def test_nan_chromatic_adaptation_Zhai2018(self):
"""
Test :func:`colour.adaptation.zhai2018.chromatic_adaptation_Zhai2018`
definition nan support.
"""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = set(permutations(cases * 3, r=3))
for case in cases:
XYZ_b = np.array(case)
XYZ_wb = np.array(case)
XYZ_wd = np.array(case)
D_b = case[0]
D_d = case[0]
XYZ_wo = np.array(case)
chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d,
XYZ_wo)
def test_n_dimensional_chromatic_adaptation_Zhai2018(self):
"""
Test :func:`colour.adaptation.zhai2018.chromatic_adaptation_Zhai2018`
definition n-dimensional arrays support.
"""
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_d = chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d)
XYZ_b = np.tile(XYZ_b, (6, 1))
XYZ_d = np.tile(XYZ_d, (6, 1))
np.testing.assert_almost_equal(
chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d),
XYZ_d,
decimal=7,
)
XYZ_wb = np.tile(XYZ_wb, (6, 1))
XYZ_wd = np.tile(XYZ_wd, (6, 1))
D_b = np.tile(D_b, (6, 1))
D_d = np.tile(D_d, (6, 1))
np.testing.assert_almost_equal(
chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d),
XYZ_d,
decimal=7,
)
XYZ_b = np.reshape(XYZ_b, (2, 3, 3))
XYZ_wb = np.reshape(XYZ_wb, (2, 3, 3))
XYZ_wd = np.reshape(XYZ_wd, (2, 3, 3))
D_b = np.reshape(D_b, (2, 3, 1))
D_d = np.reshape(D_d, (2, 3, 1))
XYZ_d = np.reshape(XYZ_d, (2, 3, 3))
np.testing.assert_almost_equal(
chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd, D_b, D_d),
XYZ_d,
decimal=7,
)
def test_domain_range_scale_chromatic_adaptation_Zhai2018(self):
"""
Test :func:`colour.adaptation.zhai2018.chromatic_adaptation_Zhai2018`
definition domain and range scale support.
"""
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])
XYZ_d = chromatic_adaptation_Zhai2018(XYZ_b, XYZ_wb, XYZ_wd)
d_r = (("reference", 1), ("1", 0.01), ("100", 1))
for scale, factor in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
chromatic_adaptation_Zhai2018(XYZ_b * factor,
XYZ_wb * factor,
XYZ_wd * factor),
XYZ_d * factor,
decimal=7,
)
def test_chromatic_adaptation_Zhai2018(self):
"""
Test :func:`colour.adaptation.zhai2018.chromatic_adaptation_Zhai2018`
definition.
"""
np.testing.assert_almost_equal(
chromatic_adaptation_Zhai2018(
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]),
),
np.array([39.18561644, 42.15461798, 19.23672036]),
decimal=7,
)
np.testing.assert_almost_equal(
chromatic_adaptation_Zhai2018(
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]),
transform="CAT16",
),
np.array([40.37398343, 43.69426311, 20.51733764]),
decimal=7,
)
np.testing.assert_almost_equal(
chromatic_adaptation_Zhai2018(
XYZ_b=np.array([52.034, 58.824, 23.703]),
XYZ_wb=np.array([92.288, 100, 38.775]),
XYZ_wd=np.array([105.432, 100, 137.392]),
D_b=0.6709,
D_d=0.5331,
XYZ_wo=np.array([97.079, 100, 141.798]),
),
np.array([57.03242915, 58.93434364, 64.76261333]),
decimal=7,
)
np.testing.assert_almost_equal(
chromatic_adaptation_Zhai2018(
XYZ_b=np.array([52.034, 58.824, 23.703]),
XYZ_wb=np.array([92.288, 100, 38.775]),
XYZ_wd=np.array([105.432, 100, 137.392]),
D_b=0.6709,
D_d=0.5331,
XYZ_wo=np.array([97.079, 100, 141.798]),
transform="CAT16",
),
np.array([56.77130011, 58.81317888, 64.66922808]),
decimal=7,
)
np.testing.assert_almost_equal(
chromatic_adaptation_Zhai2018(
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]),
),
np.array([38.72444735, 42.09232891, 20.05297620]),
decimal=7,
)
def ZCAM_to_XYZ(
specification: CAM_Specification_ZCAM,
XYZ_w: ArrayLike,
L_A: FloatingOrArrayLike,
Y_b: FloatingOrArrayLike,
surround: InductionFactors_ZCAM = VIEWING_CONDITIONS_ZCAM["Average"],
discount_illuminant: Boolean = False,
) -> NDArray:
"""
Convert from *ZCAM* specification to *CIE XYZ* tristimulus values.
