def test_domain_range_scale_XYZ_to_CIECAM02(self):
"""
Tests :func:`colour.appearance.cam16.XYZ_to_CIECAM02` definition domain
and range scale support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_w = np.array([95.05, 100.00, 108.88])
L_A = 318.31
Y_b = 20.0
surround = VIEWING_CONDITIONS_CIECAM02['Average']
specification = XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround)[:-1]
d_r = (
('reference', 1, 1),
(1, 0.01,
np.array([
1 / 100, 1 / 100, 1 / 360, 1 / 100, 1 / 100, 1 / 100, 1 / 360
])),
(100, 1, np.array([1, 1, 100 / 360, 1, 1, 1, 100 / 360])),
)
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(XYZ_to_CIECAM02(
XYZ * factor_a, XYZ_w * factor_a, L_A, Y_b, surround)[:-1],
specification * factor_b,
decimal=7)
def output_specification_from_data(self, data):
"""
Returns the *CIECAM02* colour appearance model output specification
from given data.
Parameters
----------
data : list
Fixture data.
Returns
-------
CIECAM02_Specification
*CIECAM02* colour appearance model specification.
"""
XYZ = np.array([data['X'], data['Y'], data['Z']])
XYZ_w = np.array([data['X_w'], data['Y_w'], data['Z_w']])
specification = XYZ_to_CIECAM02(XYZ,
XYZ_w,
data['L_A'],
data['Y_b'],
CIECAM02_InductionFactors(data['F'],
data['c'],
data['N_c']))
return specification
def test_domain_range_scale_XYZ_to_CIECAM02(self):
"""
Test :func:`colour.appearance.ciecam02.XYZ_to_CIECAM02` definition
domain and range scale support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_w = np.array([95.05, 100.00, 108.88])
L_A = 318.31
Y_b = 20
surround = VIEWING_CONDITIONS_CIECAM02["Average"]
specification = XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround)
d_r = (
("reference", 1, 1),
(
"1",
0.01,
np.array([
1 / 100,
1 / 100,
1 / 360,
1 / 100,
1 / 100,
1 / 100,
1 / 400,
np.nan,
]),
),
(
"100",
1,
np.array([1, 1, 100 / 360, 1, 1, 1, 100 / 400, np.nan]),
),
)
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
XYZ_to_CIECAM02(XYZ * factor_a, XYZ_w * factor_a, L_A, Y_b,
surround),
as_float_array(specification) * factor_b,
decimal=7,
)
def setUp(self):
"""Initialise the common tests attributes."""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_w = np.array([95.05, 100.00, 108.88])
L_A = 318.31
Y_b = 20.0
surround = VIEWING_CONDITIONS_CIECAM02["Average"]
specification = XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround)
self._JMh = np.array(
[specification.J, specification.M, specification.h])
def test_nan_XYZ_to_CIECAM02(self):
"""
Tests :func:`colour.appearance.ciecam02.XYZ_to_CIECAM02` 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 = np.array(case)
XYZ_w = np.array(case)
L_A = case[0]
Y_b = case[0]
surround = InductionFactors_CIECAM02(case[0], case[0], case[0])
XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround)
def test_n_dimensional_XYZ_to_CIECAM02(self):
"""
Test :func:`colour.appearance.ciecam02.XYZ_to_CIECAM02` definition
n-dimensional support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_w = np.array([95.05, 100.00, 108.88])
L_A = 318.31
Y_b = 20
surround = VIEWING_CONDITIONS_CIECAM02["Average"]
specification = XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround)
XYZ = np.tile(XYZ, (6, 1))
specification = np.tile(specification, (6, 1))
np.testing.assert_almost_equal(
XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround),
specification,
decimal=7,
)
XYZ_w = np.tile(XYZ_w, (6, 1))
np.testing.assert_almost_equal(
XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround),
specification,
decimal=7,
)
XYZ = np.reshape(XYZ, (2, 3, 3))
XYZ_w = np.reshape(XYZ_w, (2, 3, 3))
specification = np.reshape(specification, (2, 3, 8))
np.testing.assert_almost_equal(
XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround),
specification,
decimal=7,
)
def tcs_colorimetry_data(
sd_irradiance: SpectralDistribution,
sds_tcs: MultiSpectralDistributions,
cmfs: MultiSpectralDistributions,
) -> Tuple[TCS_ColorimetryData_CIE2017, ...]:
"""
Return the *test colour samples* colorimetry data under given test light
source or reference illuminant spectral distribution for the
*CIE 2017 Colour Fidelity Index* (CFI) computations.
Parameters
----------
sd_irradiance
Test light source or reference illuminant spectral distribution, i.e.
the irradiance emitter.
sds_tcs
*Test colour samples* spectral distributions.
cmfs
Standard observer colour matching functions.
Returns
-------
:class:`tuple`
*Test colour samples* colorimetry data under the given test light
source or reference illuminant spectral distribution.
Examples
--------
>>> delta_E_to_R_f(4.4410383190) # doctest: +ELLIPSIS
70.1208254...
"""
XYZ_w = sd_to_XYZ(sd_ones(), cmfs, sd_irradiance)
Y_b = 20
L_A = 100
surround = VIEWING_CONDITIONS_CIECAM02["Average"]
tcs_data = []
for sd_tcs in sds_tcs.to_sds():
XYZ = sd_to_XYZ(sd_tcs, cmfs, sd_irradiance)
CAM = XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround, True)
JMh = tstack([CAM.J, CAM.M, CAM.h])
Jpapbp = JMh_CIECAM02_to_CAM02UCS(JMh)
tcs_data.append(
TCS_ColorimetryData_CIE2017(sd_tcs.name, XYZ, CAM, JMh, Jpapbp))
return tuple(tcs_data)
def XYZ_to_UCS_Luo2006(XYZ: ArrayLike, coefficients: ArrayLike,
**kwargs: Any) -> NDArray:
"""
Convert from *CIE XYZ* tristimulus values to one of the
*Luo et al. (2006)* *CAM02-LCD*, *CAM02-SCD*, or *CAM02-UCS* colourspaces
:math:`J'a'b'` array.
Parameters
----------
XYZ
*CIE XYZ* tristimulus values.
coefficients
Coefficients of one of the *Luo et al. (2006)* *CAM02-LCD*,
*CAM02-SCD*, or *CAM02-UCS* colourspaces.
Other Parameters
----------------
kwargs
{:func:`colour.XYZ_to_CIECAM02`},
See the documentation of the previously listed definition. The default
viewing conditions are that of *IEC 61966-2-1:1999*, i.e. *sRGB* 64 Lux
ambient illumination, 80 :math:`cd/m^2`, adapting field luminance about
20% of a white object in the scene.
Returns
-------
:class:`numpy.ndarray`
*Luo et al. (2006)* *CAM02-LCD*, *CAM02-SCD*, or *CAM02-UCS*
colourspaces :math:`J'a'b'` array.
Warnings
--------
The ``XYZ_w`` parameter for :func:`colour.XYZ_to_CAM16` definition must be
given in the same domain-range scale than the ``XYZ`` parameter.
Notes
-----
+------------+------------------------+------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+========================+==================+
| ``XYZ`` | [0, 1] | [0, 1] |
+------------+------------------------+------------------+
+------------+------------------------+------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+========================+==================+
| ``Jpapbp`` | ``Jp`` : [0, 100] | ``Jp`` : [0, 1] |
| | | |
| | ``ap`` : [-100, 100] | ``ap`` : [-1, 1] |
| | | |
| | ``bp`` : [-100, 100] | ``bp`` : [-1, 1] |
+------------+------------------------+------------------+
Examples
--------
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_to_UCS_Luo2006(XYZ, COEFFICIENTS_UCS_LUO2006['CAM02-LCD'])
... # doctest: +ELLIPSIS
array([ 46.6138615..., 39.3576023..., 15.9673043...])
"""
from colour.appearance import CAM_KWARGS_CIECAM02_sRGB, XYZ_to_CIECAM02
domain_range_reference = get_domain_range_scale() == "reference"
settings = CAM_KWARGS_CIECAM02_sRGB.copy()
settings.update(**kwargs)
XYZ_w = kwargs.get("XYZ_w")
if XYZ_w is not None and domain_range_reference:
settings["XYZ_w"] = XYZ_w * 100
if domain_range_reference:
XYZ = as_float_array(XYZ) * 100
specification = XYZ_to_CIECAM02(XYZ, **settings)
JMh = tstack([specification.J, specification.M, specification.h])
return JMh_CIECAM02_to_UCS_Luo2006(JMh, coefficients)
def test_XYZ_to_CIECAM02(self):
"""
Test :func:`colour.appearance.ciecam02.XYZ_to_CIECAM02` definition.
Notes
-----
- The test values have been generated from data of the following file
by *Fairchild (2013)*:
http://rit-mcsl.org/fairchild//files/AppModEx.xls
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_w = np.array([95.05, 100.00, 108.88])
L_A = 318.31
Y_b = 20
surround = InductionFactors_CIECAM02(1, 0.69, 1)
np.testing.assert_allclose(
XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround),
np.array([41.73, 0.1, 219, 2.36, 195.37, 0.11, 278.1, np.nan]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([57.06, 43.06, 31.96])
L_A = 31.83
np.testing.assert_allclose(
XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround),
np.array([65.96, 48.57, 19.6, 52.25, 152.67, 41.67, 399.6,
np.nan]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([3.53, 6.56, 2.14])
XYZ_w = np.array([109.85, 100.00, 35.58])
L_A = 318.31
np.testing.assert_allclose(
XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround),
np.array([21.79, 46.94, 177.1, 58.79, 141.17, 48.8, 220.4,
np.nan]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([19.01, 20.00, 21.78])
L_A = 31.83
np.testing.assert_allclose(
XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround),
np.array(
[42.53, 51.92, 248.9, 60.22, 122.83, 44.54, 305.8, np.nan]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([61.45276998, 7.00421901, 82.24067384])
XYZ_w = np.array([95.05, 100, 108.88])
L_A = 4.074366543152521
np.testing.assert_allclose(
XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround),
np.array([
21.72630603341673,
411.5190338631848,
349.12875710099053,
227.15081998415593,
57.657243286322725,
297.49693233026602,
375.5788601911363,
np.nan,
]),
rtol=0.01,
atol=0.01,
)
