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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)
# Step 1: Bipartite Matching
if squared:
distances = cdist(pd_vertices, gt_vertices, metric='sqeuclidean')
else:
distances = cdist(pd_vertices, gt_vertices, metric='euclidean')
row_ind, col_ind = linear_sum_assignment(distances)
# Step 2: Vertex Translation
if squared:
translation_costs = cv * np.sqrt(np.sum(distances[row_ind, col_ind]))
else:
translation_costs = cv * np.sum(distances[row_ind, col_ind])
# Additional: Vertex Deletion
unmatched_pd_indices = set(range(len(pd_vertices))) - set(row_ind)
deletion_costs = cv * len(unmatched_pd_indices) # Assuming a fixed cost for vertex deletion
# Step 3: Vertex Insertion
unmatched_gt_indices = set(range(len(gt_vertices))) - set(col_ind)
insertion_costs = cv * len(unmatched_gt_indices) # Assuming a fixed cost for vertex insertion
# Step 4: Edge Deletion and Insertion
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)
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
insertion_edge_costs = ce * sum(np.linalg.norm(gt_vertices[edge[0]] - gt_vertices[edge[1]]) for edge in edges_to_insert)
# 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 |