二、OCR训练时，将txt文件和图片数据转为lmdb文件格式

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前言

随着人工智能的不断发展，机器学习这门技术也越来越重要，本文就介绍OCR训练前ldmb文件制作的基础内容。

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提示：以下是本篇文章正文内容，下面案例可供参考

一、背景？

示例：是基于ocr训练而作。

二、直接上内容

1.代码

代码如下（示例）：

# -*- coding:utf-8 -*-

import os
import lmdb  # ?pip install?????
import cv2
import glob  #????????????
import numpy as np


def checkImageIsValid(imageBin):
    if imageBin is None:
        return False
    imageBuf = np.fromstring(imageBin, dtype=np.uint8)
    img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE)
    if img is None:
        return False
    imgH, imgW = img.shape[0], img.shape[1]
    if imgH * imgW == 0:
        return False
    return True


def writeCache(env, cache):
    with env.begin(write=True) as txn:
        # for k, v in cache.iteritems():  #python2
        for k, v in cache.items():  #python3
            txn.put(k.encode(), str(v).encode())


def createDataset(outputPath, imagePathList, labelList, lexiconList=None, checkValid=True):
    """
    Create LMDB dataset for CRNN training.
#    ARGS:
        outputPath    : LMDB output path
        imagePathList : list of image path
        labelList     : list of corresponding groundtruth texts
        lexiconList   : (optional) list of lexicon lists
        checkValid    : if true, check the validity of every image
    """
    # print (len(imagePathList) , len(labelList))
    assert (len(imagePathList) == len(labelList))
    nSamples = len(imagePathList)
    print('...................')
    env = lmdb.open(outputPath, map_size=8589934592)  #