Vectorizing code of looping through NumPy 3D array

Question

I'm currently writing a program that processes an image and returns the same image except some pixels that are identified as a certain feature (found via truth_table) are blackened. It works, but since the images are around 5000 by 5000 pixels, it takes about 1 whole minute just to go through this part:

image_DN_no_12s = np.copy(image_DN)
for y in range(y_size):
    for x in range(x_size):
        if truth_table[y, x] == 12:
            image_DN_no_12s[y, x, :] = np.zeros((1, 1, 4))

(The 3rd dimension in np.zeros() is 4 because I am reading red, green, blue, and infrared channels.)

I would like to vectorize this code so that it will run faster. I have tried using np.where() and boolean indexing, but I can't seem to find a way to make it run faster.

Thanks in advance for the help!

Mateen Ulhaq · Accepted Answer · 2018-06-20 23:03:33Z

1

If img and truth_table are arrays where truth_table is of broadcastable shape:

img[truth_table == 12] = 0

You don't need to explicitly create another axis of 4 channels since broadcasting handles it for you.

answered Jun 20, 2018 at 23:03

Mateen Ulhaq

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