def test_n_dimensional_XYZ_to_xyY(self):
"""
Tests :func:`colour.models.cie_xyy.XYZ_to_xyY` definition n-dimensions
support.
"""
XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
illuminant = np.array([0.34570, 0.35850])
xyY = np.array([0.26414772, 0.37770001, 0.10080000])
np.testing.assert_almost_equal(XYZ_to_xyY(XYZ, illuminant),
xyY,
decimal=7)
XYZ = np.tile(XYZ, (6, 1))
xyY = np.tile(xyY, (6, 1))
np.testing.assert_almost_equal(XYZ_to_xyY(XYZ, illuminant),
xyY,
decimal=7)
illuminant = np.tile(illuminant, (6, 1))
np.testing.assert_almost_equal(XYZ_to_xyY(XYZ, illuminant),
xyY,
decimal=7)
XYZ = np.reshape(XYZ, (2, 3, 3))
illuminant = np.reshape(illuminant, (2, 3, 2))
xyY = np.reshape(xyY, (2, 3, 3))
np.testing.assert_almost_equal(XYZ_to_xyY(XYZ, illuminant),
xyY,
decimal=7)
def test_n_dimensional_XYZ_to_xyY(self):
"""
Tests :func:`colour.models.cie_xyy.XYZ_to_xyY` definition n-dimensions
support.
"""
XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
illuminant = np.array([0.31270, 0.32900])
xyY = np.array([0.54369557, 0.32107944, 0.12197225])
np.testing.assert_almost_equal(
XYZ_to_xyY(XYZ, illuminant), xyY, decimal=7)
XYZ = np.tile(XYZ, (6, 1))
xyY = np.tile(xyY, (6, 1))
np.testing.assert_almost_equal(
XYZ_to_xyY(XYZ, illuminant), xyY, decimal=7)
illuminant = np.tile(illuminant, (6, 1))
np.testing.assert_almost_equal(
XYZ_to_xyY(XYZ, illuminant), xyY, decimal=7)
XYZ = np.reshape(XYZ, (2, 3, 3))
illuminant = np.reshape(illuminant, (2, 3, 2))
xyY = np.reshape(xyY, (2, 3, 3))
np.testing.assert_almost_equal(
XYZ_to_xyY(XYZ, illuminant), xyY, decimal=7)
def chromatically_adapted_primaries(
primaries: ArrayLike,
whitepoint_t: ArrayLike,
whitepoint_r: ArrayLike,
chromatic_adaptation_transform: Union[Literal["Bianco 2010",
"Bianco PC 2010", "Bradford",
"CAT02 Brill 2008", "CAT02",
"CAT16", "CMCCAT2000",
"CMCCAT97", "Fairchild",
"Sharp", "Von Kries",
"XYZ Scaling", ],
str, ] = "CAT02",
) -> NDArray:
"""
Chromatically adapt given *primaries* :math:`xy` chromaticity coordinates
from test ``whitepoint_t`` to reference ``whitepoint_r``.
Parameters
----------
primaries
Primaries :math:`xy` chromaticity coordinates.
whitepoint_t
Test illuminant / whitepoint :math:`xy` chromaticity coordinates.
whitepoint_r
Reference illuminant / whitepoint :math:`xy` chromaticity coordinates.
chromatic_adaptation_transform
*Chromatic adaptation* transform.
Returns
-------
:class:`numpy.ndarray`
Chromatically adapted primaries :math:`xy` chromaticity coordinates.
Examples
--------
>>> p = np.array([0.64, 0.33, 0.30, 0.60, 0.15, 0.06])
>>> w_t = np.array([0.31270, 0.32900])
>>> w_r = np.array([0.34570, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> chromatically_adapted_primaries(p, w_t, w_r,
... chromatic_adaptation_transform)
... # doctest: +ELLIPSIS
array([[ 0.6484414..., 0.3308533...],
[ 0.3211951..., 0.5978443...],
[ 0.1558932..., 0.0660492...]])
"""
primaries = np.reshape(primaries, (3, 2))
XYZ_a = chromatic_adaptation_VonKries(
xy_to_XYZ(primaries),
xy_to_XYZ(whitepoint_t),
xy_to_XYZ(whitepoint_r),
chromatic_adaptation_transform,
)
P_a = XYZ_to_xyY(XYZ_a)[..., 0:2]
return P_a
def test_nan_XYZ_to_xyY(self):
"""Test :func:`colour.models.cie_xyy.XYZ_to_xyY` 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)
illuminant = np.array(case[0:2])
XYZ_to_xyY(XYZ, illuminant)
def XYZ_to_UVW(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50']):
"""
Converts from *CIE XYZ* tristimulus values to *CIE 1964 U\*V\*W\**
colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE 1964 U\*V\*W\** colourspace array.
Warning
-------
The input domain and output range of that definition are non standard!
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 100].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *CIE 1964 U\*V\*W\** colourspace array is in range [0, 100].
References
----------
- :cite:`Wikipediacj`
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313]) * 100
>>> XYZ_to_UVW(XYZ) # doctest: +ELLIPSIS
array([-28.0579733..., -0.8819449..., 37.0041149...])
"""
xyY = XYZ_to_xyY(XYZ, xyY_to_xy(illuminant))
_x, _y, Y = tsplit(xyY)
u, v = tsplit(UCS_to_uv(XYZ_to_UCS(XYZ)))
u_0, v_0 = tsplit(UCS_to_uv(XYZ_to_UCS(xyY_to_XYZ(xy_to_xyY(illuminant)))))
W = 25 * Y**(1 / 3) - 17
U = 13 * W * (u - u_0)
V = 13 * W * (v - v_0)
UVW = tstack((U, V, W))
return UVW
def chromatically_adapted_primaries(primaries,
whitepoint_t,
whitepoint_r,
chromatic_adaptation_transform='CAT02'):
"""
Chromatically adapts given *primaries* :math:`xy` chromaticity coordinates
from test `whitepoint_t` to reference `whitepoint_r`.
