There's a simpler way to create the empty image using numpy.zeros_like:
empty_img = numpy.zeros_like(img)
As Austin Hastings correctly pointed out, the trick is to use vectorized operations provided by numpy.
mask = (greens < 35) | ((reds <= greens) & (blues <= greens))
Now, one option is to use conditional indexing to modify empty_img. Since it's a 3 channel image (represented as 3 dimensional array), and our mask is only 1 channel (represented as 2 dimensional array) there are two possibilities:
- assign 3-tuples:
empty_img[mask] = (255,0,0) - provide the 3rd index:
empty_img[mask,0] = 255
If all you care about is just a single channel mask, then numpy.where is a possibility.
result = numpy.where(mask, 255, 0)