def ICTCP_to_RGB(ICTCP, L_p=10000):
"""
Converts from :math:`IC_TC_P` colour encoding to *Rec. 2020* colourspace.
Parameters
----------
ICTCP : array_like
:math:`IC_TC_P` colour encoding array.
L_p : numeric, optional
Display peak luminance :math:`cd/m^2` for *SMPTE ST 2084:2014*
non-linear encoding.
Returns
-------
ndarray
*Rec. 2020* colourspace array.
Examples
--------
>>> ICTCP = np.array([0.09554079, -0.00890639, 0.01389286])
>>> ICTCP_to_RGB(ICTCP) # doctest: +ELLIPSIS
array([ 0.3518145..., 0.2693475..., 0.2128802...])
"""
LMS_p = dot_vector(ICTCP_ICTCP_TO_LMS_P_MATRIX, ICTCP)
LMS = eotf_ST2084(LMS_p, L_p)
RGB = dot_vector(ICTCP_LMS_TO_RGB_MATRIX, LMS)
return RGB
def IPT_to_XYZ(IPT):
"""
Converts from *IPT* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
IPT : array_like
*IPT* colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Examples
--------
>>> IPT = np.array([1.00300825, 0.01906918, -0.01369292])
>>> IPT_to_XYZ(IPT) # doctest: +ELLIPSIS
array([ 0.9690723..., 1. , 1.1217921...])
"""
LMS = dot_vector(IPT_IPT_TO_LMS_MATRIX, IPT)
LMS_prime = np.sign(LMS) * np.abs(LMS) ** (1 / 0.43)
XYZ = dot_vector(IPT_LMS_TO_XYZ_MATRIX, LMS_prime)
return XYZ
def XYZ_to_IPT(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to *IPT* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
*IPT* colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values needs to be adapted for
*CIE Standard Illuminant D Series* *D65*.
Examples
--------
>>> XYZ = np.array([0.96907232, 1, 1.12179215])
>>> XYZ_to_IPT(XYZ) # doctest: +ELLIPSIS
array([ 1.0030082..., 0.0190691..., -0.0136929...])
"""
LMS = dot_vector(IPT_XYZ_TO_LMS_MATRIX, XYZ)
LMS_prime = np.sign(LMS) * np.abs(LMS) ** 0.43
IPT = dot_vector(IPT_LMS_TO_IPT_MATRIX, LMS_prime)
return IPT
def IPT_to_XYZ(IPT):
"""
Converts from *IPT* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
IPT : array_like
*IPT* colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
References
----------
- :cite:`Fairchild2013y`
Examples
--------
>>> IPT = np.array([1.00300825, 0.01906918, -0.01369292])
>>> IPT_to_XYZ(IPT) # doctest: +ELLIPSIS
array([ 0.9690723..., 1. , 1.1217921...])
"""
LMS = dot_vector(IPT_IPT_TO_LMS_MATRIX, IPT)
LMS_prime = np.sign(LMS) * np.abs(LMS) ** (1 / 0.43)
XYZ = dot_vector(IPT_LMS_TO_XYZ_MATRIX, LMS_prime)
return XYZ
def RGB_to_ICTCP(RGB, L_p=10000):
"""
Converts from *Rec. 2020* colourspace to :math:`IC_TC_P` colour encoding.
Parameters
----------
RGB : array_like
*Rec. 2020* colourspace array.
L_p : numeric, optional
Display peak luminance :math:`cd/m^2` for *SMPTE ST 2084:2014*
non-linear encoding.
Returns
-------
ndarray
:math:`IC_TC_P` colour encoding array.
Examples
--------
>>> RGB = np.array([0.35181454, 0.26934757, 0.21288023])
>>> RGB_to_ICTCP(RGB) # doctest: +ELLIPSIS
array([ 0.0955407..., -0.0089063..., 0.0138928...])
"""
LMS = dot_vector(ICTCP_RGB_TO_LMS_MATRIX, RGB)
LMS_p = oetf_ST2084(LMS, L_p)
ICTCP = dot_vector(ICTCP_LMS_P_TO_ICTCP_MATRIX, LMS_p)
return ICTCP
def hdr_IPT_to_XYZ(IPT_hdr, Y_s=0.2, Y_abs=100):
"""
Converts from *hdr-IPT* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
IPT_hdr : array_like
*hdr-IPT* 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.
Examples
--------
>>> IPT_hdr = np.array([94.65929175, 0.38041773, -0.26731187])
>>> hdr_IPT_to_XYZ(IPT_hdr) # doctest: +ELLIPSIS
array([ 0.9690723..., 1. , 1.1217921...])
"""
e = exponent_hdr_IPT(Y_s, Y_abs)[..., np.newaxis]
LMS = dot_vector(IPT_IPT_TO_LMS_MATRIX, IPT_hdr)
LMS_prime = np.sign(LMS) * np.abs(luminance_Fairchild2010(LMS, e))
XYZ = dot_vector(IPT_LMS_TO_XYZ_MATRIX, LMS_prime)
return XYZ
def XYZ_to_IPT(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to *IPT* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
*IPT* colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values needs to be adapted for
*CIE Standard Illuminant D Series* *D65*.
References
----------
- :cite:`Fairchild2013y`
Examples
--------
>>> XYZ = np.array([0.96907232, 1.00000000, 1.12179215])
>>> XYZ_to_IPT(XYZ) # doctest: +ELLIPSIS
array([ 1.0030082..., 0.0190691..., -0.0136929...])
"""
LMS = dot_vector(IPT_XYZ_TO_LMS_MATRIX, XYZ)
LMS_prime = np.sign(LMS) * np.abs(LMS) ** 0.43
IPT = dot_vector(IPT_LMS_TO_IPT_MATRIX, LMS_prime)
return IPT
def XYZ_to_hdr_IPT(XYZ, Y_s=0.2, Y_abs=100, method='Fairchild 2011'):
"""
Converts from *CIE XYZ* tristimulus values to *hdr-IPT* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
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`.
method : unicode, optional
**{'Fairchild 2011', 'Fairchild 2010'}**,
Computation method.
Returns
-------
ndarray
*hdr-IPT* colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values needs to be adapted for
*CIE Standard Illuminant D Series* *D65*.
References
----------
- :cite:`Fairchild2010`
- :cite:`Fairchild2011`
Examples
--------
>>> XYZ = np.array([0.96907232, 1.00000000, 1.12179215])
>>> XYZ_to_hdr_IPT(XYZ) # doctest: +ELLIPSIS
array([ 93.5317473..., 1.8564156..., -1.3292254...])
>>> XYZ_to_hdr_IPT(XYZ, method='Fairchild 2010') # doctest: +ELLIPSIS
array([ 94.6592917..., 0.3804177..., -0.2673118...])
"""
method_l = method.lower()
assert method.lower() in [
m.lower() for m in HDR_IPT_METHODS
], ('"{0}" method is invalid, must be one of {1}!'.format(
method, HDR_IPT_METHODS))
if method_l == 'fairchild 2010':
lightness_callable = lightness_Fairchild2010
else:
lightness_callable = lightness_Fairchild2011
e = exponent_hdr_IPT(Y_s, Y_abs, method)[..., np.newaxis]
LMS = dot_vector(IPT_XYZ_TO_LMS_MATRIX, XYZ)
LMS_prime = np.sign(LMS) * np.abs(lightness_callable(LMS, e))
IPT = dot_vector(IPT_LMS_TO_IPT_MATRIX, LMS_prime)
return IPT
def RGB_to_ICTCP(RGB, L_p=10000):
"""
Converts from *ITU-R BT.2020* colourspace to :math:`IC_TC_P` colour
encoding.
Parameters
----------
RGB : array_like
*ITU-R BT.2020* colourspace array.
L_p : numeric, optional
Display peak luminance :math:`cd/m^2` for *SMPTE ST 2084:2014*
non-linear encoding.
Returns
-------
ndarray
:math:`IC_TC_P` colour encoding array.