Parameters
----------
primaries : array_like, (3, 2)
Primaries :math:`xy` chromaticity coordinates.
whitepoint_t : array_like
Test illuminant / whitepoint :math:`xy` chromaticity coordinates.
whitepoint_r : array_like
Reference illuminant / whitepoint :math:`xy` chromaticity coordinates.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild, 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray
Chromatically adapted primaries :math:`xy` chromaticity coordinates.
Examples
--------
>>> p = np.array([0.64, 0.33, 0.30, 0.60, 0.15, 0.06])
>>> whitepoint_t = np.array([0.31271, 0.32902])
>>> whitepoint_r = np.array([0.34567, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> chromatically_adapted_primaries( # doctest: +ELLIPSIS
... p,
... whitepoint_t,
... whitepoint_r,
... chromatic_adaptation_transform)
array([[ 0.6484318..., 0.3308548...],
[ 0.3211603..., 0.5978620...],
[ 0.1558860..., 0.0660431...]])
"""
primaries = np.reshape(primaries, (3, 2))
XYZ_a = chromatic_adaptation_VonKries(
xy_to_XYZ(primaries),
xy_to_XYZ(whitepoint_t),
xy_to_XYZ(whitepoint_r),
chromatic_adaptation_transform)
P_a = XYZ_to_xyY(XYZ_a)[..., 0:2]
return P_a
def test_domain_range_scale_XYZ_to_xyY(self):
"""
Tests :func:`colour.models.cie_xyy.XYZ_to_xyY` definition domain and
range scale support.
"""
XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
xyY = XYZ_to_xyY(XYZ)
XYZ = np.tile(XYZ, (6, 1)).reshape(2, 3, 3)
xyY = np.tile(xyY, (6, 1)).reshape(2, 3, 3)
d_r = (('reference', 1, 1), (1, 1, 1), (
100,
100,
np.array([1, 1, 100]),
))
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
XYZ_to_xyY(XYZ * factor_a), xyY * factor_b, decimal=7)
def test_XYZ_to_xyY(self):
"""Test :func:`colour.models.cie_xyy.XYZ_to_xyY` definition."""
np.testing.assert_almost_equal(
XYZ_to_xyY(np.array([0.20654008, 0.12197225, 0.05136952])),
np.array([0.54369557, 0.32107944, 0.12197225]),
decimal=7,
)
np.testing.assert_almost_equal(
XYZ_to_xyY(np.array([0.14222010, 0.23042768, 0.10495772])),
np.array([0.29777735, 0.48246446, 0.23042768]),
decimal=7,
)
np.testing.assert_almost_equal(
XYZ_to_xyY(np.array([0.07818780, 0.06157201, 0.28099326])),
np.array([0.18582823, 0.14633764, 0.06157201]),
decimal=7,
)
np.testing.assert_almost_equal(
XYZ_to_xyY(np.array([0.00000000, 0.00000000, 1.00000000])),
np.array([0.00000000, 0.00000000, 0.00000000]),
decimal=7,
)
np.testing.assert_almost_equal(
XYZ_to_xyY(
np.array([
[0.20654008, 0.12197225, 0.05136952],
[0.00000000, 0.00000000, 0.00000000],
[0.00000000, 1.00000000, 0.00000000],
])),
np.array([
[0.54369557, 0.32107944, 0.12197225],
[0.31270000, 0.32900000, 0.00000000],
[0.00000000, 1.00000000, 1.00000000],
]),
decimal=7,
)
def chromatically_adapted_primaries(primaries,
whitepoint_t,
whitepoint_r,
chromatic_adaptation_transform='CAT02'):
"""
Chromatically adapts given *primaries* :math:`xy` chromaticity coordinates
from test ``whitepoint_t`` to reference ``whitepoint_r``.
Parameters
----------
primaries : array_like, (3, 2)
Primaries :math:`xy` chromaticity coordinates.
whitepoint_t : array_like
Test illuminant / whitepoint :math:`xy` chromaticity coordinates.
whitepoint_r : array_like
Reference illuminant / whitepoint :math:`xy` chromaticity coordinates.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02 Brill 2008',
'Bianco 2010', 'Bianco PC 2010'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray
Chromatically adapted primaries :math:`xy` chromaticity coordinates.
Examples
--------
>>> p = np.array([0.64, 0.33, 0.30, 0.60, 0.15, 0.06])
>>> w_t = np.array([0.31270, 0.32900])
>>> w_r = np.array([0.34570, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> chromatically_adapted_primaries(p, w_t, w_r,
... chromatic_adaptation_transform)
... # doctest: +ELLIPSIS
array([[ 0.6484414..., 0.3308533...],
[ 0.3211951..., 0.5978443...],
[ 0.1558932..., 0.0660492...]])
"""
primaries = np.reshape(primaries, (3, 2))
XYZ_a = chromatic_adaptation_VonKries(xy_to_XYZ(primaries),
xy_to_XYZ(whitepoint_t),
xy_to_XYZ(whitepoint_r),
chromatic_adaptation_transform)
P_a = XYZ_to_xyY(XYZ_a)[..., 0:2]
return P_a
def XYZ_to_UVW(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* colourspace to *CIE 1964 U\*V*\W\** colourspace.
Parameters
----------
XYZ : array_like, (3,)
*CIE XYZ* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE 1964 U\*V*\W\** colourspace matrix.
