def test_write_sameFileTwice(self):
""" Should throw an exception. """
pc = SimpleTestData.get_point_cloud()
write(pc, self.test_file_path)
# Catch most specific subclass of FileExistsError (3.6) and IOError (2.7).
with pytest.raises(Exception):
write(pc, self.test_file_path)
def test_write_loadTheSameSimpleData(self):
""" Writing point cloud data and loading it afterwards should result in the same point cloud data. """
pc_in = SimpleTestData.get_point_cloud()
write(pc_in, self.test_file_path)
data_in = SimpleTestData.get_data()
with open(self.test_file_path, 'r') as ply:
data_out = read_data(ply)
self.assertEqual(data_in, data_out)
def test_write_loadTheSameSimpleHeader(self):
""" Writing a simple point cloud and loading it afterwards should result in the same point cloud."""
pc_in = SimpleTestData.get_point_cloud()
header_in = SimpleTestData.get_header()
write(pc_in, self.test_file_path)
with open(self.test_file_path, 'r') as ply:
header_out = read_header(ply)
self.assertMultiLineEqual(header_in, header_out)
neighbors = compute_neighborhoods(pc, target, Cell(np.float(args.radius)))
iteration = 0
target_idx_base = 0
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, [
'max_z', 'min_z', 'mean_z', 'median_z', 'std_z', 'var_z',
'coeff_var_z', 'skew_z', 'kurto_z', 'sigma_z', 'perc_20', 'perc_40',
'perc_60', 'perc_80', 'perc_90', 'pulse_penetration_ratio',
'point_density', 'eigenv_1', 'eigenv_2', 'eigenv_3'
], Cell(np.float(args.radius)))
target_idx_base += len(x)
end1 = time.time()
difftime1 = end1 - start1
print(("feature calc: %f sec") % (difftime1))
iteration += 1
write(target, args.output)
#
# example usage: computefea_wtargets_cylinder.py D:/GitHub/eEcoLiDAR/develop-branch/eEcoLiDAR/ D:/GitHub/eEcoLiDAR/myPhD_escience_analysis/test_data/testdata.las D:/GitHub/eEcoLiDAR/myPhD_escience_analysis/test_data/testdata.las 2.5 D:/GitHub/eEcoLiDAR/myPhD_escience_analysis/test_data/testdata.ply
#python D:/GitHub/eEcoLiDAR/myPhD_escience_analysis/test_laserchicken/release1/computefea_wtargets_cell.py D:/GitHub/eEcoLiDAR/develop-branch/eEcoLiDAR/ D:/Koma/Paper1_ReedStructure/Data/ALS/02gz2/testmetrics/tile_00003_norm2.las D:/Koma/Paper1_ReedStructure/Data/ALS/02gz2/testmetrics/tile_00003_norm_target.las 2.5 D:/Koma/Paper1_ReedStructure/Data/ALS/02gz2/testmetrics/testdata.ply
#
print("------ Computing neighborhood is started ------")
#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, Cell(np.float(args.radius)))
#
neighbors = compute_neighborhoods(pc, target, Cell(np.float(radius)))
iteration = 0
target_idx_base = 0
for x in neighbors:
print("Computed neighborhoods list length at iteration %d is: %d" %
(iteration, len(x)))
print("------ Feature calculation is started ------")
compute_features(pc, x, target_idx_base, target, [
'min_z', 'max_z', 'mean_z', 'median_z', 'perc_10', 'perc_30',
'perc_50', 'perc_70', 'perc_90', 'point_density', 'eigenv_1',
'eigenv_2', 'eigenv_3', 'z_entropy', 'std_z', 'var_z', 'skew_z',
'kurto_z', 'pulse_penetration_ratio', 'density_absolute_mean'
], Cell(np.float(radius)))
target_idx_base += len(x)
iteration += 1
write(target, filename + str(resolution) + "m_cell.ply")
end1 = time.time()
difftime1 = end1 - start1
print(("feature calc: %f sec") % (difftime1))
from laserchicken import read_las
from laserchicken.spatial_selections import points_in_polygon_wkt
from laserchicken import write_ply
