:py:mod:`cars.applications.sparse_matching.sift`
================================================

.. py:module:: cars.applications.sparse_matching.sift

.. autoapi-nested-parse::

   this module contains the dense_matching application class.



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

Classes
~~~~~~~

.. autoapisummary::

   cars.applications.sparse_matching.sift.Sift



Functions
~~~~~~~~~

.. autoapisummary::

   cars.applications.sparse_matching.sift.compute_matches_wrapper



.. py:class:: Sift(conf=None)


   Bases: :py:obj:`cars.applications.sparse_matching.sparse_matching.SparseMatching`

   SparseMatching

   .. py:method:: check_conf(conf)

      Check configuration

      :param conf: configuration to check
      :type conf: dict

      :return: overloaded configuration
      :rtype: dict



   .. py:method:: get_save_matches()

      Get save_matches parameter

      :return: true is save_matches activated
      :rtype: bool


   .. py:method:: get_disparity_margin()

      Get disparity margin corresponding to sparse matches

      :return: margin in percent



   .. py:method:: get_strip_margin()

      Get strip margin corresponding to sparse matches

      :return: margin in percent



   .. py:method:: get_epipolar_error_upper_bound()

      Get epipolar error upper bound corresponding to sparse matches

      :return: margin



   .. py:method:: get_minimum_nb_matches()

      Get minimum_nb_matches :
      get the minimum number of matches

      :return: minimum_nb_matches



   .. py:method:: get_epipolar_error_maximum_bias()

      Get epipolar error maximum bias corresponding to sparse matches

      :return: margin



   .. py:method:: get_match_filter_knn()

      Get match_filter_knn :
      number of neighboors used to measure isolation of matches

      :return: match_filter_knn



   .. py:method:: get_match_filter_constant()

      Get get_match_filter_constant :
      constant in the formula to compute threshold of outliers

      :return: match_filter_constant



   .. py:method:: get_match_filter_mean_factor()

      Get match_filter_mean_factor :
      factor of mean in the formula
      to compute threshold of outliers

      :return: match_filter_mean_factor



   .. py:method:: get_match_filter_dev_factor()

      Get match_filter_dev_factor :
      factor of deviation in the formula
      to compute threshold of outliers

      :return: match_filter_dev_factor



   .. py:method:: get_decimation_factor()

      Get decimation_factor

      :return: decimation_factor



   .. py:method:: set_decimation_factor(value)

      set decimation_factor



   .. py:method:: run(epipolar_image_left, epipolar_image_right, disp_to_alt_ratio, orchestrator=None, pair_folder=None, pair_key='PAIR_0')

      Run Matching application.

      Create left and right CarsDataset filled with pandas.DataFrame ,
      corresponding to epipolar 2D disparities, on the same geometry
      that epipolar_image_left and epipolar_image_right.

      :param epipolar_image_left: tiled left epipolar. CarsDataset contains:

              - N x M Delayed tiles                     Each tile will be a future xarray Dataset containing:

                  - data with keys : "im", "msk", "color"
                  - attrs with keys: "margins" with "disp_min" and "disp_max"
                      "transform", "crs", "valid_pixels", "no_data_mask",
                      "no_data_img"
              - attributes containing:
                  "largest_epipolar_region","opt_epipolar_tile_size"
      :type epipolar_image_left: CarsDataset
      :param epipolar_image_right: tiled right epipolar.CarsDataset contains:

              - N x M Delayed tiles                     Each tile will be a future xarray Dataset containing:

                  - data with keys : "im", "msk", "color"
                  - attrs with keys: "margins" with "disp_min" and "disp_max"                        "transform", "crs", "valid_pixels", "no_data_mask",                        "no_data_img"
              - attributes containing:"largest_epipolar_region",                   "opt_epipolar_tile_size"
      :type epipolar_image_right: CarsDataset
      :param disp_to_alt_ratio: disp to alti ratio
      :type disp_to_alt_ratio: float
      :param orchestrator: orchestrator used
      :param pair_folder: folder used for current pair
      :type pair_folder: str
      :param pair_key: pair key id
      :type pair_key: str

      :return left matches, right matches. Each CarsDataset contains:

          - N x M Delayed tiles                 Each tile will be a future pandas DataFrame containing:
              - data : (L, 4) shape matches
          - attributes containing "disp_lower_bound",  "disp_upper_bound",                "elevation_delta_lower_bound","elevation_delta_upper_bound"

      :rtype: Tuple(CarsDataset, CarsDataset)



.. py:function:: compute_matches_wrapper(left_image_object: xarray.Dataset, right_image_object: xarray.Dataset, matching_threshold=None, n_octave=None, n_scale_per_octave=None, peak_threshold=None, edge_threshold=None, magnification=None, window_size=None, backmatching=None, disp_lower_bound=None, disp_upper_bound=None, saving_info_left=None) -> Dict[str, Tuple[xarray.Dataset, xarray.Dataset]]

   Compute matches from image objects.
   This function will be run as a delayed task.

   User must provide saving infos to save properly created datasets

   :param left_image_object: tiled Left image dataset with :

           - cst.EPI_IMAGE
           - cst.EPI_MSK (if given)
           - cst.EPI_COLOR (for left, if given)
   :type left_image_object: xr.Dataset with :

           - cst.EPI_IMAGE
           - cst.EPI_MSK (if given)
           - cst.EPI_COLOR (for left, if given)
   :param right_image_object: tiled Right image
   :type right_image_object: xr.Dataset


   :return: Left matches object, Right matches object (if exists)

   Returned objects are composed of :

       - dataframe (None for right object) with :
           - TODO