nan removal

handcrafted_solution.py

@@ -682,10 +682,11 @@ def predict(entry, visualize=False,
         vertices_3d = cv2.transform(cv2.convertPointsToHomogeneous(vertices_3d_local), cam_to_world)
         vertices_3d = cv2.convertPointsFromHomogeneous(vertices_3d).reshape(-1, 3)
 
-        # not_nan_items = np.all(~np.isnan(vertices_3d), axis=1)
-        # _, closest_fitted = points3d_kdtree.query(vertices_3d[not_nan_items])
+        if not depthmap_used:
+            not_nan_items = np.all(~np.isnan(vertices_3d), axis=1)
+            _, closest_fitted = points3d_kdtree.query(vertices_3d[not_nan_items])
 
-        # vertices_3d[not_nan_items] = points3d_kdtree.data[closest_fitted]
+            vertices_3d[not_nan_items] = points3d_kdtree.data[closest_fitted]
 
         vert_edge_per_image[i] = vertices, connections, vertices_3d
     all_3d_vertices, connections_3d = merge_vertices_3d(vert_edge_per_image, **kwargs)