def test_ransac_circle_fit_on_two_points_returns_none(self):
points = np.array(
make_circular_vertices(radius=2, center=(2, 2), num_pts=2))
result = geom.ransac_circle_fit(points,
desired_radius=2,
consensus=0.99,
tolerance=0.03,
iterations=10)
self.assertIsNone(result)
def test_ransac_circle_fit_on_linear_vertices_fails(self):
points = np.array(
make_linear_vertices(start=(2, 2), end=(5, 5), num_pts=8))
result = geom.ransac_circle_fit(points,
desired_radius=2,
consensus=0.99,
tolerance=0.03,
iterations=10)
self.assertIsNone(result)
def test_ransac_circle_fit_on_circular_vertices_with_wrong_radius_fails(
self):
points = np.array(
make_circular_vertices(radius=3, center=(2, 2), num_pts=8))
result = geom.ransac_circle_fit(points,
desired_radius=2,
consensus=0.99,
tolerance=0.03,
iterations=10)
self.assertIsNone(result)
def classify(self, vehicle_state):
"""
Classifies each cluster of points as either ball or obstacle from vehicle_state['clusters'], which is an Nx2
array of points. The resulting balls are stored into vehicle_state['balls'] as a list of circles as
((x, y), radius) and the resulting obstacles are placed into vehicle_state['obstacles'] as a list of rectangles
as ((min_x, min_y), (max_x, max_y))
"""
clusters = vehicle_state['clusters']
balls = list()
obstacles = list()
for cluster in clusters:
ball = geom.ransac_circle_fit(cluster, desired_radius=self.ball_radius, consensus=0.99, tolerance=0.03, iterations=10)
if ball is not None:
ball_pos, ball_radius = ball
balls.append(ball_pos)
else:
# Construct a bounding box and put into obstacles list
obstacles.append(geom.bounding_box(cluster))
vehicle_state['balls'] = balls
vehicle_state['obstacles'] = obstacles
def test_ransac_circle_fit_on_circular_vertices_with_correct_radius_succeeds(
self):
points = np.array(
make_circular_vertices(radius=2, center=(2, 2), num_pts=8))
result = geom.ransac_circle_fit(points,
desired_radius=2,
consensus=0.99,
tolerance=0.03,
iterations=10)
expected = ((2, 2), 2)
expected_center_x = expected[0][0]
expected_center_y = expected[0][1]
expected_radius = expected[1]
actual = result
actual_center_x = actual[0][0]
actual_center_y = actual[0][1]
actual_radius = actual[1]
self.assertAlmostEqual(expected_center_x, actual_center_x)
self.assertAlmostEqual(expected_center_y, actual_center_y)
self.assertAlmostEqual(expected_radius, actual_radius)