Python implements a method to find the closest approximation to a given color from a set of colors

This example shows how a python implementation finds the closest match to a given color from a set of colors. Share with you for your reference. Specific analysis is as follows:

This code is very useful for finding colors that are similar in color, like a set of 8 colors. Now, given a demo in RGB format, find out which of the 8 colors is closest to it. This is useful if you need to make a program that searches for images by color.

from colorsys import rgb_to_hsv

colors = dict((

((196, 2, 51), "RED"),

((255, 165, 0), "ORANGE"),

((255, 205, 0), "YELLOW"),

((0, 128, 0), "GREEN"),

((0, 0, 255), "BLUE"),

((127, 0, 255), "VIOLET"),

((0, 0, 0), "BLACK"),

((255, 255, 255), "WHITE"),))

def to_hsv( color ):

    """ converts color tuples to floats and then to hsv """

    return rgb_to_hsv(*[x/255.0 for x in color]) #rgb_to_hsv wants floats!

def color_dist( c1, c2):

    """ returns the squared euklidian distance between two color vectors in hsv space """

    return sum( (a-b)**2 for a,b in zip(to_hsv(c1),to_hsv(c2)) )

def min_color_diff( color_to_match, colors):

    """ returns the `(distance, color_name)` with the minimal distance to `colors`"""

    return min( # overal best is the best match to any color:

        (color_dist(color_to_match, test), colors[test]) # (distance to `test` color, color name)

        for test in colors)

color_to_match = (255,255,0)

print min_color_diff( color_to_match, colors)

I hope this article has helped you with your Python programming.