wrong package fix and documentation

example_tree.py

@@ -19,13 +19,13 @@
               down_sample=0.03)
     lbc.extract_skeleton()
     lbc.extract_topology()
-    #lbc.visualize()
+    lbc.visualize()
     lbc.show_graph(lbc.skeleton_graph, fig_size=(30, 30))
     lbc.show_graph(lbc.topology_graph)
     lbc.export_results('./output')
-    # lbc.animate(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=500, output='./output')
-    # lbc.animate_contracted_pcd(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=300, output='./output')
-    #lbc.animate_topology(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=300, output='./output')
+    lbc.animate(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=500, output='./output_lbc')
+    #lbc.animate_contracted_pcd(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=300, output='./output')
+    lbc.animate_topology(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=300, output='./output')
 
     # Semantic Laplacian-based Contraction
     s_lbc = SLBC(point_cloud={'trunk': pcd_trunk, 'branches': pcd_branch},
@@ -35,10 +35,10 @@
                  down_sample=0.009)
     s_lbc.extract_skeleton()
     s_lbc.extract_topology()
-    #s_lbc.visualize()
-    s_lbc.show_graph(lbc.skeleton_graph, fig_size=(30, 30))
-    s_lbc.show_graph(lbc.topology_graph)
-    s_lbc.export_results('./output')
-    #s_lbc.animate(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=500, output='./output')
+    s_lbc.visualize()
+    s_lbc.show_graph(s_lbc.skeleton_graph, fig_size=(30, 30))
+    s_lbc.show_graph(s_lbc.topology_graph)
+    s_lbc.export_results('./output_slbc')
+    s_lbc.animate(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=500, output='./output')
     #s_lbc.animate_contracted_pcd(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=300, output='./output')
-    s_lbc.animate_topology(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=300, output='./output')
+    s_lbc.animate_topology(init_rot=np.asarray([[1, 0, 0], [0, 0, 1], [0, 1, 0]]), steps=300, output='./output_slbc')

pc_skeletor/laplacian.py

@@ -30,8 +30,8 @@ def __init__(self,
                  algo_type: str,
                  point_cloud: Union[str, open3d.geometry.PointCloud, dict],
                  init_contraction: int,
-                 init_attraction: int,
-                 max_contraction: int,
+                 init_attraction: float,
+                 max_contraction: float,
                  max_attraction: int,
                  step_wise_contraction_amplification: Union[float, str],
                  termination_ratio: float,
@@ -364,6 +364,18 @@ def __init__(self,
             o3d.visualization.draw_geometries([pcd], window_name="Default Point Cloud")
 
     def __least_squares_sparse(self, pcd_points, L, laplacian_weighting, positional_weighting):
+        """
+        Perform least squares sparse solving for the Laplacian-based contraction.
+
+        Args:
+            pcd_points: The input point cloud points.
+            L: The Laplacian matrix.
+            laplacian_weighting: The Laplacian weighting matrix.
+            positional_weighting: The positional weighting matrix.
+
+        Returns:
+            The contracted point cloud.
+        """
         # Define Weights
         WL = sparse.diags(laplacian_weighting)  # I * laplacian_weighting
         WH = sparse.diags(positional_weighting)
@@ -414,7 +426,7 @@ class SLBC(LaplacianBasedContractionBase):
 
     Our semantic skeletonization algorithm based on Laplacian-Based Contraction.
 
-    Paper: tbd
+    Paper: https://arxiv.org/abs/2304.04708
 
     """
 
@@ -464,6 +476,18 @@ def __init__(self,
             o3d.visualization.draw_geometries([self.pcd], window_name="Default Point Cloud")
 
     def __least_squares_sparse(self, pcd_points, L, laplacian_weighting, positional_weighting):
+        """
+        Perform least squares sparse solving for the Semantic Laplacian-Based Contraction (S-LBC).
+
+        Args:
+            pcd_points: The input point cloud points.
+            L: The Laplacian matrix.
+            laplacian_weighting: The Laplacian weighting matrix.
+            positional_weighting: The positional weighting matrix.
+
+        Returns:
+            The contracted point cloud.
+        """
         # Define Weights
         WL = sparse.diags(laplacian_weighting)  # I * laplacian_weighting
         WH = sparse.diags(positional_weighting)
@@ -484,6 +508,7 @@ def __least_squares_sparse(self, pcd_points, L, laplacian_weighting, positional_
         num_valid = np.arange(0, pcd_points.shape[0])[mask]
         S[rows, cols] = 1
 
+        # ToDo: Speed up!
         for i in num_valid:
             S[i, L[i].nonzero()[1]] = multiplier
 

requirements.txt

@@ -5,7 +5,7 @@ matplotlib
 open3d==0.16
 tqdm
 imageio
-networkx==2.8.8
+networkx==2.6.3
 charset-normalizer
 #mistree==1.2.1
 pytest

setup.py

@@ -41,17 +41,3 @@
     ],
     zip_safe=False,
 )
-
-#[
-#        'mistree @ git+https://github.com/meyerls/mistree.git',# "mistree==1.2.1",
-#        "numpy",
-#        "scipy",
-#        "matplotlib",
-#        "open3d==0.16",
-#        "robust_laplacian==0.2.4",
-#        "dgl",
-#        "torch",
-#        "tqdm",
-#        "imageio",
-#        "wget",
-#        "networkx==2.8.8"],