def test_n_dimensional_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` definition n-dimensions
support.
"""
xyY = np.array([0.26414772, 0.37770001, 0.10080000])
XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
np.testing.assert_almost_equal(
xyY_to_XYZ(xyY),
XYZ,
decimal=7)
xyY = np.tile(xyY, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(
xyY_to_XYZ(xyY),
XYZ,
decimal=7)
xyY = np.reshape(xyY, (2, 3, 3))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(
xyY_to_XYZ(xyY),
XYZ,
decimal=7)
def test_n_dimensional_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` definition n-dimensions
support.
"""
xyY = np.array([0.26414772, 0.37770001, 0.10080000])
XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
np.testing.assert_almost_equal(
xyY_to_XYZ(xyY),
XYZ,
decimal=7)
xyY = np.tile(xyY, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(
xyY_to_XYZ(xyY),
XYZ,
decimal=7)
xyY = np.reshape(xyY, (2, 3, 3))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(
xyY_to_XYZ(xyY),
XYZ,
decimal=7)
def test_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.26414772, 0.37770001, 0.10080000])),
np.array([0.07049534, 0.10080000, 0.09558313]),
decimal=7,
)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.50453169, 0.374400000000, 0.34950000])),
np.array([0.47097710, 0.34950000, 0.11301649]),
decimal=7,
)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.47670437, 0.35790000, 0.1915])),
np.array([0.25506814, 0.19150000, 0.08849752]),
decimal=7,
)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.34567, 0.3585, 0.00000000])), np.array([0.0, 0.0, 0.0]), decimal=7
)
def test_nan_xyY_to_XYZ(self):
"""Test :func:`colour.models.cie_xyy.xyY_to_XYZ` 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:
xyY = np.array(case)
xyY_to_XYZ(xyY)
def test_nan_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` 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:
xyY = np.array(case)
xyY_to_XYZ(xyY)
def test_n_dimensional_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` definition n-dimensional
support.
"""
xyY = np.array([0.54369557, 0.32107944, 0.12197225])
XYZ = xyY_to_XYZ(xyY)
xyY = np.tile(xyY, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(xyY_to_XYZ(xyY), XYZ, decimal=7)
xyY = np.reshape(xyY, (2, 3, 3))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(xyY_to_XYZ(xyY), XYZ, decimal=7)
def _XYZ_optimal_colour_stimuli(illuminant):
"""
Returns given illuminant *Optimal Colour Stimuli* in *CIE XYZ* tristimulus
values and caches it if not existing.
Parameters
----------
illuminant : unicode
Illuminant.
Returns
-------
tuple
Illuminant *Optimal Colour Stimuli*.
"""
optimal_colour_stimuli = ILLUMINANTS_OPTIMAL_COLOUR_STIMULI.get(illuminant)
if optimal_colour_stimuli is None:
raise KeyError('"{0}" not found in factory '
'"Optimal Colour Stimuli": "{1}".'.format(
illuminant,
sorted(ILLUMINANTS_OPTIMAL_COLOUR_STIMULI.keys())))
vertices = _XYZ_OPTIMAL_COLOUR_STIMULI_CACHE.get(illuminant)
if vertices is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_CACHE[illuminant] = vertices = (
xyY_to_XYZ(optimal_colour_stimuli) / 100)
return vertices
def test_n_dimensional_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` definition n-dimensional
support.
"""
xyY = np.array([0.54369557, 0.32107944, 0.12197225])
XYZ = xyY_to_XYZ(xyY)
xyY = np.tile(xyY, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(xyY_to_XYZ(xyY), XYZ, decimal=7)
xyY = np.reshape(xyY, (2, 3, 3))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(xyY_to_XYZ(xyY), XYZ, decimal=7)
def _XYZ_optimal_colour_stimuli(illuminant):
"""
Returns given illuminant optimal colour stimuli in *CIE XYZ* colourspace
and caches it if not existing.
Parameters
----------
illuminant : unicode
Illuminant.
Returns
-------
tuple
Illuminant optimal colour stimuli.
"""
optimal_colour_stimuli = ILLUMINANTS_OPTIMAL_COLOUR_STIMULI.get(illuminant)
if optimal_colour_stimuli is None:
raise KeyError(
'"{0}" not found in factory optimal colour stimuli: "{1}".'.format(
illuminant, sorted(ILLUMINANTS_OPTIMAL_COLOUR_STIMULI.keys())))
cached_ocs = _XYZ_OPTIMAL_COLOUR_STIMULI_CACHE.get(illuminant)
if cached_ocs is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_CACHE[illuminant] = cached_ocs = (
np.array([np.ravel(xyY_to_XYZ(x) / 100)
for x in optimal_colour_stimuli]))
return cached_ocs
def _XYZ_optimal_colour_stimuli(illuminant):
"""
Returns given illuminant *Optimal Colour Stimuli* in *CIE XYZ* tristimulus
values and caches it if not existing.
Parameters
----------
illuminant : unicode
Illuminant.
Returns
-------
tuple
Illuminant *Optimal Colour Stimuli*.
"""
optimal_colour_stimuli = ILLUMINANTS_OPTIMAL_COLOUR_STIMULI.get(illuminant)
if optimal_colour_stimuli is None:
raise KeyError('"{0}" not found in factory '
'"Optimal Colour Stimuli": "{1}".'.format(
illuminant,
sorted(ILLUMINANTS_OPTIMAL_COLOUR_STIMULI.keys())))
cached_ocs = _XYZ_OPTIMAL_COLOUR_STIMULI_CACHE.get(illuminant)
if cached_ocs is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_CACHE[illuminant] = cached_ocs = (
xyY_to_XYZ(optimal_colour_stimuli) / 100)
return cached_ocs
def _XYZ_optimal_colour_stimuli(illuminant):
"""
Returns given illuminant optimal colour stimuli in *CIE XYZ* colourspace
and caches it if not existing.
Parameters
----------
illuminant : unicode
Illuminant.
Returns
-------
tuple
Illuminant optimal colour stimuli.
"""
optimal_colour_stimuli = ILLUMINANTS_OPTIMAL_COLOUR_STIMULI.get(illuminant)
if optimal_colour_stimuli is None:
raise KeyError(
'"{0}" not found in factory optimal colour stimuli: "{1}".'.format(
illuminant, sorted(ILLUMINANTS_OPTIMAL_COLOUR_STIMULI.keys())))
cached_ocs = _XYZ_OPTIMAL_COLOUR_STIMULI_CACHE.get(illuminant)
if cached_ocs is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_CACHE[illuminant] = cached_ocs = (np.array(
[np.ravel(xyY_to_XYZ(x) / 100) for x in optimal_colour_stimuli]))
return cached_ocs
def ProLab_to_XYZ(
ProLab: ArrayLike,
illuminant: ArrayLike = CCS_ILLUMINANTS[
"CIE 1931 2 Degree Standard Observer"]["D65"],
) -> NDArray:
"""
Convert from *ProLab* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
ProLab
*ProLab* colourspace array.
illuminant
Reference *illuminant* *CIE xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
:class:`numpy.ndarray`
*CIE XYZ* tristimulus values.
Notes
-----
+------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``Lab`` | ``L`` : [0, 1] | ``L`` : [0, 1] |
| | | |
| | ``a`` : [-1, 1] | ``a`` : [-1, 1] |
| | | |
| | ``b`` : [-1, 1] | ``b`` : [-1, 1] |
+------------+-----------------------+-----------------+
+------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+------------+-----------------------+-----------------+
References
----------
:cite:`Ivan2021`
Examples
--------
>>> ProLab = np.array([59.8466286, 115.0396354, 20.12510352])
>>> ProLab_to_XYZ(ProLab) # doctest: +ELLIPSIS
array([ 0.5163401..., 0.154695 ..., 0.0628957...])
"""
ProLab = to_domain_1(ProLab)
XYZ_n = xyY_to_XYZ(xy_to_xyY(illuminant))
XYZ = projective_transformation(ProLab, MATRIX_INVERSE_Q)
XYZ *= XYZ_n
return from_range_1(XYZ)
def XYZ_to_hdr_CIELab(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50'],
Y_s=0.2,
Y_abs=100):
"""
Converts from *CIE XYZ* tristimulus values to *hdr-CIELAB* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Y_s : numeric or array_like
Relative luminance :math:`Y_s` of the surround in domain [0, 1].
Y_abs : numeric or array_like
Absolute luminance :math:`Y_{abs}` of the scene diffuse white in
:math:`cd/m^2`.
Returns
-------
ndarray
*hdr-CIELAB* colourspace array.
Notes
-----
- Conversion to polar coordinates to compute the *chroma* :math:`C_{hdr}`
and *hue* :math:`h_{hdr}` correlates can be safely performed with
:func:`colour.Lab_to_LCHab` definition.
