optional result visualization

Author: mhubii

README.md

@@ -1,4 +1,4 @@
-# Roboreg
+# roboreg
 [![Unit tests](https://github.com/lbr-stack/roboreg/actions/workflows/tests.yaml/badge.svg)](https://github.com/lbr-stack/roboreg/actions/workflows/tests.yaml)
 [![PyPI version](https://img.shields.io/pypi/v/roboreg.svg)](https://pypi.org/project/roboreg/)
 [![License: Apache License 2.0](https://img.shields.io/github/license/lbr-stack/roboreg)](https://github.com/lbr-stack/roboreg?tab=Apache-2.0-1-ov-file)
@@ -160,6 +160,7 @@ rr-hydra \
     --root-link-name lbr_link_0 \
     --end-link-name lbr_link_7 \
     --number-of-points 5000 \
+    --display-results \
     --output-file HT_hydra_robust.npy
 ```
 
@@ -295,6 +296,7 @@ rr-hydra \
     --root-link-name link_base \
     --end-link-name link7 \
     --number-of-points 5000 \
+    --display-results \
     --output-file HT_hydra_robust.npy
 ```
 

cli/rr_hydra.py

@@ -14,7 +14,6 @@
     parse_hydra_data,
 )
 from roboreg.util import (
-    RegistrationVisualizer,
     clean_xyz,
     compute_vertex_normals,
     depth_to_xyz,
@@ -159,6 +158,11 @@ def args_factory() -> argparse.Namespace:
         default=10,
         help="Erosion kernel size for mask boundary. Larger value will result in larger boundary. The closer the robot, the larger the recommended kernel size.",
     )
+    parser.add_argument(
+        "--display-results",
+        action="store_true",
+        help="Display point cloud registration results.",
+    )
     validate_urdf_source(parser, parser.parse_args())
     return parser.parse_args()
 
@@ -293,13 +297,16 @@ def main():
     )
 
     # visualize
-    visualizer = RegistrationVisualizer()
-    visualizer(mesh_vertices=mesh_vertices, observed_vertices=observed_vertices)
-    visualizer(
-        mesh_vertices=mesh_vertices,
-        observed_vertices=observed_vertices,
-        HT=torch.linalg.inv(HT),
-    )
+    if args.display_results:
+        from roboreg.util import RegistrationVisualizer
+
+        visualizer = RegistrationVisualizer()
+        visualizer(mesh_vertices=mesh_vertices, observed_vertices=observed_vertices)
+        visualizer(
+            mesh_vertices=mesh_vertices,
+            observed_vertices=observed_vertices,
+            HT=torch.linalg.inv(HT),
+        )
 
     # to numpy
     HT = HT.cpu().numpy()