From e5ddf8590743b67fcafa273a68e59b830c4dca7e Mon Sep 17 00:00:00 2001
From: Pieter Holtzhausen <holtzhau@gmail.com>
Date: Sat, 20 Aug 2011 11:15:50 +0200
Subject: [PATCH] Debugging similar angles

---
 scikits/image/transform/_hough_transform.pyx | 27 ++++++++++++--------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/scikits/image/transform/_hough_transform.pyx b/scikits/image/transform/_hough_transform.pyx
index d1737f30..5d0d8b92 100644
--- a/scikits/image/transform/_hough_transform.pyx
+++ b/scikits/image/transform/_hough_transform.pyx
@@ -77,8 +77,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
     # calculate thetas if none specified
     if theta is None:
         theta = np.linspace(math.pi/2, -math.pi/2, 180)
-        p_2 = math.pi/2
-        theta = p_2-np.arange(180)/180.0*p_2*2
+        theta =  math.pi/2-np.arange(180)/180.0* math.pi
     ctheta = np.cos(theta)
     stheta = np.sin(theta)
     cdef int height = img.shape[0]
@@ -113,6 +112,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
     for i in range(num_indexes):
         mask[y_idxs[i], x_idxs[i]] = 1
     settle = 0
+    print
     while 1:
         # select random non-zero point
         count = len(points)
@@ -125,6 +125,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
         # if previously eliminated, skip
         if not mask[y, x]:
             continue
+        print "select", y, x
         value = 0
         max_value = value_threshold-1
         max_theta = -1
@@ -135,18 +136,22 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
             accum[accum_idx, j] += 1
             value = accum[accum_idx, j]
             if value > max_value:
+                print "max", j, value
                 max_value = value
                 max_theta = j
-            # if two max angles are very similar, extend the threshold for a while
-            if j != max_theta and value == max_value and abs(j - max_theta) == 1:
-                settle += 1
-                if settle > 100:
-                    other_max = 0
-                else:
-                    other_max = 1
+            # if two bordering angles have max values, extend the threshold for a while
+            if j != max_theta and value == max_value:
+                if abs(j - max_theta) <= 2:
+                    if settle > 100:
+                        other_max = 0
+                    else:
+                        other_max = 1
+            if j == 134 or j == 135:
+                print j, value, accum_idx, ctheta[j], stheta[j]
         if max_value < value_threshold or other_max:
             continue
         settle = 0
+        print "THETA", max_theta, max_value
         # from the random point walk in opposite directions and find line beginning and end
         a = -stheta[max_theta]
         b = ctheta[max_theta]
@@ -160,14 +165,14 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
             else:
                 dx0 = -1
             dy0 = <int>round(b * (1 << shift) / fabs(a)) 
-            y0 = (y0 << shift) #+ (1 << (shift - 1))
+            y0 = (y0 << shift) + (1 << (shift - 1))
         else:
             if b > 0:
                 dy0 = 1
             else:
                 dy0 = -1
             dx0 = <int>round(a * (1 << shift) / fabs(b))
-            x0 = (x0 << shift) #+ (1 << (shift - 1))
+            x0 = (x0 << shift) + (1 << (shift - 1))
   
         # pass 1: walk the line, merging lines less than specified gap length
         for k in range(2):