robokudo.annotators.grab_cut
============================

.. py:module:: robokudo.annotators.grab_cut

.. autoapi-nested-parse::

   Image segmentation using GrabCut algorithm.

   This module provides an annotator for:

   * Refining object ROIs using GrabCut
   * Processing color image regions
   * Creating segmentation masks
   * Visualizing segmentation results

   The module uses:

   * OpenCV GrabCut implementation
   * ROI-based initialization
   * Iterative segmentation
   * Mask-based visualization

   .. warning::
      Current implementation is incomplete and for reference only.



Classes
-------

.. autoapisummary::

   robokudo.annotators.grab_cut.GrabCutAnnotator


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

.. py:class:: GrabCutAnnotator(name='GrabCutAnnotator')

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


   Refine the ROI of an Object Hypothesis with the GrabCut Algorithm

   This annotator:

   * Refines object ROIs using GrabCut
   * Processes color image regions
   * Creates foreground/background models
   * Generates segmentation masks
   * Visualizes segmented regions

   .. warning::
      Current implementation only processes one object and lacks annotation creation.


   .. py:method:: compute()

      Process object hypotheses using GrabCut.

      The method:

      * Loads color image and object hypotheses
      * For each hypothesis:
        * Extracts ROI rectangle
        * Creates initial mask
        * Initializes foreground/background models
        * Runs GrabCut segmentation
        * Creates visualization

      .. note::
         Currently only processes one object and lacks annotation creation.

      :return: SUCCESS after processing
      :rtype: py_trees.Status