better grid-fitting

[imago.git] / gridf.py

diff --git a/gridf.py b/gridf.py
index d39bdd5..0c5b68f 100644 (file)
--- a/gridf.py
+++ b/gridf.py
@@ -22,104 +22,128 @@ class MyGaussianBlur(ImageFilter.Filter):
         return image.gaussian_blur(self.radius)
 
 def job_br1(args):
         return image.gaussian_blur(self.radius)
 
 def job_br1(args):

-    X, Y, im_l, a, b, c, d, s, v1, v2, k, hough, size = args
-    return [(distance(im_l, 
-             get_grid(a + X[y] * s * v1, 
-                      b + Y[y] * s * v1, 
-                      c, d, hough, size),
-             size), a + X[y] * s * v1, b + Y[y] * s * v1) for y in range(2 *k)]
+    im_l, v1, v2, h1, h2, x, y, dv, dh, size = args
+    v1 = (v1[0] + x * dv, v1[1] + x)
+    v2 = (v2[0] + y * dv, v2[1] + y)
+    return (distance(im_l, 
+                    get_grid([v1, v2], [h1, h2], size),
+                    size), x, y) 

 
 def job_br2(args):
 
 def job_br2(args):

-    X, Y, im_l, a, b, c, d, s, v1, v2, k, hough, size = args
-    return [(distance(im_l, 
-             get_grid(a, b, c + X[y] * s * v2, 
-                      d + Y[y] * s * v2, 
-                      hough, size),
-             size), c + X[y] * s * v2, d + Y[y] * s * v2) for y in range(2 *k)]
+    im_l, v1, v2, h1, h2, x, y, dv, dh, size = args
+    h1 = (h1[0] + x * dh, h1[1] + x)
+    h2 = (h2[0] + y * dh, h2[1] + y)
+    return (distance(im_l, 
+                    get_grid([v1, v2], [h1, h2], size),
+                    size), x, y) 
+
+def job_4(args):
+    im_l, v1, v2, h1, h2, x, y, w, z, dv, dh, size = args
+    v1 = (v1[0] + x * dv, v1[1] + x)
+    v2 = (v2[0] + y * dv, v2[1] + y)
+    h1 = (h1[0] + w * dh, h1[1] + w)
+    h2 = (h2[0] + z * dh, h2[1] + z)
+    return (distance(im_l, 
+                    get_grid([v1, v2], [h1, h2], size),
+                    size), x, y, w, z) 

 
 def find(lines, size, l1, l2, bounds, hough, do_something, im_h):
 
 def find(lines, size, l1, l2, bounds, hough, do_something, im_h):

-    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])
     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, b, c, d = [V(*a) for a in bounds]

     a = projection(a, l1, v1) 
     b = projection(b, l1, v1) 
     c = projection(c, l2, v2) 
     d = projection(d, l2, v2) 
     a = projection(a, l1, v1) 
     b = projection(b, l1, v1) 
     c = projection(c, l2, v2) 
     d = projection(d, l2, v2) 

+    
+    v1, v2 = hough.lines_from_list([a, b])
+    h1, h2 = hough.lines_from_list([c, d])
+
+    delta_v = ((l1[1][1] - l1[0][1]) * hough.dt) / l1[1][0]
+    delta_h = ((l2[1][1] - l2[0][1]) * hough.dt) / l2[1][0]

 
     im_l = Image.new('L', size)
     dr_l = ImageDraw.Draw(im_l)
     for line in sum(lines, []):
         dr_l.line(line_from_angl_dist(line, size), width=1, fill=255)
 
 
     im_l = Image.new('L', size)
     dr_l = ImageDraw.Draw(im_l)
     for line in sum(lines, []):
         dr_l.line(line_from_angl_dist(line, size), width=1, fill=255)
 

-    #im_l = im_h #hocus pocus    
-    im_l = im_l.filter(MyGaussianBlur(radius=2))
+    im_l = im_l.filter(MyGaussianBlur(radius=5))

     #GaussianBlur is undocumented class, may not work in future versions of PIL
     im_l_s = im_l.tostring()
 
     #GaussianBlur is undocumented class, may not work in future versions of PIL
     im_l_s = im_l.tostring()
 

-    #from gridf_analyzer import error_surface
-    #error_surface(im_l, a, b, c, d, hough, size, v1 ,v2)
+    #let's try the ULTRA bruteforce aproach:
+    pool = multiprocessing.Pool(None)

 
 

-    grid = get_grid(a, b, c, d, hough, size)
-    
-    #let's try the bruteforce aproach:
-    s = 0.001
-    k = 50
-    X, Y = [], []
-    for i in range(-k, k):
-        X.append(range(-k, k))
-        Y.append(2*k*[i])
+    #import time
+    #start = time.time()

 
 

-    pool = multiprocessing.Pool(None)
+    k = 30 
+    tasks = [(im_l_s, v1, v2, h1, h2, x, y, w, z, delta_v, delta_h, size) 
+             for x in xrange(-k, k, 2)
+             for y in xrange(-k, k, 2)
+             for z in xrange(-k, k, 2)
+             for w in xrange(-k, k, 2)]

