import numpy as np from mmdet.models.utils import mask2ndarray from mmdet.structures.bbox import BaseBoxes 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 mmyolo.registry import DATASETS, VISUALIZERS # TODO: Support for printing the change in key of results # TODO: Some bug. If you meet some bug, please use the original def parse_args(): parser = argparse.ArgumentParser(description='Browse a dataset') parser.add_argument('config', help='train config file path') 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( '--mode', '-m', default='transformed', type=str, choices=['original', 'transformed', '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; "pipeline" means show all ' 'the intermediate images. Defaults to "transformed".') parser.add_argument( '--out-dir', default='output', 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-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=3, 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 _get_adaptive_scale(img_shape: Tuple[int, int], min_scale: float = 0.3, max_scale: float = 3.0) -> float: """Get adaptive scale according to image shape. The target scale depends on the the short edge length of the image. If the short edge length equals 224, the output is 1.0. And output linear scales according the short edge length. You can also specify the minimum scale and the maximum scale to limit the linear scale. Args: img_shape (Tuple[int, int]): The shape of the canvas image. min_scale (int): The minimum scale. Defaults to 0.3. max_scale (int): The maximum scale. Defaults to 3.0. Returns: int: The adaptive scale. """ short_edge_length = min(img_shape) scale = short_edge_length / 224. return min(max(scale, min_scale), max_scale) def make_grid(imgs, names): """Concat list of pictures into a single big picture, align height here.""" visualizer = Visualizer.get_current_instance() ori_shapes = [img.shape[:2] 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'{"execution: "}{i}\n{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) visualizer.set_image(display_img) img_scale = _get_adaptive_scale(display_img.shape[:2]) visualizer.draw_texts( texts, positions=np.array(text_positions), font_sizes=img_scale * 7, colors='black', horizontal_alignments='center', font_families='monospace') return visualizer.get_image() def swap_pipeline_position(dataset_cfg): load_ann_tfm_name = 'LoadAnnotations' pipeline = dataset_cfg.get('pipeline') if (pipeline is None): return dataset_cfg all_transform_types = [tfm['type'] for tfm in pipeline] if load_ann_tfm_name in all_transform_types: load_ann_tfm_index = all_transform_types.index(load_ann_tfm_name) load_ann_tfm = pipeline.pop(load_ann_tfm_index) pipeline.insert(1, load_ann_tfm) 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() }) self.ptransforms = [ self.transforms[i] for i in range(len(self.transforms) - 1) ] for t in self.ptransforms: data = t(data) # Keep the same meta_keys in the PackDetInputs self.transforms[-1].meta_keys = [key for key in data] data_sample = self.transforms[-1](data) if data is None: return None if 'img' in data: self.intermediate_imgs.append({ 'name': t.__class__.__name__, 'dataset_sample': data_sample['data_samples'] }) 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', 'mmyolo')) dataset_cfg = cfg.get(args.phase + '_dataloader').get('dataset') if (args.phase in ['test', 'val']): swap_pipeline_position(dataset_cfg) dataset = DATASETS.build(dataset_cfg) visualizer = VISUALIZERS.build(cfg.visualizer) visualizer.dataset_meta = dataset.metainfo intermediate_imgs = [] if not hasattr(dataset, 'pipeline'): # for dataset_wrapper dataset = dataset.dataset # TODO: The dataset wrapper occasion is not considered here dataset.pipeline = InspectCompose(dataset.pipeline.transforms, intermediate_imgs) # init visualization image number assert args.show_number > 0 display_number = min(args.show_number, len(dataset)) progress_bar = ProgressBar(display_number) for i, item in zip(range(display_number), dataset): image_i = [] result_i = [result['dataset_sample'] for result in intermediate_imgs] for k, datasample in enumerate(result_i): image = datasample.img gt_instances = datasample.gt_instances image = image[..., [2, 1, 0]] # bgr to rgb 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) datasample.gt_instances = gt_instances # get filename from dataset or just use index as filename visualizer.add_datasample( 'result', image, datasample, draw_pred=False, draw_gt=True, show=False) image_show = visualizer.get_image() image_i.append(image_show) if args.mode == 'original': image = image_i[0] elif args.mode == 'transformed': image = image_i[-1] else: image = make_grid([result for result in image_i], [result['name'] for result in intermediate_imgs]) if hasattr(datasample, 'img_path'): filename = osp.basename(datasample.img_path) else: # some dataset have not image path filename = f'{i}.jpg' out_file = osp.join(args.out_dir, filename) if args.out_dir is not None else None if out_file is not None: mmcv.imwrite(image[..., ::-1], out_file) if not args.not_show: visualizer.show( image, win_name=filename, wait_time=args.show_interval) intermediate_imgs.clear() progress_bar.update() if __name__ == '__main__': main()