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from collections import namedtuple
from math import sqrt
import random
try:
import Image
except ImportError:
from PIL import Image
Point = namedtuple('Point', ('coords', 'n', 'ct'))
Cluster = namedtuple('Cluster', ('points', 'center', 'n'))
def get_points(img):
points = []
w, h = img.size
for count, color in img.getcolors(w * h):
points.append(Point(color, 3, count))
return points
rtoh = lambda rgb: '#%s' % ''.join(('%02x' % p for p in rgb))
def colorz(filename, n=3):
img = Image.open(filename)
img.thumbnail((200, 200))
w, h = img.size
points = get_points(img)
clusters = kmeans(points, n, 1)
rgbs = [map(int, c.center.coords) for c in clusters]
return map(rtoh, rgbs)
def euclidean(p1, p2):
return sqrt(sum([
(p1.coords[i] - p2.coords[i]) ** 2 for i in range(p1.n)
]))
def calculate_center(points, n):
vals = [0.0 for i in range(n)]
plen = 0
for p in points:
plen += p.ct
for i in range(n):
vals[i] += (p.coords[i] * p.ct)
return Point([(v / plen) for v in vals], n, 1)
def kmeans(points, k, min_diff):
clusters = [Cluster([p], p, p.n) for p in random.sample(points, k)]
while 1:
plists = [[] for i in range(k)]
for p in points:
smallest_distance = float('Inf')
for i in range(k):
distance = euclidean(p, clusters[i].center)
if distance < smallest_distance:
smallest_distance = distance
idx = i
plists[idx].append(p)
diff = 0
for i in range(k):
old = clusters[i]
center = calculate_center(plists[i], old.n)
new = Cluster(plists[i], center, old.n)
clusters[i] = new
diff = max(diff, euclidean(old.center, new.center))
if diff < min_diff:
break
return clusters
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