Python- Is there a function or formula to find the complementary colour of a rgb code?

colors, python, python-3.5, rgb

Solution

Here's how to calculate the complement of an RGB colour directly. It gives the same results as the algorithm using `colorsys` as shown in Iva Klass's answer, but in my tests it's about 50% faster. Note that it works for any RGB scheme, it doesn't matter whether the RGB components are integers or floats (as long as each component uses the same range!).

The function `hilo` implements a simple sorting network to sort the RGB components.

# Sum of the min & max of (a, b, c)
def hilo(a, b, c):
    if c < b: b, c = c, b
    if b < a: a, b = b, a
    if c < b: b, c = c, b
    return a + c

def complement(r, g, b):
    k = hilo(r, g, b)
    return tuple(k - u for u in (r, g, b))

Here's a short demo, using PIL / Pillow.

#!/usr/bin/env python3

''' Complement the colours in a RGB image 

    Written by PM 2Ring 2016.10.08
'''

import sys
from PIL import Image

# Sum of the min & max of (a, b, c)
def hilo(a, b, c):
    if c < b: b, c = c, b
    if b < a: a, b = b, a
    if c < b: b, c = c, b
    return a + c

def complement(r, g, b):
    k = hilo(r, g, b)
    return tuple(k - u for u in (r, g, b))

def complement_image(iname, oname):
    print('Loading', iname)
    img = Image.open(iname)
    #img.show()

    size = img.size
    mode = img.mode
    in_data = img.getdata()

    print('Complementing...')
    out_img = Image.new(mode, size)
    out_img.putdata([complement(*rgb) for rgb in in_data])
    out_img.show()
    out_img.save(oname)
    print('Saved to', oname)

def main():
    if len(sys.argv) == 3:
        complement_image(*sys.argv[1:])
    else:
        fmt = 'Complement colours.\nUsage: {} input_image output_image'
        print(fmt.format(sys.argv[0]))

if __name__ == '__main__':
    main()

Input image

Output image

Here's a Numpy version of `complement_image`. On my machine it processes the "Glasses" image about 3.7 times faster than the previous version.

import numpy as np

def complement_image(iname, oname):
    print('Loading', iname)
    img = Image.open(iname)
    #img.show()

    in_data = np.asarray(img)
    #print(in_data.shape)

    print('Complementing...')
    lo = np.amin(in_data, axis=2, keepdims=True)
    hi = np.amax(in_data, axis=2, keepdims=True)
    out_data = (lo + hi) - in_data

    out_img = Image.fromarray(out_data)
    #out_img.show()
    out_img.save(oname)
    print('Saved to', oname)

Here's a brief demo using scikit-image (and Numpy) to create complementary colours in the more perceptually uniform CIELuv colourspace.

from skimage.color import rgb2luv, luv2rgb
from skimage.util import img_as_ubyte

luv_data = rgb2luv(in_data) * (1, -1, -1)
out_data = img_as_ubyte(luv2rgb(luv_data))

Problem

Original source

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