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CIFAR100和CIFAR-10类似，它有100个类，每个类包含600个图像，其中500个训练图像和100个测试图像

100类实际是由20个类(每个类又包含5个子类)构成(5*20=100)。

 

 

# -*- coding:utf-8 -*-import pickle as pimport numpy as npimport matplotlib.pyplot as pltimport matplotlib.image as plimgfrom PIL import Imagedef load_CIFAR_batch(filename):    """ load single batch of cifar """    with open(filename, 'rb')as f:        datadict = p.load(f,encoding='bytes')        #X = datadict[b'data']        #Y = datadict[b'labels']        #X = X.reshape(10000, 3, 32, 32)        X = datadict[b'data']        Y = datadict[b'coarse_labels']+datadict[b'fine_labels']        X = X.reshape(50000, 3, 32, 32)        Y = np.array(Y)        return X, Yif __name__ == "__main__":    #imgX, imgY = load_CIFAR_batch("./cifar-10-batches-py/data_batch_1")    imgX, imgY = load_CIFAR_batch("dataset/cifar-100-python/train")    print(imgX.shape)    print("正在保存图片:")    for i in range(imgX.shape[0]):        imgs = imgX[i]        if i < 100:#只循环100张图片,这句注释掉可以便利出所有的图片,图片较多,可能要一定的时间            img0 = imgs[0]            img1 = imgs[1]            img2 = imgs[2]            i0 = Image.fromarray(img0)            i1 = Image.fromarray(img1)            i2 = Image.fromarray(img2)            img = Image.merge("RGB",(i0,i1,i2))            name = "img" + str(i)+".png"            img.save("dataset/cifar-100-python/extract-pic1/"+name,"png")#文件夹下是RGB融合后的图像            for j in range(imgs.shape[0]):                img = imgs[j]                name = "img" + str(i) + str(j) + ".jpg"                print("正在保存图片" + name)                plimg.imsave("dataset/cifar-100-python/extract-pic2/" + name, img)#文件夹下是RGB分离的图像    print("保存完毕.")

 

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