def test_row_as_diagonal(self):
"""
Tests :func:`colour.utilities.array.row_as_diagonal` definition.
"""
np.testing.assert_almost_equal(
row_as_diagonal(np.array([[0.25891593, 0.07299478, 0.36586996],
[0.30851087, 0.37131459, 0.16274825],
[0.71061831, 0.67718718, 0.09562581],
[0.71588836, 0.76772047, 0.15476079],
[0.92985142, 0.22263399, 0.88027331]])),
np.array([[[0.25891593, 0.00000000, 0.00000000],
[0.00000000, 0.07299478, 0.00000000],
[0.00000000, 0.00000000, 0.36586996]],
[[0.30851087, 0.00000000, 0.00000000],
[0.00000000, 0.37131459, 0.00000000],
[0.00000000, 0.00000000, 0.16274825]],
[[0.71061831, 0.00000000, 0.00000000],
[0.00000000, 0.67718718, 0.00000000],
[0.00000000, 0.00000000, 0.09562581]],
[[0.71588836, 0.00000000, 0.00000000],
[0.00000000, 0.76772047, 0.00000000],
[0.00000000, 0.00000000, 0.15476079]],
[[0.92985142, 0.00000000, 0.00000000],
[0.00000000, 0.22263399, 0.00000000],
[0.00000000, 0.00000000, 0.88027331]]]))
def test_row_as_diagonal(self):
"""
Tests :func:`colour.utilities.array.row_as_diagonal` definition.
"""
np.testing.assert_almost_equal(
row_as_diagonal(np.array(
[[0.25891593, 0.07299478, 0.36586996],
[0.30851087, 0.37131459, 0.16274825],
[0.71061831, 0.67718718, 0.09562581],
[0.71588836, 0.76772047, 0.15476079],
[0.92985142, 0.22263399, 0.88027331]])
),
np.array(
[[[0.25891593, 0.00000000, 0.00000000],
[0.00000000, 0.07299478, 0.00000000],
[0.00000000, 0.00000000, 0.36586996]],
[[0.30851087, 0.00000000, 0.00000000],
[0.00000000, 0.37131459, 0.00000000],
[0.00000000, 0.00000000, 0.16274825]],
[[0.71061831, 0.00000000, 0.00000000],
[0.00000000, 0.67718718, 0.00000000],
[0.00000000, 0.00000000, 0.09562581]],
[[0.71588836, 0.00000000, 0.00000000],
[0.00000000, 0.76772047, 0.00000000],
[0.00000000, 0.00000000, 0.15476079]],
[[0.92985142, 0.00000000, 0.00000000],
[0.00000000, 0.22263399, 0.00000000],
[0.00000000, 0.00000000, 0.88027331]]]
)
) # yapf: disable
def chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform='CAT02'):
M = CHROMATIC_ADAPTATION_TRANSFORMS.get(transform)
print('XYZ_w', XYZ_w)
print('XYZ_wr', XYZ_wr)
if M is None:
raise KeyError(
'"{0}" chromatic adaptation transform is not defined! Supported '
'methods: "{1}".'.format(transform,
CHROMATIC_ADAPTATION_TRANSFORMS.keys()))
print('M', M)
print('M inv', np.linalg.inv(M))
rgb_w = np.einsum('...i,...ij->...j', XYZ_w, np.transpose(M))
rgb_wr = np.einsum('...i,...ij->...j', XYZ_wr, np.transpose(M))
print('rgb_w: ', rgb_w)
print('rgb_wr: ', rgb_wr)
D = rgb_wr / rgb_w
print('D: ', D)
D = row_as_diagonal(D)
M_CAT = dot_matrix(np.linalg.inv(M), D)
M_CAT = dot_matrix(M_CAT, M)
return M_CAT
def chromatic_adaptation_Fairchild1990(
XYZ_1: ArrayLike,
XYZ_n: ArrayLike,
XYZ_r: ArrayLike,
Y_n: FloatingOrArrayLike,
discount_illuminant: Boolean = False,
) -> NDArray:
"""
Adapt given stimulus *CIE XYZ_1* tristimulus values from test viewing
conditions to reference viewing conditions using *Fairchild (1990)*
chromatic adaptation model.
Parameters
----------
XYZ_1
*CIE XYZ_1* tristimulus values of test sample / stimulus.
XYZ_n
Test viewing condition *CIE XYZ_n* tristimulus values of whitepoint.
XYZ_r
Reference viewing condition *CIE XYZ_r* tristimulus values of
whitepoint.
Y_n
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
discount_illuminant
Truth value indicating if the illuminant should be discounted.