Parameters
----------
specification
*ZCAM* colour appearance model specification.
Correlate of *Lightness* :math:`J`, correlate of *chroma* :math:`C` or
correlate of *colourfulness* :math:`M` and *hue* angle :math:`h` in
degrees must be specified, e.g. :math:`JCh` or :math:`JMh`.
XYZ_w
Absolute *CIE XYZ* tristimulus values of the white under reference
illuminant.
L_A
Test adapting field *luminance* :math:`L_A` in :math:`cd/m^2` such as
:math:`L_A = L_w * Y_b / 100` (where :math:`L_w` is luminance of the
reference white and :math:`Y_b` is the background luminance factor).
Y_b
Luminous factor of background :math:`Y_b` such as
:math:`Y_b = 100 x L_b / L_w` where :math:`L_w` is the luminance of the
light source and :math:`L_b` is the luminance of the background. For
viewing images, :math:`Y_b` can be the average :math:`Y` value for the
pixels in the entire image, or frequently, a :math:`Y` value of 20,
approximate an :math:`L^*` of 50 is used.
surround
Surround viewing conditions induction factors.
discount_illuminant
Truth value indicating if the illuminant should be discounted.
Returns
-------
:class:`numpy.ndarray`
*CIE XYZ* tristimulus values.
Raises
------
ValueError
If neither *C* or *M* correlates have been defined in the
``CAM_Specification_ZCAM`` argument.
Warnings
--------
The underlying *SMPTE ST 2084:2014* transfer function is an absolute
transfer function.
Notes
-----
- *Safdar, Hardeberg and Luo (2021)* does not specify how the chromatic
adaptation to *CIE Standard Illuminant D65* in *Step 0* should be
performed. A one-step *Von Kries* chromatic adaptation transform is not
symetrical or transitive when a degree of adptation is involved.
*Safdar, Hardeberg and Luo (2018)* uses *Zhai and Luo (2018)* two-steps
chromatic adaptation transform, thus it seems sensible to adopt this
transform for the *ZCAM* colour appearance model until more information
is available. It is worth noting that a one-step *Von Kries* chromatic
adaptation transform with support for degree of adaptation produces
values closer to the supplemental document compared to the
*Zhai and Luo (2018)* two-steps chromatic adaptation transform but then
the *ZCAM* colour appearance model does not round-trip properly.
- *Step 4* of the inverse model uses a rounded exponent of 1.3514
preventing the model to round-trip properly. Given that this
implementation takes some liberties with respect to the chromatic
adaptation transform to use, it was deemed appropriate to use an
exponent value that enables the *ZCAM* colour appearance model to
round-trip.
- The underlying *SMPTE ST 2084:2014* transfer function is an absolute
transfer function, thus the domain and range values for the *Reference*
and *1* scales are only indicative that the data is not affected by
scale transformations.
+-------------------------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+===============================+=======================+===============+
| ``CAM_Specification_ZCAM.J`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.C`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.h`` | [0, 360] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.s`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.Q`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.M`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.H`` | [0, 400] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.HC`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.V`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.K`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.H`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
+-----------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+===========+=======================+===============+
| ``XYZ`` | [UN] | [UN] |
+-----------+-----------------------+---------------+
References
----------
:cite:`Safdar2018`, :cite:`Safdar2021`, :cite:`Zhai2018`
Examples
--------
>>> specification = CAM_Specification_ZCAM(J=92.250443780723629,
... C=3.0216926733329013,
... h=196.32457375575581)
>>> XYZ_w = np.array([256, 264, 202])
>>> L_A = 264
>>> Y_b = 100
>>> surround = VIEWING_CONDITIONS_ZCAM['Average']
>>> ZCAM_to_XYZ(specification, XYZ_w, L_A, Y_b, surround)
... # doctest: +ELLIPSIS
array([ 185., 206., 163.])
