NearestColor.py

# import Image
# f = raw_input('Image:\n')
# im = Image.open(f)
# rgb_im = im.convert('RGB')
# r, g, b = rgb_im.getpixel((x, y))


# def distance(c1, c2):
#     (r1,g1,b1) = c1
#     (r2,g2,b2) = c2
#     return math.sqrt((r1 - r2)**2 + (g1 - g2) ** 2 + (b1 - b2) **2)

# colors = list(rgb_code_dictionary.keys())
# closest_colors = sorted(colors, key=lambda color: distance(color, point))
# closest_color = closest_colors[0]
# code = rgb_code_dictionary[closest_color]

import scipy.spatial as sp

input_color = (100, 50, 25)
websafe_colors = [(200, 100, 50),(100, 50, 22)]# list of web-save colors
tree = sp.KDTree(websafe_colors) # creating k-d tree from web-save colors
ditsance, result = tree.query(input_color) # get Euclidean distance and index of web-save color in tree/list
nearest_color = websafe_colors[result]