- Conversion to cartesian coordinates from the *Lightness*
:math:`L_{hdr}`, *chroma* :math:`C_{hdr}` and *hue* :math:`h_{hdr}`
correlates can be safely performed with :func:`colour.LCHab_to_Lab`
definition.
- Input *CIE XYZ* tristimulus values are in domain [0, math:`\infty`].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_to_hdr_CIELab(XYZ) # doctest: +ELLIPSIS
array([ 24.9020664..., -46.8312760..., -10.14274843])
"""
X, Y, Z = tsplit(XYZ)
X_n, Y_n, Z_n = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
e = exponent_hdr_CIELab(Y_s, Y_abs)
L_hdr = lightness_Fairchild2010(Y / Y_n, e)
a_hdr = 5 * (lightness_Fairchild2010(X / X_n, e) - L_hdr)
b_hdr = 2 * (L_hdr - lightness_Fairchild2010(Z / Z_n, e))
Lab_hdr = tstack((L_hdr, a_hdr, b_hdr))
return Lab_hdr
def Luv_to_XYZ(
Luv,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50']):
"""
Converts from *CIE L\*u\*v\** colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
Luv : array_like
*CIE L\*u\*v\** colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
- Input :math:`L^*` is in domain [0, 100].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *CIE XYZ* tristimulus values are in range [0, 1].
References
----------
- :cite:`CIETC1-482004m`
- :cite:`Wikipediaby`
Examples
--------
>>> Luv = np.array([37.9856291 , -28.80219593, -1.35800706])
>>> Luv_to_XYZ(Luv) # doctest: +ELLIPSIS
array([ 0.0704953..., 0.1008 , 0.0955831...])
"""
L, u, v = tsplit(Luv)
X_r, Y_r, Z_r = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
Y = np.where(L > CIE_E * CIE_K, ((L + 16) / 116)**3, L / CIE_K)
a = 1 / 3 * ((52 * L / (u + 13 * L * (4 * X_r /
(X_r + 15 * Y_r + 3 * Z_r)))) - 1)
b = -5 * Y
c = -1 / 3.0
d = Y * (39 * L / (v + 13 * L * (9 * Y_r /
(X_r + 15 * Y_r + 3 * Z_r))) - 5)
X = (d - b) / (a - c)
Z = X * a + b
XYZ = tstack((X, Y, Z))
return XYZ
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 XYZ_to_Lab(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* tristimulus values to *CIE Lab* 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 Lab* colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 1].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *Lightness* :math:`L^*` is in range [0, 100].
References
----------
.. [2] Lindbloom, B. (2003). XYZ to Lab. Retrieved February 24, 2014,
from http://www.brucelindbloom.com/Eqn_XYZ_to_Lab.html
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_to_Lab(XYZ) # doctest: +ELLIPSIS
array([ 37.9856291..., -23.6290768..., -4.4174661...])
"""
XYZ = np.asarray(XYZ)
XYZ_r = xyY_to_XYZ(xy_to_xyY(illuminant))
XYZ_f = XYZ / XYZ_r
XYZ_f = np.where(XYZ_f > CIE_E,
np.power(XYZ_f, 1 / 3),
(CIE_K * XYZ_f + 16) / 116)
X_f, Y_f, Z_f = tsplit(XYZ_f)
L = 116 * Y_f - 16
a = 500 * (X_f - Y_f)
b = 200 * (Y_f - Z_f)
Lab = tstack((L, a, b))
return Lab
def Luv_to_XYZ(Luv,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE Luv* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
Luv : array_like
*CIE Luv* colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
- Input :math:`L^*` is in domain [0, 100].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *CIE XYZ* tristimulus values are in range [0, 1].
References
----------
.. [3] Lindbloom, B. (2003). Luv to XYZ. Retrieved February 24, 2014,
from http://brucelindbloom.com/Eqn_Luv_to_XYZ.html
Examples
--------
>>> Luv = np.array([37.9856291 , -28.80219593, -1.35800706])
>>> Luv_to_XYZ(Luv) # doctest: +ELLIPSIS
array([ 0.0704953..., 0.1008 , 0.0955831...])
"""
L, u, v = tsplit(Luv)
X_r, Y_r, Z_r = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
Y = np.where(L > CIE_E * CIE_K, ((L + 16) / 116) ** 3, L / CIE_K)
a = 1 / 3 * ((52 * L / (u + 13 * L *
(4 * X_r / (X_r + 15 * Y_r + 3 * Z_r)))) - 1)
b = -5 * Y
c = -1 / 3.0
d = Y * (39 * L / (v + 13 * L *
(9 * Y_r / (X_r + 15 * Y_r + 3 * Z_r))) - 5)
X = (d - b) / (a - c)
Z = X * a + b
XYZ = tstack((X, Y, Z))
return XYZ
def XYZ_to_Lab(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* tristimulus values to *CIE Lab* 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 Lab* colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 1].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *Lightness* :math:`L^*` is in domain [0, 100].
References
----------
.. [2] Lindbloom, B. (2003). XYZ to Lab. Retrieved February 24, 2014,
from http://www.brucelindbloom.com/Eqn_XYZ_to_Lab.html
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_to_Lab(XYZ) # doctest: +ELLIPSIS
array([ 37.9856291..., -23.6230288..., -4.4141703...])
"""
XYZ = np.asarray(XYZ)
XYZ_r = xyY_to_XYZ(xy_to_xyY(illuminant))
XYZ_f = XYZ / XYZ_r
XYZ_f = np.where(XYZ_f > CIE_E,
np.power(XYZ_f, 1 / 3),
(CIE_K * XYZ_f + 16) / 116)
X_f, Y_f, Z_f = tsplit(XYZ_f)
L = 116 * Y_f - 16
a = 500 * (X_f - Y_f)
b = 200 * (Y_f - Z_f)
Lab = tstack((L, a, b))
return Lab
def test_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.4325, 0.3788, 0.1034])),
np.array([0.11805834, 0.1034, 0.05150892]),
decimal=7)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.3439, 0.3565, 0.0320])),
np.array([0.030869, 0.032, 0.02689257]),
decimal=7)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.4325, 0, 0.1034])),
np.array([0., 0., 0.]),
decimal=7)
def test_domain_range_scale_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` definition domain and
range scale support.
"""
xyY = np.array([0.54369557, 0.32107944, 0.12197225])
XYZ = xyY_to_XYZ(xyY)
xyY = np.tile(xyY, (6, 1)).reshape(2, 3, 3)
XYZ = np.tile(XYZ, (6, 1)).reshape(2, 3, 3)
d_r = (('reference', 1, 1), (1, 1, 1), (
100,
np.array([1, 1, 100]),
100,
))
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
xyY_to_XYZ(xyY * factor_a), XYZ * factor_b, decimal=7)
def test_domain_range_scale_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` definition domain and
range scale support.
"""
xyY = np.array([0.54369557, 0.32107944, 0.12197225])
XYZ = xyY_to_XYZ(xyY)
xyY = np.tile(xyY, (6, 1)).reshape(2, 3, 3)
XYZ = np.tile(XYZ, (6, 1)).reshape(2, 3, 3)
d_r = (('reference', 1, 1), (1, 1, 1), (
100,
np.array([1, 1, 100]),
100,
))
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
xyY_to_XYZ(xyY * factor_a), XYZ * factor_b, decimal=7)
def XYZ_to_Lab(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50']):
"""
Converts from *CIE XYZ* tristimulus values to *CIE L\*a\*b\** 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 L\*a\*b\** colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 1].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *Lightness* :math:`L^*` is in range [0, 100].
References
----------
- :cite:`CIETC1-482004m`
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_to_Lab(XYZ) # doctest: +ELLIPSIS
array([ 37.9856291..., -23.6290768..., -4.4174661...])
"""
XYZ = np.asarray(XYZ)
XYZ_r = xyY_to_XYZ(xy_to_xyY(illuminant))
XYZ_f = XYZ / XYZ_r
XYZ_f = np.where(XYZ_f > CIE_E, np.power(XYZ_f, 1 / 3),
(CIE_K * XYZ_f + 16) / 116)
X_f, Y_f, Z_f = tsplit(XYZ_f)
L = 116 * Y_f - 16
a = 500 * (X_f - Y_f)
b = 200 * (Y_f - Z_f)
Lab = tstack((L, a, b))
return Lab
def test_xyY_to_XYZ(self):
"""Test :func:`colour.models.cie_xyy.xyY_to_XYZ` definition."""
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.54369557, 0.32107944, 0.12197225])),
np.array([0.20654008, 0.12197225, 0.05136952]),
decimal=7,
)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.29777735, 0.48246446, 0.23042768])),
np.array([0.14222010, 0.23042768, 0.10495772]),
decimal=7,
)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.18582823, 0.14633764, 0.06157201])),
np.array([0.07818780, 0.06157201, 0.28099326]),
decimal=7,
)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.34567, 0.3585, 0.00000000])),
np.array([0.00000000, 0.00000000, 0.00000000]),
decimal=7,
)
np.testing.assert_almost_equal(
xyY_to_XYZ(
np.array([
[0.54369557, 0.32107944, 0.12197225],
[0.31270000, 0.32900000, 0.00000000],
[0.00000000, 1.00000000, 1.00000000],
])),
np.array([
[0.20654008, 0.12197225, 0.05136952],
[0.00000000, 0.00000000, 0.00000000],
[0.00000000, 1.00000000, 0.00000000],
]),
decimal=7,
)
def XYZ_to_Luv(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50']):
"""
Converts from *CIE XYZ* tristimulus values to *CIE L\*u\*v\** 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 L\*u\*v\** colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 1].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output :math:`L^*` is in range [0, 100].
References
----------
- :cite:`CIETC1-482004m`
- :cite:`Wikipediaby`
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_to_Luv(XYZ) # doctest: +ELLIPSIS
array([ 37.9856291..., -28.8021959..., -1.3580070...])
"""
X, Y, Z = tsplit(XYZ)
X_r, Y_r, Z_r = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
y_r = Y / Y_r
L = np.where(y_r > CIE_E, 116 * y_r**(1 / 3) - 16, CIE_K * y_r)
u = (13 * L * ((4 * X / (X + 15 * Y + 3 * Z)) -
(4 * X_r / (X_r + 15 * Y_r + 3 * Z_r))))
v = (13 * L * ((9 * Y / (X + 15 * Y + 3 * Z)) -
(9 * Y_r / (X_r + 15 * Y_r + 3 * Z_r))))
Luv = tstack((L, u, v))
return Luv
def is_within_macadam_limits(
xyY: ArrayLike,
illuminant: Union[Literal["A", "C", "D65"], str] = "D65",
tolerance: Optional[Floating] = None,
) -> NDArray:
"""
Return whether given *CIE xyY* colourspace array is within MacAdam limits
of given illuminant.
Parameters
----------
xyY
*CIE xyY* colourspace array.
illuminant
Illuminant name.
tolerance
Tolerance allowed in the inside-triangle check.
Returns
-------
:class:`numpy.ndarray`
Whether given *CIE xyY* colourspace array is within MacAdam limits.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``xyY`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
Examples
--------
>>> is_within_macadam_limits(np.array([0.3205, 0.4131, 0.51]), 'A')
array(True, dtype=bool)
>>> a = np.array([[0.3205, 0.4131, 0.51],
... [0.0005, 0.0031, 0.001]])
>>> is_within_macadam_limits(a, 'A')
array([ True, False], dtype=bool)
"""
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _CACHE_OPTIMAL_COLOUR_STIMULI_XYZ_TRIANGULATIONS.get(
illuminant)
if triangulation is None:
_CACHE_OPTIMAL_COLOUR_STIMULI_XYZ_TRIANGULATIONS[
illuminant] = triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(xyY_to_XYZ(xyY), tol=tolerance)
simplex = np.where(simplex >= 0, True, False)
return simplex
def XYZ_to_UVW(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('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.
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 UVW* colourspace array is in range [0, 100].
Warning
-------
The input / output domains of that definition are non standard!
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 XYZ_to_Luv(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* tristimulus values to *CIE Luv* 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 Luv* colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 1].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output :math:`L^*` is in range [0, 100].
References
----------
.. [2] Lindbloom, B. (2003). XYZ to Luv. Retrieved February 24, 2014,
from http://brucelindbloom.com/Eqn_XYZ_to_Luv.html
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_to_Luv(XYZ) # doctest: +ELLIPSIS
array([ 37.9856291..., -28.8021959..., -1.3580070...])
"""
X, Y, Z = tsplit(XYZ)
X_r, Y_r, Z_r = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
y_r = Y / Y_r
L = np.where(y_r > CIE_E, 116 * y_r ** (1 / 3) - 16, CIE_K * y_r)
u = (13 * L * ((4 * X / (X + 15 * Y + 3 * Z)) -
(4 * X_r / (X_r + 15 * Y_r + 3 * Z_r))))
v = (13 * L * ((9 * Y / (X + 15 * Y + 3 * Z)) -
(9 * Y_r / (X_r + 15 * Y_r + 3 * Z_r))))
Luv = tstack((L, u, v))
return Luv
def Lab_to_XYZ(Lab,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE Lab* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
Lab : array_like
*CIE Lab* colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
- Input *Lightness* :math:`L^*` is in domain [0, 100].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *CIE XYZ* tristimulus values are in domain [0, 1].
References
----------
.. [3] Lindbloom, B. (2008). Lab to XYZ. Retrieved February 24, 2014,
from http://www.brucelindbloom.com/Eqn_Lab_to_XYZ.html
Examples
--------
>>> Lab = np.array([37.98562910, -23.62302887, -4.41417036])
>>> Lab_to_XYZ(Lab) # doctest: +ELLIPSIS
array([ 0.0704953..., 0.1008 , 0.0955831...])
"""
L, a, b = tsplit(Lab)
XYZ_r = xyY_to_XYZ(xy_to_xyY(illuminant))
f_y = (L + 16) / 116
f_x = a / 500 + f_y
f_z = f_y - b / 200
x_r = np.where(f_x**3 > CIE_E, f_x**3, (116 * f_x - 16) / CIE_K)
y_r = np.where(L > CIE_K * CIE_E, ((L + 16) / 116)**3, L / CIE_K)
z_r = np.where(f_z**3 > CIE_E, f_z**3, (116 * f_z - 16) / CIE_K)
XYZ = tstack((x_r, y_r, z_r)) * XYZ_r
return XYZ
def Lab_to_XYZ(Lab,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE Lab* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
Lab : array_like
*CIE Lab* colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
- Input *Lightness* :math:`L^*` is in domain [0, 100].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *CIE XYZ* tristimulus values are in domain [0, 1].
References
----------
.. [3] Lindbloom, B. (2008). Lab to XYZ. Retrieved February 24, 2014,
from http://www.brucelindbloom.com/Eqn_Lab_to_XYZ.html
Examples
--------
>>> Lab = np.array([37.98562910, -23.62302887, -4.41417036])
>>> Lab_to_XYZ(Lab) # doctest: +ELLIPSIS
array([ 0.0704953..., 0.1008 , 0.0955831...])
"""
L, a, b = tsplit(Lab)
XYZ_r = xyY_to_XYZ(xy_to_xyY(illuminant))
f_y = (L + 16) / 116
f_x = a / 500 + f_y
f_z = f_y - b / 200
x_r = np.where(f_x ** 3 > CIE_E, f_x ** 3, (116 * f_x - 16) / CIE_K)
y_r = np.where(L > CIE_K * CIE_E, ((L + 16) / 116) ** 3, L / CIE_K)
z_r = np.where(f_z ** 3 > CIE_E, f_z ** 3, (116 * f_z - 16) / CIE_K)
XYZ = tstack((x_r, y_r, z_r)) * XYZ_r
return XYZ
def hdr_CIELab_to_XYZ(
Lab_hdr,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50'],
Y_s=0.2,
Y_abs=100):
"""
Converts from *hdr-CIELAB* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
Lab_hdr : array_like
*hdr-CIELAB* colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Y_s : numeric or array_like
Relative luminance :math:`Y_s` of the surround in domain [0, 1].
Y_abs : numeric or array_like
Absolute luminance :math:`Y_{abs}` of the scene diffuse white in
:math:`cd/m^2`.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *CIE XYZ* tristimulus values are in range [0, math:`\infty`].
Examples
--------
>>> Lab_hdr = np.array([24.90206646, -46.83127607, -10.14274843])
>>> hdr_CIELab_to_XYZ(Lab_hdr) # doctest: +ELLIPSIS
array([ 0.0704953..., 0.1008 , 0.0955831...])
"""
L_hdr, a_hdr, b_hdr = tsplit(Lab_hdr)
X_n, Y_n, Z_n = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
e = exponent_hdr_CIELab(Y_s, Y_abs)
Y = luminance_Fairchild2010(L_hdr, e) * Y_n
X = luminance_Fairchild2010((a_hdr + 5 * L_hdr) / 5, e) * X_n
Z = luminance_Fairchild2010((-b_hdr + 2 * L_hdr) / 2, e) * Z_n
XYZ = tstack((X, Y, Z))
return XYZ
def Lab_to_XYZ(
Lab,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50']):
"""
Converts from *CIE L\*a\*b\** colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
Lab : array_like
*CIE L\*a\*b\** colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
- Input *Lightness* :math:`L^*` is in domain [0, 100].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *CIE XYZ* tristimulus values are in range [0, 1].
References
----------
- :cite:`CIETC1-482004m`
Examples
--------
>>> Lab = np.array([37.98562910, -23.62907688, -4.41746615])
>>> Lab_to_XYZ(Lab) # doctest: +ELLIPSIS
array([ 0.0704953..., 0.1008 , 0.0955831...])
"""
L, a, b = tsplit(Lab)
XYZ_r = xyY_to_XYZ(xy_to_xyY(illuminant))
f_y = (L + 16) / 116
f_x = a / 500 + f_y
f_z = f_y - b / 200
x_r = np.where(f_x**3 > CIE_E, f_x**3, (116 * f_x - 16) / CIE_K)
y_r = np.where(L > CIE_K * CIE_E, ((L + 16) / 116)**3, L / CIE_K)
z_r = np.where(f_z**3 > CIE_E, f_z**3, (116 * f_z - 16) / CIE_K)
XYZ = tstack((x_r, y_r, z_r)) * XYZ_r
return XYZ
def test_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.54369557, 0.32107944, 0.12197225])),
np.array([0.20654008, 0.12197225, 0.05136952]),
decimal=7)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.29777735, 0.48246446, 0.23042768])),
np.array([0.14222010, 0.23042768, 0.10495772]),
decimal=7)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.18582823, 0.14633764, 0.06157201])),
np.array([0.07818780, 0.06157201, 0.28099326]),
decimal=7)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.34567, 0.3585, 0.00000000])),
np.array([0.00000000, 0.00000000, 0.00000000]),
decimal=7)
np.testing.assert_almost_equal(
xyY_to_XYZ(
np.array([
[0.54369557, 0.32107944, 0.12197225],
[0.31270000, 0.32900000, 0.00000000],
[0.00000000, 1.00000000, 1.00000000],
])),
np.array([
[0.20654008, 0.12197225, 0.05136952],
[0.00000000, 0.00000000, 0.00000000],
[0.00000000, 1.00000000, 0.00000000],
]),
decimal=7)
def is_within_macadam_limits(xyY, illuminant, tolerance=None):
"""
Returns if given *CIE xyY* colourspace array is within MacAdam limits of
given illuminant.
Parameters
----------
xyY : array_like
*CIE xyY* colourspace array.
illuminant : unicode
Illuminant.
tolerance : numeric, optional
Tolerance allowed in the inside-triangle check.
Returns
-------
bool
Is within MacAdam limits.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``xyY`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
Examples
--------
>>> is_within_macadam_limits(np.array([0.3205, 0.4131, 0.51]), 'A')
array(True, dtype=bool)
>>> a = np.array([[0.3205, 0.4131, 0.51],
... [0.0005, 0.0031, 0.001]])
>>> is_within_macadam_limits(a, 'A')
array([ True, False], dtype=bool)
"""
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE.get(
illuminant)
if triangulation is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE[illuminant] = \
triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(xyY_to_XYZ(xyY), tol=tolerance)
simplex = np.where(simplex >= 0, True, False)
return simplex
def is_within_macadam_limits(xyY, illuminant, tolerance=None):
"""
Returns if given *CIE xyY* colourspace array is within MacAdam limits of
given illuminant.
Parameters
----------
xyY : array_like
*CIE xyY* colourspace array.
illuminant : unicode
Illuminant.
tolerance : numeric, optional
Tolerance allowed in the inside-triangle check.
Returns
-------
bool
Is within MacAdam limits.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``xyY`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
Examples
--------
>>> is_within_macadam_limits(np.array([0.3205, 0.4131, 0.51]), 'A')
array(True, dtype=bool)
>>> a = np.array([[0.3205, 0.4131, 0.51],
... [0.0005, 0.0031, 0.001]])
>>> is_within_macadam_limits(a, 'A')
array([ True, False], dtype=bool)
"""
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE.get(
illuminant)
if triangulation is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE[illuminant] = \
triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(xyY_to_XYZ(xyY), tol=tolerance)
simplex = np.where(simplex >= 0, True, False)
return simplex
def xy_to_rgb(xy, name='ITU-R BT.2020', normalize='maximum', specific=None):
"""
xy値からRGB値を算出する。
いい感じに正規化もしておく。
Parameters
----------
xy : array_like
xy value.
name : string
color space name.
normalize : string
normalize method. You can select 'maximum', 'specific' or None.
Returns
-------
array_like
rgb value. the value is normalized.
"""
illuminant_XYZ = D65_WHITE
illuminant_RGB = D65_WHITE
chromatic_adaptation_transform = 'CAT02'
large_xyz_to_rgb_matrix = get_xyz_to_rgb_matrix(name)
if normalize == 'specific':
xyY = xy_to_xyY(xy)
xyY[..., 2] = specific
large_xyz = xyY_to_XYZ(xyY)
else:
large_xyz = xy_to_XYZ(xy)
rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB,
large_xyz_to_rgb_matrix,
chromatic_adaptation_transform)
"""
そのままだとビデオレベルが低かったりするので、
各ドット毎にRGB値を正規化＆最大化する。必要であれば。
"""
if normalize == 'maximum':
rgb = normalise_maximum(rgb, axis=-1)
else:
if(np.sum(rgb > 1.0) > 0):
print("warning: over flow has occured at xy_to_rgb")
if(np.sum(rgb < 0.0) > 0):
print("warning: under flow has occured at xy_to_rgb")
rgb[rgb < 0] = 0
rgb[rgb > 1.0] = 1.0
return rgb
def test_xyY_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xyY_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.26414772, 0.37770001, 0.10080000])),
np.array([0.07049534, 0.10080000, 0.09558313]),
decimal=7)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.50453169, 0.37440000, 0.34950000])),
np.array([0.47097710, 0.34950000, 0.11301649]),
decimal=7)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.47670437, 0.35790000, 0.19150000])),
np.array([0.25506814, 0.19150000, 0.08849752]),
decimal=7)
np.testing.assert_almost_equal(
xyY_to_XYZ(np.array([0.34567, 0.3585, 0.00000000])),
np.array([0.00000000, 0.00000000, 0.00000000]),
decimal=7)
def is_within_macadam_limits(xyY, illuminant, tolerance=None):
"""
Returns if given *CIE xyY* colourspace array is within MacAdam limits of
given illuminant.
Parameters
----------
xyY : array_like
*CIE xyY* colourspace array.
illuminant : unicode
Illuminant.
tolerance : numeric, optional
Tolerance allowed in the inside-triangle check.
Returns
-------
bool
Is within MacAdam limits.
Notes
-----
- Input *CIE xyY* colourspace array is in domain [0, 1].
- This definition requires *scipy* to be installed.
Examples
--------
>>> is_within_macadam_limits(np.array([0.3205, 0.4131, 0.51]), 'A')
array(True, dtype=bool)
>>> a = np.array([[0.3205, 0.4131, 0.51],
... [0.0005, 0.0031, 0.001]])
>>> is_within_macadam_limits(a, 'A')
array([ True, False], dtype=bool)
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE.get(
illuminant)
if triangulation is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE[illuminant] = \
triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(xyY_to_XYZ(xyY), tol=tolerance)
simplex = np.where(simplex >= 0, True, False)
return simplex
def is_within_macadam_limits(xyY, illuminant, tolerance=None):
"""
Returns if given *CIE xyY* colourspace array is within MacAdam limits of
given illuminant.
Parameters
----------
xyY : array_like
*CIE xyY* colourspace array.
illuminant : unicode
Illuminant.
tolerance : numeric, optional
Tolerance allowed in the inside-triangle check.
Returns
-------
bool
Is within MacAdam limits.
Notes
-----
- Input *CIE xyY* colourspace array is in domain [0, 1].
- This definition requires *scipy* to be installed.
Examples
--------
>>> is_within_macadam_limits(np.array([0.3205, 0.4131, 0.51]), 'A')
array(True, dtype=bool)
>>> a = np.array([[0.3205, 0.4131, 0.51],
... [0.0005, 0.0031, 0.001]])
>>> is_within_macadam_limits(a, 'A')
array([ True, False], dtype=bool)
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE.get(
illuminant)
if triangulation is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE[illuminant] = \
triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(xyY_to_XYZ(xyY), tol=tolerance)
simplex = np.where(simplex >= 0, True, False)
return simplex
def is_within_macadam_limits(xyY, illuminant):
"""
Returns if given *CIE xyY* colourspace matrix is within *MacAdam* limits of
given illuminant.
Parameters
----------
xyY : array_like, (3,)
*CIE xyY* colourspace matrix.
illuminant : unicode
Illuminant.
Returns
-------
bool
Is within *MacAdam* limits.
Notes
-----
- Input *CIE xyY* colourspace matrix is in domain [0, 1].
- This definition requires *scipy* to be installed.
Examples
--------
>>> is_within_macadam_limits((0.3205, 0.4131, 0.51), 'A')
True
>>> is_within_macadam_limits((0.0005, 0.0031, 0.001), 'A')
False
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE.get(
illuminant)
if triangulation is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE[illuminant] = \
triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(np.ravel(xyY_to_XYZ(xyY)))
return True if simplex != -1 else False
def is_within_macadam_limits(xyY, illuminant):
"""
Returns if given *CIE xyY* colourspace matrix is within *MacAdam* limits of
given illuminant.
Parameters
----------
xyY : array_like, (3,)
*CIE xyY* colourspace matrix.
illuminant : unicode
Illuminant.
Returns
-------
bool
Is within *MacAdam* limits.
Notes
-----
- Input *CIE xyY* colourspace matrix is in domain [0, 1].
- This definition requires *scipy* to be installed.
Examples
--------
>>> is_within_macadam_limits((0.3205, 0.4131, 0.51), 'A')
True
>>> is_within_macadam_limits((0.0005, 0.0031, 0.001), 'A')
False
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
optimal_colour_stimuli = _XYZ_optimal_colour_stimuli(illuminant)
triangulation = _XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE.get(
illuminant)
if triangulation is None:
_XYZ_OPTIMAL_COLOUR_STIMULI_TRIANGULATIONS_CACHE[illuminant] = \
triangulation = Delaunay(optimal_colour_stimuli)
simplex = triangulation.find_simplex(np.ravel(xyY_to_XYZ(xyY)))
return True if simplex != -1 else False
def _XYZ_optimal_colour_stimuli(
illuminant: Union[Literal["A", "C", "D65"], str] = "D65") -> NDArray:
"""
Return given illuminant *Optimal Colour Stimuli* in *CIE XYZ* tristimulus
values and caches it if not existing.
Parameters
----------
illuminant
Illuminant name.
Returns
-------
:class:`numpy.ndarray`
Illuminant *Optimal Colour Stimuli*.
"""
illuminant = validate_method(
illuminant,
list(OPTIMAL_COLOUR_STIMULI_ILLUMINANTS.keys()),
'"{0}" illuminant is invalid, it must be one of {1}!',
)
optimal_colour_stimuli = OPTIMAL_COLOUR_STIMULI_ILLUMINANTS.get(illuminant)
if optimal_colour_stimuli is None:
raise KeyError(
f'"{illuminant}" not found in factory "Optimal Colour Stimuli": '
f'"{sorted(OPTIMAL_COLOUR_STIMULI_ILLUMINANTS.keys())}".')
vertices = _CACHE_OPTIMAL_COLOUR_STIMULI_XYZ.get(illuminant)
if vertices is None:
_CACHE_OPTIMAL_COLOUR_STIMULI_XYZ[illuminant] = vertices = (
xyY_to_XYZ(optimal_colour_stimuli) / 100)
return vertices
def RGB_to_XYZ(RGB,
illuminant_RGB,
illuminant_XYZ,
RGB_to_XYZ_matrix,
chromatic_adaptation_transform='CAT02',
decoding_cctf=None):
"""
Converts given *RGB* colourspace array to *CIE XYZ* tristimulus values.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
illuminant_RGB : array_like
*RGB* colourspace *illuminant* chromaticity coordinates or *CIE xyY*
colourspace array.
illuminant_XYZ : array_like
*CIE XYZ* tristimulus values *illuminant* chromaticity coordinates or
*CIE xyY* colourspace array.
RGB_to_XYZ_matrix : array_like
*Normalised primary matrix*.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC', None}**,
*Chromatic adaptation* transform, if *None* no chromatic adaptation is
performed.
decoding_cctf : object, optional
Decoding colour component transfer function (Decoding CCTF) or
electro-optical transfer function (EOTF / EOCF).
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
+--------------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+====================+=======================+===============+
| ``RGB`` | [0, 1] | [0, 1] |
+--------------------+-----------------------+---------------+
| ``illuminant_XYZ`` | [0, 1] | [0, 1] |
+--------------------+-----------------------+---------------+
| ``illuminant_RGB`` | [0, 1] | [0, 1] |
+--------------------+-----------------------+---------------+
+--------------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+====================+=======================+===============+
| ``XYZ`` | [0, 1] | [0, 1] |
+--------------------+-----------------------+---------------+
Examples
--------
>>> RGB = np.array([0.45595571, 0.03039702, 0.04087245])
>>> illuminant_RGB = np.array([0.31270, 0.32900])
>>> illuminant_XYZ = np.array([0.34570, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> RGB_to_XYZ_matrix = np.array(
... [[0.41240000, 0.35760000, 0.18050000],
... [0.21260000, 0.71520000, 0.07220000],
... [0.01930000, 0.11920000, 0.95050000]]
... )
>>> RGB_to_XYZ(RGB, illuminant_RGB, illuminant_XYZ, RGB_to_XYZ_matrix,
... chromatic_adaptation_transform) # doctest: +ELLIPSIS
array([ 0.2163881..., 0.1257 , 0.0384749...])
"""
RGB = to_domain_1(RGB)
if decoding_cctf is not None:
with domain_range_scale('ignore'):
RGB = decoding_cctf(RGB)
XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)
if chromatic_adaptation_transform is not None:
M_CAT = chromatic_adaptation_matrix_VonKries(
xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),
xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),
transform=chromatic_adaptation_transform)
XYZ = dot_vector(M_CAT, XYZ)
return from_range_1(XYZ)
def plot_multi_colour_checkers(colour_checkers=None, **kwargs):
"""
Plots and compares given colour checkers.
Parameters
----------
colour_checkers : array_like, optional
Color checker names, must be less than or equal to 2 names.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_multi_colour_swatches`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_multi_colour_checkers(['ColorChecker 1976', 'ColorChecker 2005'])
... # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Multi_Colour_Checkers.png
:align: center
:alt: plot_multi_colour_checkers
"""
if colour_checkers is None:
colour_checkers = ['ColorChecker 1976', 'ColorChecker 2005']
else:
assert len(colour_checkers) <= 2, (
'Only two colour checkers can be compared at a time!')
colour_checkers = filter_colour_checkers(colour_checkers).values()
_figure, axes = artist(**kwargs)
compare_swatches = len(colour_checkers) == 2
colour_swatches = []
colour_checker_names = []
for colour_checker in colour_checkers:
colour_checker_names.append(colour_checker.name)
for label, xyY in colour_checker.data.items():
XYZ = xyY_to_XYZ(xyY)
RGB = XYZ_to_plotting_colourspace(XYZ, colour_checker.illuminant)
colour_swatches.append(
ColourSwatch(label.title(), np.clip(np.ravel(RGB), 0, 1)))
if compare_swatches:
colour_swatches = [
swatch
for pairs in zip(colour_swatches[0:len(colour_swatches) // 2],
colour_swatches[len(colour_swatches) // 2:])
for swatch in pairs
]
background_colour = '0.1'
width = height = 1.0
spacing = 0.25
columns = 6
settings = {
'axes': axes,
'width': width,
'height': height,
'spacing': spacing,
'columns': columns,
'text_parameters': {
'size': 8
},
'background_colour': background_colour,
'compare_swatches': 'Stacked' if compare_swatches else None,
}
settings.update(kwargs)
settings['standalone'] = False
plot_multi_colour_swatches(colour_swatches, **settings)
axes.text(
0.5,
0.005,
'{0} - {1} - Colour Rendition Chart'.format(
', '.join(colour_checker_names),
COLOUR_STYLE_CONSTANTS.colour.colourspace.name),
transform=axes.transAxes,
color=COLOUR_STYLE_CONSTANTS.colour.bright,
ha='center',
va='bottom')
settings.update({
'axes': axes,
'standalone': True,
'title': ', '.join(colour_checker_names),
})
return render(**settings)
def XYZ_to_Lab(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']):
"""
Converts from *CIE XYZ* tristimulus values to *CIE L\\*a\\*b\\**
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 L\\*a\\*b\\** colourspace array.
Notes
-----
+----------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``Lab`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``a`` : [-100, 100] | ``a`` : [-1, 1] |
| | | |
| | ``b`` : [-100, 100] | ``b`` : [-1, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004m`
Examples
--------
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_to_Lab(XYZ) # doctest: +ELLIPSIS
array([ 41.5278752..., 52.6385830..., 26.9231792...])
"""
X, Y, Z = tsplit(to_domain_1(XYZ))
X_n, Y_n, Z_n = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
f_X_X_n = intermediate_lightness_function_CIE1976(X, X_n)
f_Y_Y_n = intermediate_lightness_function_CIE1976(Y, Y_n)
f_Z_Z_n = intermediate_lightness_function_CIE1976(Z, Z_n)
L = 116 * f_Y_Y_n - 16
a = 500 * (f_X_X_n - f_Y_Y_n)
b = 200 * (f_Y_Y_n - f_Z_Z_n)
Lab = tstack([L, a, b])
return from_range_100(Lab)
def XYZ_to_Luv(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']):
"""
Converts from *CIE XYZ* tristimulus values to *CIE L\\*u\\*v\\**
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 L\\*u\\*v\\** colourspace array.
