pytorch制作自己的LMDB数据操作示例-创新互联

本文实例讲述了pytorch制作自己的LMDB数据操作。分享给大家供大家参考，具体如下：

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记录下pytorch里如何使用lmdb的code，自用

制作部分的Code

code就是ASTER里数据制作部分的代码改了点，aster_train.txt里面就算图片的完整路径每行一个，图片同目录下有同名的txt，里面记着jpg的标签

import os
import lmdb # install lmdb by "pip install lmdb"
import cv2
import numpy as np
from tqdm import tqdm
import six
from PIL import Image
import scipy.io as sio
from tqdm import tqdm
import re
def checkImageIsValid(imageBin):
 if imageBin is None:
  return False
 imageBuf = np.fromstring(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 writeCache(env, cache):
 with env.begin(write=True) as txn:
  for k, v in cache.items():
   txn.put(k.encode(), v)
def _is_difficult(word):
 assert isinstance(word, str)
 return not re.match('^[\w]+$', word)
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
 """
 assert(len(imagePathList) == len(labelList))
 nSamples = len(imagePathList)
 env = lmdb.open(outputPath, map_size=1099511627776)#大空间1048576GB
 cache = {}
 cnt = 1
 for i in range(nSamples):
  imagePath = imagePathList[i]
  label = labelList[i]
  if len(label) == 0:
   continue
  if not os.path.exists(imagePath):
   print('%s does not exist' % imagePath)
   continue
  with open(imagePath, 'rb') as f:
   imageBin = f.read()
  if checkValid:
   if not checkImageIsValid(imageBin):
    print('%s is not a valid image' % imagePath)
    continue
  #数据库中都是二进制数据
  imageKey = 'image-%09d' % cnt#9位数不足填零
  labelKey = 'label-%09d' % cnt
  cache[imageKey] = imageBin
  cache[labelKey] = label.encode()
  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).encode()
 writeCache(env, cache)
 print('Created dataset with %d samples' % nSamples)
def get_sample_list(txt_path:str):
  with open(txt_path,'r') as fr:
    jpg_list=[x.strip() for x in fr.readlines() if os.path.exists(x.replace('.jpg','.txt').strip())]
  txt_content_list=[]
  for jpg in jpg_list:
    label_path=jpg.replace('.jpg','.txt')
    with open(label_path,'r') as fr:
      try:
        str_tmp=fr.readline()
      except UnicodeDecodeError as e:
        print(label_path)
        raise(e)
      txt_content_list.append(str_tmp.strip())
  return jpg_list,txt_content_list
if __name__ == "__main__":
 txt_path='/home/gpu-server/disk/disk1/NumberData/8NumberSample/aster_train.txt'
 lmdb_output_path = '/home/gpu-server/project/aster/dataset/train'
 imagePathList,labelList=get_sample_list(txt_path)
 createDataset(lmdb_output_path, imagePathList, labelList)

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