# -*- coding: utf-8 -*-
"""
Academy Color Encoding System - Input Transform
===============================================
Defines the *Academy Color Encoding System* (ACES) *Input Transform* utilities:
- :func:`colour.sd_to_aces_relative_exposure_values`
- :func:`colour.characterisation.read_training_data_rawtoaces_v1`
- :func:`colour.characterisation.generate_illuminants_rawtoaces_v1`
- :func:`colour.characterisation.white_balance_multipliers`
- :func:`colour.characterisation.best_illuminant`
- :func:`colour.characterisation.normalise_illuminant`
- :func:`colour.characterisation.training_data_sds_to_RGB`
- :func:`colour.characterisation.training_data_sds_to_XYZ`
- :func:`colour.characterisation.optimisation_factory_rawtoaces_v1`
- :func:`colour.characterisation.optimisation_factory_JzAzBz`
- :func:`colour.matrix_idt`
References
----------
- :cite:`Dyer2017` : Dyer, S., Forsythe, A., Irons, J., Mansencal, T., & Zhu,
M. (2017). RAW to ACES (Version 1.0) [Computer software].
- :cite:`Forsythe2018` : Borer, T. (2017). Private Discussion with Mansencal,
T. and Shaw, N.
- :cite:`TheAcademyofMotionPictureArtsandSciences2014q` : The Academy of
Motion Picture Arts and Sciences, Science and Technology Council, & Academy
Color Encoding System (ACES) Project Subcommittee. (2014). Technical
Bulletin TB-2014-004 - Informative Notes on SMPTE ST 2065-1 - Academy Color
Encoding Specification (ACES) (pp. 1-40). Retrieved December 19, 2014, from
http://j.mp/TB-2014-004
- :cite:`TheAcademyofMotionPictureArtsandSciences2014r` : The Academy of
Motion Picture Arts and Sciences, Science and Technology Council, & Academy
Color Encoding System (ACES) Project Subcommittee. (2014). Technical
Bulletin TB-2014-012 - Academy Color Encoding System Version 1.0 Component
Names (pp. 1-8). Retrieved December 19, 2014, from http://j.mp/TB-2014-012
- :cite:`TheAcademyofMotionPictureArtsandSciences2015c` : The Academy of
Motion Picture Arts and Sciences, Science and Technology Council, & Academy
Color Encoding System (ACES) Project Subcommittee. (2015). Procedure
P-2013-001 - Recommended Procedures for the Creation and Use of Digital
Camera System Input Device Transforms (IDTs) (pp. 1-29). Retrieved April
24, 2015, from http://j.mp/P-2013-001
- :cite:`TheAcademyofMotionPictureArtsandSciencese` : The Academy of Motion
Picture Arts and Sciences, Science and Technology Council, & Academy Color
Encoding System (ACES) Project Subcommittee. (n.d.). Academy Color Encoding
System. Retrieved February 24, 2014, from
http://www.oscars.org/science-technology/council/projects/aces.html
"""
from __future__ import division, unicode_literals
import numpy as np
import os
from scipy.optimize import minimize
from colour.adaptation import matrix_chromatic_adaptation_VonKries
from colour.algebra import euclidean_distance
from colour.colorimetry import (
MSDS_CMFS, SDS_ILLUMINANTS, SpectralShape, sds_and_msds_to_msds,
sd_CIE_illuminant_D_series, sd_blackbody, sd_to_XYZ)
from colour.constants import DEFAULT_INT_DTYPE
from colour.characterisation import MSDS_ACES_RICD
from colour.io import read_sds_from_csv_file
from colour.models import XYZ_to_JzAzBz, XYZ_to_Lab, XYZ_to_xy, xy_to_XYZ
from colour.models.rgb import (RGB_COLOURSPACE_ACES2065_1, RGB_to_XYZ,
XYZ_to_RGB, normalised_primary_matrix)
from colour.temperature import CCT_to_xy_CIE_D
from colour.utilities import (CaseInsensitiveMapping, as_float_array,
vector_dot, from_range_1, runtime_warning,
tsplit, suppress_warnings)
__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__ = [
'FLARE_PERCENTAGE', 'S_FLARE_FACTOR',
'sd_to_aces_relative_exposure_values', 'SPECTRAL_SHAPE_RAWTOACES',
'RESOURCES_DIRECTORY_RAWTOACES', 'read_training_data_rawtoaces_v1',
'generate_illuminants_rawtoaces_v1', 'white_balance_multipliers',
'best_illuminant', 'normalise_illuminant', 'training_data_sds_to_RGB',
'training_data_sds_to_XYZ', 'optimisation_factory_rawtoaces_v1',
'optimisation_factory_JzAzBz', 'matrix_idt'
]
FLARE_PERCENTAGE = 0.00500
"""
Flare percentage in the *ACES* system.
