"Down-resolution" a numpy array

Question

I have a numpy array of shape [5sx,5sy,5sz] and I’d like to create a tensor with shape [sx,sy,sz] where the value of each element is:

new_array[x,y,z] = mean(array[5x:5x+5,5y:5y+5,5z:5z+5])

I could do this in a loop, but is there a faster one-line approach?

(I'd really ideally like to do it where the size of the origin array is not an integer multiple of the new one, but that seems like a much harder question, and I thought this would be a good first step at least.)

In pytorch I think the following broadly does it: kernel_size=[5,5,5] torch.nn.Conv3d(in_channels=1,out_channels=1,kernel_size=kernel_size, stride=kernel_size, padding=0)(tensor) — Omroth
– Omroth, Commented Feb 2, 2022 at 11:22
The new array must be smaller of at least 5 elements or you have to add a padding after the last elements. Think about the last element in x, if you do the mean from that element to the one 5 elements after, you point your index outside the matrix — DaSim
– DaSim, Commented Feb 2, 2022 at 11:24
Why the approach you wrote on the comments shouldn't be good for your purposes? — DaSim
– DaSim, Commented Feb 2, 2022 at 11:25

yann ziselman · Accepted Answer · 2022-02-02 11:31:12Z

1

You could use scikit-image block_reduce:

import numpy as np
import skimage.measure

arr5 = np.random.rand(20, 20, 20)
arr  = skimage.measure.block_reduce(arr5, (5, 5, 5), np.mean)
print(f'arr.shape = {arr.shape}')
print(f'arr5[:5, :5, :5].mean() = {arr5[:5, :5, :5].mean()}')
print(f'arr[0, 0, 0] = {arr[0, 0, 0]}')

output:

arr.shape = (4, 4, 4)
arr5[:5, :5, :5].mean() = 0.47200241666948467
arr[0, 0, 0] = 0.4720024166694848

If you're dealing with large arrays and you have a GPU available I would advise you to look into pytorch's average pooling.

answered Feb 2, 2022 at 11:31

yann ziselman

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2 Comments

Omroth Over a year ago

Average pooling may be exactly what I'm looking for in the case the arrays aren't perfectly aligned - thanks, looking into it further.

yann ziselman Over a year ago

what do you mean by "in the case the arrays aren't perfectly aligned"?

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