def __init__(
self,
sds,
shape,
cmfs=STANDARD_OBSERVER_CMFS['CIE 1931 2 Degree Standard Observer'],
illuminant=sd_ones()):
"""
Parameters
----------
sds : ndarray (n,m)
Reflectances of the ``n`` reference colours to be used for
optimisation.
shape : SpectralShape
Spectral shape of ``sds``.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
illuminant : SpectralDistribution, optional
Illuminant spectral distribution.
"""
self.shape = shape
self.wl = shape.range()
self.dw = self.wl[1] - self.wl[0]
self.cmfs = cmfs.copy().align(shape)
self.illuminant = illuminant.copy().align(shape)
self.xy_w = XYZ_to_xy(sd_to_XYZ(illuminant, cmfs=cmfs))
# The normalising constant used in sd_to_XYZ.
self.k = 1 / (np.sum(self.cmfs.values[:, 1] * self.illuminant.values) *
self.dw)
# Python 3: super().__init__(...)
super(Otsu2018Tree, self).__init__(self, Colours(self, sds))
def XYZ_to_sd_Otsu2018(
XYZ,
cmfs=STANDARD_OBSERVER_CMFS['CIE 1931 2 Degree Standard Observer'].
copy().align(OTSU_2018_SPECTRAL_SHAPE),
illuminant=ILLUMINANT_SDS['D65'].copy().align(
OTSU_2018_SPECTRAL_SHAPE),
clip=True):
XYZ = as_float_array(XYZ)
xy = XYZ_to_xy(XYZ)
cluster = select_cluster_Otsu2018(xy)
basis_functions = OTSU_2018_BASIS_FUNCTIONS[cluster]
mean = OTSU_2018_MEANS[cluster]
M = np.empty((3, 3))
for i in range(3):
sd = SpectralDistribution(
basis_functions[i, :],
OTSU_2018_SPECTRAL_SHAPE.range(),
)
M[:, i] = sd_to_XYZ(sd, illuminant=illuminant) / 100
M_inverse = np.linalg.inv(M)
sd = SpectralDistribution(mean, OTSU_2018_SPECTRAL_SHAPE.range())
XYZ_mu = sd_to_XYZ(sd, illuminant=illuminant) / 100
weights = np.dot(M_inverse, XYZ - XYZ_mu)
recovered_sd = np.dot(weights, basis_functions) + mean
if clip:
recovered_sd = np.clip(recovered_sd, 0, 1)
return SpectralDistribution(recovered_sd, OTSU_2018_SPECTRAL_SHAPE.range())
def mutiple():
cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
spd = []
for d in ['test.txt', 'test1.txt']:
with open(d) as f:
data = pd.read_csv(f, sep="\t" or ' ' or ',', header=None)
f.close()
w = [i[0] for i in data.values]
s = [i[1] for i in data.values]
data_formated = dict(zip(w, s))
spd.append(SpectralPowerDistribution('Sample', data_formated))
b = multi_spd_plot(spd,
standalone=False,
figure_size=(5, 5),
title='Spectrum')
figfile_b = StringIO()
b.savefig(figfile_b, format='svg')
figfile_b.seek(0)
figdata_svg_b = '<svg' + figfile_b.getvalue().split('<svg')[1]
b.clf()
plot.close(b)
CIE_1931_chromaticity_diagram_plot(standalone=False,
figure_size=(5, 5),
grid=False,
title='CIE 1931 Chromaticity Diagram',
bounding_box=(-0.1, 0.9, -0.05, 0.95))
illuminant = ILLUMINANTS_RELATIVE_SPDS['D50']
for s in spd:
XYZ = spectral_to_XYZ(s, cmfs, illuminant)
xy = XYZ_to_xy(XYZ)
print(xy)
x, y = xy
pylab.plot(x, y, 'o-', color='white')
pylab.annotate((("%.4f" % x), ("%.4f" % y)),
xy=xy,
xytext=(-50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=-0.2'))
a = plot.gcf()
figfile = StringIO()
a.savefig(figfile, format='svg')
figfile.seek(0)
figdata_svg = '<svg' + figfile.getvalue().split('<svg')[1]
a.clf()
plot.close(a)
del a, b
# pprint.pprint(figdata_svg)
return render_template('index.html', spd=figdata_svg_b, result=figdata_svg)
def get_cct(self, methods="Hernandez 1999"):
'''
approximate CCT using CIE 1931 xy values
'''
x, y, z = self.get_xyz()
if 0 in [x, y, z]:
return 0.0
logs.logger.debug(f"x = {x}, y = {y}, z = {z}")
if isinstance(methods, str):
methods = [methods]
ccts = list()
for curr_method in methods:
if curr_method == 'me_mccamy':
# McCamy's Approx
small_x = x / (x + y + z)
small_y = y / (x + y + z)
n = (small_x - 0.3320) / (0.1858 - small_y)
cct = 437 * (n**3) + 3601 * (n**2) + 6861 * n + 5517
if DEBUG:
logs.logger.debug(
f"[me_mccamy] calc x = {small_x}, calc y = {small_y} | Calc CCT = {cct} K"
)
elif curr_method in XY_TO_CCT_METHODS:
xyz_arr = np_array([x, y, z])
xy_arr = XYZ_to_xy(xyz_arr)
cct = xy_to_CCT(xy_arr, curr_method)
if DEBUG:
logs.logger.debug(
f"[{curr_method}] calc x,y = {xy_arr} | CCT = {cct}")
else:
options = ["me_mccamy"] + list(XY_TO_CCT_METHODS)
logs.logger.error(
f"{curr_method} Not found!\nCCT calculation methods: \n {options}"
)
return
ccts.append(int(cct))
if len(ccts) == 1:
return ccts[0]
else:
return ccts
def calc_day_light_xy(temperature=6500, cmfs_name=cm.CIE1931):
day_light_spd = cm.get_day_light_spd(temperature)
cmfs = cm.load_cmfs(cmfs_name)
normalize_coef = cm.get_nomalize_large_y_coef(
d_light_before_trim=day_light_spd, cmfs_before_trim=cmfs)
# trim
shape = cm.calc_appropriate_shape(day_light_spd, cmfs)
day_light_spd.trim(shape)
cmfs.trim(shape)
# calc
large_xyz = [
np.sum(day_light_spd.values * cmfs.values[:, x]) * normalize_coef
for x in range(3)
]
print(large_xyz)
print(XYZ_to_xy(large_xyz))
def get_normalize_large_y_param_cie1931_5nm_test():
temperature = 5000
shape = SpectralShape(380, 780)
coef = get_normalize_large_y_param_cie1931_5nm(temperature)
d_light = make_day_light_by_calculation(temperature=temperature,
interpolater=LinearInterpolator,
interval=5)
d_light.trim(shape)
cmfs_cie1931 = load_cie1931_1nm_data().trim(shape)
cmfs_cie1931_5nm = cmfs_cie1931.values[::5]
large_x = np.sum(d_light.values * cmfs_cie1931_5nm[:, 0])
large_y = np.sum(d_light.values * cmfs_cie1931_5nm[:, 1])
large_z = np.sum(d_light.values * cmfs_cie1931_5nm[:, 2])
large_xyz = [large_x * coef, large_y * coef, large_z * coef]
print(large_xyz)
print(XYZ_to_xy(large_xyz))
def calc_cie2015_d65_white_xy():
shape = SpectralShape(390, 830, 1)
cmfs = STANDARD_OBSERVERS_CMFS['CIE 2012 2 Degree Standard Observer']
d65_spd = load_d65_spd_1nmdata().trim(shape)
d65_1nm = d65_spd.values[:, 1]
cmfs_1nm = cmfs.values
large_x = np.sum(d65_1nm * cmfs_1nm[:, 0])
large_y = np.sum(d65_1nm * cmfs_1nm[:, 1])
large_z = np.sum(d65_1nm * cmfs_1nm[:, 2])
normalize_coef = 100 / large_y
large_xyz = [
large_x * normalize_coef, large_y * normalize_coef,
large_z * normalize_coef
]
print(large_xyz)
print(XYZ_to_xy(large_xyz))
def reconstruct(self, XYZ):
"""
Reconstructs the reflectance for the given *XYZ* tristimulus values.
If this is a leaf, data from this node will be used. Otherwise the
code will look for the appropriate subnode.
Parameters
==========
XYZ : ndarray, (3,)
*CIE XYZ* tristimulus values to recover the spectral distribution
from.
Returns
-------
SpectralDistribution
Recovered spectral distribution.
"""
xy = XYZ_to_xy(XYZ)
return self._reconstruct_xy(XYZ, xy)
def __init__(self, tree, reflectances):
"""
Parameters
==========
tree : tree
The parent tree. This determines what cmfs and illuminant
are used in colourimetric calculations.
reflectances : ndarray (n,m)
Reflectances of the ``n`` colours to be stored in this class.
The shape must match ``tree.shape`` with ``m`` points for
each colour.
"""
self.reflectances = reflectances
self.XYZ = np.empty((reflectances.shape[0], 3))
self.xy = np.empty((reflectances.shape[0], 2))
for i in range(len(self)):
sd = SpectralDistribution(reflectances[i, :], tree.wl)
XYZ = sd_to_XYZ(sd, illuminant=tree.illuminant) / 100
self.XYZ[i, :] = XYZ
self.xy[i, :] = XYZ_to_xy(XYZ)
def calc_d65_white_xy():
"""
とりあえず D65 White の XYZ および xy を求めてみる。
"""
# temperature = 6500
# d65_spd = make_day_light_by_calculation(temperature=temperature,
# interpolater=LinearInterpolator,
# interval=5)
# d65_spd.values = fit_significant_figures(d65_spd.values, 6)
d65_spd = load_d65_spd_1nmdata().trim(SpectralShape(380, 780))
cmfs_cie1931 = load_cie1931_1nm_data().trim(SpectralShape(380, 780))
d65_spd_5nm = d65_spd.values[::5]
cmfs_cie1931_5nm = cmfs_cie1931.values[::5]
large_x = np.sum(d65_spd_5nm[:, 1] * cmfs_cie1931_5nm[:, 0])
large_y = np.sum(d65_spd_5nm[:, 1] * cmfs_cie1931_5nm[:, 1])
large_z = np.sum(d65_spd_5nm[:, 1] * cmfs_cie1931_5nm[:, 2])
normalize_coef = 100 / large_y
large_xyz = [
large_x * normalize_coef, large_y * normalize_coef,
large_z * normalize_coef
]
print(large_xyz)
print(XYZ_to_xy(large_xyz))
from colour import (Luv_to_uv, Luv_uv_to_xy, UCS_to_uv, UCS_uv_to_xy,
xy_to_XYZ, XYZ_to_Luv, XYZ_to_UCS, XYZ_to_xy)
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2019 - Colour Developers'
__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = '*****@*****.**'
__status__ = 'Production'
__all__ = ['CHROMATICITY_DIAGRAM_TRANSFORMATIONS', 'Cycle']
CHROMATICITY_DIAGRAM_TRANSFORMATIONS = {
'CIE 1931': {
'XYZ_to_ij': lambda a, i: XYZ_to_xy(a, i),
'ij_to_XYZ': lambda a, i: xy_to_XYZ(a)
},
'CIE 1960 UCS': {
'XYZ_to_ij': lambda a, i: UCS_to_uv(XYZ_to_UCS(a)),
'ij_to_XYZ': lambda a, i: xy_to_XYZ(UCS_uv_to_xy(a))
},
'CIE 1976 UCS': {
'XYZ_to_ij': lambda a, i: Luv_to_uv(XYZ_to_Luv(a, i), i),
'ij_to_XYZ': lambda a, i: xy_to_XYZ(Luv_uv_to_xy(a))
}
}
"""
Chromaticity diagram specific helper conversion objects.
CHROMATICITY_DIAGRAM_TRANSFORMATIONS : dict
def chromatic_adaptation(XYZ, XYZ_w, XYZ_wr, method='Von Kries', **kwargs):
"""
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 the whitepoint.
XYZ_wr : array_like
Reference viewing condition *CIE XYZ* tristimulus values of the
whitepoint.
method : unicode, optional
**{'Von Kries', 'CIE 1994', 'CMCCAT2000', 'Fairchild 1990'}**,
Computation method.
Other Parameters
----------------
E_o1 : numeric
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Test illuminance :math:`E_{o1}` in :math:`cd/m^2`.
E_o2 : numeric
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Reference illuminance :math:`E_{o2}` in :math:`cd/m^2`.
