mirror of https://github.com/open-mmlab/mmocr.git
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import math
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import os.path as osp
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import mmengine
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from mmocr.utils import dump_ocr_data
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Generate training and validation set of TextOCR ')
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parser.add_argument('root_path', help='Root dir path of TextOCR')
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args = parser.parse_args()
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return args
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def collect_textocr_info(root_path, annotation_filename, print_every=1000):
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annotation_path = osp.join(root_path, annotation_filename)
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if not osp.exists(annotation_path):
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raise Exception(
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f'{annotation_path} not exists, please check and try again.')
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annotation = mmengine.load(annotation_path)
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# img_idx = img_start_idx
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img_infos = []
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for i, img_info in enumerate(annotation['imgs'].values()):
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if i > 0 and i % print_every == 0:
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print(f'{i}/{len(annotation["imgs"].values())}')
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img_info['segm_file'] = annotation_path
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ann_ids = annotation['imgToAnns'][img_info['id']]
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anno_info = []
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for ann_id in ann_ids:
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ann = annotation['anns'][ann_id]
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# Ignore illegible or non-English words
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text_label = ann['utf8_string']
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iscrowd = 1 if text_label == '.' else 0
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x, y, w, h = ann['bbox']
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x, y = max(0, math.floor(x)), max(0, math.floor(y))
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w, h = math.ceil(w), math.ceil(h)
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bbox = [x, y, w, h]
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segmentation = [max(0, int(x)) for x in ann['points']]
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anno = dict(
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iscrowd=iscrowd,
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category_id=1,
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bbox=bbox,
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area=ann['area'],
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segmentation=[segmentation])
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anno_info.append(anno)
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img_info.update(anno_info=anno_info)
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img_infos.append(img_info)
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return img_infos
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def main():
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args = parse_args()
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root_path = args.root_path
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print('Processing training set...')
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training_infos = collect_textocr_info(root_path, 'TextOCR_0.1_train.json')
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dump_ocr_data(training_infos,
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osp.join(root_path, 'instances_training.json'), 'textdet')
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print('Processing validation set...')
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val_infos = collect_textocr_info(root_path, 'TextOCR_0.1_val.json')
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dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'),
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'textdet')
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print('Finish')
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if __name__ == '__main__':
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main()
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