CIFAR原始数据转为图片

官方给出的python3解压数据文件函数，返回数据字典。

import torch
import torchvision
import numpy as np
import cv2
import os
import json

def unpickle(file):
    import pickle
    with open(file, 'rb') as fo:
        dict = pickle.load(fo, encoding='bytes')
    return dict
 
loc_1 = r'data/train_cifar10/'
loc_2 = r'data/test_cifar10/'
 
#判断文件夹是否存在，不存在的话创建文件夹
if os.path.exists(loc_1) == False:
    os.mkdir(loc_1)
if os.path.exists(loc_2) == False:
    os.mkdir(loc_2)
 
 
#训练集有五个批次，每个批次10000个图片，测试集有10000张图片
def cifar10_img(file_dir):
    for i in range(1,6):
        data_name = file_dir + '/'+'data_batch_'+ str(i)
        data_dict = unpickle(data_name)
        print(data_name + ' is processing')
 
        for j in range(10000):
            img = np.reshape(data_dict[b'data'][j],(3,32,32))
            img = np.transpose(img,(1,2,0))
            #通道顺序为RGB
            img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
            #要改成不同的形式的文件只需要将文件后缀修改即可
            img_name = loc_1 + str(data_dict[b'labels'][j]) + str((i)*10000 + j) + '.jpeg'
            cv2.imwrite(img_name,img)
 
        print(data_name + ' is done')
 
 
    test_data_name = file_dir + '/test_batch'
    print(test_data_name + ' is processing')
    test_dict = unpickle(test_data_name)
 
    for m in range(10000):
        img = np.reshape(test_dict[b'data'][m], (3, 32, 32))
        img = np.transpose(img, (1, 2, 0))
        # 通道顺序为RGB
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        # 要改成不同的形式的文件只需要将文件后缀修改即可
        img_name = loc_2 + str(test_dict[b'labels'][m]) + str(10000 + m) + '.jpeg'
        cv2.imwrite(img_name, img)
    print(test_data_name + ' is done')
    print('Finish transforming to image')
if __name__ == '__main__':
    file_dir = r'data/cifar-10-batches-py'
    cifar10_img(file_dir)