small changes

[imago.git] / src / intrsc.py

diff --git a/src/intrsc.py b/src/intrsc.py
index 2488d14..62f1498 100644 (file)
--- a/src/intrsc.py
+++ b/src/intrsc.py
@@ -26,10 +26,11 @@ def dst_sort(lines):
     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)
@@ -46,9 +47,15 @@ def board(image, lines, show_all, do_something):
                 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 = []
     
@@ -75,17 +82,27 @@ def board(image, lines, show_all, do_something):
         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)
-    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))),
+    #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, score = k_means.cluster(3, 2,zip(color_data, range(len(color_data))),
                                [[0., 0.5], [0.5, 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]],
@@ -120,7 +137,6 @@ def board(image, lines, show_all, do_something):
     except StopIteration:
         pass
     
-
     return output.Board(19, board_r)
 
 def mean_luma(cluster):
@@ -190,4 +206,5 @@ def stone_color_raw(image, (x, y)):
              sum(p[1] for p in points) / norm,
              sum(p[2] for p in points) / norm)
     hue, luma, saturation = colorsys.rgb_to_hls(*color)
-    return luma, saturation, color
+    color = colorsys.hls_to_rgb(hue, 0.5, 1.)
+    return luma, saturation, color, hue