Notes
-----
- Input *CIE XYZ* colourspace matrix is in domain [0, 100].
- Output *CIE UVW* colourspace matrix is in domain [0, 100].
Warning
-------
The input / output domains of that definition are non standard!
Examples
--------
>>> XYZ = np.array([11.80583421, 10.34, 5.15089229])
>>> XYZ_to_UVW(XYZ) # doctest: +ELLIPSIS
array([ 24.2543371..., 7.2205484..., 37.4645000...])
"""
x, y, Y = np.ravel(XYZ_to_xyY(XYZ, illuminant))
u, v = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ)))
u0, v0 = np.ravel(UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(illuminant))))
W = 25 * Y**(1 / 3) - 17
U = 13 * W * (u - u0)
V = 13 * W * (v - v0)
return np.array([U, V, W])
def test_XYZ_to_xyY(self):
"""
Tests :func:`colour.models.cie_xyy.XYZ_to_xyY` definition.
"""
np.testing.assert_almost_equal(
XYZ_to_xyY(np.array([0.07049534, 0.10080000, 0.09558313])),
np.array([0.26414772, 0.37770001, 0.10080000]),
decimal=7)
np.testing.assert_almost_equal(
XYZ_to_xyY(np.array([0.47097710, 0.34950000, 0.11301649])),
np.array([0.50453169, 0.37440000, 0.34950000]),
decimal=7)
np.testing.assert_almost_equal(
XYZ_to_xyY(np.array([0.25506814, 0.19150000, 0.08849752])),
np.array([0.47670437, 0.35790000, 0.19150000]),
decimal=7)
np.testing.assert_almost_equal(
XYZ_to_xyY(np.array([0.00000000, 0.00000000, 0.00000000])),
np.array([0.34570000, 0.35850000, 0.00000000]),
decimal=7)
np.testing.assert_almost_equal(
XYZ_to_xyY(np.array([0.00000000, 0.00000000, 1.00000000])),
np.array([0.00000000, 0.00000000, 0.00000000]),
decimal=7)
np.testing.assert_almost_equal(
XYZ_to_xyY(
np.array([
[0.07049534, 0.10080000, 0.09558313],
[0.00000000, 0.00000000, 0.00000000],
[0.00000000, 0.00000000, 1.00000000],
])),
np.array([
[0.26414772, 0.37770001, 0.10080000],
[0.34570000, 0.35850000, 0.00000000],
[0.00000000, 0.00000000, 0.00000000],
]),
decimal=7)
def XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs):
"""
Converts from *CIE XYZ* tristimulus values to given colourspace model.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like
*CIE XYZ* tristimulus values *illuminant* *xy* chromaticity
coordinates.
model : unicode
**{'CIE XYZ', 'CIE xyY', 'CIE xy', 'CIE Lab', 'CIE LCHab', 'CIE Luv',
'CIE Luv uv', 'CIE LCHuv', 'CIE UCS', 'CIE UCS uv', 'CIE UVW',
'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz, 'OSA UCS',
'hdr-CIELAB', 'hdr-IPT'}**,
Colourspace model to convert the *CIE XYZ* tristimulus values to.
Other Parameters
----------------
\\**kwargs : dict, optional
Keywords arguments.
Returns
-------
ndarray
Colourspace model values.
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> W = np.array([0.31270, 0.32900])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE XYZ')
array([ 0.2065400..., 0.1219722..., 0.0513695...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xyY')
array([ 0.5436955..., 0.3210794..., 0.1219722...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xy')
array([ 0.5436955..., 0.3210794...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Lab')
array([ 0.4152787..., 0.5263858..., 0.2692317...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHab')
array([ 0.4152787..., 0.5912425..., 0.0752458...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv')
array([ 0.4152787..., 0.9683626..., 0.1775210...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv uv')
array([ 0.3772021..., 0.5012026...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHuv')
array([ 0.4152787..., 0.9844997..., 0.0288560...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UCS uv')
array([ 0.3772021..., 0.3341350...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UVW')
array([ 0.9455035..., 0.1155536..., 0.4054757...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'DIN 99')
array([ 0.5322822..., 0.2841634..., 0.0389839...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'Hunter Lab')
array([ 0.3492452..., 0.4703302..., 0.1439330...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'Hunter Rdab')
array([ 0.1219722..., 0.5709032..., 0.1747109...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'IPT')
array([ 0.3842619..., 0.3848730..., 0.1888683...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'JzAzBz')
array([ 0.0053504..., 0.0092430..., 0.0052600...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'OSA UCS')
array([-0.0300499..., 0.0299713..., -0.0966784...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'hdr-CIELAB')
array([ 0.5187002..., 0.6047633..., 0.3214551...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'hdr-IPT')
array([ 0.4839376..., 0.4244990..., 0.2201954...])
