How can I quickly change pixels in a image from a color dictionary?

Question

I have an image, I want to change all the colors in the image from a color map eg. {(10,20,212) : (60,40,112)...}

Currently, I am reading the image OpenCV and then iterating over the image array and changing each pixel, but this is very slow.

Is there any way I can do it faster?

Answer 1

I am providing two answers to this question. This answer is more based in OpenCV and the other is more based in PIL/Pillow. Read this answer in conjunction with my other answer and potentially mix and match.

You can use Numpy's linalg.norm() to find the distances between colours and then argmin() to choose the nearest. You can then use a LUT "Look Up Table" to look up a new value based on the existing values in an image.

#!/usr/bin/env python3

import numpy as np
import cv2

def QuantizeToGivenPalette(im, palette):
    """Quantize image to a given palette.
    
    The input image is expected to be a Numpy array.
    The palette is expected to be a list of R,G,B values."""

    # Calculate the distance to each palette entry from each pixel
    distance = np.linalg.norm(im[:,:,None] - palette[None,None,:], axis=3)

    # Now choose whichever one of the palette colours is nearest for each pixel
    palettised = np.argmin(distance, axis=2).astype(np.uint8)

    return palettised

# Open input image and palettise to "inPalette" so each pixel is replaced by palette index
# ... so all black pixels become 0, all red pixels become 1, all green pixels become 2...
im=cv2.imread("image.png",cv2.IMREAD_COLOR)

inPalette = np.array([
   [0,0,0],             # black
   [0,0,255],           # red
   [0,255,0],           # green
   [255,0,0],           # blue
   [255,255,255]],      # white
   )

r = QuantizeToGivenPalette(im,inPalette)

# Now make LUT (Look Up Table) with the 5 new colours
LUT = np.zeros((5,3),dtype=np.uint8)
LUT[0]=[255,255,255]  # white
LUT[1]=[255,255,0]    # cyan
LUT[2]=[255,0,255]    # magenta
LUT[3]=[0,255,255]    # yellow
LUT[4]=[0,0,0]        # black

# Look up each pixel in the LUT
result = LUT[r]

# Save result
cv2.imwrite('result.png', result)

Input Image

Output Image

Keywords: Python, PIL, Pillow, image, image processing, quantise, quantize, specific palette, given palette, specified palette, known palette, remap, re-map, colormap, map, LUT, linalg.norm.