L_A1 : numeric or array_like
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
Luminance of test adapting field :math:`L_{A1}` in :math:`cd/m^2`.
L_A2 : numeric or array_like
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
Luminance of reference adapting field :math:`L_{A2}` in :math:`cd/m^2`.
Y_n : numeric or array_like
{:func:`colour.adaptation.chromatic_adaptation_Fairchild1990`},
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
Y_o : numeric
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Luminance factor :math:`Y_o` of achromatic background normalised to
domain [0.18, 1] in **'Reference'** domain-range scale.
direction : unicode, optional
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
**{'Forward', 'Reverse'}**,
Chromatic adaptation direction.
discount_illuminant : bool, optional
{:func:`colour.adaptation.chromatic_adaptation_Fairchild1990`},
Truth value indicating if the illuminant should be discounted.
n : numeric, optional
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Noise component in fundamental primary system.
surround : CMCCAT2000_InductionFactors, optional
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
Surround viewing conditions induction factors.
transform : unicode, optional
{:func:`colour.adaptation.chromatic_adaptation_VonKries`},
**{'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_w`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_wr`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``Y_o`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_c`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`CIETC1-321994b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`,
:cite:`Fairchild2013t`, :cite:`Li2002a`, :cite:`Westland2012k`
Examples
--------
*Von Kries* chromatic adaptation:
>>> import numpy as np
>>> 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(XYZ, XYZ_w, XYZ_wr)
... # doctest: +ELLIPSIS
array([ 0.2163881..., 0.1257 , 0.0384749...])
*CIE 1994* chromatic adaptation, requires extra *kwargs*:
>>> XYZ = np.array([0.2800, 0.2126, 0.0527])
>>> XYZ_w = np.array([1.09867452, 1.00000000, 0.35591556])
>>> XYZ_wr = np.array([0.95045593, 1.00000000, 1.08905775])
>>> Y_o = 0.20
>>> E_o = 1000
>>> chromatic_adaptation(
... XYZ, XYZ_w, XYZ_wr, method='CIE 1994', Y_o=Y_o, E_o1=E_o, E_o2=E_o)
... # doctest: +ELLIPSIS
array([ 0.2403379..., 0.2115621..., 0.1764301...])
*CMCCAT2000* chromatic adaptation, requires extra *kwargs*:
>>> XYZ = np.array([0.2248, 0.2274, 0.0854])
>>> XYZ_w = np.array([1.1115, 1.0000, 0.3520])
>>> XYZ_wr = np.array([0.9481, 1.0000, 1.0730])
>>> L_A = 200
>>> chromatic_adaptation(
... XYZ, XYZ_w, XYZ_wr, method='CMCCAT2000', L_A1=L_A, L_A2=L_A)
... # doctest: +ELLIPSIS
array([ 0.1952698..., 0.2306834..., 0.2497175...])
*Fairchild (1990)* chromatic adaptation, requires extra *kwargs*:
>>> XYZ = np.array([0.1953, 0.2307, 0.2497])
>>> Y_n = 200
>>> chromatic_adaptation(
... XYZ, XYZ_w, XYZ_wr, method='Fairchild 1990', Y_n=Y_n)
... # doctest: +ELLIPSIS
array([ 0.2332526..., 0.2332455..., 0.7611593...])
"""
function = CHROMATIC_ADAPTATION_METHODS[method]
domain_range_reference = get_domain_range_scale() == 'reference'
domain_100 = (chromatic_adaptation_CIE1994,
chromatic_adaptation_CMCCAT2000,
chromatic_adaptation_Fairchild1990)
if function in domain_100 and domain_range_reference:
XYZ = as_float_array(XYZ) * 100
XYZ_w = as_float_array(XYZ_w) * 100
XYZ_wr = as_float_array(XYZ_wr) * 100
if kwargs.get('Y_o'):
kwargs['Y_o'] = kwargs['Y_o'] * 100
kwargs.update({'XYZ_w': XYZ_w, 'XYZ_wr': XYZ_wr})
if function is chromatic_adaptation_CIE1994:
from colour import XYZ_to_xy
kwargs.update({'xy_o1': XYZ_to_xy(XYZ_w), 'xy_o2': XYZ_to_xy(XYZ_wr)})
elif function is chromatic_adaptation_Fairchild1990:
kwargs.update({'XYZ_n': XYZ_w, 'XYZ_r': XYZ_wr})
XYZ_c = function(XYZ, **filter_kwargs(function, **kwargs))
if function in domain_100 and domain_range_reference:
XYZ_c /= 100
return XYZ_c
def index():
cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
with open('test.txt') as f:
data = pd.read_csv(f, sep="\t" or ' ' or ',', header=None)
f.close()
w = [i[0] for i in data.values]
s = [i[1] for i in data.values]
data_formated = dict(zip(w, s))
spd = SpectralPowerDistribution('Sample', data_formated)
b = single_spd_plot(spd,
standalone=False,
figure_size=(5, 5),
title='Spectrum')
figfile_b = StringIO()
b.savefig(figfile_b, format='svg')
figfile_b.seek(0)
figdata_svg_b = '<svg' + figfile_b.getvalue().split('<svg')[1]
b.clf()
plot.close(b)
illuminant = ILLUMINANTS_RELATIVE_SPDS['D50']
XYZ = spectral_to_XYZ(spd, cmfs, illuminant)
xy = XYZ_to_xy(XYZ)
print(xy)
cct = xy_to_CCT(xy)
print(cct)
cri = colour_rendering_index(spd, additional_data=True)
print(cri.Q_a)
Q_as = cri.Q_as
y = [s[1].Q_a for s in sorted(Q_as.items(), key=lambda s: s[0])]
print(y)
single_spd_colour_rendering_index_bars_plot(spd,
standalone=False,
figure_size=(7, 7),
title='Colour rendering index')
c = plot.gcf()
figfile_c = StringIO()
c.savefig(figfile_c, format='svg')
figfile_c.seek(0)
figdata_svg_c = '<svg' + figfile_c.getvalue().split('<svg')[1]
c.clf()
plot.close(c)
CIE_1931_chromaticity_diagram_plot(standalone=False,
figure_size=(6, 5),
grid=False,
title='CIE 1931 Chromaticity Diagram',
bounding_box=(-0.1, 0.9, -0.05, 0.95))
x, y = xy
pylab.plot(x, y, 'o-', color='white')
pylab.annotate((("%.4f" % x), ("%.4f" % y)),
xy=xy,
xytext=(-50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=-0.2'))
a = plot.gcf()
figfile = StringIO()
a.savefig(figfile, format='svg')
figfile.seek(0)
figdata_svg = '<svg' + figfile.getvalue().split('<svg')[1]
a.clf()
plot.close(a)
del a, b, c
# pprint.pprint(figdata_svg)
return render_template('index.html',
spd=figdata_svg_b,
result=figdata_svg,
colour_rendering_index=figdata_svg_c)
def RGB_to_CCT(rgb):
RGB = np.array(rgb)
XYZ = sRGB_to_XYZ(RGB / 255)
xy = XYZ_to_xy(XYZ)
CCT = xy_to_CCT(xy, "hernandez1999")
return CCT
def chromatic_adaptation(
XYZ: ArrayLike,
XYZ_w: ArrayLike,
XYZ_wr: ArrayLike,
method: Union[Literal["CIE 1994", "CMCCAT2000", "Fairchild 1990",
"Zhai 2018", "Von Kries", ], str, ] = "Von Kries",
**kwargs: Any,
) -> NDArray:
"""
Adapt given stimulus from test viewing conditions to reference viewing
conditions.
Parameters
----------
XYZ
*CIE XYZ* tristimulus values of stimulus to adapt.
XYZ_w
Test viewing condition *CIE XYZ* tristimulus values of the whitepoint.
XYZ_wr
Reference viewing condition *CIE XYZ* tristimulus values of the
whitepoint.
method
Computation method.
Other Parameters
----------------
E_o1
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Test illuminance :math:`E_{o1}` in :math:`cd/m^2`.
E_o2
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Reference illuminance :math:`E_{o2}` in :math:`cd/m^2`.
n
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Noise component in fundamental primary system.
Y_o
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Luminance factor :math:`Y_o` of achromatic background normalised to
domain [0.18, 1] in **'Reference'** domain-range scale.
direction
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
Chromatic adaptation direction.
L_A1
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
Luminance of test adapting field :math:`L_{A1}` in :math:`cd/m^2`.
L_A2
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
Luminance of reference adapting field :math:`L_{A2}` in :math:`cd/m^2`.
surround
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
Surround viewing conditions induction factors.
discount_illuminant
{:func:`colour.adaptation.chromatic_adaptation_Fairchild1990`},
Truth value indicating if the illuminant should be discounted.
Y_n
{:func:`colour.adaptation.chromatic_adaptation_Fairchild1990`},
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
D_b
{:func:`colour.adaptation.chromatic_adaptation_Zhai2018`},
Degree of adaptation :math:`D_\\beta` of input illuminant
:math:`\\beta`.
D_d
{:func:`colour.adaptation.chromatic_adaptation_Zhai2018`},
Degree of adaptation :math:`D_\\Delta` of output illuminant
:math:`\\Delta`.
transform
{:func:`colour.adaptation.chromatic_adaptation_VonKries`,
:func:`colour.adaptation.chromatic_adaptation_Zhai2018`},
Chromatic adaptation transform.
XYZ_wo
{:func:`colour.adaptation.chromatic_adaptation_Zhai2018`},
Baseline illuminant (:math:`BI`) :math:`o`.
Returns
-------
:class:`numpy.ndarray`
*CIE XYZ_c* tristimulus values of the stimulus corresponding colour.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_w`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_wr`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``XYZ_wo`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
| ``Y_o`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ_c`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`CIETC1-321994b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`,
:cite:`Fairchild2013t`, :cite:`Li2002a`, :cite:`Westland2012k`
Examples
--------
*Von Kries* chromatic adaptation:
>>> import numpy as np
>>> 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(XYZ, XYZ_w, XYZ_wr)
... # doctest: +ELLIPSIS
array([ 0.2163881..., 0.1257 , 0.0384749...])
*CIE 1994* chromatic adaptation, requires extra *kwargs*:
>>> XYZ = np.array([0.2800, 0.2126, 0.0527])
>>> XYZ_w = np.array([1.09867452, 1.00000000, 0.35591556])
>>> XYZ_wr = np.array([0.95045593, 1.00000000, 1.08905775])
>>> Y_o = 0.20
>>> E_o = 1000
>>> chromatic_adaptation(
... XYZ, XYZ_w, XYZ_wr, method='CIE 1994', Y_o=Y_o, E_o1=E_o, E_o2=E_o)
... # doctest: +ELLIPSIS
array([ 0.2403379..., 0.2115621..., 0.1764301...])
*CMCCAT2000* chromatic adaptation, requires extra *kwargs*:
>>> XYZ = np.array([0.2248, 0.2274, 0.0854])
>>> XYZ_w = np.array([1.1115, 1.0000, 0.3520])
>>> XYZ_wr = np.array([0.9481, 1.0000, 1.0730])
>>> L_A = 200
>>> chromatic_adaptation(
... XYZ, XYZ_w, XYZ_wr, method='CMCCAT2000', L_A1=L_A, L_A2=L_A)
... # doctest: +ELLIPSIS
array([ 0.1952698..., 0.2306834..., 0.2497175...])
*Fairchild (1990)* chromatic adaptation, requires extra *kwargs*:
>>> XYZ = np.array([0.1953, 0.2307, 0.2497])
>>> Y_n = 200
>>> chromatic_adaptation(
... XYZ, XYZ_w, XYZ_wr, method='Fairchild 1990', Y_n=Y_n)
... # doctest: +ELLIPSIS
array([ 0.2332526..., 0.2332455..., 0.7611593...])
*Zhai and Luo (2018)* chromatic adaptation:
>>> 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(XYZ, XYZ_w, XYZ_wr, method='Zhai 2018')
... # doctest: +ELLIPSIS
array([ 0.2163881..., 0.1257 , 0.0384749...])
>>> chromatic_adaptation(
... XYZ, XYZ_w, XYZ_wr, method='Zhai 2018', D_b=0.9,
... XYZ_wo=np.array([100, 100, 100]))
... # doctest: +ELLIPSIS
array([ 0.2152436..., 0.1253522..., 0.0388406...])
"""
method = validate_method(method, CHROMATIC_ADAPTATION_METHODS)
function = CHROMATIC_ADAPTATION_METHODS[method]
domain_range_reference = get_domain_range_scale() == "reference"
domain_100 = (
chromatic_adaptation_CIE1994,
chromatic_adaptation_CMCCAT2000,
chromatic_adaptation_Fairchild1990,
chromatic_adaptation_Zhai2018,
)
if function in domain_100 and domain_range_reference:
XYZ = as_float_array(XYZ) * 100
XYZ_w = as_float_array(XYZ_w) * 100
XYZ_wr = as_float_array(XYZ_wr) * 100
if "Y_o" in kwargs:
kwargs["Y_o"] = kwargs["Y_o"] * 100
if "XYZ_wo" in kwargs:
kwargs["XYZ_wo"] = kwargs["XYZ_wo"] * 100
kwargs.update({"XYZ_w": XYZ_w, "XYZ_wr": XYZ_wr})
if function is chromatic_adaptation_CIE1994:
from colour import XYZ_to_xy
kwargs.update({"xy_o1": XYZ_to_xy(XYZ_w), "xy_o2": XYZ_to_xy(XYZ_wr)})
elif function is chromatic_adaptation_Fairchild1990:
kwargs.update({"XYZ_n": XYZ_w, "XYZ_r": XYZ_wr})
elif function is chromatic_adaptation_Zhai2018:
kwargs.update({"XYZ_wb": XYZ_w, "XYZ_wd": XYZ_wr})
XYZ_c = function(XYZ, **filter_kwargs(function, **kwargs))
if function in domain_100 and domain_range_reference:
XYZ_c /= 100
return XYZ_c
def chromatic_adaptation(XYZ, XYZ_w, XYZ_wr, method='Von Kries', **kwargs):
"""
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 the whitepoint.
XYZ_wr : array_like
Reference viewing condition *CIE XYZ* tristimulus values of the
whitepoint.
method : unicode, optional
**{'Von Kries', 'CIE 1994', 'CMCCAT2000', 'Fairchild 1990'}**,
Computation method.
Other Parameters
----------------
E_o1 : numeric
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Test illuminance :math:`E_{o1}` in :math:`cd/m^2`.
E_o2 : numeric
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Reference illuminance :math:`E_{o2}` in :math:`cd/m^2`.
L_A1 : numeric or array_like
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
Luminance of test adapting field :math:`L_{A1}` in :math:`cd/m^2`.
L_A2 : numeric or array_like
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
Luminance of reference adapting field :math:`L_{A2}` in :math:`cd/m^2`.
Y_n : numeric or array_like
{:func:`colour.adaptation.chromatic_adaptation_Fairchild1990`},
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
Y_o : numeric
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Luminance factor :math:`Y_o` of achromatic background as percentage in
domain [18, 100].
direction : unicode, optional
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
**{'Forward', 'Reverse'}**,
Chromatic adaptation direction.
discount_illuminant : bool, optional
{:func:`colour.adaptation.chromatic_adaptation_Fairchild1990`},
Truth value indicating if the illuminant should be discounted.
n : numeric, optional
{:func:`colour.adaptation.chromatic_adaptation_CIE1994`},
Noise component in fundamental primary system.
surround : CMCCAT2000_InductionFactors, optional
{:func:`colour.adaptation.chromatic_adaptation_CMCCAT2000`},
Surround viewing conditions induction factors.
transform : unicode, optional
{:func:`colour.adaptation.chromatic_adaptation_VonKries`},
**{'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:`CIETC1-321994b`
- :cite:`Fairchild1991a`
- :cite:`Fairchild2013s`
- :cite:`Fairchild2013t`
- :cite:`Li2002a`
- :cite:`Westland2012k`
Examples
--------
*Von Kries* chromatic adaptation:
>>> import numpy as np
>>> 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(XYZ, XYZ_w, XYZ_wr)
... # doctest: +ELLIPSIS
array([ 0.0839746..., 0.1141321..., 0.2862554...])
*CIE 1994* chromatic adaptation, requires extra *kwargs*:
>>> XYZ = np.array([0.2800, 0.2126, 0.0527])
>>> XYZ_w = np.array([1.09867452, 1.00000000, 0.35591556])
>>> XYZ_wr = np.array([0.95045593, 1.00000000, 1.08905775])
>>> Y_o = 20
>>> E_o = 1000
>>> chromatic_adaptation(
... XYZ, XYZ_w, XYZ_wr, method='CIE 1994', Y_o=Y_o, E_o1=E_o, E_o2=E_o)
... # doctest: +ELLIPSIS
array([ 0.2403379..., 0.2115621..., 0.1764301...])
