def setUp(self):
"""
Set up the test.
Load in a bunch of real data from AHN3.
"""
np.random.seed(1234)
_TEST_FILE_NAME = 'AHN3.las'
_TEST_DATA_SOURCE = 'testdata'
_CYLINDER = InfiniteCylinder(4)
_PC_260807 = load(os.path.join(_TEST_DATA_SOURCE, _TEST_FILE_NAME))
_PC_1000 = copy_point_cloud(
_PC_260807,
array_mask=(np.random.choice(range(
len(_PC_260807[keys.point]['x']['data'])),
size=1000,
replace=False)))
_1000_NEIGHBORHOODS_IN_260807 = list(
compute_neighbors.compute_neighborhoods(_PC_260807, _PC_1000,
_CYLINDER))
self.point_cloud = _PC_260807
self.neigh = _1000_NEIGHBORHOODS_IN_260807
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 setUp(self):
"""
Set up the test.
Create a sphere and a cylinder of points and a central point
The cylinder has no points on the equator of the sphere
"""
# create the points
self.radius = 0.5
self.xyz = [[0., 0., 0.]]
self._set_sphere_data()
self._set_cylinder_data()
# create the pc
self.point_cloud, self.npts = self._get_pc(np.array(self.xyz))
self.target_point_cloud = self._get_central_point(0)
self.indexpc = 0
# create the volume/neighborhood
self.cylinder = InfiniteCylinder(self.radius + 1E-3)
self.neighborhoods = list(
compute_neighborhoods(self.point_cloud, self.target_point_cloud,
self.cylinder))
# theoretical value of the echo ratio
self.theoretical_value = (self.npt_sphere + 1) / (self.npt_sphere +
self.npt_cyl + 1)
def test_valid(self):
"""Compute the echo ratio for a sphere/cylinder at different target points without crashing."""
# read the data
self.point_cloud = read_las.read(
os.path.join(self._test_data_source, self._test_file_name))
# get the target point clouds
random.seed(102938482634)
self.target_point_cloud = self._get_random_targets()
self.target_point_cloud_index = 0
# volume descriptions
radius = 0.5
self.cyl = InfiniteCylinder(radius)
neighbors = compute_neighborhoods(self.point_cloud,
self.target_point_cloud, self.cyl)
cylinder_index = []
for x in neighbors:
cylinder_index += x
# extractor
extractor = PulsePenetrationFeatureExtractor()
for index in cylinder_index:
extractor.extract(self.point_cloud, index, None, None, None)
def assert_std_for_z_function_in_xy_grid(z_checkered, expected):
"""Assert that the standard deviation of z values in a grid of unit x and y"""
n_points, points = create_points_in_xy_grid(z_checkered)
point_cloud = create_point_cloud(points[:, 0], points[:, 1], points[:, 2])
targets = create_point_cloud([0], [0], [0])
compute_features(point_cloud, [range(n_points)], 0, targets, ['sigma_z'],
InfiniteCylinder(10))
np.testing.assert_almost_equal(targets[keys.point]['sigma_z']['data'][0],
expected)
def test_compute_neighbors_cylinderVolume(self):
"""Compute neighbors should detect cylinder volume and find neighbors accordingly"""
target_point_cloud = self._get_random_targets()
cylinder = InfiniteCylinder(0.5)
neighborhoods = compute_neighborhoods(self.point_cloud,
target_point_cloud, cylinder)
self._assert_all_points_within_cylinder(neighborhoods,
target_point_cloud,
cylinder.radius)
def setUp(self):
"""Set up the test."""
self.point_cloud = load(os.path.join(self._test_data_source, self._test_file_name))
random.seed(102938482634)
self.target_point_cloud = self._get_random_targets()
radius = 0.5
self.sphere = Sphere(radius)
self.cyl = InfiniteCylinder(radius)
def test_eigenvalues_of_too_few_points_results_in_0():
"""If there are too few points to calculate the eigen values don't output NaN or inf."""
a = np.array([5])
pc = create_point_cloud(a, a, a)
compute_features(pc, [[0]], pc, ["eigenv_1", "eigenv_2", "eigenv_3"],
InfiniteCylinder(5))
eigen_val_123 = np.array(
[pc[keys.point]['eigenv_{}'.format(i)]['data'] for i in [1, 2, 3]])
assert not np.any(np.isnan(eigen_val_123))
assert not np.any(np.isinf(eigen_val_123))
def setUp(self):
"""Set up the test."""
# generate the points
self._set_cylinder_data()
# get the central point as targetpc
self.targetpc = self._get_central_point()
# get the cylinder
self.cyl = InfiniteCylinder(np.mean(self.radius))
# get the theoretical value +1 for central point
npts = self.points_per_cylinder + 1
self.areadens = npts / self.cyl.calculate_base_area()
def _find_neighbors_for_random_targets_and_compute_entropy(self):
num_all_pc_points = len(self.point_cloud[keys.point]["x"]["data"])
rand_indices = [
random.randint(0, num_all_pc_points) for _ in range(20)
]
target_point_cloud = utils.copy_point_cloud(self.point_cloud,
rand_indices)
radius = 25
neighborhoods = list(
compute_cylinder_neighborhood(self.point_cloud, target_point_cloud,
radius))
compute_features(self.point_cloud,
neighborhoods,
target_point_cloud, ["entropy_z"],
InfiniteCylinder(5),
layer_thickness=0.1)
return target_point_cloud
def setUp(self):
# read the data
self.point_cloud = read_las.read(
os.path.join(self._test_data_source, self._test_file_name))
# get the target point clouds
random.seed(102938482634)
self.target_pc_sequential = self._get_random_targets()
self.target_pc_vector = utils.copy_point_cloud(
self.target_pc_sequential)
self.target_pc_index = 0
# volume descriptions
radius = 0.5
self.cyl = InfiniteCylinder(radius)
self.neighbors = compute_neighborhoods(self.point_cloud,
self.target_pc_sequential,
self.cyl)
self.cylinder_index = []
for x in self.neighbors:
self.cylinder_index += x
def test_eigenvalues_in_cylinders(self):
"""Test provenance added (This should actually be part the general feature extractor test suite)."""
