lines.sort(key=itemgetter(1))
return lines
-def board(image, lines, show_all, do_something):
- """Compute intersections, find stone colors and return board situation."""
+def b_intersects(image, lines, show_all, do_something, logger):
+ """Compute intersections."""
# TODO refactor show_all, do_something
# TODO refactor this into smaller functions
+ logger("finding the stones")
lines = [dst_sort(l) for l in lines]
an0 = (sum([l[0] for l in lines[0]]) / len(lines[0]) - pi / 2)
an1 = (sum([l[0] for l in lines[1]]) / len(lines[1]) - pi / 2)
draw.point((x , y), fill=(120, 255, 120))
do_something(image_g, "intersections")
- image_c = filters.color_enhance(image)
- if show_all:
- do_something(image_c, "white balance")
+ return intersections
+
+def board(image, intersections, show_all, do_something, logger):
+ """Find stone colors and return board situation."""
+
+# image_c = filters.color_enhance(image)
+# if show_all:
+# do_something(image_c, "white balance")
+ image_c = image
board_raw = []
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]
-
- clusters = k_means.cluster(3, 2,zip(color_data, range(len(color_data))),
- [[0., 0.5], [0.5, 0.5], [1., 0.5]])
-
+ #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]
+
+ init_x = sum(c[0] for c in color_data) / float(len(color_data))
+
+ clusters, score = k_means.cluster(3, 2,zip(color_data, range(len(color_data))),
+ [[0., 0.5], [init_x, 0.5], [1., 0.5]])
+# clusters1, score1 = k_means.cluster(1, 2,zip(color_data, range(len(color_data))),
+# [[0.5, 0.5]])
+# clusters2, score2 = k_means.cluster(2, 2,zip(color_data, range(len(color_data))),
+# [[0., 0.5], [0.75, 0.5]])
+# import sys
+# print >> sys.stderr, score1, score2, score
+#
if show_all:
fig = pyplot.figure(figsize=(8, 6))
pyplot.scatter([d[0][0] for d in clusters[0]], [d[0][1] for d in clusters[0]],
except StopIteration:
pass
-
return output.Board(19, board_r)
def mean_luma(cluster):
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