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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import math | |
| import os | |
| import os.path as osp | |
| from functools import partial | |
| import mmcv | |
| from mmocr.utils.fileio import list_to_file | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='Generate training and validation set of TextOCR ' | |
| 'by cropping box image.') | |
| parser.add_argument('root_path', help='Root dir path of TextOCR') | |
| parser.add_argument( | |
| 'n_proc', default=1, type=int, help='Number of processes to run') | |
| args = parser.parse_args() | |
| return args | |
| def process_img(args, src_image_root, dst_image_root): | |
| # Dirty hack for multi-processing | |
| img_idx, img_info, anns = args | |
| src_img = mmcv.imread(osp.join(src_image_root, img_info['file_name'])) | |
| labels = [] | |
| for ann_idx, ann in enumerate(anns): | |
| text_label = ann['utf8_string'] | |
| # Ignore illegible or non-English words | |
| if text_label == '.': | |
| continue | |
| x, y, w, h = ann['bbox'] | |
| x, y = max(0, math.floor(x)), max(0, math.floor(y)) | |
| w, h = math.ceil(w), math.ceil(h) | |
| dst_img = src_img[y:y + h, x:x + w] | |
| dst_img_name = f'img_{img_idx}_{ann_idx}.jpg' | |
| dst_img_path = osp.join(dst_image_root, dst_img_name) | |
| mmcv.imwrite(dst_img, dst_img_path) | |
| labels.append(f'{osp.basename(dst_image_root)}/{dst_img_name}' | |
| f' {text_label}') | |
| return labels | |
| def convert_textocr(root_path, | |
| dst_image_path, | |
| dst_label_filename, | |
| annotation_filename, | |
| img_start_idx=0, | |
| nproc=1): | |
| annotation_path = osp.join(root_path, annotation_filename) | |
| if not osp.exists(annotation_path): | |
| raise Exception( | |
| f'{annotation_path} not exists, please check and try again.') | |
| src_image_root = root_path | |
| # outputs | |
| dst_label_file = osp.join(root_path, dst_label_filename) | |
| dst_image_root = osp.join(root_path, dst_image_path) | |
| os.makedirs(dst_image_root, exist_ok=True) | |
| annotation = mmcv.load(annotation_path) | |
| process_img_with_path = partial( | |
| process_img, | |
| src_image_root=src_image_root, | |
| dst_image_root=dst_image_root) | |
| tasks = [] | |
| for img_idx, img_info in enumerate(annotation['imgs'].values()): | |
| ann_ids = annotation['imgToAnns'][img_info['id']] | |
| anns = [annotation['anns'][ann_id] for ann_id in ann_ids] | |
| tasks.append((img_idx + img_start_idx, img_info, anns)) | |
| labels_list = mmcv.track_parallel_progress( | |
| process_img_with_path, tasks, keep_order=True, nproc=nproc) | |
| final_labels = [] | |
| for label_list in labels_list: | |
| final_labels += label_list | |
| list_to_file(dst_label_file, final_labels) | |
| return len(annotation['imgs']) | |
| def main(): | |
| args = parse_args() | |
| root_path = args.root_path | |
| print('Processing training set...') | |
| num_train_imgs = convert_textocr( | |
| root_path=root_path, | |
| dst_image_path='image', | |
| dst_label_filename='train_label.txt', | |
| annotation_filename='TextOCR_0.1_train.json', | |
| nproc=args.n_proc) | |
| print('Processing validation set...') | |
| convert_textocr( | |
| root_path=root_path, | |
| dst_image_path='image', | |
| dst_label_filename='val_label.txt', | |
| annotation_filename='TextOCR_0.1_val.json', | |
| img_start_idx=num_train_imgs, | |
| nproc=args.n_proc) | |
| print('Finish') | |
| if __name__ == '__main__': | |
| main() | |