pandora.validation.interpolated_disparity

This module contains classes and functions associated to the interpolation of the disparity map for the validation step.

Classes

AbstractInterpolation	Abstract Interpolation class
McCnnInterpolation	McCnnInterpolation class allows to perform the interpolation of the disparity map
SgmInterpolation	SgmInterpolation class allows to perform the interpolation of the disparity map

Module Contents

class pandora.validation.interpolated_disparity.AbstractInterpolation

Abstract Interpolation class

__metaclass__

interpolation_methods_avail: Dict

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 disparity interpolation method for the validation step :return: None

abstract interpolated_disparity(left: xarray.Dataset, img_left: xarray.Dataset = None, img_right: xarray.Dataset = None, cv: xarray.Dataset = None) → None

Interpolation of the left disparity map to resolve occlusion and mismatch conflicts.

Parameters:

• 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)

• 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

  • edges (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

  • edges (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:

None

class pandora.validation.interpolated_disparity.McCnnInterpolation(**cfg: dict)

Bases: AbstractInterpolation

McCnnInterpolation class allows to perform the interpolation of the disparity map

check_config(**cfg: dict) → None

Check and update the configuration

Parameters:

cfg (dictionary) – optional configuration, {}

Returns:

None

desc() → None

Describes the disparity interpolation method :return: None

interpolated_disparity(left: xarray.Dataset, img_left: xarray.Dataset = None, img_right: xarray.Dataset = None, cv: xarray.Dataset = None) → None

Interpolation of the left disparity map to resolve occlusion and mismatch conflicts.

Parameters:

• 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)

• 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

  • edges (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

  • edges (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:

None

static interpolate_occlusion_mc_cnn(disp: numpy.ndarray, valid: numpy.ndarray)

static interpolate_mismatch_mc_cnn(disp: numpy.ndarray, valid: numpy.ndarray)

class pandora.validation.interpolated_disparity.SgmInterpolation(**cfg: dict)

Bases: AbstractInterpolation

SgmInterpolation class allows to perform the interpolation of the disparity map

check_config(**cfg: dict) → None

Check and update the configuration

Parameters:

cfg (dictionary) – optional configuration, {}

Returns:

None

desc() → None

Describes the disparity interpolation method :return: None

interpolated_disparity(left: xarray.Dataset, img_left: xarray.Dataset = None, img_right: xarray.Dataset = None, cv: xarray.Dataset = None) → None

Interpolation of the left disparity map to resolve occlusion and mismatch conflicts.

Parameters:

• 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)

• 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

  • edges (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

  • edges (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:

None

static interpolate_occlusion_sgm(disp: numpy.ndarray, valid: numpy.ndarray)

static interpolate_mismatch_sgm(disp: numpy.ndarray, valid: numpy.ndarray)