`pandora.validation.validation`

This module contains classes and functions associated to the validation step.

Module Contents

Classes

`AbstractValidation`	Abstract Validation class
`CrossCheckingAccurate`	CrossChecking class allows to perform the validation step

class pandora.validation.validation.AbstractValidation[source]

Abstract Validation class

__metaclass__[source]

validation_methods_avail: Dict[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() → None[source]: Describes the validation method :return: None

abstract disparity_checking(dataset_left: xarray.Dataset, dataset_right: xarray.Dataset, img_left: xarray.Dataset = None, img_right: xarray.Dataset = None, cv: xarray.Dataset = None) → xarray.Dataset[source]

Determination of occlusions and false matches by performing a consistency check on valid pixels. Update the validity_mask :

If out & MSK_PIXEL_OCCLUSION != 0 : Invalid pixel : occluded pixel

If out & MSK_PIXEL_MISMATCH != 0 : Invalid pixel : mismatched pixel

Update the measure map: add the disp RL / disp LR distances

Parameters:

dataset_left (xarray.Dataset) –
left 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)
dataset_right (xarray.Dataset) –
right 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)
img_left (xarray.Dataset) –
left Datset image containing :
- im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
- disparity (optional): 3D (disp, row, col) xarray.DataArray float32
- msk (optional): 2D (row, col) xarray.DataArray int16
- classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
- segm (optional): 2D (row, col) xarray.DataArray int16
img_right (xarray.Dataset) –
right Dataset image containing :
- im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
- disparity (optional): 3D (disp, row, col) xarray.DataArray float32
- msk (optional): 2D (row, col) xarray.DataArray int16
- classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
- segm (optional): 2D (row, col) xarray.DataArray int16
cv (xarray.Dataset) –
cost_volume Dataset with the variables:
- cost_volume 3D xarray.DataArray (row, col, disp)
- confidence_measure 3D xarray.DataArray (row, col, indicator)

Returns:

the left 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) with the bit 8 and 9 of the validity_mask :
- If out & MSK_PIXEL_OCCLUSION != 0 : Invalid pixel : occluded pixel
- If out & MSK_PIXEL_MISMATCH != 0 : Invalid pixel : mismatched pixel

Return type:

xarray.Dataset

class pandora.validation.validation.CrossCheckingAccurate(**cfg)[source]

Bases: AbstractValidation

CrossChecking class allows to perform the validation step

_THRESHOLD = 1.0[source]

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

Parameters:: cfg (dict) – optimization configuration
Returns:: optimization configuration updated
Return type:: dict

desc() → None[source]: Describes the validation method :return: None

disparity_checking(dataset_left: xarray.Dataset, dataset_right: xarray.Dataset, img_left: xarray.Dataset = None, img_right: xarray.Dataset = None, cv: xarray.Dataset = None) → xarray.Dataset[source]

Determination of occlusions and false matches by performing a consistency check on valid pixels.

Update the validity_mask :