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)

or, using numpy.amax

mask = (greens < 35) | (numpy.amax(img, axis=2) != 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)

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:

RED, GREEN, BLUE = (2, 1, 0)

reds = img[:, :, RED]
greens = img[:, :, GREEN]
blues = img[:, :, BLUE]

mask = (greens < 35) | (reds > greens) | (blues > greens)

or, using numpy.amax

mask = (greens < 35) | (numpy.amax(img, axis=2) != 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)

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)

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)

or, using numpy.amax

mask = (greens < 35) | (numpy.amax(img, axis=2) != 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)