grid -- naive optimalization

[imago.git] / gridf.py

diff --git a/gridf.py b/gridf.py
index c8f4b90..17be372 100644 (file)
--- a/gridf.py
+++ b/gridf.py
@@ -1,8 +1,127 @@
+import Image, ImageDraw, ImageFilter
+

 from manual import lines as g_grid, l2ad
 from intrsc import intersections_from_angl_dist
 from manual import lines as g_grid, l2ad
 from intrsc import intersections_from_angl_dist

+from linef import line_from_angl_dist
+
+class GridFittingFailedError(Exception):
+    pass
+
+class MyGaussianBlur(ImageFilter.Filter):
+    name = "GaussianBlur"
+
+    def __init__(self, radius=2):
+        self.radius = 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 t(self):
+        return (self.x, self.y)
+
+def find(lines, size, l1, l2, bounds, hough, do_something):
+    a, b, c, d = [V(*a) for a in bounds]
+    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])
+    grid = get_grid(a, b, c, d, hough, size)
+    dist = distance(lines, grid, size)
+    print dist
+    
+    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

 
 

-def find(lines, size, l1, l2):
-    c = intersections_from_angl_dist(lines, size)
-    corners = [c[0][0], c[0][-1], c[-1][0], c[-1][-1]]
+    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]]]
+    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()])
+    c = intersections_from_angl_dist([l1, l2], size, get_all=True)
+    corners = (c[0] + c[1])
+    if len(corners) < 4:
+        print l1, l2, c
+        raise GridFittingFailedError

     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
+    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_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]
+                score +=  bg_l[x, y]
+                area += 1
+
+    return res, float(score)/area