diff options
Diffstat (limited to 'ratpoison/py')
| -rw-r--r-- | ratpoison/py/color_detect.py | 108 | ||||
| -rw-r--r-- | ratpoison/py/colorz.py | 71 |
2 files changed, 0 insertions, 179 deletions
diff --git a/ratpoison/py/color_detect.py b/ratpoison/py/color_detect.py deleted file mode 100644 index 30661f8..0000000 --- a/ratpoison/py/color_detect.py +++ /dev/null @@ -1,108 +0,0 @@ -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 deleted file mode 100644 index 8c00f0c..0000000 --- a/ratpoison/py/colorz.py +++ /dev/null @@ -1,71 +0,0 @@ -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 |