"""
J_z, C_z, h_z, _S_z, _Q_z, M_z, _H, _H_Z, _V_z, _K_z, _W_z = astuple(
specification)
J_z = to_domain_1(J_z)
C_z = to_domain_1(C_z)
h_z = to_domain_degrees(h_z)
M_z = to_domain_1(M_z)
XYZ_w = to_domain_1(XYZ_w)
_X_w, Y_w, _Z_w = tsplit(XYZ_w)
L_A = as_float_array(L_A)
Y_b = as_float_array(Y_b)
F_s, F, c, N_c = surround
# Step 0 (Forward) - Chromatic adaptation from reference illuminant to
# "CIE Standard Illuminant D65" illuminant using "CAT02".
# Computing degree of adaptation :math:`D`.
D = (degree_of_adaptation(surround.F, L_A)
if not discount_illuminant else ones(L_A.shape))
# Step 1 (Forward) - Computing factors related with viewing conditions and
# independent of the test stimulus.
# Background factor :math:`F_b`
F_b = np.sqrt(Y_b / Y_w)
# Luminance level adaptation factor :math:`F_L`
F_L = 0.171 * spow(L_A, 1 / 3) * (1 - np.exp(-48 / 9 * L_A))
# Step 2 (Forward) - Computing achromatic response (:math:`I_{z,w}`),
# redness-greenness (:math:`a_{z,w}`), and yellowness-blueness
# (:math:`b_{z,w}`).
with domain_range_scale("ignore"):
I_z_w, _A_z_w, B_z_w = tsplit(
XYZ_to_Izazbz(XYZ_w, method="Safdar 2021"))
# Step 1 (Inverse) - Computing achromatic response (:math:`I_z`).
Q_z_p = (1.6 * F_s) / F_b**0.12
Q_z_m = F_s**2.2 * F_b**0.5 * spow(F_L, 0.2)
Q_z_w = 2700 * spow(I_z_w, Q_z_p) * Q_z_m
I_z_p = (F_b**0.12) / (1.6 * F_s)
I_z_d = 2700 * 100 * Q_z_m
I_z = spow((J_z * Q_z_w) / I_z_d, I_z_p)
# Step 2 (Inverse) - Computing chroma :math:`C_z`.
if has_only_nan(M_z) and not has_only_nan(C_z):
M_z = (C_z * Q_z_w) / 100
elif has_only_nan(M_z):
raise ValueError('Either "C" or "M" correlate must be defined in '
'the "CAM_Specification_ZCAM" argument!')
# Step 3 (Inverse) - Computing hue angle :math:`h_z`
# :math:`h_z` is currently required as an input.
# Computing eccentricity factor :math:`e_z`.
e_z = 1.015 + np.cos(np.radians(89.038 + h_z % 360))
h_z_r = np.radians(h_z)
# Step 4 (Inverse) - Computing redness-greenness (:math:`a_z`), and
# yellowness-blueness (:math:`b_z`).
# C_z_p_e = 1.3514
C_z_p_e = 50 / 37
C_z_p = spow(
(M_z * spow(I_z_w, 0.78) * F_b**0.1) /
(100 * e_z**0.068 * spow(F_L, 0.2)),
C_z_p_e,
)
a_z = C_z_p * np.cos(h_z_r)
b_z = C_z_p * np.sin(h_z_r)
# Step 5 (Inverse) - Computing tristimulus values :math:`XYZ_{D65}`.
with domain_range_scale("ignore"):
XYZ_D65 = Izazbz_to_XYZ(tstack([I_z, a_z, b_z]), method="Safdar 2021")
XYZ = chromatic_adaptation_Zhai2018(XYZ_D65,
TVS_D65,
XYZ_w,
D,
D,
transform="CAT02")
return from_range_1(XYZ)
def XYZ_to_ZCAM(
XYZ: ArrayLike,
XYZ_w: ArrayLike,
L_A: FloatingOrArrayLike,
Y_b: FloatingOrArrayLike,
surround: InductionFactors_ZCAM = VIEWING_CONDITIONS_ZCAM["Average"],
discount_illuminant: Boolean = False,
) -> CAM_Specification_ZCAM:
"""
Compute the *ZCAM* colour appearance model correlates from given *CIE XYZ*
tristimulus values.
Parameters
----------
XYZ
Absolute *CIE XYZ* tristimulus values of test sample / stimulus.
XYZ_w
Absolute *CIE XYZ* tristimulus values of the white under reference
illuminant.
L_A
Test adapting field *luminance* :math:`L_A` in :math:`cd/m^2` such as
:math:`L_A = L_w * Y_b / 100` (where :math:`L_w` is luminance of the
reference white and :math:`Y_b` is the background luminance factor).
Y_b
Luminous factor of background :math:`Y_b` such as
:math:`Y_b = 100 * L_b / L_w` where :math:`L_w` is the luminance of the
light source and :math:`L_b` is the luminance of the background. For
viewing images, :math:`Y_b` can be the average :math:`Y` value for the
pixels in the entire image, or frequently, a :math:`Y` value of 20,
approximate an :math:`L^*` of 50 is used.
surround
Surround viewing conditions induction factors.
discount_illuminant
Truth value indicating if the illuminant should be discounted.
