"""Imago grid-fitting module"""
import multiprocessing
+from functools import partial
import Image, ImageDraw, ImageFilter
-from geometry import V, projection
-from manual import lines as g_grid, l2ad
+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 job_br1(args):
- im_l, v1, v2, h1, h2, x, y, dv, dh, size = args
- v1 = (v1[0] + x * dv, v1[1] + x)
- v2 = (v2[0] + y * dv, v2[1] + y)
- return (distance(im_l,
- get_grid([v1, v2], [h1, h2], size),
- size), x, y)
-
-def job_br2(args):
- im_l, v1, v2, h1, h2, x, y, dv, dh, size = args
- h1 = (h1[0] + x * dh, h1[1] + x)
- h2 = (h2[0] + y * dh, h2[1] + y)
- return (distance(im_l,
- get_grid([v1, v2], [h1, h2], size),
- size), x, y)
-
-def job_4(args):
- im_l, v1, v2, h1, h2, x, y, w, z, dv, dh, size = args
+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), x, y, w, 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):
+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])
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=5))
+ 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()
- #let's try the ULTRA bruteforce aproach:
- pool = multiprocessing.Pool(None)
-
#import time
#start = time.time()
- k = 30
- tasks = [(im_l_s, v1, v2, h1, h2, x, y, w, z, delta_v, delta_h, size)
- for x in xrange(-k, k, 2)
- for y in xrange(-k, k, 2)
- for z in xrange(-k, k, 2)
- for w in xrange(-k, k, 2)]
-
- opt = pool.map(job_4, tasks)
- _, x_v, y_v, x_h, y_h = max(opt)
-
- 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)
-
- k = 5
- tasks = [(im_l_s, v1, v2, h1, h2, x, y, w, z, delta_v, delta_h, size)
- for x in xrange(-k, k)
- for y in xrange(-k, k)
- for z in xrange(-k, k)
- for w in xrange(-k, k)]
+ 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)
- opt = pool.map(job_4, tasks)
- _, x_v, y_v, x_h, y_h = max(opt)
+ 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)
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]]]
-
- pool.terminate()
- pool.join()
#print time.time() - start
# 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")
+ 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
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
+ 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)
projekty / imago.git / blobdiff