pandora.multiscale.fixed_zoom_pyramid

This module contains functions associated to the multi-scale pyramid method.

Module Contents

Classes

FixedZoomPyramid

FixedZoomPyramid class, allows to perform the multiscale processing
class pandora.multiscale.fixed_zoom_pyramid.FixedZoomPyramid(left_img: xarray.Dataset, right_img: xarray.Dataset, **cfg: dict)[source]

Bases: pandora.multiscale.multiscale.AbstractMultiscale

FixedZoomPyramid class, allows to perform the multiscale processing

_PYRAMID_NUM_SCALES = 2[source]
_PYRAMID_SCALE_FACTOR = 2[source]
_PYRAMID_MARGE = 1[source]
check_conf(left_img: xarray.Dataset, right_img: xarray.Dataset, **cfg: str | float | int) → Dict[str, str | float | int][source]

Add default values to the dictionary if there are missing elements and check if the dictionary is correct

Parameters:

  • left_img (xarray.Dataset) – xarray.Dataset of left image

  • right_img (xarray.Dataset) – xarray.Dataset of right image

  • cfg (dict) – aggregation configuration

Return cfg:

aggregation configuration updated

Return type:

dict

desc()[source]

Describes the aggregation method

disparity_range(disp: xarray.Dataset, disp_min: numpy.ndarray, disp_max: numpy.ndarray) → Tuple[numpy.ndarray, numpy.ndarray][source]

Disparity range computation by seeking the max and min values in the window. Invalid disparities are given the full disparity range

Parameters:

  • disp (xarray.Dataset with the data variables : - disparity_map 2D xarray.DataArray (row, col) - confidence_measure 3D xarray.DataArray(row, col, indicator)) – the disparity dataset

  • disp_min (np.ndarray) – absolute min disparity

  • disp_max (np.ndarray) – absolute max disparity

Returns:

Two np.darray : - disp_min_range : minimum disparity value for all pixels. - disp_max_range : maximum disparity value for all pixels.

Return type:

tuple (np.ndarray, np.ndarray)