# TODO comments, refactoring, move methods to appropriate modules
-def plot_line(line, c):
- points = linef.line_from_angl_dist(line, (520, 390))
+class GridFittingFailedError(Exception):
+ pass
+
+class BadGenError(Exception):
+ pass
+
+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):
if l1[i] and l2[j]:
yield (l1[i], l2[j])
+ def remove(self, data):
+ self.data = list(set(self.data) - set(data))
+
def initial(self):
try:
- return self.gen.next()
+ nxt = self.gen.next()
except StopIteration:
self.gen = self.initial_g()
- return self.gen.next()
+ nxt = self.gen.next()
+ return nxt
def get(self, sample):
if len(sample) == 2:
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)
+ l1 = None
+ l2 = None
+ for p in self.data:
+ d = dst(p)
+ if d <= dist:
+ cons.append(p)
+ if p.l1 == l1 or p.l2 == l2:
+ return float("inf"), []
+ else:
+ l1, l2 = p.l1, p.l2
+ score += min(d, dist)
+
+ return score, cons
+
def intersection((a1, b1, c1), (a2, b2, c2)):
delim = float(a1 * b2 - b1 * a2)
+ if delim == 0:
+ return None
x = (b1 * c2 - c1 * b2) / delim
y = (c1 * a2 - a1 * c2) / delim
return x, y
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]
- yield map(lambda p: p.to_tuple(), [c1, c2, c3, c4])
+ 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):
+ pass
+ # the square was too small to fit 17 lines inside
+ # TODO define SquareTooSmallError or something
def dst(p, l):
(x, y), (a, b, c) = p, ransac.points_to_line(*l)
points = [l.points for l in new_lines1]
- line1, cons = ransac.estimate(points, 2, 800, Diagonal_model)
- points2 = map(lambda l: [(p if not p in cons else None) for p in l], points)
- line2, cons2 = ransac.estimate(points2, 2, 800, Diagonal_model)
- center = intersection(line1, line2)
- data = sum(points, [])
- diag1 = Line(line1)
- diag1.points = ransac.filter_near(data, diag1, 2)
- diag2 = Line(line2)
- diag2.points = ransac.filter_near(data, diag2, 2)
-
- grids = map(manual.lines, list(gen_corners(diag1, diag2)))
-
- sc, grid = min(map(lambda g: (score(sum(g, []), data), g), grids))
-
+ def dst_p(x, y):
+ x = x - size[0] / 2
+ y = y - size[1] / 2
+ return sqrt(x * x + y * y)
+
+ for n_tries in xrange(3):
+ model = Diagonal_model(points)
+ diag_lines = ransac.ransac_multi(6, points, 2, 800, model=model)
+ diag_lines = [l[0] for l in diag_lines]
+ centers = []
+ cen_lin = []
+ for i in xrange(len(diag_lines)):
+ line1 = diag_lines[i]
+ for line2 in diag_lines[i+1:]:
+ c = intersection(line1, line2)
+ if c and dst_p(*c) < min(size) / 2:
+ cen_lin.append((line1, line2, c))
+ centers.append(c)
+
+ 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))
+ for l in diag_lines:
+ plot_line_g(l, size[0])
+ pyplot.scatter(*zip(*sum(points, [])))
+ if len(centers) >= 1:
+ pyplot.scatter([c[0] for c in centers], [c[1] for c in centers], 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 diagonals")
+
+ data = sum(points, [])
+ # TODO what if lines are missing?
+ sc = float("inf")
+ grid = None
+ for (line1, line2, c) in cen_lin:
+ diag1 = Line(line1)
+ diag1.points = ransac.filter_near(data, diag1, 2)
+ diag2 = Line(line2)
+ diag2.points = ransac.filter_near(data, diag2, 2)
+
+
+ grids = list(gen_corners(diag1, diag2))
+
+ try:
+ new_sc, new_grid = min(map(lambda g: (score(sum(g, []), data), g), grids))
+ if new_sc < sc:
+ sc, grid = new_sc, new_grid
+ except ValueError:
+ pass
+ if grid:
+ break
+ else:
+ raise GridFittingFailedError
+
grid_lines = [[l2ad(l, size) for l in grid[0]],
[l2ad(l, size) for l in grid[1]]]
+ grid_lines[0].sort(key=lambda l: l[1])
+ grid_lines[1].sort(key=lambda l: l[1])
+ if grid_lines[0][0][0] > grid_lines[1][0][0]:
+ grid_lines = grid_lines[1], grid_lines[0]
return grid, grid_lines
-def test():
- import pickle
- import matplotlib.pyplot as pyplot
-
- lines = pickle.load(open('lines.pickle'))
-
- size = (520, 390)
- new_lines1 = map(lambda l: Line.from_ad(l, size), lines[0])
- new_lines2 = map(lambda l: Line.from_ad(l, size), lines[1])
- for l1 in new_lines1:
- for l2 in new_lines2:
- p = Point(intersection(l1, l2))
- p.l1 = l1
- p.l2 = l2
- l1.points.append(p)
- l2.points.append(p)
-
- points = [l.points for l in new_lines1]
-
- line1, cons = ransac.estimate(points, 2, 800, Diagonal_model)
- points2 = map(lambda l: [(p if not p in cons else None) for p in l], points)
- line2, cons2 = ransac.estimate(points2, 2, 800, Diagonal_model)
- center = intersection(line1, line2)
- data = sum(points, [])
- diag1 = Line(line1)
- diag1.points = ransac.filter_near(data, diag1, 2)
- diag2 = Line(line2)
- diag2.points = ransac.filter_near(data, diag2, 2)
-
- plot_line_g(diag1, 520)
- plot_line_g(diag2, 520)
- pyplot.scatter(*zip(*sum(points, [])))
- pyplot.scatter([center[0]], [center[1]], color='r')
- pyplot.xlim(0, 520)
- pyplot.ylim(0, 390)
- pyplot.show()
-
- grids = map(manual.lines, list(gen_corners(diag1, diag2)))
- plot_grid = lambda g: map(lambda l: pyplot.plot(*zip(*l), color='g'), sum(g, []))
- map(plot_grid, grids)
- pyplot.show()
-
- sc, grid = min(map(lambda g: (score(sum(g, []), data), g), grids))
-
- map(lambda l: pyplot.plot(*zip(*l), color='g'), sum(grid, []))
- pyplot.scatter(*zip(*sum(points, [])))
- pyplot.xlim(0, 520)
- pyplot.ylim(0, 390)
- pyplot.show()
projekty / imago.git / blobdiff