better peak extraction

[imago.git] / gridf.py

diff --git a/gridf.py b/gridf.py
index dcffd92..e957880 100644 (file)
--- a/gridf.py
+++ b/gridf.py
@@ -1,15 +1,16 @@
 """Imago grid-fitting module"""
 
 import multiprocessing
-import itertools
+from functools import partial
 
 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
+import pso
 
 class GridFittingFailedError(Exception):
     pass
@@ -22,21 +23,12 @@ class MyGaussianBlur(ImageFilter.Filter):
     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)
-    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) 
+    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, do_something, im_h):
     l1 = line_from_angl_dist(l1, size)
@@ -64,18 +56,13 @@ def find(lines, size, l1, l2, bounds, hough, do_something, im_h):
     #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
+    import time
+    start = time.time()
 
-    pool = multiprocessing.Pool(None)
+    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)
 
-    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))]
-
-    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 = pso.optimize(4, 30, f_dist, 32, 1028)
 
     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]]]
-
-    pool.terminate()
-    pool.join()
+    
+    print time.time() - start
     
 ### Show error surface
 #
@@ -98,18 +84,18 @@ def find(lines, size, l1, l2, bounds, hough, do_something, im_h):
 
 ### 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
@@ -136,8 +122,8 @@ 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)
-#        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)