解析cifar的python数据集中的图片

以下代码根据这篇文章修改：http://blog.csdn.net/guohuifengby/article/details/62424299

运行环境：
win7
numpy-1.13.1+mkl-cp36-cp36m-win_amd64.whl
scipy-0.19.1-cp36-cp36m-win_amd64.whl

#encoding:utf-8
from scipy.misc import imsave
import numpy as np

# 解压缩，返回解压后的字典
def unpickle(file):
    import pickle
    with open(file, 'rb') as fo:
        dict = pickle.load(fo, encoding='bytes')
    return dict



# 生成训练集图片，如果需要png格式，只需要改图片后缀名即可。
for j in range(1, 6):

    # 读取当前目录下的data_batch12345文件，dataName其实也是data_batch文件的路径，本文和脚本文件在同一目录下。
    dataName = "data_batch_" + str(j)  

    Xtr = unpickle(dataName)
    print (dataName + " is loading...")

    for i in range(0, 10000):
        img = np.reshape(Xtr[b'data'][i], (3, 32, 32))  # Xtr['data']为图片二进制数据
        img = img.transpose(1, 2, 0)  # 读取image

        # Xtr['labels']为图片的标签，值范围0-9，本文中，train文件夹需要存在，并与脚本文件在同一目录下。
        picName = 'train/' + str(Xtr[b'labels'][i]) + '_' + str(i + (j - 1)*10000) + '.jpg'  

        imsave(picName, img)
    print (dataName + " loaded.")

print ("test_batch is loading...")

# 生成测试集图片
testXtr = unpickle("test_batch")
for i in range(0, 10000):
    img = np.reshape(testXtr[b'data'][i], (3, 32, 32))
    img = img.transpose(1, 2, 0)
    picName = 'test/' + str(testXtr[b'labels'][i]) + '_' + str(i) + '.jpg'
    imsave(picName, img)
print ("test_batch loaded.")