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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import os | |
| import urllib | |
| import numpy as np | |
| import torch | |
| from mmengine.utils import scandir | |
| from prettytable import PrettyTable | |
| from mmyolo.models import RepVGGBlock | |
| IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', | |
| '.tiff', '.webp') | |
| def switch_to_deploy(model): | |
| """Model switch to deploy status.""" | |
| for layer in model.modules(): | |
| if isinstance(layer, RepVGGBlock): | |
| layer.switch_to_deploy() | |
| print('Switch model to deploy modality.') | |
| def auto_arrange_images(image_list: list, image_column: int = 2) -> np.ndarray: | |
| """Auto arrange image to image_column x N row. | |
| Args: | |
| image_list (list): cv2 image list. | |
| image_column (int): Arrange to N column. Default: 2. | |
| Return: | |
| (np.ndarray): image_column x N row merge image | |
| """ | |
| img_count = len(image_list) | |
| if img_count <= image_column: | |
| # no need to arrange | |
| image_show = np.concatenate(image_list, axis=1) | |
| else: | |
| # arrange image according to image_column | |
| image_row = round(img_count / image_column) | |
| fill_img_list = [np.ones(image_list[0].shape, dtype=np.uint8) * 255 | |
| ] * ( | |
| image_row * image_column - img_count) | |
| image_list.extend(fill_img_list) | |
| merge_imgs_col = [] | |
| for i in range(image_row): | |
| start_col = image_column * i | |
| end_col = image_column * (i + 1) | |
| merge_col = np.hstack(image_list[start_col:end_col]) | |
| merge_imgs_col.append(merge_col) | |
| # merge to one image | |
| image_show = np.vstack(merge_imgs_col) | |
| return image_show | |
| def get_file_list(source_root: str) -> [list, dict]: | |
| """Get file list. | |
| Args: | |
| source_root (str): image or video source path | |
| Return: | |
| source_file_path_list (list): A list for all source file. | |
| source_type (dict): Source type: file or url or dir. | |
| """ | |
| is_dir = os.path.isdir(source_root) | |
| is_url = source_root.startswith(('http:/', 'https:/')) | |
| is_file = os.path.splitext(source_root)[-1].lower() in IMG_EXTENSIONS | |
| source_file_path_list = [] | |
| if is_dir: | |
| # when input source is dir | |
| for file in scandir(source_root, IMG_EXTENSIONS, recursive=True): | |
| source_file_path_list.append(os.path.join(source_root, file)) | |
| elif is_url: | |
| # when input source is url | |
| filename = os.path.basename( | |
| urllib.parse.unquote(source_root).split('?')[0]) | |
| file_save_path = os.path.join(os.getcwd(), filename) | |
| print(f'Downloading source file to {file_save_path}') | |
| torch.hub.download_url_to_file(source_root, file_save_path) | |
| source_file_path_list = [file_save_path] | |
| elif is_file: | |
| # when input source is single image | |
| source_file_path_list = [source_root] | |
| else: | |
| print('Cannot find image file.') | |
| source_type = dict(is_dir=is_dir, is_url=is_url, is_file=is_file) | |
| return source_file_path_list, source_type | |
| def show_data_classes(data_classes): | |
| """When printing an error, all class names of the dataset.""" | |
| print('\n\nThe name of the class contained in the dataset:') | |
| data_classes_info = PrettyTable() | |
| data_classes_info.title = 'Information of dataset class' | |
| # List Print Settings | |
| # If the quantity is too large, 25 rows will be displayed in each column | |
| if len(data_classes) < 25: | |
| data_classes_info.add_column('Class name', data_classes) | |
| elif len(data_classes) % 25 != 0 and len(data_classes) > 25: | |
| col_num = int(len(data_classes) / 25) + 1 | |
| data_name_list = list(data_classes) | |
| for i in range(0, (col_num * 25) - len(data_classes)): | |
| data_name_list.append('') | |
| for i in range(0, len(data_name_list), 25): | |
| data_classes_info.add_column('Class name', | |
| data_name_list[i:i + 25]) | |
| # Align display data to the left | |
| data_classes_info.align['Class name'] = 'l' | |
| print(data_classes_info) | |
| def is_metainfo_lower(cfg): | |
| """Determine whether the custom metainfo fields are all lowercase.""" | |
| def judge_keys(dataloader_cfg): | |
| while 'dataset' in dataloader_cfg: | |
| dataloader_cfg = dataloader_cfg['dataset'] | |
| if 'metainfo' in dataloader_cfg: | |
| all_keys = dataloader_cfg['metainfo'].keys() | |
| all_is_lower = all([str(k).islower() for k in all_keys]) | |
| assert all_is_lower, f'The keys in dataset metainfo must be all lowercase, but got {all_keys}. ' \ | |
| f'Please refer to https://github.com/open-mmlab/mmyolo/blob/e62c8c4593/configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py#L8' # noqa | |
| judge_keys(cfg.get('train_dataloader', {})) | |
| judge_keys(cfg.get('val_dataloader', {})) | |
| judge_keys(cfg.get('test_dataloader', {})) | |