Metric fix

hoho/wed.py

@@ -2,9 +2,20 @@ from scipy.spatial.distance import cdist
 from scipy.optimize import linear_sum_assignment
 import numpy as np 
 
+
+def zeromean_normalize(vertices):
+    vertices = np.array(vertices)
+    vertices = vertices - vertices.mean(axis=0)
+    vertices = vertices / (1e-6 + np.linalg.norm(vertices, axis=1)[:, None])
+    return vertices
+
+
 def compute_WED(pd_vertices, pd_edges, gt_vertices, gt_edges, cv=1.0, ce=1.0, normalized=True, squared=False):
     pd_vertices = np.array(pd_vertices)
     gt_vertices = np.array(gt_vertices)
+    pd_vertices = zeromean_normalize(pd_vertices)
+    gt_vertices = zeromean_normalize(gt_vertices)
+
     pd_edges = np.array(pd_edges)
     gt_edges = np.array(gt_edges)
     
@@ -15,7 +26,6 @@ def compute_WED(pd_vertices, pd_edges, gt_vertices, gt_edges, cv=1.0, ce=1.0, no
         distances = cdist(pd_vertices, gt_vertices, metric='euclidean')
 
     row_ind, col_ind = linear_sum_assignment(distances)
-    
     # Step 2: Vertex Translation
     
     if squared:
@@ -32,13 +42,18 @@ def compute_WED(pd_vertices, pd_edges, gt_vertices, gt_edges, cv=1.0, ce=1.0, no
     insertion_costs = cv * len(unmatched_gt_indices)  # Assuming a fixed cost for vertex insertion
     
     # Step 4: Edge Deletion and Insertion
-    updated_pd_edges = [(row_ind[np.where(col_ind == edge[0])[0][0]], row_ind[np.where(col_ind == edge[1])[0][0]]) for edge in pd_edges if edge[0] in col_ind and edge[1] in col_ind]
-    pd_edges_set = set(map(tuple, updated_pd_edges))
-    gt_edges_set = set(map(tuple, gt_edges))
+    updated_pd_edges = [(col_ind[np.where(row_ind == edge[0])[0][0]], col_ind[np.where(row_ind == edge[1])[0][0]]) for edge in pd_edges if edge[0] in row_ind and edge[1] in row_ind]
+    pd_edges_set = set(map(tuple, [set(edge) for edge in updated_pd_edges]))
+    gt_edges_set = set(map(tuple, [set(edge) for edge in gt_edges]))
+
     
     # Delete edges not in ground truth
     edges_to_delete = pd_edges_set - gt_edges_set
-    deletion_edge_costs = ce * sum(np.linalg.norm(pd_vertices[edge[0]] - pd_vertices[edge[1]]) for edge in edges_to_delete)
+    
+    #deletion_edge_costs = ce * sum(np.linalg.norm(pd_vertices[edge[0]] - pd_vertices[edge[1]]) for edge in edges_to_delete)
+    vert_tf = [np.where(col_ind == v)[0][0] if v in col_ind else 0 for v in range(len(gt_vertices))]
+    deletion_edge_costs = ce * sum(np.linalg.norm(pd_vertices[vert_tf[edge[0]]] - pd_vertices[vert_tf[edge[1]]]) for edge in edges_to_delete)
+
     
     # Insert missing edges from ground truth
     edges_to_insert = gt_edges_set - pd_edges_set
@@ -46,9 +61,11 @@ def compute_WED(pd_vertices, pd_edges, gt_vertices, gt_edges, cv=1.0, ce=1.0, no
     
     # Step 5: Calculation of WED
     WED = translation_costs + deletion_costs + insertion_costs + deletion_edge_costs + insertion_edge_costs
+    print ("translation_costs, deletion_costs, insertion_costs, deletion_edge_costs, insertion_edge_costs")
+    print (translation_costs, deletion_costs, insertion_costs, deletion_edge_costs, insertion_edge_costs)
     
     if normalized:
         total_length_of_gt_edges = np.linalg.norm((gt_vertices[gt_edges[:, 0]] - gt_vertices[gt_edges[:, 1]]), axis=1).sum()
         WED = WED / total_length_of_gt_edges
-       
+    print ("Total length", total_length_of_gt_edges)
     return WED

requirements.txt

@@ -3,4 +3,6 @@ pillow
 webdataset
 trimesh
 scipy
-datasets
+datasets
+pycolmap
+plotly

setup.py

@@ -6,7 +6,7 @@ with open('requirements.txt') as f:
     required = f.read().splitlines()
 
 setup(name='hoho',
-	version='0.0.2',
+	version='0.0.3',
 	description='Tools and utilites for the HoHo Dataset and S23DR Competition',
 	url='usm3d.github.io',
 	author='Jack Langerman, Dmytro Mishkin, S23DR Orgainizing Team',