robokudo.descriptors.analysis_engines.parallel
==============================================

.. py:module:: robokudo.descriptors.analysis_engines.parallel

.. autoapi-nested-parse::

   Analysis engine demonstrating parallel processing capabilities.

   This module provides an analysis engine that demonstrates how to implement
   parallel processing within a pipeline. It shows how to configure and execute
   multiple annotators concurrently using a parallel execution policy.

   The pipeline implements the following functionality:

   * Kinect camera data input
   * Pipeline trigger for controlled execution
   * Parallel execution of slow annotators
   * Synchronized completion policy

   .. note::
       This is a demonstration pipeline that uses simulated slow annotators to
       illustrate parallel processing behavior. The annotators have different
       sleep durations to show asynchronous execution.



Classes
-------

.. autoapisummary::

   robokudo.descriptors.analysis_engines.parallel.AnalysisEngine


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

.. py:class:: AnalysisEngine

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


   Analysis engine demonstrating parallel processing capabilities.

   This class implements a pipeline that demonstrates parallel execution of
   annotators. It uses the BetterParallel component with a synchronized
   completion policy to ensure all parallel tasks complete before proceeding.

   The pipeline includes:

   * Pipeline trigger for controlled execution
   * Camera data collection and preprocessing
   * Fast annotator (1s processing time)
   * Parallel slow annotators (2s and 4s processing times)
   * Final slow annotator (2s processing time)

   .. note::
       The pipeline uses simulated processing times to demonstrate the
       benefits of parallel execution compared to sequential processing.


   .. 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 pipeline with parallel processing capabilities.

      This method constructs a processing pipeline that demonstrates parallel
      execution of annotators. It configures two slow annotators to run in
      parallel, with different processing times to show asynchronous execution.

      Pipeline execution sequence:

      1. Pipeline trigger (wait for user)
      2. Clear outputs
      3. Read camera data
      4. Preprocess image
      5. Fast annotator (1s)
      6. Parallel annotators (2s and 4s)
      7. Final slow annotator (2s)

      :return: The configured pipeline with parallel processing
      :rtype: robokudo.pipeline.Pipeline