import Image, ImageDraw, ImageFilter
+from geometry import V
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
def filter(self, image):
return image.gaussian_blur(self.radius)
-class V(object):
- 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 __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):
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(2 * k)]
-
def job_br1(args):
X, Y, im_l, a, b, c, d, s, v1, v2, k, hough, size = args
return [(distance(im_l,
hough, size),
size), c + X[y] * s * v2, d + Y[y] * s * v2) for y in range(2 *k)]
-def error_surface(im_l, a, b, c, d, hough, size, v1):
- import matplotlib.pyplot as plt
- from matplotlib import cm
- 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):
+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)
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=30))
+ #im_l = im_l.filter(MyGaussianBlur(radius=30))
#GaussianBlur is undocumented class, may not work in future versions of PIL
- im_l = im_l.tostring()
+ #im_l = im_l.tostring()
+ im_l = im_h.tostring() # hocus pocus
- #error_surface(im_l, a, b, c, d, hough, size, v1)
+ #from gridf_analyzer import error_surface
+ #error_surface(im_l, a, b, c, d, hough, size, v1 ,v2)
grid = get_grid(a, b, c, d, hough, size)
dist = distance(im_l, grid, size)
X.append(range(-k, k))
Y.append(2*k*[i])
+ pool = multiprocessing.Pool(None)
+
tasks = [(X[x], Y[x], im_l, a, b, c, d, s, v1, v2, k, hough, size) for x in xrange(0, 2 * k)]
- pool = multiprocessing.Pool(None)
-
#start = time.time()
opt_ab = pool.map(job_br1, tasks, 1)
opt_cd = pool.map(job_br2, tasks, 1)
for tpl in lst:
if tpl[0] > d1:
d1 = tpl[0]
- an, bn = tpl[1], tpl[2]
+ a, b = tpl[1], tpl[2]
d1 = 0
for lst in opt_cd:
for tpl in lst:
if tpl[0] > d1:
d1 = tpl[0]
- cn, dn = tpl[1], tpl[2]
+ c, d = tpl[1], tpl[2]
#print time.time() - start
- grid = get_grid(an, bn, cn, dn, hough, size)
+ 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]]]
return grid, grid_lines
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)
--- /dev/null
+import matplotlib.pyplot as plt
+from matplotlib import cm
+import time
+import sys
+import pickle
+import multiprocessing
+
+import gridf
+
+def job1(args):
+ X, Y, im_l, a, b, c, d, s, v1, k, hough, size = args
+ return [gridf.distance(im_l,
+ gridf.get_grid(a + X[y] * s * v1,
+ b + Y[y] * s * v1,
+ c, d, hough, size),
+ size) for y in range(2 * k)]
+def job2(args):
+ X, Y, im_l, a, b, c, d, s, v1, v2, k, hough, size = args
+ return [gridf.distance(im_l,
+ gridf.get_grid(a, b, c+ X[y] * s * v2,
+ d + Y[y] * s * v2,
+ hough, size),
+ size) for y in range(2 * k)]
+
+def error_surface(im_l, a, b, c, d, hough, size, v1, v2):
+ 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, v2, k, hough, size) for x in xrange(0, 2 * k)]
+
+ pool = multiprocessing.Pool(None)
+
+ start = time.time()
+ Z = pool.map(job2, 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()