pandora.cost_volume_confidence.cost_volume_confidence

This module contains classes and functions to estimate confidence.

Module Contents

Classes

AbstractCostVolumeConfidence

Abstract Cost Volume Confidence class
class pandora.cost_volume_confidence.cost_volume_confidence.AbstractCostVolumeConfidence[source]

Abstract Cost Volume Confidence class

__metaclass__[source]
confidence_methods_avail[source]
cfg[source]
classmethod register_subclass(short_name: str)[source]

Allows to register the subclass with its short name

Parameters:

short_name (string) – the subclass to be registered

abstract desc()[source]

Describes the confidence method

abstract confidence_prediction(disp: xarray.Dataset, img_left: xarray.Dataset, img_right: xarray.Dataset, cv: xarray.Dataset) → Tuple[xarray.Dataset, xarray.Dataset][source]

Computes a confidence prediction.

Parameters:

  • disp (xarray.Dataset or None) – the disparity map dataset or None

  • img_left – left Dataset image

  • img_right (xarray.Dataset) – right Dataset image

  • cv (xarray.Dataset) – cost volume dataset

Tye img_left:

xarray.Dataset

Returns:

None

static allocate_confidence_map(name_confidence_measure: str, confidence_map: numpy.ndarray, disp: xarray.Dataset, cv: xarray.Dataset) → Tuple[xarray.Dataset, xarray.Dataset][source]

Create or update the confidence measure : confidence_measure (xarray.DataArray of the cost volume and the disparity map) by adding a the indicator

Parameters:

  • name_confidence_measure (string) – the name of the new confidence indicator

  • confidence_map (2D np.array (row, col) dtype=np.float32) – the condidence map

  • disp (xarray.Dataset or None) – the disparity map dataset or None

  • cv (xarray.Dataset) – cost volume dataset

Returns:

the disparity map and the cost volume with updated confidence measure

Return type:

Tuple(xarray.Dataset, xarray.Dataset) with the data variables:

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