pandora.cost_volume_confidence.std_intensity

This module contains functions for estimating confidence from image.

Module Contents

Classes

StdIntensity

StdIntensity class allows to estimate a confidence measure from the left image by calculating the standard

class pandora.cost_volume_confidence.std_intensity.StdIntensity(**cfg: str)

Bases: pandora.cost_volume_confidence.cost_volume_confidence.AbstractCostVolumeConfidence

StdIntensity class allows to estimate a confidence measure from the left image by calculating the standard

deviation of the intensity

_method = 'intensity_std'

_indicator = ''

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

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

Parameters:

cfg (dict) – std_intensity configuration

Return cfg:

std_intensity 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 confidence measure that evaluates the standard deviation of intensity of the left image

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 a new indicator ‘ambiguity_confidence’ in the DataArray confidence_measure

Return type:

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

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