Notes
-----
+------------+-----------------------+------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+==================+
| ``RGB`` | [0, 1] | [0, 1] |
+------------+-----------------------+------------------+
+------------+-----------------------+------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+==================+
| ``ICTCP`` | ``I`` : [0, 1] | ``I`` : [0, 1] |
| | | |
| | ``CT`` : [-1, 1] | ``CT`` : [-1, 1] |
| | | |
| | ``CP`` : [-1, 1] | ``CP`` : [-1, 1] |
+------------+-----------------------+------------------+
References
----------
:cite:`Dolby2016a`, :cite:`Lu2016c`
Examples
--------
>>> RGB = np.array([0.45620519, 0.03081071, 0.04091952])
>>> RGB_to_ICTCP(RGB) # doctest: +ELLIPSIS
array([ 0.0735136..., 0.0047525..., 0.0935159...])
"""
RGB = to_domain_1(RGB)
LMS = dot_vector(ICTCP_RGB_TO_LMS_MATRIX, RGB)
with domain_range_scale('ignore'):
LMS_p = eotf_inverse_ST2084(LMS, L_p)
ICTCP = dot_vector(ICTCP_LMS_P_TO_ICTCP_MATRIX, LMS_p)
return from_range_1(ICTCP)
def ICTCP_to_RGB(ICTCP, L_p=10000):
"""
Converts from :math:`IC_TC_P` colour encoding to *ITU-R BT.2020*
colourspace.
Parameters
----------
ICTCP : array_like
:math:`IC_TC_P` colour encoding array.
L_p : numeric, optional
Display peak luminance :math:`cd/m^2` for *SMPTE ST 2084:2014*
non-linear encoding.
Returns
-------
ndarray
*ITU-R BT.2020* colourspace array.
Notes
-----
+------------+-----------------------+------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+==================+
| ``ICTCP`` | ``I`` : [0, 1] | ``I`` : [0, 1] |
| | | |
| | ``CT`` : [-1, 1] | ``CT`` : [-1, 1] |
| | | |
| | ``CP`` : [-1, 1] | ``CP`` : [-1, 1] |
+------------+-----------------------+------------------+
+------------+-----------------------+------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+==================+
| ``RGB`` | [0, 1] | [0, 1] |
+------------+-----------------------+------------------+
References
----------
:cite:`Dolby2016a`, :cite:`Lu2016c`
Examples
--------
>>> ICTCP = np.array([0.07351364, 0.00475253, 0.09351596])
>>> ICTCP_to_RGB(ICTCP) # doctest: +ELLIPSIS
array([ 0.4562052..., 0.0308107..., 0.0409195...])
"""
ICTCP = to_domain_1(ICTCP)
LMS_p = dot_vector(ICTCP_ICTCP_TO_LMS_P_MATRIX, ICTCP)
with domain_range_scale('ignore'):
LMS = eotf_ST2084(LMS_p, L_p)
RGB = dot_vector(ICTCP_LMS_TO_RGB_MATRIX, LMS)
return from_range_1(RGB)
def hdr_IPT_to_XYZ(IPT_hdr, Y_s=0.2, Y_abs=100, method='Fairchild 2011'):
"""
Converts from *hdr-IPT* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
IPT_hdr : array_like
*hdr-IPT* 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`.
method : unicode, optional
**{'Fairchild 2011', 'Fairchild 2010'}**,
Computation method.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
References
----------
- :cite:`Fairchild2010`
- :cite:`Fairchild2011`
Examples
--------
>>> IPT_hdr = np.array([93.53174734, 1.85641567, -1.32922546])
>>> hdr_IPT_to_XYZ(IPT_hdr) # doctest: +ELLIPSIS
array([ 0.9690723..., 1. , 1.1217921...])
>>> IPT_hdr = np.array([94.65929175, 0.38041773, -0.26731187])
>>> hdr_IPT_to_XYZ(IPT_hdr, method='Fairchild 2010')
... # doctest: +ELLIPSIS
array([ 0.9690723..., 1. , 1.1217921...])
"""
method_l = method.lower()
assert method.lower() in [
m.lower() for m in HDR_IPT_METHODS
], ('"{0}" method is invalid, must be one of {1}!'.format(
method, HDR_IPT_METHODS))
if method_l == 'fairchild 2010':
luminance_callable = luminance_Fairchild2010
else:
luminance_callable = luminance_Fairchild2011
e = exponent_hdr_IPT(Y_s, Y_abs, method)[..., np.newaxis]
LMS = dot_vector(IPT_IPT_TO_LMS_MATRIX, IPT_hdr)
LMS_prime = np.sign(LMS) * np.abs(luminance_callable(LMS, e))
XYZ = dot_vector(IPT_LMS_TO_XYZ_MATRIX, LMS_prime)
return XYZ
def XYZ_to_IPT(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to *IPT* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
*IPT* colourspace array.
Notes
-----
+------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+------------+-----------------------+-----------------+
+------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``IPT`` | ``I`` : [0, 1] | ``I`` : [0, 1] |
| | | |
| | ``P`` : [-1, 1] | ``P`` : [-1, 1] |
| | | |
| | ``T`` : [-1, 1] | ``T`` : [-1, 1] |
+------------+-----------------------+-----------------+
- Input *CIE XYZ* tristimulus values needs to be adapted for
*CIE Standard Illuminant D Series* *D65*.
References
----------
:cite:`Fairchild2013y`
Examples
--------
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_to_IPT(XYZ) # doctest: +ELLIPSIS
array([ 0.3842619..., 0.3848730..., 0.1888683...])
"""
XYZ = to_domain_1(XYZ)
LMS = dot_vector(IPT_XYZ_TO_LMS_MATRIX, XYZ)
LMS_prime = spow(LMS, 0.43)
IPT = dot_vector(IPT_LMS_TO_IPT_MATRIX, LMS_prime)
return from_range_1(IPT)
def XYZ_to_IPT(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to *IPT* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
*IPT* colourspace array.
Notes
-----
+------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+------------+-----------------------+-----------------+
+------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``IPT`` | ``I`` : [0, 1] | ``I`` : [0, 1] |
| | | |
| | ``P`` : [-1, 1] | ``P`` : [-1, 1] |
| | | |
| | ``T`` : [-1, 1] | ``T`` : [-1, 1] |
+------------+-----------------------+-----------------+
- Input *CIE XYZ* tristimulus values needs to be adapted for
*CIE Standard Illuminant D Series* *D65*.
References
----------
:cite:`Fairchild2013y`
Examples
--------
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_to_IPT(XYZ) # doctest: +ELLIPSIS
array([ 0.3842619..., 0.3848730..., 0.1888683...])
"""
XYZ = to_domain_1(XYZ)
LMS = dot_vector(IPT_XYZ_TO_LMS_MATRIX, XYZ)
LMS_prime = spow(LMS, 0.43)
IPT = dot_vector(IPT_LMS_TO_IPT_MATRIX, LMS_prime)
return from_range_1(IPT)
def IPT_to_XYZ(IPT):
"""
Converts from *IPT* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
IPT : array_like
*IPT* colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
+------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``IPT`` | ``I`` : [0, 1] | ``I`` : [0, 1] |
| | | |
| | ``P`` : [-1, 1] | ``P`` : [-1, 1] |
| | | |
| | ``T`` : [-1, 1] | ``T`` : [-1, 1] |
+------------+-----------------------+-----------------+
+------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+------------+-----------------------+-----------------+
References
----------
:cite:`Fairchild2013y`
Examples
--------
>>> IPT = np.array([0.38426191, 0.38487306, 0.18886838])
>>> IPT_to_XYZ(IPT) # doctest: +ELLIPSIS
array([ 0.2065400..., 0.1219722..., 0.0513695...])
"""
IPT = to_domain_1(IPT)
LMS = dot_vector(IPT_IPT_TO_LMS_MATRIX, IPT)
LMS_prime = spow(LMS, 1 / 0.43)
XYZ = dot_vector(IPT_LMS_TO_XYZ_MATRIX, LMS_prime)
return from_range_1(XYZ)
def IPT_to_XYZ(IPT):
"""
Converts from *IPT* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
IPT : array_like
*IPT* colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
+------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``IPT`` | ``I`` : [0, 1] | ``I`` : [0, 1] |
| | | |
| | ``P`` : [-1, 1] | ``P`` : [-1, 1] |
| | | |
| | ``T`` : [-1, 1] | ``T`` : [-1, 1] |
+------------+-----------------------+-----------------+
+------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+------------+-----------------------+-----------------+
References
----------
:cite:`Fairchild2013y`
Examples
--------
>>> IPT = np.array([0.38426191, 0.38487306, 0.18886838])
>>> IPT_to_XYZ(IPT) # doctest: +ELLIPSIS
array([ 0.2065400..., 0.1219722..., 0.0513695...])
"""
IPT = to_domain_1(IPT)
LMS = dot_vector(IPT_IPT_TO_LMS_MATRIX, IPT)
LMS_prime = spow(LMS, 1 / 0.43)
XYZ = dot_vector(IPT_LMS_TO_XYZ_MATRIX, LMS_prime)
return from_range_1(XYZ)
def plot_cvd_simulation_Machado2009(RGB,
deficiency='Protanomaly',
severity=0.5,
M_a=None,
**kwargs):
"""
Performs colour vision deficiency simulation on given *RGB* colourspace
array using *Machado et al. (2009)* model.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
deficiency : unicode, optional
{'Protanomaly', 'Deuteranomaly', 'Tritanomaly'}
Colour blindness / vision deficiency type.
severity : numeric, optional
Severity of the colour vision deficiency in domain [0, 1].
M_a : array_like, optional
Anomalous trichromacy matrix to use instead of Machado (2010)
pre-computed matrix.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.plot_image`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Notes
-----
- Input *RGB* array is expected to be linearly encoded.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> import numpy as np
>>> RGB = np.random.rand(32, 32, 3)
>>> plot_cvd_simulation_Machado2009(RGB) # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_CVD_Simulation_Machado2009.png
:align: center
:alt: plot_cvd_simulation_Machado2009
"""
if M_a is None:
M_a = cvd_matrix_Machado2009(deficiency, severity)
text = 'Deficiency: {0} - Severity: {1}'.format(deficiency, severity)
settings = {'text_parameters': {'text': None if M_a is None else text}}
settings.update(kwargs)
return plot_image(
COLOUR_STYLE_CONSTANTS.colour.colourspace.encoding_cctf(
dot_vector(M_a, RGB)), **settings)
def XYZ_to_RGB_LLAB(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to normalised cone responses.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
Normalised cone responses.
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_to_RGB_LLAB(XYZ) # doctest: +ELLIPSIS
array([ 0.9414279..., 1.0404012..., 1.0897088...])
"""
_X, Y, _Z = tsplit(XYZ)
Y = tstack((Y, Y, Y))
XYZ_n = XYZ / Y
return dot_vector(LLAB_XYZ_TO_RGB_MATRIX, XYZ_n)
def XYZ_to_RGB_LLAB(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to normalised cone responses.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
Normalised cone responses.
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_to_RGB_LLAB(XYZ) # doctest: +ELLIPSIS
array([ 0.9414279..., 1.0404012..., 1.0897088...])
"""
_X, Y, Z = tsplit(XYZ)
Y = tstack((Y, Y, Y))
XYZ_n = XYZ / Y
return dot_vector(LLAB_XYZ_TO_RGB_MATRIX, XYZ_n)
def XYZ_to_LMS_ATD95(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to *LMS* cone responses.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
*LMS* cone responses.
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_to_LMS_ATD95(XYZ) # doctest: +ELLIPSIS
array([ 6.2283272..., 7.4780666..., 3.8859772...])
"""
LMS = dot_vector([
[0.2435, 0.8524, -0.0516],
[-0.3954, 1.1642, 0.0837],
[0.0000, 0.0400, 0.6225],
], XYZ)
LMS *= np.array([0.66, 1.0, 0.43])
LMS = spow(LMS, 0.7)
LMS += np.array([0.024, 0.036, 0.31])
return LMS
def test_dot_vector(self):
"""
Tests :func:`colour.utilities.array.dot_vector` definition.
"""
m = np.array([
[0.7328, 0.4296, -0.1624],
[-0.7036, 1.6975, 0.0061],
[0.0030, 0.0136, 0.9834],
])
m = np.reshape(np.tile(m, (6, 1)), (6, 3, 3))
v = np.array([0.20654008, 0.12197225, 0.05136952])
v = np.tile(v, (6, 1))
np.testing.assert_almost_equal(
dot_vector(m, v),
np.array([
[0.19540944, 0.06203965, 0.05279523],
[0.19540944, 0.06203965, 0.05279523],
[0.19540944, 0.06203965, 0.05279523],
[0.19540944, 0.06203965, 0.05279523],
[0.19540944, 0.06203965, 0.05279523],
[0.19540944, 0.06203965, 0.05279523],
]),
decimal=7)
def RGB_to_YCoCg(RGB):
"""
Converts an array of *R'G'B'* values to the corresponding *YCoCg* colour
encoding values array.
Parameters
----------
RGB : array_like
Input *R'G'B'* array.
Returns
-------
ndarray
*YCoCg* colour encoding array.
References
----------
:cite:`Malvar2003`
Examples
--------
>>> RGB_to_YCoCg(np.array([1.0, 1.0, 1.0]))
array([ 1., 0., 0.])
>>> RGB_to_YCoCg(np.array([0.75, 0.5, 0.5]))
array([ 0.5625, 0.125 , -0.0625])
"""
return dot_vector(RGB_TO_YCOCG_MATRIX, RGB)
def YCoCg_to_RGB(YCoCg):
"""
Converts an array of *YCoCg* colour encoding values to the corresponding
*R'G'B'* values array.
Parameters
----------
YCoCg : array_like
*YCoCg* colour encoding array.
Returns
-------
ndarray
Output *R'G'B'* array.
References
----------
:cite:`Malvar2003`
Examples
--------
>>> YCoCg_to_RGB(np.array([1.0, 0.0, 0.0]))
array([ 1., 1., 1.])
>>> YCoCg_to_RGB(np.array([0.5625, 0.125, -0.0625]))
array([ 0.75, 0.5 , 0.5 ])
"""
return dot_vector(YCOCG_TO_RGB_MATRIX, YCoCg)
def RGB_to_YCoCg(RGB):
"""
Converts an array of *R'G'B'* values to the corresponding *YCoCg* colour
encoding values array.
Parameters
----------
RGB : array_like
Input *R'G'B'* array.
Returns
-------
ndarray
*YCoCg* colour encoding array.
References
----------
:cite:`Malvar2003`
Examples
--------
>>> RGB_to_YCoCg(np.array([1.0, 1.0, 1.0]))
array([ 1., 0., 0.])
>>> RGB_to_YCoCg(np.array([0.75, 0.5, 0.5]))
array([ 0.5625, 0.125 , -0.0625])
"""
return dot_vector(RGB_TO_YCOCG_MATRIX, RGB)
def RGB_to_rgb(RGB):
"""
Converts given *RGB* array to *Hunt-Pointer-Estevez*
:math:`\\rho\gamma\\beta` colourspace.
Parameters
----------
RGB : array_like
*RGB* array.
Returns
-------
ndarray
*Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array.
Examples
--------
>>> RGB = np.array([19.99370783, 20.00393634, 20.01326387])
>>> RGB_to_rgb(RGB) # doctest: +ELLIPSIS
array([ 19.9969397..., 20.0018612..., 20.0135053...])
"""
rgb = dot_vector(dot_matrix(XYZ_TO_HPE_MATRIX, CAT02_INVERSE_CAT), RGB)
return rgb
def rgb_to_RGB(rgb):
"""
Converts given *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace
array to *RGB* array.
Parameters
----------
rgb : array_like
*Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array.
Returns
-------
ndarray
*RGB* array.
Examples
--------
>>> rgb = np.array([19.99693975, 20.00186123, 20.01350530])
>>> rgb_to_RGB(rgb) # doctest: +ELLIPSIS
array([ 19.9937078..., 20.0039363..., 20.0132638...])
"""
RGB = dot_vector(dot_matrix(CAT02_CAT, HPE_TO_XYZ_MATRIX), rgb)
return RGB
def RGB_to_rgb(RGB):
"""
Converts given *RGB* array to *Hunt-Pointer-Estevez*
:math:`\\rho\gamma\\beta` colourspace.
Parameters
----------
RGB : array_like
*RGB* array.
Returns
-------
ndarray
*Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array.
Examples
--------
>>> RGB = np.array([19.99370783, 20.00393634, 20.01326387])
>>> RGB_to_rgb(RGB) # doctest: +ELLIPSIS
array([ 19.9969397..., 20.0018612..., 20.0135053...])
"""
rgb = dot_vector(dot_matrix(XYZ_TO_HPE_MATRIX, CAT02_INVERSE_CAT), RGB)
return rgb
def YCoCg_to_RGB(YCoCg):
"""
Converts an array of *YCoCg* colour encoding values to the corresponding
*R'G'B'* values array.
Parameters
----------
YCoCg : array_like
*YCoCg* colour encoding array.
Returns
-------
ndarray
Output *R'G'B'* array.
References
----------
:cite:`Malvar2003`
Examples
--------
>>> YCoCg_to_RGB(np.array([1.0, 0.0, 0.0]))
array([ 1., 1., 1.])
>>> YCoCg_to_RGB(np.array([0.5625, 0.125, -0.0625]))
array([ 0.75, 0.5 , 0.5 ])
"""
return dot_vector(YCOCG_TO_RGB_MATRIX, YCoCg)
def rgb_to_RGB(rgb):
"""
Converts given *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace
array to *RGB* array.
Parameters
----------
rgb : array_like
*Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array.
Returns
-------
ndarray
*RGB* array.
Examples
--------
>>> rgb = np.array([19.99693975, 20.00186123, 20.01350530])
>>> rgb_to_RGB(rgb) # doctest: +ELLIPSIS
array([ 19.9937078..., 20.0039363..., 20.0132638...])
"""
RGB = dot_vector(dot_matrix(CAT02_CAT, HPE_TO_XYZ_MATRIX), rgb)
return RGB
def test_dot_vector(self):
"""
Tests :func:`colour.utilities.array.dot_vector` definition.
"""
m = np.array([
[0.7328, 0.4296, -0.1624],
[-0.7036, 1.6975, 0.0061],
[0.0030, 0.0136, 0.9834],
])
m = np.reshape(np.tile(m, (6, 1)), (6, 3, 3))
v = np.array([0.20654008, 0.12197225, 0.05136952])
v = np.tile(v, (6, 1))
np.testing.assert_almost_equal(
dot_vector(m, v),
np.array([
[0.19540944, 0.06203965, 0.05279523],
[0.19540944, 0.06203965, 0.05279523],
[0.19540944, 0.06203965, 0.05279523],
[0.19540944, 0.06203965, 0.05279523],
[0.19540944, 0.06203965, 0.05279523],
[0.19540944, 0.06203965, 0.05279523],
]),
decimal=7)
def test_dot_vector(self):
"""
Tests :func:`colour.utilities.array.dot_vector` definition.
"""
m = np.array([
[0.7328, 0.4296, -0.1624],
[-0.7036, 1.6975, 0.0061],
[0.0030, 0.0136, 0.9834],
])
m = np.reshape(np.tile(m, (6, 1)), (6, 3, 3))
v = np.array([0.07049534, 0.10080000, 0.09558313])
v = np.tile(v, (6, 1))
np.testing.assert_almost_equal(
dot_vector(m, v),
np.array([
[0.07943996, 0.12209054, 0.09557882],
[0.07943996, 0.12209054, 0.09557882],
[0.07943996, 0.12209054, 0.09557882],
[0.07943996, 0.12209054, 0.09557882],
[0.07943996, 0.12209054, 0.09557882],
[0.07943996, 0.12209054, 0.09557882],
]),
decimal=7)
def plot_cvd_simulation_Machado2009(RGB,
deficiency='Protanomaly',
severity=0.5,
M_a=None,
**kwargs):
"""
Performs colour vision deficiency simulation on given *RGB* colourspace
array using *Machado et al. (2009)* model.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
deficiency : unicode, optional
{'Protanomaly', 'Deuteranomaly', 'Tritanomaly'}
Colour blindness / vision deficiency type.
severity : numeric, optional
Severity of the colour vision deficiency in domain [0, 1].
M_a : array_like, optional
Anomalous trichromacy matrix to use instead of Machado (2010)
pre-computed matrix.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.plot_image`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Notes
-----
- Input *RGB* array is expected to be linearly encoded.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> import numpy as np
>>> RGB = np.random.rand(32, 32, 3)
>>> plot_cvd_simulation_Machado2009(RGB) # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_CVD_Simulation_Machado2009.png
:align: center
:alt: plot_cvd_simulation_Machado2009
"""
if M_a is None:
M_a = cvd_matrix_Machado2009(deficiency, severity)
text = 'Deficiency: {0} - Severity: {1}'.format(deficiency, severity)
settings = {'text_parameters': {'text': None if M_a is None else text}}
settings.update(kwargs)
return plot_image(
COLOUR_STYLE_CONSTANTS.colour.colourspace.encoding_cctf(
dot_vector(M_a, RGB)), **settings)
def highlights_recovery_blend(RGB, multipliers, threshold=0.99):
"""
Performs highlights recovery using *Coffin (1997)* method from *dcraw*.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
multipliers : array_like
Normalised camera white level or white balance multipliers.
threshold : numeric, optional
Threshold for highlights selection.
Returns
-------
ndarray
Highlights recovered *RGB* colourspace array.
References
----------
:cite:`Coffin2015a`
"""
M = np.array(
[[1.0000000, 1.0000000, 1.0000000],
[1.7320508, -1.7320508, 0.0000000],
[-1.0000000, -1.0000000, 2.0000000]]) # yapf: disable
clipping_level = np.min(multipliers) * threshold
Lab = dot_vector(M, RGB)
Lab_c = dot_vector(M, np.minimum(RGB, clipping_level))
s = np.sum((Lab * Lab)[..., 1:3], axis=2)
s_c = np.sum((Lab_c * Lab_c)[..., 1:3], axis=2)
ratio = np.sqrt(s_c / s)
ratio[np.logical_or(np.isnan(ratio), np.isinf(ratio))] = 1
Lab[:, :, 1:3] *= np.rollaxis(ratio[np.newaxis], 0, 3)
RGB_o = dot_vector(np.linalg.inv(M), Lab)
return RGB_o
def degrees_of_adaptation(LMS, Y_n, v=1 / 3, discount_illuminant=False):
"""
Computes the degrees of adaptation :math:`p_L`, :math:`p_M` and
:math:`p_S`.
Parameters
----------
LMS : array_like
Cone responses.
Y_n : numeric or array_like
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
v : numeric or array_like, optional
Exponent :math:`v`.
discount_illuminant : bool, optional
Truth value indicating if the illuminant should be discounted.
Returns
-------
ndarray
Degrees of adaptation :math:`p_L`, :math:`p_M` and :math:`p_S`.
Examples
--------
>>> LMS = np.array([20.00052060, 19.99978300, 19.99883160])
>>> Y_n = 31.83
>>> degrees_of_adaptation(LMS, Y_n) # doctest: +ELLIPSIS
array([ 0.9799324..., 0.9960035..., 1.0233041...])
>>> degrees_of_adaptation(LMS, Y_n, 1 / 3, True)
array([ 1., 1., 1.])
"""
LMS = np.asarray(LMS)
if discount_illuminant:
return np.ones(LMS.shape)
Y_n = np.asarray(Y_n)
v = np.asarray(v)
L, M, S = tsplit(LMS)
LMS_E = dot_vector(VON_KRIES_CAT, np.ones(LMS.shape)) # E illuminant.
L_E, M_E, S_E = tsplit(LMS_E)
Ye_n = Y_n ** v
f_E = lambda x, y: (3 * (x / y)) / (L / L_E + M / M_E + S / S_E)
f_P = lambda x: (1 + Ye_n + x) / (1 + Ye_n + 1 / x)
p_L = f_P(f_E(L, L_E))
p_M = f_P(f_E(M, M_E))
p_S = f_P(f_E(S, S_E))
p_LMS = tstack((p_L, p_M, p_S))
return p_LMS
def RGB_to_RGB(RGB,
input_colourspace,
output_colourspace,
chromatic_adaptation_transform='CAT02'):
"""
Converts from given input *RGB* colourspace to output *RGB* colourspace
using given *chromatic adaptation* method.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
input_colourspace : RGB_Colourspace
*RGB* input colourspace.
output_colourspace : RGB_Colourspace
*RGB* output colourspace.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray
*RGB* colourspace array.
Notes
-----
- Input / output *RGB* colourspace arrays are in domain / range [0, 1].
- Input / output *RGB* colourspace arrays are assumed to be representing
linear light values.
Examples
--------
>>> from colour import sRGB_COLOURSPACE, PROPHOTO_RGB_COLOURSPACE
>>> RGB = np.array([0.01103742, 0.12734226, 0.11632971])
>>> RGB_to_RGB(
... RGB,
... sRGB_COLOURSPACE,
... PROPHOTO_RGB_COLOURSPACE) # doctest: +ELLIPSIS
array([ 0.0643538..., 0.1157289..., 0.1158038...])
"""
cat = chromatic_adaptation_matrix_VonKries(
xy_to_XYZ(input_colourspace.whitepoint),
xy_to_XYZ(output_colourspace.whitepoint),
chromatic_adaptation_transform)
M = dot_matrix(cat, input_colourspace.RGB_to_XYZ_matrix)
M = dot_matrix(output_colourspace.XYZ_to_RGB_matrix, M)
RGB = dot_vector(M, RGB)
return RGB
def degrees_of_adaptation(LMS, Y_n, v=1 / 3, discount_illuminant=False):
"""
Computes the degrees of adaptation :math:`p_L`, :math:`p_M` and
:math:`p_S`.
Parameters
----------
LMS : array_like
Cone responses.
Y_n : numeric or array_like
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
v : numeric or array_like, optional
Exponent :math:`v`.
discount_illuminant : bool, optional
Truth value indicating if the illuminant should be discounted.
Returns
-------
ndarray
Degrees of adaptation :math:`p_L`, :math:`p_M` and :math:`p_S`.
Examples
--------
>>> LMS = np.array([20.00052060, 19.99978300, 19.99883160])
>>> Y_n = 31.83
>>> degrees_of_adaptation(LMS, Y_n) # doctest: +ELLIPSIS
array([ 0.9799324..., 0.9960035..., 1.0233041...])
>>> degrees_of_adaptation(LMS, Y_n, 1 / 3, True)
array([ 1., 1., 1.])
"""
LMS = np.asarray(LMS)
if discount_illuminant:
return np.ones(LMS.shape)
Y_n = np.asarray(Y_n)
v = np.asarray(v)
L, M, S = tsplit(LMS)
LMS_E = dot_vector(VON_KRIES_CAT, np.ones(LMS.shape)) # E illuminant.
L_E, M_E, S_E = tsplit(LMS_E)
Ye_n = Y_n**v
f_E = lambda x, y: (3 * (x / y)) / (L / L_E + M / M_E + S / S_E)
f_P = lambda x: (1 + Ye_n + x) / (1 + Ye_n + 1 / x)
p_L = f_P(f_E(L, L_E))
p_M = f_P(f_E(M, M_E))
p_S = f_P(f_E(S, S_E))
p_LMS = tstack((p_L, p_M, p_S))
return p_LMS
def RGB_to_RGB(RGB,
input_colourspace,
output_colourspace,
chromatic_adaptation_transform='CAT02'):
"""
Converts from given input *RGB* colourspace to output *RGB* colourspace
using given *chromatic adaptation* method.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
input_colourspace : RGB_Colourspace
*RGB* input colourspace.
output_colourspace : RGB_Colourspace
*RGB* output colourspace.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild, 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray
*RGB* colourspace array.
Notes
-----
- *RGB* colourspace arrays are in domain [0, 1].
Examples
--------
>>> from colour import sRGB_COLOURSPACE, PROPHOTO_RGB_COLOURSPACE
>>> RGB = np.array([0.01103604, 0.12734466, 0.11631037])
>>> RGB_to_RGB(
... RGB,
... sRGB_COLOURSPACE,
... PROPHOTO_RGB_COLOURSPACE) # doctest: +ELLIPSIS
array([ 0.0643338..., 0.1157362..., 0.1157614...])
"""
cat = chromatic_adaptation_matrix_VonKries(
xy_to_XYZ(input_colourspace.whitepoint),
xy_to_XYZ(output_colourspace.whitepoint),
chromatic_adaptation_transform)
M = dot_matrix(cat, input_colourspace.RGB_to_XYZ_matrix)
M = dot_matrix(output_colourspace.XYZ_to_RGB_matrix, M)
RGB = dot_vector(M, RGB)
return RGB
def chromatic_adaptation(RGB, RGB_0, RGB_0r, Y, D=1):
"""
Applies chromatic adaptation to given *RGB* normalised cone responses
array.
Parameters
----------
RGB : array_like
*RGB* normalised cone responses array of test sample / stimulus.
RGB_0 : array_like
*RGB* normalised cone responses array of reference white.
RGB_0r : array_like
*RGB* normalised cone responses array of reference illuminant
*CIE Standard Illuminant D Series* *D65*.
Y : numeric or array_like
Tristimulus values :math:`Y` of the stimulus.
D : numeric or array_like, optional
*Discounting-the-Illuminant* factor in domain [0, 1].
Returns
-------
ndarray
Adapted *CIE XYZ* tristimulus values.
Examples
--------
>>> RGB = np.array([0.94142795, 1.04040120, 1.08970885])
>>> RGB_0 = np.array([0.94146023, 1.04039386, 1.08950293])
>>> RGB_0r = np.array([0.94146023, 1.04039386, 1.08950293])
>>> Y = 20.0
>>> chromatic_adaptation(RGB, RGB_0, RGB_0r, Y) # doctest: +ELLIPSIS
array([ 19.01, 20. , 21.78])
"""
R, G, B = tsplit(RGB)
R_0, G_0, B_0 = tsplit(RGB_0)
R_0r, G_0r, B_0r = tsplit(RGB_0r)
Y = np.asarray(Y)
beta = (B_0 / B_0r) ** 0.0834
R_r = (D * (R_0r / R_0) + 1 - D) * R
G_r = (D * (G_0r / G_0) + 1 - D) * G
B_r = (D * (B_0r / (B_0 ** beta)) + 1 - D) * (abs(B) ** beta)
RGB_r = tstack((R_r, G_r, B_r))
Y = tstack((Y, Y, Y))
XYZ_r = dot_vector(LLAB_RGB_TO_XYZ_MATRIX, RGB_r * Y)
return XYZ_r
def chromatic_adaptation(RGB, RGB_0, RGB_0r, Y, D=1):
"""
Applies chromatic adaptation to given *RGB* normalised cone responses
array.
Parameters
----------
RGB : array_like
*RGB* normalised cone responses array of test sample / stimulus.
RGB_0 : array_like
*RGB* normalised cone responses array of reference white.
RGB_0r : array_like
*RGB* normalised cone responses array of reference illuminant
*CIE Standard Illuminant D Series* *D65*.
Y : numeric or array_like
Tristimulus values :math:`Y` of the stimulus.
D : numeric or array_like, optional
*Discounting-the-Illuminant* factor in domain [0, 1].
Returns
-------
ndarray
Adapted *CIE XYZ* tristimulus values.
