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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,144 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| import argparse | ||
| import math | ||
| import os.path as osp | ||
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| import mmcv | ||
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| from mmocr.utils import convert_annotations | ||
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| def parse_args(): | ||
| parser = argparse.ArgumentParser( | ||
| description='Generate training and validation set of ArT ') | ||
| parser.add_argument('root_path', help='Root dir path of ArT') | ||
| parser.add_argument( | ||
| '--val-ratio', help='Split ratio for val set', default=0.0, type=float) | ||
| args = parser.parse_args() | ||
| return args | ||
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| def collect_art_info(root_path, split, ratio, print_every=1000): | ||
| """Collect the annotation information. | ||
| The annotation format is as the following: | ||
| { | ||
| 'gt_1726': # 'gt_1726' is file name | ||
| [ | ||
| { | ||
| 'transcription': '燎申集团', | ||
| 'points': [ | ||
| [141, 199], | ||
| [237, 201], | ||
| [313, 236], | ||
| [357, 283], | ||
| [359, 300], | ||
| [309, 261], | ||
| [233, 230], | ||
| [140, 231] | ||
| ], | ||
| 'language': 'Chinese', | ||
| 'illegibility': False | ||
| }, | ||
| ... | ||
| ], | ||
| ... | ||
| } | ||
| Args: | ||
| root_path (str): Root path to the dataset | ||
| split (str): Dataset split, which should be 'train' or 'val' | ||
| ratio (float): Split ratio for val set | ||
| print_every (int): Print log info per iteration | ||
| Returns: | ||
| img_info (dict): The dict of the img and annotation information | ||
| """ | ||
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| annotation_path = osp.join(root_path, 'annotations/train_labels.json') | ||
| if not osp.exists(annotation_path): | ||
| raise Exception( | ||
| f'{annotation_path} not exists, please check and try again.') | ||
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| annotation = mmcv.load(annotation_path) | ||
| img_prefixes = annotation.keys() | ||
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| trn_files, val_files = [], [] | ||
| if ratio > 0: | ||
| for i, file in enumerate(img_prefixes): | ||
| if i % math.floor(1 / ratio): | ||
| trn_files.append(file) | ||
| else: | ||
| val_files.append(file) | ||
| else: | ||
| trn_files, val_files = img_prefixes, [] | ||
| print(f'training #{len(trn_files)}, val #{len(val_files)}') | ||
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| if split == 'train': | ||
| img_prefixes = trn_files | ||
| elif split == 'val': | ||
| img_prefixes = val_files | ||
| else: | ||
| raise NotImplementedError | ||
|
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| img_infos = [] | ||
| for i, prefix in enumerate(img_prefixes): | ||
| if i > 0 and i % print_every == 0: | ||
| print(f'{i}/{len(img_prefixes)}') | ||
| img_file = osp.join(root_path, 'imgs', prefix + '.jpg') | ||
| # Skip not exist images | ||
| if not osp.exists(img_file): | ||
| continue | ||
| img = mmcv.imread(img_file) | ||
|
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| 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(annotation_path))) | ||
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| anno_info = [] | ||
| for ann in annotation[prefix]: | ||
| segmentation = [] | ||
| for x, y in ann['points']: | ||
| segmentation.append(max(0, x)) | ||
| segmentation.append(max(0, y)) | ||
| xs, ys = segmentation[::2], segmentation[1::2] | ||
| x, y = min(xs), min(ys) | ||
| w, h = max(xs) - x, max(ys) - y | ||
| bbox = [x, y, w, h] | ||
| if ann['transcription'] == '###' or ann['illegibility']: | ||
| iscrowd = 1 | ||
| else: | ||
| iscrowd = 0 | ||
| anno = dict( | ||
| iscrowd=iscrowd, | ||
| category_id=1, | ||
| bbox=bbox, | ||
| area=w * h, | ||
| segmentation=[segmentation]) | ||
| anno_info.append(anno) | ||
| img_info.update(anno_info=anno_info) | ||
| img_infos.append(img_info) | ||
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| return img_infos | ||
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| def main(): | ||
| args = parse_args() | ||
| root_path = args.root_path | ||
| print('Processing training set...') | ||
| training_infos = collect_art_info(root_path, 'train', args.val_ratio) | ||
| convert_annotations(training_infos, | ||
| osp.join(root_path, 'instances_training.json')) | ||
| if args.val_ratio > 0: | ||
| print('Processing validation set...') | ||
