clustering_eps=150

handcrafted_solution.py

@@ -430,7 +430,7 @@ def prune_not_connected(all_3d_vertices, connections_3d):
     return np.array(new_verts), connected_out
 
 
-def predict(entry, visualize=False, scale_estimation_coefficient=2.5, **kwargs) -> Tuple[np.ndarray, List[int]]:
+def predict(entry, visualize=False, scale_estimation_coefficient=2.5, clustering_eps = 100, **kwargs) -> Tuple[np.ndarray, List[int]]:
     if 'gestalt' not in entry or 'depthcm' not in entry or 'K' not in entry or 'R' not in entry or 't' not in entry:
         print('Missing required fields in the entry')
         return (entry['__key__'], *empty_solution())
@@ -450,7 +450,7 @@ def predict(entry, visualize=False, scale_estimation_coefficient=2.5, **kwargs)
 
         # print(len(points))
 
-        clustered = DBSCAN(eps=100, min_samples=10).fit(points).labels_
+        clustered = DBSCAN(eps=clustering_eps, min_samples=10).fit(points).labels_
         clustered_indices = np.argsort(clustered)
 
         points = points[clustered_indices]

script.py

@@ -137,6 +137,7 @@ if __name__ == "__main__":
                                        merge_th=100.0,
                                        min_missing_distance=30000000.0,
                                        scale_estimation_coefficient=2.54,
+                                       clustering_eps=150,
                                        ))
         
         for i, result in enumerate(tqdm(results)):