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python生成lmdb格式的文件实例

更新时间:2020-06-11 09:24 作者:startmvc
在crnn训练的时候需要用到lmdb格式的数据集,下面是python生成lmdb个是数据集的代码,注意一

在crnn训练的时候需要用到lmdb格式的数据集,下面是python生成lmdb个是数据集的代码,注意一定要在linux系统下,否则会读入图像的时候出问题,可能遇到的问题都在代码里面注释了,看代码即可。


#-*- coding:utf-8 -*-
 
import os
import lmdb#先pip install这个模块哦
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():
 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)所需要的磁盘空间的最小值,之前是1T,我改成了8g,否则会报磁盘空间不足,这个数字是字节
 
 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,否则f.read就不可用了,就会输出这个信息
 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 输出目录
 outputPath = 'D:/ruanjianxiazai/tuxiangyangben/fengehou/train'#训练集和验证集要跑两遍这个程序,分两次生成
 
 path = "D:/ruanjianxiazai/tuxiangyangben/fengehou/chenguang/*.jpg"#将txt与jpg的都放在同一个文件里面
 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)
 

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