[amelius]

Yeah, but in the classical approach your users don't need to have a GPU installed.

This seems to be a case of "if you're holding a hammer, everything looks like a nail".

[dr_zoidberg]

Edit: I'm pretty sure I missclicked to which comment I wanted to answer and now I can't find it... It said something along the ML approach being more developer-time efficient.

You can definitely do it in 20 minutes if you know how (which is also applicable to the ML version):

    import cv2
    import numpy as np

    red_duck = cv2.imread("red_duck.png", cv2.IMREAD_GRAYSCALE)

    # boilertplate etc, up to the point where you want to match your ducks on screen:

    res = cv2.matchTemplate(img_screen, red_duck, cv2.TM_CCOEFF_NORMED)
    positions = np.zeros_like(img_screen)
    positions[res > 0.7] = 1  # we found a duck
    # now do whatever you want with each position
    # on real life images you may need to do some additional post-processing, on 8-bit rendered images you probably don't need to