def test_nan_uv_to_CCT_Robertson1968(self):
"""
Tests :func:`colour.temperature.cct.uv_to_CCT_Robertson1968` definition
nan support.
"""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = set(permutations(cases * 3, r=2))
for case in cases:
uv = np.array(case)
uv_to_CCT_Robertson1968(uv)
def test_nan_uv_to_CCT_Robertson1968(self):
"""
Test :func:`colour.temperature.robertson1968.uv_to_CCT_Robertson1968`
definition nan support.
"""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = set(permutations(cases * 3, r=2))
for case in cases:
uv = np.array(case)
uv_to_CCT_Robertson1968(uv)
def test_n_dimensional_uv_to_CCT_Robertson1968(self):
"""
Tests :func:`colour.temperature.cct.uv_to_CCT_Robertson1968` definition
n-dimensional arrays support.
"""
uv = np.array([0.1978, 0.3122])
CCT_D_uv = uv_to_CCT_Robertson1968(uv)
uv = np.tile(uv, (6, 1))
CCT_D_uv = np.tile(CCT_D_uv, (6, 1))
np.testing.assert_almost_equal(
uv_to_CCT_Robertson1968(uv), CCT_D_uv, decimal=7)
uv = np.reshape(uv, (2, 3, 2))
CCT_D_uv = np.reshape(CCT_D_uv, (2, 3, 2))
np.testing.assert_almost_equal(
uv_to_CCT_Robertson1968(uv), CCT_D_uv, decimal=7)
def test_uv_to_CCT_Robertson1968(self):
"""
Tests :func:`colour.temperature.cct.uv_to_CCT_Robertson1968`
definition.
"""
for key, value in TEMPERATURE_DUV_TO_UV.items():
np.testing.assert_allclose(
uv_to_CCT_Robertson1968(value), key, atol=0.25)
def test_uv_to_CCT_Robertson1968(self):
"""
Tests :func:`colour.temperature.cct.uv_to_CCT_Robertson1968`
definition.
"""
for key, value in TEMPERATURE_DUV_TO_UV.items():
np.testing.assert_allclose(uv_to_CCT_Robertson1968(value),
key,
atol=0.25)
def test_n_dimensional_uv_to_CCT_Robertson1968(self):
"""
Test :func:`colour.temperature.robertson1968.uv_to_CCT_Robertson1968`
definition n-dimensional arrays support.
"""
uv = np.array([0.1978, 0.3122])
CCT_D_uv = uv_to_CCT_Robertson1968(uv)
uv = np.tile(uv, (6, 1))
CCT_D_uv = np.tile(CCT_D_uv, (6, 1))
np.testing.assert_almost_equal(uv_to_CCT_Robertson1968(uv),
CCT_D_uv,
decimal=7)
uv = np.reshape(uv, (2, 3, 2))
CCT_D_uv = np.reshape(CCT_D_uv, (2, 3, 2))
np.testing.assert_almost_equal(uv_to_CCT_Robertson1968(uv),
CCT_D_uv,
decimal=7)
def colour_rendering_index(spd_test, additional_data=False):
"""
Returns the *colour rendering index* :math:`Q_a` of given spectral power
distribution.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
additional_data : bool, optional
Output additional data.
Returns
-------
numeric or CRI_Specification
Colour rendering index.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> spd = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> colour_rendering_index(spd) # doctest: +ELLIPSIS
64.1507331...
"""
cmfs = STANDARD_OBSERVERS_CMFS.get('CIE 1931 2 Degree Standard Observer')
shape = cmfs.shape
spd_test = spd_test.clone().align(shape)
tcs_spds = {}
for index, tcs_spd in TCS_SPDS.items():
tcs_spds[index] = tcs_spd.clone().align(shape)
XYZ = spectral_to_XYZ(spd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
spd_reference = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
spd_reference = D_illuminant_relative_spd(xy)
spd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
spd_test,
spd_reference,
tcs_spds,
cmfs,
chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
spd_reference,
spd_reference,
tcs_spds,
cmfs)
Q_as = colour_rendering_indexes(
test_tcs_colorimetry_data, reference_tcs_colorimetry_data)
Q_a = np.average([v.Q_a for k, v in Q_as.items()
if k in (1, 2, 3, 4, 5, 6, 7, 8)])
if additional_data:
return CRI_Specification(spd_test.name,
Q_a,
Q_as,
(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data))
else:
return Q_a
def colour_rendering_index(sd_test, additional_data=False):
"""
Returns the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
distribution.
Parameters
----------
sd_test : SpectralDistribution
Test spectral distribution.
additional_data : bool, optional
Whether to output additional data.
Returns
-------
numeric or CRI_Specification
*Colour Rendering Index* (CRI).
References
----------
:cite:`Ohno2008a`
Examples
--------
>>> from colour import ILLUMINANTS_SDS
>>> sd = ILLUMINANTS_SDS['FL2']
>>> colour_rendering_index(sd) # doctest: +ELLIPSIS
64.1515202...
"""
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'].copy(
).trim(DEFAULT_SPECTRAL_SHAPE)
shape = cmfs.shape
sd_test = sd_test.copy().align(shape)
tcs_sds = {sd.name: sd.copy().align(shape) for sd in TCS_SDS.values()}
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(sd_test,
sd_reference,
tcs_sds,
cmfs,
chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
sd_reference, sd_reference, tcs_sds, cmfs)
Q_as = colour_rendering_indexes(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data)
Q_a = np.average(
[v.Q_a for k, v in Q_as.items() if k in (1, 2, 3, 4, 5, 6, 7, 8)])
if additional_data:
return CRI_Specification(
sd_test.name, Q_a, Q_as,
(test_tcs_colorimetry_data, reference_tcs_colorimetry_data))
else:
return Q_a
def colour_rendering_index(spd_test, additional_data=False):
"""
Returns the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
power distribution.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
additional_data : bool, optional
Output additional data.
Returns
-------
numeric or CRI_Specification
*Colour Rendering Index* (CRI).
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> spd = ILLUMINANTS_RELATIVE_SPDS['F2']
>>> colour_rendering_index(spd) # doctest: +ELLIPSIS
64.1515202...
"""
cmfs = STANDARD_OBSERVERS_CMFS[
'CIE 1931 2 Degree Standard Observer'].clone().trim_wavelengths(
ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
spd_test = spd_test.clone().align(shape)
tcs_spds = {
spd.name: spd.clone().align(shape)
for spd in TCS_SPDS.values()
}
XYZ = spectral_to_XYZ(spd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
spd_reference = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
spd_reference = D_illuminant_relative_spd(xy)
spd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
spd_test, spd_reference, tcs_spds, cmfs, chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
spd_reference, spd_reference, tcs_spds, cmfs)
Q_as = colour_rendering_indexes(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data)
Q_a = np.average(
[v.Q_a for k, v in Q_as.items() if k in (1, 2, 3, 4, 5, 6, 7, 8)])
if additional_data:
return CRI_Specification(spd_test.name, Q_a, Q_as,
(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data))
else:
return Q_a
def camera_space_to_XYZ_matrix(xy,
CCT_calibration_illuminant_1,
CCT_calibration_illuminant_2,
M_color_matrix_1,
M_color_matrix_2,
M_camera_calibration_1,
M_camera_calibration_2,
analog_balance,
M_forward_matrix_1,
M_forward_matrix_2,
chromatic_adaptation_transform='Bradford'):
"""
Returns the *Camera Space* to *CIE XYZ* matrix for given *xy* white
balance chromaticity coordinates.
Parameters
----------
xy : array_like
*xy* white balance chromaticity coordinates.
CCT_calibration_illuminant_1 : numeric
Correlated colour temperature of *CalibrationIlluminant1*.
