X-Git-Url: http://git.tomasm.cz/imago.git/blobdiff_plain/7f4334b61a001eb1a2df878f804a9f6771263709..04c7dfcf51881df1f6a2058c8a74c179ab3aee13:/src/gridf3.py?ds=inline diff --git a/src/gridf3.py b/src/gridf3.py index 4f2a1e3..ca8decb 100644 --- a/src/gridf3.py +++ b/src/gridf3.py @@ -9,6 +9,12 @@ from geometry import l2ad # TODO comments, refactoring, move methods to appropriate modules +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) @@ -29,12 +35,16 @@ class Diagonal_model: 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: @@ -42,8 +52,30 @@ class Diagonal_model: 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 + else: # TODO delete this or refactor + 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 @@ -95,7 +127,7 @@ class Line: elif key == 2: return self.c -def gen_corners(d1, d2): +def gen_corners(d1, d2, min_size): for c1 in d1.points: if c1 in d2.points: continue @@ -104,13 +136,22 @@ def gen_corners(d1, d2): 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] + x_min = min([c1[0], c2[0], c3[0], c4[0]]) + x_max = max([c1[0], c2[0], c3[0], c4[0]]) + if x_max - x_min < min_size: + continue + y_min = min([c1[1], c2[1], c3[1], c4[1]]) + y_max = max([c1[1], c2[1], c3[1], c4[1]]) + if y_max - y_min < min_size: + continue + 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: + except (TypeError): pass # the square was too small to fit 17 lines inside # TODO define SquareTooSmallError or something @@ -120,6 +161,8 @@ def dst(p, l): return abs(a * x + b * y + c) / sqrt(a*a+b*b) def score(lines, points): + # TODO find whether the point actualy lies on the line or just in the same + # direction score = 0 for p in points: s = min(map(lambda l: dst(p, l), lines)) @@ -129,7 +172,6 @@ def score(lines, points): def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger): - logger("finding the grid") 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: @@ -142,42 +184,74 @@ def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger): 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) - - 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]) - 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)) - + 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): + logger("finding the diagonals") + model = Diagonal_model(points) + diag_lines = ransac.ransac_multi(6, points, 2, 400, 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 + from PIL 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") + + logger("finding the grid") + 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, min(size) / 3)) + + 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]) @@ -187,52 +261,3 @@ def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger): 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()