常用图像数据集原始数据(.png或.jpg格式)生成方法

引言

在计算机视觉方面的工作,我们常常需要用到很多图像数据集.像ImageNet这样早已大名鼎鼎的数据集,我等的百十个G的硬盘容量怕是怎么也承载不下;本文中,将给出一些Hello world级的图像数据集生成方法,以及其他相关图像数据资源的整理.

本文的主要内容包括:

  1. MNIST, CIFAR-10, CIFAR-100等.png或.jpg格式数据集的生成方法;
  2. 如何编写脚本生成图像数据,并更根据标签文件自动归类;
  3. 如何使用Digist工具生成这些数据集;
  4. .h5格式数据文件格式查看方式;
  5. 相关数据集的下载地址.

生成图像数据

MNIST, CIFAR-10, CIFAR-100等数据在其官网都有相关的介绍,这里也给出相关的数据集的官方地址:

  1. MNIST: http://yann.lecun.com/exdb/mnist/
  2. CIFAR系列: https://www.cs.toronto.edu/~kriz/cifar.html

通过官网的介绍可以看出,官网给出的数据集大多都是二进制格式和一些python,matlab格式;有时候我们需要的是原始图像数据,这个时候我们就需要使用代码或者借助其他工具自己生成了.

代码生成的方式,在网上也有很多,但良莠不齐.大多需要自己根据官网给出的数据格式,自己更具格式特征生成原始数据,这里就不做具体介绍了,网上有很多.这里介绍一些比较简单快捷的方式,来帮助我们快速得到原始图像数据.


CIFAR-10 图像数据

这部分是我从kaggle cifar-10 官网提供的CIFAR-10数据集生成的,原始数据集(.png格式,比较符合我们的要求),但存在一个问题,所给的图片混乱的排列在train目录下,未按照原始10分类进行分类,但好在给出了trainLabels.csv类别映射文件;所以,我们需要解决的首要问题就是,根据这个映射文件自动分成10类别,并存放在10个文件目录下.

下边直接给出我的代码:

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# coding: utf-8

import csv
import os
import shutil
import sys

# 获取文件名(除去后缀)
def getImageFilePre(filename):
if filename.endswith(".png"):
temp = filename.split(".")
filePre = temp[0]
return filePre

# string 转 int
def str2Int(stringValue):
return int(stringValue)

# int 转 string
def int2Str(intValue):
return str(intValue)

# 文件重命名
def fileRename(dirPath):
# 三个参数:分别返回
# 1.父目录
# 2.所有文件夹名字(不含路径)
# 3.所有文件名字
for parent, dirnames, filenames in os.walk(dirPath):
for dirname in dirnames: #输出文件夹信息
count = 1
newTmpPath = os.path.join(dirPath, dirname)
os.chdir(newTmpPath)
fileContents = os.listdir(newTmpPath)

for curFile in fileContents:
if curFile.endswith(".png"):
newName = dirname + "."+ int2Str(count) + ".png"
count = count + 1
shutil.move(curFile, newName)
print curFile + " -> " + newName + " ------> OK!"

def main():
# 读取标签文件内容
csvfile = file('trainLabels.csv', 'rb')
reader = csv.reader(csvfile)
reader = list(reader) # 转化为list列表

# 读取目录下文件列表
dirPath = "F:\\xxxxx\\data_origin\\train_200"
os.chdir(dirPath)
dirContents = os.listdir(dirPath)
dirContents.sort(key=lambda x:int(x[:-4])) #按文件名排序

totalFiles = 50001
for num in range(1, totalFiles): # 0-199
labelContent = reader[num]
labelID = reader[num][0]
labelName = reader[num][1]
imageFilename = dirContents[num-1]
tmpFilePre = getImageFilePre(dirContents[num-1])

if str2Int(labelID) == str2Int(tmpFilePre):
print "labelID == filePre !!!"
baseDirPath = "F:\\xxxxx\\data_origin\\train_with_class"
new_dir_name = labelName
new_dir_path = os.path.join(baseDirPath, new_dir_name)

isExists = os.path.isdir(new_dir_path)
if not isExists:
os.makedirs(new_dir_path)
print new_dir_path + " 创建成功!"
else:
print new_dir_path + "目录已存在!"

shutil.copy(imageFilename, new_dir_path)

print ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>"

csvfile.close()

rootPath = "F:\\xxxxx\\data_origin\\train_with_class"
fileRename(rootPath)

if __name__ == '__main__':
main()

这样,便分成了10个类别,并根据类别存放在不同的目录下,每一类别5000张图片;在我的Windows平台下耗时1.5个小时(包括文件重命名)才跑完,确实有点慢.下图为最终的结果图:

CIFAR-10 with class

CIFAR-10 examples


caffe图像化操作工具digits工具生成图像数据集

详细的使用方法可移步这篇博文:http://www.cnblogs.com/denny402/p/5136155.html

需要安装caffe和digits工具,使用工具可直接生成自动归类的图片数据,速度很快可以一试.