Examples
--------
>>> RGB = np.array([0.94142795, 1.04040120, 1.08970885])
>>> RGB_0 = np.array([0.94146023, 1.04039386, 1.08950293])
>>> RGB_0r = np.array([0.94146023, 1.04039386, 1.08950293])
>>> Y = 20.0
>>> chromatic_adaptation(RGB, RGB_0, RGB_0r, Y) # doctest: +ELLIPSIS
array([ 19.01, 20. , 21.78])
"""
R, G, B = tsplit(RGB)
R_0, G_0, B_0 = tsplit(RGB_0)
R_0r, G_0r, B_0r = tsplit(RGB_0r)
Y = np.asarray(Y)
beta = (B_0 / B_0r) ** 0.0834
R_r = (D * (R_0r / R_0) + 1 - D) * R
G_r = (D * (G_0r / G_0) + 1 - D) * G
B_r = (D * (B_0r / (B_0 ** beta)) + 1 - D) * (abs(B) ** beta)
RGB_r = tstack((R_r, G_r, B_r))
Y = tstack((Y, Y, Y))
XYZ_r = dot_vector(LLAB_RGB_TO_XYZ_MATRIX, RGB_r * Y)
return XYZ_r
def chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr, transform='CAT02'):
"""
Adapts given stimulus from test viewing conditions to reference viewing
conditions.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of stimulus to adapt.
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
*CIE XYZ_c* tristimulus values of the stimulus corresponding colour.
References
----------
- :cite:`Fairchild2013t`
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr) # doctest: +ELLIPSIS
array([ 0.0839746..., 0.1141321..., 0.2862554...])
Using Bradford method:
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> transform = 'Bradford'
>>> chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr, transform)
... # doctest: +ELLIPSIS
array([ 0.0854032..., 0.1140122..., 0.2972149...])
"""
cat = chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform)
XYZ_a = dot_vector(cat, XYZ)
return XYZ_a
def XYZ_to_hdr_IPT(XYZ, Y_s=0.2, Y_abs=100):
"""
Converts from *CIE XYZ* tristimulus values to *hdr-IPT* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
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-IPT* colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values needs to be adapted for
*CIE Standard Illuminant D Series* *D65*.
Examples
--------
>>> XYZ = np.array([0.96907232, 1.00000000, 1.12179215])
>>> XYZ_to_hdr_IPT(XYZ) # doctest: +ELLIPSIS
array([ 94.6592917..., 0.3804177..., -0.2673118...])
"""
e = exponent_hdr_IPT(Y_s, Y_abs)[..., np.newaxis]
LMS = dot_vector(IPT_XYZ_TO_LMS_MATRIX, XYZ)
LMS_prime = np.sign(LMS) * np.abs(lightness_Fairchild2010(LMS, e))
IPT = dot_vector(IPT_LMS_TO_IPT_MATRIX, LMS_prime)
return IPT
def RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength):
"""
Converts *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching
functions into the *CIE 1964 10 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 1964 10 Degree Standard Observer* spectral tristimulus values.
See Also
--------
:attr:`colour.colorimetry.dataset.cmfs.RGB_CMFS`
Notes
-----
- Data for the *CIE 1964 10 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
.. [2] Wyszecki, G., & Stiles, W. S. (2000). The CIE 1964 Standard
Observer. In Color Science: Concepts and Methods, Quantitative
Data and Formulae (p. 141). Wiley. ISBN:978-0471399186
Examples
--------
>>> RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 9.6432150...e-03, 3.7526317...e-03, -4.1078830...e-06])
"""
cmfs = RGB_CMFS['Stiles & Burch 1959 10 Degree RGB CMFs']
rgb_bar = cmfs.get(wavelength)
M = np.array(
[[0.341080, 0.189145, 0.387529],
[0.139058, 0.837460, 0.073316],
[0.000000, 0.039553, 2.026200]]) # yapf: disable
xyz_bar = dot_vector(M, rgb_bar)
return xyz_bar
def RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(wavelength):
"""
Converts *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching
functions into the *Stockman & Sharpe 10 Degree Cone Fundamentals*
spectral sensitivity functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*Stockman & Sharpe 10 Degree Cone Fundamentals* spectral tristimulus
values.
Notes
-----
- Data for the *Stockman & Sharpe 10 Degree Cone Fundamentals* already
exists, this definition is intended for educational purpose.
References
----------
- :cite:`CIETC1-362006a`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0052860..., 0.0003252..., 0. ])
"""
cmfs = RGB_CMFS['Stiles & Burch 1959 10 Degree RGB CMFs']
rgb_bar = cmfs[wavelength]
M = np.array([
[0.1923252690, 0.749548882, 0.0675726702],
[0.0192290085, 0.940908496, 0.113830196],
[0.0000000000, 0.0105107859, 0.991427669],
])
lms_bar = dot_vector(M, rgb_bar)
lms_bar[..., -1][np.asarray(np.asarray(wavelength) > 505)] = 0
return lms_bar
def RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength):
"""
Converts *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching
functions into the *CIE 1964 10 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 1964 10 Degree Standard Observer* spectral tristimulus values.
See Also
--------
:attr:`colour.colorimetry.dataset.cmfs.RGB_CMFS`
Notes
-----
- Data for the *CIE 1964 10 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
.. [2] Wyszecki, G., & Stiles, W. S. (2000). The CIE 1964 Standard
Observer. In Color Science: Concepts and Methods, Quantitative
Data and Formulae (p. 141). Wiley. ISBN:978-0471399186
Examples
--------
>>> RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 9.6432150...e-03, 3.7526317...e-03, -4.1078830...e-06])
"""
cmfs = RGB_CMFS.get('Stiles & Burch 1959 10 Degree RGB CMFs')
rgb_bar = cmfs.get(wavelength)
M = np.array([[0.341080, 0.189145, 0.387529],
[0.139058, 0.837460, 0.073316],
[0.000000, 0.039553, 2.026200]])
xyz_bar = dot_vector(M, rgb_bar)
return xyz_bar
def RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(wavelength):
"""
Converts *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching
functions into the *Stockman & Sharpe 10 Degree Cone Fundamentals*
spectral sensitivity functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*Stockman & Sharpe 10 Degree Cone Fundamentals* spectral tristimulus
values.
Notes
-----
- Data for the *Stockman & Sharpe 10 Degree Cone Fundamentals* already
exists, this definition is intended for educational purpose.
References
----------
.. [3] CIE TC 1-36. (2006). CIE 170-1:2006 Fundamental Chromaticity
Diagram with Physiological Axes - Part 1 (pp. 1–56).
ISBN:978-3-901-90646-6
Examples
--------
>>> RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0052860..., 0.0003252..., 0. ])
"""
cmfs = RGB_CMFS['Stiles & Burch 1959 10 Degree RGB CMFs']
rgb_bar = cmfs.get(wavelength)
M = np.array(
[[0.1923252690, 0.749548882, 0.0675726702],
[0.0192290085, 0.940908496, 0.113830196],
[0.0000000000, 0.0105107859, 0.991427669]]) # yapf: disable
lms_bar = dot_vector(M, rgb_bar)
lms_bar[..., -1][np.asarray(np.asarray(wavelength) > 505)] = 0
return lms_bar
def chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr, transform='CAT02'):
"""
Adapts given stimulus from test viewing conditions to reference viewing
conditions.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of stimulus to adapt.
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
*CIE XYZ_c* tristimulus values of the stimulus corresponding colour.
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr) # doctest: +ELLIPSIS
array([ 0.0839746..., 0.1141321..., 0.2862554...])
Using Bradford method:
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> method = 'Bradford'
>>> chromatic_adaptation_VonKries( # doctest: +ELLIPSIS
... XYZ, XYZ_w, XYZ_wr, method)
array([ 0.0854032..., 0.1140122..., 0.2972149...])
"""
cat = chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform)
XYZ_a = dot_vector(cat, XYZ)
return XYZ_a
def LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength):
"""
Converts *Stockman & Sharpe 10 Degree Cone Fundamentals* colour matching
functions into the *CIE 2012 10 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 2012 10 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 2012 10 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
- :cite:`CVRLp`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0098162..., 0.0037761..., 0. ])
"""
cmfs = LMS_CMFS['Stockman & Sharpe 10 Degree Cone Fundamentals']
lms_bar = cmfs[wavelength]
M = np.array([
[1.93986443, -1.34664359, 0.43044935],
[0.69283932, 0.34967567, 0.00000000],
[0.00000000, 0.00000000, 2.14687945],
])
xyz_bar = dot_vector(M, lms_bar)
return xyz_bar
def RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(wavelength):
"""
Converts *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching
functions into the *Stockman & Sharpe 10 Degree Cone Fundamentals*
spectral sensitivity functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*Stockman & Sharpe 10 Degree Cone Fundamentals* spectral tristimulus
values.
