generalized RANSAC

[imago.git] / src / gridf3.py

diff --git a/src/gridf3.py b/src/gridf3.py
index ac7fa2a..5852115 100644 (file)
--- a/src/gridf3.py
+++ b/src/gridf3.py
@@ -12,6 +12,9 @@ from geometry import l2ad
 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)
@@ -32,12 +35,16 @@ class Diagonal_model:
                 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:
-            return self.gen.next()
+            nxt = self.gen.next()
         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:
@@ -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)
+        l1 = None
+        l2 = None
         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)
+
         return score, cons
 
 def intersection((a1, b1, c1), (a2, b2, c2)):
@@ -125,7 +139,7 @@ def gen_corners(d1, d2):
             # TODO create an intersection?
         try:
             yield manual.lines(map(lambda p: p.to_tuple(), [c2, c1, c3, c4]))
-        except TypeError:
+        except (TypeError, ZeroDivisionError):
             pass
             # the square was too small to fit 17 lines inside
             # TODO define SquareTooSmallError or something
@@ -158,9 +172,9 @@ def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger):
     points = [l.points for l in new_lines1]
 
     for trial in xrange(3):
-        line1, cons = ransac.estimate(points, 2, 800, Diagonal_model)
-        points2 = map(lambda l: [(p if not p in cons else None) for p in l], points)
-        line2, cons2 = ransac.estimate(points2, 2, 800, Diagonal_model)
+        model = Diagonal_model(points)
+        diag_lines = ransac.ransac_multi(2, points, 2, 800, model=model)
+        [(line1, cons), (line2, cons2)] = diag_lines
         center = intersection(line1, line2)
         data = sum(points, [])
         diag1 = Line(line1)
@@ -210,52 +224,3 @@ def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger):
 
     return grid, grid_lines
 
-def test():
-    import pickle
-    import matplotlib.pyplot as pyplot
-
-    lines = pickle.load(open('lines.pickle'))
-
-    size = (520, 390)
-    new_lines1 = map(lambda l: Line.from_ad(l, size), lines[0])
-    new_lines2 = map(lambda l: Line.from_ad(l, size), lines[1])
-    for l1 in new_lines1:
-        for l2 in new_lines2:
-            p = Point(intersection(l1, l2))
-            p.l1 = l1
-            p.l2 = l2
-            l1.points.append(p)
-            l2.points.append(p)
-
-    points = [l.points for l in new_lines1]
-
-    line1, cons = ransac.estimate(points, 2, 800, Diagonal_model)
-    points2 = map(lambda l: [(p if not p in cons else None) for p in l], points)
-    line2, cons2 = ransac.estimate(points2, 2, 800, Diagonal_model)
-    center = intersection(line1, line2)
-    data = sum(points, [])
-    diag1 = Line(line1)
-    diag1.points = ransac.filter_near(data, diag1, 2)
-    diag2 = Line(line2)
-    diag2.points = ransac.filter_near(data, diag2, 2)
-
-    plot_line_g(diag1, 520)
-    plot_line_g(diag2, 520)
-    pyplot.scatter(*zip(*sum(points, [])))
-    pyplot.scatter([center[0]], [center[1]], color='r')
-    pyplot.xlim(0, 520)
-    pyplot.ylim(0, 390)
-    pyplot.show()
-
-    grids = map(manual.lines, list(gen_corners(diag1, diag2)))
-    plot_grid = lambda g: map(lambda l: pyplot.plot(*zip(*l), color='g'), sum(g, []))
-    map(plot_grid, grids)
-    pyplot.show()
-
-    sc, grid = min(map(lambda g: (score(sum(g, []), data), g), grids))
-
-    map(lambda l: pyplot.plot(*zip(*l), color='g'), sum(grid, []))
-    pyplot.scatter(*zip(*sum(points, [])))
-    pyplot.xlim(0, 520)
-    pyplot.ylim(0, 390)
-    pyplot.show()