robokudo.descriptors.analysis_engines.trigger_example
=====================================================

.. py:module:: robokudo.descriptors.analysis_engines.trigger_example

.. autoapi-nested-parse::

   Analysis engine demonstrating pipeline trigger functionality.

   This module provides an analysis engine that demonstrates how to use pipeline
   triggers to control the execution flow. It implements a pipeline that waits for
   user input (keypress) before processing each frame from a Kinect camera.

   The pipeline implements the following functionality:
   - Pipeline trigger for user-controlled execution
   - Reading data from a Kinect camera
   - Image preprocessing
   - Simulated slow processing (for demonstration)

   .. note::
       This example is particularly useful for debugging and step-by-step analysis
       of pipeline behavior, as it allows manual control over frame processing.



Classes
-------

.. autoapisummary::

   robokudo.descriptors.analysis_engines.trigger_example.AnalysisEngine


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

.. py:class:: AnalysisEngine

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


   Analysis engine with pipeline trigger functionality.

   This class implements a pipeline that demonstrates the use of pipeline
   triggers for controlled execution. The pipeline waits for user input
   before processing each frame, making it useful for debugging and
   step-by-step analysis.

   The pipeline includes:
   - Pipeline trigger for user control
   - Collection reader for Kinect camera data
   - Image preprocessing
   - Simulated slow processing

   .. note::
       The pipeline uses a SlowAnnotator to simulate time-consuming processing.
       This helps demonstrate the effect of the trigger mechanism on pipeline
       execution.


   .. 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 trigger-controlled execution.

      This method constructs a processing pipeline that includes a trigger
      mechanism. The pipeline will pause and wait for user input (keypress)
      before processing each frame from the Kinect camera.

      The pipeline execution sequence is:
      1. Wait for trigger (keypress)
      2. Initialize pipeline
      3. Read frame from Kinect
      4. Preprocess image
      5. Simulate slow processing
      6. Return to step 1

      :return: The configured pipeline with trigger mechanism
      :rtype: robokudo.pipeline.Pipeline