from manual import lines as g_grid, l2ad, intersection, line as g_line
from intrsc import intersections_from_angl_dist
from linef import line_from_angl_dist
+import pcf
class GridFittingFailedError(Exception):
pass
def filter(self, image):
return image.gaussian_blur(self.radius)
-class V():
+class V(object):
def __init__(self, x, y):
self.x = x
self.y = y
def __rmul__(self, other):
return V(other * self.x, other * self.y)
- def t(self):
- return (self.x, self.y)
+ def __len__(self):
+ return 2;
+ def __getitem__(self, key):
+ if key == 0:
+ return self.x
+ elif key == 1:
+ return self.y
+ elif type(key) != int:
+ raise TypeError("V indices must be integers")
+ else:
+ raise KeyError("V index ({}) out of range".format(key))
+
+ def __iter__(self):
+ yield self.x
+ yield self.y
+
+ @property
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)))
+ return V(*intersection(g_line(point, point + vector.normal), g_line(*line)))
+
+def job(args):
+ X, Y, im_l, a, b, c, d, s, v1, 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) for y in range(0,2 * k)]
+def error_surface(im_l, a, b, c, d, hough, size, v1):
+ import matplotlib.pyplot as plt
+ from matplotlib import cm
+ import multiprocessing
+ import time
+ import sys
+ import pickle
+
+ X = []
+ Y = []
+ Z = []
+ s = 0.001
+ k = 250
+ for i in range(-k, k):
+ X.append(range(-k, k))
+ Y.append(2*k*[i])
+
+ tasks = [(X[x], Y[x], im_l, a, b, c, d, s, v1, k, hough, size) for x in xrange(0, 2 * k)]
+ #everything is passed by value here; can it somehow be passed by reference?
+
+ pool = multiprocessing.Pool(None)
+
+ start = time.time()
+ Z = pool.map(job, tasks, 1)
+ print time.time() - start
+
+ s_file = open('surface' + str(k), 'w')
+ pickle.dump((X, Y, Z), s_file)
+ s_file.close()
+ plt.imshow(Z, cmap=cm.jet, interpolation='bicubic',
+ origin='upper', extent=(-k, k, -k, k), aspect='equal')
+ plt.colorbar()
+
+ plt.show()
+
+ sys.exit()
def find(lines, size, l1, l2, bounds, hough, do_something):
a, b, c, d = [V(*a) for a in bounds]
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_l.filter(MyGaussianBlur(radius=50))
+ #GaussianBlur is undocumented class, may not work in future versions of PIL
+ im_l = im_l.tostring()
+
+ #error_surface(im_l, a, b, c, d, hough, size, v1)
+
grid = get_grid(a, b, c, d, hough, size)
- dist = distance(lines, grid, size)
+ dist = distance(im_l, 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),
+ distances = [(distance(im_l, grid, size),
grid, t) for grid, t in grids]
distances.sort(reverse=True)
if distances[0][0] > dist:
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),
+ distances = [(distance(im_l, grid, size),
grid, t) for grid, t in grids]
distances.sort(reverse=True)
if distances[0][0] > dist:
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()])
+ 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
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 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)
#im_g = im_g.filter(MyGaussianBlur(radius=3))
- im_d, distance = combine(im_l, im_g)
+ #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
def combine(bg, fg):
bg_l = bg.load()
fg_l = fg.load()
- res = Image.new('L', fg.size)
- res_l = res.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]
+ #res_l[x, y] = bg_l[x, y] * fg_l[x, y]
score += bg_l[x, y]
area += 1
- return res, float(score)/area
+ #return res, float(score)/area
+ return None, float(score)/area
projekty / imago.git / blobdiff