python读取numpy图像数据时将灰度图像转为3通道并更改尺寸的方法

在用深度网络训练时，大部分网络都要求输入为3通道，而有时现有的数据为单通道的灰度图，并且尺寸也不符合网络输入，可用下面的函数转换，以minist数据集为例。

import numpy as np
from keras.datasets import mnist
from keras.utils import to_categorical

image_size = 224
def load_mnist(image_size):
    (x_train,y_train),(x_test,y_test) = mnist.load_data()
    train_image = [cv2.cvtColor(cv2.resize(img,(image_size,image_size)),cv2.COLOR_GRAY2BGR) for img in x_train]
    test_image = [cv2.cvtColor(cv2.resize(img,(image_size,image_size)),cv2.COLOR_GRAY2BGR) for img in x_test]    
    train_image = np.asarray(train_image)
    test_image = np.asarray(test_image)
    train_label = to_categorical(y_train)
    test_label = to_categorical(y_test)
    print('finish loading data!')
    return train_image, train_label, test_image, test_label