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feature_solution.py

@@ -624,7 +624,6 @@ def predict(entry, visualize=False) -> Tuple[np.ndarray, List[int]]:
         # save_image_with_lines(f'line_{i}.png', np.array(good_entry['gestalt'][i]), line_0, (255, 0, 0))
         # save_image_with_lines(f'line_{j}.png', np.array(good_entry['gestalt'][j]), line_1, (255, 0, 0))
 
-
         # Triangulation with matched keypoints
         R_0 = good_entry['R'][i]
         t_0 = good_entry['t'][i]
@@ -639,13 +638,13 @@ def predict(entry, visualize=False) -> Tuple[np.ndarray, List[int]]:
         connections = [(0,1)]
         # ours method
         vert_edge_per_image[i] = connections, points_3d
-
         
     all_3d_vertices, connections_3d = merge_vertices_3d_ours(vert_edge_per_image, 3.0)
 
     # all_3d_vertices_clean, connections_3d_clean  = prune_not_connected(all_3d_vertices, connections_3d)
     all_3d_vertices_clean = all_3d_vertices
     connections_3d_clean = connections
+
     if (len(all_3d_vertices_clean) < 2) or len(connections_3d_clean) < 1:
         print (f'Not enough vertices or connections in the 3D vertices')
         return (good_entry['__key__'], *empty_solution())