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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
from mmocr.utils import dump_ocr_data
def convert_annotations(root_path, split):
"""Convert original annotations to mmocr format.
The annotation format is as the following:
Crops/val/11/1/1.png weighted
Crops/val/11/1/2.png 26
Crops/val/11/1/3.png casting
Crops/val/11/1/4.png 28
After this module, the annotation has been changed to the format below:
jsonl:
{'filename': 'Crops/val/11/1/1.png', 'text': 'weighted'}
{'filename': 'Crops/val/11/1/1.png', 'text': '26'}
{'filename': 'Crops/val/11/1/1.png', 'text': 'casting'}
{'filename': 'Crops/val/11/1/1.png', 'text': '28'}
Args:
root_path (str): The root path of the dataset
split (str): The split of dataset. Namely: training or test
"""
assert isinstance(root_path, str)
assert isinstance(split, str)
img_info = []
with open(
osp.join(root_path, f'{split}_label.txt'),
encoding='"utf-8-sig') as f:
annos = f.readlines()
for anno in annos:
if anno:
# Text may contain spaces
dst_img_name, word = anno.split('png ')
word = word.strip('\n')
img_info.append({
'file_name': dst_img_name + 'png',
'anno_info': [{
'text': word
}]
})
dump_ocr_data(img_info, osp.join(root_path, f'{split.lower()}_label.json'),
'textrecog')
def parse_args():
parser = argparse.ArgumentParser(
description='Generate training and test set of Lecture Video DB')
parser.add_argument('root_path', help='Root dir path of Lecture Video DB')
args = parser.parse_args()
return args
def main():
args = parse_args()
root_path = args.root_path
for split in ['train', 'val', 'test']:
convert_annotations(root_path, split)
print(f'{split} split converted.')
if __name__ == '__main__':
main()
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