Returns
-------
:class:`colour.CAM_Specification_ZCAM`
*ZCAM* colour appearance model specification.
Warnings
--------
The underlying *SMPTE ST 2084:2014* transfer function is an absolute
transfer function.
Notes
-----
- *Safdar, Hardeberg and Luo (2021)* does not specify how the chromatic
adaptation to *CIE Standard Illuminant D65* in *Step 0* should be
performed. A one-step *Von Kries* chromatic adaptation transform is not
symmetrical or transitive when a degree of adaptation is involved.
*Safdar, Hardeberg and Luo (2018)* uses *Zhai and Luo (2018)* two-steps
chromatic adaptation transform, thus it seems sensible to adopt this
transform for the *ZCAM* colour appearance model until more information
is available. It is worth noting that a one-step *Von Kries* chromatic
adaptation transform with support for degree of adaptation produces
values closer to the supplemental document compared to the
*Zhai and Luo (2018)* two-steps chromatic adaptation transform but then
the *ZCAM* colour appearance model does not round-trip properly.
- The underlying *SMPTE ST 2084:2014* transfer function is an absolute
transfer function, thus the domain and range values for the *Reference*
and *1* scales are only indicative that the data is not affected by
scale transformations.
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ`` | [UN] | [UN] |
+------------+-----------------------+---------------+
| ``XYZ_tw`` | [UN] | [UN] |
+------------+-----------------------+---------------+
| ``XYZ_rw`` | [UN] | [UN] |
+------------+-----------------------+---------------+
+-------------------------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+===============================+=======================+===============+
| ``CAM_Specification_ZCAM.J`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.C`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.h`` | [0, 360] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.s`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.Q`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.M`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.H`` | [0, 400] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.HC`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.V`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.K`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
| ``CAM_Specification_ZCAM.H`` | [UN] | [0, 1] |
+-------------------------------+-----------------------+---------------+
References
----------
:cite:`Safdar2018`, :cite:`Safdar2021`, :cite:`Zhai2018`
Examples
--------
>>> XYZ = np.array([185, 206, 163])
>>> XYZ_w = np.array([256, 264, 202])
>>> L_A = 264
>>> Y_b = 100
>>> surround = VIEWING_CONDITIONS_ZCAM['Average']
>>> XYZ_to_ZCAM(XYZ, XYZ_w, L_A, Y_b, surround)
... # doctest: +ELLIPSIS
CAM_Specification_ZCAM(J=92.2504437..., C=3.0216926..., h=196.3245737..., \
s=19.1319556..., Q=321.3408463..., M=10.5256217..., H=237.6114442..., \
HC=None, V=34.7006776..., K=25.8835968..., W=91.6821728...)
"""
XYZ = to_domain_1(XYZ)
XYZ_w = to_domain_1(XYZ_w)
_X_w, Y_w, _Z_w = tsplit(XYZ_w)
L_A = as_float_array(L_A)
Y_b = as_float_array(Y_b)
F_s, _F, _c, _N_c = surround
# Step 0 (Forward) - Chromatic adaptation from reference illuminant to
# "CIE Standard Illuminant D65" illuminant using "CAT02".
# Computing degree of adaptation :math:`D`.
D = (degree_of_adaptation(surround.F, L_A)
if not discount_illuminant else ones(L_A.shape))
XYZ_D65 = chromatic_adaptation_Zhai2018(XYZ,
XYZ_w,
TVS_D65,
D,
D,
transform="CAT02")
# Step 1 (Forward) - Computing factors related with viewing conditions and
# independent of the test stimulus.
# Background factor :math:`F_b`
F_b = np.sqrt(Y_b / Y_w)
# Luminance level adaptation factor :math:`F_L`
F_L = 0.171 * spow(L_A, 1 / 3) * (1 - np.exp(-48 / 9 * L_A))
# Step 2 (Forward) - Computing achromatic response (:math:`I_z` and
# :math:`I_{z,w}`), redness-greenness (:math:`a_z` and :math:`a_{z,w}`),
# and yellowness-blueness (:math:`b_z`, :math:`b_{z,w}`).
with domain_range_scale("ignore"):
I_z, a_z, b_z = tsplit(XYZ_to_Izazbz(XYZ_D65, method="Safdar 2021"))
I_z_w, _a_z_w, b_z_w = tsplit(
XYZ_to_Izazbz(XYZ_w, method="Safdar 2021"))
# Step 3 (Forward) - Computing hue angle :math:`h_z`
h_z = hue_angle(a_z, b_z)
