# TODO comments, refactoring, move methods to appropriate modules
-def plot_line(line, c):
- points = linef.line_from_angl_dist(line, (520, 390))
+def plot_line(line, c, size):
+ points = linef.line_from_angl_dist(line, size)
pyplot.plot(*zip(*points), color=c)
-def plot_line_g((a, b, c), max_x):
- find_y = lambda x: - (c + a * x) / b
- pyplot.plot([0, max_x], [find_y(0), find_y(max_x)], color='b')
class Diagonal_model:
def __init__(self, data):
else:
return ransac.least_squares(sample)
+ def score(self, est, dist):
+ cons = []
+ score = 0
+ a, b, c = est
+ dst = lambda (x, y): abs(a * x + b * y + c) / sqrt(a*a+b*b)
+ for p in self.data:
+ d = dst(p)
+ if d <= dist:
+ cons.append(p)
+ score += min(d, dist)
+ return score, cons
+
def intersection((a1, b1, c1), (a2, b2, c2)):
delim = float(a1 * b2 - b1 * a2)
x = (b1 * c2 - c1 * b2) / delim
if c1 in d2.points:
continue
pass
- c2 = [p for p in d2.points if p in c1.l1.points][0]
- c3 = [p for p in d1.points if p in c2.l2.points][0]
- c4 = [p for p in d2.points if p in c3.l1.points][0]
+ try:
+ c2 = [p for p in d2.points if p in c1.l1.points][0]
+ c3 = [p for p in d1.points if p in c2.l2.points][0]
+ c4 = [p for p in d2.points if p in c3.l1.points][0]
+ except IndexError:
+ continue
+ # there is not a corresponding intersection
+ # TODO create an intersection?
try:
yield manual.lines(map(lambda p: p.to_tuple(), [c2, c1, c3, c4]))
except TypeError:
diag2 = Line(line2)
diag2.points = ransac.filter_near(data, diag2, 2)
+ if show_all:
+ import matplotlib.pyplot as pyplot
+ import Image
+
+ def plot_line_g((a, b, c), max_x):
+ find_y = lambda x: - (c + a * x) / b
+ pyplot.plot([0, max_x], [find_y(0), find_y(max_x)], color='b')
+
+ fig = pyplot.figure(figsize=(8, 6))
+ plot_line_g(diag1, size[0])
+ plot_line_g(diag2, size[0])
+ pyplot.scatter(*zip(*sum(points, [])))
+ pyplot.scatter([center[0]], [center[1]], color='r')
+ pyplot.xlim(0, size[0])
+ pyplot.ylim(0, size[1])
+ pyplot.gca().invert_yaxis()
+ fig.canvas.draw()
+ size_f = fig.canvas.get_width_height()
+ buff = fig.canvas.tostring_rgb()
+ image_p = Image.fromstring('RGB', size_f, buff, 'raw')
+ do_something(image_p, "finding diagonal")
+
+
grids = list(gen_corners(diag1, diag2))
sc, grid = min(map(lambda g: (score(sum(g, []), data), g), grids))