Notes
-----
+----------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``Luv`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``u`` : [-100, 100] | ``u`` : [-1, 1] |
| | | |
| | ``v`` : [-100, 100] | ``v`` : [-1, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004m`, :cite:`Wikipedia2007b`
Examples
--------
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_to_Luv(XYZ) # doctest: +ELLIPSIS
array([ 41.5278752..., 96.8362605..., 17.7521014...])
"""
X, Y, Z = tsplit(to_domain_1(XYZ))
X_r, Y_r, Z_r = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
with domain_range_scale('100'):
L = lightness_CIE1976(Y, Y_r)
u = (13 * L * ((4 * X / (X + 15 * Y + 3 * Z)) -
(4 * X_r / (X_r + 15 * Y_r + 3 * Z_r))))
v = (13 * L * ((9 * Y / (X + 15 * Y + 3 * Z)) -
(9 * Y_r / (X_r + 15 * Y_r + 3 * Z_r))))
Luv = tstack([L, u, v])
return from_range_100(Luv)
def Luv_to_XYZ(
Luv,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']):
"""
Converts from *CIE L\\*u\\*v\\** colourspace to *CIE XYZ* tristimulus
values.
Parameters
----------
Luv : array_like
*CIE L\\*u\\*v\\** colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
+----------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``Luv`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``u`` : [-100, 100] | ``u`` : [-1, 1] |
| | | |
| | ``v`` : [-100, 100] | ``v`` : [-1, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004m`, :cite:`Wikipedia2007b`
Examples
--------
>>> Luv = np.array([41.52787529, 96.83626054, 17.75210149])
>>> Luv_to_XYZ(Luv) # doctest: +ELLIPSIS
array([ 0.2065400..., 0.1219722..., 0.0513695...])
"""
L, u, v = tsplit(to_domain_100(Luv))
X_r, Y_r, Z_r = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
with domain_range_scale('100'):
Y = luminance_CIE1976(L, Y_r)
a = 1 / 3 * (
(52 * L / (u + 13 * L * (4 * X_r / (X_r + 15 * Y_r + 3 * Z_r)))) - 1)
b = -5 * Y
c = -1 / 3.0
d = Y * (39 * L / (v + 13 * L * (9 * Y_r /
(X_r + 15 * Y_r + 3 * Z_r))) - 5)
X = (d - b) / (a - c)
Z = X * a + b
XYZ = tstack([X, Y, Z])
return from_range_1(XYZ)
def XYZ_to_RGB(XYZ,
illuminant_XYZ,
illuminant_RGB,
XYZ_to_RGB_matrix,
chromatic_adaptation_transform='CAT02',
OECF=None):
"""
Converts from *CIE XYZ* tristimulus values to given *RGB* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant_XYZ : array_like
*CIE XYZ* tristimulus values *illuminant* *xy* chromaticity coordinates
or *CIE xyY* colourspace array.
illuminant_RGB : array_like
*RGB* colourspace *illuminant* *xy* chromaticity coordinates or
*CIE xyY* colourspace array.
XYZ_to_RGB_matrix : array_like
*Normalised primary matrix*.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild, 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* transform.
OECF : object, optional
*Opto-electronic conversion function*.
Returns
-------
ndarray
*RGB* colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 1].
- Input *illuminant_XYZ* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Input *illuminant_RGB* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *RGB* colourspace array is in domain [0, 1].
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> illuminant_XYZ = np.array([0.34567, 0.35850])
>>> illuminant_RGB = np.array([0.31271, 0.32902])
>>> chromatic_adaptation_transform = 'Bradford'
>>> XYZ_to_RGB_matrix = np.array([
... [3.24100326, -1.53739899, -0.49861587],
... [-0.96922426, 1.87592999, 0.04155422],
... [0.05563942, -0.20401120, 1.05714897]])
>>> XYZ_to_RGB(
... XYZ,
... illuminant_XYZ,
... illuminant_RGB,
... XYZ_to_RGB_matrix,
... chromatic_adaptation_transform) # doctest: +ELLIPSIS
array([ 0.0110360..., 0.1273446..., 0.1163103...])
"""
M = chromatic_adaptation_matrix_VonKries(
xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),
xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),
transform=chromatic_adaptation_transform)
XYZ_a = dot_vector(M, XYZ)
RGB = dot_vector(XYZ_to_RGB_matrix, XYZ_a)
if OECF is not None:
RGB = OECF(RGB)
return RGB
def Luv_to_XYZ(
Luv,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']):
"""
Converts from *CIE L\\*u\\*v\\** colourspace to *CIE XYZ* tristimulus
values.
Parameters
----------
Luv : array_like
*CIE L\\*u\\*v\\** colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
+----------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``Luv`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``u`` : [-100, 100] | ``u`` : [-1, 1] |
| | | |
| | ``v`` : [-100, 100] | ``v`` : [-1, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004m`, :cite:`Wikipedia2007b`
Examples
--------
>>> Luv = np.array([41.52787529, 96.83626054, 17.75210149])
>>> Luv_to_XYZ(Luv) # doctest: +ELLIPSIS
array([ 0.2065400..., 0.1219722..., 0.0513695...])
"""
L, u, v = tsplit(to_domain_100(Luv))
X_r, Y_r, Z_r = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
with domain_range_scale('100'):
Y = luminance_CIE1976(L, Y_r)
a = 1 / 3 * ((52 * L / (u + 13 * L * (4 * X_r /
(X_r + 15 * Y_r + 3 * Z_r)))) - 1)
b = -5 * Y
c = -1 / 3.0
d = Y * (39 * L / (v + 13 * L * (9 * Y_r /
(X_r + 15 * Y_r + 3 * Z_r))) - 5)
X = (d - b) / (a - c)
Z = X * a + b
XYZ = tstack([X, Y, Z])
return from_range_1(XYZ)
def XYZ_to_RGB(XYZ,
illuminant_XYZ,
illuminant_RGB,
XYZ_to_RGB_matrix,
chromatic_adaptation_transform='CAT02',
encoding_cctf=None):
"""
Converts from *CIE XYZ* tristimulus values to *RGB* colourspace array.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant_XYZ : array_like
*CIE XYZ* tristimulus values *illuminant* *xy* chromaticity coordinates
or *CIE xyY* colourspace array.
illuminant_RGB : array_like
*RGB* colourspace *illuminant* *xy* chromaticity coordinates or
*CIE xyY* colourspace array.
XYZ_to_RGB_matrix : array_like
*Normalised primary matrix*.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC', None}**,
*Chromatic adaptation* transform, if *None* no chromatic adaptation is
performed.
encoding_cctf : object, optional
Encoding colour component transfer function (Encoding CCTF) or
opto-electronic transfer function (OETF / OECF).
Returns
-------
ndarray
*RGB* colourspace array.
Notes
-----
+--------------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+====================+=======================+===============+
| ``XYZ`` | [0, 1] | [0, 1] |
+--------------------+-----------------------+---------------+
| ``illuminant_XYZ`` | [0, 1] | [0, 1] |
+--------------------+-----------------------+---------------+
| ``illuminant_RGB`` | [0, 1] | [0, 1] |
+--------------------+-----------------------+---------------+
+--------------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+====================+=======================+===============+
| ``RGB`` | [0, 1] | [0, 1] |
+--------------------+-----------------------+---------------+
Examples
--------
>>> XYZ = np.array([0.21638819, 0.12570000, 0.03847493])
>>> illuminant_XYZ = np.array([0.34570, 0.35850])
>>> illuminant_RGB = np.array([0.31270, 0.32900])
>>> chromatic_adaptation_transform = 'Bradford'
>>> XYZ_to_RGB_matrix = np.array(
... [[3.24062548, -1.53720797, -0.49862860],
... [-0.96893071, 1.87575606, 0.04151752],
... [0.05571012, -0.20402105, 1.05699594]]
... )
>>> XYZ_to_RGB(XYZ, illuminant_XYZ, illuminant_RGB, XYZ_to_RGB_matrix,
... chromatic_adaptation_transform) # doctest: +ELLIPSIS
array([ 0.4559557..., 0.0303970..., 0.0408724...])
"""
XYZ = to_domain_1(XYZ)
if chromatic_adaptation_transform is not None:
M_CAT = chromatic_adaptation_matrix_VonKries(
xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),
xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),
transform=chromatic_adaptation_transform)
XYZ = dot_vector(M_CAT, XYZ)
RGB = dot_vector(XYZ_to_RGB_matrix, XYZ)
if encoding_cctf is not None:
with domain_range_scale('ignore'):
RGB = encoding_cctf(RGB)
return from_range_1(RGB)
def convert_experiment_results_Breneman1987(experiment):
"""
Converts *Breneman (1987)* experiment results to a
:class:`colour.CorrespondingColourDataset` class instance.
