robokudo.annotators.region_filter
=================================

.. py:module:: robokudo.annotators.region_filter

.. autoapi-nested-parse::

   Point cloud filtering based on semantic regions.

   This module provides an annotator for:

   * Filtering point clouds using semantic map regions
   * Supporting regions in world and local frames
   * Creating region hypotheses with poses
   * Visualizing filtered regions and clouds

   The module uses:

   * Semantic maps to define regions of interest
   * Frame transformations for region alignment
   * Oriented bounding boxes for point filtering

   .. note::
      Regions can be defined in either world frame or local frames.



Classes
-------

.. autoapisummary::

   robokudo.annotators.region_filter.RegionFilter


Module Contents
---------------

.. py:class:: RegionFilter(name='RegionFilter', descriptor=Descriptor())

   Bases: :py:obj:`robokudo.annotators.core.ThreadedAnnotator`


   Point cloud filtering using semantic map regions.

   The RegionFilter can be used to filter point clouds based on a environment model based on different
   regions. These regions are collected in a 'SemanticMap' which has one 'SemanticMapEntry' per region of interest.
   Semantics to these regions are linked by referencing well-known names from your URDF and/or knowledge base.

   .. note::
      Regions are defined in a SemanticMap with SemanticMapEntry objects.


   .. py:class:: Descriptor

      Bases: :py:obj:`robokudo.annotators.core.BaseAnnotator.Descriptor`


      Configuration descriptor for region filtering.


      .. py:class:: Parameters

         Parameters for configuring region filtering.

         Frame parameters:

         :ivar world_frame_name: Name of the world coordinate frame, defaults to "map"
         :type world_frame_name: str

         Semantic map parameters:

         :ivar semantic_map_ros_package: ROS package containing semantic map, defaults to "robokudo"
         :type semantic_map_ros_package: str
         :ivar semantic_map_name: Name of semantic map module, defaults to "semantic_map_iai_kitchen"
         :type semantic_map_name: str

         Region selection:

         :ivar active_region: Name of active region to filter, empty for all regions
         :type active_region: str


         .. py:attribute:: world_frame_name
            :value: 'map'



         .. py:attribute:: semantic_map_ros_package
            :value: 'robokudo'



         .. py:attribute:: semantic_map_name
            :value: 'semantic_map_iai_kitchen'



         .. py:attribute:: active_region
            :value: ''




      .. py:attribute:: parameters



   .. py:attribute:: world_frame_name


   .. py:attribute:: semantic_map
      :value: None



   .. py:attribute:: active_region


   .. py:method:: load_semantic_map() -> None

      Load semantic map from configured package and module.

      Uses ModuleLoader to dynamically load the semantic map module.



   .. py:method:: compute()

      Filter point cloud using semantic map regions.

      The method:

      * Loads point cloud and query from CAS
      * Updates semantic map and active regions
      * For each active region:
        * Transforms region to camera frame
        * Creates oriented bounding box
        * Filters points within box
        * Creates region hypothesis with pose
      * Updates CAS with filtered cloud
      * Creates visualization markers

      :return: SUCCESS after processing
      :rtype: py_trees.Status
      :raises Exception: If queried region not found in map