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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import json
import os
import os.path as osp
import cv2
import lmdb
import numpy as np
from mmocr.utils import list_from_file
def parse_line(line, format):
if format == 'txt':
img_name, text = line.split(' ')
else:
line = json.loads(line)
img_name = line['filename']
text = line['text']
return img_name, text
def check_image_is_valid(imageBin):
if imageBin is None:
return False
imageBuf = np.frombuffer(imageBin, dtype=np.uint8)
img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE)
imgH, imgW = img.shape[0], img.shape[1]
if imgH * imgW == 0:
return False
return True
def write_cache(env, cache):
with env.begin(write=True) as txn:
cursor = txn.cursor()
cursor.putmulti(cache, dupdata=False, overwrite=True)
def recog2lmdb(img_root,
label_path,
output,
label_format='txt',
label_only=False,
batch_size=1000,
encoding='utf-8',
lmdb_map_size=1099511627776,
verify=True):
"""Create text recognition dataset to LMDB format.
Args:
img_root (str): Path to images.
label_path (str): Path to label file.
output (str): LMDB output path.
label_format (str): Format of the label file, either txt or jsonl.
label_only (bool): Only convert label to lmdb format.
batch_size (int): Number of files written to the cache each time.
encoding (str): Label encoding method.
lmdb_map_size (int): Maximum size database may grow to.
verify (bool): If true, check the validity of
every image.Defaults to True.
E.g.
This function supports MMOCR's recognition data format and the label file
can be txt or jsonl, as follows:
├──img_root
| |—— img1.jpg
| |—— img2.jpg
| |—— ...
|——label.txt (or label.jsonl)
label.txt: img1.jpg HELLO
img2.jpg WORLD
...
label.jsonl: {'filename':'img1.jpg', 'text':'HELLO'}
{'filename':'img2.jpg', 'text':'WORLD'}
...
"""
# check label format
assert osp.basename(label_path).split('.')[-1] == label_format
# create lmdb env
os.makedirs(output, exist_ok=True)
env = lmdb.open(output, map_size=lmdb_map_size)
# load label file
anno_list = list_from_file(label_path, encoding=encoding)
cache = []
# index start from 1
cnt = 1
n_samples = len(anno_list)
for anno in anno_list:
label_key = 'label-%09d'.encode(encoding) % cnt
img_name, text = parse_line(anno, label_format)
if label_only:
# convert only labels to lmdb
line = json.dumps(
dict(filename=img_name, text=text), ensure_ascii=False)
cache.append((label_key, line.encode(encoding)))
else:
# convert both images and labels to lmdb
img_path = osp.join(img_root, img_name)
if not osp.exists(img_path):
print('%s does not exist' % img_path)
continue
with open(img_path, 'rb') as f:
image_bin = f.read()
if verify:
try:
if not check_image_is_valid(image_bin):
print('%s is not a valid image' % img_path)
continue
except Exception:
print('error occurred at ', img_name)
image_key = 'image-%09d'.encode(encoding) % cnt
cache.append((image_key, image_bin))
cache.append((label_key, text.encode(encoding)))
if cnt % batch_size == 0:
write_cache(env, cache)
cache = []
print('Written %d / %d' % (cnt, n_samples))
cnt += 1
n_samples = cnt - 1
cache.append(
('num-samples'.encode(encoding), str(n_samples).encode(encoding)))
write_cache(env, cache)
print('Created lmdb dataset with %d samples' % n_samples)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('label_path', type=str, help='Path to label file')
parser.add_argument('output', type=str, help='Output lmdb path')
parser.add_argument(
'--img-root', '-i', type=str, help='Input imglist path')
parser.add_argument(
'--label-only',
action='store_true',
help='Only converter label to lmdb')
parser.add_argument(
'--label-format',
'-f',
default='txt',
choices=['txt', 'jsonl'],
help='The format of the label file, either txt or jsonl')
parser.add_argument(
'--batch-size',
'-b',
type=int,
default=1000,
help='Processing batch size, defaults to 1000')
parser.add_argument(
'--encoding',
'-e',
type=str,
default='utf8',
help='Bytes coding scheme, defaults to utf8')
parser.add_argument(
'--lmdb-map-size',
'-m',
type=int,
default=1099511627776,
help='Maximum size database may grow to, '
'defaults to 1099511627776 bytes (1TB)')
opt = parser.parse_args()
assert opt.img_root or opt.label_only
recog2lmdb(opt.img_root, opt.label_path, opt.output, opt.label_format,
opt.label_only, opt.batch_size, opt.encoding, opt.lmdb_map_size)
if __name__ == '__main__':
main()
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