Parameters
----------
experiment : integer
{1, 2, 3, 4, 6, 8, 9, 11, 12}
*Breneman (1987)* experiment number.
Returns
-------
CorrespondingColourDataset
:class:`colour.CorrespondingColourDataset` class instance.
Examples
--------
>>> from pprint import pprint
>>> pprint(tuple(convert_experiment_results_Breneman1987(2)))
... # doctest: +ELLIPSIS
(2,
array([ 0.9582463..., 1. , 0.9436325...]),
array([ 0.9587332..., 1. , 0.4385796...]),
array([[ 388.125 , 405. , 345.625 ],
[ 266.8957925..., 135. , 28.5983365...],
[ 474.5717821..., 405. , 222.75 ...],
[ 538.3899082..., 405. , 24.8944954...],
[ 178.7430167..., 135. , 19.6089385...],
[ 436.6749547..., 405. , 26.5483725...],
[ 124.7746282..., 135. , 36.1965613...],
[ 77.0794172..., 135. , 60.5850563...],
[ 279.9390889..., 405. , 455.8395127...],
[ 149.5808157..., 135. , 498.7046827...],
[ 372.1113689..., 405. , 669.9883990...],
[ 212.3638968..., 135. , 414.6704871...]]),
array([[ 400.1039651..., 405. , 191.7287234...],
[ 271.0384615..., 135. , 13.5 ...],
[ 495.4705323..., 405. , 119.7290874...],
[ 580.7967033..., 405. , 6.6758241...],
[ 190.1933701..., 135. , 7.4585635...],
[ 473.7184115..., 405. , 10.2346570...],
[ 135.4936014..., 135. , 20.2376599...],
[ 86.4689781..., 135. , 35.2281021...],
[ 283.5396281..., 405. , 258.1775929...],
[ 119.7044335..., 135. , 282.6354679...],
[ 359.9532224..., 405. , 381.0031185...],
[ 181.8271461..., 135. , 204.0661252...]]),
array(1500),
array(1500),
0.3,
0.3,
{})
"""
valid_experiment_results = (1, 2, 3, 4, 6, 8, 9, 11, 12)
assert experiment in valid_experiment_results, (
'"Breneman (1987)" experiment result must be one of "{0}"!'.format(
valid_experiment_results))
samples_luminance = [
0.270,
0.090,
0.270,
0.270,
0.090,
0.270,
0.090,
0.090,
0.270,
0.090,
0.270,
0.090,
]
experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])
illuminant_chromaticities = experiment_results.pop(0)
Y_r = Y_t = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES[experiment].Y
B_r = B_t = 0.3
XYZ_t, XYZ_r = xy_to_XYZ(
np.hstack([
Luv_uv_to_xy(illuminant_chromaticities[1:3]),
np.full([2, 1], Y_r)
])) / Y_r
xyY_cr, xyY_ct = [], []
for i, experiment_result in enumerate(experiment_results):
xyY_cr.append(
np.hstack([
Luv_uv_to_xy(experiment_result[2]), samples_luminance[i] * Y_r
]))
xyY_ct.append(
np.hstack([
Luv_uv_to_xy(experiment_result[1]), samples_luminance[i] * Y_t
]))
XYZ_cr = xyY_to_XYZ(xyY_cr)
XYZ_ct = xyY_to_XYZ(xyY_ct)
return CorrespondingColourDataset(experiment, XYZ_r, XYZ_t, XYZ_cr, XYZ_ct,
Y_r, Y_t, B_r, B_t, {})
def Lab_to_XYZ(
Lab,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']):
"""
Converts from *CIE L\\*a\\*b\\** colourspace to *CIE XYZ* tristimulus
values.
Parameters
----------
Lab : array_like
*CIE L\\*a\\*b\\** colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
+----------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``Lab`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``a`` : [-100, 100] | ``a`` : [-1, 1] |
| | | |
| | ``b`` : [-100, 100] | ``b`` : [-1, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004m`
Examples
--------
>>> Lab = np.array([41.52787529, 52.63858304, 26.92317922])
>>> Lab_to_XYZ(Lab) # doctest: +ELLIPSIS
array([ 0.2065400..., 0.1219722..., 0.0513695...])
"""
L, a, b = tsplit(to_domain_100(Lab))
X_n, Y_n, Z_n = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
f_Y_Y_n = (L + 16) / 116
f_X_X_n = a / 500 + f_Y_Y_n
f_Z_Z_n = f_Y_Y_n - b / 200
X = intermediate_luminance_function_CIE1976(f_X_X_n, X_n)
Y = intermediate_luminance_function_CIE1976(f_Y_Y_n, Y_n)
Z = intermediate_luminance_function_CIE1976(f_Z_Z_n, Z_n)
XYZ = tstack([X, Y, Z])
return from_range_1(XYZ)
def colour_checker_plot(colour_checker='ColorChecker 2005', **kwargs):
"""
Plots given colour checker.
Parameters
----------
colour_checker : unicode, optional
Color checker name.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Raises
------
KeyError
If the given colour rendition chart is not found in the factory colour
rendition charts.
Examples
--------
>>> colour_checker_plot() # doctest: +SKIP
True
"""
canvas(**kwargs)
colour_checker, name = COLOURCHECKERS.get(colour_checker), colour_checker
if colour_checker is None:
raise KeyError(
('Colour checker "{0}" not found in '
'factory colour checkers: "{1}".').format(
name, sorted(COLOURCHECKERS.keys())))
_name, data, illuminant = colour_checker
colour_parameters = []
for _index, label, x, y, Y in data:
XYZ = xyY_to_XYZ((x, y, Y))
RGB = XYZ_to_sRGB(XYZ, illuminant)
colour_parameters.append(
ColourParameter(label.title(), np.clip(np.ravel(RGB), 0, 1)))
background_colour = '0.1'
matplotlib.pyplot.gca().patch.set_facecolor(background_colour)
width = height = 1.0
spacing = 0.25
across = 6
settings = {
'standalone': False,
'width': width,
'height': height,
'spacing': spacing,
'across': across,
'text_size': 8,
'margins': (-0.125, 0.125, -0.5, 0.125)}
settings.update(kwargs)
multi_colour_plot(colour_parameters, **settings)
text_x = width * (across / 2) + (across * (spacing / 2)) - spacing / 2
text_y = -(len(colour_parameters) / across + spacing / 2)
pylab.text(text_x,
text_y,
'{0} - {1} - Colour Rendition Chart'.format(
name, RGB_COLOURSPACES.get('sRGB').name),
color='0.95',
clip_on=True,
ha='center')
settings.update({
'title': name,
'facecolor': background_colour,
'edgecolor': None,
'standalone': True})
boundaries(**settings)
decorate(**settings)
return display(**settings)
def hdr_CIELab_to_XYZ(
Lab_hdr,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65'],
Y_s=0.2,
Y_abs=100,
method='Fairchild 2011'):
"""
Converts from *hdr-CIELAB* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
Lab_hdr : array_like
*hdr-CIELAB* colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Y_s : numeric or array_like
Relative luminance :math:`Y_s` of the surround.
Y_abs : numeric or array_like
Absolute luminance :math:`Y_{abs}` of the scene diffuse white in
:math:`cd/m^2`.
method : unicode, optional
**{'Fairchild 2011', 'Fairchild 2010'}**,
Computation method.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
+----------------+-------------------------+---------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=========================+=====================+
| ``Lab_hdr`` | ``L_hdr`` : [0, 100] | ``L_hdr`` : [0, 1] |
| | | |
| | ``a_hdr`` : [-100, 100] | ``a_hdr`` : [-1, 1] |
| | | |
| | ``b_hdr`` : [-100, 100] | ``b_hdr`` : [-1, 1] |
+----------------+-------------------------+---------------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-------------------------+---------------------+
| ``Y_s`` | [0, 1] | [0, 1] |
+----------------+-------------------------+---------------------+
+----------------+-------------------------+---------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=========================+=====================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-------------------------+---------------------+
References
----------
:cite:`Fairchild2010`, :cite:`Fairchild2011`
Examples
--------
>>> Lab_hdr = np.array([51.87002062, 60.4763385, 32.14551912])
>>> hdr_CIELab_to_XYZ(Lab_hdr) # doctest: +ELLIPSIS
array([ 0.2065400..., 0.1219722..., 0.0513695...])
>>> Lab_hdr = np.array([31.99621114, 128.00763036, 48.76952309])
>>> hdr_CIELab_to_XYZ(Lab_hdr, method='Fairchild 2010')
... # doctest: +ELLIPSIS
array([ 0.2065400..., 0.1219722..., 0.0513695...])
