def test_correctTimesLogged(self):
log = read(self.test_file_path)['log']
self.assertListEqual([2018, 1, 18, 16, 1, 0, 3, 18, -1],
list(log[0]['time'].timetuple()))
self.assertListEqual([2018, 1, 18, 16, 3, 0, 3, 18, -1],
list(log[1]['time'].timetuple()))
def test_correctPoints(self):
data = read(self.test_file_path)
points = data[keys.point]
point = np.array([
points['x']['data'][0], points['y']['data'][0],
points['z']['data'][0], points['return']['data'][0]
])
np.testing.assert_allclose(point, np.array([0.11, 0.12, 0.13, 1]))
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,
sample_size=500))
def test_correctModulesLogged(self):
log = read(self.test_file_path)['log']
modules = [entry['module'] for entry in log]
self.assertListEqual(['load', 'filter'], modules)
def test_allLogEntriesContainAllColumns(self):
log = read(self.test_file_path)['log']
for entry in log:
for key in ['time', 'module', 'parameters', 'version']:
self.assertIn(key, entry)
def test_correctPointCloudWithInvalidComments(self):
"""Invalid comments should not cause error."""
data = read(self.test_file_with_invalid_comments_path)
point_cloud = data['pointcloud']
offset = point_cloud['offset']['data'][0]
np.testing.assert_allclose(offset, 12.1)
def test_correctPointCloudWithoutComments(self):
"""Missing comment section should not cause error (regression test)."""
data = read(self.test_file_without_comments_path)
point_cloud = data['pointcloud']
offset = point_cloud['offset']['data'][0]
np.testing.assert_allclose(offset, 12.1)
def test_correctPointCloud(self):
data = read(self.test_file_path)
point_cloud = data['pointcloud']
offset = point_cloud['offset']['data'][0]
np.testing.assert_allclose(offset, 12.1)
def test_rightNumberOfPoints(self):
data = read(self.test_file_path)
self.assertEqual(len(data[keys.point]['x']['data']), 3)
def test_containsXElement(self):
data = read(self.test_file_path)
self.assertIn('x', data[keys.point])
def test_containsPointsElement(self):
data = read(self.test_file_path)
self.assertIn(keys.point, data)
def test_existentPly_noError(self):
read(self.test_file_path)
def test_wrongFormat_error(self):
with raises(ValueError):
read(self.las_file_path)
def test_nonexistentFile_error(self):
# Catch most specific subclass of FileNotFoundException (3.6) and IOError (2.7).
with raises(Exception):
read('nonexistentfile.ply')