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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import os | |
| import os.path as osp | |
| import mmcv | |
| import mmengine | |
| from mmocr.utils import crop_img, dump_ocr_data | |
| def collect_files(img_dir, gt_dir): | |
| """Collect all images and their corresponding groundtruth files. | |
| Args: | |
| img_dir (str): The image directory | |
| gt_dir (str): The groundtruth directory | |
| Returns: | |
| files (list): The list of tuples (img_file, groundtruth_file) | |
| """ | |
| assert isinstance(img_dir, str) | |
| assert img_dir | |
| assert isinstance(gt_dir, str) | |
| assert gt_dir | |
| ann_list, imgs_list = [], [] | |
| for img_file in os.listdir(img_dir): | |
| ann_file = 'gt_' + str(int(img_file[2:6])) + '.txt' | |
| ann_list.append(osp.join(gt_dir, ann_file)) | |
| imgs_list.append(osp.join(img_dir, img_file)) | |
| files = list(zip(imgs_list, ann_list)) | |
| assert len(files), f'No images found in {img_dir}' | |
| print(f'Loaded {len(files)} images from {img_dir}') | |
| return files | |
| def collect_annotations(files, nproc=1): | |
| """Collect the annotation information. | |
| Args: | |
| files (list): The list of tuples (image_file, groundtruth_file) | |
| nproc (int): The number of process to collect annotations | |
| Returns: | |
| images (list): The list of image information dicts | |
| """ | |
| assert isinstance(files, list) | |
| assert isinstance(nproc, int) | |
| if nproc > 1: | |
| images = mmengine.track_parallel_progress( | |
| load_img_info, files, nproc=nproc) | |
| else: | |
| images = mmengine.track_progress(load_img_info, files) | |
| return images | |
| def load_img_info(files): | |
| """Load the information of one image. | |
| Args: | |
| files (tuple): The tuple of (img_file, groundtruth_file) | |
| Returns: | |
| img_info (dict): The dict of the img and annotation information | |
| """ | |
| assert isinstance(files, tuple) | |
| img_file, gt_file = files | |
| assert int(osp.basename(gt_file)[3:-4]) == int( | |
| osp.basename(img_file)[2:-4]) | |
| # read imgs while ignoring orientations | |
| img = mmcv.imread(img_file, 'unchanged') | |
| img_info = dict( | |
| file_name=osp.join(osp.basename(img_file)), | |
| height=img.shape[0], | |
| width=img.shape[1], | |
| segm_file=osp.join(osp.basename(gt_file))) | |
| if osp.splitext(gt_file)[1] == '.txt': | |
| img_info = load_txt_info(gt_file, img_info) | |
| else: | |
| raise NotImplementedError | |
| return img_info | |
| def load_txt_info(gt_file, img_info): | |
| """Collect the annotation information. | |
| The annotation format is as the following: | |
| x1,y1,x2,y2,x3,y3,x4,y4,text | |
| 118,15,147,15,148,46,118,46,LƯỢNG | |
| 149,9,165,9,165,43,150,43,TỐT | |
| 167,9,180,9,179,43,167,42,ĐỂ | |
| 181,12,193,12,193,43,181,43,CÓ | |
| 195,13,215,14,215,46,196,46,VIỆC | |
| 217,13,237,14,239,47,217,46,LÀM, | |
| Args: | |
| gt_file (str): The path to ground-truth | |
| img_info (dict): The dict of the img and annotation information | |
| Returns: | |
| img_info (dict): The dict of the img and annotation information | |
| """ | |
| with open(gt_file, encoding='utf-8') as f: | |
| anno_info = [] | |
| for line in f: | |
| line = line.strip('\n') | |
| ann = line.split(',') | |
| bbox = ann[0:8] | |
| word = line[len(','.join(bbox)) + 1:] | |
| bbox = [int(coord) for coord in bbox] | |
| # Ignore hard samples | |
| if word == '###': | |
| continue | |
| assert len(bbox) == 8 | |
| anno = dict(bbox=bbox, word=word) | |
| anno_info.append(anno) | |
| img_info.update(anno_info=anno_info) | |
| return img_info | |
| def generate_ann(root_path, split, image_infos, preserve_vertical): | |
| """Generate cropped annotations and label txt file. | |
| Args: | |
| root_path (str): The root path of the dataset | |
| split (str): The split of dataset. Namely: training or test | |
| image_infos (list[dict]): A list of dicts of the img and | |
| annotation information | |
| preserve_vertical (bool): Whether to preserve vertical texts | |
| """ | |
| dst_image_root = osp.join(root_path, 'crops', split) | |
| ignore_image_root = osp.join(root_path, 'ignores', split) | |
| if split == 'training': | |
| dst_label_file = osp.join(root_path, 'train_label.json') | |
| elif split == 'test': | |
| dst_label_file = osp.join(root_path, 'test_label.json') | |
| elif split == 'unseen_test': | |
| dst_label_file = osp.join(root_path, 'unseen_test_label.json') | |
| os.makedirs(dst_image_root, exist_ok=True) | |
| img_info = [] | |
| for image_info in image_infos: | |
| index = 1 | |
| src_img_path = osp.join(root_path, 'imgs', split, | |
| image_info['file_name']) | |
| image = mmcv.imread(src_img_path) | |
| src_img_root = image_info['file_name'].split('.')[0] | |
| for anno in image_info['anno_info']: | |
| word = anno['word'] | |
| dst_img = crop_img(image, anno['bbox'], 0, 0) | |
| h, w, _ = dst_img.shape | |
| dst_img_name = f'{src_img_root}_{index}.png' | |
| index += 1 | |
| # Skip invalid annotations | |
| if min(dst_img.shape) == 0: | |
| continue | |
| # Skip vertical texts | |
| if not preserve_vertical and h / w > 2 and split == 'training': | |
| dst_img_path = osp.join(ignore_image_root, dst_img_name) | |
| mmcv.imwrite(dst_img, dst_img_path) | |
| continue | |
| dst_img_path = osp.join(dst_image_root, dst_img_name) | |
| mmcv.imwrite(dst_img, dst_img_path) | |
| img_info.append({ | |
| 'file_name': dst_img_name, | |
| 'anno_info': [{ | |
| 'text': word | |
| }] | |
| }) | |
| dump_ocr_data(img_info, dst_label_file, 'textrecog') | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='Generate training and test set of VinText ') | |
| parser.add_argument('root_path', help='Root dir path of VinText') | |
| parser.add_argument( | |
| '--preserve-vertical', | |
| help='Preserve samples containing vertical texts', | |
| action='store_true') | |
| parser.add_argument( | |
| '--nproc', default=1, type=int, help='Number of processes') | |
| args = parser.parse_args() | |
| return args | |
| def main(): | |
| args = parse_args() | |
| root_path = args.root_path | |
| for split in ['training', 'test', 'unseen_test']: | |
| print(f'Processing {split} set...') | |
| with mmengine.Timer( | |
| print_tmpl='It takes {}s to convert VinText annotation'): | |
| files = collect_files( | |
| osp.join(root_path, 'imgs', split), | |
| osp.join(root_path, 'annotations')) | |
| image_infos = collect_annotations(files, nproc=args.nproc) | |
| generate_ann(root_path, split, image_infos, args.preserve_vertical) | |
| if __name__ == '__main__': | |
| main() | |