Source code for colour.recovery.meng2015

# -*- coding: utf-8 -*-
"""
Meng et al. (2015) - Reflectance Recovery
=========================================

Defines the objects for reflectance recovery using
*Meng, Simon and Hanika (2015)* method:

-   :func:`colour.recovery.XYZ_to_sd_Meng2015`

References
----------
-   :cite:`Meng2015c` : Meng, J., Simon, F., Hanika, J., & Dachsbacher, C.
    (2015). Physically Meaningful Rendering using Tristimulus Colours. Computer
    Graphics Forum, 34(4), 31-40. doi:10.1111/cgf.12676
"""

import numpy as np
from scipy.optimize import minimize

from colour.colorimetry import (MSDS_CMFS_STANDARD_OBSERVER, SDS_ILLUMINANTS,
                                SpectralDistribution, SpectralShape, sd_ones,
                                sd_to_XYZ_integration)
from colour.utilities import to_domain_1, from_range_100, runtime_warning

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2021 - 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__ = ['SPECTRAL_SHAPE_MENG2015', 'XYZ_to_sd_Meng2015']

SPECTRAL_SHAPE_MENG2015 = SpectralShape(360, 780, 5)
"""
Spectral shape according to *ASTM E308-15* practise shape but using an interval
of 5.

SPECTRAL_SHAPE_MENG2015 : SpectralShape
"""


[docs]def XYZ_to_sd_Meng2015( XYZ, cmfs=MSDS_CMFS_STANDARD_OBSERVER['CIE 1931 2 Degree Standard Observer'] .copy().align(SPECTRAL_SHAPE_MENG2015), illuminant=SDS_ILLUMINANTS['D65'].copy().align( SPECTRAL_SHAPE_MENG2015), optimisation_kwargs=None): """ Recovers the spectral distribution of given *CIE XYZ* tristimulus values using *Meng et al. (2015)* method. Parameters ---------- XYZ : array_like, (3,) *CIE XYZ* tristimulus values to recover the spectral distribution from. cmfs : XYZ_ColourMatchingFunctions Standard observer colour matching functions. The wavelength :math:`\\lambda_{i}` range interval of the colour matching functions affects directly the time the computations take. The current default interval of 5 is a good compromise between precision and time spent. illuminant : SpectralDistribution, optional Illuminant spectral distribution. optimisation_kwargs : dict_like, optional Parameters for :func:`scipy.optimize.minimize` definition. Returns ------- SpectralDistribution Recovered spectral distribution. Notes ----- +------------+-----------------------+---------------+ | **Domain** | **Scale - Reference** | **Scale - 1** | +============+=======================+===============+ | ``XYZ`` | [0, 1] | [0, 1] | +------------+-----------------------+---------------+ - The definition used to convert spectrum to *CIE XYZ* tristimulus values is :func:`colour.colorimetry.spectral_to_XYZ_integration` definition because it processes any measurement interval opposed to :func:`colour.colorimetry.sd_to_XYZ_ASTME308` definition that handles only measurement interval of 1, 5, 10 or 20nm. References ---------- :cite:`Meng2015c` Examples -------- >>> from colour.utilities import numpy_print_options >>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) >>> cmfs = ( ... MSDS_CMFS_STANDARD_OBSERVER['CIE 1931 2 Degree Standard Observer']. ... copy().align(SpectralShape(360, 780, 10)) ... ) >>> illuminant = SDS_ILLUMINANTS['D65'].copy().align(cmfs.shape) >>> sd = XYZ_to_sd_Meng2015(XYZ, cmfs, illuminant) >>> with numpy_print_options(suppress=True): ... sd # doctest: +ELLIPSIS SpectralDistribution([[ 360. , 0.0765153...], [ 370. , 0.0764771...], [ 380. , 0.0764286...], [ 390. , 0.0764329...], [ 400. , 0.0765863...], [ 410. , 0.0764339...], [ 420. , 0.0757213...], [ 430. , 0.0733091...], [ 440. , 0.0676493...], [ 450. , 0.0577616...], [ 460. , 0.0440805...], [ 470. , 0.0284802...], [ 480. , 0.0138019...], [ 490. , 0.0033557...], [ 500. , 0. ...], [ 510. , 0. ...], [ 520. , 0. ...], [ 530. , 0. ...], [ 540. , 0.0055360...], [ 550. , 0.0317335...], [ 560. , 0.075457 ...], [ 570. , 0.1314930...], [ 580. , 0.1938219...], [ 590. , 0.2559747...], [ 600. , 0.3122869...], [ 610. , 0.3584363...], [ 620. , 0.3927112...], [ 630. , 0.4158866...], [ 640. , 0.4305832...], [ 650. , 0.4391142...], [ 660. , 0.4439484...], [ 670. , 0.4464121...], [ 680. , 0.4475718...], [ 690. , 0.4481182...], [ 700. , 0.4483734...], [ 710. , 0.4484743...], [ 720. , 0.4485753...], [ 730. , 0.4486474...], [ 740. , 0.4486629...], [ 750. , 0.4486995...], [ 760. , 0.4486925...], [ 770. , 0.4486794...], [ 780. , 0.4486982...]], interpolator=SpragueInterpolator, interpolator_kwargs={}, extrapolator=Extrapolator, extrapolator_kwargs={...}) >>> sd_to_XYZ_integration(sd, cmfs, illuminant) / 100 # doctest: +ELLIPSIS array([ 0.2065400..., 0.1219722..., 0.0513695...]) """ XYZ = to_domain_1(XYZ) if illuminant.shape != cmfs.shape: runtime_warning( 'Aligning "{0}" illuminant shape to "{1}" colour matching ' 'functions shape.'.format(illuminant.name, cmfs.name)) illuminant = illuminant.copy().align(cmfs.shape) sd = sd_ones(cmfs.shape) def objective_function(a): """ Objective function. """ return np.sum(np.diff(a) ** 2) def constraint_function(a): """ Function defining the constraint. """ sd[:] = a return sd_to_XYZ_integration( sd, cmfs=cmfs, illuminant=illuminant) - XYZ wavelengths = sd.wavelengths bins = wavelengths.size optimisation_settings = { 'method': 'SLSQP', 'constraints': { 'type': 'eq', 'fun': constraint_function }, 'bounds': np.tile(np.array([0, 1000]), (bins, 1)), 'options': { 'ftol': 1e-10, }, } if optimisation_kwargs is not None: optimisation_settings.update(optimisation_kwargs) result = minimize(objective_function, sd.values, **optimisation_settings) if not result.success: raise RuntimeError( 'Optimization failed for {0} after {1} iterations: "{2}".'.format( XYZ, result.nit, result.message)) return SpectralDistribution( from_range_100(result.x * 100), wavelengths, name='{0} (XYZ) - Meng (2015)'.format(XYZ))