How to resize N-d numpy image?

Question

I don't just want to subsample it, but to interpolate / average the pixels.

For example, if I start with

array([[[3, 1, 3, 1],
        [3, 1, 3, 1],
        [3, 1, 3, 1],
        [3, 1, 3, 1]],

       [[3, 1, 3, 1],
        [3, 1, 3, 1],
        [3, 1, 3, 1],
        [3, 1, 3, 1]]], dtype=uint8)

and shrink it by a factor of 2 in all dimensions, I want the output to be

array([[[2, 2],
        [2, 2]]], dtype=uint8)

Attempted solutions:

A. SciPy ndimage:

>>> scipy.ndimage.interpolation.zoom(x, .5, mode='nearest')

array([[[3, 1],
        [3, 1]]], dtype=uint8)

(The optional order parameter makes no difference)

B. Looping over 2**3 possible offsets: ugly, slow, works only for integer zoom factors, and needs extra steps to avoid overflows.

C. OpenCV and PIL work only with 2D images.

Divakar · Accepted Answer · 2017-10-19T10:45:44.427

Reshape to split each axes into one more axis each of length 2, giving us a 6D array and then get the mean along those latter ones (axes : 1,3,5) -

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

Extending to n-dim array, it would be -

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)))

Sample run -

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 ]]])

How to resize N-d numpy image?

1 Answers1