How to map colors in an image to a palette fast with numpy?-CodePudding

I have two arrays. One is an image array and the other is a palette array. Both have elements containing 8-bit RGB channels. I need to replace every color in the image with the closest color in the palette.

Currently I'm measuring distance in the RGB-space, which is not ideal, but easy to implement.

This is my implementation:

image_array = np.array(image) # converts PIL image, values are uint8
# palette values are also 8-bit but I use int so I don't have to cast types
palette_array = np.array(palette, dtype=[('red', np.int), ('green', np.int), ('blue', np.int)])
mapped_image = np.empty((image_height, image_width, 3), dtype=np.uint8)
for x in range(image_width):
    for y in range(image_height):
        r, g, b = image_array[y, x]
        distances_squared = (r-palette['red'])**2   (g-palette['green'])**2   (b-palette['blue'])**2
        closest_index = np.argmin(distances_squared)
        closest_color = palette.flat[closest_index]
        mapped_image[y, x] = closest_color

The palette has 4096 random colors (simple conversion is not possible). When mapping a 600x448 sized image this takes roughly a minute even on my core i5 machine. I plan to use this on lower-end devices like a raspberry pi, where it takes roughly 3 minutes to map a small image.

This is way too slow. I believe this can be sped up significantly when the full loop is implemented with numpy syntax, but I can't figure out how to do this.

How do I get from the original image to the mapped one all implemented with numpy syntax?

CodePudding user response：

You can try using cKDTree function from scipy.

import numpy as np
from scipy.spatial import cKDTree
palette=np.random.randint(0, 255, size=(4096,3), dtype=np.uint8) # random palette
image_in=np.random.randint(0, 255, size=(800, 600, 3), dtype=np.uint8) # random image
size=image_in.shape
vor=cKDTree(palette)
test_points=np.reshape(image_in, (-1,3))
_, test_point_regions = vor.query(test_points, k=1)
image_out=palette[test_point_regions]
np.reshape(image_out, size)

This program runs for approximately 0.8 seconds.