Expanding/Zooming in a numpy array

Question

I have the following array:

import numpy as np
a = np.array([[2, 3, 5],
              [4, 6, 7],
              [1, 5, 7]])

I want to expand it to this array:

b = [[2 2 2 3 3 3 5 5 5]
     [2 2 2 3 3 3 5 5 5]
     [2 2 2 3 3 3 5 5 5]
     [4 4 4 6 6 6 7 7 7]
     [4 4 4 6 6 6 7 7 7]
     [4 4 4 6 6 6 7 7 7]
     [1 1 1 5 5 5 7 7 7]
     [1 1 1 5 5 5 7 7 7]
     [1 1 1 5 5 5 7 7 7]]

So I’m using the following command:

import scipy.ndimage
b = scipy.ndimage.interpolation.zoom(a, 3, order=0)

based on this question and answer here Resampling a numpy array representing an image.

However, what I’m getting is this:

b = [[2 2 3 3 3 3 5 5 5]
     [2 2 3 3 3 3 5 5 5]
     [4 4 6 6 6 6 7 7 7]
     [4 4 6 6 6 6 7 7 7]
     [4 4 6 6 6 6 7 7 7]
     [4 4 6 6 6 6 7 7 7]
     [1 1 5 5 5 5 7 7 7]
     [1 1 5 5 5 5 7 7 7]
     [1 1 5 5 5 5 7 7 7]]

I want the expansion to be exactly by 3, or whatever the zoom factor is, but currently it’s different for each element of the array.

Is there a direct way to do this? Or shall I do it manually with some coding?

Asked By: philippos

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Source

Answer 1

I don’t know if there’s a function that does exactly what you want in NumPy or SciPy but it’s easy to create one yourself:

from __future__ import division
import numpy as np

def zoom(a, factor):
    a = np.asarray(a)
    slices = [slice(0, old, 1/factor) for old in a.shape]
    idxs = (np.mgrid[slices]).astype('i')
    return a[tuple(idxs)]

It gives the expected result:

>>> a = [[2,3,5], [4,6,7], [1,5,7]]

>>> zoom(a,3)
array([[2, 2, 2, 3, 3, 3, 5, 5, 5],
       [2, 2, 2, 3, 3, 3, 5, 5, 5],
       [2, 2, 2, 3, 3, 3, 5, 5, 5],
       [4, 4, 4, 6, 6, 6, 7, 7, 7],
       [4, 4, 4, 6, 6, 6, 7, 7, 7],
       [4, 4, 4, 6, 6, 6, 7, 7, 7],
       [1, 1, 1, 5, 5, 5, 7, 7, 7],
       [1, 1, 1, 5, 5, 5, 7, 7, 7],
       [1, 1, 1, 5, 5, 5, 7, 7, 7]])

I haven’t tested it for all factors and shapes, maybe that approach could have trouble because of floating point accuracy (the step argument in the slices).

Answered By: MSeifert

Answer 2

Maybe a little late, but for the sake of completness: Numpy Kron does the job perfectly

>>> import numpy as np
>>> a = np.array([[2,3,5], [4,6,7], [1,5,7]])
>>> np.kron(a, np.ones((3,3)))
array([[ 2.,  2.,  2.,  3.,  3.,  3.,  5.,  5.,  5.],
       [ 2.,  2.,  2.,  3.,  3.,  3.,  5.,  5.,  5.],
       [ 2.,  2.,  2.,  3.,  3.,  3.,  5.,  5.,  5.],
       [ 4.,  4.,  4.,  6.,  6.,  6.,  7.,  7.,  7.],
       [ 4.,  4.,  4.,  6.,  6.,  6.,  7.,  7.,  7.],
       [ 4.,  4.,  4.,  6.,  6.,  6.,  7.,  7.,  7.],
       [ 1.,  1.,  1.,  5.,  5.,  5.,  7.,  7.,  7.],
       [ 1.,  1.,  1.,  5.,  5.,  5.,  7.,  7.,  7.],
       [ 1.,  1.,  1.,  5.,  5.,  5.,  7.,  7.,  7.]])

Answered By: thilo_dual

Answer 3

Here is my approach, which uses simple numpy functions. It returns the same dtype as the input.

import numpy as np

def zoom(a, factor):
    sx, sy = (factor * dim for dim in a.shape)
    X, Y = np.ogrid[0:sx, 0:sy]
    return a[X//factor, Y//factor]

Answered By: Mike T

Expanding/Zooming in a numpy array

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