+"""Imago grid-fitting module"""
+
+import multiprocessing
+
+import Image, ImageDraw, ImageFilter
+
+from geometry import V, projection
from manual import lines as g_grid, l2ad
from intrsc import intersections_from_angl_dist
+from linef import line_from_angl_dist
+import pcf
+
+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)
+
+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 find(lines, size, l1, l2, bounds, hough, do_something, im_h):
+ 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])
+ a = projection(a, l1, v1)
+ b = projection(b, l1, v1)
+ c = projection(c, l2, v2)
+ d = projection(d, l2, v2)
+
+ 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_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_s = im_l.tostring()
+
+ #from gridf_analyzer import error_surface
+ #error_surface(im_l, a, b, c, d, hough, size, v1 ,v2)
-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]]
+ grid = get_grid(a, b, c, d, hough, 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])
+
+ pool = multiprocessing.Pool(None)
+
+ 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)]
+
+ import time
+ start = time.time()
+ opt_ab = pool.map(job_br1, tasks, 1)
+ opt_cd = pool.map(job_br2, tasks, 1)
+ d1 = 0
+ for lst in opt_ab:
+ for tpl in lst:
+ if tpl[0] > d1:
+ d1 = tpl[0]
+ a, b = tpl[1], tpl[2]
+ d1 = 0
+ for lst in opt_cd:
+ for tpl in lst:
+ if tpl[0] > d1:
+ d1 = tpl[0]
+ c, d = tpl[1], tpl[2]
+ 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]]]
+
+ ###
+
+ 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
+
+def get_grid(a, b, c, d, hough, size):
+ l1 = hough.lines_from_list([a, b])
+ l2 = hough.lines_from_list([c, d])
+ 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])
+ if len(corners) < 4:
+ print l1, l2, c
+ raise GridFittingFailedError
grid = g_grid(corners)
return grid
+
+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)
+ 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)
+ distance_d = pcf.combine(im_l, im_g.tostring())
+ return distance_d