gridf bruteforce

diff --git a/gridf.py b/gridf.py
index 35bbc80..406285c 100644 (file)
--- a/gridf.py
+++ b/gridf.py
@@ -1,3 +1,5 @@
+import multiprocessing
+
 import Image, ImageDraw, ImageFilter
 
 from manual import lines as g_grid, l2ad, intersection, line as g_line
@@ -60,12 +62,27 @@ def job(args):
                      get_grid(a + X[y] * s * v1, 
                               b + Y[y] * s * v1, 
                               c, d, hough, size),
-                     size) for y in range(0,2 * k)]
-    
+                     size) for y in range(2 * k)]
+
+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)]
+
+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)]
+
 def error_surface(im_l, a, b, c, d, hough, size, v1):
     import matplotlib.pyplot as plt
     from matplotlib import cm
-    import multiprocessing
     import time
     import sys
     import pickle
@@ -114,7 +131,7 @@ def find(lines, size, l1, l2, bounds, hough, do_something):
     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=50))
+    im_l = im_l.filter(MyGaussianBlur(radius=30))
     #GaussianBlur is undocumented class, may not work in future versions of PIL
     im_l = im_l.tostring()
 
@@ -122,25 +139,73 @@ def find(lines, size, l1, l2, bounds, hough, do_something):
 
     grid = get_grid(a, b, c, d, hough, size)
     dist = distance(im_l, grid, 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])
+
+    tasks = [(X[x], Y[x], im_l, a, b, c, d, s, v1, v2, k, hough, size) for x in xrange(0, 2 * k)]
+
+    pool = multiprocessing.Pool(None)
+    
+    #start = time.time()
+    opt_ab = pool.map(job_br1, tasks, 1)
+    opt_cd = pool.map(job_br2, tasks, 1)
+    an, bn, cn, dn = 4 * [0]
+    d1 = 0
+    for lst in opt_ab:
+        for tpl in lst:
+            if tpl[0] > d1:
+                d1 = tpl[0]
+                an, bn = tpl[1], tpl[2]
+    d1 = 0
+    for lst in opt_cd:
+        for tpl in lst:
+            if tpl[0] > d1:
+                d1 = tpl[0]
+                cn, dn = tpl[1], tpl[2]
+    #print time.time() - start
+    grid = get_grid(an, bn, cn, dn, hough, size) 
+    grid_lines = [[l2ad(l, size) for l in grid[0]], [l2ad(l, size) for l in grid[1]]]
+    return grid, grid_lines
+
+    #old optimization experiments: 
     print dist
    
-    s = 0.02
-    while True:
-        ts1 = [(s, 0), (-s, 0), (s, s), (-s, -s), (-s, s), (s, -s), (0, s),  (0, -s)]
+    path = [(0,0)] #MNTR
+    s = 0.01
+    for _ in range(10):
+        ts1 = [(s, 0), (0, s), (-s, 0), (0, -s)]
         grids = [(get_grid(a + t[0] * v1, b + t[1] * v1, 
                            c, d, hough, size), t) for t in ts1]
-        distances = [(distance(im_l, 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
+        distances = [distance(im_l, grid, size) for (grid, t) in grids]
+        gradient = [(di - dist) for di in distances]
+        gradient = [gradient[0] - gradient[2], gradient[1] - gradient[3]]
+        norm = (gradient[0] ** 2 + gradient[1] ** 2) ** 0.5
+        gradient = [g / (100 * norm) for g in gradient]
+        path.append(gradient)
+        a, b = a + gradient[0] * v1, b + gradient[1] * v1
+        dist = distance(im_l, grid, size)
+        print dist
+
+    ###MNTR
+    import matplotlib.pyplot as plt
+    from matplotlib import cm
+    import pickle
+
+    X, Y, Z = pickle.load(open('surface250'))
+
+    plt.imshow(Z, cmap=cm.jet, interpolation='none', 
+               origin='upper', extent=(-0.250, 0.250, -0.250, 0.250), aspect='equal')
+    plt.colorbar()
+    plt.plot([y for (x, y) in path], [x for (x, y) in path], 'go-')
+
+    plt.show()
+    ###MNTR
 
     print "---"
 

diff --git a/linef.py b/linef.py
old mode 100755 (executable)
new mode 100644 (file)
index 37690cc..29ce2e3
--- a/linef.py
+++ b/linef.py
@@ -1,5 +1,3 @@
-#!/usr/bin/env python
-
 """Go image recognition lines-finding module"""
 
 import sys