*CMCCAT2000* chromatic adaptation, requires extra *kwargs*:
>>> XYZ = np.array([0.2248, 0.2274, 0.0854])
>>> XYZ_w = np.array([1.1115, 1.0000, 0.3520])
>>> XYZ_wr = np.array([0.9481, 1.0000, 1.0730])
>>> L_A = 200
>>> chromatic_adaptation(
... XYZ, XYZ_w, XYZ_wr, method='CMCCAT2000', L_A1=L_A, L_A2=L_A)
... # doctest: +ELLIPSIS
array([ 0.1952698..., 0.2306834..., 0.2497175...])
*Fairchild (1990)* chromatic adaptation, requires extra *kwargs*:
>>> XYZ = np.array([0.1953, 0.2307, 0.2497])
>>> Y_n = 200
>>> chromatic_adaptation(
... XYZ, XYZ_w, XYZ_wr, method='Fairchild 1990', Y_n=Y_n)
... # doctest: +ELLIPSIS
array([ 0.2332526..., 0.2332455..., 0.7611593...])
"""
XYZ = np.asarray(XYZ)
XYZ_w = np.asarray(XYZ_w)
XYZ_wr = np.asarray(XYZ_wr)
function = CHROMATIC_ADAPTATION_METHODS[method]
# Callables with percentage domain.
# TODO: Handle scaling with metadata.
percentage_domain = (chromatic_adaptation_CIE1994,
chromatic_adaptation_CMCCAT2000,
chromatic_adaptation_Fairchild1990)
if function in percentage_domain:
XYZ = XYZ * 100
XYZ_w = XYZ_w * 100
XYZ_wr = XYZ_wr * 100
kwargs.update({'XYZ_w': XYZ_w, 'XYZ_wr': XYZ_wr})
if function is chromatic_adaptation_CIE1994:
from colour import XYZ_to_xy
kwargs.update({'xy_o1': XYZ_to_xy(XYZ_w), 'xy_o2': XYZ_to_xy(XYZ_wr)})
elif function is chromatic_adaptation_Fairchild1990:
kwargs.update({'XYZ_n': XYZ_w, 'XYZ_r': XYZ_wr})
XYZ_c = function(XYZ, **filter_kwargs(function, **kwargs))
if function in percentage_domain:
XYZ_c /= 100
return XYZ_c
cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
with open('test.txt') as f:
data = pd.read_csv(f, sep="\t" or ' ' or ',', header=None)
f.close()
w = [i[0] for i in data.values]
s = [i[1] for i in data.values]
data_formated = dict(zip(w, s))
print(data)
print(data.values)
print(data_formated)
spd = SpectralPowerDistribution('Sample', data_formated)
illuminant = ILLUMINANTS_RELATIVE_SPDS['D50']
XYZ = spectral_to_XYZ(spd, cmfs, illuminant)
print(XYZ)
xy = XYZ_to_xy(XYZ)
print(xy)
CIE_1931_chromaticity_diagram_plot(standalone=False)
x, y = xy
pylab.plot(x, y, 'o-', color='white')
pylab.annotate('test',
xy=xy,
xytext=(-50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=-0.2'))
a = display(standalone=False)
print(type(a))
figfile = BytesIO()
from Quartz.CoreGraphics import CGGetDisplayTransferByTable, CGMainDisplayID
from colour import xy_to_CCT_Hernandez1999, XYZ_to_xy, sRGB_to_XYZ
from time import sleep
lasttemp = None
while True:
(_, red, green, blue,
_) = CGGetDisplayTransferByTable(CGMainDisplayID(), 3, None, None, None,
None)
rgb = [red[2], green[2], blue[2]]
# print("rgb", rgb)
temp = xy_to_CCT_Hernandez1999(XYZ_to_xy(sRGB_to_XYZ(rgb)))
if temp != lasttemp:
print temp
sleeptime = 0.1
else:
sleeptime = 1
lasttemp = temp
sleep(sleeptime)