在crnn訓練的時候需要用到lmdb格式的數據集，下面是python生成lmdb個是數據集的代碼，注意一定要在linux系統下，否則會讀入圖像的時候出問題，可能遇到的問題都在代碼里面注釋了，看代碼即可。

#-*- coding:utf-8 -*- import osimport lmdb#先pip install這個模塊哦import cv2import globimport 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():   txn.put(k, v) 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)#1099511627776)所需要的磁盤空間的最小值，之前是1T，我改成了8g，否則會報磁盤空間不足，這個數字是字節  cache = {} cnt = 1 for i in xrange(nSamples):  imagePath = imagePathList[i]  label = labelList[i]  if not os.path.exists(imagePath):   print('%s does not exist' % imagePath)   continue  with open(imagePath, 'r') as f:   imageBin = f.read()  if checkValid:   if not checkImageIsValid(imageBin):    print('%s is not a valid image' % imagePath)#注意一定要在linux下，否則f.read就不可用了，就會輸出這個信息    continue   imageKey = 'image-%09d' % cnt  labelKey = 'label-%09d' % cnt  cache[imageKey] = imageBin  cache[labelKey] = label  if lexiconList:   lexiconKey = 'lexicon-%09d' % cnt   cache[lexiconKey] = ' '.join(lexiconList[i])  if cnt % 1000 == 0:   writeCache(env, cache)   cache = {}   print('Written %d / %d' % (cnt, nSamples))  cnt += 1 nSamples = cnt - 1 cache['num-samples'] = str(nSamples) writeCache(env, cache) print('Created dataset with %d samples' % nSamples)  def read_text(path):  with open(path) as f:  text = f.read() text = text.strip()  return text  if __name__ == '__main__': # lmdb 輸出目錄 outputPath = 'D:/ruanjianxiazai/tuxiangyangben/fengehou/train'#訓練集和驗證集要跑兩遍這個程序，分兩次生成  path = "D:/ruanjianxiazai/tuxiangyangben/fengehou/chenguang/*.jpg"#將txt與jpg的都放在同一個文件里面 imagePathList = glob.glob(path) print '------------',len(imagePathList),'------------' imgLabelLists = [] for p in imagePathList:  try:   imgLabelLists.append((p, read_text(p.replace('.jpg', '.txt'))))  except:   continue    # imgLabelList = [ (p, read_text(p.replace('.jpg', '.txt'))) for p in imagePathList] # sort by labelList imgLabelList = sorted(imgLabelLists, key = lambda x:len(x[1])) imgPaths = [ p[0] for p in imgLabelList] txtLists = [ p[1] for p in imgLabelList]  createDataset(outputPath, imgPaths, txtLists, lexiconList=None, checkValid=True)