2 files changed, 179 insertions, 0 deletions
diff --git a/ratpoison/py/color_detect.py b/ratpoison/py/color_detect.py
new file mode 100644
index 0000000..30661f8
--- /dev/null
+++ b/ratpoison/py/color_detect.py
@@ -0,0 +1,108 @@
+import sys
+import colorsys
+from colorz import colorz
+from math import sqrt
+
+try:
+    import Image
+except ImportError:
+    from PIL import Image
+
+if len(sys.argv) < 2:
+  print "Usage: {0} FILENAME [num_colors]".format(sys.argv[0])
+  sys.exit()
+
+
+print sys.argv[1]
+
+WALLPAPER = sys.argv[1]
+filename = WALLPAPER.split('/').pop()
+COLORS = ".{0}.colors".format(filename)
+XRESOURCES = ".{0}.Xres".format(filename)
+SAMPLE = ".{0}.sample.png".format(filename)
+
+cols = ''
+xres = ''
+
+def torgb(hexv):
+    hexv = hexv[1:]
+    r, g, b = (
+        int(hexv[0:2], 16) / 256.0,
+        int(hexv[2:4], 16) / 256.0,
+        int(hexv[4:6], 16) / 256.0,
+    )
+    return r, g, b
+
+def normalize(hexv, minv=128, maxv=256):
+    r, g, b = torgb(hexv)
+    h, s, v = colorsys.rgb_to_hsv(r, g, b)
+    minv = minv / 256.0
+    maxv = maxv / 256.0
+    if v < minv:
+        v = minv
+    if v > maxv:
+        v = maxv
+    r, g, b = colorsys.hsv_to_rgb(h, s, v)
+    return '#{:02x}{:02x}{:02x}'.format(int(r * 256), int(g * 256), int(b * 256))
+
+def darkness(hexv):
+  r, g, b = torgb(hexv)
+  darkness = sqrt((255 - r) ** 2 + (255 - g) ** 2 + (255 - b) ** 2)
+  return darkness
+
+def to_hsv(c):
+    r, g, b = torgb(c)
+    h, s, v = colorsys.rgb_to_hsv(r, g, b)
+    return h, s, v
+
+def hex_color_to_rgb(color):
+    color = color[1:] if color[0]=="#" else color
+    return (
+        int(color[:2], 16),
+        int(color[2:4], 16),
+        int(color[4:], 16)
+        )
+
+def create_sample(f, colors):
+    im = Image.new("RGB", (1000, 100), "white")
+    pix = im.load()
+
+    width_sample = im.size[0]/len(colors)
+
+    for i, c in enumerate(colors):
+        for j in range(width_sample*i, width_sample*i+width_sample):
+            for k in range(0, 100):
+                pix[j, k] = hex_color_to_rgb(c)
+
+    im.save(f)
+
+if __name__ == '__main__':
+    if len(sys.argv) == 2:
+        n = 16
+    else:
+        n = int(sys.argv[2])
+
+
+    i = 0
+    # sort by value, saturation, then hue
+    colors = colorz(WALLPAPER, n=n)
+    colors.sort(key=lambda  x:darkness(x), reverse=True)
+    for c in colors:
+        if i == 0:
+            c = normalize(c, minv=0, maxv=32)
+        elif i == 8:
+            c = normalize(c, minv=128, maxv=192)
+        elif i < 8:
+            c = normalize(c, minv=160, maxv=224)
+        else:
+            c = normalize(c, minv=200, maxv=256)
+        c = normalize(c, minv=32, maxv=224)
+        xres += """*color{}: {}\n""".format(i, c)
+        cols += """export COLOR{}="{}"\n""".format(i, c)
+        i += 1
+
+    create_sample(SAMPLE, colors)
+    with open(XRESOURCES, 'w') as f:
+        f.write(xres)
+    with open(COLORS, 'w') as f:
+        f.write(cols)
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