random.seed(102938482634)
point_cloud = load(os.path.join('testdata', 'AHN3.las'))
num_all_pc_points = len(point_cloud[keys.point]["x"]["data"])
rand_indices = [
random.randint(0, num_all_pc_points) for _ in range(20)
]
target_point_cloud = utils.copy_point_cloud(point_cloud, rand_indices)
radius = 2.5
neighbors = compute_neighbors.compute_cylinder_neighborhood(
point_cloud, target_point_cloud, radius)
compute_features(point_cloud, neighbors, target_point_cloud,
["eigenv_1", "eigenv_2", "eigenv_3"],
InfiniteCylinder(5))
self.assertEqual(
"laserchicken.feature_extractor.eigenvals_feature_extractor",
target_point_cloud[keys.provenance][-1]["module"])
def setUp(self):
"""
Create a grid of 4 targets and an environment point cloud and neighbors and the echo ratios of those targets.
"""
self.radius = 0.5
targets = np.array([[10., 0., 5.], [10., 10., 5.], [0., 0., 5.],
[0., 10., 5.]]) # Grid w. steps 10 & height 5
self.echo_ratios = np.array([0., 0.9, 0.5, 0.8])
environment_parts = [
self._create_environment_part(t, ratio, self.radius)
for t, ratio in zip(targets, self.echo_ratios)
]
environment = np.vstack(environment_parts)
self.target_pc = create_point_cloud(targets[:, 0], targets[:, 1],
targets[:, 2])
self.environment_pc = create_point_cloud(environment[:, 0],
environment[:, 1],
environment[:, 2])
self.cylinder = InfiniteCylinder(self.radius)
self.neighbors = list(
compute_neighborhoods(self.environment_pc, self.target_pc,
self.cylinder))
from laserchicken import read_ply
from laserchicken.feature_extractor import *
from laserchicken.feature_extractor.pulse_penetration_feature_extractor import GROUND_TAGS
from laserchicken.keys import point, normalized_height
from laserchicken.utils import copy_point_cloud
from laserchicken.volume_specification import InfiniteCylinder, Cell
from . import compute_features
from .feature_map import create_default_feature_map, _create_name_extractor_pairs
np.random.seed(1234)
_TEST_FILE_NAME = 'AHN3.ply'
_TEST_NEIGHBORHOODS_FILE_NAME = 'AHN3_1000_random_neighbors.json'
_TEST_DATA_SOURCE = 'testdata'
_CYLINDER = InfiniteCylinder(4)
_PC_260807 = read_ply.read(os.path.join(_TEST_DATA_SOURCE, _TEST_FILE_NAME))
_PC_1000 = copy_point_cloud(_PC_260807,
array_mask=(np.random.choice(range(
len(_PC_260807[keys.point]['x']['data'])),
size=1000,
replace=False)))
_PC_10 = copy_point_cloud(_PC_260807,
array_mask=(np.random.choice(range(
len(_PC_260807[keys.point]['x']['data'])),
size=10,
replace=False)))
_1000_NEIGHBORHOODS_IN_260807 = next(
compute_neighbors.compute_neighborhoods(_PC_260807,
_PC_1000,
_CYLINDER,
def test_infiniteCylinder_type():
assert_equal(InfiniteCylinder(2).get_type(), InfiniteCylinder.TYPE)
def test_infiniteCylinder_calculateArea():
assert_almost_equal(
InfiniteCylinder(2).calculate_base_area(), 12.56637061436)
print(("Number of points: %s ") % (pc[point]['x']['data'].shape[0]))
print(("Number of points in target: %s ") %
(target[point]['x']['data'].shape[0]))
print("------ Computing neighborhood is started ------")
start = time.time()
#compute_neighborhoods is now a generator. To get the result of a generator the user
#needs to call next(compute_neighborhoods). The following shows how to get the results.
#
#indices_cyl=compute_neighborhoods(pc, target, InfiniteCylinder(np.float(args.radius)))
#
neighbors = compute_neighborhoods(pc, target,
InfiniteCylinder(np.float(args.radius)))
iteration = 0
target_idx_base = 0
fullstarttime = time.time()
for x in neighbors:
end = time.time()
difftime = end - start
print(("build kd-tree: %f sec") % (difftime))
print("Computed neighborhoods list length at iteration %d is: %d" %
(iteration, len(x)))
start1 = time.time()
print("------ Feature calculation is started ------")
compute_features(pc, x, target_idx_base, target,
def test_cell_volume(self):
assert_expected_ratio(volume=InfiniteCylinder(5))