cars.applications.sparse_matching.sparse_matching_algo
======================================================

.. py:module:: cars.applications.sparse_matching.sparse_matching_algo

.. autoapi-nested-parse::

   Sparse matching Sift module:
   contains sift sparse matching method



Functions
---------

.. autoapisummary::

   cars.applications.sparse_matching.sparse_matching_algo.compute_matches
   cars.applications.sparse_matching.sparse_matching_algo.dataset_matching


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

.. py:function:: compute_matches(left: numpy.ndarray, right: numpy.ndarray, left_mask: numpy.ndarray = None, right_mask: numpy.ndarray = None, left_origin: [float, float] = None, right_origin: [float, float] = None, matching_threshold: float = 0.7, n_octave: int = 8, n_scale_per_octave: int = 3, peak_threshold: float = 4.0, edge_threshold: float = 10.0, magnification: float = 7.0, window_size: int = 2, backmatching: bool = True, disp_lower_bound=None, disp_upper_bound=None)

   Compute matches between left and right
   Convention for masks: True is a valid pixel

   :param left: left image as numpy array
   :type left: np.ndarray
   :param right: right image as numpy array
   :type right: np.ndarray
   :param left_mask: left mask as numpy array
   :type left_mask: np.ndarray
   :param right_mask: right mask as numpy array
   :type right_mask: np.ndarray
   :param left_origin: left image origin in the full image
   :type left_origin: [float, float]
   :param right_origin: right image origin in the full image
   :type right_origin: [float, float]
   :param matching_threshold: threshold for the ratio to nearest second match
   :type matching_threshold: float
   :param n_octave: the number of octaves of the DoG scale space
   :type n_octave: int
   :param n_scale_per_octave: the nb of levels / octave of the DoG scale space
   :type n_scale_per_octave: int
   :param peak_threshold: the peak selection threshold
   :type peak_threshold: float
   :param edge_threshold: the edge selection threshold
   :type edge_threshold: float
   :param magnification: set the descriptor magnification factor
   :type magnification: float
   :param window_size: size of the window
   :type window_size: int
   :param backmatching: also check that right vs. left gives same match
   :type backmatching: bool

   :return: matches
   :rtype: numpy buffer of shape (nb_matches,4)



.. py:function:: dataset_matching(ds1, ds2, used_band, matching_threshold=0.7, n_octave=8, n_scale_per_octave=3, peak_threshold=4.0, edge_threshold=10.0, magnification=7.0, window_size=2, backmatching=True, disp_lower_bound=None, disp_upper_bound=None, classif_bands_to_mask=None)

   Compute sift matches between two datasets
   produced by stereo.epipolar_rectify_images

   :param ds1: Left image dataset
   :type ds1: xarray.Dataset as produced by stereo.epipolar_rectify_images
   :param ds2: Right image dataset
   :type ds2: xarray.Dataset as produced by stereo.epipolar_rectify_images
   :param matching_threshold: threshold for the ratio to nearest second match
   :type matching_threshold: float
   :param n_octave: the number of octaves of the DoG scale space
   :type n_octave: int
   :param n_scale_per_octave: the nb of levels / octave of the DoG scale space
   :type n_scale_per_octave: int
   :param peak_threshold: the peak selection threshold
   :type peak_threshold: int
   :param edge_threshold: the edge selection threshold.
   :param magnification: set the descriptor magnification factor
   :type magnification: float
   :param window_size: size of the window
   :type window_size: int
   :param backmatching: also check that right vs. left gives same match
   :type backmatching: bool
   :param classif_bands_to_mask: bands from classif to mask
   :type classif_bands_to_mask: list of str / int

   :return: matches
   :rtype: numpy buffer of shape (nb_matches,4)