from math import sqrt
import random
import sys
+import time
import src.linef as linef
import src.gridf as gridf
import new_geometry as gm
+random.seed(12345)
+
def plot_line(line, c):
points = linef.line_from_angl_dist(line, (520, 390))
pyplot.plot(*zip(*points), color=c)
def nearest(lines, point):
return min(map(lambda l: dst(point, l), lines))
+def nearest2(lines, point):
+ return min(map(lambda l: dst(point, points_to_line(*l)), lines))
+
size = (520, 390)
+def generate_models(sgrid, lh):
+ for f in [0, 1, 2, 3, 5, 7, 8, 11, 15, 17]:
+ grid = gm.fill(sgrid[0], sgrid[1], lh , f)
+ grid = [sgrid[0]] + grid + [sgrid[1]]
+ for s in xrange(17 - f):
+ grid = [gm.expand_left(grid, lh)] + grid
+ yield grid
+ for i in xrange(17 - f):
+ grid = grid[1:]
+ grid.append(gm.expand_right(grid, lh))
+ yield grid
+
+def score(grid, points, limit):
+ d = max(map(lambda l: dst((0, 0), points_to_line(*l)), grid + grid))
+ if d > limit:
+ return 0
+ return len([p for p in points if nearest2(grid, p) <= 2])
+
points = pickle.load(open('edges.pickle'))
lines = pickle.load(open('lines.pickle'))
l1, l2 = lines
+lines_general = map(to_general, sum(lines, []))
+near_points = [p for p in points if nearest(lines_general, p) <= 2]
+
while True:
- l1s = random.sample(l2, 2)
+ t0 = time.time()
+ #l1s = random.sample(l1, 2)
+ l1s = [l1[0], l1[-1]]
l1s.sort(key=lambda l: l[1])
- corners = map(lambda l:linef.line_from_angl_dist(l, size), l1s)
- middle = ((0, 195),(520, 195))
- # TODO! can I assume anything to be perspectively disorted square?
- # TODO! take lower and middle and construct top
- lh = (gm.intersection(corners[0], middle), gm.intersection(corners[1], middle))
- grid = gm.fill(corners[0], corners[1], lh , 3)
- grid = [corners[0]] + grid + [corners[1]]
- grid.append(gm.expand(grid[-2], grid[-1], ((gm.intersection(middle, grid[-2]),
- (gm.intersection(middle, grid[-1]))))))
- grid.append(gm.expand(grid[-2], grid[-1], ((gm.intersection(middle, grid[-2]),
- (gm.intersection(middle, grid[-1]))))))
+ sgrid = map(lambda l:linef.line_from_angl_dist(l, size), l1s)
+ middle = lambda m: ((m, 0),(m, 390))
+ middle = middle(gm.intersection((sgrid[0][0], sgrid[1][1]),
+ (sgrid[0][1], sgrid[1][0]))[0])
+ lh = (gm.intersection(sgrid[0], middle), gm.intersection(sgrid[1], middle))
+ sc, grid = max(map(lambda g: (score(g, points, 400), g), generate_models(sgrid, lh)))
map(lambda l: pyplot.plot(*zip(*l), color='b'), grid)
-
- plot_line(l1s[0], 'g')
+ #l2s = random.sample(l2, 2)
+ l2s = [l2[0], l2[-1]]
+ l2s.sort(key=lambda l: l[1])
+ sgrid = map(lambda l:linef.line_from_angl_dist(l, size), l2s)
+ middle = lambda m: ((0, m),(520, m))
+ middle = middle(gm.intersection((sgrid[0][0], sgrid[1][1]),
+ (sgrid[0][1], sgrid[1][0]))[1])
+ lh = (gm.intersection(sgrid[0], middle), gm.intersection(sgrid[1], middle))
+ sc, grid = max(map(lambda g: (score(g, points, 530), g), generate_models(sgrid, lh)))
+ print time.time() - t0
+
+ pyplot.scatter(*zip(*near_points))
+ map(lambda l: pyplot.plot(*zip(*l), color='b'), grid)
+ plot_line(l2s[0], 'r')
+ plot_line(l2s[1], 'r')
+ plot_line(l1s[0], 'r')
plot_line(l1s[1], 'r')
-
pyplot.xlim(0, 520)
pyplot.ylim(0, 390)
pyplot.show()
sys.exit()
-lines_general = map(to_general, sum(lines, []))
-
-near_points = [p for p in points if nearest(lines_general, p) <= 2]
-pyplot.scatter(*zip(*near_points))
for l in lines[0]: