def test_domain_range_scale_XYZ_to_CAM16(self):
"""
Tests :func:`colour.appearance.cam16.XYZ_to_CAM16` 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 = CAM16_VIEWING_CONDITIONS['Average']
specification = XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b, surround)[:-1]
d_r = (
('reference', 1, 1),
(1, 0.01, np.array([1, 1, 1 / 360, 1, 1, 1, 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_CAM16(XYZ * factor_a, XYZ_w * factor_a, L_A, Y_b,
surround)[:-1],
specification * factor_b,
decimal=7)
def test_domain_range_scale_XYZ_to_CAM16(self):
"""
Test :func:`colour.appearance.cam16.XYZ_to_CAM16` 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_CAM16["Average"]
specification = XYZ_to_CAM16(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_CAM16(XYZ * factor_a, XYZ_w * factor_a, L_A, Y_b,
surround),
as_float_array(specification) * factor_b,
decimal=7,
)
def test_nan_XYZ_to_CAM16(self):
"""
Tests :func:`colour.appearance.cam16.XYZ_to_CAM16` 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 = CAM16_InductionFactors(case[0], case[0], case[0])
XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b, surround)
def test_n_dimensional_XYZ_to_CAM16(self):
"""
Test :func:`colour.appearance.cam16.XYZ_to_CAM16` 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_CAM16["Average"]
specification = XYZ_to_CAM16(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_CAM16(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_CAM16(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_CAM16(XYZ, XYZ_w, L_A, Y_b, surround),
specification,
decimal=7,
)
def output_specification_from_data(self, data):
"""
Returns the *CAM16* colour appearance model output specification from
given data.
Parameters
----------
data : list
Fixture data.
Returns
-------
CAM16_Specification
*CAM16* colour appearance model specification.
"""
XYZ = tstack([data['X'], data['Y'], data['Z']])
XYZ_w = tstack([data['X_w'], data['Y_w'], data['Z_w']])
specification = XYZ_to_CAM16(
XYZ, XYZ_w, data['L_A'], data['Y_b'],
CAM16_InductionFactors(data['F'], data['c'], data['N_c']))
return specification
def XYZ_to_UCS_Li2017(XYZ: ArrayLike, coefficients: ArrayLike,
**kwargs: Any) -> NDArray:
"""
Convert from *CIE XYZ* tristimulus values to one of the *Li et al. (2017)*
*CAM16-LCD*, *CAM16-SCD*, or *CAM16-UCS* colourspaces :math:`J'a'b'` array.
Parameters
----------
XYZ
*CIE XYZ* tristimulus values.
coefficients
Coefficients of one of the *Li et al. (2017)* *CAM16-LCD*, *CAM16-SCD*,
or *CAM16-UCS* colourspaces.
Other Parameters
----------------
kwargs
{:func:`colour.XYZ_to_CAM16`},
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`
*Li et al. (2017)* *CAM16-LCD*, *CAM16-SCD*, or *CAM16-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
--------
>>> import numpy as np
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_to_UCS_Li2017(XYZ, COEFFICIENTS_UCS_LUO2006['CAM02-LCD'])
... # doctest: +ELLIPSIS
array([ 46.0658603..., 41.0758649..., 14.5102582...])
>>> from colour.appearance import CAM_KWARGS_CIECAM02_sRGB
>>> XYZ_w = CAM_KWARGS_CIECAM02_sRGB['XYZ_w']
>>> XYZ_to_UCS_Li2017(
... XYZ, COEFFICIENTS_UCS_LUO2006['CAM02-LCD'], XYZ_w=XYZ_w / 100)
... # doctest: +ELLIPSIS
array([ 46.0658603..., 41.0758649..., 14.5102582...])
"""
from colour.appearance import CAM_KWARGS_CIECAM02_sRGB, XYZ_to_CAM16
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_CAM16(XYZ, **settings)
JMh = tstack([specification.J, specification.M, specification.h])
return JMh_CAM16_to_UCS_Li2017(JMh, coefficients)
def test_XYZ_to_CAM16(self):
"""Test :func:`colour.appearance.cam16.XYZ_to_CAM16` definition."""
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_CAM16["Average"]
np.testing.assert_almost_equal(
XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b, surround),
np.array([
41.73120791,
0.10335574,
217.06795977,
2.34501507,
195.37170899,
0.10743677,
275.59498615,
np.nan,
]),
decimal=7,
)
XYZ = np.array([57.06, 43.06, 31.96])
L_A = 31.83
np.testing.assert_almost_equal(
XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b, surround),
np.array([
65.42828069,
49.67956420,
17.48659243,
52.94308868,
152.06985268,
42.62473321,
398.03047943,
np.nan,
]),
decimal=7,
)
XYZ = np.array([3.53, 6.56, 2.14])
XYZ_w = np.array([109.85, 100, 35.58])
L_A = 318.31
np.testing.assert_almost_equal(
XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b, surround),
np.array([
21.36052893,
50.99381895,
178.86724266,
61.57953092,
139.78582768,
53.00732582,
223.01823806,
np.nan,
]),
decimal=7,
)
XYZ = np.array([19.01, 20.00, 21.78])
L_A = 318.31
np.testing.assert_almost_equal(
XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b, surround),
np.array([
41.36326063,
52.81154022,
258.88676291,
53.12406914,
194.52011798,
54.89682038,
311.24768647,
np.nan,
]),
decimal=7,
)
XYZ = np.array([61.45276998, 7.00421901, 82.2406738])
XYZ_w = np.array([95.05, 100.00, 108.88])
L_A = 4.074366543152521
np.testing.assert_almost_equal(
XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b, surround),
np.array([
21.03801957,
457.78881613,
350.06445098,
241.50642846,
56.74143988,
330.94646237,
376.43915877,
np.nan,
]),
decimal=7,
)
