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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import math | |
| import os | |
| import os.path as osp | |
| import mmcv | |
| import mmengine | |
| from mmocr.utils import dump_ocr_data | |
| def collect_files(img_dir, gt_dir, ratio): | |
| """Collect all images and their corresponding groundtruth files. | |
| Args: | |
| img_dir (str): The image directory | |
| gt_dir (str): The groundtruth directory | |
| ratio (float): Split ratio for val set | |
| 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 | |
| assert isinstance(ratio, float) | |
| assert ratio < 1.0, 'val_ratio should be a float between 0.0 to 1.0' | |
| ann_list, imgs_list = [], [] | |
| for ann_file in os.listdir(gt_dir): | |
| ann_list.append(osp.join(gt_dir, ann_file)) | |
| imgs_list.append(osp.join(img_dir, ann_file.replace('json', 'jpg'))) | |
| all_files = list(zip(imgs_list, ann_list)) | |
| assert len(all_files), f'No images found in {img_dir}' | |
| print(f'Loaded {len(all_files)} images from {img_dir}') | |
| trn_files, val_files = [], [] | |
| if ratio > 0: | |
| for i, file in enumerate(all_files): | |
| if i % math.floor(1 / ratio): | |
| trn_files.append(file) | |
| else: | |
| val_files.append(file) | |
| else: | |
| trn_files, val_files = all_files, [] | |
| print(f'training #{len(trn_files)}, val #{len(val_files)}') | |
| return trn_files, val_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 osp.basename(gt_file).split('.')[0] == osp.basename(img_file).split( | |
| '.')[0] | |
| # read imgs while ignoring orientations | |
| img = mmcv.imread(img_file) | |
| 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] == '.json': | |
| img_info = load_json_info(gt_file, img_info) | |
| else: | |
| raise NotImplementedError | |
| return img_info | |
| def load_json_info(gt_file, img_info): | |
| """Collect the annotation information. | |
| The annotation format is as the following: | |
| { | |
| "chars": [ | |
| { | |
| "ignore": 0, | |
| "transcription": "H", | |
| "points": [25, 175, 112, 175, 112, 286, 25, 286] | |
| }, | |
| { | |
| "ignore": 0, | |
| "transcription": "O", | |
| "points": [102, 182, 210, 182, 210, 273, 102, 273] | |
| }, ... | |
| ] | |
| "lines": [ | |
| { | |
| "ignore": 0, | |
| "transcription": "HOKI", | |
| "points": [23, 173, 327, 180, 327, 290, 23, 283] | |
| }, | |
| { | |
| "ignore": 0, | |
| "transcription": "TEA", | |
| "points": [368, 180, 621, 180, 621, 294, 368, 294] | |
| }, ... | |
| ] | |
| } | |
| 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 | |
| """ | |
| annotation = mmengine.load(gt_file) | |
| anno_info = [] | |
| for line in annotation['lines']: | |
| segmentation = line['points'] | |
| x = max(0, min(segmentation[0::2])) | |
| y = max(0, min(segmentation[1::2])) | |
| w = abs(max(segmentation[0::2]) - x) | |
| h = abs(max(segmentation[1::2]) - y) | |
| bbox = [x, y, w, h] | |
| anno = dict( | |
| iscrowd=line['ignore'], | |
| category_id=1, | |
| bbox=bbox, | |
| area=w * h, | |
| segmentation=[segmentation]) | |
| anno_info.append(anno) | |
| img_info.update(anno_info=anno_info) | |
| return img_info | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='Generate training and val set of ReCTS.') | |
| parser.add_argument('root_path', help='Root dir path of ReCTS') | |
| parser.add_argument( | |
| '--val-ratio', help='Split ratio for val set', default=0.0, type=float) | |
| parser.add_argument( | |
| '--nproc', default=1, type=int, help='Number of process') | |
| args = parser.parse_args() | |
| return args | |
| def main(): | |
| args = parse_args() | |
| root_path = args.root_path | |
| ratio = args.val_ratio | |
| trn_files, val_files = collect_files( | |
| osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), ratio) | |
| # Train set | |
| trn_infos = collect_annotations(trn_files, nproc=args.nproc) | |
| with mmengine.Timer( | |
| print_tmpl='It takes {}s to convert ReCTS Training annotation'): | |
| dump_ocr_data(trn_infos, osp.join(root_path, | |
| 'instances_training.json'), | |
| 'textdet') | |
| # Val set | |
| if len(val_files) > 0: | |
| val_infos = collect_annotations(val_files, nproc=args.nproc) | |
| with mmengine.Timer( | |
| print_tmpl='It takes {}s to convert ReCTS Val annotation'): | |
| dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'), | |
| 'textdet') | |
| if __name__ == '__main__': | |
| main() | |