+import multiprocessing
+
import Image, ImageDraw, ImageFilter
from manual import lines as g_grid, l2ad, intersection, line as g_line
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)))
-
-def error_surface(lines, a, b, c, d, hough, size, v1):
+ 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,
+ 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 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 = 200
+ 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()
- for x in range(0, 2*k):
- try:
- Z.append([distance(lines, get_grid(a + X[x][y] * s * v1, b + Y[x][y] * s * v1,
- c, d, hough, size),
- size) for y in range(0, 2* k)])
- except Exception:
- Z.append(Z[-1])
- o = ((time.time() - start) * (2 * k - (x + 1))) / (60 * (x + 1))
- print x + 1, "{0} h {1:2.2f} m".format(int(o) / 60, o % 60)
+ 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.gnuplot2, interpolation='bicubic',
+ 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]
l1 = line_from_angl_dist(l1, size)
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=30))
+ #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)
- print dist
+ dist = distance(im_l, 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])
+
+ tasks = [(X[x], Y[x], im_l, a, b, c, d, s, v1, v2, k, hough, size) for x in xrange(0, 2 * k)]
- #error_surface(lines, a, b, c, d, hough, size, v1)
+ pool = multiprocessing.Pool(None)
- s = 0.02
- while True:
- ts1 = [(s, 0), (-s, 0), (s, s), (-s, -s), (-s, s), (s, -s), (0, s), (0, -s)]
+ #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]
+ an, bn = 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]
+ #print time.time() - start
+ grid = get_grid(an, bn, cn, dn, hough, size)
+ grid_lines = [[l2ad(l, size) for l in grid[0]], [l2ad(l, size) for l in grid[1]]]
+ return grid, grid_lines
+
+ #old optimization experiments:
+ print dist
+
+ path = [(0,0)] #MNTR
+ s = 0.01
+ for _ in range(10):
+ ts1 = [(s, 0), (0, s), (-s, 0), (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
+ distances = [distance(im_l, grid, size) for (grid, t) in grids]
+ gradient = [(di - dist) for di in distances]
+ gradient = [gradient[0] - gradient[2], gradient[1] - gradient[3]]
+ norm = (gradient[0] ** 2 + gradient[1] ** 2) ** 0.5
+ gradient = [g / (100 * norm) for g in gradient]
+ path.append(gradient)
+ a, b = a + gradient[0] * v1, b + gradient[1] * v1
+ dist = distance(im_l, grid, size)
+ print dist
+
+ ###MNTR
+ import matplotlib.pyplot as plt
+ from matplotlib import cm
+ import pickle
+
+ X, Y, Z = pickle.load(open('surface250'))
+
+ plt.imshow(Z, cmap=cm.jet, interpolation='none',
+ origin='upper', extent=(-0.250, 0.250, -0.250, 0.250), aspect='equal')
+ plt.colorbar()
+ plt.plot([y for (x, y) in path], [x for (x, y) in path], 'go-')
+
+ plt.show()
+ ###MNTR
print "---"
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])
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))
+ #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.tostring(), im_g.tostring())
+ distance = pcf.combine(im_l, im_g.tostring())
return distance
def combine(bg, fg):
projekty / imago.git / blobdiff