Notes
-----
- Data for the *Stockman & Sharpe 10 Degree Cone Fundamentals* already
exists, this definition is intended for educational purpose.
References
----------
.. [3] CIE TC 1-36. (2006). CIE 170-1:2006 Fundamental Chromaticity
Diagram with Physiological Axes - Part 1 (pp. 1–56).
ISBN:978-3-901-90646-6
Examples
--------
>>> RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0052860..., 0.0003252..., 0. ])
"""
cmfs = RGB_CMFS.get('Stiles & Burch 1959 10 Degree RGB CMFs')
rgb_bar = cmfs.get(wavelength)
M = np.array([[0.1923252690, 0.749548882, 0.0675726702],
[0.0192290085, 0.940908496, 0.113830196],
[0.0000000000, 0.0105107859, 0.991427669]])
lms_bar = dot_vector(M, rgb_bar)
lms_bar[..., -1][np.asarray(np.asarray(wavelength) > 505)] = 0
return lms_bar
def LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength):
"""
Converts *Stockman & Sharpe 10 Degree Cone Fundamentals* colour matching
functions into the *CIE 2012 10 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 2012 10 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 2012 10 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
.. [5] CVRL. (n.d.). CIE (2012) 10-deg XYZ “physiologically-relevant”
colour matching functions. Retrieved June 25, 2014, from
http://www.cvrl.org/database/text/cienewxyz/cie2012xyz10.htm
Examples
--------
>>> LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0098162..., 0.0037761..., 0. ])
"""
cmfs = LMS_CMFS.get('Stockman & Sharpe 10 Degree Cone Fundamentals')
lms_bar = cmfs.get(wavelength)
M = np.array([[1.93986443, -1.34664359, 0.43044935],
[0.69283932, 0.34967567, 0.00000000],
[0.00000000, 0.00000000, 2.14687945]])
xyz_bar = dot_vector(M, lms_bar)
return xyz_bar
def LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength):
"""
Converts *Stockman & Sharpe 2 Degree Cone Fundamentals* colour matching
functions into the *CIE 2012 2 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 2012 2 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 2012 2 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
.. [4] CVRL. (n.d.). CIE (2012) 2-deg XYZ “physiologically-relevant”
colour matching functions. Retrieved June 25, 2014, from
http://www.cvrl.org/database/text/cienewxyz/cie2012xyz2.htm
Examples
--------
>>> LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0109677..., 0.0041959..., 0. ])
"""
cmfs = LMS_CMFS.get('Stockman & Sharpe 2 Degree Cone Fundamentals')
lms_bar = cmfs.get(wavelength)
M = np.array([[1.94735469, -1.41445123, 0.36476327],
[0.68990272, 0.34832189, 0.00000000],
[0.00000000, 0.00000000, 1.93485343]])
xyz_bar = dot_vector(M, lms_bar)
return xyz_bar
def XYZ_to_RGB_cie1994(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to cone responses.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
Cone responses.
Examples
--------
>>> XYZ = np.array([28.00, 21.26, 5.27])
>>> XYZ_to_RGB_cie1994(XYZ) # doctest: +ELLIPSIS
array([ 25.8244273..., 18.6791422..., 4.8390194...])
"""
return dot_vector(CIE1994_XYZ_TO_RGB_MATRIX, XYZ)
def RGB_to_XYZ_cie1994(RGB):
"""
Converts from cone responses to *CIE XYZ* tristimulus values.
Parameters
----------
RGB : array_like
Cone responses.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Examples
--------
>>> RGB = np.array([25.82442730, 18.67914220, 4.83901940])
>>> RGB_to_XYZ_cie1994(RGB) # doctest: +ELLIPSIS
array([ 28. , 21.26, 5.27])
"""
return dot_vector(CIE1994_RGB_TO_XYZ_MATRIX, RGB)
def XYZ_to_RGB_Nayatani95(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to cone responses.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
Cone responses.
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_to_RGB_Nayatani95(XYZ) # doctest: +ELLIPSIS
array([ 20.000520..., 19.999783..., 19.998831...])
"""
return dot_vector(NAYATANI95_XYZ_TO_RGB_MATRIX, XYZ)
def RGB_to_XYZ_Fairchild1990(RGB):
"""
Converts from cone responses to *CIE XYZ* tristimulus values.
Parameters
----------
RGB : array_like
Cone responses.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Examples
--------
>>> RGB = np.array([22.12319350, 23.60542240, 22.92795340])
>>> RGB_to_XYZ_Fairchild1990(RGB) # doctest: +ELLIPSIS
array([ 19.53, 23.07, 24.97])
"""
return dot_vector(FAIRCHILD1990_RGB_TO_XYZ_MATRIX, RGB)
def XYZ_to_RGB_Fairchild1990(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to cone responses.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
Cone responses.
Examples
--------
>>> XYZ = np.array([19.53, 23.07, 24.97])
>>> XYZ_to_RGB_Fairchild1990(XYZ) # doctest: +ELLIPSIS
array([ 22.1231935..., 23.6054224..., 22.9279534...])
"""
return dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ)
def test_dot_vector(self):
"""
Tests :func:`colour.utilities.array.dot_vector` definition.
"""
m = np.array([[0.7328, 0.4296, -0.1624],
[-0.7036, 1.6975, 0.0061],
[0.0030, 0.0136, 0.9834]])
m = np.reshape(np.tile(m, (6, 1)), (6, 3, 3))
v = np.array([0.07049534, 0.10080000, 0.09558313])
v = np.tile(v, (6, 1))
np.testing.assert_almost_equal(
dot_vector(m, v),
np.array([[0.07943996, 0.12209054, 0.09557882],
[0.07943996, 0.12209054, 0.09557882],
[0.07943996, 0.12209054, 0.09557882],
[0.07943996, 0.12209054, 0.09557882],
[0.07943996, 0.12209054, 0.09557882],
[0.07943996, 0.12209054, 0.09557882]]),
decimal=7)
def XYZ_to_rgb(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to *Hunt-Pointer-Estevez*
:math:`\\rho\gamma\\beta` colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
*Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace.
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_to_rgb(XYZ) # doctest: +ELLIPSIS
array([ 19.4743367..., 20.3101217..., 21.78 ])
"""
return dot_vector(XYZ_TO_HPE_MATRIX, XYZ)
def XYZ_to_CIECAM02(XYZ,
XYZ_w,
L_A,
Y_b,
surround=CIECAM02_VIEWING_CONDITIONS.get('Average'),
discount_illuminant=False):
"""
Computes the CIECAM02 colour appearance model correlates from given
*CIE XYZ* tristimulus values.
This is the *forward* implementation.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of test sample / stimulus in domain
[0, 100].
XYZ_w : array_like
*CIE XYZ* tristimulus values of reference white in domain [0, 100].
L_A : numeric or array_like
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
Y_b : numeric or array_like
Adapting field *Y* tristimulus value :math:`Y_b`.
surround : CIECAM02_InductionFactors, optional
Surround viewing conditions induction factors.
discount_illuminant : bool, optional
Truth value indicating if the illuminant should be discounted.
Returns
-------
CIECAM02_Specification
CIECAM02 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_w* tristimulus values are in domain [0, 100].
