Source code for colour.quality.cqs

# -*- coding: utf-8 -*-
"""
Colour Quality Scale
====================

Defines *Colour Quality Scale* (CQS) computation objects:

-   :class:`colour.quality.CQS_Specification`
-   :func:`colour.colour_quality_scale`

References
----------
-   :cite:`Davis2010a` : Davis, W., & Ohno, Y. (2010). Color quality scale.
    Optical Engineering, 49(3), 033602. doi:10.1117/1.3360335
-   :cite:`Ohno2008a` : Ohno, Yoshiro, & Davis, W. (2008). NIST CQS simulation
    (Version 7.4) [Computer software].
    https://drive.google.com/file/d/1PsuU6QjUJjCX6tQyCud6ul2Tbs8rYWW9/view?\
usp=sharing
-   :cite:`Ohno2013` : Ohno, Yoshiro, & Davis, W. (2008). NIST CQS simulation
    (Version 7.4) [Computer software].
    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
from colour.colorimetry import (
    DEFAULT_SPECTRAL_SHAPE, sd_CIE_illuminant_D_series, ILLUMINANTS,
    STANDARD_OBSERVER_CMFS, sd_blackbody, sd_to_XYZ)
from colour.quality.datasets.vs import VS_INDEXES_TO_NAMES, VS_SDS
from colour.models import (Lab_to_LCHab, UCS_to_uv, XYZ_to_Lab, XYZ_to_UCS,
                           XYZ_to_xy, xy_to_XYZ)
from colour.temperature import CCT_to_xy_CIE_D, uv_to_CCT_Ohno2013
from colour.adaptation import chromatic_adaptation_VonKries
from colour.utilities import as_float_array, domain_range_scale, tsplit
from colour.utilities.documentation import DocstringTuple

__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__ = [
    'D65_GAMUT_AREA', 'VS_ColorimetryData', 'VS_ColourQualityScaleData',
    'CQS_Specification', 'COLOUR_QUALITY_SCALE_METHODS',
    'colour_quality_scale', 'gamut_area', 'vs_colorimetry_data', 'CCT_factor',
    'scale_conversion', 'delta_E_RMS', 'colour_quality_scales'
]

D65_GAMUT_AREA = 8210


class VS_ColorimetryData(
        namedtuple('VS_ColorimetryData', ('name', 'XYZ', 'Lab', 'C'))):
    """
    Defines the the class storing *VS test colour samples* colorimetry data.
    """


class VS_ColourQualityScaleData(
        namedtuple('VS_ColourQualityScaleData',
                   ('name', 'Q_a', 'D_C_ab', 'D_E_ab', 'D_Ep_ab'))):
    """
    Defines the the class storing *VS test colour samples* colour quality
    scale data.
    """


