from math import cos, tan, pi
from operator import itemgetter
+import colorsys
import ImageDraw
board_raw = sum(board_raw, [])
### Show color distribution
- luma = [s[0] for s in board_raw]
- saturation = [s[1] for s in board_raw]
if show_all:
import matplotlib.pyplot as pyplot
import Image
fig = pyplot.figure(figsize=(8, 6))
+ luma = [s[0] for s in board_raw]
+ saturation = [s[1] for s in board_raw]
pyplot.scatter(luma, saturation,
- color=[(s[2][0]/255.,
- s[2][1]/255.,
- s[2][2]/255., 1.)
- for s in board_raw])
+ color=[s[2] for s in board_raw])
pyplot.xlim(0,1)
pyplot.ylim(0,1)
fig.canvas.draw()
image_p = Image.fromstring('RGB', size, buff, 'raw')
do_something(image_p, "color distribution")
- clusters = k_means.cluster(3, 2,zip(zip(luma, saturation), range(len(luma))),
+ max_s0 = max(s[0] for s in board_raw)
+ min_s0 = min(s[0] for s in board_raw)
+ norm_s0 = lambda x: (x - min_s0) / (max_s0 - min_s0)
+ max_s1 = max(s[1] for s in board_raw)
+ min_s1 = min(s[1] for s in board_raw)
+ norm_s1 = lambda x: (x - min_s1) / (max_s1 - min_s1)
+ max_s1 = max(s[1] for s in board_raw)
+ min_s1 = min(s[1] for s in board_raw)
+ norm_s1 = lambda x: (x - min_s1) / (max_s1 - min_s1)
+ color_data = [(norm_s0(s[0]), norm_s1(s[1])) for s in board_raw]
+
+ clusters = k_means.cluster(3, 2,zip(color_data, range(len(color_data))),
[[0., 0.5], [0.5, 0.5], [1., 0.5]])
if show_all:
return output.Board(19, board_r)
def mean_luma(cluster):
- """Return mean luma of the *cluster* of points."""
+ """Return mean luminanace of the *cluster* of points."""
return sum(c[0][0] for c in cluster) / float(len(cluster))
def to_general(line, size):
return intersections
def rgb2lumsat(color):
- """Convert RGB to luma and HSI model saturation."""
+ """Convert RGB to luminance and HSI model saturation."""
r, g, b = color
luma = (0.30 * r + 0.59 * g + 0.11 * b) / 255.0
max_diff = max(color) - min(color)
norm = float(len(points))
if norm == 0:
return 0, 0, (0, 0, 0) #TODO trow exception here
+ norm = float(norm*255)
color = (sum(p[0] for p in points) / norm,
sum(p[1] for p in points) / norm,
sum(p[2] for p in points) / norm)
- luma, saturation = rgb2lumsat(color)
- return luma, saturation, color
+ hue, luma, saturation = colorsys.rgb_to_hls(*color)
+ color = colorsys.hls_to_rgb(hue, 0.5, 1.)
+ print color
+ return luma, saturation, color, hue