Spectral Recovery and Up-sampling#

Reflectance Recovery#

CIE XYZ Colourspace to Spectral#

colour

XYZ_to_sd(XYZ[, method])

Recover the spectral distribution of the specified CIE XYZ tristimulus values using the specified method.

XYZ_TO_SD_METHODS

Supported spectral distribution recovery methods.

XYZ_to_msds(XYZ[, method])

Recover spectral values from the specified CIE XYZ tristimulus values using the specified method.

XYZ_TO_MSDS_METHODS

Supported multi-spectral distributions recovery methods.

Jakob and Hanika (2019)#

colour.recovery

LUT3D_Jakob2019()

Define a class for working with pre-computed lookup tables for the Jakob and Hanika (2019) spectral upsampling method.

XYZ_to_sd_Jakob2019(...)

Recover the spectral distribution from the specified CIE XYZ tristimulus values using Jakob and Hanika (2019) method.

Ancillary Objects

colour.recovery

find_coefficients_Jakob2019(XYZ[, cmfs, ...])

Find the coefficients for the Jakob and Hanika (2019) reflectance spectral model.

sd_Jakob2019(coefficients[, shape])

Generate a spectral distribution using the spectral model specified by Jakob and Hanika (2019).

Mallett and Yuksel (2019)#

colour.recovery

RGB_to_sd_Mallett2019(RGB[, basis_functions])

Recover the spectral distribution of the specified RGB colourspace array using Mallett and Yuksel (2019) method.

Ancillary Objects

colour.recovery

MSDS_BASIS_FUNCTIONS_sRGB_MALLETT2019

the base object for multi-spectral computations.

spectral_primary_decomposition_Mallett2019(...)

Perform spectral primary decomposition as described in Mallett and Yuksel (2019) for the specified RGB colourspace.

SPECTRAL_SHAPE_sRGB_MALLETT2019

Shape for Mallett and Yuksel (2019) sRGB colourspace basis functions: (380, 780, 5).

Meng, Simon and Hanika (2015)#

colour.recovery

XYZ_to_sd_Meng2015(XYZ[, cmfs, illuminant, ...])

Recover the spectral distribution from the specified CIE XYZ tristimulus values using the Meng et al. (2015) method.

Otsu, Yamamoto and Hachisuka (2018)#

colour.recovery

XYZ_to_sd_Otsu2018(XYZ[, cmfs, illuminant, ...])

Recover the spectral distribution of the specified CIE XYZ tristimulus values using Otsu et al. (2018) method.

Ancillary Objects

colour.recovery

Dataset_Otsu2018([shape, basis_functions, ...])

Store all information required for the Otsu et al. (2018) spectral upsampling method.

Tree_Otsu2018(*args, **kwargs)

Sub-class of colour.recovery.otsu2018.Node representing the root node of a tree containing information shared with all nodes, such as the standard observer colour matching functions and the illuminant, if any is used.

Smits (1999)#

colour.recovery

RGB_to_msds_Smits1999(RGB[, basis])

Recover spectral values from RGB colourspace array using the Smits (1999) decomposition algorithm.

RGB_to_sd_Smits1999(RGB[, basis, name])

Generate a spectral distribution from RGB values using the Smits (1999) decomposition algorithm.

MSDS_SMITS1999

Smits (1999) multi-spectral distributions.

SDS_SMITS1999

Smits (1999) spectral distributions.

Gaussian Basis#

colour.recovery

RGB_to_msds_Gaussian(RGB)

Recover spectral values from RGB colourspace array using Gaussian basis spectra and the Smits (1999) decomposition algorithm.

RGB_to_sd_Gaussian(RGB)

Recover the spectral distribution of the specified RGB colourspace array using Gaussian basis spectra and the Smits (1999) decomposition algorithm.

MSDS_GAUSSIAN_BASIS

Gaussian basis multi-spectral distributions for spectral upsampling.

Ancillary Objects

colour.recovery

FWHM_GAUSSIAN_BASIS

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).

PEAK_WAVELENGTHS_GAUSSIAN_BASIS

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).

generate_gaussian_basis([shape, ...])

Generate a set of Gaussian basis multi-spectral distributions.

optimise_gaussian_basis_parameters([shape, ...])

Optimise Gaussian basis parameters for colorimetric accuracy.

Camera RGB Sensitivities Recovery#

Jiang, Liu, Gu and Süsstrunk (2013)#

colour.recovery

RGB_to_msds_camera_sensitivities_Jiang2013(...)

Recover the camera RGB sensitivities for the specified camera RGB values using Jiang et al. (2013) method.

RGB_to_sd_camera_sensitivity_Jiang2013(RGB, ...)

Recover a single camera RGB sensitivity for the specified camera RGB values using Jiang et al. (2013) method.

Ancillary Objects

colour.recovery

PCA_Jiang2013(-> Tuple[Tuple[NDArrayFloat, ...)

Perform Principal Component Analysis (PCA) on specified camera RGB sensitivities.