[docs]class CQS_Specification( namedtuple('CQS_Specification', ('name', 'Q_a', 'Q_f', 'Q_p', 'Q_g', 'Q_d', 'Q_as', 'colorimetry_data'))): """ Defines the *Colour Quality Scale* (CQS) colour quality specification. Parameters ---------- name : unicode Name of the test spectral distribution. Q_a : numeric Colour quality scale :math:`Q_a`. Q_f : numeric Colour fidelity scale :math:`Q_f` intended to evaluate the fidelity of object colour appearances (compared to the reference illuminant of the same correlated colour temperature and illuminance). Q_p : numeric Colour preference scale :math:`Q_p` similar to colour quality scale :math:`Q_a` but placing additional weight on preference of object colour appearance, set to *None* in *NIST CQS 9.0* method. This metric is based on the notion that increases in chroma are generally preferred and should be rewarded. Q_g : numeric Gamut area scale :math:`Q_g` representing the relative gamut formed by the (:math:`a^*`, :math:`b^*`) coordinates of the 15 samples illuminated by the test light source in the *CIE L\\*a\\*b\\** object colourspace. Q_d : numeric Relative gamut area scale :math:`Q_d`, set to *None* in *NIST CQS 9.0* method. Q_as : dict Individual *Colour Quality Scale* (CQS) data for each sample. colorimetry_data : tuple Colorimetry data for the test and reference computations. References ---------- :cite:`Davis2010a`, :cite:`Ohno2008a`, :cite:`Ohno2013` """
COLOUR_QUALITY_SCALE_METHODS = DocstringTuple(['NIST CQS 7.4', 'NIST CQS 9.0']) COLOUR_QUALITY_SCALE_METHODS.__doc__ = """ Supported *Colour Quality Scale* (CQS) computation methods. References ---------- :cite:`Davis2010a`, :cite:`Ohno2008a`, :cite:`Ohno2013` COLOUR_QUALITY_SCALE_METHODS : tuple **{ 'NIST CQS 9.0', 'NIST CQS 7.4'}** """
[docs]def colour_quality_scale(sd_test, additional_data=False, method='NIST CQS 9.0'): """ Returns the *Colour Quality Scale* (CQS) of given spectral distribution using given method. Parameters ---------- sd_test : SpectralDistribution Test spectral distribution. additional_data : bool, optional Whether to output additional data. method : unicode, optional **{'NIST CQS 9.0', 'NIST CQS 7.4'}**, Computation method. Returns ------- numeric or CQS_Specification Color quality scale. References ---------- :cite:`Davis2010a`, :cite:`Ohno2008a`, :cite:`Ohno2013` Examples -------- >>> from colour import ILLUMINANT_SDS >>> sd = ILLUMINANT_SDS['FL2'] >>> colour_quality_scale(sd) # doctest: +ELLIPSIS 64.1117031... """ method = method.lower() assert method.lower() in [ m.lower() for m in COLOUR_QUALITY_SCALE_METHODS ], ('"{0}" method is invalid, must be one of {1}!'.format( method, COLOUR_QUALITY_SCALE_METHODS)) cmfs = STANDARD_OBSERVER_CMFS['CIE 1931 2 Degree Standard Observer'].copy( ).trim(DEFAULT_SPECTRAL_SHAPE) shape = cmfs.shape sd_test = sd_test.copy().align(shape) vs_sds = { sd.name: sd.copy().align(shape) for sd in VS_SDS[method].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_Ohno2013(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_vs_colorimetry_data = vs_colorimetry_data( sd_test, sd_reference, vs_sds, cmfs, chromatic_adaptation=True) reference_vs_colorimetry_data = vs_colorimetry_data( sd_reference, sd_reference, vs_sds, cmfs) if method == 'nist cqs 9.0': CCT_f = 1 scaling_f = 3.2 else: XYZ_r = sd_to_XYZ(sd_reference, cmfs) XYZ_r /= XYZ_r[1] CCT_f = CCT_factor(reference_vs_colorimetry_data, XYZ_r) scaling_f = 3.104 Q_as = colour_quality_scales(test_vs_colorimetry_data, reference_vs_colorimetry_data, scaling_f, CCT_f) D_E_RMS = delta_E_RMS(Q_as, 'D_E_ab') D_Ep_RMS = delta_E_RMS(Q_as, 'D_Ep_ab') Q_a = scale_conversion(D_Ep_RMS, CCT_f, scaling_f) if method == 'nist cqs 9.0': scaling_f = 2.93 * 1.0343 else: scaling_f = 2.928 Q_f = scale_conversion(D_E_RMS, CCT_f, scaling_f) G_t = gamut_area( [vs_CQS_data.Lab for vs_CQS_data in test_vs_colorimetry_data]) G_r = gamut_area( [vs_CQS_data.Lab for vs_CQS_data in reference_vs_colorimetry_data]) Q_g = G_t / D65_GAMUT_AREA * 100 if method == 'nist cqs 9.0': Q_d = Q_p = None else: p_delta_C = np.average([ sample_data.D_C_ab if sample_data.D_C_ab > 0 else 0 for sample_data in Q_as.values() ]) Q_p = 100 - 3.6 * (D_Ep_RMS - p_delta_C) Q_d = G_t / G_r * CCT_f * 100 if additional_data: return CQS_Specification( sd_test.name, Q_a, Q_f, Q_p, Q_g, Q_d, Q_as, (test_vs_colorimetry_data, reference_vs_colorimetry_data)) else: return Q_a
