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DFFastGlobalRegistration component

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DFFastGlobalRegistration component#

Register two point clouds together in a fast manner.

Inputs:

i_cloud_source (pointcloud ,item)

The source point cloud.

i_cloud_target (pointcloud ,item)

The target cloud.

i_radius_kd_search (float ,item)

The radius used to search for neighbors in the KDTree.it is expressed relative to the point cloud size (0.01 means radiusKDTreeSearch = 1% of maxSize(pointCloud). It is used for the calculation of FPFHFeatures.

i_neighbours_kd_search (int ,item)

The maximum number of neighbors to search for in the KDTree. It is used for the calculation of FPFHFeatures. A higher value will result in heavier computation but potentially more precise.

i_max_corrspondence_dist (float ,item)

The maximum distance between correspondences. A higher value will result in more correspondences, but potentially include wrong ones.

i_iteration_number (int ,item)

The number of iterations to run the RanSaC registration algorithm. A higher value will take more time to compute but increases the chances of finding a good transformation. As parameter of the FastGlobalRegistrationOption options.

i_max_tuple_count (int ,item)

The maximum number of tuples to consider in the FPFH hyperspace. A higher value will result in heavier computation but potentially more precise. As parameter of the FastGlobalRegistrationOption options.

Outputs:

o_x_form

The computed transformation.

Code:

#! python3


import Rhino
from ghpythonlib.componentbase import executingcomponent as component

from Grasshopper.Kernel import GH_RuntimeMessageLevel as RML

from diffCheck import diffcheck_bindings
from diffCheck import df_cvt_bindings


class DFFastGlobalRegistration(component):
    def RunScript(self,
            i_cloud_source: Rhino.Geometry.PointCloud,
            i_cloud_target: Rhino.Geometry.PointCloud,
            i_radius_kd_search: float,
            i_neighbours_kd_search: int,
            i_max_corrspondence_dist: float,
            i_iteration_number: int,
            i_max_tuple_count: int) -> Rhino.Geometry.Transform:
        if i_cloud_source is None or i_cloud_target is None:
            ghenv.Component.AddRuntimeMessage(RML.Warning, "Please provide both objects of type point clouds to align")  # noqa: F821
            return None
        if not i_cloud_source.ContainsNormals or not i_cloud_target.ContainsNormals:
            ghenv.Component.AddRuntimeMessage(RML.Error, "Please compute cloud's normals with a component before")  # noqa: F821

        # set default values
        if i_radius_kd_search is None:
            i_radius_kd_search = 0.8
        if i_neighbours_kd_search is None:
            i_neighbours_kd_search = 50
        if i_max_corrspondence_dist is None:
            i_max_corrspondence_dist = 0.05
        if i_iteration_number is None:
            i_iteration_number = 128
        if i_max_tuple_count is None:
            i_max_tuple_count = 1000

        df_cloud_source = df_cvt_bindings.cvt_rhcloud_2_dfcloud(i_cloud_source)
        df_cloud_target = df_cvt_bindings.cvt_rhcloud_2_dfcloud(i_cloud_target)

        df_xform = diffcheck_bindings.dfb_registrations.DFGlobalRegistrations.O3DFastGlobalRegistrationFeatureMatching(
            source=df_cloud_source,
            target=df_cloud_target,
            voxelize=False,  # set as default
            voxel_size=0.1,  # set as default
            radius_kd_tree_search=i_radius_kd_search,
            max_neighbor_kd_tree_search=i_neighbours_kd_search,
            max_correspondence_distance=i_max_corrspondence_dist,
            iteration_number=i_iteration_number,
            max_tuple_count=i_max_tuple_count
        )
        print("-------------------")
        print("Estimated transformation matrix:")
        print(df_xform.transformation_matrix)
        print("-------------------")

        # cvt df xform to rhino xform
        df_xform_matrix = df_xform.transformation_matrix
        rh_form = Rhino.Geometry.Transform()
        for i in range(4):
            for j in range(4):
                rh_form[i, j] = df_xform_matrix[i, j]
        if rh_form == Rhino.Geometry.Transform.Identity:
            ghenv.Component.AddRuntimeMessage(RML.Warning, "The transformation matrix is identity, no transformation is applied")  # noqa: F821
            return None

        o_x_form = rh_form

        return o_x_form
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