# Step 4 (Forward) - Computing hue quadrature :math:`H`.
H = hue_quadrature(h_z)
# Computing eccentricity factor :math:`e_z`.
e_z = 1.015 + np.cos(np.radians(89.038 + h_z % 360))
# Step 5 (Forward) - Computing brightness :math:`Q_z`,
# lightness :math:`J_z`, colourfulness :math`M_z`, and chroma :math:`C_z`
Q_z_p = (1.6 * F_s) / F_b**0.12
Q_z_m = F_s**2.2 * F_b**0.5 * spow(F_L, 0.2)
Q_z = 2700 * spow(I_z, Q_z_p) * Q_z_m
Q_z_w = 2700 * spow(I_z_w, Q_z_p) * Q_z_m
J_z = 100 * (Q_z / Q_z_w)
M_z = (100 * (a_z**2 + b_z**2)**0.37 *
((spow(e_z, 0.068) * spow(F_L, 0.2)) /
(F_b**0.1 * spow(I_z_w, 0.78))))
C_z = 100 * (M_z / Q_z_w)
# Step 6 (Forward) - Computing saturation :math:`S_z`,
# vividness :math:`V_z`, blackness :math:`K_z`, and whiteness :math:`W_z`.
S_z = 100 * spow(F_L, 0.6) * np.sqrt(M_z / Q_z)
V_z = np.sqrt((J_z - 58)**2 + 3.4 * C_z**2)
K_z = 100 - 0.8 * np.sqrt(J_z**2 + 8 * C_z**2)
W_z = 100 - np.sqrt((100 - J_z)**2 + C_z**2)
return CAM_Specification_ZCAM(
as_float(from_range_1(J_z)),
as_float(from_range_1(C_z)),
as_float(from_range_degrees(h_z)),
as_float(from_range_1(S_z)),
as_float(from_range_1(Q_z)),
as_float(from_range_1(M_z)),
as_float(from_range_degrees(H, 400)),
None,
as_float(from_range_1(V_z)),
as_float(from_range_1(K_z)),
as_float(from_range_1(W_z)),
)
def corresponding_chromaticities_prediction_Zhai2018(
experiment: Union[Literal[1, 2, 3, 4, 6, 8, 9, 11, 12],
CorrespondingColourDataset] = 1,
D_b: FloatingOrArrayLike = 1,
D_d: FloatingOrArrayLike = 1,
XYZ_wo: ArrayLike = np.array([1, 1, 1]),
transform: Union[Literal["CAT02", "CAT16", ], str, ] = "CAT02",
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
"""
Return the corresponding chromaticities prediction for
*Zhai and Luo (2018)* chromatic adaptation model using given transform.
Parameters
----------
experiment
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
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:`tuple`
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`Zhai2018`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_Zhai2018(2)
>>> pr = [(p.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2082238..., 0.4727943...])),
(array([ 0.449, 0.511]), array([ 0.4474691..., 0.5076681...])),
(array([ 0.263, 0.505]), array([ 0.2640379..., 0.4954003...])),
(array([ 0.322, 0.545]), array([ 0.3336937..., 0.5435500...])),
(array([ 0.316, 0.537]), array([ 0.3238490..., 0.5359889...])),
(array([ 0.265, 0.553]), array([ 0.2724846..., 0.5506939...])),
(array([ 0.221, 0.538]), array([ 0.2267596..., 0.5295259...])),
(array([ 0.135, 0.532]), array([ 0.1443208..., 0.5190035...])),
(array([ 0.145, 0.472]), array([ 0.1500723..., 0.4561352...])),
(array([ 0.163, 0.331]), array([ 0.1570902..., 0.3245137...])),
(array([ 0.176, 0.431]), array([ 0.1763887..., 0.4146000...])),
(array([ 0.244, 0.349]), array([ 0.2267005..., 0.3551480...]))]
"""
experiment_results = (experiment if isinstance(
experiment, CorrespondingColourDataset) else
convert_experiment_results_Breneman1987(experiment))
with domain_range_scale("1"):
XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r
xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr])
uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w)
uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr)
XYZ_1 = experiment_results.XYZ_ct
XYZ_2 = chromatic_adaptation_Zhai2018(XYZ_1, XYZ_w, XYZ_wr, D_b, D_d,
XYZ_wo, transform)
uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
return tuple(
CorrespondingChromaticitiesPrediction(experiment_results.name,
uv_t[i], uv_m[i], uv_p[i])
for i in range(len(uv_t)))