better grid-fitting

[imago.git] / gridf.py

diff --git a/gridf.py b/gridf.py
index 17be372..0c5b68f 100644 (file)
--- a/gridf.py
+++ b/gridf.py
@@ -1,8 +1,14 @@
+"""Imago grid-fitting module"""
+
+import multiprocessing
+

 import Image, ImageDraw, ImageFilter
 
 import Image, ImageDraw, ImageFilter
 

+from geometry import V, projection

 from manual import lines as g_grid, l2ad
 from intrsc import intersections_from_angl_dist
 from linef import line_from_angl_dist
 from manual import lines as g_grid, l2ad
 from intrsc import intersections_from_angl_dist
 from linef import line_from_angl_dist

+import pcf

 
 class GridFittingFailedError(Exception):
     pass
 
 class GridFittingFailedError(Exception):
     pass


@@ -15,78 +21,131 @@ class MyGaussianBlur(ImageFilter.Filter):
     def filter(self, image):
         return image.gaussian_blur(self.radius)
 
     def filter(self, image):
         return image.gaussian_blur(self.radius)
 

-class V():
-    def __init__(self, x, y):
-        self.x = x
-        self.y = y
+def job_br1(args):
+    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):
+    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):
+    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) 

     
     

-    def __add__(self, other):
-        return V(self.x + other.x, self.y + other.y)
+    v1, v2 = hough.lines_from_list([a, b])
+    h1, h2 = hough.lines_from_list([c, d])

 
 

-    def __sub__(self, other):
-        return V(self.x - other.x, self.y - other.y)
+    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]

 
 

-    def __rmul__(self, other):
-        return V(other * self.x, other * self.y)
+    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)

 
 

-    def t(self):
-        return (self.x, self.y)
+    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()

 
 

-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])
-    grid = get_grid(a, b, c, d, hough, size)
-    dist = distance(lines, grid, size)
-    print dist
+    #let's try the ULTRA bruteforce aproach:
+    pool = multiprocessing.Pool(None)
+
+    #import time
+    #start = time.time()
+
+    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)]
+
+    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)
+
+    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)]
+
+    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

     
     

-    s = 0.02
-    while True:
-        ts1 = [(s, 0), (-s, 0), (s, s), (-s, -s), (-s, s), (s, -s), (0, s),  (0, -s)]
-        grids = [(get_grid(a + t[0] * v1, b + t[1] * v1, 
-                           c, d, hough, size), t) for t in ts1]
-        distances = [(distance(lines, grid, size), 
-                      grid, t) for grid, t in grids]
-        distances.sort(reverse=True)
-        if distances[0][0] > dist:
-            dist = distances[0][0]
-            grid = distances[0][1]
-            t = distances[0][2]
-            a, b = a + t[0] * v1, b + t[1] * v1
-            print dist
-            s *= 0.75
-        else: 
-           break
-
-    print "---"
-
-    s = 0.02
-    while True:
-        ts1 = [(s, 0), (-s, 0), (s, s), (-s, -s), (-s, s), (s, -s), (0, s),  (0, -s)]
-        grids = [(get_grid(a, b, 
-                           c + t[0] * v2, d + t[1] * v2, hough, size), t) for t in ts1]
-        distances = [(distance(lines, grid, size), 
-                      grid, t) for grid, t in grids]
-        distances.sort(reverse=True)
-        if distances[0][0] > dist:
-            dist = distances[0][0]
-            grid = distances[0][1]
-            t = distances[0][2]
-            c, d = c + t[0] * v2, d + t[1] * v2
-            print dist
-            s *= 0.75
-        else:
-            break
-
-    grid_lines = [[l2ad(l, size) for l in grid[0]], [l2ad(l, size) for l in grid[1]]]
+### 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.t(), b.t()])
-    l2 = hough.lines_from_list([c.t(), d.t()])
+def get_grid(l1, l2, size):

     c = intersections_from_angl_dist([l1, l2], size, get_all=True)
     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
     corners = (c[0] + c[1])
     if len(corners) < 4:
         print l1, l2, c


@@ -94,34 +153,22 @@ def get_grid(a, b, c, d, hough, size):
     grid = g_grid(corners)
     return grid
 
     grid = g_grid(corners)
     return grid
 

-def distance(lines, grid, size):
-    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_l.filter(MyGaussianBlur(radius=3))
-    # GaussianBlur is undocumented class, may not work in future versions of PIL
+def line_out(line, size):
+    for p in line:
+        if p[0] < 0 or p[0] > size[0] or p[1] < 0 or p[1] > size[1]:
+            return True
+    else:
+        return False
+
+def distance(im_l, grid, size):

     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_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)
-    return distance
-
-def combine(bg, fg):
-    bg_l = bg.load()
-    fg_l = fg.load()
-    res = Image.new('L', fg.size)
-    res_l = res.load()
-
-    score = 0
-    area = 0
-
-    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]
-                score +=  bg_l[x, y]
-                area += 1
-
-    return res, float(score)/area
+#        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)
+    distance_d = pcf.combine(im_l, im_g.tostring())
+    return distance_d