pandora.aggregation.cbca
========================

.. py:module:: pandora.aggregation.cbca

.. autoapi-nested-parse::

   This module contains functions associated to the Cross Based Cost Aggregation (cbca) method.



Attributes
----------

.. autoapisummary::

   pandora.aggregation.cbca.cross_support


Classes
-------

.. autoapisummary::

   pandora.aggregation.cbca.CrossBasedCostAggregation


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

.. py:class:: CrossBasedCostAggregation(**cfg: dict)

   Bases: :py:obj:`pandora.aggregation.aggregation.AbstractAggregation`


   CrossBasedCostAggregation class, allows to perform the aggregation step


   .. py:attribute:: _CBCA_INTENSITY
      :value: 30.0



   .. py:attribute:: _CBCA_DISTANCE
      :value: 5



   .. py:attribute:: cfg


   .. py:attribute:: _cbca_intensity


   .. py:attribute:: _cbca_distance


   .. py:method:: check_conf(**cfg: Union[str, float, int]) -> Dict[str, Union[str, float, int]]

      Add default values to the dictionary if there are missing elements and check if the dictionary is correct

      :param cfg: aggregation configuration
      :type cfg: dict
      :return cfg: aggregation configuration updated
      :rtype: dict



   .. py:method:: desc()

      Describes the aggregation method



   .. py:method:: cost_volume_aggregation(img_left: xarray.Dataset, img_right: xarray.Dataset, cv: xarray.Dataset, **cfg: Union[str, int]) -> None

      Aggregated the cost volume with Cross-Based Cost Aggregation, using the pipeline define in
      Zhang, K., Lu, J., & Lafruit, G. (2009).
      Cross-based local stereo matching using orthogonal integral images.
      IEEE transactions on circuits and systems for video technology, 19(7), 1073-1079.

      :param img_left: left Dataset image containing :

              - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
              - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
              - msk (optional): 2D (row, col) xarray.DataArray int16
              - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
              - segm (optional): 2D (row, col) xarray.DataArray int16
              - edges (optional): 2D (row, col) xarray.DataArray int16
      :type img_left: xarray.Dataset
      :param img_right: right Dataset image containing :

              - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
              - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
              - msk (optional): 2D (row, col) xarray.DataArray int16
              - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
              - segm (optional): 2D (row, col) xarray.DataArray int16
              - edges (optional): 2D (row, col) xarray.DataArray int16
      :type img_right: xarray.Dataset
      :param cv: cost volume dataset with the data variables:

              - cost_volume 3D xarray.DataArray (row, col, disp)
              - confidence_measure 3D xarray.DataArray (row, col, indicator)
      :type cv: xarray.Dataset
      :param cfg: images configuration containing the mask convention : valid_pixels, no_data
      :type cfg: dict
      :return: None



   .. py:method:: computes_cross_supports(img_left: xarray.Dataset, img_right: xarray.Dataset, cv: xarray.Dataset) -> Tuple[numpy.ndarray, List[numpy.ndarray]]

      Prepare images and compute the cross support region of the left and right images.
      A 3x3 median filter is applied to the images before calculating the cross support region.

      :param img_left: left Dataset image containing :

              - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
              - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
              - msk (optional): 2D (row, col) xarray.DataArray int16
              - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
              - segm (optional): 2D (row, col) xarray.DataArray int16
              - edges (optional): 2D (row, col) xarray.DataArray int16
      :type img_left: xarray.Dataset
      :param img_right: right Dataset image containing :

              - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
              - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
              - msk (optional): 2D (row, col) xarray.DataArray int16
              - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
              - segm (optional): 2D (row, col) xarray.DataArray int16
              - edges (optional): 2D (row, col) xarray.DataArray int16
      :type img_right: xarray.Dataset
      :param cv: cost volume dataset with the data variables:

              - cost_volume 3D xarray.DataArray (row, col, disp)
              - confidence_measure 3D xarray.DataArray (row, col, indicator)
      :type cv: xarray.Dataset
      :return: the left and right cross support region
      :rtype: Tuples(left cross support region, List(right cross support region))



.. py:data:: cross_support