FLARE_PERCENTAGE : float
"""
S_FLARE_FACTOR = 0.18000 / (0.18000 + FLARE_PERCENTAGE)
"""
Flare modulation factor in the *ACES* system.
S_FLARE_FACTOR : float
"""
[docs]def sd_to_aces_relative_exposure_values(
sd,
illuminant=SDS_ILLUMINANTS['D65'],
apply_chromatic_adaptation=False,
chromatic_adaptation_transform='CAT02'):
"""
Converts given spectral distribution to *ACES2065-1* colourspace relative
exposure values.
Parameters
----------
sd : SpectralDistribution
Spectral distribution.
illuminant : SpectralDistribution, optional
*Illuminant* spectral distribution.
apply_chromatic_adaptation : bool, optional
Whether to apply chromatic adaptation using given transform.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02 Brill 2008',
'Bianco 2010', 'Bianco PC 2010'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray, (3,)
*ACES2065-1* colourspace relative exposure values array.
Notes
-----
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ`` | [0, 100] | [0, 1] |
+------------+-----------------------+---------------+
- The chromatic adaptation method implemented here is a bit unusual
as it involves building a new colourspace based on *ACES2065-1*
colourspace primaries but using the whitepoint of the illuminant that
the spectral distribution was measured under.
References
----------
:cite:`Forsythe2018`,
:cite:`TheAcademyofMotionPictureArtsandSciences2014q`,
:cite:`TheAcademyofMotionPictureArtsandSciences2014r`,
:cite:`TheAcademyofMotionPictureArtsandSciencese`
Examples
--------
>>> from colour import SDS_COLOURCHECKERS
>>> sd = SDS_COLOURCHECKERS['ColorChecker N Ohta']['dark skin']
>>> sd_to_aces_relative_exposure_values(sd) # doctest: +ELLIPSIS
array([ 0.1171814..., 0.0866360..., 0.0589726...])
>>> sd_to_aces_relative_exposure_values(sd,
... apply_chromatic_adaptation=True) # doctest: +ELLIPSIS
array([ 0.1180779..., 0.0869031..., 0.0589125...])
"""
shape = MSDS_ACES_RICD.shape
if sd.shape != MSDS_ACES_RICD.shape:
sd = sd.copy().align(shape)
if illuminant.shape != MSDS_ACES_RICD.shape:
illuminant = illuminant.copy().align(shape)
s_v = sd.values
i_v = illuminant.values
r_bar, g_bar, b_bar = tsplit(MSDS_ACES_RICD.values)
def k(x, y):
"""
Computes the :math:`K_r`, :math:`K_g` or :math:`K_b` scale factors.
"""
return 1 / np.sum(x * y)
k_r = k(i_v, r_bar)
k_g = k(i_v, g_bar)
k_b = k(i_v, b_bar)
E_r = k_r * np.sum(i_v * s_v * r_bar)
E_g = k_g * np.sum(i_v * s_v * g_bar)
E_b = k_b * np.sum(i_v * s_v * b_bar)
E_rgb = np.array([E_r, E_g, E_b])
# Accounting for flare.
E_rgb += FLARE_PERCENTAGE
E_rgb *= S_FLARE_FACTOR
if apply_chromatic_adaptation:
xy = XYZ_to_xy(sd_to_XYZ(illuminant) / 100)
NPM = normalised_primary_matrix(RGB_COLOURSPACE_ACES2065_1.primaries,
xy)
XYZ = RGB_to_XYZ(E_rgb, xy, RGB_COLOURSPACE_ACES2065_1.whitepoint, NPM,
chromatic_adaptation_transform)
E_rgb = XYZ_to_RGB(XYZ, RGB_COLOURSPACE_ACES2065_1.whitepoint,
RGB_COLOURSPACE_ACES2065_1.whitepoint,
RGB_COLOURSPACE_ACES2065_1.matrix_XYZ_to_RGB)
return from_range_1(E_rgb)
SPECTRAL_SHAPE_RAWTOACES = SpectralShape(380, 780, 5)
"""
Default spectral shape according to *RAW to ACES* v1.
SPECTRAL_SHAPE_RAWTOACES : SpectralShape
"""
RESOURCES_DIRECTORY_RAWTOACES = os.path.join(
os.path.dirname(__file__), 'datasets', 'rawtoaces')
"""
*RAW to ACES* resources directory.