"""
L_hdr, a_hdr, b_hdr = tsplit(to_domain_100(Lab_hdr))
X_n, Y_n, Z_n = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
method_l = method.lower()
assert method.lower() in [
m.lower() for m in HDR_CIELAB_METHODS
], ('"{0}" method is invalid, must be one of {1}!'.format(
method, HDR_CIELAB_METHODS))
if method_l == 'fairchild 2010':
luminance_callable = luminance_Fairchild2010
else:
luminance_callable = luminance_Fairchild2011
e = exponent_hdr_CIELab(Y_s, Y_abs, method)
# Domain and range scaling has already be handled.
with domain_range_scale('ignore'):
Y = luminance_callable(L_hdr, e) * Y_n
X = luminance_callable((a_hdr + 5 * L_hdr) / 5, e) * X_n
Z = luminance_callable((-b_hdr + 2 * L_hdr) / 2, e) * Z_n
XYZ = tstack([X, Y, Z])
return from_range_1(XYZ)
def RGB_to_XYZ(RGB,
illuminant_RGB,
illuminant_XYZ,
RGB_to_XYZ_matrix,
chromatic_adaptation_transform='CAT02',
EOCF=None):
"""
Converts from given *RGB* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
illuminant_RGB : array_like
*RGB* colourspace *illuminant* chromaticity coordinates or *CIE xyY*
colourspace array.
illuminant_XYZ : array_like
*CIE XYZ* tristimulus values *illuminant* chromaticity coordinates or
*CIE xyY* colourspace array.
RGB_to_XYZ_matrix : array_like
*Normalised primary matrix*.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild, 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* transform.
EOCF : object, optional
*Electro-optical conversion function*.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
- Input *RGB* colourspace array is in domain [0, 1].
- Input *illuminant_RGB* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Input *illuminant_XYZ* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *CIE XYZ* tristimulus values are in domain [0, 1].
Examples
--------
>>> RGB = np.array([0.01103604, 0.12734466, 0.11631037])
>>> illuminant_RGB = np.array([0.31271, 0.32902])
>>> illuminant_XYZ = np.array([0.34567, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> RGB_to_XYZ_matrix = np.array([
... [0.41238656, 0.35759149, 0.18045049],
... [0.21263682, 0.71518298, 0.07218020],
... [0.01933062, 0.11919716, 0.95037259]])
>>> RGB_to_XYZ(
... RGB,
... illuminant_RGB,
... illuminant_XYZ,
... RGB_to_XYZ_matrix,
... chromatic_adaptation_transform) # doctest: +ELLIPSIS
array([ 0.0704953..., 0.1008 , 0.0955831...])
"""
if EOCF is not None:
RGB = EOCF(RGB)
M = chromatic_adaptation_matrix_VonKries(
xyY_to_XYZ(xy_to_xyY(illuminant_RGB)),
xyY_to_XYZ(xy_to_xyY(illuminant_XYZ)),
transform=chromatic_adaptation_transform)
XYZ = dot_vector(RGB_to_XYZ_matrix, RGB)
XYZ_a = dot_vector(M, XYZ)
return XYZ_a
def XYZ_to_hdr_CIELab(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65'],
Y_s=0.2,
Y_abs=100,
method='Fairchild 2011'):
"""
Converts from *CIE XYZ* tristimulus values to *hdr-CIELAB* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Y_s : numeric or array_like
Relative luminance :math:`Y_s` of the surround.
Y_abs : numeric or array_like
Absolute luminance :math:`Y_{abs}` of the scene diffuse white in
:math:`cd/m^2`.
method : unicode, optional
**{'Fairchild 2011', 'Fairchild 2010'}**,
Computation method.
Returns
-------
ndarray
*hdr-CIELAB* colourspace array.
Notes
-----
+----------------+-------------------------+---------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=========================+=====================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-------------------------+---------------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-------------------------+---------------------+
| ``Y_s`` | [0, 1] | [0, 1] |
+----------------+-------------------------+---------------------+
+----------------+-------------------------+---------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=========================+=====================+
| ``Lab_hdr`` | ``L_hdr`` : [0, 100] | ``L_hdr`` : [0, 1] |
| | | |
| | ``a_hdr`` : [-100, 100] | ``a_hdr`` : [-1, 1] |
| | | |
| | ``b_hdr`` : [-100, 100] | ``b_hdr`` : [-1, 1] |
+----------------+-------------------------+---------------------+
- Conversion to polar coordinates to compute the *chroma* :math:`C_{hdr}`
and *hue* :math:`h_{hdr}` correlates can be safely performed with
:func:`colour.Lab_to_LCHab` definition.
- Conversion to cartesian coordinates from the *Lightness*
:math:`L_{hdr}`, *chroma* :math:`C_{hdr}` and *hue* :math:`h_{hdr}`
correlates can be safely performed with :func:`colour.LCHab_to_Lab`
definition.
References
----------
:cite:`Fairchild2010`, :cite:`Fairchild2011`
Examples
--------
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_to_hdr_CIELab(XYZ) # doctest: +ELLIPSIS
array([ 51.8700206..., 60.4763385..., 32.1455191...])
>>> XYZ_to_hdr_CIELab(XYZ, method='Fairchild 2010') # doctest: +ELLIPSIS
array([ 31.9962111..., 128.0076303..., 48.7695230...])
"""
X, Y, Z = tsplit(to_domain_1(XYZ))
X_n, Y_n, Z_n = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
method_l = method.lower()
assert method.lower() in [
m.lower() for m in HDR_CIELAB_METHODS
], ('"{0}" method is invalid, must be one of {1}!'.format(
method, HDR_CIELAB_METHODS))
if method_l == 'fairchild 2010':
lightness_callable = lightness_Fairchild2010
else:
lightness_callable = lightness_Fairchild2011
e = exponent_hdr_CIELab(Y_s, Y_abs, method)
# Domain and range scaling has already be handled.
with domain_range_scale('ignore'):
L_hdr = lightness_callable(Y / Y_n, e)
a_hdr = 5 * (lightness_callable(X / X_n, e) - L_hdr)
b_hdr = 2 * (L_hdr - lightness_callable(Z / Z_n, e))
Lab_hdr = tstack([L_hdr, a_hdr, b_hdr])
return from_range_100(Lab_hdr)
def XYZ_to_Luv(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']):
"""
Converts from *CIE XYZ* tristimulus values to *CIE L\\*u\\*v\\**
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 L\\*u\\*v\\** colourspace array.
Notes
-----
+----------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``Luv`` | ``L`` : [0, 100] | ``L`` : [0, 1] |
| | | |
| | ``u`` : [-100, 100] | ``u`` : [-1, 1] |
| | | |
| | ``v`` : [-100, 100] | ``v`` : [-1, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`CIETC1-482004m`, :cite:`Wikipedia2007b`
Examples
--------
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_to_Luv(XYZ) # doctest: +ELLIPSIS
array([ 41.5278752..., 96.8362605..., 17.7521014...])
"""
X, Y, Z = tsplit(to_domain_1(XYZ))
X_r, Y_r, Z_r = tsplit(xyY_to_XYZ(xy_to_xyY(illuminant)))
with domain_range_scale('100'):
L = lightness_CIE1976(Y, Y_r)
u = (13 * L * ((4 * X / (X + 15 * Y + 3 * Z)) -
(4 * X_r / (X_r + 15 * Y_r + 3 * Z_r))))
v = (13 * L * ((9 * Y / (X + 15 * Y + 3 * Z)) -
(9 * Y_r / (X_r + 15 * Y_r + 3 * Z_r))))
Luv = tstack([L, u, v])
return from_range_100(Luv)
def UVW_to_XYZ(
UVW,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']):
"""
Converts *CIE 1964 U\\*V\\*W\\** colourspace to *CIE XYZ* tristimulus
values.
Parameters
----------
UVW : array_like
*CIE 1964 U\\*V\\*W\\** colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Warning
-------
The input domain and output range of that definition are non standard!
Notes
-----
+----------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``UVW`` | ``U`` : [-100, 100] | ``U`` : [-1, 1] |
| | | |
| | ``V`` : [-100, 100] | ``V`` : [-1, 1] |
| | | |
| | ``W`` : [0, 100] | ``W`` : [0, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`Wikipedia2008a`
Examples
--------
>>> import numpy as np
>>> UVW = np.array([94.55035725, 11.55536523, 40.54757405])
>>> UVW_to_XYZ(UVW)
array([ 20.654008, 12.197225, 5.136952])
"""
U, V, W = tsplit(to_domain_100(UVW))
u_0, v_0 = tsplit(xy_to_UCS_uv(xyY_to_xy(illuminant)))
Y = ((W + 17) / 25) ** 3
u = U / (13 * W) + u_0
v = V / (13 * W) + v_0
x, y = tsplit(UCS_uv_to_xy(tstack([u, v])))
XYZ = xyY_to_XYZ(tstack([x, y, Y]))
return from_range_100(XYZ)