From 17fc734d20035c84fc903f185dd10f85fdc489b3 Mon Sep 17 00:00:00 2001
From: Vasil Zlatanov <vasil.zlatanov@gmail.com>
Date: Wed, 28 Jan 2015 18:05:14 +0100
Subject: new colorschemes

---
 ratpoison/py/colorz.py | 71 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 ratpoison/py/colorz.py

(limited to 'ratpoison/py/colorz.py')

diff --git a/ratpoison/py/colorz.py b/ratpoison/py/colorz.py
new file mode 100644
index 0000000..8c00f0c
--- /dev/null
+++ b/ratpoison/py/colorz.py
@@ -0,0 +1,71 @@
+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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