robokudo.annotators.blur
========================

.. py:module:: robokudo.annotators.blur

.. autoapi-nested-parse::

   Image blur detection for RoboKudo.

   This module provides an annotator for calculating and visualizing image blur metrics
   using the Laplacian variance method. It can optionally halt pipeline execution if
   blur exceeds a threshold.

   .. note::
      The blur metric is calculated using the Laplacian operator variance, where:

      * Higher values indicate sharper images
      * Lower values indicate more blur



Classes
-------

.. autoapisummary::

   robokudo.annotators.blur.BlurAnnotator


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

.. py:class:: BlurAnnotator(name: str = 'BlurAnnotator', descriptor: BlurAnnotator = Descriptor())

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


   Annotator for calculating and visualizing image blur metrics.

   This annotator:

   * Calculates a blur metric using Laplacian variance
   * Visualizes the blur value on the image
   * Optionally halts pipeline if blur exceeds threshold

   .. warning::
      Setting return_failure_above_threshold to True will stop pipeline
      advancement when the blur threshold is exceeded.


   .. py:class:: Descriptor

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


      Configuration descriptor for blur detection.


      .. py:class:: Parameters

         Parameters for configuring blur detection behavior.


         .. py:attribute:: blur_threshold
            :type:  float
            :value: 100.0


            Threshold for acceptable blur level, defaults to 100



         .. py:attribute:: return_failure_above_threshold
            :type:  bool
            :value: True


            Let this behaviour return failure to stop the advancement of the current pipeline




      .. py:attribute:: parameters



   .. py:method:: compute_bluriness(img: numpy.typing.NDArray) -> float

      Compute the blur metric for an image.

      Uses Laplacian variance to measure image sharpness:

      * Converts image to grayscale
      * Computes Laplacian operator
      * Returns variance of Laplacian values

      :param img: Input image in BGR format
      :return: Blur metric value (higher = sharper)



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

      Update blur detection and visualization.

      Creates visualizations containing:

      * Color image with blur metric overlay
      * Point cloud data

      :return: SUCCESS if blur is acceptable, FAILURE if above threshold (if configured)