pointcloud_depth_coeff=1.05

handcrafted_solution.py

@@ -454,7 +454,7 @@ 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, clustering_eps=100, dist_coeff=0, **kwargs) -> Tuple[
+def predict(entry, visualize=False, scale_estimation_coefficient=2.5, clustering_eps=100, dist_coeff=0, pointcloud_depth_coeff = 1, **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')
@@ -525,7 +525,7 @@ def predict(entry, visualize=False, scale_estimation_coefficient=2.5, clustering
             homo_belonging_points = cv2.convertPointsToHomogeneous(np.array([i.xyz for i in belonging_points]))
             depth = cv2.convertPointsFromHomogeneous(cv2.transform(homo_belonging_points, world_to_cam))
             depth = depth[:, 0, 2]
-            depth = depth[important[0]]
+            depth = depth[important[0]] * pointcloud_depth_coeff
             projected2d = projected2d[important]
             if len(depth) < 1:
                 print(f'No 3D points in image {i}')

script.py

@@ -139,6 +139,7 @@ if __name__ == "__main__":
                                        scale_estimation_coefficient=2.54,
                                        clustering_eps=100,
                                        dist_coeff=0.1,
+                                       pointcloud_depth_coeff=1.05,
                                        ))
         
         for i, result in enumerate(tqdm(results)):