Local Standardization from the input of an Image Array

#!/usr/bin/python3

from numpy import asarray

def local_standardization():
    x = 3
    y = 2
    z = 3
    matrix = []
    # taking 3*2*3 matric from the user as an image input
    for i in range(x):
        matrix.append([])
        for j in range(y):
            matrix[i].append([])
            for k in range(z):
                matrix[i][j].append(input(""))
    pixels=asarray(matrix)
    pixels=pixels.astype('float32')
    #calculate per-channel means and standard deviations
    means=pixels.mean(axis=(0,1),dtype='float64')
    std=pixels.std(axis=(0,1),dtype='float64')
    print('Means(Before Standardization): %s, Stds(Before Standardization): %s' % (means,std))
    #per-channel standardization of pixels
    pixels=(pixels-means)/std
    means=pixels.mean(axis=(0,1),dtype='float64')
    std=pixels.std(axis=(0,1),dtype='float64')
    print('Means(After Standardization): %s, Stds(After Standardization): %s' % (means, std))
    # print(pixels.shape)
    return pixels
print(local_standardization())



# sample input([[[111,12,33],
#         [ 44,15,16]],
#         [[ 75,98,19],
#         [120,131,112]],
#         [[ 13,141,15],
#         [ 16,127,183]]])