def test_entropy_in_cylinders(self):
"""Test computing of eigenvalues in cylinder."""
num_all_pc_points = len(self.point_cloud[keys.point]["x"]["data"])
rand_indices = [
random.randint(0, num_all_pc_points) for p in range(20)
]
target_point_cloud = utils.copy_point_cloud(self.point_cloud,
rand_indices)
n_targets = len(target_point_cloud[keys.point]["x"]["data"])
radius = 25
neighbors = compute_neighbors.compute_cylinder_neighborhood(
self.point_cloud, target_point_cloud, radius)
target_idx_base = 0
for x in neighbors:
feature_extractor.compute_features(self.point_cloud,
x,
target_idx_base,
target_point_cloud,
["entropy_z"],
InfiniteCylinder(5),
layer_thickness=0.1)
target_idx_base += len(x)
for i in range(n_targets):
H = utils.get_attribute_value(target_point_cloud, i, "entropy_z")
self.assertTrue(H >= 0)
self.assertEqual(
"laserchicken.feature_extractor.entropy_feature_extractor",
target_point_cloud[keys.provenance][0]["module"])
self.assertEqual([0.1],
target_point_cloud[keys.provenance][0]["parameters"])
def test_normalize_tiny_unequal_point_cloud(self):
point_cloud = create_point_cloud([0, 0, 0], [0, 0, 0], [1, 2, 3])
normalized_point_cloud = normalize(point_cloud)
normalized_values = get_attribute_value(normalized_point_cloud,
range(3), normalized_height)
np.testing.assert_allclose(normalized_values,
np.array([0, 1, 2]),
atol=1e-7)
def test_GetPointCloudPointFeature(self):
""" Should not raise exception. """
pc = test_tools.generate_tiny_test_point_cloud()
cols = 0.5 * (pc[keys.point]["x"]["data"] +
pc[keys.point]["y"]["data"])
pc[keys.point]["color"] = {"type": "double", "data": cols}
x, y, z = utils.get_point(pc, 1)
c = utils.get_attribute_value(pc, 1, "color")
self.assertEqual(c, 0.5 * (x + y))
def test_normalize_tiny_unequal_point_cloud_multiple_cells(self):
"""Last of the 3 points is not in the neighborhood of the others."""
point_cloud = create_point_cloud([0, 0, 5], [0, 0, 0], [1, 2, 3])
normalized_point_cloud = normalize(point_cloud, cell_size=2)
normalized_values = get_attribute_value(normalized_point_cloud,
range(3), normalized_height)
np.testing.assert_allclose(normalized_values,
np.array([0, 1, 0]),
atol=1e-7)
def test_entropy_positive_value(self):
"""Test computing of eigenvalues in cylinder."""
target_point_cloud = self._find_neighbors_for_random_targets_and_compute_entropy(
)
n_targets = len(target_point_cloud[keys.point]["x"]["data"])
for i in range(n_targets):
entropy_z = utils.get_attribute_value(target_point_cloud, i,
"entropy_z")
self.assertTrue(entropy_z >= 0)
def extract(self, sourcepc, neighborhood, targetpc, targetindex, volume):
t1b = utils.get_attribute_value(targetpc, targetindex,
self.requires()[0])
x, y, z = utils.get_point(targetpc, targetindex)
return [x + t1b, y + t1b, z + t1b] # x + 3z/2, y + 3z/2, 5z/2
def _extract_one(self, target_point_cloud, target_index):
t1b = utils.get_attribute_value(target_point_cloud, target_index,
self.requires()[0])
x, y, z = utils.get_point(target_point_cloud, target_index)
return [x + t1b, y + t1b, z + t1b] # x + 3z/2, y + 3z/2, 5z/2
def _assert_feature_name_all_valued(expected, feature_name, n, target):
v = get_attribute_value(target, range(n), feature_name)
np.testing.assert_allclose(v, expected)
