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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import math
import os.path as osp
import mmcv
import mmengine
from mmocr.utils import dump_ocr_data
def parse_args():
parser = argparse.ArgumentParser(
description='Generate training and validation set of LSVT ')
parser.add_argument('root_path', help='Root dir path of LSVT')
parser.add_argument(
'--val-ratio', help='Split ratio for val set', default=0.0, type=float)
args = parser.parse_args()
return args
def collect_lsvt_info(root_path, split, ratio, print_every=1000):
"""Collect the annotation information.
The annotation format is as the following:
[
{'gt_1234': # 'gt_1234' is file name
[
{
'transcription': '一站式购物中心',
'points': [[45, 272], [215, 273], [212, 296], [45, 290]]
'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
"""
annotation_path = osp.join(root_path, 'annotations/train_full_labels.json')
if not osp.exists(annotation_path):
raise Exception(
f'{annotation_path} not exists, please check and try again.')
annotation = mmengine.load(annotation_path)
img_prefixes = annotation.keys()
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)}')
if split == 'train':
img_prefixes = trn_files
elif split == 'val':
img_prefixes = val_files
else:
raise NotImplementedError
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)
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)))
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]
anno = dict(
iscrowd=1 if ann['illegibility'] else 0,
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)
return img_infos
def main():
args = parse_args()
root_path = args.root_path
print('Processing training set...')
training_infos = collect_lsvt_info(root_path, 'train', args.val_ratio)
dump_ocr_data(training_infos,
osp.join(root_path, 'instances_training.json'), 'textdet')
if args.val_ratio > 0:
print('Processing validation set...')
val_infos = collect_lsvt_info(root_path, 'val', args.val_ratio)
dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'),
'textdet')
print('Finish')
if __name__ == '__main__':
main()
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