+"""Imago grid-fitting module"""
+
+import multiprocessing
+from functools import partial
+
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
+import pcf
+import pso
class GridFittingFailedError(Exception):
pass
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 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 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):
- a, b, c, d = [V(*a) for a in bounds]
+def find(lines, size, l1, l2, bounds, hough, do_something, im_h):
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)
- 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
-
- 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]]]
+ 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_l.filter(MyGaussianBlur(radius=5))
+ #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 = pso.optimize(4, 30, f_dist, 32, 1028)
+
+ 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]]]
+
+ 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)
+###
+
+### 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
-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])
if len(corners) < 4:
print l1, l2, c
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)
+ if line_out(line, size):
+ return 0
#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
+ #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
projekty / imago.git / blobdiff