def test_n_dimensional_Lab_to_XYZ(self):
"""
Tests :func:`colour.models.cie_lab.Lab_to_XYZ` definition n-dimensions
support.
"""
Lab = np.array([41.52787529, 52.63858304, 26.92317922])
illuminant = np.array([0.31270, 0.32900])
XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
np.testing.assert_almost_equal(Lab_to_XYZ(Lab, illuminant),
XYZ,
decimal=7)
Lab = np.tile(Lab, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(Lab_to_XYZ(Lab, illuminant),
XYZ,
decimal=7)
illuminant = np.tile(illuminant, (6, 1))
np.testing.assert_almost_equal(Lab_to_XYZ(Lab, illuminant),
XYZ,
decimal=7)
Lab = np.reshape(Lab, (2, 3, 3))
illuminant = np.reshape(illuminant, (2, 3, 2))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(Lab_to_XYZ(Lab, illuminant),
XYZ,
decimal=7)
def test_n_dimensional_Lab_to_XYZ(self):
"""
Tests :func:`colour.models.cie_lab.Lab_to_XYZ` definition n-dimensions
support.
"""
Lab = np.array([37.98562910, -23.62907688, -4.41746615])
illuminant = np.array([0.34570, 0.35850])
XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
np.testing.assert_almost_equal(Lab_to_XYZ(Lab, illuminant),
XYZ,
decimal=7)
Lab = np.tile(Lab, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(Lab_to_XYZ(Lab, illuminant),
XYZ,
decimal=7)
illuminant = np.tile(illuminant, (6, 1))
np.testing.assert_almost_equal(Lab_to_XYZ(Lab, illuminant),
XYZ,
decimal=7)
Lab = np.reshape(Lab, (2, 3, 3))
illuminant = np.reshape(illuminant, (2, 3, 2))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(Lab_to_XYZ(Lab, illuminant),
XYZ,
decimal=7)
def is_within_pointer_gamut(XYZ, tolerance=None):
"""
Returns if given *CIE XYZ* tristimulus values are within Pointer's Gamut
volume.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
tolerance : numeric, optional
Tolerance allowed in the inside-triangle check.
Returns
-------
bool
Is within Pointer's Gamut.
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 1].
Examples
--------
>>> import numpy as np
>>> is_within_pointer_gamut(np.array([0.3205, 0.4131, 0.5100]))
array(True, dtype=bool)
>>> a = np.array([[0.3205, 0.4131, 0.5100], [0.0005, 0.0031, 0.0010]])
>>> is_within_pointer_gamut(a)
array([ True, False], dtype=bool)
"""
XYZ_p = Lab_to_XYZ(
LCHab_to_Lab(POINTER_GAMUT_DATA), POINTER_GAMUT_ILLUMINANT)
return is_within_mesh_volume(XYZ, XYZ_p, tolerance)
def test_domain_range_scale_Lab_to_XYZ(self):
"""
Tests :func:`colour.models.cie_lab.Lab_to_XYZ` definition
domain and range scale support.
"""
Lab = np.array([41.52787529, 52.63858304, 26.92317922])
illuminant = np.array([0.31270, 0.32900])
XYZ = Lab_to_XYZ(Lab, illuminant)
d_r = (('reference', 1, 1), (1, 0.01, 1), (100, 1, 100))
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
Lab_to_XYZ(Lab * factor_a, illuminant),
XYZ * factor_b,
decimal=7)
def test_nan_Lab_to_XYZ(self):
"""Test :func:`colour.models.cie_lab.Lab_to_XYZ` definition nan support."""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = set(permutations(cases * 3, r=3))
for case in cases:
Lab = np.array(case)
illuminant = np.array(case[0:2])
Lab_to_XYZ(Lab, illuminant)
def test_Lab_to_XYZ(self):
"""
Tests :func:`colour.models.cie_lab.Lab_to_XYZ` definition.
"""
np.testing.assert_almost_equal(Lab_to_XYZ(
np.array([100, 0.83871284, -21.55579303])),
np.array([0.96907232, 1., 1.12179215]),
decimal=7)
np.testing.assert_almost_equal(Lab_to_XYZ(
np.array([100, 129.04406346, 406.69765889])),
np.array([1.92001986, 1., -0.1241347]),
decimal=7)
np.testing.assert_almost_equal(Lab_to_XYZ(
np.array([100, 8.32281957, -73.58297716])),
np.array([1.0131677, 1., 2.11217686]),
decimal=7)
np.testing.assert_almost_equal(Lab_to_XYZ(
np.array([100, -13.29228089, -162.12804888]), (0.44757, 0.40745)),
np.array([1.0131677, 1., 2.11217686]),
decimal=7)
np.testing.assert_almost_equal(Lab_to_XYZ(
np.array([100, 2.18505384, -56.60990888]), (1 / 3, 1 / 3)),
np.array([1.0131677, 1., 2.11217686]),
decimal=7)
np.testing.assert_almost_equal(Lab_to_XYZ(
np.array([100, 10.76832763, -49.42733157]), (0.31271, 0.32902)),
np.array([1.0131677, 1., 2.11217686]),
decimal=7)
def highlights_recovery_LCHab(RGB,
threshold=None,
RGB_colourspace=sRGB_COLOURSPACE):
"""
Performs highlights recovery in *CIE L\\*C\\*Hab* colourspace.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
threshold : numeric, optional
Threshold for highlights selection, automatically computed
if not given.
RGB_colourspace : RGB_Colourspace, optional
Working *RGB* colourspace to perform the *CIE L\\*C\\*Hab* to and from.
Returns
-------
ndarray
Highlights recovered *RGB* colourspace array.
"""
L, _C, H = tsplit(
Lab_to_LCHab(
XYZ_to_Lab(
RGB_to_XYZ(RGB, RGB_colourspace.whitepoint,
RGB_colourspace.whitepoint,
RGB_colourspace.RGB_to_XYZ_matrix),
RGB_colourspace.whitepoint)))
_L_c, C_c, _H_c = tsplit(
Lab_to_LCHab(
XYZ_to_Lab(
RGB_to_XYZ(np.clip(RGB, 0,
threshold), RGB_colourspace.whitepoint,
RGB_colourspace.whitepoint,
RGB_colourspace.RGB_to_XYZ_matrix),
RGB_colourspace.whitepoint)))
return XYZ_to_RGB(
Lab_to_XYZ(LCHab_to_Lab(tstack([L, C_c, H])),
RGB_colourspace.whitepoint), RGB_colourspace.whitepoint,
RGB_colourspace.whitepoint, RGB_colourspace.XYZ_to_RGB_matrix)
def is_within_pointer_gamut(XYZ: ArrayLike,
tolerance: Optional[Floating] = None) -> NDArray:
"""
Return whether given *CIE XYZ* tristimulus values are within Pointer's
Gamut volume.
Parameters
----------
XYZ
*CIE XYZ* tristimulus values.
tolerance
Tolerance allowed in the inside-triangle check.
Returns
-------
:class:`numpy.ndarray`
Wether given *CIE XYZ* tristimulus values are within Pointer's Gamut
volume.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
Examples
--------
>>> import numpy as np
>>> is_within_pointer_gamut(np.array([0.3205, 0.4131, 0.5100]))
array(True, dtype=bool)
>>> a = np.array([[0.3205, 0.4131, 0.5100], [0.0005, 0.0031, 0.0010]])
>>> is_within_pointer_gamut(a)
array([ True, False], dtype=bool)
"""
XYZ_p = Lab_to_XYZ(LCHab_to_Lab(DATA_POINTER_GAMUT_VOLUME),
CCS_ILLUMINANT_POINTER_GAMUT)
return is_within_mesh_volume(XYZ, XYZ_p, tolerance)
def test_Lab_to_XYZ(self):
"""
Tests :func:`colour.models.cie_lab.Lab_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([37.98562910, -23.62907688, -4.41746615])),
np.array([0.07049534, 0.10080000, 0.09558313]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([65.70971880, 41.56438554, 37.78303554])),
np.array([0.47097710, 0.34950000, 0.11301649]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([50.86223896, 32.76150086, 20.25483590])),
np.array([0.25506814, 0.19150000, 0.08849752]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([37.98562910, -32.51333979, -35.96770745]),
np.array([0.44757, 0.40745])),
np.array([0.07049534, 0.10080000, 0.09558313]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([37.98562910, -22.61920654, 4.19811236]),
np.array([0.31270, 0.32900])),
np.array([0.07049534, 0.10080000, 0.09558313]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([37.98562910, -25.55521883, -11.26139386]),
np.array([0.37208, 0.37529])),
np.array([0.07049534, 0.10080000, 0.09558313]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([37.98562910, -25.55521883, -11.26139386]),
np.array([0.37208, 0.37529, 0.10080])),
np.array([0.00710593, 0.01016064, 0.00963478]),
decimal=7)
def test_Lab_to_XYZ(self):
"""
Tests :func:`colour.models.cie_lab.Lab_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([37.98562910, -23.62302887, -4.41417036])),
np.array([0.07049534, 0.10080000, 0.09558313]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([65.70971880, 41.57577646, 37.78652063])),
np.array([0.47097710, 0.34950000, 0.11301649]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([50.86223896, 32.77078577, 20.25804815])),
np.array([0.25506814, 0.19150000, 0.08849752]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([37.98562910, -32.51333979, -35.96770745]),
np.array([0.44757, 0.40745])),
np.array([0.07049534, 0.10080000, 0.09558313]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([37.98562910, -22.61718913, 4.19383056]),
np.array([0.31271, 0.32902])),
np.array([0.07049534, 0.10080000, 0.09558313]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([37.98562910, -25.55521883, -11.26139386]),
np.array([0.37208, 0.37529])),
np.array([0.07049534, 0.10080000, 0.09558313]),
decimal=7)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([37.98562910, -25.55521883, -11.26139386]),
np.array([0.37208, 0.37529, 0.10080])),
np.array([0.00710593, 0.01016064, 0.00963478]),
decimal=7)
def test_Lab_to_XYZ(self):
"""Test :func:`colour.models.cie_lab.Lab_to_XYZ` definition."""
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([41.52787529, 52.63858304, 26.92317922])),
np.array([0.20654008, 0.12197225, 0.05136952]),
decimal=7,
)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([55.11636304, -41.08791787, 30.91825778])),
np.array([0.14222010, 0.23042768, 0.10495772]),
decimal=7,
)
np.testing.assert_almost_equal(
Lab_to_XYZ(np.array([29.80565520, 20.01830466, -48.34913874])),
np.array([0.07818780, 0.06157201, 0.28099326]),
decimal=7,
)
np.testing.assert_almost_equal(
Lab_to_XYZ(
np.array([41.52787529, 38.48089305, -5.73295122]),
np.array([0.44757, 0.40745]),
),
np.array([0.20654008, 0.12197225, 0.05136952]),
decimal=7,
)
np.testing.assert_almost_equal(
Lab_to_XYZ(
np.array([41.52787529, 51.19354174, 19.91843098]),
np.array([0.34570, 0.35850]),
),
np.array([0.20654008, 0.12197225, 0.05136952]),
decimal=7,
)
np.testing.assert_almost_equal(
Lab_to_XYZ(
np.array([41.52787529, 51.19354174, 19.91843098]),
np.array([0.34570, 0.35850, 1.00000]),
),
np.array([0.20654008, 0.12197225, 0.05136952]),
decimal=7,
)
Notes
-----
- *X-Rite* data is given as *CIE L\\*a\\*b\\** colourspace values under
*CIE Illuminant D Series D50* for the
*CIE 1931 2 Degree Standard Observer*.
COLORCHECKER24_BEFORE_NOV2014_LAB_DATA : ndarray
"""
COLORCHECKER24_BEFORE_NOV2014_DATA = OrderedDict(
zip(
COLORCHECKER24_BEFORE_NOV2014_LAB_DATA.keys(),
XYZ_to_xyY(
Lab_to_XYZ(
list(COLORCHECKER24_BEFORE_NOV2014_LAB_DATA.values()),
ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50']))))
COLORCHECKER24_BEFORE_NOV2014_ILLUMINANT = (
ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50'])
"""
*ColorChecker24 - Before November 2014* illuminant.
COLORCHECKER24_BEFORE_NOV2014_ILLUMINANT : ndarray
"""
COLORCHECKER24_BEFORE_NOV2014 = ColourChecker(
'ColorChecker24 - Before November 2014',
COLORCHECKER24_BEFORE_NOV2014_DATA,
COLORCHECKER24_BEFORE_NOV2014_ILLUMINANT)
"""
def sample_RGB_colourspace_volume_MonteCarlo(
colourspace,
samples=10e6,
limits=np.array([[0, 100], [-150, 150], [-150, 150]]),
illuminant_Lab=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']
['D65'],
chromatic_adaptation_method='CAT02',
random_generator=random_triplet_generator,
random_state=None):
"""
Randomly samples the *Lab* colourspace volume and returns the ratio of
samples within the given *RGB* colourspace volume.
Parameters
----------
colourspace : RGB_Colourspace
*RGB* colourspace to compute the volume of.
samples : numeric, optional
Samples count.
limits : array_like, optional
*Lab* colourspace volume.
illuminant_Lab : array_like, optional
*Lab* colourspace *illuminant* chromaticity coordinates.
chromatic_adaptation_method : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* method.
random_generator : generator, optional
Random triplet generator providing the random samples within the *Lab*
colourspace volume.
random_state : RandomState, optional
Mersenne Twister pseudo-random number generator to use in the random
number generator.
Returns
-------
integer
Within *RGB* colourspace volume samples count.
Notes
-----
- The doctest is assuming that :func:`np.random.RandomState` definition
will return the same sequence no matter which *OS* or *Python*
version is used. There is however no formal promise about the *prng*
sequence reproducibility of either *Python* or *Numpy*
implementations: Laurent. (2012). Reproducibility of python
pseudo-random numbers across systems and versions? Retrieved January
20, 2015, from http://stackoverflow.com/questions/8786084/\
reproducibility-of-python-pseudo-random-numbers-across-systems-and-versions
Examples
--------
>>> from colour.models import sRGB_COLOURSPACE as sRGB
>>> prng = np.random.RandomState(2)
>>> sample_RGB_colourspace_volume_MonteCarlo(sRGB, 10e3, random_state=prng)
... # doctest: +ELLIPSIS
9...
"""
random_state = (random_state
if random_state is not None else np.random.RandomState())
Lab = as_float_array(list(random_generator(samples, limits, random_state)))
RGB = XYZ_to_RGB(
Lab_to_XYZ(Lab, illuminant_Lab),
illuminant_Lab,
colourspace.whitepoint,
colourspace.XYZ_to_RGB_matrix,
chromatic_adaptation_transform=chromatic_adaptation_method)
RGB_w = RGB[np.logical_and(
np.min(RGB, axis=-1) >= 0,
np.max(RGB, axis=-1) <= 1)]
return len(RGB_w)
def RGB_colourspaces_CIE_1931_chromaticity_diagram_plot(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces in *CIE 1931 Chromaticity Diagram*.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
show_diagram_colours : bool, optional
{:func:`CIE_1931_chromaticity_diagram_plot`},
Whether to display the chromaticity diagram background colours.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_CIE_1931_chromaticity_diagram_plot(
... c) # doctest: +SKIP
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg', 'S-Gamut', 'Pointer Gamut')
cmfs, name = get_cmfs(cmfs), cmfs
settings = {
'title':
'{0} - {1} - CIE 1931 Chromaticity Diagram'.format(
', '.join(colourspaces), name),
'standalone':
False
}
settings.update(kwargs)
CIE_1931_chromaticity_diagram_plot(**settings)
x_limit_min, x_limit_max = [-0.1], [0.9]
y_limit_min, y_limit_max = [-0.1], [0.9]
settings = {
'colour_cycle_map': 'rainbow',
'colour_cycle_count': len(colourspaces)
}
settings.update(kwargs)
cycle = colour_cycle(**settings)
for colourspace in colourspaces:
if colourspace == 'Pointer Gamut':
xy = np.asarray(POINTER_GAMUT_BOUNDARIES)
alpha_p, colour_p = 0.85, '0.95'
pylab.plot(xy[..., 0],
xy[..., 1],
label='Pointer\'s Gamut',
color=colour_p,
alpha=alpha_p,
linewidth=2)
pylab.plot((xy[-1][0], xy[0][0]), (xy[-1][1], xy[0][1]),
color=colour_p,
alpha=alpha_p,
linewidth=2)
XYZ = Lab_to_XYZ(LCHab_to_Lab(POINTER_GAMUT_DATA),
POINTER_GAMUT_ILLUMINANT)
xy = XYZ_to_xy(XYZ, POINTER_GAMUT_ILLUMINANT)
pylab.scatter(xy[..., 0],
xy[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
else:
colourspace, name = get_RGB_colourspace(colourspace), colourspace
r, g, b, _a = next(cycle)
primaries = colourspace.primaries
whitepoint = colourspace.whitepoint
pylab.plot((whitepoint[0], whitepoint[0]),
(whitepoint[1], whitepoint[1]),
color=(r, g, b),
label=colourspace.name,
linewidth=2)
pylab.plot((whitepoint[0], whitepoint[0]),
(whitepoint[1], whitepoint[1]),
'o',
color=(r, g, b),
linewidth=2)
pylab.plot((primaries[0, 0], primaries[1, 0]),
(primaries[0, 1], primaries[1, 1]),
'o-',
color=(r, g, b),
linewidth=2)
pylab.plot((primaries[1, 0], primaries[2, 0]),
(primaries[1, 1], primaries[2, 1]),
'o-',
color=(r, g, b),
linewidth=2)
pylab.plot((primaries[2, 0], primaries[0, 0]),
(primaries[2, 1], primaries[0, 1]),
'o-',
color=(r, g, b),
linewidth=2)
x_limit_min.append(np.amin(primaries[..., 0]) - 0.1)
y_limit_min.append(np.amin(primaries[..., 1]) - 0.1)
x_limit_max.append(np.amax(primaries[..., 0]) + 0.1)
y_limit_max.append(np.amax(primaries[..., 1]) + 0.1)
settings.update({
'legend':
True,
'legend_location':
'upper right',
'x_tighten':
True,
'y_tighten':
True,
'limits': (min(x_limit_min), max(x_limit_max), min(y_limit_min),
max(y_limit_max)),
'standalone':
True
})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def RGB_colourspaces_CIE_1976_UCS_chromaticity_diagram_plot(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces in *CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
show_diagram_colours : bool, optional
{:func:`CIE_1976_UCS_chromaticity_diagram_plot`},
Whether to display the chromaticity diagram background colours.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_CIE_1976_UCS_chromaticity_diagram_plot(
... c) # doctest: +SKIP
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg', 'S-Gamut', 'Pointer Gamut')
cmfs, name = get_cmfs(cmfs), cmfs
illuminant = DEFAULT_PLOTTING_ILLUMINANT
settings = {
'title':
'{0} - {1} - CIE 1976 UCS Chromaticity Diagram'.format(
', '.join(colourspaces), name),
'standalone':
False
}
settings.update(kwargs)
CIE_1976_UCS_chromaticity_diagram_plot(**settings)
x_limit_min, x_limit_max = [-0.1], [0.7]
y_limit_min, y_limit_max = [-0.1], [0.7]
settings = {
'colour_cycle_map': 'rainbow',
'colour_cycle_count': len(colourspaces)
}
settings.update(kwargs)
cycle = colour_cycle(**settings)
for colourspace in colourspaces:
if colourspace == 'Pointer Gamut':
uv = Luv_to_uv(
XYZ_to_Luv(xy_to_XYZ(POINTER_GAMUT_BOUNDARIES), illuminant),
illuminant)
alpha_p, colour_p = 0.85, '0.95'
pylab.plot(uv[..., 0],
uv[..., 1],
label='Pointer\'s Gamut',
color=colour_p,
alpha=alpha_p,
linewidth=2)
pylab.plot((uv[-1][0], uv[0][0]), (uv[-1][1], uv[0][1]),
color=colour_p,
alpha=alpha_p,
linewidth=2)
XYZ = Lab_to_XYZ(LCHab_to_Lab(POINTER_GAMUT_DATA),
POINTER_GAMUT_ILLUMINANT)
uv = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)
pylab.scatter(uv[..., 0],
uv[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
else:
colourspace, name = get_RGB_colourspace(colourspace), colourspace
r, g, b, _a = next(cycle)
# RGB colourspaces such as *ACES2065-1* have primaries with
# chromaticity coordinates set to 0 thus we prevent nan from being
# yield by zero division in later colour transformations.
P = np.where(colourspace.primaries == 0, EPSILON,
colourspace.primaries)
P = Luv_to_uv(XYZ_to_Luv(xy_to_XYZ(P), illuminant), illuminant)
W = Luv_to_uv(
XYZ_to_Luv(xy_to_XYZ(colourspace.whitepoint), illuminant),
illuminant)
pylab.plot((W[0], W[0]), (W[1], W[1]),
color=(r, g, b),
label=colourspace.name,
linewidth=2)
pylab.plot((W[0], W[0]), (W[1], W[1]),
'o',
color=(r, g, b),
linewidth=2)
pylab.plot((P[0, 0], P[1, 0]), (P[0, 1], P[1, 1]),
'o-',
color=(r, g, b),
linewidth=2)
pylab.plot((P[1, 0], P[2, 0]), (P[1, 1], P[2, 1]),
'o-',
color=(r, g, b),
linewidth=2)
pylab.plot((P[2, 0], P[0, 0]), (P[2, 1], P[0, 1]),
'o-',
color=(r, g, b),
linewidth=2)
x_limit_min.append(np.amin(P[..., 0]) - 0.1)
y_limit_min.append(np.amin(P[..., 1]) - 0.1)
x_limit_max.append(np.amax(P[..., 0]) + 0.1)
y_limit_max.append(np.amax(P[..., 1]) + 0.1)
settings.update({
'legend':
True,
'legend_location':
'upper right',
'x_tighten':
True,
'y_tighten':
True,
'limits': (min(x_limit_min), max(x_limit_max), min(y_limit_min),
max(y_limit_max)),
'standalone':
True
})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def DIN99_to_XYZ(
Lab_99: ArrayLike,
illuminant: ArrayLike = CCS_ILLUMINANTS[
"CIE 1931 2 Degree Standard Observer"]["D65"],
k_E: Floating = 1,
k_CH: Floating = 1,
method: Union[Literal["ASTMD2244-07", "DIN99", "DIN99b", "DIN99c",
"DIN99d"], str] = "DIN99",
) -> NDArray:
"""
Convert from *DIN99* colourspace or one of the *DIN99b*, *DIN99c*,
*DIN99d* refined formulas according to *Cui et al. (2002)* to *CIE XYZ*
tristimulus values.
Parameters
----------
Lab_99
*DIN99* colourspace array.
illuminant
Reference *illuminant* *CIE xy* chromaticity coordinates or *CIE xyY*
colourspace array.
k_E
Parametric factor :math:`K_E` used to compensate for texture and other
specimen presentation effects.
k_CH
Parametric factor :math:`K_{CH}` used to compensate for texture and
other specimen presentation effects.
method
Computation method to choose between the :cite:`ASTMInternational2007`
formula and the refined formulas according to *Cui et al. (2002)*.
Returns
-------
:class:`numpy.ndarray`
*CIE XYZ* tristimulus values.
Notes
-----
+----------------+------------------------+--------------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+========================+====================+
| ``Lab_99`` | ``L_99`` : [0, 100] | ``L_99`` : [0, 1] |
| | | |
| | ``a_99`` : [-100, 100] | ``a_99`` : [-1, 1] |
| | | |
| | ``b_99`` : [-100, 100] | ``b_99`` : [-1, 1] |
+----------------+------------------------+--------------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+------------------------+--------------------+
+----------------+-----------------------+---------------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=====================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+---------------------+
References
----------
:cite:`ASTMInternational2007`
Examples
--------
>>> import numpy as np
>>> Lab_99 = np.array([53.22821989, 28.41634656, 3.89839552])
>>> DIN99_to_XYZ(Lab_99) # doctest: +ELLIPSIS
array([ 0.2065400..., 0.1219722..., 0.0513695...])
"""
Lab = DIN99_to_Lab(Lab_99, k_E, k_CH, method)
return Lab_to_XYZ(Lab, illuminant)
def sample_RGB_colourspace_volume_MonteCarlo(
colourspace: RGB_Colourspace,
samples: Integer = 1000000,
limits: ArrayLike = np.array([[0, 100], [-150, 150], [-150, 150]]),
illuminant_Lab: ArrayLike = CCS_ILLUMINANTS[
"CIE 1931 2 Degree Standard Observer"]["D65"],
chromatic_adaptation_transform: Union[Literal["Bianco 2010",
"Bianco PC 2010", "Bradford",
"CAT02 Brill 2008", "CAT02",
"CAT16", "CMCCAT2000",
"CMCCAT97", "Fairchild",
"Sharp", "Von Kries",
"XYZ Scaling", ],
str, ] = "CAT02",
random_generator: Callable = random_triplet_generator,
random_state: np.random.RandomState = None,
) -> Integer:
"""
Randomly sample the *CIE L\\*a\\*b\\** colourspace volume and returns the
ratio of samples within the given *RGB* colourspace volume.
Parameters
----------
colourspace
*RGB* colourspace to compute the volume of.
samples
Samples count.
limits
*CIE L\\*a\\*b\\** colourspace volume.
illuminant_Lab
*CIE L\\*a\\*b\\** colourspace *illuminant* chromaticity coordinates.
chromatic_adaptation_transform
*Chromatic adaptation* transform.
random_generator
Random triplet generator providing the random samples within the
*CIE L\\*a\\*b\\** colourspace volume.
random_state
Mersenne Twister pseudo-random number generator to use in the random
number generator.
Returns
-------
:class:`numpy.integer`
Within *RGB* colourspace volume samples count.
Notes
-----
- The doctest is assuming that :func:`np.random.RandomState` definition
will return the same sequence no matter which *OS* or *Python*
version is used. There is however no formal promise about the *prng*
sequence reproducibility of either *Python* or *Numpy*
implementations: Laurent. (2012). Reproducibility of python
pseudo-random numbers across systems and versions? Retrieved January
20, 2015, from http://stackoverflow.com/questions/8786084/\
reproducibility-of-python-pseudo-random-numbers-across-systems-and-versions
Examples
--------
>>> from colour.models import RGB_COLOURSPACE_sRGB as sRGB
>>> prng = np.random.RandomState(2)
>>> sample_RGB_colourspace_volume_MonteCarlo(sRGB, 10e3, random_state=prng)
... # doctest: +ELLIPSIS
9...
"""
random_state = (random_state
if random_state is not None else np.random.RandomState())
Lab = random_generator(DEFAULT_INT_DTYPE(samples), limits, random_state)
RGB = XYZ_to_RGB(
Lab_to_XYZ(Lab, illuminant_Lab),
illuminant_Lab,
colourspace.whitepoint,
colourspace.matrix_XYZ_to_RGB,
chromatic_adaptation_transform=chromatic_adaptation_transform,
)
RGB_w = RGB[np.logical_and(
np.min(RGB, axis=-1) >= 0,
np.max(RGB, axis=-1) <= 1)]
return len(RGB_w)
def RGB_colourspaces_CIE_1960_UCS_chromaticity_diagram_plot(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces in *CIE 1960 UCS Chromaticity Diagram*.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_CIE_1960_UCS_chromaticity_diagram_plot(
... c) # doctest: +SKIP
True
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg', 'S-Gamut', 'Pointer Gamut')
cmfs, name = get_cmfs(cmfs), cmfs
settings = {
'title': '{0} - {1} - CIE 1960 UCS Chromaticity Diagram'.format(
', '.join(colourspaces), name),
'standalone': False}
settings.update(kwargs)
CIE_1960_UCS_chromaticity_diagram_plot(**settings)
x_limit_min, x_limit_max = [-0.1], [0.7]
y_limit_min, y_limit_max = [-0.2], [0.6]
settings = {'colour_cycle_map': 'rainbow',
'colour_cycle_count': len(colourspaces)}
settings.update(kwargs)
cycle = colour_cycle(**settings)
for colourspace in colourspaces:
if colourspace == 'Pointer Gamut':
uv = UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(POINTER_GAMUT_BOUNDARIES)))
alpha_p, colour_p = 0.85, '0.95'
pylab.plot(uv[..., 0],
uv[..., 1],
label='Pointer\'s Gamut',
color=colour_p,
alpha=alpha_p,
linewidth=2)
pylab.plot((uv[-1][0], uv[0][0]),
(uv[-1][1], uv[0][1]),
color=colour_p,
alpha=alpha_p,
linewidth=2)
XYZ = Lab_to_XYZ(LCHab_to_Lab(POINTER_GAMUT_DATA),
POINTER_GAMUT_ILLUMINANT)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
pylab.scatter(uv[..., 0],
uv[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
else:
colourspace, name = get_RGB_colourspace(colourspace), colourspace
r, g, b, _a = next(cycle)
# RGB colourspaces such as *ACES2065-1* have primaries with
# chromaticity coordinates set to 0 thus we prevent nan from being
# yield by zero division in later colour transformations.
primaries = np.where(colourspace.primaries == 0,
EPSILON,
colourspace.primaries)
primaries = UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(primaries)))
whitepoint = UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(
colourspace.whitepoint)))
pylab.plot((whitepoint[0], whitepoint[0]),
(whitepoint[1], whitepoint[1]),
color=(r, g, b),
label=colourspace.name,
linewidth=2)
pylab.plot((whitepoint[0], whitepoint[0]),
(whitepoint[1], whitepoint[1]),
'o',
color=(r, g, b),
linewidth=2)
pylab.plot((primaries[0, 0], primaries[1, 0]),
(primaries[0, 1], primaries[1, 1]),
'o-',
color=(r, g, b),
linewidth=2)
pylab.plot((primaries[1, 0], primaries[2, 0]),
(primaries[1, 1], primaries[2, 1]),
'o-',
color=(r, g, b),
linewidth=2)
pylab.plot((primaries[2, 0], primaries[0, 0]),
(primaries[2, 1], primaries[0, 1]),
'o-',
color=(r, g, b),
linewidth=2)
x_limit_min.append(np.amin(primaries[..., 0]) - 0.1)
y_limit_min.append(np.amin(primaries[..., 1]) - 0.1)
x_limit_max.append(np.amax(primaries[..., 0]) + 0.1)
y_limit_max.append(np.amax(primaries[..., 1]) + 0.1)
settings.update({
'legend': True,
'legend_location': 'upper right',
'x_tighten': True,
'y_tighten': True,
'limits': (min(x_limit_min), max(x_limit_max),
min(y_limit_min), max(y_limit_max)),
'standalone': True})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
Notes
-----
- *X-Rite* data is given as *CIE L\\*a\\*b\\** colourspace values under
*CIE Illuminant D Series D50* for the
*CIE 1931 2 Degree Standard Observer*.
DATA_COLORCHECKER24_BEFORE_NOV2014 : ndarray
"""
DATA_COLORCHECKER24_BEFORE_NOV2014 = OrderedDict(
zip(
SAMPLE_LABELS_COLORCHECKER_CLASSIC,
XYZ_to_xyY(
Lab_to_XYZ(
list(DATA_COLORCHECKER24_BEFORE_NOV2014.values()),
CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']
['ICC D50']))))
CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014 = (
CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['ICC D50'])
"""
*ColorChecker24 - Before November 2014* illuminant.
CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014 : ndarray
"""
CCS_COLORCHECKER24_BEFORE_NOV2014 = ColourChecker(
'ColorChecker24 - Before November 2014',
DATA_COLORCHECKER24_BEFORE_NOV2014,
CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014)
"""
def plot_pointer_gamut(method='CIE 1931', **kwargs):
"""
Plots *Pointer's Gamut* according to given method.
Parameters
----------
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
Plotting method.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_pointer_gamut() # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Pointer_Gamut.png
:align: center
:alt: plot_pointer_gamut
"""
settings = {'uniform': True}
settings.update(kwargs)
figure, axes = artist(**settings)
method = method.upper()
if method == 'CIE 1931':
def XYZ_to_ij(XYZ, *args):
"""
Converts given *CIE XYZ* tristimulus values to *ij* chromaticity
coordinates.
"""
return XYZ_to_xy(XYZ, *args)
def xy_to_ij(xy):
"""
Converts given *xy* chromaticity coordinates to *ij* chromaticity
coordinates.
"""
return xy
elif method == 'CIE 1960 UCS':
def XYZ_to_ij(XYZ, *args):
"""
Converts given *CIE XYZ* tristimulus values to *ij* chromaticity
coordinates.
"""
return UCS_to_uv(XYZ_to_UCS(XYZ))
def xy_to_ij(xy):
"""
Converts given *xy* chromaticity coordinates to *ij* chromaticity
coordinates.
"""
return xy_to_UCS_uv(xy)
elif method == 'CIE 1976 UCS':
def XYZ_to_ij(XYZ, *args):
"""
Converts given *CIE XYZ* tristimulus values to *ij* chromaticity
coordinates.
"""
return Luv_to_uv(XYZ_to_Luv(XYZ, *args), *args)
def xy_to_ij(xy):
"""
Converts given *xy* chromaticity coordinates to *ij* chromaticity
coordinates.
"""
return xy_to_Luv_uv(xy)
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}'.format(
method))
ij = xy_to_ij(as_float_array(POINTER_GAMUT_BOUNDARIES))
alpha_p = COLOUR_STYLE_CONSTANTS.opacity.high
colour_p = COLOUR_STYLE_CONSTANTS.colour.darkest
axes.plot(ij[..., 0],
ij[..., 1],
label='Pointer\'s Gamut',
color=colour_p,
alpha=alpha_p)
axes.plot((ij[-1][0], ij[0][0]), (ij[-1][1], ij[0][1]),
color=colour_p,
alpha=alpha_p)
XYZ = Lab_to_XYZ(LCHab_to_Lab(POINTER_GAMUT_DATA),
POINTER_GAMUT_ILLUMINANT)
ij = XYZ_to_ij(XYZ, POINTER_GAMUT_ILLUMINANT)
axes.scatter(ij[..., 0],
ij[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
settings.update({'axes': axes})
settings.update(kwargs)
return render(**settings)
np.array([96.539, -0.425, 1.186]),
np.array([81.257, -0.638, -0.335]),
np.array([66.766, -0.734, -0.504]),
np.array([50.867, -0.153, -0.27]),
np.array([35.656, -0.421, -1.231]),
np.array([20.461, -0.079, -0.973]),
],
))
DATA_COLORCHECKER24_BEFORE_NOV2014: Dict = dict(
zip(
SAMPLE_LABELS_COLORCHECKER_CLASSIC,
XYZ_to_xyY(
Lab_to_XYZ(
list(DATA_COLORCHECKER24_BEFORE_NOV2014_CIE_LAB.values()),
CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]
["ICC D50"],
)),
))
CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014: NDArray = CCS_ILLUMINANTS[
"CIE 1931 2 Degree Standard Observer"]["ICC D50"]
"""*ColorChecker24 - Before November 2014* illuminant."""
CCS_COLORCHECKER24_BEFORE_NOV2014: ColourChecker = ColourChecker(
"ColorChecker24 - Before November 2014",
DATA_COLORCHECKER24_BEFORE_NOV2014,
CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014,
)
"""
Reference *ColorChecker Classic* data from *X-Rite (2015)*.
