robokudo.descriptors.analysis_engines.stretch_demo
==================================================

.. py:module:: robokudo.descriptors.analysis_engines.stretch_demo

.. autoapi-nested-parse::

   Analysis engine answering perception queries for the Stretch robot.

   Localizes objects standing on the dominant plane within the apartment demo's second
   shelf layer, in view of the Stretch's RealSense, and reports their poses in response to
   a :class:`~robokudo_msgs.action.Query`, so a plan can correct an object's pose before
   grasping it.

   .. note::
       The pipeline localizes but does not recognize: it attaches no
       :class:`~robokudo.types.annotation.Classification`, so every reported object designator
       has an empty ``type`` and the caller decides what it asked for. Adding a classifying
       annotator (for example
       :class:`~robokudo.annotators.clip_annotator.ClipAnnotator` or
       :class:`~robokudo.annotators.simple_yolo_annotator.SimpleYoloAnnotator`) before
       :class:`~robokudo.annotators.query.GenerateQueryResult` fills that field in without any
       other change.



Attributes
----------

.. autoapisummary::

   robokudo.descriptors.analysis_engines.stretch_demo.CAMERA_CONFIG_NAME
   robokudo.descriptors.analysis_engines.stretch_demo.TARGET_SHELF_LAYER_MIN_WORLD_Z
   robokudo.descriptors.analysis_engines.stretch_demo.TARGET_SHELF_LAYER_MAX_WORLD_Z
   robokudo.descriptors.analysis_engines.stretch_demo.TARGET_SHELF_MIN_WORLD_X
   robokudo.descriptors.analysis_engines.stretch_demo.TARGET_SHELF_MAX_WORLD_X
   robokudo.descriptors.analysis_engines.stretch_demo.TARGET_SHELF_MIN_WORLD_Y
   robokudo.descriptors.analysis_engines.stretch_demo.TARGET_SHELF_MAX_WORLD_Y


Classes
-------

.. autoapisummary::

   robokudo.descriptors.analysis_engines.stretch_demo.AnalysisEngine


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

.. py:data:: CAMERA_CONFIG_NAME
   :value: 'realsense'


   Camera configuration this engine reads from.

   The Stretch carries a RealSense D435i publishing on the stock ``realsense2_camera`` topics,
   and its colour frame is named ``camera_color_optical_frame``, so the shared RealSense config
   applies unchanged. Pass overrides to
   :meth:`~robokudo.descriptors.factories.cr_descriptor_factory.CollectionReaderDescriptorFactory.create_descriptor`
   if a particular robot publishes elsewhere.


.. py:data:: TARGET_SHELF_LAYER_MIN_WORLD_Z
   :value: 0.4565


   Lower world-frame height bound of the crop, in metres.

   The apartment demo's shelf has layers at world heights 0.283m, 0.63m, 1.265m and 1.613m
   (see ``experiments.real_stretch_apartment_demo.demo``); this engine targets the second
   layer at 0.63m. The bound is the midpoint between that layer and the one below it, so
   the crop keeps whatever sits on the target layer while excluding its neighbours.


.. py:data:: TARGET_SHELF_LAYER_MAX_WORLD_Z
   :value: 0.9475


   Upper world-frame height bound of the crop, in metres.

   The midpoint between the target layer (0.63m) and the one above it (1.265m).


.. py:data:: TARGET_SHELF_MIN_WORLD_X
   :value: 0.4


.. py:data:: TARGET_SHELF_MAX_WORLD_X
   :value: 1.35


.. py:data:: TARGET_SHELF_MIN_WORLD_Y
   :value: -0.35


.. py:data:: TARGET_SHELF_MAX_WORLD_Y
   :value: 0.05


   World-frame lateral bounds of the crop, in metres.

   The shelf (``experiments.real_stretch_apartment_demo.demo``) is a 0.305m x 0.85m
   footprint centred at world (0.88, -0.17) with a -90 degree yaw, which rotates its outer
   footprint to world X in [0.455, 1.305] and world Y in [-0.3225, -0.0175]. These bounds
   add roughly 5cm of margin on every side of that footprint, since the height bound alone
   left the shelf's own case (side walls, the layer's front lip) and, without a lateral
   bound at all, the rest of the room in view as further untyped candidates.


.. py:class:: AnalysisEngine

   Bases: :py:obj:`robokudo.analysis_engine.AnalysisEngineInterface`


   Query-driven tabletop localization for the Stretch.


   .. py:method:: name() -> str

      Get the name of the analysis engine.

      :return: The name identifier of this analysis engine



   .. py:method:: implementation() -> robokudo.pipeline.Pipeline

      Build the pipeline that answers a query with the poses of the objects in view.

      The pipeline waits for a query, reads a frame, isolates the dominant plane,
      treats what stands on it as objects, estimates a pose per object from its
      bounding box, and replies.

      :return: The configured pipeline for Stretch perception