Returns
-------
:class:`numpy.ndarray`
Adapted *CIE XYZ_2* tristimulus values of stimulus.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_1`` | [0, 100] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_n`` | [0, 100] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_r`` | [0, 100] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_2`` | [0, 100] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Fairchild1991a`, :cite:`Fairchild2013s`
Examples
--------
>>> XYZ_1 = np.array([19.53, 23.07, 24.97])
>>> XYZ_n = np.array([111.15, 100.00, 35.20])
>>> XYZ_r = np.array([94.81, 100.00, 107.30])
>>> Y_n = 200
>>> chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_n, XYZ_r, Y_n)
... # doctest: +ELLIPSIS
array([ 23.3252634..., 23.3245581..., 76.1159375...])
"""
XYZ_1 = to_domain_100(XYZ_1)
XYZ_n = to_domain_100(XYZ_n)
XYZ_r = to_domain_100(XYZ_r)
Y_n = as_float_array(Y_n)
LMS_1 = vector_dot(MATRIX_XYZ_TO_RGB_FAIRCHILD1990, XYZ_1)
LMS_n = vector_dot(MATRIX_XYZ_TO_RGB_FAIRCHILD1990, XYZ_n)
LMS_r = vector_dot(MATRIX_XYZ_TO_RGB_FAIRCHILD1990, XYZ_r)
p_LMS = degrees_of_adaptation(
LMS_1, Y_n, discount_illuminant=discount_illuminant
)
a_LMS_1 = p_LMS / LMS_n
a_LMS_2 = p_LMS / LMS_r
A_1 = row_as_diagonal(a_LMS_1)
A_2 = row_as_diagonal(a_LMS_2)
LMSp_1 = vector_dot(A_1, LMS_1)
c = 0.219 - 0.0784 * np.log10(Y_n)
C = row_as_diagonal(tstack([c, c, c]))
LMS_a = vector_dot(C, LMSp_1)
LMSp_2 = vector_dot(np.linalg.inv(C), LMS_a)
LMS_c = vector_dot(np.linalg.inv(A_2), LMSp_2)
XYZ_c = vector_dot(MATRIX_RGB_TO_XYZ_FAIRCHILD1990, LMS_c)
return from_range_100(XYZ_c)
def chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform='CAT02'):
"""
Computes the *chromatic adaptation* matrix from test viewing conditions
to reference viewing conditions.
Parameters
----------
XYZ_w : array_like
Test viewing condition *CIE XYZ* tristimulus values of whitepoint.
XYZ_wr : array_like
Reference viewing condition *CIE XYZ* tristimulus values of whitepoint.
transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
ndarray
Chromatic adaptation matrix :math:`M_{cat}`.
Raises
------
KeyError
If chromatic adaptation method is not defined.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_w`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_wr`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Fairchild2013t`
Examples
--------
>>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
>>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
>>> chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr)
... # doctest: +ELLIPSIS
array([[ 1.0425738..., 0.0308910..., -0.0528125...],
[ 0.0221934..., 1.0018566..., -0.0210737...],
[-0.0011648..., -0.0034205..., 0.7617890...]])
Using Bradford method:
>>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
>>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
>>> method = 'Bradford'
>>> chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, method)
... # doctest: +ELLIPSIS
array([[ 1.0479297..., 0.0229468..., -0.0501922...],
[ 0.0296278..., 0.9904344..., -0.0170738...],
[-0.0092430..., 0.0150551..., 0.7518742...]])
"""
XYZ_w = to_domain_1(XYZ_w)
XYZ_wr = to_domain_1(XYZ_wr)
M = CHROMATIC_ADAPTATION_TRANSFORMS.get(transform)
if M is None:
raise KeyError(
'"{0}" chromatic adaptation transform is not defined! Supported '
'methods: "{1}".'.format(transform,
CHROMATIC_ADAPTATION_TRANSFORMS.keys()))
rgb_w = np.einsum('...i,...ij->...j', XYZ_w, np.transpose(M))
rgb_wr = np.einsum('...i,...ij->...j', XYZ_wr, np.transpose(M))
D = rgb_wr / rgb_w
D = row_as_diagonal(D)
M_CAT = dot_matrix(np.linalg.inv(M), D)
M_CAT = dot_matrix(M_CAT, M)
return M_CAT
def XYZ_to_RLAB(XYZ,
XYZ_n,
Y_n,
sigma=VIEWING_CONDITIONS_RLAB['Average'],
D=D_FACTOR_RLAB['Hard Copy Images']):
"""
Computes the *RLAB* model color appearance correlates.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of test sample / stimulus.
XYZ_n : array_like
*CIE XYZ* tristimulus values of reference white.
Y_n : numeric or array_like
Absolute adapting luminance in :math:`cd/m^2`.
sigma : numeric or array_like, optional
Relative luminance of the surround, see
:attr:`colour.VIEWING_CONDITIONS_RLAB` for reference.
D : numeric or array_like, optional
*Discounting-the-Illuminant* factor normalised to domain [0, 1].
Returns
-------
CAM_Specification_RLAB
*RLAB* colour appearance model specification.
Notes
-----
+--------------------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+==========================+=======================+===============+
| ``XYZ`` | [0, 100] | [0, 1] |
+--------------------------+-----------------------+---------------+
| ``XYZ_n`` | [0, 100] | [0, 1] |
+--------------------------+-----------------------+---------------+
+------------------------------+-----------------------\
+---------------+
| **Range** | **Scale - Reference** \
| **Scale - 1** |
+==============================+=======================\
+===============+
| ``CAM_Specification_RLAB.h`` | [0, 360] \
| [0, 1] |
+------------------------------+-----------------------\
+---------------+
References
----------
:cite:`Fairchild1996a`, :cite:`Fairchild2013w`
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_n = np.array([109.85, 100, 35.58])
>>> Y_n = 31.83
>>> sigma = VIEWING_CONDITIONS_RLAB['Average']
>>> D = D_FACTOR_RLAB['Hard Copy Images']
>>> XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma, D) # doctest: +ELLIPSIS
CAM_Specification_RLAB(J=49.8347069..., C=54.8700585..., \
h=286.4860208..., s=1.1010410..., HC=None, a=15.5711021..., b=-52.6142956...)
"""
XYZ = to_domain_100(XYZ)
XYZ_n = to_domain_100(XYZ_n)
Y_n = as_float_array(Y_n)
D = as_float_array(D)
sigma = as_float_array(sigma)
# Converting to cone responses.
LMS_n = XYZ_to_rgb(XYZ_n)
# Computing the :math:`A` matrix.
LMS_l_E = (3 * LMS_n) / (LMS_n[0] + LMS_n[1] + LMS_n[2])
LMS_p_L = ((1 + spow(Y_n[..., np.newaxis], 1 / 3) + LMS_l_E) /
(1 + spow(Y_n[..., np.newaxis], 1 / 3) + (1 / LMS_l_E)))
LMS_a_L = (LMS_p_L + D[..., np.newaxis] * (1 - LMS_p_L)) / LMS_n
aR = row_as_diagonal(LMS_a_L)
M = matrix_dot(matrix_dot(MATRIX_R, aR), MATRIX_XYZ_TO_HPE)
XYZ_ref = vector_dot(M, XYZ)
X_ref, Y_ref, Z_ref = tsplit(XYZ_ref)
# Computing the correlate of *Lightness* :math:`L^R`.
LR = 100 * spow(Y_ref, sigma)
# Computing opponent colour dimensions :math:`a^R` and :math:`b^R`.
aR = 430 * (spow(X_ref, sigma) - spow(Y_ref, sigma))
bR = 170 * (spow(Y_ref, sigma) - spow(Z_ref, sigma))
# Computing the *hue* angle :math:`h^R`.
hR = np.degrees(np.arctan2(bR, aR)) % 360
# TODO: Implement hue composition computation.
# Computing the correlate of *chroma* :math:`C^R`.
CR = np.hypot(aR, bR)
# Computing the correlate of *saturation* :math:`s^R`.
sR = CR / LR
return CAM_Specification_RLAB(LR, CR, from_range_degrees(hR), sR, None, aR,
bR)
def XYZ_to_RLAB(XYZ,
XYZ_n,
Y_n,
sigma=RLAB_VIEWING_CONDITIONS.get('Average'),
D=RLAB_D_FACTOR.get('Hard Copy Images')):
"""
Computes the RLAB model color appearance correlates.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of test sample / stimulus in domain
[0, 100].
XYZ_n : array_like
*CIE XYZ* tristimulus values of reference white in domain [0, 100].
Y_n : numeric or array_like
Absolute adapting luminance in :math:`cd/m^2`.
sigma : numeric or array_like, optional
Relative luminance of the surround, see :attr:`RLAB_VIEWING_CONDITIONS`
for reference.
D : numeric or array_like, optional
*Discounting-the-Illuminant* factor in domain [0, 1].
Returns
-------
RLAB_Specification
RLAB colour appearance model specification.
Warning
-------
The input domain of that definition is non standard!
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 100].
- Input *CIE XYZ_n* tristimulus values are in domain [0, 100].
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_n = np.array([109.85, 100, 35.58])
>>> Y_n = 31.83
>>> sigma = RLAB_VIEWING_CONDITIONS['Average']
>>> D = RLAB_D_FACTOR['Hard Copy Images']
>>> XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma, D) # doctest: +ELLIPSIS
RLAB_Specification(J=49.8347069..., C=54.8700585..., h=286.4860208..., \
s=1.1010410..., HC=None, a=15.5711021..., b=-52.6142956...)
