robokudo.annotators.semantic_world_connector
============================================

.. py:module:: robokudo.annotators.semantic_world_connector


Classes
-------

.. autoapisummary::

   robokudo.annotators.semantic_world_connector.SemanticDigitalTwinConnector


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

.. py:class:: SemanticDigitalTwinConnector(name: str = 'SemanticDigitalTwinSynchronization', descriptor: SemanticDigitalTwinConnector | None = None)

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


   An annotator that synchronizes the current state of the world with the semdt.


   .. py:class:: Descriptor

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


      .. py:class:: Parameters

         .. py:attribute:: confidence_threshold
            :value: 0.05




      .. py:attribute:: parameters



   .. py:attribute:: object_comparator


   .. py:method:: compute() -> py_trees.common.Status

      Synchronise the current RoboKudo state with the current semdt state.



   .. py:method:: associate_hypotheses_with_beliefs(object_hypotheses: list[robokudo.types.scene.ObjectHypothesis], object_beliefs: list[robokudo.types.belief_state.ObjectBeliefState], cas: robokudo.cas.CAS) -> list[tuple[robokudo.types.scene.ObjectHypothesis, robokudo.types.belief_state.ObjectBeliefState]]

      Associate current object hypotheses with existing or new object beliefs.



   .. py:method:: add_world_pose_annotations(object_hypotheses: list[robokudo.types.scene.ObjectHypothesis], cas: robokudo.cas.CAS) -> None

      Add world-frame stamped poses to hypotheses when a camera pose is available.



   .. py:method:: get_latest_pose_annotation(object_hypothesis: robokudo.types.scene.ObjectHypothesis) -> robokudo.types.annotation.PoseAnnotation | None
      :staticmethod:


      Return the latest plain pose annotation for a hypothesis.



   .. py:method:: create_association_cost_matrix(object_hypotheses: list[robokudo.types.scene.ObjectHypothesis], object_beliefs: list[robokudo.types.belief_state.ObjectBeliefState]) -> numpy.ndarray

      Create a negative-similarity cost matrix for Hungarian assignment.



   .. py:method:: apply_assignment(object_hypotheses: list[robokudo.types.scene.ObjectHypothesis], object_beliefs: list[robokudo.types.belief_state.ObjectBeliefState], cost_matrix: numpy.ndarray, hypothesis_indices: numpy.ndarray, belief_indices: numpy.ndarray, cas: robokudo.cas.CAS) -> list[tuple[robokudo.types.scene.ObjectHypothesis, robokudo.types.belief_state.ObjectBeliefState]]

      Update matched beliefs or create new beliefs for low-confidence matches.



   .. py:method:: create_new_beliefs_for_hypotheses(object_hypotheses: list[robokudo.types.scene.ObjectHypothesis], cas: robokudo.cas.CAS) -> list[tuple[robokudo.types.scene.ObjectHypothesis, robokudo.types.belief_state.ObjectBeliefState]]

      Create object beliefs for all given hypotheses.



   .. py:method:: create_new_beliefs_for_unmatched_hypotheses(object_hypotheses: list[robokudo.types.scene.ObjectHypothesis], matched_hypothesis_indices: set[int], cas: robokudo.cas.CAS) -> list[tuple[robokudo.types.scene.ObjectHypothesis, robokudo.types.belief_state.ObjectBeliefState]]

      Create object beliefs for hypotheses not returned by Hungarian assignment.



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

      Log a compact summary of the current SemDT world contents.



   .. py:method:: create_association_visualization(associated_hypotheses: list[tuple[robokudo.types.scene.ObjectHypothesis, robokudo.types.belief_state.ObjectBeliefState]]) -> None

      Publish a 2D image showing which SemDT UUID each hypothesis maps to.



   .. py:method:: get_object_belief_label(object_belief: robokudo.types.belief_state.ObjectBeliefState) -> str
      :staticmethod:


      Return a compact display label for an object belief UUID.