MNIST数据集转换为图像

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字符集下载地址：
http://yann.lecun.com/exdb/mnist/

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t10k-images-idx3-ubyte的处理代码：

在同一级目录创建一个test的文件夹存放.txt还有.png文件

#!/usr/bin/env python
# -*- coding: utf-8 -*-
from PIL import Image
import struct


def read_image(filename):
 f = open(filename, 'rb')


 index = 0
 buf = f.read()


 f.close()


 magic, images, rows, columns = struct.unpack_from('>IIII' , buf , index)
 index += struct.calcsize('>IIII')


 for i in range(images):
 #for i in xrange(2000):
   image = Image.new('L', (columns, rows))


   for x in range(rows):
     for y in  range(columns):
       image.putpixel((y, x), int(struct.unpack_from('>B', buf, index)[0]))
       index += struct.calcsize('>B')


   print('save ' + str(i) + 'image')
   image.save('test/' + str(i) + '.png')


def read_label(filename, saveFilename):
 f = open(filename, 'rb')
 index = 0
 buf = f.read()


 f.close()


 magic, labels = struct.unpack_from('>II' , buf , index)
 index += struct.calcsize('>II')
 
 labelArr = [0] * labels
 #labelArr = [0] * 2000


 for x in range(labels):
 #for x in xrange(2000):
   labelArr[x] = int(struct.unpack_from('>B', buf, index)[0])
   index += struct.calcsize('>B')


 save = open(saveFilename, 'w')


 save.write(','.join(map(lambda x: str(x), labelArr)))
 save.write('\n')


 save.close()
 print('save labels success')


if __name__ == '__main__':
# 以下是1万个检测数据。
 read_image('t10k-images.idx3-ubyte')
 read_label('t10k-labels.idx1-ubyte', 'test/label.txt')

# 以下是6万个训练数据。
#read_image('train-images.idx3-ubyte')
#read_label('train-labels.idx1-ubyte', 'test/label.txt')

image.png