"""
Y_n = np.asarray(Y_n)
D = np.asarray(D)
sigma = np.asarray(sigma)
# Converting to cone responses.
LMS_n = XYZ_to_rgb(XYZ_n)
# Computing the :math:`A` matrix.
LMS_l_E = (3 * LMS_n) / (LMS_n[0] + LMS_n[1] + LMS_n[2])
LMS_p_L = ((1 + (Y_n[..., np.newaxis] ** (1 / 3)) + LMS_l_E) /
(1 + (Y_n[..., np.newaxis] ** (1 / 3)) + (1 / LMS_l_E)))
LMS_a_L = (LMS_p_L + D[..., np.newaxis] * (1 - LMS_p_L)) / LMS_n
aR = row_as_diagonal(LMS_a_L)
M = dot_matrix(dot_matrix(R_MATRIX, aR), XYZ_TO_HPE_MATRIX)
XYZ_ref = dot_vector(M, XYZ)
X_ref, Y_ref, Z_ref = tsplit(XYZ_ref)
# -------------------------------------------------------------------------
# Computing the correlate of *Lightness* :math:`L^R`.
# -------------------------------------------------------------------------
LR = 100 * (Y_ref ** sigma)
# Computing opponent colour dimensions :math:`a^R` and :math:`b^R`.
aR = 430 * ((X_ref ** sigma) - (Y_ref ** sigma))
bR = 170 * ((Y_ref ** sigma) - (Z_ref ** sigma))
# -------------------------------------------------------------------------
# Computing the *hue* angle :math:`h^R`.
# -------------------------------------------------------------------------
hR = np.degrees(np.arctan2(bR, aR)) % 360
# TODO: Implement hue composition computation.
# -------------------------------------------------------------------------
# Computing the correlate of *chroma* :math:`C^R`.
# -------------------------------------------------------------------------
CR = np.sqrt((aR ** 2) + (bR ** 2))
# -------------------------------------------------------------------------
# Computing the correlate of *saturation* :math:`s^R`.
# -------------------------------------------------------------------------
sR = CR / LR
return RLAB_Specification(LR, CR, hR, sR, None, aR, bR)
def XYZ_to_RLAB(XYZ,
XYZ_n,
Y_n,
sigma=RLAB_VIEWING_CONDITIONS['Average'],
D=RLAB_D_FACTOR['Hard Copy Images']):
"""
Computes the *RLAB* model color appearance correlates.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of test sample / stimulus in domain
[0, 100].
XYZ_n : array_like
*CIE XYZ* tristimulus values of reference white in domain [0, 100].
Y_n : numeric or array_like
Absolute adapting luminance in :math:`cd/m^2`.
sigma : numeric or array_like, optional
Relative luminance of the surround, see
:attr:`colour.RLAB_VIEWING_CONDITIONS` for reference.
D : numeric or array_like, optional
*Discounting-the-Illuminant* factor in domain [0, 1].
Returns
-------
RLAB_Specification
*RLAB* colour appearance model specification.
Warning
-------
The input domain of that definition is non standard!
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 100].
- Input *CIE XYZ_n* tristimulus values are in domain [0, 100].
References
----------
- :cite:`Fairchild1996a`
- :cite:`Fairchild2013w`
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_n = np.array([109.85, 100, 35.58])
>>> Y_n = 31.83
>>> sigma = RLAB_VIEWING_CONDITIONS['Average']
>>> D = RLAB_D_FACTOR['Hard Copy Images']
>>> XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma, D) # doctest: +ELLIPSIS
RLAB_Specification(J=49.8347069..., C=54.8700585..., h=286.4860208..., \
s=1.1010410..., HC=None, a=15.5711021..., b=-52.6142956...)
"""
Y_n = np.asarray(Y_n)
D = np.asarray(D)
sigma = np.asarray(sigma)
# Converting to cone responses.
LMS_n = XYZ_to_rgb(XYZ_n)
# Computing the :math:`A` matrix.
LMS_l_E = (3 * LMS_n) / (LMS_n[0] + LMS_n[1] + LMS_n[2])
LMS_p_L = ((1 + (Y_n[..., np.newaxis] ** (1 / 3)) + LMS_l_E) /
(1 + (Y_n[..., np.newaxis] ** (1 / 3)) + (1 / LMS_l_E)))
LMS_a_L = (LMS_p_L + D[..., np.newaxis] * (1 - LMS_p_L)) / LMS_n
aR = row_as_diagonal(LMS_a_L)
M = dot_matrix(dot_matrix(R_MATRIX, aR), XYZ_TO_HPE_MATRIX)
XYZ_ref = dot_vector(M, XYZ)
X_ref, Y_ref, Z_ref = tsplit(XYZ_ref)
# Computing the correlate of *Lightness* :math:`L^R`.
LR = 100 * (Y_ref ** sigma)
# Computing opponent colour dimensions :math:`a^R` and :math:`b^R`.
aR = 430 * ((X_ref ** sigma) - (Y_ref ** sigma))
bR = 170 * ((Y_ref ** sigma) - (Z_ref ** sigma))
# Computing the *hue* angle :math:`h^R`.
hR = np.degrees(np.arctan2(bR, aR)) % 360
# TODO: Implement hue composition computation.
# Computing the correlate of *chroma* :math:`C^R`.
CR = np.hypot(aR, bR)
# Computing the correlate of *saturation* :math:`s^R`.
sR = CR / LR
return RLAB_Specification(LR, CR, hR, sR, None, aR, bR)
def chromatic_adaptation_Fairchild1990(XYZ_1,
XYZ_n,
XYZ_r,
Y_n,
discount_illuminant=False):
"""
Adapts given stimulus *CIE XYZ_1* tristimulus values from test viewing
conditions to reference viewing conditions using Fairchild (1990) chromatic
adaptation model.
Parameters
----------
XYZ_1 : array_like
*CIE XYZ_1* tristimulus values of test sample / stimulus in domain
[0, 100].
XYZ_n : array_like
Test viewing condition *CIE XYZ_n* tristimulus values of whitepoint.
XYZ_r : array_like
Reference viewing condition *CIE XYZ_r* tristimulus values of
whitepoint.
Y_n : numeric or array_like
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
discount_illuminant : bool, optional
Truth value indicating if the illuminant should be discounted.
Returns
-------
ndarray
Adapted *CIE XYZ_2* tristimulus values of stimulus.
Warning
-------
The input domain of that definition is non standard!
Notes
-----
- Input *CIE XYZ_1*, *CIE XYZ_n* and *CIE XYZ_r* tristimulus values are
in domain [0, 100].
- Output *CIE XYZ_2* tristimulus values are in domain [0, 100].
Examples
--------
>>> XYZ_1 = np.array([19.53, 23.07, 24.97])
>>> XYZ_n = np.array([111.15, 100.00, 35.20])
>>> XYZ_r = np.array([94.81, 100.00, 107.30])
>>> Y_n = 200
>>> chromatic_adaptation_Fairchild1990( # doctest: +ELLIPSIS
... XYZ_1, XYZ_n, XYZ_r, Y_n)
array([ 23.3252634..., 23.3245581..., 76.1159375...])
"""
XYZ_1 = np.asarray(XYZ_1)
XYZ_n = np.asarray(XYZ_n)
XYZ_r = np.asarray(XYZ_r)
Y_n = np.asarray(Y_n)
LMS_1 = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_1)
LMS_n = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_n)
LMS_r = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_r)
p_LMS = degrees_of_adaptation(LMS_1,
Y_n,
discount_illuminant=discount_illuminant)
a_LMS_1 = p_LMS / LMS_n
a_LMS_2 = p_LMS / LMS_r
A_1 = row_as_diagonal(a_LMS_1)
A_2 = row_as_diagonal(a_LMS_2)
LMSp_1 = dot_vector(A_1, LMS_1)
c = 0.219 - 0.0784 * np.log10(Y_n)
C = row_as_diagonal(tstack((c, c, c)))
LMS_a = dot_vector(C, LMSp_1)
LMSp_2 = dot_vector(np.linalg.inv(C), LMS_a)
LMS_c = dot_vector(np.linalg.inv(A_2), LMSp_2)
XYZ_c = dot_vector(FAIRCHILD1990_RGB_TO_XYZ_MATRIX, LMS_c)
return XYZ_c
def chromatic_adaptation_Fairchild1990(XYZ_1,
XYZ_n,
XYZ_r,
Y_n,
discount_illuminant=False):
"""
Adapts given stimulus *CIE XYZ_1* tristimulus values from test viewing
conditions to reference viewing conditions using *Fairchild (1990)*
chromatic adaptation model.
Parameters
----------
XYZ_1 : array_like
*CIE XYZ_1* tristimulus values of test sample / stimulus.
XYZ_n : array_like
Test viewing condition *CIE XYZ_n* tristimulus values of whitepoint.
XYZ_r : array_like
Reference viewing condition *CIE XYZ_r* tristimulus values of
whitepoint.
Y_n : numeric or array_like
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
discount_illuminant : bool, optional
Truth value indicating if the illuminant should be discounted.
Returns
-------
ndarray
Adapted *CIE XYZ_2* tristimulus values of stimulus.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_1`` | [0, 100] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_n`` | [0, 100] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_r`` | [0, 100] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_2`` | [0, 100] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Fairchild1991a`, :cite:`Fairchild2013s`
Examples
--------
>>> XYZ_1 = np.array([19.53, 23.07, 24.97])
>>> XYZ_n = np.array([111.15, 100.00, 35.20])
>>> XYZ_r = np.array([94.81, 100.00, 107.30])
>>> Y_n = 200
>>> chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_n, XYZ_r, Y_n)
... # doctest: +ELLIPSIS
array([ 23.3252634..., 23.3245581..., 76.1159375...])