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_w = np.array([95.05, 100.00, 108.88])
>>> L_A = 318.31
>>> Y_b = 20.0
>>> surround = CIECAM02_VIEWING_CONDITIONS['Average']
>>> XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround) # doctest: +ELLIPSIS
CIECAM02_Specification(J=41.7310911..., C=0.1047077..., h=219.0484326..., \
s=2.3603053..., Q=195.3713259..., M=0.1088421..., H=array(278.0607358...), \
HC=None)
"""
_X_w, Y_w, _Z_w = tsplit(XYZ_w)
L_A = np.asarray(L_A)
Y_b = np.asarray(Y_b)
n, F_L, N_bb, N_cb, z = tsplit(viewing_condition_dependent_parameters(
Y_b, Y_w, L_A))
# Converting *CIE XYZ* tristimulus values to CMCCAT2000 transform
# sharpened *RGB* values.
RGB = dot_vector(CAT02_CAT, XYZ)
RGB_w = dot_vector(CAT02_CAT, XYZ_w)
# Computing degree of adaptation :math:`D`.
D = degree_of_adaptation(surround.F,
L_A) if not discount_illuminant else 1
# Computing full chromatic adaptation.
RGB_c = full_chromatic_adaptation_forward(
RGB, RGB_w, Y_w, D)
RGB_wc = full_chromatic_adaptation_forward(
RGB_w, RGB_w, Y_w, D)
# Converting to *Hunt-Pointer-Estevez* colourspace.
RGB_p = RGB_to_rgb(RGB_c)
RGB_pw = RGB_to_rgb(RGB_wc)
# Applying forward post-adaptation non linear response compression.
RGB_a = post_adaptation_non_linear_response_compression_forward(
RGB_p, F_L)
RGB_aw = post_adaptation_non_linear_response_compression_forward(
RGB_pw, F_L)
# Converting to preliminary cartesian coordinates.
a, b = tsplit(opponent_colour_dimensions_forward(RGB_a))
# -------------------------------------------------------------------------
# Computing the *hue* angle :math:`h`.
h = hue_angle(a, b)
# -------------------------------------------------------------------------
# Computing hue :math:`h` quadrature :math:`H`.
H = hue_quadrature(h)
# TODO: Compute hue composition.
# Computing eccentricity factor *e_t*.
e_t = eccentricity_factor(h)
# Computing achromatic responses for the stimulus and the whitepoint.
A = achromatic_response_forward(RGB_a, N_bb)
A_w = achromatic_response_forward(RGB_aw, N_bb)
# -------------------------------------------------------------------------
# Computing the correlate of *Lightness* :math:`J`.
# -------------------------------------------------------------------------
J = lightness_correlate(A, A_w, surround.c, z)
# -------------------------------------------------------------------------
# Computing the correlate of *brightness* :math:`Q`.
# -------------------------------------------------------------------------
Q = brightness_correlate(surround.c, J, A_w, F_L)
# -------------------------------------------------------------------------
# Computing the correlate of *chroma* :math:`C`.
# -------------------------------------------------------------------------
C = chroma_correlate(J, n, surround.N_c, N_cb, e_t, a, b, RGB_a)
# -------------------------------------------------------------------------
# Computing the correlate of *colourfulness* :math:`M`.
# -------------------------------------------------------------------------
M = colourfulness_correlate(C, F_L)
# -------------------------------------------------------------------------
# Computing the correlate of *saturation* :math:`s`.
# -------------------------------------------------------------------------
s = saturation_correlate(M, Q)
return CIECAM02_Specification(J, C, h, s, Q, M, H, None)
def CIECAM02_to_XYZ(J,
C,
h,
XYZ_w,
L_A,
Y_b,
surround=CIECAM02_VIEWING_CONDITIONS.get(
'Average'),
discount_illuminant=False):
"""
Converts CIECAM02 specification to *CIE XYZ* tristimulus values.
This is the *reverse* implementation.
Parameters
----------
J : numeric or array_like
Correlate of *Lightness* :math:`J`.
C : numeric or array_like
Correlate of *chroma* :math:`C`.
h : numeric or array_like
*Hue* angle :math:`h` in degrees.
XYZ_w : array_like
*CIE XYZ* tristimulus values of reference white.
L_A : numeric or array_like
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
Y_b : numeric or array_like
Adapting field *Y* tristimulus value :math:`Y_b`.
surround : CIECAM02_Surround, optional
Surround viewing conditions.
discount_illuminant : bool, optional
Discount the illuminant.
Returns
-------
XYZ : ndarray
*CIE XYZ* tristimulus values.
Warning
-------
The output range of that definition is non standard!
Notes
-----
- Input *CIE XYZ_w* tristimulus values are in domain [0, 100].
- Output *CIE XYZ* tristimulus values are in range [0, 100].
Examples
--------
>>> J = 41.731091132513917
>>> C = 0.104707757171105
>>> h = 219.04843265827190
>>> XYZ_w = np.array([95.05, 100.00, 108.88])
>>> L_A = 318.31
>>> Y_b = 20.0
>>> CIECAM02_to_XYZ(J, C, h, XYZ_w, L_A, Y_b) # doctest: +ELLIPSIS
array([ 19.01..., 20... , 21.78...])
"""
_X_w, Y_w, _Zw = tsplit(XYZ_w)
n, F_L, N_bb, N_cb, z = tsplit(viewing_condition_dependent_parameters(
Y_b, Y_w, L_A))
# Converting *CIE XYZ* tristimulus values to CMCCAT2000 transform
# sharpened *RGB* values.
RGB_w = dot_vector(CAT02_CAT, XYZ_w)
# Computing degree of adaptation :math:`D`.
D = degree_of_adaptation(surround.F, L_A) if not discount_illuminant else 1
# Computation full chromatic adaptation.
RGB_wc = full_chromatic_adaptation_forward(RGB_w, RGB_w, Y_w, D)
# Converting to *Hunt-Pointer-Estevez* colourspace.
RGB_pw = RGB_to_rgb(RGB_wc)
# Applying post-adaptation non linear response compression.
RGB_aw = post_adaptation_non_linear_response_compression_forward(
RGB_pw, F_L)
# Computing achromatic responses for the stimulus and the whitepoint.
A_w = achromatic_response_forward(RGB_aw, N_bb)
# Computing temporary magnitude quantity :math:`t`.
t = temporary_magnitude_quantity_reverse(C, J, n)
# Computing eccentricity factor *e_t*.
e_t = eccentricity_factor(h)
# Computing achromatic response :math:`A` for the stimulus.
A = achromatic_response_reverse(A_w, J, surround.c, z)
# Computing *P_1* to *P_3*.
P_n = P(surround.N_c, N_cb, e_t, t, A, N_bb)
_P_1, P_2, _P_3 = tsplit(P_n)
# Computing opponent colour dimensions :math:`a` and :math:`b`.
a, b = tsplit(opponent_colour_dimensions_reverse(P_n, h))
# Computing post-adaptation non linear response compression matrix.
RGB_a = post_adaptation_non_linear_response_compression_matrix(
P_2, a, b)
# Applying reverse post-adaptation non linear response compression.
RGB_p = post_adaptation_non_linear_response_compression_reverse(
RGB_a, F_L)
# Converting to *Hunt-Pointer-Estevez* colourspace.
RGB_c = rgb_to_RGB(RGB_p)
# Applying reverse full chromatic adaptation.
RGB = full_chromatic_adaptation_reverse(RGB_c, RGB_w, Y_w, D)
# Converting CMCCAT2000 transform sharpened *RGB* values to *CIE XYZ*
# tristimulus values.
XYZ = dot_vector(CAT02_INVERSE_CAT, RGB)
return XYZ
def chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr, transform='CAT02'):
"""
Adapts given stimulus from test viewing conditions to reference viewing
conditions.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of stimulus to adapt.
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
*CIE XYZ_c* tristimulus values of the stimulus corresponding colour.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_n`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_r`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_c`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Fairchild2013t`
Examples
--------
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
>>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
>>> chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr) # doctest: +ELLIPSIS
array([ 0.2163881..., 0.1257 , 0.0384749...])
Using Bradford method:
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
>>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
>>> transform = 'Bradford'
>>> chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr, transform)
... # doctest: +ELLIPSIS
array([ 0.2166600..., 0.1260477..., 0.0385506...])
"""
XYZ = to_domain_1(XYZ)
M_CAT = chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform)
XYZ_a = dot_vector(M_CAT, XYZ)
return from_range_1(XYZ_a)
