pcf -- combine

[imago.git] / gridf.py

diff --git a/gridf.py b/gridf.py
index 17be372..50beb81 100644 (file)
--- a/gridf.py
+++ b/gridf.py
@@ -1,8 +1,9 @@
 import Image, ImageDraw, ImageFilter
 
-from manual import lines as g_grid, l2ad
+from manual import lines as g_grid, l2ad, intersection, line as g_line
 from intrsc import intersections_from_angl_dist
 from linef import line_from_angl_dist
+import pcf
 
 class GridFittingFailedError(Exception):
     pass
@@ -32,15 +33,62 @@ class V():
     def t(self):
         return (self.x, self.y)
 
+    def normal(self):
+        return V(-self.y, self.x)
+
+def projection(point, line, vector):
+    n = vector.normal()
+    l2 = g_line(point.t(), (point + n).t())
+    return V(*intersection(l2, g_line(*line)))
+    
+def error_surface(lines, a, b, c, d, hough, size, v1):
+    import matplotlib.pyplot as plt
+    from matplotlib import cm
+    import time
+    import pickle
+    X = []
+    Y = []
+    Z = []
+    s = 0.001
+    k = 200
+    for i in range(-k, k):
+        X.append(range(-k, k))
+        Y.append(2*k*[i])
+        
+    start = time.time()
+    for x in range(0, 2*k):
+        try:
+            Z.append([distance(lines, get_grid(a + X[x][y] * s * v1, b + Y[x][y] * s * v1, 
+                                           c, d, hough, size),
+                              size) for y in range(0, 2* k)])
+        except Exception:
+            Z.append(Z[-1])
+        o = ((time.time() - start) * (2 * k - (x + 1))) / (60 * (x + 1))
+        print x + 1, "{0} h {1:2.2f} m".format(int(o) / 60, o % 60)
+    s_file = open('surface' + str(k), 'w')
+    pickle.dump((X, Y, Z), s_file)
+    s_file.close()
+    plt.imshow(Z, cmap=cm.gnuplot2, interpolation='bicubic', 
+               origin='upper', extent=(-k, k, -k, k), aspect='equal')
+    plt.colorbar()
+
+    plt.show()
+
 def find(lines, size, l1, l2, bounds, hough, do_something):
     a, b, c, d = [V(*a) for a in bounds]
     l1 = line_from_angl_dist(l1, size)
     l2 = line_from_angl_dist(l2, size)
     v1 = V(*l1[0]) - V(*l1[1])
     v2 = V(*l2[0]) - V(*l2[1])
+    a = projection(a, l1, v1) 
+    b = projection(b, l1, v1) 
+    c = projection(c, l2, v2) 
+    d = projection(d, l2, v2) 
     grid = get_grid(a, b, c, d, hough, size)
     dist = distance(lines, grid, size)
     print dist
+
+    #error_surface(lines, a, b, c, d, hough, size, v1)
     
     s = 0.02
     while True:
@@ -87,6 +135,7 @@ def get_grid(a, b, c, d, hough, size):
     l1 = hough.lines_from_list([a.t(), b.t()])
     l2 = hough.lines_from_list([c.t(), d.t()])
     c = intersections_from_angl_dist([l1, l2], size, get_all=True)
+    #TODO do something when a corner is outside the image
     corners = (c[0] + c[1])
     if len(corners) < 4:
         print l1, l2, c
@@ -100,19 +149,21 @@ def distance(lines, grid, size):
     for line in sum(lines, []):
         dr_l.line(line_from_angl_dist(line, size), width=1, fill=255)
     im_l = im_l.filter(MyGaussianBlur(radius=3))
-    # GaussianBlur is undocumented class, may not work in future versions of PIL
+    #GaussianBlur is undocumented class, may not work in future versions of PIL
     im_g = Image.new('L', size)
     dr_g = ImageDraw.Draw(im_g)
     for line in grid[0] + grid[1]:
         dr_g.line(line, width=1, fill=255)
-    im_d, distance = combine(im_l, im_g)
+    #im_g = im_g.filter(MyGaussianBlur(radius=3))
+    #im_d, distance = combine(im_l, im_g)
+    distance = pcf.combine(im_l.tostring(), im_g.tostring())
     return distance
 
 def combine(bg, fg):
     bg_l = bg.load()
     fg_l = fg.load()
-    res = Image.new('L', fg.size)
-    res_l = res.load()
+    #res = Image.new('L', fg.size)
+    #res_l = res.load()
 
     score = 0
     area = 0
@@ -120,8 +171,9 @@ def combine(bg, fg):
     for x in xrange(fg.size[0]):
         for y in xrange(fg.size[1]):
             if fg_l[x, y]:
-                res_l[x, y] = bg_l[x, y]
+                #res_l[x, y] = bg_l[x, y] * fg_l[x, y]
                 score +=  bg_l[x, y]
                 area += 1
 
-    return res, float(score)/area
+    #return res, float(score)/area
+    return None, float(score)/area