"""
Zhai and Luo (2018) Chromatic Adaptation Model
==============================================
Defines the *Zhai and Luo (2018)* chromatic adaptation model object:
- :func:`colour.adaptation.chromatic_adaptation_Zhai2018`
References
----------
- :cite:`Zhai2018` : Zhai, Q., & Luo, M. R. (2018). Study of chromatic
adaptation via neutral white matches on different viewing media. Optics
Express, 26(6), 7724. doi:10.1364/OE.26.007724
"""
import numpy as np
from colour.adaptation import CHROMATIC_ADAPTATION_TRANSFORMS
from colour.algebra import vector_dot
from colour.hints import ArrayLike, Literal, NDArrayFloat, Union
from colour.utilities import (
as_float_array,
from_range_100,
to_domain_100,
validate_method,
)
__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "colour-developers@colour-science.org"
__status__ = "Production"
__all__ = [
"chromatic_adaptation_Zhai2018",
]
[docs]
def chromatic_adaptation_Zhai2018(
XYZ_b: ArrayLike,
XYZ_wb: ArrayLike,
XYZ_wd: ArrayLike,
D_b: ArrayLike = 1,
D_d: ArrayLike = 1,
XYZ_wo: ArrayLike = np.array([1, 1, 1]),
transform: Union[Literal["CAT02", "CAT16"], str] = "CAT02",
) -> NDArrayFloat:
"""
Adapt given sample colour :math:`XYZ_{\\beta}` tristimulus values from
input viewing conditions under :math:`\\beta` illuminant to output viewing
conditions under :math:`\\delta` illuminant using *Zhai and Luo (2018)*
chromatic adaptation model.
According to the definition of :math:`D`, a one-step CAT such as CAT02 can
only be used to transform colors from an incomplete adapted field into a
complete adapted field. When CAT02 are used to transform an incomplete to
incomplete case, :math:`D` has no baseline level to refer to.
*Smet et al. (2017)* proposed a new concept of two-step CAT to replace the
present CATs such as CAT02 with only one-step transform in order to define
:math:`D` more clearly. A two-step CAT involves an illuminant representing
the baseline states between the test and reference illuminants for the
calculation. In the first step the test color is transformed from test
illuminant to the baseline illuminant (:math:`BI`), and it is then
transformed to the reference illuminant Degrees of adaptation under the
other illuminants should be calculated relative to the adaptation under the
:math:`BI`. When :math:`D` becomes lower towards zero, the adaptation point
of the observer moves towards the :math:`BI`. Therefore, the chromaticity
of the :math:`BI` should be an intrinsic property of the human vision
system.
Parameters
----------
XYZ_b
Sample colour :math:`XYZ_{\\beta}` under input illuminant
:math:`\\beta`.
XYZ_wb
Input illuminant :math:`\\beta`.
XYZ_wd
Output illuminant :math:`\\delta`.
D_b
Degree of adaptation :math:`D_{\\beta}` of input illuminant
:math:`\\beta`.
D_d
Degree of adaptation :math:`D_{\\delta}` of output illuminant
:math:`\\delta`.
XYZ_wo
Baseline illuminant (:math:`BI`) :math:`o`.
transform
Chromatic adaptation transform.
Returns
-------
:class:`numpy.ndarray`
Sample corresponding colour :math:`XYZ_{\\delta}` tristimulus values
under output illuminant :math:`D_{\\delta}`.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_b`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_wb`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_wd`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_wo`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_d`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Zhai2018`
Examples
--------
>>> XYZ_b = np.array([48.900, 43.620, 6.250])
>>> XYZ_wb = np.array([109.850, 100, 35.585])
>>> XYZ_wd = np.array([95.047, 100, 108.883])
>>> D_b = 0.9407
>>> D_d = 0.9800
>>> XYZ_wo = np.array([100, 100, 100])
>>> chromatic_adaptation_Zhai2018(
... XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d, XYZ_wo
... ) # doctest: +ELLIPSIS
array([ 39.1856164..., 42.1546179..., 19.2367203...])
>>> XYZ_d = np.array([39.18561644, 42.15461798, 19.23672036])
>>> chromatic_adaptation_Zhai2018(
... XYZ_d, XYZ_wd, XYZ_wb, D_d, D_b, XYZ_wo
... ) # doctest: +ELLIPSIS
array([ 48.9 , 43.62, 6.25])
"""
XYZ_b = to_domain_100(XYZ_b)
XYZ_wb = to_domain_100(XYZ_wb)
XYZ_wd = to_domain_100(XYZ_wd)
XYZ_wo = to_domain_100(XYZ_wo)
D_b = as_float_array(D_b)
D_d = as_float_array(D_d)
Y_wb = XYZ_wb[..., 1][..., None]
Y_wd = XYZ_wd[..., 1][..., None]
Y_wo = XYZ_wo[..., 1][..., None]
transform = validate_method(transform, ("CAT02", "CAT16"))
M = CHROMATIC_ADAPTATION_TRANSFORMS[transform]
RGB_b = vector_dot(M, XYZ_b)
RGB_wb = vector_dot(M, XYZ_wb)
RGB_wd = vector_dot(M, XYZ_wd)
RGB_wo = vector_dot(M, XYZ_wo)
D_RGB_b = D_b * (Y_wb / Y_wo) * (RGB_wo / RGB_wb) + 1 - D_b
D_RGB_d = D_d * (Y_wd / Y_wo) * (RGB_wo / RGB_wd) + 1 - D_d
D_RGB = D_RGB_b / D_RGB_d
RGB_d = D_RGB * RGB_b
XYZ_d = vector_dot(np.linalg.inv(M), RGB_d)
return from_range_100(XYZ_d)