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)