def test_find_circles(self):
rrld = ReadRobotLineData()
points = rrld.get_points(os.path.abspath("embedding_darwin_-{}".format(4)))
points = [(p[0]*100, p[1]*100) for p in points]
ccd = CenterCircleDetection(points)
ccd.find_circles()
def test_minimum_span_tree_from_point_cloud(self):
rrld = ReadRobotLineData()
points = rrld.get_points(os.path.abspath("embedding_darwin_-{}".format(4)))
points = [(int(e[0]), int(e[1])) for e in points]
points = list(set(points))
ccd = CenterCircleDetection(points)
result = ccd.calculate_minimum_span_tree()
plt.plot([e[0] for e in points], [e[1] for e in points], 'ro')
for edge in result:
x1, y1 = points[edge[0]]
x2, y2 = points[edge[1]]
plt.plot([x1, x2], [y1, y2], 'g-')
plt.show()
if sgrid.get(sx + 1, None) is not None:
if sgrid[sx + 1].get(sy, 0) > 2:
counter += 1
if sgrid.get(sx - 1, None) is not None:
if sgrid[sx - 1].get(sy - 1, 0) > 2:
counter += 1
if sgrid.get(sx, None) is not None:
if sgrid[sx].get(sy - 1, 0) > 2:
counter += 1
if sgrid.get(sx + 1, None) is not None:
if sgrid[sx + 1].get(sy - 1, 0) > 2:
counter += 1
return counter
rrld = ReadRobotLineData()
points = rrld.get_points(os.path.abspath("embedding_darwin_-{}".format(18)))
grid = {}
for point in points:
x = int(point[0] / 50)
y = int(point[1] / 50)
if x not in grid:
grid[x] = {}
if y not in grid[x]:
grid[x][y] = 0
grid[x][y] += 1
# print grid[x][y]
for x in grid.keys():
return linemapping
def plot_line_assignment(self, line_assignment):
lins = itertools.cycle(["r-", "y-", "g-", "m-", "b-", "c-"])
cols = itertools.cycle(["ro", "yo", "go", "mo", "bo", "co"])
for key, values in self.linemapping.items():
print "K",
key = eval(key)
plt.plot([key[0], key[2]], [key[1], key[3]], lins.next())
plt.plot([e[0] for e in values], [e[1] for e in values], cols.next())
plt.xlim([0, 200])
plt.ylim([0, 100])
plt.show()
rrld = ReadRobotLineData()
points = rrld.get_points("data_darwin/embedding_darwin_-36")
points, data = rrld.get_image(points)
# Receives input data points
# data, points = giveit()
# points = [(x*random.random(), 2*random.random()) for x in range(40)] + [(4*random.random(), y*random.random()) for y in range(80)]
print "Point sTatistics"
xmax = max([e[0] for e in points])
xmin = min([e[0] for e in points])
ymax = max([e[1] for e in points])
ymin = min([e[1] for e in points])
