image_p = Image.fromstring('RGB', size, buff, 'raw')
do_something(image_p, "color distribution")
- 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]
+ #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]
+ color_data = [(s[0], 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]])
sum(p[2] for p in points) / norm)
hue, luma, saturation = colorsys.rgb_to_hls(*color)
color = colorsys.hls_to_rgb(hue, 0.5, 1.)
- print color
return luma, saturation, color, hue