pc = read_las.read("D:/GitHub/eEcoLiDAR/eEcoLiDAR/testdata/AHN2.las")
pc_out = points_in_polygon_wkt(
pc,
"POLYGON(( 243590.0 572110.0, 243640.0 572160.0, 243700.0 572110.0, 243640.0 572060.0, 243590.0 572110.0 ))"
)
write_ply.write(
pc,
"D:/GitHub/komazsofi/myPhD_escience_analysis/test_data/testply_orig1.ply")
write_ply.write(
pc_out,
"D:/GitHub/komazsofi/myPhD_escience_analysis/test_data/withinplygonply1.ply"
)
print(("Number of points in target: %s ") %
(target[point]['x']['data'].shape[0]))
print("------ Computing neighborhood is started ------")
#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, Cell(np.float(args.radius)))
#
neighbors = compute_neighborhoods(pc, target, Cell(np.float(radius)))
iteration = 0
target_idx_base = 0
for x in neighbors:
print("Computed neighborhoods list length at iteration %d is: %d" %
(iteration, len(x)))
print("------ Feature calculation is started ------")
compute_features(pc, x, target_idx_base, target, [
'max_z', 'min_z', 'mean_z', 'median_z', 'std_z', 'var_z',
'coeff_var_z', 'skew_z', 'kurto_z'
], Cell(np.float(radius)))
target_idx_base += len(x)
iteration += 1
write(target, workingdirectory + filename + "_cell_10m.ply")
end1 = time.time()
difftime1 = end1 - start1
print(("feature calc: %f sec") % (difftime1))
pc = read_las.read(args.input)
f = open(args.kdtree, 'rb')
kdtree = pickle.load(f)
f.close()
start1 = time.time()
compute_features(pc, kdtree, 0, pc, [
'max_z', 'min_z', 'mean_z', 'median_z', 'std_z', 'var_z', 'range',
'coeff_var_z', 'skew_z', 'kurto_z', 'sigma_z', 'perc_20', 'perc_40',
'perc_60', 'perc_80', 'pulse_penetration_ratio', 'density_absolute_mean'
], Sphere(np.float(args.radius)))
"""
feadataframe=pd.DataFrame({'_x':pc[point]['x']['data'],'_y':pc[point]['y']['data'],'_z':pc[point]['z']['data'],
'max_z':pc[point]['max_z']['data'],'min_z':pc[point]['min_z']['data'], 'mean_z':pc[point]['mean_z']['data'],
'median_z':pc[point]['median_z']['data'],'std_z':pc[point]['std_z']['data'], 'var_z':pc[point]['var_z']['data'],
'range':pc[point]['range']['data'],'coeff_var_z':pc[point]['coeff_var_z']['data'], 'skew_z':pc[point]['skew_z']['data'],'kurto_z':pc[point]['kurto_z']['data'],
'pulse_penetration_ratio':pc[point]['pulse_penetration_ratio']['data'],'density_absolute_mean':pc[point]['density_absolute_mean']['data'],
'sigma_z':pc[point]['sigma_z']['data'], 'perc_20':pc[point]['perc_20']['data'],
'perc_40':pc[point]['perc_40']['data'],'perc_60':pc[point]['perc_60']['data'], 'perc_80':pc[point]['perc_80']['data']})
feadataframe.to_csv(args.output,sep=";",index=False)
"""
end1 = time.time()
difftime1 = end1 - start1
print(("feature calc: %f sec") % (difftime1))
write(pc, args.output)
def load(input_las_path, output_ply_path):
pc = read_las.read(input_las_path)
write_ply.write(pc, output_ply_path)
def test_write_loadTheSame(self):
""" Writing a point cloud and loading it afterwards should result in the same point cloud. """
pc = generate_test_point_cloud()
write(pc, self.test_file_path)
loaded = read_ply(self.test_file_path)
np.testing.assert_allclose(loaded, pc)
def test_write_sameFileTwice(self):
""" Should not throw an exception. """
pc = generate_test_point_cloud()
write(pc, self.test_file_path)
write(pc, self.test_file_path)
def test_write_nonExistingFile(self):
""" Should create a file. """
pc = generate_test_point_cloud()
write(pc, self.test_file_path)
assert (os.path.exists(self.test_file_path))