pandora.refinement.cpp.refinement_cpp
=====================================

.. py:module:: pandora.refinement.cpp.refinement_cpp


Functions
---------

.. autoapisummary::

   pandora.refinement.cpp.refinement_cpp.quadratic_refinement_method
   pandora.refinement.cpp.refinement_cpp.loop_refinement
   pandora.refinement.cpp.refinement_cpp.loop_approximate_refinement
   pandora.refinement.cpp.refinement_cpp.vfit_refinement_method


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

.. py:function:: quadratic_refinement_method(cost, disp, measure, cst_pandora_msk_pixel_stopped_interpolation)

   Return the subpixel disparity and cost, by fitting a quadratic curve

   :param cost: cost of the values disp - 1, disp, disp + 1
   :type cost: 1D numpy array : [cost[disp -1], cost[disp], cost[disp + 1]]
   :param disp: the disparity
   :type disp: float
   :param measure: the type of measure used to create the cost volume
   :param measure: string = min | max
   :param cst_pandora_msk_pixel_stopped_interpolation: value for the PANDORA_MSK_PIXEL_STOPPED_INTERPOLATION     constant in pandora.constants
   :param cst_pandora_msk_pixel_stopped_interpolation: int
   :return: the disparity shift, the refined cost and the state of the pixel ( Information:     calculations stopped at the pixel step, sub-pixel interpolation did not succeed )
   :rtype: float, float, int


.. py:function:: loop_refinement(cv, disp, mask, d_min, d_max, subpixel, measure, method, cst_pandora_msk_pixel_invalid, cst_pandora_msk_pixel_stopped_interpolation)

   Apply for each pixels the refinement method

   :param cv: cost volume to refine
   :type cv: 3D numpy array (row, col, disp)
   :param disp: disparity map
   :type disp: 2D numpy array (row, col)
   :param mask: validity mask
   :type mask: 2D numpy array (row, col)
   :param d_min: minimal disparity
   :type d_min: int
   :param d_max: maximal disparity
   :type d_max: int
   :param subpixel: subpixel precision used to create the cost volume
   :type subpixel: int ( 1 | 2 | 4 )
   :param measure: the measure used to create the cot volume
   :param measure: string
   :param method: the refinement method
   :param method: function
   :param cst_pandora_msk_pixel_invalid: value for the PANDORA_MSK_PIXEL_INVALID constant in pandora.constants
   :param cst_pandora_msk_pixel_invalid: int
   :param cst_pandora_msk_pixel_stopped_interpolation: value for the PANDORA_MSK_PIXEL_STOPPED_INTERPOLATION     constant in pandora.constants
   :param cst_pandora_msk_pixel_stopped_interpolation: int
   :return: the refine coefficient, the refine disparity map, and the validity mask
   :rtype: tuple(2D numpy array (row, col), 2D numpy array (row, col), 2D numpy array (row, col))


.. py:function:: loop_approximate_refinement(cv, disp, mask, d_min, d_max, subpixel, measure, method, cst_pandora_msk_pixel_invalid, cst_pandora_msk_pixel_stopped_interpolation)

   Apply for each pixels the refinement method on the right disparity map which was created with the     approximate method : a diagonal search for the minimum on the left cost volume

   :param cv: the left cost volume
   :type cv: 3D numpy array (row, col, disp)
   :param disp: right disparity map
   :type disp: 2D numpy array (row, col)
   :param mask: right validity mask
   :type mask: 2D numpy array (row, col)
   :param d_min: minimal disparity
   :type d_min: int
   :param d_max: maximal disparity
   :type d_max: int
   :param subpixel: subpixel precision used to create the cost volume
   :type subpixel: int ( 1 | 2 | 4 )
   :param measure: the type of measure used to create the cost volume
   :type measure: string = min | max
   :param method: the refinement method
   :type method: function
   :param cst_pandora_msk_pixel_invalid: value for the PANDORA_MSK_PIXEL_INVALID constant in pandora.constants
   :param cst_pandora_msk_pixel_invalid: int
   :param cst_pandora_msk_pixel_stopped_interpolation: value for the PANDORA_MSK_PIXEL_STOPPED_INTERPOLATION     constant in pandora.constants
   :param cst_pandora_msk_pixel_stopped_interpolation: int
   :return: the refine coefficient, the refine disparity map, and the validity mask
   :rtype: tuple(2D numpy array (row, col), 2D numpy array (row, col), 2D numpy array (row, col))


.. py:function:: vfit_refinement_method(cost, disp, measure, cst_pandora_msk_pixel_stopped_interpolation) -> Tuple[float, float, int]

   Return the subpixel disparity and cost, by matching a symmetric V shape (linear interpolation)

   :param cost: cost of the values disp - 1, disp, disp + 1
   :type cost: 1D numpy array : [cost[disp -1], cost[disp], cost[disp + 1]]
   :param disp: the disparity
   :type disp: float
   :param measure: the type of measure used to create the cost volume
   :param measure: string = min | max
   :param cst_pandora_msk_pixel_stopped_interpolation: value for the PANDORA_MSK_PIXEL_STOPPED_INTERPOLATION     constant in pandora.constants
   :param cst_pandora_msk_pixel_stopped_interpolation: int
   :return: the disparity shift, the refined cost and the state of the pixel( Information: calculations     stopped at the pixel step, sub-pixel interpolation did not succeed )
   :rtype: float, float, int