robokudo.descriptors.analysis_engines.demo
==========================================

.. py:module:: robokudo.descriptors.analysis_engines.demo

.. autoapi-nested-parse::

   Analysis engine demonstrating basic tabletop segmentation.

   This module provides a basic analysis engine that demonstrates tabletop
   segmentation using a Kinect camera. It implements a straightforward pipeline
   for processing point cloud data to identify objects on a table surface.

   The pipeline implements the following functionality:
   - Reading data from a Kinect camera (without transform lookup)
   - Image preprocessing
   - Point cloud cropping
   - Plane detection (table surface)
   - Point cloud cluster extraction (objects)

   .. note::
       This is a basic demonstration pipeline that can be used as a starting point
       for more complex object detection and segmentation tasks.



Classes
-------

.. autoapisummary::

   robokudo.descriptors.analysis_engines.demo.AnalysisEngine


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

.. py:class:: AnalysisEngine

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


   Analysis engine for basic tabletop segmentation.

   This class implements a simple pipeline for tabletop segmentation using
   a Kinect camera. It processes point cloud data to identify and segment
   objects on a table surface.

   The pipeline includes:
   - Collection reader for Kinect camera data
   - Image preprocessing
   - Point cloud cropping
   - Plane detection
   - Point cloud cluster extraction

   .. note::
       The pipeline uses the Kinect configuration without transform lookup
       for simplicity. For more advanced applications, consider using the
       version with transform lookup enabled.


   .. py:method:: name()

      Get the name of the analysis engine.

      :return: The name identifier of this analysis engine
      :rtype: str



   .. py:method:: implementation()

      Create a basic pipeline for tabletop segmentation.

      This method constructs a processing pipeline that performs tabletop
      segmentation using point cloud data from a Kinect camera. The pipeline
      processes the data through several stages to identify objects on a
      table surface.

      The pipeline execution sequence is:
      1. Initialize pipeline
      2. Read frame from Kinect
      3. Preprocess image
      4. Crop point cloud to region of interest
      5. Detect table plane
      6. Extract object clusters

      :return: The configured pipeline for tabletop segmentation
      :rtype: robokudo.pipeline.Pipeline

      .. note::
          The pipeline includes commented-out options for adding triggers
          and slow processing simulation, which can be useful for debugging.