pytorch制作自己的LMDB数据操作示例

yipeiwu_com5年前Python基础

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:

前言

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)#最大空间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)

读取部分

这里用的pytorch的dataloader,简单记录一下,人比较懒,代码就直接抄过来,不整理拆分了,重点看__getitem__

from __future__ import absolute_import
# import sys
# sys.path.append('./')
import os
# import moxing as mox
import pickle
from tqdm import tqdm
from PIL import Image, ImageFile
import numpy as np
import random
import cv2
import lmdb
import sys
import six
import torch
from torch.utils import data
from torch.utils.data import sampler
from torchvision import transforms
from lib.utils.labelmaps import get_vocabulary, labels2strs
from lib.utils import to_numpy
ImageFile.LOAD_TRUNCATED_IMAGES = True
from config import get_args
global_args = get_args(sys.argv[1:])
if global_args.run_on_remote:
 import moxing as mox
 #moxing是一个分布式的框架 跳过
class LmdbDataset(data.Dataset):
 def __init__(self, root, voc_type, max_len, num_samples, transform=None):
  super(LmdbDataset, self).__init__()
  if global_args.run_on_remote:
   dataset_name = os.path.basename(root)
   data_cache_url = "/cache/%s" % dataset_name
   if not os.path.exists(data_cache_url):
    os.makedirs(data_cache_url)
   if mox.file.exists(root):
    mox.file.copy_parallel(root, data_cache_url)
   else:
    raise ValueError("%s not exists!" % root)
   self.env = lmdb.open(data_cache_url, max_readers=32, readonly=True)
  else:
   self.env = lmdb.open(root, max_readers=32, readonly=True)
  assert self.env is not None, "cannot create lmdb from %s" % root
  self.txn = self.env.begin()
  self.voc_type = voc_type
  self.transform = transform
  self.max_len = max_len
  self.nSamples = int(self.txn.get(b"num-samples"))
  self.nSamples = min(self.nSamples, num_samples)
  assert voc_type in ['LOWERCASE', 'ALLCASES', 'ALLCASES_SYMBOLS','DIGITS']
  self.EOS = 'EOS'
  self.PADDING = 'PADDING'
  self.UNKNOWN = 'UNKNOWN'
  self.voc = get_vocabulary(voc_type, EOS=self.EOS, PADDING=self.PADDING, UNKNOWN=self.UNKNOWN)
  self.char2id = dict(zip(self.voc, range(len(self.voc))))
  self.id2char = dict(zip(range(len(self.voc)), self.voc))
  self.rec_num_classes = len(self.voc)
  self.lowercase = (voc_type == 'LOWERCASE')
 def __len__(self):
  return self.nSamples
 def __getitem__(self, index):
  assert index <= len(self), 'index range error'
  index += 1
  img_key = b'image-%09d' % index
  imgbuf = self.txn.get(img_key)
  #由于Image.open需要一个类文件对象 所以这里需要把二进制转为一个类文件对象
  buf = six.BytesIO()
  buf.write(imgbuf)
  buf.seek(0)
  try:
   img = Image.open(buf).convert('RGB')
   # img = Image.open(buf).convert('L')
   # img = img.convert('RGB')
  except IOError:
   print('Corrupted image for %d' % index)
   return self[index + 1]
  # reconition labels
  label_key = b'label-%09d' % index
  word = self.txn.get(label_key).decode()
  if self.lowercase:
   word = word.lower()
  ## fill with the padding token
  label = np.full((self.max_len,), self.char2id[self.PADDING], dtype=np.int)
  label_list = []
  for char in word:
   if char in self.char2id:
    label_list.append(self.char2id[char])
   else:
    ## add the unknown token
    print('{0} is out of vocabulary.'.format(char))
    label_list.append(self.char2id[self.UNKNOWN])
  ## add a stop token
  label_list = label_list + [self.char2id[self.EOS]]
  assert len(label_list) <= self.max_len
  label[:len(label_list)] = np.array(label_list)
  if len(label) <= 0:
   return self[index + 1]
  # label length
  label_len = len(label_list)
  if self.transform is not None:
   img = self.transform(img)
  return img, label, label_len

更多关于Python相关内容可查看本站专题:《Python数学运算技巧总结》、《Python图片操作技巧总结》、《Python数据结构与算法教程》、《Python函数使用技巧总结》、《Python字符串操作技巧汇总》及《Python入门与进阶经典教程

希望本文所述对大家Python程序设计有所帮助。

相关文章

详解Python3序列赋值、序列解包

上节我们提到解决赋值中等号两边参数不一致的方法可以通过切片,但在Python3中我们可以利用特定的语法更加方便的处理这种情况,如下示例。 当带 * 出现在结尾间时 L = [1, 2...

Python实现图片滑动式验证识别方法

Python实现图片滑动式验证识别方法

1 abstract 验证码作为一种自然人的机器人的判别工具,被广泛的用于各种防止程序做自动化的场景中。传统的字符型验证安全性已经名存实亡的情况下,各种新型的验证码如雨后春笋般涌现。目前...

简单了解python中的与或非运算

简单了解python中的与或非运算

真的很重要,栽了个跟头!!!(虽然以前好像知道。。。) print(True or False and False) print((True or False) and False)...

Python中pymysql 模块的使用详解

pymysql 模块的使用 一、pymysql的下载和使用 (1)pymysql模块的下载 pip3 install pymysql (2)pymysql的使用 # 实现:使用...

浅谈python脚本设置运行参数的方法

浅谈python脚本设置运行参数的方法

正在学习Django框架,在运行manage.py的时候需要给它设置要监听的端口,就是给这个脚本一个运行参数。教学视频中,是在Eclipse中设置的运行参数,网上Django大部分都是在...