Note

You are reading the documentation for MMOCR 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMOCR 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the maintenance plan, changelog, code and documentation of MMOCR 1.0 for more details.

Source code for mmocr.utils.lmdb_util

# Copyright (c) OpenMMLab. All rights reserved.
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 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 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 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)