def test_difference_of_normals_estimation_simple():
pc = pclpy.read(test_data("street.las"), "PointXYZRGBA")
pc_subsampled = pclpy.octree_voxel_downsample(pc, 0.05)
subsampled = pclpy.octree_voxel_downsample(pc_subsampled, 0.4)
normals_large = pcl.PointCloud.PointNormal()
pc_subsampled.compute_normals(radius=4,
output_cloud=normals_large,
num_threads=8,
search_surface=subsampled)
normals_small = pcl.PointCloud.PointNormal()
pc_subsampled.compute_normals(radius=0.10,
output_cloud=normals_small,
num_threads=8)
don = pcl.features.DifferenceOfNormalsEstimation.PointXYZRGBA_PointNormal_PointNormal(
)
don.setInputCloud(pc_subsampled)
don.setNormalScaleLarge(normals_large)
don.setNormalScaleSmall(normals_small)
output = pcl.PointCloud.PointNormal(pc_subsampled.xyz)
don.computeFeature(output)
indices = np.argwhere(output.curvature > 0.4)
output2 = pcl.PointCloud.PointNormal(output, pcl.vectors.Int(indices))
clusters = pclpy.extract_clusters(pcl.PointCloud.PointXYZ(output2.xyz),
0.1, 50, 100000)
assert len(clusters) == 32
def test_fpfh_simple():
pc = pclpy.read(test_data("street.las"), "PointXYZ")
pc = pclpy.octree_voxel_downsample(pc, 0.25)
normals = pc.compute_normals(radius=0.5, num_threads=8)
fpfh = pcl.features.FPFHEstimation.PointXYZ_Normal_FPFHSignature33()
fpfh.setInputCloud(pc)
fpfh.setInputNormals(normals)
fpfh.setRadiusSearch(0.5)
fpfhs = pcl.PointCloud.FPFHSignature33()
fpfh.compute(fpfhs)
expected_bottom_right = np.array([
[6.2739134, 2.528063, 2.078603],
[1.3269598, 1.0290794, 0.27428713],
[0., 0., 0.],
])
assert np.allclose(fpfhs.histogram[-3:, -3:], expected_bottom_right)
def test_voxel_centroid_api_rgba():
pc = pclpy.read(test_data("street_thinned.las"), "PointXYZRGBA")
output = pclpy.octree_voxel_downsample(pc, resolution=0.3, centroids=False)
assert output.size() == 3148