:py:mod:`cars.applications.hole_detection.hole_detection_tools`
===============================================================

.. py:module:: cars.applications.hole_detection.hole_detection_tools

.. autoapi-nested-parse::

   This module contains function for holes detection.



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


Functions
~~~~~~~~~

.. autoapisummary::

   cars.applications.hole_detection.hole_detection_tools.get_roi_coverage_as_poly_with_margins
   cars.applications.hole_detection.hole_detection_tools.localize_masked_areas
   cars.applications.hole_detection.hole_detection_tools.classif_to_stacked_array



.. py:function:: get_roi_coverage_as_poly_with_margins(msk_values: numpy.ndarray, row_offset=0, col_offset=0, margin=0) -> List[shapely.geometry.Polygon]

   Finds all roi existing in binary msk and stores their coverage as
   list of Polygon

   :param msk_values: msk layer of left/right epipolar image dataset
   :type msk_values: np.ndarray
   :param row_offset: offset on row to apply
   :type row_offset: int
   :param col_offset: offset on col to apply
   :type col_offset: int
   :param margin: margin added to bbox in case masked region is
       localized at tile border (to ensure later disparity values
       at mask border extraction)
   :type margin: int

   :return: list of polygon



.. py:function:: localize_masked_areas(dataset: xarray.Dataset, classification: List[str], row_offset: int = 0, col_offset: int = 0, margin: int = 0) -> numpy.ndarray

   Calculates bbox of masked region(s) if mask exists for
   input image file (see configuration "mask" and "mask_classes"
   in input .json configuration file)

   :param dataset: epipolar image dataset
   :type dataset: CarsDataset
   :param classification: label of masked region to use
   :type classification: List of str
   :param row_offset: offset on row to apply
   :type row_offset: int
   :param col_offset: offset on col to apply
   :type col_offset: int
   :param margin: margin added to bbox in case masked region is
       localized at tile border (to ensure later disparity values
       at mask border extraction)
   :type margin: int

   :return: bounding box of masked area(s)



.. py:function:: classif_to_stacked_array(disp_map, class_index)

   Convert disparity dataset to mask correspoding to all classes

   :param disp_map: disparity dataset
   :type disp_map: xarray Dataset
   :param class_index: classification tags
   :type class_index: list of str