.h5文件结构查看器

在做卷积神经网络的时候,我们经常需要保存.h5数据文件,但有时候我们需要利用这些.h5文件,比如在进行transfor Learning的时候,就需要根据.h5文件的格式进行层冻结.

除了自己用代码一窥.h5文件结构外,还有什么快捷的工具吗?有的,matlab就提供了现成的调用方法.文档地址在这里:http://cn.mathworks.com/help/matlab/ref/h5disp.html

如,我们可以使用matlab命令查看vgg16模型的权重结构

1
>> h5disp('vgg16_weights.h5')

结果显示如下:

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>> h5disp('vgg16_weights.h5')
HDF5 vgg16_weights.h5
Group '/'
Attributes:
'nb_layers': 37
Group '/layer_0'
Attributes:
'nb_params': 0
Group '/layer_1'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x3x64
MaxSize: 3x3x3x64
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 64
MaxSize: 64
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_10'
Attributes:
'nb_params': 0
Group '/layer_11'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x128x256
MaxSize: 3x3x128x256
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 256
MaxSize: 256
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_12'
Attributes:
'nb_params': 0
Group '/layer_13'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x256x256
MaxSize: 3x3x256x256
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 256
MaxSize: 256
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_14'
Attributes:
'nb_params': 0
Group '/layer_15'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x256x256
MaxSize: 3x3x256x256
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 256
MaxSize: 256
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_16'
Attributes:
'nb_params': 0
Group '/layer_17'
Attributes:
'nb_params': 0
Group '/layer_18'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x256x512
MaxSize: 3x3x256x512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 512
MaxSize: 512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_19'
Attributes:
'nb_params': 0
Group '/layer_2'
Attributes:
'nb_params': 0
Group '/layer_20'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x512x512
MaxSize: 3x3x512x512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 512
MaxSize: 512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_21'
Attributes:
'nb_params': 0
Group '/layer_22'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x512x512
MaxSize: 3x3x512x512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 512
MaxSize: 512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_23'
Attributes:
'nb_params': 0
Group '/layer_24'
Attributes:
'nb_params': 0
Group '/layer_25'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x512x512
MaxSize: 3x3x512x512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 512
MaxSize: 512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_26'
Attributes:
'nb_params': 0
Group '/layer_27'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x512x512
MaxSize: 3x3x512x512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 512
MaxSize: 512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_28'
Attributes:
'nb_params': 0
Group '/layer_29'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x512x512
MaxSize: 3x3x512x512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 512
MaxSize: 512
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_3'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x64x64
MaxSize: 3x3x64x64
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 64
MaxSize: 64
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_30'
Attributes:
'nb_params': 0
Group '/layer_31'
Attributes:
'nb_params': 0
Group '/layer_32'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 4096x25088
MaxSize: 4096x25088
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 4096
MaxSize: 4096
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_33'
Attributes:
'nb_params': 0
Group '/layer_34'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 4096x4096
MaxSize: 4096x4096
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 4096
MaxSize: 4096
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_35'
Attributes:
'nb_params': 0
Group '/layer_36'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 1000x4096
MaxSize: 1000x4096
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 1000
MaxSize: 1000
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_4'
Attributes:
'nb_params': 0
Group '/layer_5'
Attributes:
'nb_params': 0
Group '/layer_6'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x64x128
MaxSize: 3x3x64x128
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 128
MaxSize: 128
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_7'
Attributes:
'nb_params': 0
Group '/layer_8'
Attributes:
'nb_params': 2
Dataset 'param_0'
Size: 3x3x128x128
MaxSize: 3x3x128x128
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'param_1'
Size: 128
MaxSize: 128
Datatype: H5T_IEEE_F32LE (single)
ChunkSize: []
Filters: none
FillValue: 0.000000
Group '/layer_9'
Attributes:
'nb_params': 0

参考资料

[1]. https://www.cs.toronto.edu/~kriz/cifar.html
[2]. https://www.kaggle.com/c/cifar-10/data
[3]. http://cn.mathworks.com/help/matlab/ref/h5disp.html
[4]. http://www.cnblogs.com/denny402/p/5136155.html


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