cars.applications.resampling.resampling_algo
============================================

.. py:module:: cars.applications.resampling.resampling_algo

.. autoapi-nested-parse::

   Resampling module:
   contains functions used for epipolar resampling



Functions
---------

.. autoapisummary::

   cars.applications.resampling.resampling_algo.epipolar_rectify_images
   cars.applications.resampling.resampling_algo.resample_image
   cars.applications.resampling.resampling_algo.oversampling_func
   cars.applications.resampling.resampling_algo.to_band_format


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

.. py:function:: epipolar_rectify_images(left_imgs, right_imgs, grid1, grid2, region, margins, epipolar_size_x, epipolar_size_y, interpolator_image='bicubic', interpolator_classif='nearest', interpolator_mask='nearest', interpolators_edges=None, step=None, mask1=None, mask2=None, edges1=None, edges2=None, left_classifs=None, right_classifs=None, nodata1=0, nodata2=0, add_classif=True, add_edges=True)

   Resample left and right images


.. py:function:: resample_image(imgs, grid, largest_size, step=None, region=None, nodata=None, mask=None, band_coords=False, interpolator_img='bicubic', interpolator_mask='nearest', img_transform=None)

   Resample image according to grid and largest size.

   :param img: Path to the image to resample
   :type img: string
   :param grid: rectification grid dict
   :type grid: dict
   :param largest_size: Size of full output image
   :type largest_size: list of two int
   :param step: horizontal step of resampling (useful for strip resampling)
   :type step: int
   :param region: A subset of the output image to produce
   :type region: None (full output is produced) or array of four floats
                 [xmin,ymin,xmax,ymax]
   :param nodata: Nodata value to use (both for input and output)
   :type nodata: None or float
   :param mask: Mask to resample as well
   :type mask: None or path to mask image
   :param band_coords: Force bands coordinate in output dataset
   :type band_coords: boolean
   :param interpolator: interpolator type (bicubic (default) or nearest)
   :type interpolator: str ("nearest" "linear" "bco")
   :rtype: xarray.Dataset with resampled image and mask


.. py:function:: oversampling_func(grid_reader, img_reader, img_transform, block_region, interpolator_img, band_coords, nb_bands, bands, resamp, nodata, msk, mask, nodata_msk, interpolator_mask, ysize, xsize, ystart, xstart)

   Do the resampling calculus


.. py:function:: to_band_format(edges_dict)

   Helper function, that takes the edges input dict and transforms it into a
   band format image, so resampling can understand it