numpy - Pixel neighbors in 2d array (image) using Python

Question

I have a numpy array like this:

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

I need to create a function let's call it "neighbors" with the following input parameter:

• x: a numpy 2d array
• (i,j): the index of an element in a 2d array
• d: neighborhood radius

As output I want to get the neighbors of the cell i,j with a given distance d. So if I run

neighbors(im, i, j, d=1) with i = 1 and j = 1 (element value = 5) 

I should get the indices of the following values: [1,2,3,4,6,7,8,9]. I hope I make it clear. Is there any library like scipy which deal with this?

I've done something working but it's a rough solution.

def pixel_neighbours(self, p):

    rows, cols = self.im.shape

    i, j = p[0], p[1]

    rmin = i - 1 if i - 1 >= 0 else 0
    rmax = i + 1 if i + 1 < rows else i

    cmin = j - 1 if j - 1 >= 0 else 0
    cmax = j + 1 if j + 1 < cols else j

    neighbours = []

    for x in xrange(rmin, rmax + 1):
        for y in xrange(cmin, cmax + 1):
            neighbours.append([x, y])
    neighbours.remove([p[0], p[1]])

    return neighbours

How can I improve this?

Answer 1

Have a look at scipy.ndimage.generic_filter.

As an example:

import numpy as np
import scipy.ndimage as ndimage

def test_func(values):
    print(values)
    return values.sum()


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

footprint = np.array([[1,1,1],
                      [1,0,1],
                      [1,1,1]])

results = ndimage.generic_filter(x, test_func, footprint=footprint)

By default, it will "reflect" the values at the boundaries. You can control this with the mode keyword argument.

However, if you're wanting to do something like this, there's a good chance that you can express your problem as a convolution of some sort. If so, it will be much faster to break it down into convolutional steps and use more optimized functions (e.g. most of scipy.ndimage).