preliminary work on generalized RANSAC

[imago.git] / src / gridf3.py

diff --git a/src/gridf3.py b/src/gridf3.py
index 0697b92..693b8ee 100644 (file)
--- a/src/gridf3.py
+++ b/src/gridf3.py
@@ -12,6 +12,9 @@ from geometry import l2ad
 class GridFittingFailedError(Exception):
     pass
 
 class GridFittingFailedError(Exception):
     pass
 

+class BadGenError(Exception):
+    pass
+

 def plot_line(line, c, size):
     points = linef.line_from_angl_dist(line, size)
     pyplot.plot(*zip(*points), color=c)
 def plot_line(line, c, size):
     points = linef.line_from_angl_dist(line, size)
     pyplot.plot(*zip(*points), color=c)


@@ -32,12 +35,16 @@ class Diagonal_model:
                 if l1[i] and l2[j]:
                     yield (l1[i], l2[j])
 
                 if l1[i] and l2[j]:
                     yield (l1[i], l2[j])
 

+    def remove(self, data):
+        self.data = list(set(self.data) - set(data))
+

     def initial(self):
         try:
     def initial(self):
         try:

-            return self.gen.next()
+            nxt = self.gen.next()

         except StopIteration:
             self.gen = self.initial_g()
         except StopIteration:
             self.gen = self.initial_g()

-            return self.gen.next()
+            nxt = self.gen.next()
+        return nxt

 
     def get(self, sample):
         if len(sample) == 2:
 
     def get(self, sample):
         if len(sample) == 2:


@@ -50,11 +57,18 @@ class Diagonal_model:
         score = 0
         a, b, c = est
         dst = lambda (x, y): abs(a * x + b * y + c) / sqrt(a*a+b*b)
         score = 0
         a, b, c = est
         dst = lambda (x, y): abs(a * x + b * y + c) / sqrt(a*a+b*b)

+        l1 = None
+        l2 = None

         for p in self.data:
             d = dst(p)
             if d <= dist:
                 cons.append(p)
         for p in self.data:
             d = dst(p)
             if d <= dist:
                 cons.append(p)

+                if p.l1 == l1 or p.l2 == l2:
+                    return float("inf"), []
+                else:
+                    l1, l2 = p.l1, p.l2

             score += min(d, dist)
             score += min(d, dist)

+

         return score, cons
 
 def intersection((a1, b1, c1), (a2, b2, c2)):
         return score, cons
 
 def intersection((a1, b1, c1), (a2, b2, c2)):