pandora
=======

.. py:module:: pandora

.. autoapi-nested-parse::

   This module contains functions to run Pandora pipeline.



Submodules
----------

.. toctree::
   :maxdepth: 1

   /api_reference/pandora/Pandora/index
   /api_reference/pandora/aggregation/index
   /api_reference/pandora/check_configuration/index
   /api_reference/pandora/common/index
   /api_reference/pandora/constants/index
   /api_reference/pandora/cost_volume_confidence/index
   /api_reference/pandora/cpp/index
   /api_reference/pandora/criteria/index
   /api_reference/pandora/disparity/index
   /api_reference/pandora/filter/index
   /api_reference/pandora/img_tools/index
   /api_reference/pandora/interval_tools/index
   /api_reference/pandora/marge/index
   /api_reference/pandora/margins/index
   /api_reference/pandora/matching_cost/index
   /api_reference/pandora/multiscale/index
   /api_reference/pandora/optimization/index
   /api_reference/pandora/output_tree_design/index
   /api_reference/pandora/profiler/index
   /api_reference/pandora/refinement/index
   /api_reference/pandora/semantic_segmentation/index
   /api_reference/pandora/state_machine/index
   /api_reference/pandora/validation/index


Functions
---------

.. autoapisummary::

   pandora.run
   pandora.setup_logging
   pandora.import_plugin
   pandora.main


Package Contents
----------------

.. py:function:: run(pandora_machine: state_machine.PandoraMachine, img_left: xarray.Dataset, img_right: xarray.Dataset, cfg: Dict[str, dict]) -> Tuple[xarray.Dataset, xarray.Dataset]

   Run the pandora pipeline

   :param pandora_machine: instance of PandoraMachine
   :type pandora_machine: PandoraMachine
   :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 cfg: pipeline configuration
   :type cfg: Dict[str, dict]
   :return: Two xarray.Dataset :


           - left : the left dataset, which contains the variables :
               - disparity_map : the disparity map in the geometry of the left image 2D DataArray (row, col)
               - confidence_measure : the confidence measure in the geometry of the left image 3D DataArray                     (row, col, indicator)
               - validity_mask : the validity mask in the geometry of the left image 2D DataArray (row, col)
               - classif_mask : information about a classification
               - segm_mask : information about a segmentation

           - right : the right dataset. If there is no validation step, the right Dataset will be empty.If a             validation step is configured, the dataset will contain the variables :
               - disparity_map : the disparity map in the geometry of the right image 2D DataArray (row, col)
               - confidence_measure : the confidence measure in the geometry of the left image 3D DataArray                     (row, col, indicator)
               - validity_mask : the validity mask in the geometry of the left image 2D DataArray (row, col)
               - classif_mask : information about a classification
               - segm_mask : information about a segmentation

   :rtype: tuple (xarray.Dataset, xarray.Dataset)


.. py:function:: setup_logging(verbose: bool) -> None

   Setup the logging configuration

   :param verbose: verbose mode
   :type verbose: bool
   :return: None


.. py:function:: import_plugin() -> None

   Load all the registered entry points
   :return: None


.. py:function:: main(cfg_path: os.PathLike | str, output: str, verbose: bool) -> None

   Check config file and run pandora framework accordingly

   :param cfg_path: path to the json configuration file
   :type cfg_path: string
   :param output: Path to output directory
   :type output: string
   :param verbose: verbose mode
   :type verbose: bool
   :return: None