# -*- coding: utf-8 -*-
"""
Colour Rendering Index
======================
Defines *Colour Rendering Index* (CRI) computation objects:
- :class:`colour.quality.CRI_Specification`
- :func:`colour.colour_rendering_index`
See Also
--------
`Colour Rendering Index Jupyter Notebook
<http://nbviewer.jupyter.org/github/colour-science/colour-notebooks/\
blob/master/notebooks/quality/cri.ipynb>`_
References
----------
- :cite:`Ohno2008a` : Ohno, Y., & Davis, W. (2008). NIST CQS simulation 7.4.
Retrieved from https://drive.google.com/file/d/\
1PsuU6QjUJjCX6tQyCud6ul2Tbs8rYWW9/view?usp=sharing
"""
from __future__ import division, unicode_literals
import numpy as np
from collections import namedtuple
from colour.algebra import euclidean_distance, spow
from colour.colorimetry import (
DEFAULT_SPECTRAL_SHAPE, sd_CIE_illuminant_D_series,
STANDARD_OBSERVERS_CMFS, sd_blackbody, sd_to_XYZ)
from colour.quality.datasets.tcs import TCS_INDEXES_TO_NAMES, TCS_SDS
from colour.models import UCS_to_uv, XYZ_to_UCS, XYZ_to_xyY
from colour.temperature import CCT_to_xy_CIE_D, uv_to_CCT_Robertson1968
from colour.utilities import domain_range_scale
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2020 - Colour Developers'
__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-developers@colour-science.org'
__status__ = 'Production'
__all__ = [
'TCS_ColorimetryData', 'TCS_ColourQualityScaleData', 'CRI_Specification',
'colour_rendering_index', 'tcs_colorimetry_data',
'colour_rendering_indexes'
]
class TCS_ColorimetryData(
namedtuple('TCS_ColorimetryData', ('name', 'XYZ', 'uv', 'UVW'))):
"""
Defines the the class storing *test colour samples* colorimetry data.
"""
class TCS_ColourQualityScaleData(
namedtuple('TCS_ColourQualityScaleData', ('name', 'Q_a'))):
"""
Defines the the class storing *test colour samples* colour rendering
index data.
"""
[docs]class CRI_Specification(
namedtuple('CRI_Specification',
('name', 'Q_a', 'Q_as', 'colorimetry_data'))):
"""
Defines the *Colour Rendering Index* (CRI) colour quality specification.
Parameters
----------
name : unicode
Name of the test spectral distribution.
Q_a : numeric
*Colour Rendering Index* (CRI) :math:`Q_a`.
Q_as : dict
Individual *colour rendering indexes* data for each sample.
colorimetry_data : tuple
Colorimetry data for the test and reference computations.
References
----------
:cite:`Ohno2008a`
"""
[docs]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 tcs_colorimetry_data(sd_t, sd_r, sds_tcs, cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
sd_t : SpectralDistribution
Test spectral distribution.
sd_r : SpectralDistribution
Reference spectral distribution.
sds_tcs : dict
*Test colour samples* spectral distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
XYZ_t = sd_to_XYZ(sd_t, cmfs)
uv_t = UCS_to_uv(XYZ_to_UCS(XYZ_t))
u_t, v_t = uv_t[0], uv_t[1]
XYZ_r = sd_to_XYZ(sd_r, cmfs)
uv_r = UCS_to_uv(XYZ_to_UCS(XYZ_r))
u_r, v_r = uv_r[0], uv_r[1]
tcs_data = []
for _key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
sd_tcs = sds_tcs[value]
XYZ_tcs = sd_to_XYZ(sd_tcs, cmfs, sd_t)
xyY_tcs = XYZ_to_xyY(XYZ_tcs)
uv_tcs = UCS_to_uv(XYZ_to_UCS(XYZ_tcs))
u_tcs, v_tcs = uv_tcs[0], uv_tcs[1]
if chromatic_adaptation:
def c(x, y):
"""
Computes the :math:`c` term.
"""
return (4 - x - 10 * y) / y
def d(x, y):
"""
Computes the :math:`d` term.
"""
return (1.708 * y + 0.404 - 1.481 * x) / y
c_t, d_t = c(u_t, v_t), d(u_t, v_t)
c_r, d_r = c(u_r, v_r), d(u_r, v_r)
tcs_c, tcs_d = c(u_tcs, v_tcs), d(u_tcs, v_tcs)
u_tcs = (
(10.872 + 0.404 * c_r / c_t * tcs_c - 4 * d_r / d_t * tcs_d) /
(16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d))
v_tcs = (5.52 /
(16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d))
W_tcs = 25 * spow(xyY_tcs[-1], 1 / 3) - 17
U_tcs = 13 * W_tcs * (u_tcs - u_r)
V_tcs = 13 * W_tcs * (v_tcs - v_r)
tcs_data.append(
TCS_ColorimetryData(sd_tcs.name, XYZ_tcs, uv_tcs,
np.array([U_tcs, V_tcs, W_tcs])))
return tcs_data
def colour_rendering_indexes(test_data, reference_data):
"""
Returns the *test colour samples* rendering indexes :math:`Q_a`.
Parameters
----------
test_data : list
Test data.
reference_data : list
Reference data.
Returns
-------
dict
*Test colour samples* *Colour Rendering Index* (CRI).
"""
Q_as = {}
for i, _ in enumerate(test_data):
Q_as[i + 1] = TCS_ColourQualityScaleData(
test_data[i].name, 100 -
4.6 * euclidean_distance(reference_data[i].UVW, test_data[i].UVW))
return Q_as