dcnum.segm.segmenter_sto
========================

.. py:module:: dcnum.segm.segmenter_sto


Classes
-------

.. autoapisummary::

   dcnum.segm.segmenter_sto.STOSegmenter


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

.. py:class:: STOSegmenter(*, num_workers: int | None = None, kwargs_mask: dict | None = None, debug: bool = False, **kwargs)

   Bases: :py:obj:`dcnum.segm.segmenter.Segmenter`, :py:obj:`abc.ABC`


   Segmenter with single thread operation

   :param kwargs_mask: Keyword arguments for mask post-processing (see `process_labels`)
   :type kwargs_mask: dict
   :param debug: Debugging parameters
   :type debug: bool
   :param kwargs: Additional, optional keyword arguments for ``segment_algorithm``
                  defined in the subclass.


   .. py:attribute:: hardware_processor
      :value: 'gpu'


      Required hardware ("cpu" or "gpu") defined in first-level subclass.


   .. py:method:: segment_batch(images: numpy.ndarray, bg_off: numpy.ndarray | None = None)

      Perform batch segmentation of `images`

      Before segmentation, an optional background offset correction with
      ``bg_off`` is performed. After segmentation, mask postprocessing is
      performed according to the class definition.

      :param images: The time-series image data. First axis is time.
      :type images: 3d np.ndarray of shape (N, Y, X)
      :param bg_off: Optional 1D numpy array with background offset
      :type bg_off: 1D np.ndarray of length N

      .. rubric:: Notes

      - If the segmentation algorithm only accepts background-corrected
        images, then `images` must already be background-corrected,
        except for the optional `bg_off`.



   .. py:method:: segment_single(image, bg_off: float | None = None)

      This is a convenience-wrapper around `segment_batch`