pandora.cost_volume_confidence.risk
===================================

.. py:module:: pandora.cost_volume_confidence.risk

.. autoapi-nested-parse::

   This module contains functions for estimating the risk.



Classes
-------

.. autoapisummary::

   pandora.cost_volume_confidence.risk.Risk


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

.. py:class:: Risk(**cfg: str)

   Bases: :py:obj:`pandora.cost_volume_confidence.cost_volume_confidence.AbstractCostVolumeConfidence`


   Allows to estimate a risk confidence from the cost volume


   .. py:attribute:: _ETA_MIN
      :value: 0.0



   .. py:attribute:: _ETA_MAX
      :value: 0.7



   .. py:attribute:: _ETA_STEP
      :value: 0.01



   .. py:attribute:: _PERCENTILE
      :value: 1.0



   .. py:attribute:: _method_max
      :value: 'risk_max'



   .. py:attribute:: _method_min
      :value: 'risk_min'



   .. py:attribute:: _method_disp_inf
      :value: 'disp_inf_from_risk'



   .. py:attribute:: _method_disp_sup
      :value: 'disp_sup_from_risk'



   .. py:attribute:: cfg


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



   .. py:attribute:: _percentile
      :value: 1.0



   .. py:attribute:: _eta_step


   .. py:attribute:: _eta_max


   .. py:attribute:: _indicator_max
      :value: 'risk_max'



   .. py:attribute:: _indicator_min
      :value: 'risk_min'



   .. py:attribute:: _indicator_disp_sup
      :value: 'disp_sup_from_risk'



   .. py:attribute:: _indicator_disp_inf
      :value: 'disp_inf_from_risk'



   .. py:attribute:: _etas


   .. py:attribute:: _nbr_etas


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

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

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



   .. py:method:: desc() -> None

      Describes the confidence method
      :return: None



   .. py:method:: confidence_prediction(disp: xarray.Dataset, img_left: xarray.Dataset = None, img_right: xarray.Dataset = None, cv: xarray.Dataset = None) -> Tuple[xarray.Dataset, xarray.Dataset]

      Computes a risk confidence measure that evaluates the matching cost function at each point

      :param disp: the disparity map dataset
      :type disp: xarray.Dataset
      :param img_left: left Dataset image
      :tye img_left: xarray.Dataset
      :param img_right: right Dataset image
      :type img_right: xarray.Dataset
      :param cv: cost volume dataset
      :type cv: xarray.Dataset
      :return: the disparity map and the cost volume with new indicators 'risk_max_confidence'
              and 'risk_min_confidence' in the
      :rtype: Tuple(xarray.Dataset, xarray.Dataset)



   .. py:method:: compute_risk(cv: numpy.ndarray, sampled_ambiguity: numpy.ndarray, etas: numpy.ndarray, nbr_etas: int, grids: numpy.ndarray, disparity_range: numpy.ndarray) -> Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]
      :staticmethod:


      Computes minimum and maximum risk.
      Cost Volume must correspond to min similarity measure.

      :param cv: cost volume
      :type cv: 3D np.ndarray (row, col, disp)
      :param sampled_ambiguity: sampled cost volume ambiguity
      :type sampled_ambiguity: 3D np.ndarray (row, col, eta)
      :param etas: range between eta_min and eta_max with step eta_step
      :type etas: np.ndarray
      :param nbr_etas: number of etas
      :type nbr_etas: int
      :param grids: array containing min and max disparity grids
      :type grids: 2D np.ndarray (min, max)
      :param disparity_range: array containing disparity range
      :type disparity_range: np.ndarray
      :return: the minimum and maximum risk
      :rtype: Tuple(2D np.ndarray (row, col) dtype = float32, 2D np.ndarray (row, col) dtype = float32        2D np.ndarray (row, col) dtype = float32, 2D np.ndarray (row, col) dtype = float32)



   .. py:method:: compute_risk_and_sampled_risk(cv: numpy.ndarray, sampled_ambiguity: numpy.ndarray, etas: numpy.ndarray, nbr_etas: int, grids: numpy.ndarray, disparity_range: numpy.ndarray) -> Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]
      :staticmethod:


      Computes minimum and maximum risk and sampled_risk.
      Cost Volume must correspond to min similarity measure.

      :param cv: cost volume
      :type cv: 3D np.ndarray (row, col, disp)
      :param sampled_ambiguity: sampled cost volume ambiguity
      :type sampled_ambiguity: 3D np.ndarray (row, col, eta)
      :param etas: range between eta_min and eta_max with step eta_step
      :type etas: np.ndarray
      :param nbr_etas: nuber of etas
      :type nbr_etas: int
      :param grids: array containing min and max disparity grids
      :type grids: 2D np.ndarray (min, max)
      :param disparity_range: array containing disparity range
      :type disparity_range: np.ndarray
      :return: the risk
      :rtype: Tuple(2D np.ndarray (row, col) dtype = float32, 2D np.ndarray (row, col) dtype = float32,
                   2D np.ndarray (row, col) dtype = float32, 2D np.ndarray (row, col) dtype = float32,
                   3D np.ndarray (row, col) dtype = float32, 3D np.ndarray (row, col) dtype = float32)