#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Colour Rendering Index
======================
Defines *colour rendering index* computation objects:
- :class:`CRI_Specification`
- :func:`colour_rendering_index`
See Also
--------
`Colour Rendering Index IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/quality/cri.ipynb>`_ # noqa
References
----------
.. [1] Ohno, Y., & Davis, W. (2008). NIST CQS simulation 7.4. Retrieved from
http://cie2.nist.gov/TC1-69/NIST CQS simulation 7.4.xls
"""
from __future__ import division, unicode_literals
import numpy as np
from collections import namedtuple
from colour.colorimetry import (
D_illuminant_relative_spd,
STANDARD_OBSERVERS_CMFS,
blackbody_spd,
spectral_to_XYZ)
from colour.quality.dataset.tcs import TCS_INDEXES_TO_NAMES, TCS_SPDS
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
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2015 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['TCS_ColorimetryData',
'TCS_ColourQualityScaleData',
'CRI_Specification',
'colour_rendering_index',
'tcs_colorimetry_data',
'colour_rendering_indexes']
[docs]class TCS_ColorimetryData(namedtuple('TCS_ColorimetryData',
('name', 'XYZ', 'uv', 'UVW'))):
"""
Defines the the class holding *test colour samples* colorimetry data.
"""
[docs]class TCS_ColourQualityScaleData(
namedtuple('TCS_ColourQualityScaleData',
('name', 'Q_a'))):
"""
Defines the the class holding *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* colour quality specification.
Parameters
----------
name : unicode
Name of the test spectral power distribution.
Q_a : numeric
*Colour rendering index* :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.
"""
[docs]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
[docs]def tcs_colorimetry_data(spd_t,
spd_r,
spds_tcs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
spd_t : SpectralPowerDistribution
Test spectral power distribution.
spd_r : SpectralPowerDistribution
Reference spectral power distribution.
spds_tcs : dict
*Test colour samples* spectral power 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 = spectral_to_XYZ(spd_t, cmfs)
uv_t = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ_t)))
u_t, v_t = uv_t[0], uv_t[1]
XYZ_r = spectral_to_XYZ(spd_r, cmfs)
uv_r = np.ravel(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()):
spd_tcs = spds_tcs.get(value)
XYZ_tcs = spectral_to_XYZ(spd_tcs, cmfs, spd_t)
xyY_tcs = np.ravel(XYZ_to_xyY(XYZ_tcs))
uv_tcs = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ_tcs)))
u_tcs, v_tcs = uv_tcs[0], uv_tcs[1]
if chromatic_adaptation:
c = lambda x, y: (4 - x - 10 * y) / y
d = lambda x, y: (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 * 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(spd_tcs.name,
XYZ_tcs,
uv_tcs,
np.array([U_tcs, V_tcs, W_tcs])))
return tcs_data
[docs]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 indexes.
"""
Q_as = {}
for i, _ in enumerate(test_data):
Q_as[i + 1] = TCS_ColourQualityScaleData(
test_data[i].name,
100 - 4.6 * np.linalg.norm(
reference_data[i].UVW - test_data[i].UVW))
return Q_as
Colour 0.3