CCT_calibration_illuminant_2 : numeric
Correlated colour temperature of *CalibrationIlluminant2*.
M_color_matrix_1 : array_like
*ColorMatrix1* tag matrix.
M_color_matrix_2 : array_like
*ColorMatrix2* tag matrix.
M_camera_calibration_1 : array_like
*CameraCalibration1* tag matrix.
M_camera_calibration_2 : array_like
*CameraCalibration2* tag matrix.
analog_balance : array_like
*AnalogBalance* tag vector.
M_forward_matrix_1 : array_like
*ForwardMatrix1* tag matrix.
M_forward_matrix_2 : array_like
*ForwardMatrix2* tag matrix.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
ndarray
*Camera Space* to *CIE XYZ* matrix.
Notes
-----
- The reference illuminant is D50 as defined per
:attr:`colour_hdri.models.dataset.dng.ADOBE_DNG_XYZ_ILLUMINANT`
attribute.
References
----------
- :cite:`AdobeSystems2012f`
- :cite:`AdobeSystems2012g`
- :cite:`AdobeSystems2015d`
- :cite:`McGuffog2012a`
Examples
--------
>>> M_color_matrix_1 = np.array(
... [[0.5309, -0.0229, -0.0336],
... [-0.6241, 1.3265, 0.3337],
... [-0.0817, 0.1215, 0.6664]])
>>> M_color_matrix_2 = np.array(
... [[0.4716, 0.0603, -0.0830],
... [-0.7798, 1.5474, 0.2480],
... [-0.1496, 0.1937, 0.6651]])
>>> M_camera_calibration_1 = np.identity(3)
>>> M_camera_calibration_2 = np.identity(3)
>>> analog_balance = np.ones(3)
>>> M_forward_matrix_1 = np.array(
... [[0.8924, -0.1041, 0.1760],
... [0.4351, 0.6621, -0.0972],
... [0.0505, -0.1562, 0.9308]])
>>> M_forward_matrix_2 = np.array(
... [[0.8924, -0.1041, 0.1760],
... [0.4351, 0.6621, -0.0972],
... [0.0505, -0.1562, 0.9308]])
>>> camera_space_to_XYZ_matrix( # doctest: +ELLIPSIS
... np.array([0.32816244, 0.34698169]),
... 2850,
... 6500,
... M_color_matrix_1,
... M_color_matrix_2,
... M_camera_calibration_1,
... M_camera_calibration_2,
... analog_balance,
... M_forward_matrix_1,
... M_forward_matrix_2)
array([[ 2.1604087..., -0.1041... , 0.2722498...],
[ 1.0533324..., 0.6621... , -0.1503561...],
[ 0.1222553..., -0.1562... , 1.4398304...]])
"""
# *ForwardMatrix1* and *ForwardMatrix2* are not included in the camera
# profile.
if is_identity(M_forward_matrix_1) and is_identity(M_forward_matrix_2):
M_camera_to_XYZ = np.linalg.inv(
XYZ_to_camera_space_matrix(xy, CCT_calibration_illuminant_1,
CCT_calibration_illuminant_2,
M_color_matrix_1, M_color_matrix_2,
M_camera_calibration_1,
M_camera_calibration_2, analog_balance))
M_CAT = chromatic_adaptation_matrix_VonKries(
xy_to_XYZ(xy), xy_to_XYZ(ADOBE_DNG_XYZ_ILLUMINANT),
chromatic_adaptation_transform)
M_camera_space_to_XYZ = dot_matrix(M_CAT, M_camera_to_XYZ)
else:
uv = UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(xy)))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
M_CC = interpolated_matrix(CCT, CCT_calibration_illuminant_1,
CCT_calibration_illuminant_2,
M_camera_calibration_1,
M_camera_calibration_2)
# The reference implementation :cite:`AdobeSystems2015d` diverges from
# the white-paper :cite:`AdobeSystems2012f`:
# The reference implementation directly computes the camera neutral by
# multiplying directly the interpolated colour matrix :math:`CM` with
# the tristimulus values of the *xy* white balance chromaticity
# coordinates.
# The current implementation is based on the white-paper so that the
# interpolated camera calibration matrix :math:`CC` and the
# analog balance matrix :math:`AB` are accounted for.
camera_neutral = xy_to_camera_neutral(
xy, CCT_calibration_illuminant_1, CCT_calibration_illuminant_2,
M_color_matrix_1, M_color_matrix_2, M_camera_calibration_1,
M_camera_calibration_2, analog_balance)
M_AB = np.diagflat(analog_balance)
M_reference_neutral = dot_vector(np.linalg.inv(dot_matrix(M_AB, M_CC)),
camera_neutral)
M_D = np.linalg.inv(np.diagflat(M_reference_neutral))
M_FM = interpolated_matrix(CCT, CCT_calibration_illuminant_1,
CCT_calibration_illuminant_2,
M_forward_matrix_1, M_forward_matrix_2)
M_camera_space_to_XYZ = dot_matrix(
dot_matrix(M_FM, M_D), np.linalg.inv(dot_matrix(M_AB, M_CC)))
return M_camera_space_to_XYZ
def XYZ_to_camera_space_matrix(xy, CCT_calibration_illuminant_1,
CCT_calibration_illuminant_2, M_color_matrix_1,
M_color_matrix_2, M_camera_calibration_1,
M_camera_calibration_2, analog_balance):
"""
Returns the *CIE XYZ* to *Camera Space* matrix for given *xy* white balance
chromaticity coordinates.
Parameters
----------
xy : array_like
*xy* white balance chromaticity coordinates.
CCT_calibration_illuminant_1 : numeric
Correlated colour temperature of *CalibrationIlluminant1*.
CCT_calibration_illuminant_2 : numeric
Correlated colour temperature of *CalibrationIlluminant2*.
M_color_matrix_1 : array_like
*ColorMatrix1* tag matrix.
M_color_matrix_2 : array_like
*ColorMatrix2* tag matrix.
M_camera_calibration_1 : array_like
*CameraCalibration1* tag matrix.
M_camera_calibration_2 : array_like
*CameraCalibration2* tag matrix.
analog_balance : array_like
*AnalogBalance* tag vector.
Returns
-------
ndarray
*CIE XYZ* to *Camera Space* matrix.
Notes
-----
- The reference illuminant is D50 as defined per
:attr:`colour_hdri.models.dataset.dng.ADOBE_DNG_XYZ_ILLUMINANT`
attribute.
References
----------
- :cite:`AdobeSystems2012f`
- :cite:`AdobeSystems2015d`
- :cite:`McGuffog2012a`
Examples
--------
>>> M_color_matrix_1 = np.array(
... [[0.5309, -0.0229, -0.0336],
... [-0.6241, 1.3265, 0.3337],
... [-0.0817, 0.1215, 0.6664]])
>>> M_color_matrix_2 = np.array(
... [[0.4716, 0.0603, -0.0830],
... [-0.7798, 1.5474, 0.2480],
... [-0.1496, 0.1937, 0.6651]])
>>> M_camera_calibration_1 = np.identity(3)
>>> M_camera_calibration_2 = np.identity(3)
>>> analog_balance = np.ones(3)
>>> XYZ_to_camera_space_matrix( # doctest: +ELLIPSIS
... np.array([0.34510414, 0.35162252]),
... 2850,
... 6500,
... M_color_matrix_1,
... M_color_matrix_2,
... M_camera_calibration_1,
... M_camera_calibration_2,
... analog_balance)
array([[ 0.4854908..., 0.0408106..., -0.0714282...],
[-0.7433278..., 1.4956549..., 0.2680749...],
[-0.1336946..., 0.1767874..., 0.6654045...]])
"""
M_AB = np.diagflat(analog_balance)
uv = UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(xy)))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if is_identity(M_color_matrix_1) or is_identity(M_color_matrix_2):
M_CM = (M_color_matrix_1
if is_identity(M_color_matrix_2) else M_color_matrix_2)
else:
M_CM = interpolated_matrix(CCT, CCT_calibration_illuminant_1,
CCT_calibration_illuminant_2,
M_color_matrix_1, M_color_matrix_2)
M_CC = interpolated_matrix(CCT, CCT_calibration_illuminant_1,
CCT_calibration_illuminant_2,
M_camera_calibration_1, M_camera_calibration_2)
M_XYZ_to_camera_space = dot_matrix(dot_matrix(M_AB, M_CC), M_CM)
return M_XYZ_to_camera_space
def colour_rendering_index(sd_test, additional_data=False):
"""
Returns the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
distribution.
Parameters
----------
sd_test : SpectralDistribution
Test spectral distribution.
additional_data : bool, optional
Whether to output additional data.
Returns
-------
numeric or CRI_Specification
*Colour Rendering Index* (CRI).
References
----------
:cite:`Ohno2008a`
Examples
--------
>>> from colour import ILLUMINANTS_SDS
>>> sd = ILLUMINANTS_SDS['FL2']
>>> colour_rendering_index(sd) # doctest: +ELLIPSIS
64.1515202...
"""
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'].copy(
).trim(ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
sd_test = sd_test.copy().align(shape)
tcs_sds = {sd.name: sd.copy().align(shape) for sd in TCS_SDS.values()}
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
sd_test, sd_reference, tcs_sds, cmfs, chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
sd_reference, sd_reference, tcs_sds, cmfs)
Q_as = colour_rendering_indexes(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data)
Q_a = np.average(
[v.Q_a for k, v in Q_as.items() if k in (1, 2, 3, 4, 5, 6, 7, 8)])
if additional_data:
return CRI_Specification(
sd_test.name, Q_a, Q_as,
(test_tcs_colorimetry_data, reference_tcs_colorimetry_data))
else:
return Q_a
def colour_rendering_index(
sd_test: SpectralDistribution, additional_data: Boolean = False
) -> Union[Floating, ColourRendering_Specification_CRI]:
"""
Return the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
distribution.
Parameters
----------
sd_test
Test spectral distribution.
additional_data
Whether to output additional data.
Returns
-------
:class:`numpy.floating` or \
:class:`colour.quality.ColourRendering_Specification_CRI`
*Colour Rendering Index* (CRI).
References
----------
:cite:`Ohno2008a`
Examples
--------
>>> from colour import SDS_ILLUMINANTS
>>> sd = SDS_ILLUMINANTS['FL2']
>>> colour_rendering_index(sd) # doctest: +ELLIPSIS
64.2337241...
"""
# pylint: disable=E1102
cmfs = reshape_msds(
MSDS_CMFS["CIE 1931 2 Degree Standard Observer"],
SPECTRAL_SHAPE_DEFAULT,
)
shape = cmfs.shape
sd_test = reshape_sd(sd_test, shape)
tcs_sds = {sd.name: reshape_sd(sd, shape) for sd in SDS_TCS.values()}
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
sd_test, sd_reference, tcs_sds, cmfs, chromatic_adaptation=True
)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
sd_reference, sd_reference, tcs_sds, cmfs
)
Q_as = colour_rendering_indexes(
test_tcs_colorimetry_data, reference_tcs_colorimetry_data
)
Q_a = as_float_scalar(
np.average(
[v.Q_a for k, v in Q_as.items() if k in (1, 2, 3, 4, 5, 6, 7, 8)]
)
)
if additional_data:
return ColourRendering_Specification_CRI(
sd_test.name,
Q_a,
Q_as,
(test_tcs_colorimetry_data, reference_tcs_colorimetry_data),
)
else:
return Q_a
