colour.adaptation.matrix_chromatic_adaptation_vk20#
- colour.adaptation.matrix_chromatic_adaptation_vk20(XYZ_p: ArrayLike, XYZ_n: ArrayLike, XYZ_r: ArrayLike = TVS_XYZ_R_VK20, transform: Literal['Bianco 2010', 'Bianco PC 2010', 'Bradford', 'CAT02 Brill 2008', 'CAT02', 'CAT16', 'CMCCAT2000', 'CMCCAT97', 'Fairchild', 'Sharp', 'Von Kries', 'XYZ Scaling'] | str = 'CAT02', coefficients: Coefficients_DegreeOfAdaptation_vK20 = CONDITIONS_DEGREE_OF_ADAPTATION_VK20['Fairchild']) NDArrayFloat [source]#
Compute the chromatic adaptation matrix from previous viewing conditions to adapting viewing conditions using Von Kries 2020 (vK20) method.
- Parameters:
XYZ_p (ArrayLike) – Previous viewing conditions CIE XYZ tristimulus values of whitepoint.
XYZ_n (ArrayLike) – Adapting viewing conditions CIE XYZ tristimulus values of whitepoint.
XYZ_r (ArrayLike) – Reference viewing conditions CIE XYZ tristimulus values of whitepoint.
transform (Literal['Bianco 2010', 'Bianco PC 2010', 'Bradford', 'CAT02 Brill 2008', 'CAT02', 'CAT16', 'CMCCAT2000', 'CMCCAT97', 'Fairchild', 'Sharp', 'Von Kries', 'XYZ Scaling'] | str) – Chromatic adaptation transform.
coefficients (Coefficients_DegreeOfAdaptation_vK20) – vK20 degree of adaptation coefficients.
- Returns:
Chromatic adaptation matrix \(M_{cat}\).
- Return type:
Notes
Domain
Scale - Reference
Scale - 1
XYZ_p
[0, 1]
[0, 1]
XYZ_n
[0, 1]
[0, 1]
XYZ_r
[0, 1]
[0, 1]
References
[Fai20]
Examples
>>> XYZ_p = np.array([0.95045593, 1.00000000, 1.08905775]) >>> XYZ_n = np.array([0.96429568, 1.00000000, 0.82510460]) >>> matrix_chromatic_adaptation_vk20(XYZ_p, XYZ_n) ... array([[ 1.0279139...e+00, 2.9137117...e-02, -2.2794068...e-02], [ 2.0702840...e-02, 9.9005316...e-01, -9.2143464...e-03], [ -6.3758553...e-04, -1.1577319...e-03, 9.1296320...e-01]])
Using Bradford transform:
>>> XYZ_p = np.array([0.95045593, 1.00000000, 1.08905775]) >>> XYZ_n = np.array([0.96429568, 1.00000000, 0.82510460]) >>> transform = "Bradford" >>> matrix_chromatic_adaptation_vk20(XYZ_p, XYZ_n, transform=transform) ... array([[ 1.0367230..., 0.0195580..., -0.0219321...], [ 0.0276321..., 0.9822296..., -0.0082419...], [-0.0029508..., 0.0040690..., 0.9102430...]])