dir_file目录下需有这几个文件

 

 

源代码

#coding=utf-8
import cv2
import numpy as np
import os#文件夹名
str_2 = './train_cifar10'
str_1 = './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(str_1) == False:
os.mkdir(str_1)
if os.path.exists(str_2) == False:
os.mkdir(str_2)# 解压缩，返回解压后的字典，f,encoding='bytes'
def unpickle(file):
import pickle
fo = open(file, 'rb')
dict = pickle.load(fo, encoding='bytes')
fo.close()
return dictdef cifar_jpg(dir_file):
# 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):
dataName = dir_file + '/' + "data_batch_" + str(j) # 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。
Xtr = unpickle(dataName)
#print(Xtr)
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
picName = './train_cifar10/' + str(Xtr[b'labels'][i]) + '_' + str(
i + (j - 1) * 10000) + '.jpg' # 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。
cv2.imwrite(picName, img)
print(dataName + " loaded.")print("test_batch is loading...")# 生成测试集图片
testName = dir_file + '/' + 'test_batch'
testXtr = unpickle(testName)
for i in range(0, 10000):
img = np.reshape(testXtr[b'data'][i], (3, 32, 32))
img = img.transpose(1, 2, 0)
picName = './test_cifar10/' + str(testXtr[b'labels'][i]) + '_' + str(i) + '.jpg'
cv2.imwrite(picName, img)
print("test_batch loaded.")
return#标签与名字的对应关系
def label_name():
label_name_dict = {
'airplane': "0",
'automobile': "1",
'bird': "2",
'cat': "3",
'deer': "4",
'dog': "5",
'frog': "6",
'horse': "7",
'ship': "8",
'truck': "9"
}
return label_name_dictif __name__ == '__main__':
dir_file = './cifar-10-batches-py'
try:
cifar_jpg(dir_file)
except:
print('函数报错')