1 def get_files(filename):
 2     class_train = []
 3     label_train = []
 4     for train_class in os.listdir(filename):
 5          for pic in os.listdir(filename+train_class):
 6              class_train.append(filename+train_class+'/'+pic)
 7              label_train.append(train_class)
 8     temp = np.array([class_train,label_train])
 9     temp = temp.transpose()
10     #shuffle the samples
11     np.random.shuffle(temp)
12     #after transpose, images is in dimension 0 and label in dimension 1
13     image_list = list(temp[:,0])
14     label_list = list(temp[:,1])
15     label_list = [int(i) for i in label_list]
16     #print(label_list)
17     return image_list,label_list
18 TrainData,labels=get_files(path)

 

 1 import numpy as np
 2 import glob
 3 from skimage import io
 4 from skimage import transform 
 5 #读取图片
 6 path='C:/Users/hsy/Desktop/train/'
 7 def read_img(path):
 8     cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]
 9     imgs=[]
10     labels=[]
11     for idx,folder in enumerate(cate):
12         for im in glob.glob(folder+'/*.jpg'):
13             print('reading the images:%s'%(im))
14             img=io.imread(im)
15             img=transform.resize(img,(64,64))
16             imgs.append(img)
17             labels.append(idx)
18     return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)
19 data,label=read_img(path)
20