Notes
-----
- *Colour* only ships a minimal dataset from *RAW to ACES*, please see
`Colour - Datasets <https://github.com/colour-science/colour-datasets>`_
for the complete *RAW to ACES* v1 dataset, i.e. *3372171*.
RESOURCES_DIRECTORY_RAWTOACES : unicode
"""
_TRAINING_DATA_RAWTOACES_V1 = None
[docs]def read_training_data_rawtoaces_v1():
"""
Reads the *RAW to ACES* v1 190 patches.
Returns
-------
MultiSpectralDistributions
*RAW to ACES* v1 190 patches.
References
----------
:cite:`Dyer2017`
Examples
--------
>>> len(read_training_data_rawtoaces_v1().labels)
190
"""
global _TRAINING_DATA_RAWTOACES_V1
if _TRAINING_DATA_RAWTOACES_V1 is not None:
training_data = _TRAINING_DATA_RAWTOACES_V1
else:
path = os.path.join(RESOURCES_DIRECTORY_RAWTOACES, '190_Patches.csv')
training_data = sds_and_msds_to_msds(
read_sds_from_csv_file(path).values())
_TRAINING_DATA_RAWTOACES_V1 = training_data
return training_data
_ILLUMINANTS_RAWTOACES_V1 = None
[docs]def generate_illuminants_rawtoaces_v1():
"""
Generates a series of illuminants according to *RAW to ACES* v1:
- *CIE Illuminant D Series* in range [4000, 25000] kelvin degrees.
- *Blackbodies* in range [1000, 3500] kelvin degrees.
- A.M.P.A.S. variant of *ISO 7589 Studio Tungsten*.
Returns
-------
CaseInsensitiveMapping
Series of illuminants.
Notes
-----
- This definition introduces a few differences compared to
*RAW to ACES* v1: *CIE Illuminant D Series* are computed in range
[4002.15, 7003.77] kelvin degrees and the :math:`C_2` change is not
used in *RAW to ACES* v1.
References
----------
:cite:`Dyer2017`
Examples
--------
>>> # Doctests skip for Python 2.x compatibility.
>>> list(sorted(generate_illuminants_rawtoaces_v1().keys()))
... # doctest: +SKIP
['1000K Blackbody', '1500K Blackbody', '2000K Blackbody', \
'2500K Blackbody', '3000K Blackbody', '3500K Blackbody', 'D100', 'D105', \
'D110', 'D115', 'D120', 'D125', 'D130', 'D135', 'D140', 'D145', 'D150', \
'D155', 'D160', 'D165', 'D170', 'D175', 'D180', 'D185', 'D190', 'D195', \
'D200', 'D205', 'D210', 'D215', 'D220', 'D225', 'D230', 'D235', 'D240', \
'D245', 'D250', 'D40', 'D45', 'D50', 'D55', 'D60', 'D65', 'D70', 'D75', \
'D80', 'D85', 'D90', 'D95', 'iso7589']
"""
global _ILLUMINANTS_RAWTOACES_V1
if _ILLUMINANTS_RAWTOACES_V1 is not None:
illuminants = _ILLUMINANTS_RAWTOACES_V1
else:
illuminants = CaseInsensitiveMapping()
# CIE Illuminants D Series from 4000K to 25000K.
for i in np.arange(4000, 25000 + 500, 500):
CCT = i * 1.4388 / 1.4380
xy = CCT_to_xy_CIE_D(CCT)
sd = sd_CIE_illuminant_D_series(xy)
sd.name = 'D{0:d}'.format(DEFAULT_INT_DTYPE(CCT / 100))
illuminants[sd.name] = sd.align(SPECTRAL_SHAPE_RAWTOACES)
# TODO: Remove when removing the "colour.sd_blackbody" definition
# warning.
with suppress_warnings(colour_usage_warnings=True):
# Blackbody from 1000K to 4000K.
for i in np.arange(1000, 4000, 500):
sd = sd_blackbody(i, SPECTRAL_SHAPE_RAWTOACES)
illuminants[sd.name] = sd
# A.M.P.A.S. variant of ISO 7589 Studio Tungsten.
sd = read_sds_from_csv_file(
os.path.join(RESOURCES_DIRECTORY_RAWTOACES,
'AMPAS_ISO_7589_Tungsten.csv'))['iso7589']
illuminants.update({sd.name: sd})
_ILLUMINANTS_RAWTOACES_V1 = illuminants
return illuminants
[docs]def white_balance_multipliers(sensitivities, illuminant):
"""
Computes the *RGB* white balance multipliers for given camera *RGB*
spectral sensitivities and illuminant.
