k-means clustering

[imago.git] / gridf.py

diff --git a/gridf.py b/gridf.py
index bc84ea8..3bb7c54 100644 (file)
--- a/gridf.py
+++ b/gridf.py
@@ -1,8 +1,16 @@
+"""Imago grid-fitting module"""
+
+import multiprocessing
+from functools import partial
+

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

-from manual import lines as g_grid, l2ad, intersection, line as g_line
+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
 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


@@ -15,124 +23,86 @@ 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 __add__(self, other):
-        return V(self.x + other.x, self.y + other.y)
-
-    def __sub__(self, other):
-        return V(self.x - other.x, self.y - other.y)
-
-    def __rmul__(self, other):
-        return V(other * self.x, other * self.y)
+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)
+    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 t(self):
-        return (self.x, self.y)
-
-    def normal(self):
-        return V(-self.y, self.x)
-
-def projection(point, line, vector):
-    n = vector.normal()
-    l2 = g_line(point.t(), (point + n).t())
-    return V(*intersection(l2, g_line(*line)))
-    
-def error_surface(lines, a, b, c, d, hough, size, v1):
-    import matplotlib.pyplot as plt
-    from matplotlib import cm
-    import time
-    import pickle
-    X = []
-    Y = []
-    Z = []
-    s = 0.001
-    k = 5
-    for i in range(-k, k):
-        X.append(range(-k, k))
-        Y.append(2*k*[i])
-        
-    start = time.clock()
-    for x in range(0, 2*k):
-        try:
-            Z.append([distance(lines, get_grid(a + X[x][y] * s * v1, b + Y[x][y] * s * v1, 
-                                           c, d, hough, size),
-                              size) for y in range(0, 2* k)])
-        except Exception:
-            Z.append(Z[-1])
-        o = ((time.clock() - start) * (2 * k - (x + 1))) / (60 * (x + 1))
-        print x + 1, "{0} h {1:2.2f} m".format(int(o) / 60, o % 60)
-    s_file = open('surface' + str(k), 'w')
-    pickle.dump((X, Y, Z), s_file)
-    s_file.close()
-    plt.imshow(Z, cmap=cm.gnuplot2, interpolation='bicubic', 
-               origin='upper', extent=(-k, k, -k, k), aspect='equal')
-    plt.colorbar()
-
-    plt.show()
-
-def find(lines, size, l1, l2, bounds, hough, do_something):
-    a, b, c, d = [V(*a) for a in bounds]
+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])
     v2 = V(*l2[0]) - V(*l2[1])
     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) 
     a = projection(a, l1, v1) 
     b = projection(b, l1, v1) 
     c = projection(c, l2, v2) 
     d = projection(d, l2, v2) 

-    grid = get_grid(a, b, c, d, hough, size)
-    dist = distance(lines, grid, size)
-    print dist
+    
+    v1, v2 = hough.lines_from_list([a, b])
+    h1, h2 = hough.lines_from_list([c, d])
+
+    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]
+
+    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)

 
 

-    #error_surface(lines, a, b, c, d, hough, size, v1)
+    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()
+
+    #import time
+    #start = time.time()
+
+    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)
+
+    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)
+    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]]]

     
     

-    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]]]
+    #print time.time() - start
+    
+### 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)
+###
+
+    if show_all:
+
+### 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)
     #TODO do something when a corner is outside the image
     corners = (c[0] + c[1])
     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])


@@ -142,36 +112,22 @@ 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)

+        if line_out(line, size):
+            return 0

     #im_g = im_g.filter(MyGaussianBlur(radius=3))
     #im_g = im_g.filter(MyGaussianBlur(radius=3))

-    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] * fg_l[x, y]
-                score +=  bg_l[x, y]
-                area += 1
-
-    #return res, float(score)/area
-    return None, float(score)/area
+    #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