def gamut_area(Lab): """ Returns the gamut area :math:`G` covered by given *CIE L\\*a\\*b\\** matrices. Parameters ---------- Lab : array_like *CIE L\\*a\\*b\\** colourspace matrices. Returns ------- numeric Gamut area :math:`G`. Examples -------- >>> Lab = [ ... np.array([39.94996006, 34.59018231, -19.86046321]), ... np.array([38.88395498, 21.44348519, -34.87805301]), ... np.array([36.60576301, 7.06742454, -43.21461177]), ... np.array([46.60142558, -15.90481586, -34.64616865]), ... np.array([56.50196523, -29.54655550, -20.50177194]), ... np.array([55.73912101, -43.39520959, -5.08956953]), ... np.array([56.20776870, -53.68997662, 20.21134410]), ... np.array([66.16683122, -38.64600327, 42.77396631]), ... np.array([76.72952110, -23.92148210, 61.04740432]), ... np.array([82.85370708, -3.98679065, 75.43320144]), ... np.array([69.26458861, 13.11066359, 68.83858372]), ... np.array([69.63154351, 28.24532497, 59.45609803]), ... np.array([61.26281449, 40.87950839, 44.97606172]), ... np.array([41.62567821, 57.34129516, 27.46718170]), ... np.array([40.52565174, 48.87449192, 3.45121680]) ... ] >>> gamut_area(Lab) # doctest: +ELLIPSIS 8335.9482018... """ Lab = as_float_array(Lab) Lab_s = np.roll(np.copy(Lab), -3) _L, a, b = tsplit(Lab) _L_s, a_s, b_s = tsplit(Lab_s) A = np.linalg.norm(Lab[..., 1:3], axis=-1) B = np.linalg.norm(Lab_s[..., 1:3], axis=-1) C = np.linalg.norm(np.dstack([a_s - a, b_s - b]), axis=-1) t = (A + B + C) / 2 S = np.sqrt(t * (t - A) * (t - B) * (t - C)) return np.sum(S) def vs_colorimetry_data(sd_test, sd_reference, sds_vs, cmfs, chromatic_adaptation=False): """ Returns the *VS test colour samples* colorimetry data. Parameters ---------- sd_test : SpectralDistribution Test spectral distribution. sd_reference : SpectralDistribution Reference spectral distribution. sds_vs : dict *VS test colour samples* spectral distributions. cmfs : XYZ_ColourMatchingFunctions Standard observer colour matching functions. chromatic_adaptation : bool, optional Perform chromatic adaptation. Returns ------- list *VS test colour samples* colorimetry data. """ XYZ_t = sd_to_XYZ(sd_test, cmfs) XYZ_t /= XYZ_t[1] XYZ_r = sd_to_XYZ(sd_reference, cmfs) XYZ_r /= XYZ_r[1] xy_r = XYZ_to_xy(XYZ_r) vs_data = [] for _key, value in sorted(VS_INDEXES_TO_NAMES.items()): sd_vs = sds_vs[value] with domain_range_scale('1'): XYZ_vs = sd_to_XYZ(sd_vs, cmfs, sd_test) if chromatic_adaptation: XYZ_vs = chromatic_adaptation_VonKries( XYZ_vs, XYZ_t, XYZ_r, transform='CMCCAT2000') Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r) _L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs) vs_data.append(VS_ColorimetryData(sd_vs.name, XYZ_vs, Lab_vs, C_vs)) return vs_data def CCT_factor(reference_data, XYZ_r): """ Returns the correlated colour temperature factor penalizing lamps with extremely low correlated colour temperatures. Parameters ---------- reference_data : VS_ColorimetryData Reference colorimetry data. XYZ_r : array_like *CIE XYZ* tristimulus values for reference. Returns ------- numeric Correlated colour temperature factor. """ xy_w = ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65'] XYZ_w = xy_to_XYZ(xy_w) Labs = [] for vs_colorimetry_data_ in reference_data: _name, XYZ, _Lab, _C = vs_colorimetry_data_ XYZ_a = chromatic_adaptation_VonKries( XYZ, XYZ_r, XYZ_w, transform='CMCCAT2000') Lab = XYZ_to_Lab(XYZ_a, illuminant=xy_w) Labs.append(Lab) G_r = gamut_area(Labs) / D65_GAMUT_AREA CCT_f = 1 if G_r > 1 else G_r return CCT_f def scale_conversion(D_E_ab, CCT_f, scaling_f): """ Returns the *Colour Quality Scale* (CQS) for given :math:`\\Delta E_{ab}` value and given correlated colour temperature penalizing factor. Parameters ---------- D_E_ab : numeric :math:`\\Delta E_{ab}` value. CCT_f : numeric Correlated colour temperature penalizing factor. scaling_f : numeric, optional Scaling factor constant. Returns ------- numeric *Colour Quality Scale* (CQS). """ Q_a = 10 * np.log(np.exp((100 - scaling_f * D_E_ab) / 10) + 1) * CCT_f return Q_a def delta_E_RMS(cqs_data, attribute): """ Computes the root-mean-square average for given *Colour Quality Scale* (CQS) data. Parameters ---------- cqs_data : VS_ColourQualityScaleData *Colour Quality Scale* (CQS) data. attribute : unicode Colorimetry data attribute to use to compute the root-mean-square average. Returns ------- numeric Root-mean-square average. """ return np.sqrt(1 / len(cqs_data) * np.sum([ getattr(sample_data, attribute) ** 2 for sample_data in cqs_data.values() ])) def colour_quality_scales(test_data, reference_data, scaling_f, CCT_f): """ Returns the *VS test colour samples* rendering scales. Parameters ---------- test_data : list Test data. reference_data : list Reference data. scaling_f : numeric, optional Scaling factor constant. CCT_f : numeric Factor penalizing lamps with extremely low correlated colour temperatures. Returns ------- dict *VS Test colour samples* colour rendering scales. """ Q_as = {} for i, _ in enumerate(test_data): D_C_ab = test_data[i].C - reference_data[i].C D_E_ab = euclidean_distance(test_data[i].Lab, reference_data[i].Lab) if D_C_ab > 0: D_Ep_ab = np.sqrt(D_E_ab ** 2 - D_C_ab ** 2) else: D_Ep_ab = D_E_ab Q_a = scale_conversion(D_Ep_ab, CCT_f, scaling_f) Q_as[i + 1] = VS_ColourQualityScaleData(test_data[i].name, Q_a, D_C_ab, D_E_ab, D_Ep_ab) return Q_as