DFCADSegmentator component#
Inputs:
|
The point cloud clusters already segmented by normals. |
|
The DFAssembly object. |
|
From 0 to 1, it’s the sin value. By default 0.1. The closer to 0 the less permissive and viceversa to 1. |
|
From 0 to infinite. By default 0.1. The closer to 0 the less permissive your point. |
|
The maximum correspondence distance for the segmentation. Default is 0.1 |
|
The radius used for normal estimation. Default is 0.01 |
|
Whether to make a registration of the clusters on the mesh faces. Default is True. |
|
The maximum correspondence distance for the ICP algorithm. Default is 0.1 |
Outputs:
o_beam_clouds |
The merged clouds associated to each beam. |
o_face_clouds |
The datatree of clouds associated to each face. |
o_poses_from_icp |
The list of poses resulting from the ICP registration. If i_make_registration is False, this output will contain the original assembly beam planes (no ICP applied). |
Code:
#! python3
import System
import Rhino
from ghpythonlib.componentbase import executingcomponent as component
from Grasshopper.Kernel import GH_RuntimeMessageLevel as RML
import ghpythonlib.treehelpers as th
from diffCheck.diffcheck_bindings import dfb_segmentation
from diffCheck.diffcheck_bindings import dfb_geometry, dfb_registrations
from diffCheck import df_cvt_bindings
class DFCADSegmentator(component):
def RunScript(self,
i_clouds: System.Collections.Generic.List[Rhino.Geometry.PointCloud],
i_assembly,
i_angle_threshold: float,
i_association_threshold: float,
i_maximum_face_segment_distance: float,
i_radius_normal_estimation: float,
i_make_registration: bool,
i_max_correspondence_distance_icp: float):
if i_clouds is None or i_assembly is None:
self.AddRuntimeMessage(RML.Warning, "Please provide a cloud and an assembly to segment.")
return None
if i_angle_threshold is None:
i_angle_threshold = 0.1
if i_association_threshold is None:
i_association_threshold = 0.1
if i_radius_normal_estimation is None:
i_radius_normal_estimation = 0.01
if i_make_registration is None:
i_make_registration = True
if i_max_correspondence_distance_icp is None:
i_max_correspondence_distance_icp = 0.1
if i_maximum_face_segment_distance is None:
i_maximum_face_segment_distance = 0.1
o_face_clusters = []
o_poses_from_icp = []
transforms = []
df_clusters = []
# we make a deepcopy of the input clouds
df_clouds = [df_cvt_bindings.cvt_rhcloud_2_dfcloud(cloud.Duplicate()) for cloud in i_clouds]
df_merged_cloud = dfb_geometry.DFPointCloud()
for pc in df_clouds:
df_merged_cloud.add_points(pc)
df_merged_cloud.remove_statistical_outliers(100, 1.5)
df_beams = i_assembly.beams
rh_meshes = []
for i, df_b in enumerate(df_beams):
rh_b_mesh_faces = [df_b_f.to_mesh() for df_b_f in df_b.side_faces]
for df_j_face in df_b.joint_faces:
rh_b_mesh_faces.append(df_j_face.to_mesh())
rh_mesh = Rhino.Geometry.Mesh()
for rh_b_mesh_face in rh_b_mesh_faces:
rh_mesh.Append(rh_b_mesh_face)
df_b_mesh = df_cvt_bindings.cvt_rhmesh_2_dfmesh(rh_mesh)
df_sampled_cloud = df_b_mesh.sample_points_uniformly(1000)
df_sampled_cloud.estimate_normals(use_cilantro_evaluator=False,
search_radius = i_radius_normal_estimation,
)
if i_make_registration:
transform = dfb_registrations.DFRefinedRegistration.O3DICP(
source=df_sampled_cloud,
target=df_merged_cloud,
max_correspondence_distance= i_max_correspondence_distance_icp,
max_iteration = 1000
)
df_xform = transform.transformation_matrix
rh_xform = Rhino.Geometry.Transform()
for i in range(4):
for j in range(4):
rh_xform[i, j] = df_xform[i, j]
else:
rh_xform = Rhino.Geometry.Transform(1)
transforms.append(rh_xform)
df_asssociated_cluster_faces_per_beam = []
for i, df_b in enumerate(df_beams):
beam_detected_pose = Rhino.Geometry.Plane(df_b.plane)
if beam_detected_pose.Transform(transforms[i]):
o_poses_from_icp.append(beam_detected_pose)
else:
o_poses_from_icp.append(None)
rh_b_mesh_faces = [df_b_f.to_mesh() for df_b_f in df_b.side_faces]
rh_test_mesh = Rhino.Geometry.Mesh()
for j in range(len(rh_b_mesh_faces)):
sucess = rh_b_mesh_faces[j].Transform(transforms[i])
if sucess:
rh_test_mesh.Append(rh_b_mesh_faces[j])
rh_meshes.append(rh_test_mesh)
df_b_mesh_faces = [df_cvt_bindings.cvt_rhmesh_2_dfmesh(rh_b_mesh_face) for rh_b_mesh_face in rh_b_mesh_faces]
# different association depending on the type of beam
df_new_asssociated_cluster_faces = dfb_segmentation.DFSegmentation.associate_clusters(
is_roundwood=df_b.is_roundwood,
discriminate_points=True,
reference_mesh=df_b_mesh_faces,
unassociated_clusters=df_clouds,
angle_threshold=i_angle_threshold,
association_threshold=i_association_threshold,
maximum_face_segment_distance=i_maximum_face_segment_distance
)
df_asssociated_cluster_faces_per_beam.append(df_new_asssociated_cluster_faces)
for i, df_b in enumerate(df_beams):
o_face_clusters.append([])
rh_b_mesh_faces = [df_b_f.to_mesh() for df_b_f in df_b.side_faces]
for j in range(len(rh_b_mesh_faces)):
rh_b_mesh_faces[j].Transform(transforms[i])
df_b_mesh_faces = [df_cvt_bindings.cvt_rhmesh_2_dfmesh(rh_b_mesh_face) for rh_b_mesh_face in rh_b_mesh_faces]
dfb_segmentation.DFSegmentation.clean_unassociated_clusters(
is_roundwood=df_b.is_roundwood,
discriminate_points=True,
unassociated_clusters=df_clouds,
associated_clusters=[df_asssociated_cluster_faces_per_beam[i]],
reference_mesh=[df_b_mesh_faces],
angle_threshold=i_angle_threshold,
association_threshold=i_association_threshold,
maximum_face_segment_distance=i_maximum_face_segment_distance
)
o_face_clusters[-1] = [df_cvt_bindings.cvt_dfcloud_2_rhcloud(cluster) for cluster in df_asssociated_cluster_faces_per_beam[i]]
df_asssociated_cluster = dfb_geometry.DFPointCloud()
for df_associated_face in df_asssociated_cluster_faces_per_beam[i]:
df_asssociated_cluster.add_points(df_associated_face)
df_clusters.append(df_asssociated_cluster)
o_beam_clouds = [df_cvt_bindings.cvt_dfcloud_2_rhcloud(cluster) for cluster in df_clusters]
for i, o_beam_cloud in enumerate(o_beam_clouds):
if not o_beam_cloud.IsValid:
o_beam_clouds[i] = None
ghenv.Component.AddRuntimeMessage(RML.Warning, "Some beams could not be segmented and were replaced by 'None'") # noqa: F821
o_face_clouds = th.list_to_tree(o_face_clusters)
return [o_beam_clouds, o_face_clouds, o_poses_from_icp]