the problem is to take a black-white image, detect all the places where white borders on black, keep that white, and turn all other white pixels black. I know how to do this using normal for-loops and lists, but I want to do it w/ numpy, which I am not that familiar with. Here is what I have so far:

>>>from PIL Import Image >>>import numpy as np >>>a = Image.open('a.png') >>>a = a.convert('L') >>>a_np = np.array(a) >>>a_np array([[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]], dtype=uint8) >>>mask = np.pad(a_np[1:-1,1:-1],1,mode='wrap') != 0 >>>mask array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], ..., [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]]) >>> np.where(mask == True) (array([ 98, 98, 98, ..., 981, 981, 981]), array([393, 394, 395, ..., 684, 685, 686])) >>> a_np[mask] = 0 >>> a_np array([[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]], dtype=uint8) >>> np.where(a_np == 1) (array([], dtype=int64), array([], dtype=int64))

Basically, trying to create a mask that finds the neighbors of every element in the array and for those that do not have a black neighbor, turn them black – but no matter what I try I either get all black elements or the same array that I started with. Numpy or OpenCV solutions are welcome.

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## Answer

With numpy and help of scipy, this can be achieved with a `binary_erosion`

.

One can erode the pixels with a kernel of 3×3, then compare the original image to the erosion to find the difference:

from scipy.ndimage import binary_erosion kernel = np.ones((3,3)) edges = a_np - binary_erosion(a_np, kernel)*255 out = Image.fromarray(edges.astype('uint8'), 'L')

output image: