pandora.cost_volume_confidence.risk

This module contains functions for estimating the risk.

Classes

Risk	Allows to estimate a risk confidence from the cost volume

Module Contents

class pandora.cost_volume_confidence.risk.Risk(**cfg: str)

Bases: pandora.cost_volume_confidence.cost_volume_confidence.AbstractCostVolumeConfidence

Allows to estimate a risk confidence from the cost volume

_ETA_MIN = 0.0

_ETA_MAX = 0.7

_ETA_STEP = 0.01

_PERCENTILE = 1.0

_method_max = 'risk_max'

_method_min = 'risk_min'

_indicator = ''

check_conf(**cfg: str | float) → Dict[str, str | float]

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

Parameters:

cfg (dict) – ambiguity configuration

Return cfg:

ambiguity configuration updated

Return type:

dict

desc() → None

Describes the confidence method :return: None

confidence_prediction(disp: xarray.Dataset, img_left: xarray.Dataset = None, img_right: xarray.Dataset = None, cv: xarray.Dataset = None) → Tuple[xarray.Dataset, xarray.Dataset]

Computes a risk confidence measure that evaluates the matching cost function at each point

Parameters:

• disp (xarray.Dataset) – the disparity map dataset

• img_left – left Dataset image

• img_right (xarray.Dataset) – right Dataset image

• cv (xarray.Dataset) – cost volume dataset

Tye img_left:

xarray.Dataset

Returns:

the disparity map and the cost volume with new indicators ‘risk_max_confidence’ and ‘risk_min_confidence’ in the

Return type:

Tuple(xarray.Dataset, xarray.Dataset)

static compute_risk(cv: numpy.ndarray, sampled_ambiguity: numpy.ndarray, etas: numpy.ndarray, nbr_etas: int, grids: numpy.ndarray, disparity_range: numpy.ndarray) → Tuple[numpy.ndarray, numpy.ndarray]

Computes minimum and maximum risk.

Parameters:

• cv (3D np.ndarray (row, col, disp)) – cost volume

• sampled_ambiguity (3D np.ndarray (row, col, eta)) – sampled cost volume ambiguity

• etas (np.ndarray) – range between eta_min and eta_max with step eta_step

• nbr_etas (int) – number of etas

• grids (2D np.ndarray (min, max)) – array containing min and max disparity grids

• disparity_range (np.ndarray) – array containing disparity range

Returns:

the minimum and maximum risk

Return type:

Tuple(2D np.ndarray (row, col) dtype = float32, 2D np.ndarray (row, col) dtype = float32)

static compute_risk_and_sampled_risk(cv: numpy.ndarray, sampled_ambiguity: numpy.ndarray, etas: numpy.ndarray, nbr_etas: int, grids: numpy.ndarray, disparity_range: numpy.ndarray) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]

Computes minimum and maximum risk and sampled_risk.

Parameters:

• cv (3D np.ndarray (row, col, disp)) – cost volume

• sampled_ambiguity (3D np.ndarray (row, col, eta)) – sampled cost volume ambiguity

• etas (np.ndarray) – range between eta_min and eta_max with step eta_step

• nbr_etas (int) – nuber of etas

• grids (2D np.ndarray (min, max)) – array containing min and max disparity grids

• disparity_range (np.ndarray) – array containing disparity range

Returns:

the risk

Return type:

Tuple(2D np.ndarray (row, col) dtype = float32, 2D np.ndarray (row, col) dtype = float32, 3D np.ndarray (row, col) dtype = float32, 3D np.ndarray (row, col) dtype = float32)