X-Git-Url: http://git.tomasm.cz/imago.git/blobdiff_plain/b91b0e43b25e64b3516b9156ca42e7309665628b..1e473b2725c875b2d4e6e69530a95eec2f3a901e:/gridf.py?ds=sidebyside

diff --git a/gridf.py b/gridf.py
index 6f044f1..0955d7e 100644
--- a/gridf.py
+++ b/gridf.py
@@ -53,16 +53,17 @@ def find(lines, size, l1, l2, bounds, hough, do_something, im_h):
     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=30))
+
+    #im_l = im_h #hocus pocus    
+    im_l = im_l.filter(MyGaussianBlur(radius=2))
     #GaussianBlur is undocumented class, may not work in future versions of PIL
-    #im_l = im_l.tostring()
-    im_l = im_h.tostring() # hocus pocus
+    im_l_s = im_l.tostring()
 
     #from gridf_analyzer import error_surface
     #error_surface(im_l, a, b, c, d, hough, size, v1 ,v2)
 
     grid = get_grid(a, b, c, d, hough, size)
-    dist = distance(im_l, grid, size)
+    dist = distance(im_l_s, grid, size)
     
     #let's try the bruteforce aproach:
     s = 0.001
@@ -74,9 +75,10 @@ def find(lines, size, l1, l2, bounds, hough, do_something, im_h):
 
     pool = multiprocessing.Pool(None)
 
-    tasks = [(X[x], Y[x], im_l, a, b, c, d, s, v1, v2, k, hough, size) for x in xrange(0, 2 * k)]
+    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)]
 
-    #start = time.time()
+    import time
+    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]
@@ -92,66 +94,29 @@ def find(lines, size, l1, l2, bounds, hough, do_something, im_h):
             if tpl[0] > d1:
                 d1 = tpl[0]
                 c, d = tpl[1], tpl[2]
-    #print time.time() - start
+    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]]]
-    return grid, grid_lines
+    
+    ### 
 
-    #old optimization experiments: 
-    print dist
-   
-    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) 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 "---"
-
-    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(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]
-            c, d = c + t[0] * v2, d + t[1] * v2
-            print dist
-            s *= 0.75
-        else:
-            break
+    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")
+
+
+    ###
 
-    grid_lines = [[l2ad(l, size) for l in grid[0]], [l2ad(l, size) for l in grid[1]]]
     return grid, grid_lines
 
 def get_grid(a, b, c, d, hough, size):
@@ -185,22 +150,3 @@ def distance(im_l, grid, size):
     #im_d, distance = combine(im_l, im_g)
     distance = pcf.combine(im_l, im_g.tostring())
     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] * fg_l[x, y]
-                score +=  bg_l[x, y]
-                area += 1
-
-    #return res, float(score)/area
-    return None, float(score)/area