pandora2d.cost_volume_confidence.ambiguity
==========================================

.. py:module:: pandora2d.cost_volume_confidence.ambiguity

.. autoapi-nested-parse::

   This module contains functions associated to the cost volume confidence computation step
   with ambiguity method.



Classes
-------

.. autoapisummary::

   pandora2d.cost_volume_confidence.ambiguity.Ambiguity


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

.. py:class:: Ambiguity(cfg: dict)

   Bases: :py:obj:`pandora2d.cost_volume_confidence.cost_volume_confidence.CostVolumeConfidence`


   Ambiguity class


   .. py:attribute:: _normalization


   .. py:attribute:: _eta_min
      :value: 0.0



   .. py:attribute:: _eta_max


   .. py:attribute:: _eta_step


   .. py:attribute:: _percentile
      :value: 1



   .. py:property:: schema


   .. py:property:: defaults


   .. py:method:: confidence_prediction(left_image: xarray.Dataset, cost_volumes: xarray.Dataset, dataset_disp_maps: xarray.Dataset) -> tuple[xarray.Dataset, xarray.Dataset]

      Compute a confidence prediction.

      :param left_image: left Dataset image
      :param right_image: right Dataset image
      :param cost_volumes: cost volume dataset
      :param dataset_disp_maps: dataset containing row and col disparity maps
      :return: the disparity map and the cost volume updated with the confidence measure



   .. py:method:: normalize_with_extremum(confidence: numpy.ndarray, nbr_disparities: int, nbr_etas: int) -> numpy.ndarray
      :staticmethod:


      Normalize ambiguity with extremum

      :param confidence: confidence
      :param nbr_disparities: number of disparity (row_disparity * col_disparity)
      :param nbr_etas: size of etas
      :return: the normalized confidence