de_noise.py 文件源码

def get_train_batch(noise=500):
    ran = np.random.randint(600,5800,size=10,dtype='int')
    #print(ran)
    image = []
    label = []
    label_0 = []
    n_pic = ran
    # print(n_pic)
    for i in range(10):
        frame_0 = cv2.imread('./cropedoriginalPixel2/%d.jpg' % (n_pic[i]), 0)
        frame_0 = add_noise(frame_0, n = noise)
        frame_0 = cv2.resize(frame_0, (24, 24))
        frame_0 = np.array(frame_0).reshape(-1)
        frame_0 = frame_0 / 255.0
        image.append(frame_0)
        #print(np.shape(image))
    for i in range(10):
        frame_1 = cv2.imread('./cropedoriginalPixel2/%d.jpg' % (n_pic[i]), 0)
        frame_1 = cv2.resize(frame_1, (24, 24))
        frame_1 = np.array(frame_1).reshape(-1)
        frame_1 = gray2binary(frame_1)
        label.append(frame_1)
    return np.array(image,dtype='float') , np.array(label,dtype='float')