+"""Imago grid-fitting module"""
+
import multiprocessing
+from functools import partial
import Image, ImageDraw, ImageFilter
-from geometry import V
-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
import pcf
+import cs as Optimizer
class GridFittingFailedError(Exception):
pass
def filter(self, image):
return image.gaussian_blur(self.radius)
-def projection(point, line, vector):
- return V(*intersection(g_line(point, point + vector.normal), g_line(*line)))
-
-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 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 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, 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)
+
+ 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)
- #im_l = im_h #hocus pocus
- im_l = im_l.filter(MyGaussianBlur(radius=2))
+ 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()
- #from gridf_analyzer import error_surface
- #error_surface(im_l, a, b, c, d, hough, size, v1 ,v2)
+ import time
+ start = time.time()
- grid = get_grid(a, b, c, d, hough, size)
- dist = distance(im_l_s, 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])
+ 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)
- pool = multiprocessing.Pool(None)
+ x_v, y_v, x_h, y_h = Optimizer.optimize(4, 30, f_dist, 128, 256)
- 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)]
+ 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)
- 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]
- 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]
+ 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]]]
+
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]]]
- ###
-
+### 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)
+###
+
+### 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()
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])
+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])
#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 = pcf.combine(im_l, im_g.tostring())
- return distance
+ distance_d = pcf.combine(im_l, im_g.tostring())
+ return distance_d
projekty / imago.git / blobdiff