def test_single_point():
'''Test measure_length function from patty.utils'''
with np.errstate(all='raise'):
line = np.array([[0, -1., 0]])
expected = 0
length = measure_length(line)
assert_almost_equal(expected, length)
def get_stick_scale(pointcloud, eps=0.1, min_samples=20):
"""Takes a point cloud, as a numpy array, looks for red segments
of scale sticks and returns the scale estimation with most support.
Method:
pointcloud --dbscan--> clusters --lengthEstimation-->
lengths --ransac--> best length
Arguments:
pointcloud Point cloud containing only measuring stick segments
(only the red, or only the white parts)
eps DBSCAN parameter: Maximum distance between two samples
for them to be considered as in the same neighborhood.
min_samples DBSCAN parameter: The number of samples in a neighborhood
for a point to be considered as a core point.
Returns:
scale Estimate of the size of one actual meter in expressed
in units of the pointcloud's coordinates.
confidence A number expressing the reliability of the estimated
scale. Confidence is in [0, 1]. With a confidence greater
than .5, the estimate can be considered useable for
further calculations.
"""
# quickly return for trivial case
if pointcloud.size == 0:
return 1, 0
# find the red segments to measure
pc_reds = extract_mask(pointcloud, get_red_mask(pointcloud))
if len(pc_reds) == 0:
# unit scale, zero confidence (ie. any other estimation is better)
return 1.0, 0.0
cluster_generator = segment_dbscan(
pc_reds, eps, min_samples, algorithm='kd_tree')
sizes = [{'len': len(cluster),
'meter': measure_length(cluster) * SEGMENTS_PER_METER}
for cluster in cluster_generator]
if len(sizes) == 0:
return 1.0, 0.0
scale, votes, n_clusters = ransac(sizes)
confidence = get_confidence_level(votes, n_clusters)
return scale, confidence
def test_centered_line_on_x_axis():
'''Test measure_length function from patty.utils'''
line = np.array([[-5., 0, 0], [0, 0, 0], [5., 0, 0]])
expected = 10
length = measure_length(line)
assert_almost_equal(expected, length)
def test_no_points():
'''Test measure_length function from patty.utils'''
line = np.array([])
expected = 0
length = measure_length(line)
assert_almost_equal(expected, length)
def test_rectangle_in_y_z_plane():
'''Test measure_length function from patty.utils'''
line = np.array([[0, -1., 0], [0, 9., 0], [0, -1., 4], [0, 9., 4]])
expected = 10
length = measure_length(line)
assert_almost_equal(expected, length)
