script for converting ASCII to sgf

[imago.git] / gridf.py

diff --git a/gridf.py b/gridf.py
index dcffd92..3bb7c54 100644 (file)
--- a/gridf.py
+++ b/gridf.py
@@ -1,15 +1,16 @@
 """Imago grid-fitting module"""
 
 import multiprocessing
 """Imago grid-fitting module"""
 
 import multiprocessing

-import itertools
+from functools import partial

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

-from geometry import V, projection
-from manual import lines as g_grid, l2ad
+from geometry import V, projection, l2ad
+from manual import lines as g_grid

 from intrsc import intersections_from_angl_dist
 from linef import line_from_angl_dist
 import pcf
 from intrsc import intersections_from_angl_dist
 from linef import line_from_angl_dist
 import pcf

+import cs as Optimizer

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


@@ -22,23 +23,14 @@ class MyGaussianBlur(ImageFilter.Filter):
     def filter(self, image):
         return image.gaussian_blur(self.radius)
 
     def filter(self, image):
         return image.gaussian_blur(self.radius)
 

-def job_br1(args):
-    im_l, v1, v2, h1, h2, x, y, dv, dh, size = args
+def job_4(x, y, w, z, im_l, v1, v2, h1, h2, dv, dh, size):

     v1 = (v1[0] + x * dv, v1[1] + x)
     v2 = (v2[0] + y * dv, v2[1] + y)
     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 find(lines, size, l1, l2, bounds, hough, do_something, im_h):
+    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))
+
+def find(lines, size, l1, l2, bounds, hough, show_all, do_something):

     l1 = line_from_angl_dist(l1, size)
     l2 = line_from_angl_dist(l2, size)
     v1 = V(*l1[0]) - V(*l1[1])
     l1 = line_from_angl_dist(l1, size)
     l2 = line_from_angl_dist(l2, size)
     v1 = V(*l1[0]) - V(*l1[1])


@@ -60,22 +52,17 @@ def find(lines, size, l1, l2, bounds, hough, do_something, im_h):
     for line in sum(lines, []):
         dr_l.line(line_from_angl_dist(line, size), width=1, fill=255)
 
     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=5))
+    im_l = im_l.filter(MyGaussianBlur(radius=3))

     #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()
 

-    #let's try the bruteforce aproach:
-    k = 30
-
-    pool = multiprocessing.Pool(None)
+    #import time
+    #start = time.time()

 
 

-    tasks = [(im_l_s, v1, v2, h1, h2, x, y, delta_v, delta_h, size) for (x, y) in
-             itertools.product(xrange(-k, k), xrange(-k, k))]
+    f_dist = partial(job_4, im_l=im_l_s, v1=v1, v2=v2, h1=h1, h2=h2,
+                     dv=delta_v, dh=delta_h, size=size)

 
 

-    opt_v = pool.map(job_br1, tasks, 8)
-    opt_h = pool.map(job_br2, tasks, 8)
-    _, x_v, y_v = max(opt_v)
-    _, x_h, y_h = max(opt_h)
+    x_v, y_v, x_h, y_h = Optimizer.optimize(4, 30, f_dist, 128, 256)

 
     v1 = (v1[0] + x_v * delta_v, v1[1] + x_v)
     v2 = (v2[0] + y_v * delta_v, v2[1] + y_v)
 
     v1 = (v1[0] + x_v * delta_v, v1[1] + x_v)
     v2 = (v2[0] + y_v * delta_v, v2[1] + y_v)


@@ -85,9 +72,8 @@ def find(lines, size, l1, l2, bounds, hough, do_something, im_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]]]
     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
 #
     
 ### Show error surface
 #


@@ -96,20 +82,22 @@ def find(lines, size, l1, l2, bounds, hough, do_something, im_h):
 #                  delta_v, delta_h, x_v, y_v, x_h, y_h, size)
 ###
 
 #                  delta_v, delta_h, x_v, y_v, x_h, y_h, size)
 ###
 

+    if show_all:
+

 ### Show grid over lines
 #
 ### 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")
+        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


@@ -136,8 +124,8 @@ 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)