"""
with domain_range_scale(1):
values = None
if model == 'CIE XYZ':
values = XYZ
elif model == 'CIE xyY':
values = XYZ_to_xyY(XYZ, illuminant)
elif model == 'CIE xy':
values = XYZ_to_xy(XYZ, illuminant)
elif model == 'CIE Lab':
values = XYZ_to_Lab(XYZ, illuminant)
elif model == 'CIE LCHab':
values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))
elif model == 'CIE Luv':
values = XYZ_to_Luv(XYZ, illuminant)
elif model == 'CIE Luv uv':
values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)
elif model == 'CIE LCHuv':
values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))
elif model == 'CIE UCS':
values = XYZ_to_UCS(XYZ)
elif model == 'CIE UCS uv':
values = UCS_to_uv(XYZ_to_UCS(XYZ))
elif model == 'CIE UVW':
values = XYZ_to_UVW(XYZ, illuminant)
elif model == 'DIN 99':
values = Lab_to_DIN99(XYZ_to_Lab(XYZ, illuminant))
elif model == 'Hunter Lab':
values = XYZ_to_Hunter_Lab(XYZ, xy_to_XYZ(illuminant))
elif model == 'Hunter Rdab':
values = XYZ_to_Hunter_Rdab(XYZ, xy_to_XYZ(illuminant))
elif model == 'IPT':
values = XYZ_to_IPT(XYZ)
elif model == 'JzAzBz':
values = XYZ_to_JzAzBz(XYZ)
elif model == 'OSA UCS':
values = XYZ_to_OSA_UCS(XYZ)
elif model == 'hdr-CIELAB':
values = XYZ_to_hdr_CIELab(XYZ, illuminant, **kwargs)
elif model == 'hdr-IPT':
values = XYZ_to_hdr_IPT(XYZ, **kwargs)
if values is None:
raise ValueError(
'"{0}" not found in colourspace models: "{1}".'.format(
model, ', '.join(COLOURSPACE_MODELS)))
return values
def XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs):
"""
Converts from *CIE XYZ* tristimulus values to given colourspace model.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like
Reference *illuminant* *CIE xy* chromaticity coordinates or *CIE xyY*
colourspace array.
model : unicode
**{'CIE XYZ', 'CIE xyY', 'CIE xy', 'CIE Lab', 'CIE LCHab', 'CIE Luv',
'CIE Luv uv', 'CIE LCHuv', 'CIE UCS', 'CIE UCS uv', 'CIE UVW',
'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz, 'OSA UCS',
'hdr-CIELAB', 'hdr-IPT'}**,
Colourspace model to convert the *CIE XYZ* tristimulus values to.
Other Parameters
----------------
\\**kwargs : dict, optional
Keywords arguments.
Returns
-------
ndarray
Colourspace model values.
Warnings
--------
This definition is is deprecated and will be removed in a future release.
:func:`colour.convert` definition should be used instead.
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> W = np.array([0.31270, 0.32900])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE XYZ')
array([ 0.2065400..., 0.1219722..., 0.0513695...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xyY')
array([ 0.5436955..., 0.3210794..., 0.1219722...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xy')
array([ 0.5436955..., 0.3210794...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Lab')
array([ 0.4152787..., 0.5263858..., 0.2692317...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHab')
array([ 0.4152787..., 0.5912425..., 0.0752458...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv')
array([ 0.4152787..., 0.9683626..., 0.1775210...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv uv')
array([ 0.3772021..., 0.5012026...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHuv')
array([ 0.4152787..., 0.9844997..., 0.0288560...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UCS')
array([ 0.1376933..., 0.1219722..., 0.1053731...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UCS uv')
array([ 0.3772021..., 0.3341350...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UVW')
array([ 0.9455035..., 0.1155536..., 0.4054757...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'DIN 99')
array([ 0.5322822..., 0.2841634..., 0.0389839...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'Hunter Lab')
array([ 0.3492452..., 0.4703302..., 0.1439330...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'Hunter Rdab')
array([ 0.1219722..., 0.5709032..., 0.1747109...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'IPT')
array([ 0.3842619..., 0.3848730..., 0.1888683...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'JzAzBz')
array([ 0.0053504..., 0.0092430..., 0.0052600...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'OSA UCS')
array([-0.0300499..., 0.0299713..., -0.0966784...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'hdr-CIELAB')
array([ 0.5187002..., 0.6047633..., 0.3214551...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'hdr-IPT')
array([ 0.4839376..., 0.4244990..., 0.2201954...])
>>> try:
... XYZ_to_colourspace_model(XYZ, W, 'Undefined')
... except ValueError as error:
... print(error)
"Undefined" not found in colourspace models: "CIE XYZ, CIE xyY, CIE Lab, \
CIE LCHab, CIE Luv, CIE Luv uv, CIE LCHuv, CIE UCS, CIE UCS uv, CIE UVW, \
DIN 99, Hunter Lab, Hunter Rdab, IPT, JzAzBz, OSA UCS, hdr-CIELAB, hdr-IPT".
"""
from colour.models import (
Lab_to_DIN99, Lab_to_LCHab, Luv_to_LCHuv, Luv_to_uv, UCS_to_uv,
XYZ_to_IPT, XYZ_to_Hunter_Lab, XYZ_to_Hunter_Rdab, XYZ_to_Lab,
XYZ_to_Luv, XYZ_to_OSA_UCS, XYZ_to_UCS, XYZ_to_UVW, XYZ_to_hdr_CIELab,
XYZ_to_hdr_IPT, XYZ_to_JzAzBz, XYZ_to_xy, XYZ_to_xyY, xy_to_XYZ)
with domain_range_scale(1):
values = None
if model == 'CIE XYZ':
values = XYZ
elif model == 'CIE xyY':
values = XYZ_to_xyY(XYZ, illuminant)
elif model == 'CIE xy':
values = XYZ_to_xy(XYZ, illuminant)
elif model == 'CIE Lab':
values = XYZ_to_Lab(XYZ, illuminant)
elif model == 'CIE LCHab':
values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))
elif model == 'CIE Luv':
values = XYZ_to_Luv(XYZ, illuminant)
elif model == 'CIE Luv uv':
values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)
elif model == 'CIE LCHuv':
values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))
elif model == 'CIE UCS':
values = XYZ_to_UCS(XYZ)
elif model == 'CIE UCS uv':
values = UCS_to_uv(XYZ_to_UCS(XYZ))
elif model == 'CIE UVW':
values = XYZ_to_UVW(XYZ, illuminant)
elif model == 'DIN 99':
values = Lab_to_DIN99(XYZ_to_Lab(XYZ, illuminant))
elif model == 'Hunter Lab':
values = XYZ_to_Hunter_Lab(XYZ, xy_to_XYZ(illuminant))
elif model == 'Hunter Rdab':
values = XYZ_to_Hunter_Rdab(XYZ, xy_to_XYZ(illuminant))
elif model == 'IPT':
values = XYZ_to_IPT(XYZ)
elif model == 'JzAzBz':
values = XYZ_to_JzAzBz(XYZ)
elif model == 'OSA UCS':
values = XYZ_to_OSA_UCS(XYZ)
elif model == 'hdr-CIELAB':
values = XYZ_to_hdr_CIELab(XYZ, illuminant, **kwargs)
elif model == 'hdr-IPT':
values = XYZ_to_hdr_IPT(XYZ, **kwargs)
if values is None:
raise ValueError(
'"{0}" not found in colourspace models: "{1}".'.format(
model, ', '.join(COLOURSPACE_MODELS)))
return values
def XYZ_to_reference_colourspace(XYZ, illuminant, reference_colourspace):
"""
Converts from *CIE XYZ* tristimulus values to given reference colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like
*CIE XYZ* tristimulus values *illuminant* *xy* chromaticity
coordinates.
reference_colourspace : unicode
**{'CIE XYZ', 'CIE xyY', 'CIE xy', 'CIE Lab', 'CIE Luv', 'CIE Luv uv',
'CIE UCS', 'CIE UCS uv', 'CIE UVW', 'IPT'}**,
Reference colourspace to convert the *CIE XYZ* tristimulus values to.
Returns
-------
ndarray
Reference colourspace values.
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> W = np.array([0.34567, 0.35850])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'CIE XYZ')
array([ 0.0704953..., 0.1008 , 0.0955831...])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xyY')
array([ 0.2641477..., 0.3777000..., 0.1008 ])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xy')
array([ 0.2641477..., 0.3777000...])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Lab')
array([-23.6230288..., -4.4141703..., 37.9856291...])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHab')
array([ 24.0319036..., 190.5841597..., 37.9856291...])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv')
array([-28.7922944..., -1.3558195..., 37.9856291...])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv uv')
array([ 0.1508531..., 0.4853297...])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHuv')
array([ 28.82419932, 182.69604747, 37.9856291 ])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UCS uv')
array([ 0.1508531..., 0.32355314...])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UVW')
array([-28.0483277..., -0.8805242..., 37.0041149...])
>>> XYZ_to_reference_colourspace( # doctest: +ELLIPSIS
... XYZ, W, 'IPT')
array([-0.1111479..., 0.0159474..., 0.3657112...])
"""
value = None
if reference_colourspace == 'CIE XYZ':
value = XYZ
if reference_colourspace == 'CIE xyY':
value = XYZ_to_xyY(XYZ, illuminant)
if reference_colourspace == 'CIE xy': # Used for Chromaticity Diagram.
value = XYZ_to_xy(XYZ, illuminant)
if reference_colourspace == 'CIE Lab':
L, a, b = tsplit(XYZ_to_Lab(XYZ, illuminant))
value = tstack((a, b, L))
if reference_colourspace == 'CIE LCHab':
L, CH, ab = tsplit(Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant)))
value = tstack((CH, ab, L))
if reference_colourspace == 'CIE Luv':
L, u, v = tsplit(XYZ_to_Luv(XYZ, illuminant))
value = tstack((u, v, L))
if reference_colourspace == 'CIE Luv uv': # Used for Chromaticity Diagram.
u, v = tsplit(Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant))
value = tstack((u, v))
if reference_colourspace == 'CIE LCHuv':
L, CH, uv = tsplit(Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant)))
value = tstack((CH, uv, L))
if reference_colourspace == 'CIE UCS':
value = XYZ_to_UCS(XYZ)
if reference_colourspace == 'CIE UCS uv': # Used for Chromaticity Diagram.
u, v = tsplit(UCS_to_uv(XYZ_to_UCS(XYZ)))
value = tstack((u, v))
if reference_colourspace == 'CIE UVW':
value = XYZ_to_UVW(XYZ * 100, illuminant)
if reference_colourspace == 'IPT':
I, P, T = tsplit(XYZ_to_IPT(XYZ))
value = tstack((P, T, I))
if value is None:
raise ValueError(('"{0}" not found in reference colourspace models: '
'"{1}".').format(reference_colourspace,
', '.join(REFERENCE_COLOURSPACES)))
return value
def tcs_colorimetry_data(
sd_t: SpectralDistribution,
sd_r: SpectralDistribution,
sds_tcs: Dict[str, SpectralDistribution],
cmfs: MultiSpectralDistributions,
chromatic_adaptation: Boolean = False,
) -> Tuple[TCS_ColorimetryData, ...]:
"""
Return the *test colour samples* colorimetry data.
Parameters
----------
sd_t
Test spectral distribution.
sd_r
Reference spectral distribution.
sds_tcs
*Test colour samples* spectral distributions.
cmfs
Standard observer colour matching functions.
chromatic_adaptation
Perform chromatic adaptation.
Returns
-------
:class:`tuple`
*Test colour samples* colorimetry data.
"""
XYZ_t = sd_to_XYZ(sd_t, cmfs)
uv_t = UCS_to_uv(XYZ_to_UCS(XYZ_t))
u_t, v_t = uv_t[0], uv_t[1]
XYZ_r = sd_to_XYZ(sd_r, cmfs)
uv_r = UCS_to_uv(XYZ_to_UCS(XYZ_r))
u_r, v_r = uv_r[0], uv_r[1]
tcs_data = []
for _key, value in sorted(INDEXES_TO_NAMES_TCS.items()):
sd_tcs = sds_tcs[value]
XYZ_tcs = sd_to_XYZ(sd_tcs, cmfs, sd_t)
xyY_tcs = XYZ_to_xyY(XYZ_tcs)
uv_tcs = UCS_to_uv(XYZ_to_UCS(XYZ_tcs))
u_tcs, v_tcs = uv_tcs[0], uv_tcs[1]
if chromatic_adaptation:
def c(
x: FloatingOrNDArray, y: FloatingOrNDArray
) -> FloatingOrNDArray:
"""Compute the :math:`c` term."""
return (4 - x - 10 * y) / y
def d(
x: FloatingOrNDArray, y: FloatingOrNDArray
) -> FloatingOrNDArray:
"""Compute the :math:`d` term."""
return (1.708 * y + 0.404 - 1.481 * x) / y
c_t, d_t = c(u_t, v_t), d(u_t, v_t)
c_r, d_r = c(u_r, v_r), d(u_r, v_r)
tcs_c, tcs_d = c(u_tcs, v_tcs), d(u_tcs, v_tcs)
u_tcs = (
10.872 + 0.404 * c_r / c_t * tcs_c - 4 * d_r / d_t * tcs_d
) / (16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d)
v_tcs = 5.52 / (
16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d
)
W_tcs = 25 * spow(xyY_tcs[-1], 1 / 3) - 17
U_tcs = 13 * W_tcs * (u_tcs - u_r)
V_tcs = 13 * W_tcs * (v_tcs - v_r)
tcs_data.append(
TCS_ColorimetryData(
sd_tcs.name, XYZ_tcs, uv_tcs, np.array([U_tcs, V_tcs, W_tcs])
)
)
return tuple(tcs_data)
def _tcs_colorimetry_data(test_spd,
reference_spd,
tsc_spds,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
test_spd : SpectralPowerDistribution
Test spectral power distribution.
reference_spd : SpectralPowerDistribution
Reference spectral power distribution.
tsc_spds : dict
Test colour samples.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
test_XYZ = spectral_to_XYZ(test_spd, cmfs)
test_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(test_XYZ)))
test_u, test_v = test_uv[0], test_uv[1]
reference_XYZ = spectral_to_XYZ(reference_spd, cmfs)
reference_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(reference_XYZ)))
reference_u, reference_v = reference_uv[0], reference_uv[1]
tcs_data = []
for key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
tcs_spd = tsc_spds.get(value)
tcs_XYZ = spectral_to_XYZ(tcs_spd, cmfs, test_spd)
tcs_xyY = np.ravel(XYZ_to_xyY(tcs_XYZ))
tcs_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(tcs_XYZ)))
tcs_u, tcs_v = tcs_uv[0], tcs_uv[1]
if chromatic_adaptation:
c = lambda x, y: (4 - x - 10 * y) / y
d = lambda x, y: (1.708 * y + 0.404 - 1.481 * x) / y
test_c, test_d = c(test_u, test_v), d(test_u, test_v)
reference_c, reference_d = (c(reference_u, reference_v),
d(reference_u, reference_v))
tcs_c, tcs_d = c(tcs_u, tcs_v), d(tcs_u, tcs_v)
tcs_u = ((10.872 + 0.404 * reference_c / test_c * tcs_c -
4 * reference_d / test_d * tcs_d) /
(16.518 + 1.481 * reference_c / test_c * tcs_c -
reference_d / test_d * tcs_d))
tcs_v = (5.52 / (16.518 + 1.481 * reference_c / test_c * tcs_c -
reference_d / test_d * tcs_d))
tcs_W = 25 * tcs_xyY[-1]**(1 / 3) - 17
tcs_U = 13 * tcs_W * (tcs_u - reference_u)
tcs_V = 13 * tcs_W * (tcs_v - reference_v)
tcs_data.append(
TSC_COLORIMETRY_DATA_NXYZUVUVW(tcs_spd.name, tcs_XYZ, tcs_uv,
np.array([tcs_U, tcs_V, tcs_W])))
return tcs_data
def XYZ_to_colourspace_model(XYZ, illuminant, model):
"""
Converts from *CIE XYZ* tristimulus values to given colourspace model.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like
*CIE XYZ* tristimulus values *illuminant* *xy* chromaticity
coordinates.
model : unicode
**{'CIE XYZ', 'CIE xyY', 'CIE xy', 'CIE Lab', 'CIE LCHab', 'CIE Luv',
'CIE Luv uv', 'CIE LCHuv', 'CIE UCS', 'CIE UCS uv', 'CIE UVW', 'IPT',
'Hunter Lab', 'Hunter Rdab'}**,
Colourspace model to convert the *CIE XYZ* tristimulus values to.
Returns
-------
ndarray
Colourspace model values.
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> W = np.array([0.34570, 0.35850])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE XYZ')
array([ 0.0704953..., 0.1008 , 0.0955831...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xyY')
array([ 0.2641477..., 0.3777000..., 0.1008 ])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xy')
array([ 0.2641477..., 0.3777000...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Lab')
array([ 37.9856291..., -23.6290768..., -4.4174661...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHab')
array([ 37.9856291..., 24.0384542..., 190.5892337...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv')
array([ 37.9856291..., -28.8021959..., -1.3580070...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv uv')
array([ 0.1508531..., 0.4853297...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHuv')
array([ 37.9856291..., 28.8341927..., 182.6994640...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UCS uv')
array([ 0.1508531..., 0.32355314...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UVW')
array([-28.0579733..., -0.8819449..., 37.0041149...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'IPT')
array([ 0.3657112..., -0.1111479..., 0.0159474...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'Hunter Lab')
array([ 31.7490157..., -15.1351736..., -2.7709606...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'Hunter Rdab')
array([ 10.08..., -18.7019271..., -3.4239649...])
"""
values = None
if model == 'CIE XYZ':
values = XYZ
elif model == 'CIE xyY':
values = XYZ_to_xyY(XYZ, illuminant)
elif model == 'CIE xy':
values = XYZ_to_xy(XYZ, illuminant)
elif model == 'CIE Lab':
values = XYZ_to_Lab(XYZ, illuminant)
elif model == 'CIE LCHab':
values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))
elif model == 'CIE Luv':
values = XYZ_to_Luv(XYZ, illuminant)
elif model == 'CIE Luv uv':
values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)
elif model == 'CIE LCHuv':
values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))
elif model == 'CIE UCS':
values = XYZ_to_UCS(XYZ)
elif model == 'CIE UCS uv':
values = UCS_to_uv(XYZ_to_UCS(XYZ))
elif model == 'CIE UVW':
values = XYZ_to_UVW(XYZ * 100, illuminant)
elif model == 'IPT':
values = XYZ_to_IPT(XYZ)
elif model == 'Hunter Lab':
values = XYZ_to_Hunter_Lab(XYZ * 100, xy_to_XYZ(illuminant) * 100)
elif model == 'Hunter Rdab':
values = XYZ_to_Hunter_Rdab(XYZ * 100, xy_to_XYZ(illuminant) * 100)
if values is None:
raise ValueError(
'"{0}" not found in colourspace models: "{1}".'.format(
model, ', '.join(COLOURSPACE_MODELS)))
return values
def tcs_colorimetry_data(sd_t,
sd_r,
sds_tcs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
sd_t : SpectralDistribution
Test spectral distribution.
sd_r : SpectralDistribution
Reference spectral distribution.
sds_tcs : dict
*Test colour samples* spectral distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
XYZ_t = sd_to_XYZ(sd_t, cmfs)
uv_t = UCS_to_uv(XYZ_to_UCS(XYZ_t))
u_t, v_t = uv_t[0], uv_t[1]
XYZ_r = sd_to_XYZ(sd_r, cmfs)
uv_r = UCS_to_uv(XYZ_to_UCS(XYZ_r))
u_r, v_r = uv_r[0], uv_r[1]
tcs_data = []
for _key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
sd_tcs = sds_tcs[value]
XYZ_tcs = sd_to_XYZ(sd_tcs, cmfs, sd_t)
xyY_tcs = XYZ_to_xyY(XYZ_tcs)
uv_tcs = UCS_to_uv(XYZ_to_UCS(XYZ_tcs))
u_tcs, v_tcs = uv_tcs[0], uv_tcs[1]
if chromatic_adaptation:
def c(x, y):
"""
Computes the :math:`c` term.
"""
return (4 - x - 10 * y) / y
def d(x, y):
"""
Computes the :math:`d` term.
"""
return (1.708 * y + 0.404 - 1.481 * x) / y
c_t, d_t = c(u_t, v_t), d(u_t, v_t)
c_r, d_r = c(u_r, v_r), d(u_r, v_r)
tcs_c, tcs_d = c(u_tcs, v_tcs), d(u_tcs, v_tcs)
u_tcs = (
(10.872 + 0.404 * c_r / c_t * tcs_c - 4 * d_r / d_t * tcs_d) /
(16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d))
v_tcs = (5.52 /
(16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d))
W_tcs = 25 * spow(xyY_tcs[-1], 1 / 3) - 17
U_tcs = 13 * W_tcs * (u_tcs - u_r)
V_tcs = 13 * W_tcs * (v_tcs - v_r)
tcs_data.append(
TCS_ColorimetryData(sd_tcs.name, XYZ_tcs, uv_tcs,
np.array([U_tcs, V_tcs, W_tcs])))
return tcs_data
def XYZ_to_OSA_UCS(XYZ):
"""
Converts from *CIE XYZ* tristimulus values under the
*CIE 1964 10 Degree Standard Observer* to *OSA UCS* colourspace.
The lightness axis, *L* is usually in range [-9, 5] and centered around
middle gray (Munsell N/6). The yellow-blue axis, *j* is usually in range
[-15, 15]. The red-green axis, *g* is usually in range [-20, 15].
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values under the
*CIE 1964 10 Degree Standard Observer*.
Returns
-------
ndarray
*OSA UCS* :math:`Ljg` lightness, jaune (yellowness), and greenness.
Notes
-----
+------------+-----------------------+--------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+====================+
| ``XYZ`` | [0, 100] | [0, 1] |
+------------+-----------------------+--------------------+
+------------+-----------------------+--------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+====================+
| ``Ljg`` | ``L`` : [-100, 100] | ``L`` : [-1, 1] |
| | | |
| | ``j`` : [-100, 100] | ``j`` : [-1, 1] |
| | | |
| | ``g`` : [-100, 100] | ``g`` : [-1, 1] |
+------------+-----------------------+--------------------+
- *OSA UCS* uses the *CIE 1964 10 Degree Standard Observer*.
References
----------
:cite:`Cao2013`, :cite:`Moroney2003`
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) * 100
>>> XYZ_to_OSA_UCS(XYZ) # doctest: +ELLIPSIS
array([-3.0049979..., 2.9971369..., -9.6678423...])
"""
XYZ = to_domain_100(XYZ)
x, y, Y = tsplit(XYZ_to_xyY(XYZ))
Y_0 = Y * (4.4934 * x**2 + 4.3034 * y**2 - 4.276 * x * y - 1.3744 * x -
2.5643 * y + 1.8103)
o_3 = 1 / 3
Y_0_es = spow(Y_0, o_3) - 2 / 3
# Gracefully handles Y_0 < 30.
Y_0_s = Y_0 - 30
Lambda = 5.9 * (Y_0_es + 0.042 * spow(Y_0_s, o_3))
RGB = dot_vector(M_XYZ_TO_RGB_OSA_UCS, XYZ)
RGB_3 = spow(RGB, 1 / 3)
C = Lambda / (5.9 * Y_0_es)
L = (Lambda - 14.4) / spow(2, 1 / 2)
j = C * np.dot(RGB_3, np.array([1.7, 8, -9.7]))
g = C * np.dot(RGB_3, np.array([-13.7, 17.7, -4]))
Ljg = tstack([L, j, g])
return from_range_100(Ljg)
def XYZ_to_UVW(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']):
"""
Converts from *CIE XYZ* tristimulus values to *CIE 1964 U\\*V\\*W\\**
colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE 1964 U\\*V\\*W\\** colourspace array.
Warning
-------
The input domain and output range of that definition are non standard!
Notes
-----
+----------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``UVW`` | ``U`` : [-100, 100] | ``U`` : [-1, 1] |
| | | |
| | ``V`` : [-100, 100] | ``V`` : [-1, 1] |
| | | |
| | ``W`` : [0, 100] | ``W`` : [0, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`Wikipedia2008a`
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) * 100
>>> XYZ_to_UVW(XYZ) # doctest: +ELLIPSIS
array([ 94.5503572..., 11.5553652..., 40.5475740...])
"""
XYZ = to_domain_100(XYZ)
xy = xyY_to_xy(illuminant)
xyY = XYZ_to_xyY(XYZ, xy)
_x, _y, Y = tsplit(xyY)
u, v = tsplit(UCS_to_uv(XYZ_to_UCS(XYZ)))
u_0, v_0 = tsplit(xy_to_UCS_uv(xy))
W = 25 * spow(Y, 1 / 3) - 17
U = 13 * W * (u - u_0)
V = 13 * W * (v - v_0)
UVW = tstack([U, V, W])
return from_range_100(UVW)
Notes
-----
- *X-Rite* data is given as *CIE L\\*a\\*b\\** colourspace values under
*CIE Illuminant D Series D50* for the
*CIE 1931 2 Degree Standard Observer*.
DATA_COLORCHECKER24_BEFORE_NOV2014 : ndarray
"""
DATA_COLORCHECKER24_BEFORE_NOV2014 = OrderedDict(
zip(
SAMPLE_LABELS_COLORCHECKER_CLASSIC,
XYZ_to_xyY(
Lab_to_XYZ(
list(DATA_COLORCHECKER24_BEFORE_NOV2014.values()),
CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']
['ICC D50']))))
CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014 = (
CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['ICC D50'])
"""
*ColorChecker24 - Before November 2014* illuminant.
CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014 : ndarray
"""
CCS_COLORCHECKER24_BEFORE_NOV2014 = ColourChecker(
'ColorChecker24 - Before November 2014',
DATA_COLORCHECKER24_BEFORE_NOV2014,
CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014)
*ColorChecker24 - Before November 2014* illuminant.
Notes
-----
- *X-Rite* data is given as *CIE L\\*a\\*b\\** colourspace values under
*CIE Illuminant D Series D50* for the
*CIE 1931 2 Degree Standard Observer*.
COLORCHECKER24_BEFORE_NOV2014_LAB_DATA : ndarray
"""
COLORCHECKER24_BEFORE_NOV2014_DATA = OrderedDict(
zip(
COLORCHECKER24_BEFORE_NOV2014_LAB_DATA.keys(),
XYZ_to_xyY(
Lab_to_XYZ(
list(COLORCHECKER24_BEFORE_NOV2014_LAB_DATA.values()),
ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50']))))
COLORCHECKER24_BEFORE_NOV2014_ILLUMINANT = (
ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50'])
"""
*ColorChecker24 - Before November 2014* illuminant.
COLORCHECKER24_BEFORE_NOV2014_ILLUMINANT : ndarray
"""
COLORCHECKER24_BEFORE_NOV2014 = ColourChecker(
'ColorChecker24 - Before November 2014',
COLORCHECKER24_BEFORE_NOV2014_DATA,
COLORCHECKER24_BEFORE_NOV2014_ILLUMINANT)
"""
np.array([51.038, -28.631, -28.638]),
np.array([96.539, -0.425, 1.186]),
np.array([81.257, -0.638, -0.335]),
np.array([66.766, -0.734, -0.504]),
np.array([50.867, -0.153, -0.27]),
np.array([35.656, -0.421, -1.231]),
np.array([20.461, -0.079, -0.973]),
],
))
DATA_COLORCHECKER24_BEFORE_NOV2014: Dict = dict(
zip(
SAMPLE_LABELS_COLORCHECKER_CLASSIC,
XYZ_to_xyY(
Lab_to_XYZ(
list(DATA_COLORCHECKER24_BEFORE_NOV2014_CIE_LAB.values()),
CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]
["ICC D50"],
)),
))
CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014: NDArray = CCS_ILLUMINANTS[
"CIE 1931 2 Degree Standard Observer"]["ICC D50"]
"""*ColorChecker24 - Before November 2014* illuminant."""
CCS_COLORCHECKER24_BEFORE_NOV2014: ColourChecker = ColourChecker(
"ColorChecker24 - Before November 2014",
DATA_COLORCHECKER24_BEFORE_NOV2014,
CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014,
)
"""
Reference *ColorChecker Classic* data from *X-Rite (2015)*.