feat: update and cleanup script with new registration step prior to association

Author: DamienGilliard

src/gh/components/DF_CAD_segmentator/code.py

@@ -9,7 +9,7 @@
 
 
 from diffCheck.diffcheck_bindings import dfb_segmentation
-from diffCheck.diffcheck_bindings import dfb_geometry, dfb_registrations, dfb_transformation
+from diffCheck.diffcheck_bindings import dfb_geometry, dfb_registrations
 
 from diffCheck import df_cvt_bindings
 
@@ -24,7 +24,7 @@ def RunScript(self,
             i_angle_association_threshold: float,
             i_maximum_face_segment_distance: float,
             i_radius_normal_estimation: float,
-            i_max_correspondence_distance_icp: float,):
+            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.")
@@ -38,60 +38,17 @@ def RunScript(self,
         if i_radius_normal_estimation is None:
             i_radius_normal_estimation = 0.01
         o_face_clusters = []
-        df_clusters_temp = []
+        o_transforms = []
         df_clusters = []
         # we make a deepcopy of the input clouds
-        df_clouds_copy = [df_cvt_bindings.cvt_rhcloud_2_dfcloud(cloud.Duplicate()) for cloud in i_clouds]
         df_clouds = [df_cvt_bindings.cvt_rhcloud_2_dfcloud(cloud.Duplicate()) for cloud in i_clouds]
         df_merged_cloud = dfb_geometry.DFPointCloud()
+        df_merged_cloud.remove_statistical_outliers(100, 1.5)
         for pc in df_clouds:
             df_merged_cloud.add_points(pc)
 
         df_beams = i_assembly.beams
-        df_asssociated_cluster_faces_per_beam = []
-
-        for df_b in df_beams:
-            rh_b_mesh_faces = [df_b_f.to_mesh() for df_b_f in df_b.side_faces]
-            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_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_copy,
-                angle_threshold=i_angle_threshold,
-                association_threshold=i_association_threshold ,
-                angle_association_threshold=i_angle_association_threshold,
-                maximum_face_segment_distance=i_maximum_face_segment_distance
-            )
-            df_asssociated_cluster_faces_per_beam.append(df_asssociated_cluster_faces)
-
-        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]
-            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_copy,
-                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 ,
-                angle_association_threshold=i_angle_association_threshold,
-                maximum_face_segment_distance=i_maximum_face_segment_distance
-            )
-
-            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_temp.append(df_asssociated_cluster)
 
-        # Now with the df_clusters, we sample a point cloud on the beam of the assembly, and perform an ICP to align the beam mesh to the point cloud.
-        # Then we re-compute the association on the scan with thigher thresholds to have a better segmentation.
-        o_transforms = []
         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]
@@ -106,30 +63,22 @@ def RunScript(self,
             df_sampled_cloud.estimate_normals(use_cilantro_evaluator=False,
             search_radius = i_radius_normal_estimation,
             )
-            cluster_without_normals = df_clusters_temp[i]
-            cluster_without_normals.estimate_normals(use_cilantro_evaluator=False,
-            search_radius = i_radius_normal_estimation,
-            )
-            df_merged_cloud.remove_statistical_outliers(100, 1.5)
-            self.AddRuntimeMessage(RML.Warning, f"size of pc: {cluster_without_normals.get_num_points()}")
-            if cluster_without_normals.get_num_points() != 0:
-                transform = dfb_registrations.DFRefinedRegistration.O3DICP(
-                    source=df_sampled_cloud,
-                    target=df_merged_cloud,
-                    max_correspondence_distance= i_max_correspondence_distance_icp,
-                    max_iteration = 1000
 
+            transform = dfb_registrations.DFRefinedRegistration.O3DICP(
+                source=df_sampled_cloud,
+                target=df_merged_cloud,
+                max_correspondence_distance= i_max_correspondence_distance_icp,
+                max_iteration = 1000
                 )
-            else:
-                transform = dfb_transformation.DFTransformation()
+
             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]
             o_transforms.append(rh_xform)
 
-        df_new_asssociated_cluster_faces_per_beam = []
+        df_asssociated_cluster_faces_per_beam = []
         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]
             rh_test_mesh = Rhino.Geometry.Mesh()
@@ -151,7 +100,7 @@ def RunScript(self,
                 angle_association_threshold=i_angle_association_threshold,
                 maximum_face_segment_distance=i_maximum_face_segment_distance
             )
-            df_new_asssociated_cluster_faces_per_beam.append(df_new_asssociated_cluster_faces)
+            df_asssociated_cluster_faces_per_beam.append(df_new_asssociated_cluster_faces)
 
         for i, df_b in enumerate(df_beams):
             o_face_clusters.append([])
@@ -164,23 +113,22 @@ def RunScript(self,
                 is_roundwood=df_b.is_roundwood,
                 discriminate_points=True,
                 unassociated_clusters=df_clouds,
-                associated_clusters=[df_new_asssociated_cluster_faces_per_beam[i]],
+                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,
                 angle_association_threshold=i_angle_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_new_asssociated_cluster_faces_per_beam[i]]
+            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_new_asssociated_cluster_faces_per_beam[i]:
+            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_intermediary_clouds = [df_cvt_bindings.cvt_dfcloud_2_rhcloud(cluster) for cluster in df_clusters_temp]
         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):
@@ -190,4 +138,4 @@ def RunScript(self,
 
         o_face_clouds = th.list_to_tree(o_face_clusters)
 
-        return [o_beam_clouds, o_face_clouds, o_transforms, o_beam_intermediary_clouds, rh_meshes]
+        return [o_beam_clouds, o_face_clouds]