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pythonimagenumpypixel

Numpy filter pixel indices

I want to extract the sum count of all pixels of an RGB image where R=0 and B=0 and where the x,y coordinates of those pixels are lying on the border of an image.

First I get the coordinates of the pixels with R=0 and B=0:

import cv2
import numpy as np
i = cv2.imread("test2.png")
indices = np.where((i[:, :, 0] == 0) & (i[:, :, 2] == 0))

Which gives me a list of the coordinates. Now I want to get the sum of all pixels where the x position is 0 or the image width (in this case 21).

I could sort the list but I would like to stick to numpy arrays if possible. Is there an fancy way to do it?

enter image description here

Solution

  • Approach #1

    With X along the second axis, here's one fancy way -

    (i[...,[0,2]]==0).all(-1)[:,[0,-1]].sum()

    Approach #2

    With multi-dim indexing -

    (i[:,[0,-1],[0,2]]==0).sum()

    Approach #3

    For performance, use more of slicing -

    mask = (i[...,0]==0) & (i[...,2]==0)
out_x = (mask[:,0] + mask[:,-1]).sum()

    On older NumPy versions, np.count_nonzero might be better than .sum().