• 本文利用了keras中自带的MINIST数据库,这个自带的数据库已经进行了train set 和test set的划分.因此,本文将相应的数据集保存到了本地.
• 直接上代码:

• import necessary packages

import os
import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from matplotlib.image import imsave
import itertools

• get the data which has been shuffled and split between train and test sets.

# the data, shuffled and split between tran and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print("X_train original shape", X_train.shape)
print("y_train original shape", y_train.shape)

• results: 此处可以看到, 训练集6w张图片.

  
  
  image.png

• show some demons

for i in range(9):
    plt.subplot(3,3,i+1)
    plt.imshow(X_train[i], cmap='gray', interpolation='none')
    plt.title("Class {}".format(y_train[i]))

• the destination folder structure we tend to use:
  外层结构:

  
  
  image.png

  内层结构

  
  
  image.png
• 对训练集的处理: 图片名称从0开始编号.

image_counter = itertools.count(0)
for image, label in zip(X_train, y_train):
    dest_folder = os.path.join(train_path, str(label))
    image_name = next(image_counter)
    image_path = os.path.join(dest_folder, str(image_name)+'.png')
    
    if not os.path.exists(dest_folder):
        os.mkdir(dest_folder)
    
    imsave(image_path, image, cmap = 'gray')  

• 对测试集图片的处理:图片接着训练集编号.

for image, label in zip(X_test, y_test):
    dest_folder = os.path.join(test_path, str(label))
    image_name = next(image_counter)
    image_path = os.path.join(dest_folder, str(image_name)+'.png')
    
    if not os.path.exists(dest_folder):
        os.mkdir(dest_folder)
    
    imsave(image_path, image, cmap = 'gray')    

• 结果:

  
  
  image.png