"""
XYZ_1 = to_domain_100(XYZ_1)
XYZ_n = to_domain_100(XYZ_n)
XYZ_r = to_domain_100(XYZ_r)
Y_n = as_float_array(Y_n)
LMS_1 = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_1)
LMS_n = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_n)
LMS_r = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_r)
p_LMS = degrees_of_adaptation(
LMS_1, Y_n, discount_illuminant=discount_illuminant)
a_LMS_1 = p_LMS / LMS_n
a_LMS_2 = p_LMS / LMS_r
A_1 = row_as_diagonal(a_LMS_1)
A_2 = row_as_diagonal(a_LMS_2)
LMSp_1 = dot_vector(A_1, LMS_1)
c = 0.219 - 0.0784 * np.log10(Y_n)
C = row_as_diagonal(tstack([c, c, c]))
LMS_a = dot_vector(C, LMSp_1)
LMSp_2 = dot_vector(np.linalg.inv(C), LMS_a)
LMS_c = dot_vector(np.linalg.inv(A_2), LMSp_2)
XYZ_c = dot_vector(FAIRCHILD1990_RGB_TO_XYZ_MATRIX, LMS_c)
return from_range_100(XYZ_c)
def chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform='CAT02'):
"""
Computes the *chromatic adaptation* matrix from test viewing conditions
to reference viewing conditions.
Parameters
----------
XYZ_w : array_like
Test viewing condition *CIE XYZ* tristimulus values of whitepoint.
XYZ_wr : array_like
Reference viewing condition *CIE XYZ* tristimulus values of whitepoint.
transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
ndarray
Chromatic adaptation matrix.
Raises
------
KeyError
If chromatic adaptation method is not defined.
Examples
--------
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> chromatic_adaptation_matrix_VonKries( # doctest: +ELLIPSIS
... XYZ_w, XYZ_wr)
array([[ 0.8687653..., -0.1416539..., 0.3871961...],
[-0.1030072..., 1.0584014..., 0.1538646...],
[ 0.0078167..., 0.0267875..., 2.9608177...]])
Using Bradford method:
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> method = 'Bradford'
>>> chromatic_adaptation_matrix_VonKries( # doctest: +ELLIPSIS
... XYZ_w, XYZ_wr, method)
array([[ 0.8446794..., -0.1179355..., 0.3948940...],
[-0.1366408..., 1.1041236..., 0.1291981...],
[ 0.0798671..., -0.1349315..., 3.1928829...]])
"""
M = CHROMATIC_ADAPTATION_TRANSFORMS.get(transform)
if M is None:
raise KeyError(
'"{0}" chromatic adaptation transform is not defined! Supported '
'methods: "{1}".'.format(transform,
CHROMATIC_ADAPTATION_TRANSFORMS.keys()))
rgb_w = np.einsum('...i,...ij->...j', XYZ_w, np.transpose(M))
rgb_wr = np.einsum('...i,...ij->...j', XYZ_wr, np.transpose(M))
D = rgb_wr / rgb_w
D = row_as_diagonal(D)
cat = dot_matrix(np.linalg.inv(M), D)
cat = dot_matrix(cat, M)
return cat
def chromatic_adaptation_Fairchild1990(XYZ_1,
XYZ_n,
XYZ_r,
Y_n,
discount_illuminant=False):
"""
Adapts given stimulus *CIE XYZ_1* tristimulus values from test viewing
conditions to reference viewing conditions using *Fairchild (1990)*
chromatic adaptation model.
Parameters
----------
XYZ_1 : array_like
*CIE XYZ_1* tristimulus values of test sample / stimulus in domain
[0, 100].
XYZ_n : array_like
Test viewing condition *CIE XYZ_n* tristimulus values of whitepoint.
XYZ_r : array_like
Reference viewing condition *CIE XYZ_r* tristimulus values of
whitepoint.
Y_n : numeric or array_like
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
discount_illuminant : bool, optional
Truth value indicating if the illuminant should be discounted.
Returns
-------
ndarray
Adapted *CIE XYZ_2* tristimulus values of stimulus.
Warning
-------
The input domain and output range of that definition are non standard!
Notes
-----
- Input *CIE XYZ_1*, *CIE XYZ_n* and *CIE XYZ_r* tristimulus values are
in domain [0, 100].
- Output *CIE XYZ_2* tristimulus values are in range [0, 100].