 
 

-    tasks = [(X[x], Y[x], im_l_s, a, b, c, d, s, 
-              v1, v2, k, hough, size) for x in xrange(0, 2 * k)]
-
-    import time
-    start = time.time()
-    opt_ab = pool.map(job_br1, tasks, 1)
-    opt_cd = pool.map(job_br2, tasks, 1)
-    d1 = 0
-    for lst in opt_ab:
-        for tpl in lst:
-            if tpl[0] > d1:
-                d1 = tpl[0]
-                a, b = tpl[1], tpl[2]
-    d1 = 0
-    for lst in opt_cd:
-        for tpl in lst:
-            if tpl[0] > d1:
-                d1 = tpl[0]
-                c, d = tpl[1], tpl[2]
-    print time.time() - start
-    grid = get_grid(a, b, c, d, hough, size) 
-    grid_lines = [[l2ad(l, size) for l in grid[0]], 
-                  [l2ad(l, size) for l in grid[1]]]
-    
-    ### 
+    opt = pool.map(job_4, tasks)
+    _, x_v, y_v, x_h, y_h = max(opt)

 
 

-    im_t = Image.new('RGB', im_l.size, None)
-    im_t_l = im_t.load()
-    im_l_l = im_l.load()
-    for x in xrange(im_t.size[0]):
-        for y in xrange(im_t.size[1]):
-            im_t_l[x, y] = (im_l_l[x, y], 0, 0)
+    v1 = (v1[0] + x_v * delta_v, v1[1] + x_v)
+    v2 = (v2[0] + y_v * delta_v, v2[1] + y_v)
+    h1 = (h1[0] + x_h * delta_h, h1[1] + x_h)
+    h2 = (h2[0] + y_h * delta_h, h2[1] + y_h)

 
 

-    #im_t_d = ImageDraw.Draw(im_t)
-    #for l in grid[0] + grid[1]:
-    #    im_t_d.line(l, width=1, fill=(0, 255, 0))
+    k = 5 
+    tasks = [(im_l_s, v1, v2, h1, h2, x, y, w, z, delta_v, delta_h, size) 
+             for x in xrange(-k, k)
+             for y in xrange(-k, k)
+             for z in xrange(-k, k)
+             for w in xrange(-k, k)]

 
 

-    #do_something(im_t, "lines and grid")
+    opt = pool.map(job_4, tasks)
+    _, x_v, y_v, x_h, y_h = max(opt)

 
 

+    v1 = (v1[0] + x_v * delta_v, v1[1] + x_v)
+    v2 = (v2[0] + y_v * delta_v, v2[1] + y_v)
+    h1 = (h1[0] + x_h * delta_h, h1[1] + x_h)
+    h2 = (h2[0] + y_h * delta_h, h2[1] + y_h)

 
 

-    ###
+    grid = get_grid([v1, v2], [h1, h2], size) 
+    grid_lines = [[l2ad(l, size) for l in grid[0]], 
+                  [l2ad(l, size) for l in grid[1]]]
+
+    pool.terminate()
+    pool.join()
+    
+    #print time.time() - start
+    
+### Show error surface
+#
+#    from gridf_analyzer import error_surface
+#    error_surface(k, im_l_s, v1_i, v2_i, h1_i, h2_i, 
+#                  delta_v, delta_h, x_v, y_v, x_h, y_h, size)
+###
+
+### Show grid over lines
+#
+#    im_t = Image.new('RGB', im_l.size, None)
+#    im_t_l = im_t.load()
+#    im_l_l = im_l.load()
+#    for x in xrange(im_t.size[0]):
+#        for y in xrange(im_t.size[1]):
+#            im_t_l[x, y] = (im_l_l[x, y], 0, 0)
+#
+#    im_t_d = ImageDraw.Draw(im_t)
+#    for l in grid[0] + grid[1]:
+#        im_t_d.line(l, width=1, fill=(0, 255, 0))
+#
+#    do_something(im_t, "lines and grid")
+###

 
     return grid, grid_lines
 
 
     return grid, grid_lines
 

-def get_grid(a, b, c, d, hough, size):
-    l1 = hough.lines_from_list([a, b])
-    l2 = hough.lines_from_list([c, d])
+def get_grid(l1, l2, size):

     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])
     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])


@@ -141,8 +165,8 @@ def distance(im_l, grid, size):
     dr_g = ImageDraw.Draw(im_g)
     for line in grid[0] + grid[1]:
         dr_g.line(line, width=1, fill=255)
     dr_g = ImageDraw.Draw(im_g)
     for line in grid[0] + grid[1]:
         dr_g.line(line, width=1, fill=255)

-        if line_out(line, size):
-            return 0
+#        if line_out(line, size):
+#            return 0

     #im_g = im_g.filter(MyGaussianBlur(radius=3))
     #GaussianBlur is undocumented class, may not work in future versions of PIL
     #im_d, distance = combine(im_l, im_g)
     #im_g = im_g.filter(MyGaussianBlur(radius=3))
     #GaussianBlur is undocumented class, may not work in future versions of PIL
     #im_d, distance = combine(im_l, im_g)