text: import os
  import lmdb # install lmdb by "pip install lmdb"
  import cv2
  import numpy as np
  #from genLineText import GenTextImage
  
  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=1099511627776)
      
      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)
                  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
  
  
  import glob
  if __name__ == '__main__':
      
      ##lmdb 输出目录
      outputPath = '../data/lmdb/train'
      
      path = '../data/dataline/*.jpg'
      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 lebelList 
      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)
  
  
  
  
  