References
----------
- :cite:`Fairchild1991a`
- :cite:`Fairchild2013s`
Examples
--------
>>> XYZ_1 = np.array([19.53, 23.07, 24.97])
>>> XYZ_n = np.array([111.15, 100.00, 35.20])
>>> XYZ_r = np.array([94.81, 100.00, 107.30])
>>> Y_n = 200
>>> chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_n, XYZ_r, Y_n)
... # doctest: +ELLIPSIS
array([ 23.3252634..., 23.3245581..., 76.1159375...])
"""
XYZ_1 = np.asarray(XYZ_1)
XYZ_n = np.asarray(XYZ_n)
XYZ_r = np.asarray(XYZ_r)
Y_n = np.asarray(Y_n)
LMS_1 = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_1)
LMS_n = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_n)
LMS_r = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_r)
p_LMS = degrees_of_adaptation(
LMS_1, Y_n, discount_illuminant=discount_illuminant)
a_LMS_1 = p_LMS / LMS_n
a_LMS_2 = p_LMS / LMS_r
A_1 = row_as_diagonal(a_LMS_1)
A_2 = row_as_diagonal(a_LMS_2)
LMSp_1 = dot_vector(A_1, LMS_1)
c = 0.219 - 0.0784 * np.log10(Y_n)
C = row_as_diagonal(tstack((c, c, c)))
LMS_a = dot_vector(C, LMSp_1)
LMSp_2 = dot_vector(np.linalg.inv(C), LMS_a)
LMS_c = dot_vector(np.linalg.inv(A_2), LMSp_2)
XYZ_c = dot_vector(FAIRCHILD1990_RGB_TO_XYZ_MATRIX, LMS_c)
return XYZ_c
def XYZ_to_RLAB(XYZ,
XYZ_n,
Y_n,
sigma=RLAB_VIEWING_CONDITIONS['Average'],
D=RLAB_D_FACTOR['Hard Copy Images']):
"""
Computes the *RLAB* model color appearance correlates.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of test sample / stimulus.
XYZ_n : array_like
*CIE XYZ* tristimulus values of reference white.
Y_n : numeric or array_like
Absolute adapting luminance in :math:`cd/m^2`.
sigma : numeric or array_like, optional
Relative luminance of the surround, see
:attr:`colour.RLAB_VIEWING_CONDITIONS` for reference.
D : numeric or array_like, optional
*Discounting-the-Illuminant* factor normalised to domain [0, 1].
Returns
-------
RLAB_Specification
*RLAB* colour appearance model specification.
Notes
-----
+--------------------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+==========================+=======================+===============+
| ``XYZ`` | [0, 100] | [0, 1] |
+--------------------------+-----------------------+---------------+
| ``XYZ_n`` | [0, 100] | [0, 1] |
+--------------------------+-----------------------+---------------+
+--------------------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+==========================+=======================+===============+
| ``RLAB_Specification.h`` | [0, 360] | [0, 1] |
+--------------------------+-----------------------+---------------+
References
----------
:cite:`Fairchild1996a`, :cite:`Fairchild2013w`
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_n = np.array([109.85, 100, 35.58])
>>> Y_n = 31.83
>>> sigma = RLAB_VIEWING_CONDITIONS['Average']
>>> D = RLAB_D_FACTOR['Hard Copy Images']
>>> XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma, D) # doctest: +ELLIPSIS
RLAB_Specification(J=49.8347069..., C=54.8700585..., h=286.4860208..., \
s=1.1010410..., HC=None, a=15.5711021..., b=-52.6142956...)
"""
XYZ = to_domain_100(XYZ)
XYZ_n = to_domain_100(XYZ_n)
Y_n = as_float_array(Y_n)
D = as_float_array(D)
sigma = as_float_array(sigma)
# Converting to cone responses.
LMS_n = XYZ_to_rgb(XYZ_n)
# Computing the :math:`A` matrix.
LMS_l_E = (3 * LMS_n) / (LMS_n[0] + LMS_n[1] + LMS_n[2])
LMS_p_L = ((1 + spow(Y_n[..., np.newaxis], 1 / 3) + LMS_l_E) /
(1 + spow(Y_n[..., np.newaxis], 1 / 3) + (1 / LMS_l_E)))
LMS_a_L = (LMS_p_L + D[..., np.newaxis] * (1 - LMS_p_L)) / LMS_n
aR = row_as_diagonal(LMS_a_L)
M = dot_matrix(dot_matrix(R_MATRIX, aR), XYZ_TO_HPE_MATRIX)
XYZ_ref = dot_vector(M, XYZ)
X_ref, Y_ref, Z_ref = tsplit(XYZ_ref)
# Computing the correlate of *Lightness* :math:`L^R`.
LR = 100 * spow(Y_ref, sigma)
# Computing opponent colour dimensions :math:`a^R` and :math:`b^R`.
aR = 430 * (spow(X_ref, sigma) - spow(Y_ref, sigma))
bR = 170 * (spow(Y_ref, sigma) - spow(Z_ref, sigma))
# Computing the *hue* angle :math:`h^R`.
hR = np.degrees(np.arctan2(bR, aR)) % 360
# TODO: Implement hue composition computation.
# Computing the correlate of *chroma* :math:`C^R`.
CR = np.hypot(aR, bR)
# Computing the correlate of *saturation* :math:`s^R`.
sR = CR / LR
return RLAB_Specification(LR, CR, from_range_degrees(hR), sR, None, aR, bR)
def chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform='CAT02'):
"""
Computes the *chromatic adaptation* matrix from test viewing conditions
to reference viewing conditions.
