measuring performance

[imago.git] / src / intrsc.py

diff --git a/src/intrsc.py b/src/intrsc.py
index 73de142..3c736d9 100644 (file)
--- a/src/intrsc.py
+++ b/src/intrsc.py
@@ -2,6 +2,7 @@
 
 from math import cos, tan, pi
 from operator import itemgetter
+import colorsys
 
 import ImageDraw
 
@@ -25,10 +26,11 @@ def dst_sort(lines):
     lines.sort(key=itemgetter(1))
     return lines
 
-def board(image, lines, show_all, do_something):
+def board(image, lines, show_all, do_something, logger):
     """Compute intersections, find stone colors and return board situation."""
     # 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)
@@ -65,10 +67,7 @@ def board(image, lines, show_all, do_something):
         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()
@@ -77,13 +76,17 @@ 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]
+    #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]])
@@ -187,8 +190,10 @@ def stone_color_raw(image, (x, y)):
     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.)
+    return luma, saturation, color, hue