pandora.refinement.refinement

This module contains classes and functions associated to the subpixel refinement step.

Classes

AbstractRefinement

Abstract Refinement class

Module Contents

class pandora.refinement.refinement.AbstractRefinement

Abstract Refinement class

__metaclass__

subpixel_methods_avail: Dict

_refinement_method_name = None

cfg = None

margins

subpixel_refinement(cv: xarray.Dataset, disp: xarray.Dataset) → None

Subpixel refinement of disparities and costs.

Parameters:

• cv (xarray.Dataset) –

  the cost volume dataset with the data variables:

  • cost_volume 3D xarray.DataArray (row, col, disp)

  • confidence_measure 3D xarray.DataArray (row, col, indicator)

• disp (xarray.Dataset) –

  Dataset with the variables :

  • disparity_map 2D xarray.DataArray (row, col)

  • confidence_measure 3D xarray.DataArray (row, col, indicator)

  • validity_mask 2D xarray.DataArray (row, col)

Returns:

None

approximate_subpixel_refinement(cv_left: xarray.Dataset, disp_right: xarray.Dataset) → xarray.Dataset

Subpixel refinement of the right disparities map, which was created with the approximate method : a diagonal search for the minimum on the left cost volume

Parameters:

• cv_left (xarray.Dataset) –

  the left cost volume dataset with the data variables:

  • cost_volume 3D xarray.DataArray (row, col, disp)

  • confidence_measure 3D xarray.DataArray (row, col, indicator)

• disp_right (xarray.Dataset) –

  right disparity map with the variables :

  • disparity_map 2D xarray.DataArray (row, col)

  • confidence_measure 3D xarray.DataArray (row, col, indicator)

  • validity_mask 2D xarray.DataArray (row, col)

Returns:

disp_right Dataset with the variables :

• disparity_map 2D xarray.DataArray (row, col) that contains the refined disparities

• confidence_measure 3D xarray.DataArray (row, col, indicator) (unchanged)

• validity_mask 2D xarray.DataArray (row, col) with the value of bit 3 ( Information: calculations stopped at the pixel step, sub-pixel interpolation did not succeed )

• interpolated_coeff 2D xarray.DataArray (row, col) that contains the refined cost

Return type:

xarray.Dataset

classmethod register_subclass(short_name: str)

Allows to register the subclass with its short name

Parameters:

short_name (string) – the subclass to be registered

abstract desc() → None

Describes the subpixel method :return: None

static refinement_method(cost: numpy.ndarray, disp: float, measure: str) → Tuple[float, float, int]

Abstractmethod:

Return the subpixel disparity and cost

Parameters:

• cost (1D numpy array : [cost[disp -1], cost[disp], cost[disp + 1]]) – cost of the values disp - 1, disp, disp + 1

• disp (float) – the disparity

• measure (string = min | max) – the type of measure used to create the cost volume

Returns:

the refined disparity (disp + (sub_disp/subpix)), the refined cost and the state of the pixel ( Information: calculations stopped at the pixel step, sub-pixel interpolation did not succeed )

Return type:

float, float, int