Parameters
----------
XYZ_w : array_like
Test viewing condition *CIE XYZ* tristimulus values of whitepoint.
XYZ_wr : array_like
Reference viewing condition *CIE XYZ* tristimulus values of whitepoint.
transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
ndarray
Chromatic adaptation matrix.
Raises
------
KeyError
If chromatic adaptation method is not defined.
Examples
--------
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> chromatic_adaptation_matrix_VonKries( # doctest: +ELLIPSIS
... XYZ_w, XYZ_wr)
array([[ 0.8687653..., -0.1416539..., 0.3871961...],
[-0.1030072..., 1.0584014..., 0.1538646...],
[ 0.0078167..., 0.0267875..., 2.9608177...]])
Using Bradford method:
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> method = 'Bradford'
>>> chromatic_adaptation_matrix_VonKries( # doctest: +ELLIPSIS
... XYZ_w, XYZ_wr, method)
array([[ 0.8446794..., -0.1179355..., 0.3948940...],
[-0.1366408..., 1.1041236..., 0.1291981...],
[ 0.0798671..., -0.1349315..., 3.1928829...]])
"""
M = CHROMATIC_ADAPTATION_TRANSFORMS.get(transform)
if M is None:
raise KeyError(
'"{0}" chromatic adaptation transform is not defined! Supported '
'methods: "{1}".'.format(transform,
CHROMATIC_ADAPTATION_TRANSFORMS.keys()))
rgb_w = np.einsum('...i,...ij->...j', XYZ_w, np.transpose(M))
rgb_wr = np.einsum('...i,...ij->...j', XYZ_wr, np.transpose(M))
D = rgb_wr / rgb_w
D = row_as_diagonal(D)
cat = dot_matrix(np.linalg.inv(M), D)
cat = dot_matrix(cat, M)
return cat
def matrix_chromatic_adaptation_VonKries(
XYZ_w: ArrayLike,
XYZ_wr: ArrayLike,
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:
"""
Compute the *chromatic adaptation* matrix from test viewing conditions
to reference viewing conditions.
Parameters
----------
XYZ_w
Test viewing conditions *CIE XYZ* tristimulus values of whitepoint.
XYZ_wr
Reference viewing conditions *CIE XYZ* tristimulus values of
whitepoint.
transform
Chromatic adaptation transform.
Returns
-------
:class:`numpy.ndarray`
Chromatic adaptation matrix :math:`M_{cat}`.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_w`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_wr`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Fairchild2013t`
Examples
--------
>>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
>>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
>>> matrix_chromatic_adaptation_VonKries(XYZ_w, XYZ_wr)
... # doctest: +ELLIPSIS
array([[ 1.0425738..., 0.0308910..., -0.0528125...],
[ 0.0221934..., 1.0018566..., -0.0210737...],
[-0.0011648..., -0.0034205..., 0.7617890...]])
Using Bradford method:
>>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
>>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
>>> method = 'Bradford'
>>> matrix_chromatic_adaptation_VonKries(XYZ_w, XYZ_wr, method)
... # doctest: +ELLIPSIS
array([[ 1.0479297..., 0.0229468..., -0.0501922...],
[ 0.0296278..., 0.9904344..., -0.0170738...],
[-0.0092430..., 0.0150551..., 0.7518742...]])
"""
XYZ_w = to_domain_1(XYZ_w)
XYZ_wr = to_domain_1(XYZ_wr)
transform = validate_method(
transform,
CHROMATIC_ADAPTATION_TRANSFORMS,
'"{0}" chromatic adaptation transform is invalid, '
"it must be one of {1}!",
)
M = CHROMATIC_ADAPTATION_TRANSFORMS[transform]
RGB_w = np.einsum("...i,...ij->...j", XYZ_w, np.transpose(M))
RGB_wr = np.einsum("...i,...ij->...j", XYZ_wr, np.transpose(M))
D = RGB_wr / RGB_w
D = row_as_diagonal(D)
M_CAT = matrix_dot(np.linalg.inv(M), D)
M_CAT = matrix_dot(M_CAT, M)
return M_CAT
