cars.applications.sparse_matching.sparse_matching_wrappers
==========================================================

.. py:module:: cars.applications.sparse_matching.sparse_matching_wrappers

.. autoapi-nested-parse::

   Sparse matching Sift module:
   contains sift sparse matching method



Functions
---------

.. autoapisummary::

   cars.applications.sparse_matching.sparse_matching_wrappers.euclidean_matrix_distance
   cars.applications.sparse_matching.sparse_matching_wrappers.remove_epipolar_outliers
   cars.applications.sparse_matching.sparse_matching_wrappers.compute_disparity_range
   cars.applications.sparse_matching.sparse_matching_wrappers.compute_disp_min_disp_max
   cars.applications.sparse_matching.sparse_matching_wrappers.transform_triangulated_matches_to_dataframe
   cars.applications.sparse_matching.sparse_matching_wrappers.get_margins


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

.. py:function:: euclidean_matrix_distance(descr1: numpy.array, descr2: numpy.array)

   Compute a matrix containing cross euclidean distance
   :param descr1: first keypoints descriptor
   :type descr1: numpy.ndarray
   :param descr2: second keypoints descriptor
   :type descr2: numpy.ndarray
   :return euclidean matrix distance
   :rtype: float


.. py:function:: remove_epipolar_outliers(matches, percent=0.1)

   This function will filter the match vector
   according to a quantile of epipolar error
   used for testing compute_disparity_range sparse method

   :param matches: the [4,N] matches array
   :type matches: numpy array
   :param percent: the quantile to remove at each extrema
   :type percent: float
   :return: the filtered match array
   :rtype: numpy array


.. py:function:: compute_disparity_range(matches, percent=0.1)

   This function will compute the disparity range
   from matches by filtering percent outliers

   :param matches: the [4,N] matches array
   :type matches: numpy array
   :param percent: the quantile to remove at each extrema (in %)
   :type percent: float
   :return: the disparity range
   :rtype: float, float


.. py:function:: compute_disp_min_disp_max(pd_cloud, orchestrator, disp_margin=0.1, pair_key=None, disp_to_alt_ratio=None)

   Compute disp min and disp max from triangulated and filtered matches

   :param pd_cloud: triangulated_matches
   :type pd_cloud: pandas Dataframe
   :param orchestrator: orchestrator used
   :type orchestrator: Orchestrator
   :param disp_margin: disparity margin
   :type disp_margin: float
   :param disp_to_alt_ratio: used for logging info
   :type disp_to_alt_ratio: float

   :return: disp min and disp max
   :rtype: float, float


.. py:function:: transform_triangulated_matches_to_dataframe(triangulated_matches)

   :param triangulated_matches: triangulated matches
   :type: cars_dataset


.. py:function:: get_margins(margin_left, margin_right, disp_min, disp_max)

   Get margins for the dense matching steps

   :param margin: margins object
   :type margin: Margins
   :param disp_min: Minimum disparity
   :type disp_min: int
   :param disp_max: Maximum disparity
   :type disp_max: int
   :return: margins of the matching algorithm used