| val_infos = collect_art_info(root_path, 'val', args.val_ratio) | ||
| convert_annotations(val_infos, osp.join(root_path, | ||
| 'instances_val.json')) | ||
| print('Finish') | ||
|
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| if __name__ == '__main__': | ||
| main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,129 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| import argparse | ||
| import json | ||
| import math | ||
| import os.path as osp | ||
|
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| import mmcv | ||
|
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| from mmocr.utils.fileio import list_to_file | ||
|
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||
|
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| def parse_args(): | ||
| parser = argparse.ArgumentParser( | ||
| description='Generate training and validation set of ArT ') | ||
| parser.add_argument('root_path', help='Root dir path of ArT') | ||
| 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 processes') | ||
| parser.add_argument( | ||
| '--format', | ||
| default='jsonl', | ||
| help='Use jsonl or string to format annotations', | ||
| choices=['jsonl', 'txt']) | ||
| args = parser.parse_args() | ||
| return args | ||
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| def convert_art(root_path, split, ratio, format): | ||
| """Collect the annotation information and crop the images. | ||
| The annotation format is as the following: | ||
| { | ||
| "gt_2836_0": [ | ||
| { | ||
| "transcription": "URDER", | ||
| "points": [ | ||
| [25, 51], | ||
| [0, 2], | ||
| [21, 0], | ||
| [42, 43] | ||
| ], | ||
| "language": "Latin", | ||
| "illegibility": false | ||
| } | ||
| ], ... | ||
| } | ||
| Args: | ||
| root_path (str): The root path of the dataset | ||
| split (str): The split of dataset. Namely: training or val | ||
| ratio (float): Split ratio for val set | ||
| format (str): Annotation format, whether be txt or jsonl | ||
| Returns: | ||
| img_info (dict): The dict of the img and annotation information | ||
| """ | ||
|
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| annotation_path = osp.join(root_path, | ||
| 'annotations/train_task2_labels.json') | ||
| if not osp.exists(annotation_path): | ||
| raise Exception( | ||
| f'{annotation_path} not exists, please check and try again.') | ||
|
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| annotation = mmcv.load(annotation_path) | ||
| # outputs | ||
| dst_label_file = osp.join(root_path, f'{split}_label.{format}') | ||
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| img_prefixes = annotation.keys() | ||
|
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| trn_files, val_files = [], [] | ||
| if ratio > 0: | ||
| for i, file in enumerate(img_prefixes): | ||
| if i % math.floor(1 / ratio): | ||
| trn_files.append(file) | ||
| else: | ||
| val_files.append(file) | ||
| else: | ||
| trn_files, val_files = img_prefixes, [] | ||
| print(f'training #{len(trn_files)}, val #{len(val_files)}') | ||
|
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| if split == 'train': | ||
| img_prefixes = trn_files | ||
| elif split == 'val': | ||
| img_prefixes = val_files | ||
| else: | ||
| raise NotImplementedError | ||
|
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| labels = [] | ||
| for prefix in img_prefixes: | ||
| text_label = annotation[prefix][0]['transcription'] | ||
| dst_img_name = prefix + '.jpg' | ||
|
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| if format == 'txt': | ||
| labels.append(f'crops/{dst_img_name}' f' {text_label}') | ||
| elif format == 'jsonl': | ||
| labels.append( | ||
| json.dumps( | ||
| { | ||
| 'filename': f'crops/{dst_img_name}', | ||
| 'text': text_label | ||
| }, | ||
| ensure_ascii=False)) | ||
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| list_to_file(dst_label_file, labels) | ||
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| def main(): | ||
| args = parse_args() | ||
| root_path = args.root_path | ||
| print('Processing training set...') | ||
| convert_art( | ||
| root_path=root_path, | ||
| split='train', | ||
| ratio=args.val_ratio, | ||
| format=args.format) | ||
| if args.val_ratio > 0: | ||
| print('Processing validation set...') | ||
| convert_art( | ||
| root_path=root_path, | ||
| split='val', | ||
| ratio=args.val_ratio, | ||
| format=args.format) | ||
| print('Finish') | ||
|
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|
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| if __name__ == '__main__': | ||
| main() |