Generate pixel density map (heatmap) from image with numpy array manipulation

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The specific problem I try to solve is: I have a binary image binary map that I want to generate a heatmap (density map) for, my idea is to get the 2D array of this image, let say it is 12x12

a = np.random.randint(20, size=(12, 12));

index and process it with a fixed-size submatrix (let say 3x3), so for every submatrix, a pixel percentage value will be calculated (nonzero pixels/total pixel).

submatrix = a[0:3, 0:3]
pixel_density = np.count_nonzero(submatrix) / submatrix.size

At the end, all the percentage values will made up a new 2D array (a smaller, 4x4 density array) that represent the density estimation of the original image. Lower resolution is fine because the data it will be compared to has a lower resolution as well.

I am not sure how to do that through numpy, especially for the indexing part. Also if there is a better way for generating heatmap like that, please let me know as well.

Thank you!

image-processing numpy python
1 Answers

Maybe a 2-D convolution? Basically this will sweep through the a matrix with the b matrix, which is just 1s below. So it will do the summation you were looking for. This link has a visual demo of convolution near the bottom.

import numpy as np
from scipy import signal

a = np.random.randint(2, size=(12, 12))
b = np.ones((4,4))
signal.convolve2d(a,b, 'valid') / b.sum()
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