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DFJointSegmentator component

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DFJointSegmentator component#

Extracts the joints from a point cloud.

Inputs:

i_clusters (pointcloud ,list)

The cluster point clouds from normal segmentation.

i_assembly (ghdoc ,item)

The assembly to extract the joints from

i_angle_threshold (float ,item)

From 0 to 1, it’s the sin value. By default 0.1. The closer to 0 the less permissive and viceversa to 1.

i_distance_threshold (float ,item)

From 0 to infinite. By default 0.1. The closer to 0 the less permissive your point.

i_correspondence_distance (float ,item)

The maximum correspondence distance for the local ICP registration that re-aligns the joints for per-face analysis of each joint. The default value is 0.005

Outputs:

o_joint_face_segments

The individual faces of the extracted joints.
o_joint_segments

The extracted joints.
o_reference_point_clouds

The reference point clouds of the joints. They are generated from the

Code:

#! python3

import System

import Rhino
import ghpythonlib.treehelpers

from diffCheck import diffcheck_bindings
from diffCheck import df_cvt_bindings as df_cvt

from ghpythonlib.componentbase import executingcomponent as component


ABSTOL = Rhino.RhinoDoc.ActiveDoc.ModelAbsoluteTolerance

class DFJointSegmentator(component):
    def __init__(self):
        super(DFJointSegmentator, self).__init__()
    def RunScript(self,
            i_clusters: System.Collections.Generic.List[Rhino.Geometry.PointCloud],
            i_assembly,
            i_angle_threshold: float,
            i_distance_threshold: float,
            i_correspondence_distance: float,):

        if i_clusters is None or i_assembly is None:
            return None
        if i_angle_threshold is None:
            i_angle_threshold = 0.1
        if i_distance_threshold is None:
            i_distance_threshold = 0.1
        if i_correspondence_distance is None:
            i_correspondence_distance = 0.005
        if len(i_clusters) == 0:
            raise ValueError("No clusters given.")
        if not isinstance(i_clusters[0], Rhino.Geometry.PointCloud):
            raise ValueError("The input clusters must be PointClouds.")

        # get number of joints
        n_joints = i_assembly.total_number_joints

        # prepping the reference meshes
        df_joints = [[] for _ in range(n_joints)]
        for joint in i_assembly.all_joints:
            for face in joint.faces:
                face = face.to_mesh()
                face.Subdivide()
                face.Faces.ConvertQuadsToTriangles()
                df_joints[joint.id].append(df_cvt.cvt_rhmesh_2_dfmesh(face))

        o_reference_point_clouds = []
        o_joint_faces_segments = []
        df_cloud_clusters = [df_cvt.cvt_rhcloud_2_dfcloud(cluster) for cluster in i_clusters]
        df_joint_clouds = []

        # for each joint, find the corresponding faces, store them as such but also merge them, generate a reference point cloud, and register the merged clusters to the reference point cloud
        for df_joint in df_joints:
            rh_joint_faces_segments = []

            # create the reference point cloud
            ref_df_joint_cloud = diffcheck_bindings.dfb_geometry.DFPointCloud()
            for face in df_joint:
                ref_face_cloud = face.sample_points_uniformly(1000)
                ref_df_joint_cloud.add_points(ref_face_cloud)
            o_reference_point_clouds.append(df_cvt.cvt_dfcloud_2_rhcloud(ref_df_joint_cloud))

            # find the corresponding clusters and merge them
            df_joint_cloud = diffcheck_bindings.dfb_geometry.DFPointCloud()
            df_joint_face_segments = diffcheck_bindings.dfb_segmentation.DFSegmentation.associate_clusters(df_joint, df_cloud_clusters, i_angle_threshold, i_distance_threshold)
            for df_joint_face_segment in df_joint_face_segments:
                        df_joint_cloud.add_points(df_joint_face_segment)

            # register the joint faces to the reference point cloud
            transform = diffcheck_bindings.dfb_registrations.DFRefinedRegistration.O3DICP(df_joint_cloud, ref_df_joint_cloud, max_correspondence_distance = i_correspondence_distance)
            for df_joint_face_segment in df_joint_face_segments:
                df_joint_face_segment.apply_transformation(transform)
                rh_joint_faces_segments.append(df_cvt.cvt_dfcloud_2_rhcloud(df_joint_face_segment))
            df_joint_clouds.append(df_joint_cloud)
            o_joint_faces_segments.append(rh_joint_faces_segments)

        o_joint_segments = [df_cvt.cvt_dfcloud_2_rhcloud(df_joint_cloud) for df_joint_cloud in df_joint_clouds]

        o_gh_tree_joint_faces_segments = ghpythonlib.treehelpers.list_to_tree(o_joint_faces_segments)

        return o_gh_tree_joint_faces_segments, o_joint_segments, o_reference_point_clouds
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