Use `tf.image.resize_image_with_crop_or_pad` to resize numpy array

import numpy as np

a = np.array([[1,2,3,4], [5,6,7,8], [9,8,7,6], [5,4,3,2]])
a[::2, ::2]

array([[1, 3],
       [9, 7]])

m,n,r = a.shape
out = a.reshape(m//2,2,n//2,2,r//2,2).mean((1,3,5))

def shrink(a, S=2): # S : shrink factor
    new_shp = np.vstack((np.array(a.shape)//S,[S]*a.ndim)).ravel('F')
    return a.reshape(new_shp).mean(tuple(1+2*np.arange(a.ndim)))

In [407]: a
Out[407]: 
array([[[1, 5, 8, 2],
        [5, 6, 4, 0],
        [8, 5, 5, 5],
        [1, 0, 0, 0]],

       [[0, 0, 7, 6],
        [3, 5, 4, 3],
        [4, 5, 1, 3],
        [6, 7, 4, 0]]])

In [408]: a[:2,:2,:2].mean()
Out[408]: 3.125

In [409]: a[:2,:2,2:4].mean()
Out[409]: 4.25

In [410]: a[:2,2:4,:2].mean()
Out[410]: 4.5

In [411]: a[:2,2:4,2:4].mean()
Out[411]: 2.25

In [412]: shrink(a, S=2)
Out[412]: 
array([[[ 3.125,  4.25 ],
        [ 4.5  ,  2.25 ]]])

import cv2
import numpy as np

img = cv2.imread('your_image.jpg')
res = cv2.resize(img, dsize=(54, 140), interpolation=cv2.INTER_CUBIC)

target_z = 170
# x is shape of (166, 256, 256)
zp = 170-x.get_shape().as_list()[0]
# what if zp is negative ?
paddings = tf.constant([[0, zp], [0, 0], [0,0]])
tf.pad(x, paddings, "CONSTANT")