Question

I’m swapping values of a multidimensional numpy array in Python. But the code is too slow. Another thread says:

Typically, you avoid iterating through them directly. … there’s a good chance that it’s easy to vectorize.

So, do you know a way to optimize the following code?

import PIL.Image
import numpy

pil_image = PIL.Image.open('Image.jpg').convert('RGB')
cv_image = numpy.array(pil_image)
# Convert RGB to BGR
for y in range(len(cv_image)):
    for x in range(len(cv_image[y])):
        (cv_image[y][x][0], cv_image[y][x][2]) = (cv_image[y][x][2],
            cv_image[y][x][0])

For an 509×359 image this last more than one second, which is way too much. It should perform it’s task in no time.

Answer 1

How about this single operation inverting the matrix along the last axis?

cv_image = cv_image[:,:,::-1]