faster gridf, cancel scaling

[imago.git] / src / gridf3.py

diff --git a/src/gridf3.py b/src/gridf3.py
index 5852115..6f6f386 100644 (file)
--- a/src/gridf3.py
+++ b/src/gridf3.py
@@ -67,12 +67,15 @@ class Diagonal_model:
                     return float("inf"), []
                 else:
                     l1, l2 = p.l1, p.l2
                     return float("inf"), []
                 else:
                     l1, l2 = p.l1, p.l2

-            score += min(d, dist)
+            else: # TODO delete this or refactor
+                score += min(d, dist)

 
         return score, cons
 
 def intersection((a1, b1, c1), (a2, b2, c2)):
     delim = float(a1 * b2 - b1 * a2)
 
         return score, cons
 
 def intersection((a1, b1, c1), (a2, b2, c2)):
     delim = float(a1 * b2 - b1 * a2)

+    if delim == 0:
+        return None

     x = (b1 * c2 - c1 * b2) / delim
     y = (c1 * a2 - a1 * c2) / delim
     return x, y
     x = (b1 * c2 - c1 * b2) / delim
     y = (c1 * a2 - a1 * c2) / delim
     return x, y


@@ -124,7 +127,7 @@ class Line:
         elif key == 2:
             return self.c
 
         elif key == 2:
             return self.c
 

-def gen_corners(d1, d2):
+def gen_corners(d1, d2, min_size):

     for c1 in d1.points:
         if c1 in d2.points:
             continue
     for c1 in d1.points:
         if c1 in d2.points:
             continue


@@ -133,13 +136,22 @@ def gen_corners(d1, d2):
             c2 = [p for p in d2.points if p in c1.l1.points][0]
             c3 = [p for p in d1.points if p in c2.l2.points][0]
             c4 = [p for p in d2.points if p in c3.l1.points][0]
             c2 = [p for p in d2.points if p in c1.l1.points][0]
             c3 = [p for p in d1.points if p in c2.l2.points][0]
             c4 = [p for p in d2.points if p in c3.l1.points][0]

+            x_min = min([c1[0], c2[0], c3[0], c4[0]])
+            x_max = max([c1[0], c2[0], c3[0], c4[0]])
+            if x_max - x_min < min_size:
+                continue
+            y_min = min([c1[1], c2[1], c3[1], c4[1]])
+            y_max = max([c1[1], c2[1], c3[1], c4[1]])
+            if y_max - y_min < min_size:
+                continue
+

         except IndexError:
             continue
             # there is not a corresponding intersection
             # TODO create an intersection?
         try:
             yield manual.lines(map(lambda p: p.to_tuple(), [c2, c1, c3, c4]))
         except IndexError:
             continue
             # there is not a corresponding intersection
             # TODO create an intersection?
         try:
             yield manual.lines(map(lambda p: p.to_tuple(), [c2, c1, c3, c4]))

-        except (TypeError, ZeroDivisionError):
+        except (TypeError):

             pass
             # the square was too small to fit 17 lines inside
             # TODO define SquareTooSmallError or something
             pass
             # the square was too small to fit 17 lines inside
             # TODO define SquareTooSmallError or something


@@ -149,6 +161,8 @@ def dst(p, l):
     return abs(a * x + b * y + c) / sqrt(a*a+b*b)
 
 def score(lines, points):
     return abs(a * x + b * y + c) / sqrt(a*a+b*b)
 
 def score(lines, points):

+    # TODO find whether the point actualy lies on the line or just in the same
+    # direction

     score = 0
     for p in points:
         s = min(map(lambda l: dst(p, l), lines))
     score = 0
     for p in points:
         s = min(map(lambda l: dst(p, l), lines))


@@ -171,16 +185,24 @@ def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger):
 
     points = [l.points for l in new_lines1]
 
 
     points = [l.points for l in new_lines1]
 

-    for trial in xrange(3):
+    def dst_p(x, y):
+        x = x - size[0] / 2
+        y = y - size[1] / 2
+        return sqrt(x * x + y * y)
+
+    for n_tries in xrange(3):

         model = Diagonal_model(points)
         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)
-        diag1.points = ransac.filter_near(data, diag1, 2)
-        diag2 = Line(line2)
-        diag2.points = ransac.filter_near(data, diag2, 2)
+        diag_lines = ransac.ransac_multi(6, points, 2, 800, model=model)
+        diag_lines = [l[0] for l in diag_lines]
+        centers = []
+        cen_lin = []
+        for i in xrange(len(diag_lines)):
+            line1 = diag_lines[i]
+            for line2 in diag_lines[i+1:]:
+                c = intersection(line1, line2)
+                if c and dst_p(*c) < min(size) / 2:
+                    cen_lin.append((line1, line2, c))
+                    centers.append(c)

 
         if show_all:
             import matplotlib.pyplot as pyplot
 
         if show_all:
             import matplotlib.pyplot as pyplot


@@ -191,10 +213,11 @@ def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger):
                 pyplot.plot([0, max_x], [find_y(0), find_y(max_x)], color='b')
 
             fig = pyplot.figure(figsize=(8, 6))
                 pyplot.plot([0, max_x], [find_y(0), find_y(max_x)], color='b')
 
             fig = pyplot.figure(figsize=(8, 6))

-            plot_line_g(diag1, size[0])
-            plot_line_g(diag2, size[0])
+            for l in diag_lines:
+                plot_line_g(l, size[0])

             pyplot.scatter(*zip(*sum(points, [])))
             pyplot.scatter(*zip(*sum(points, [])))

-            pyplot.scatter([center[0]], [center[1]], color='r')
+            if len(centers) >= 1:
+                pyplot.scatter([c[0] for c in centers], [c[1] for c in centers], color='r')

             pyplot.xlim(0, size[0])
             pyplot.ylim(0, size[1])
             pyplot.gca().invert_yaxis()
             pyplot.xlim(0, size[0])
             pyplot.ylim(0, size[1])
             pyplot.gca().invert_yaxis()


@@ -202,16 +225,29 @@ def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger):
             size_f = fig.canvas.get_width_height()
             buff = fig.canvas.tostring_rgb()
             image_p = Image.fromstring('RGB', size_f, buff, 'raw')
             size_f = fig.canvas.get_width_height()
             buff = fig.canvas.tostring_rgb()
             image_p = Image.fromstring('RGB', size_f, buff, 'raw')

-            do_something(image_p, "finding diagonal")
-
+            do_something(image_p, "finding diagonals")

 
 

-        grids = list(gen_corners(diag1, diag2))
-        
-        try:
-            sc, grid = min(map(lambda g: (score(sum(g, []), data), g), grids))
+        data = sum(points, [])
+        # TODO what if lines are missing?
+        sc = float("inf")
+        grid = None
+        for (line1, line2, c) in cen_lin:
+            diag1 = Line(line1)
+            diag1.points = ransac.filter_near(data, diag1, 2)
+            diag2 = Line(line2)
+            diag2.points = ransac.filter_near(data, diag2, 2)
+
+
+            grids = list(gen_corners(diag1, diag2, min(size) / 3))
+            
+            try:
+                new_sc, new_grid = min(map(lambda g: (score(sum(g, []), data), g), grids))
+                if new_sc < sc:
+                    sc, grid = new_sc, new_grid
+            except ValueError:
+                pass
+        if grid:

             break
             break

-        except ValueError:
-            pass

     else:
         raise GridFittingFailedError
         
     else:
         raise GridFittingFailedError