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)
希望本文所述对大家的Python程序设计有所帮助。
