pandora2d.refinement.dichotomy_cpp
==================================

.. py:module:: pandora2d.refinement.dichotomy_cpp

.. autoapi-nested-parse::

   Module for Dichotomy refinement method (cpp version).



Classes
-------

.. autoapisummary::

   pandora2d.refinement.dichotomy_cpp.Dichotomy


Functions
---------

.. autoapisummary::

   pandora2d.refinement.dichotomy_cpp.disparity_to_index
   pandora2d.refinement.dichotomy_cpp.index_to_disparity
   pandora2d.refinement.dichotomy_cpp.create_cost_values_map


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

.. py:class:: Dichotomy(cfg: dict = None, _: list = None, __: int = 5)

   Bases: :py:obj:`pandora2d.refinement.refinement.AbstractRefinement`


   Subpixel refinement method by dichotomy (cpp version).


   .. py:attribute:: NB_MAX_ITER
      :value: 9



   .. py:attribute:: schema


   .. py:attribute:: filter


   .. py:method:: check_conf(cfg: dict) -> dict
      :classmethod:


      Check the refinement method configuration.

      Will change `number_of_iterations` value by `Dichotomy.NB_MAX_ITER` if above `Dichotomy.NB_MAX_ITER`.

      :param cfg: user_config for refinement method
      :return: cfg: global configuration



   .. py:property:: margins

      Create margins for dichotomy object.

      It will be used for ROI and for dichotomy window extraction from cost volumes.



   .. py:method:: refinement_method(cost_volumes: xarray.Dataset, disp_map: xarray.Dataset, img_left: xarray.Dataset, img_right: xarray.Dataset) -> tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

      Return the subpixel disparity maps

      :param cost_volumes: cost_volumes 4D row, col, disp_col, disp_row
      :param disp_map: pixel disparity maps
      :param img_left: left image dataset
      :param img_right: right image dataset
      :return: the refined disparity maps



.. py:function:: disparity_to_index(disparity_map: xarray.DataArray, shift: int, subpixel: int) -> numpy.ndarray

   Transform a disparity map to index map. Indexes correspond to (row/col) disparities in cost volume.

   Example:
       - with subpixel=1 :

           * disparity_map = -2 -1 -1  1
                             -1  0 -1 -1
                              0  1  1  1

           * disparities range = [-4 -3 -2 -1 0 1 2 3]

           * index_map = 2 3 3 5
                         3 4 3 3
                         4 5 5 5

       - with subpixel=2 :

           * disparity_map = -4  -2   -1.5 -2.5
                             -4  -1   -1   -1
                             -4  -1.5 -1   -1.5

           * disparities range = [-4 -3.5 -3 -2.5 -2 -1.5 -1]

           * index_map = 0 4 5 3
                         0 6 6 6
                         0 5 6 5

   :param disparity_map: 2D map
   :param shift: the first value of the disparity coordinates in the cost volume
   :param subpixel: :sub-sampling of cost_volume
   :return: the index map


.. py:function:: index_to_disparity(index_map: numpy.ndarray, shift: int, subpixel: int) -> numpy.ndarray

   Transform an index map to disparity map. Indexes correspond to (row/col) disparities in cost volume.

   For examples, see disparity_to_index method.

   :param index_map: 2D map
   :param shift: the first value of the disparity coordinates in the cost volume
   :param subpixel: :sub-sampling of cost_volume
   :return: the index map


.. py:function:: create_cost_values_map(cost_volumes: xarray.Dataset, disp_map: xarray.Dataset) -> numpy.ndarray

   Return the map with best matching score

   :param cost_volumes: cost_volumes 4D row, col, disp_col, disp_row
   :param disp_map: pixel disparity maps
   :return: the cost_value map and the invalid_disparity_map