Parameters
----------
sensitivities : RGB_CameraSensitivities
Camera *RGB* spectral sensitivities.
illuminant : SpectralDistribution
Illuminant spectral distribution.
Returns
-------
ndarray
*RGB* white balance multipliers.
References
----------
:cite:`Dyer2017`
Examples
--------
>>> path = os.path.join(
... RESOURCES_DIRECTORY_RAWTOACES,
... 'CANON_EOS_5DMark_II_RGB_Sensitivities.csv')
>>> sensitivities = sds_and_msds_to_msds(
... read_sds_from_csv_file(path).values())
>>> illuminant = SDS_ILLUMINANTS['D55']
>>> white_balance_multipliers(sensitivities, illuminant)
... # doctest: +ELLIPSIS
array([ 2.3414154..., 1. , 1.5163375...])
"""
shape = sensitivities.shape
if illuminant.shape != shape:
runtime_warning('Aligning "{0}" illuminant shape to "{1}".'.format(
illuminant.name, shape))
illuminant = illuminant.copy().align(shape)
RGB_w = 1 / np.sum(
sensitivities.values * illuminant.values[..., np.newaxis], axis=0)
RGB_w *= 1 / np.min(RGB_w)
return RGB_w
[docs]def best_illuminant(RGB_w, sensitivities, illuminants):
"""
Select the best illuminant for given *RGB* white balance multipliers,
and sensitivities in given series of illuminants.
Parameters
----------
RGB_w : array_like
*RGB* white balance multipliers.
sensitivities : RGB_CameraSensitivities
Camera *RGB* spectral sensitivities.
illuminants : SpectralDistribution
Illuminant spectral distributions to choose the best illuminant from.
Returns
-------
SpectralDistribution
Best illuminant.
Examples
--------
>>> path = os.path.join(
... RESOURCES_DIRECTORY_RAWTOACES,
... 'CANON_EOS_5DMark_II_RGB_Sensitivities.csv')
>>> sensitivities = sds_and_msds_to_msds(
... read_sds_from_csv_file(path).values())
>>> illuminants = generate_illuminants_rawtoaces_v1()
>>> RGB_w = white_balance_multipliers(
... sensitivities, SDS_ILLUMINANTS['FL2'])
>>> # Doctests skip for Python 2.x compatibility.
>>> best_illuminant(RGB_w, sensitivities, illuminants).name
... # doctest: +SKIP
'D40'
"""
RGB_w = as_float_array(RGB_w)
sse = np.inf
illuminant_b = None
for illuminant in illuminants.values():
RGB_wi = white_balance_multipliers(sensitivities, illuminant)
sse_c = np.sum((RGB_wi / RGB_w - 1) ** 2)
if sse_c < sse:
sse = sse_c
illuminant_b = illuminant
return illuminant_b
[docs]def normalise_illuminant(illuminant, sensitivities):
"""
Normalises given illuminant with given camera *RGB* spectral sensitivities.
The multiplicative inverse scaling factor :math:`k` is computed by
multiplying the illuminant by the sensitivies channel with the maximum
value.
Parameters
----------
illuminant : SpectralDistribution
Illuminant spectral distribution.
sensitivities : RGB_CameraSensitivities
Camera *RGB* spectral sensitivities.
Returns
-------
SpectralDistribution
Normalised illuminant.
Examples
--------
>>> path = os.path.join(
... RESOURCES_DIRECTORY_RAWTOACES,
... 'CANON_EOS_5DMark_II_RGB_Sensitivities.csv')
>>> sensitivities = sds_and_msds_to_msds(
... read_sds_from_csv_file(path).values())
>>> illuminant = SDS_ILLUMINANTS['D55']
>>> np.sum(illuminant.values) # doctest: +ELLIPSIS
7276.1490000...
>>> np.sum(normalise_illuminant(illuminant, sensitivities).values)
... # doctest: +ELLIPSIS
3.4390373...
"""
shape = sensitivities.shape
if illuminant.shape != shape:
runtime_warning('Aligning "{0}" illuminant shape to "{1}".'.format(
illuminant.name, shape))
illuminant = illuminant.copy().align(shape)
c_i = np.argmax(np.max(sensitivities.values, axis=0))
k = 1 / np.sum(illuminant.values * sensitivities.values[..., c_i])
return illuminant * k
[docs]def training_data_sds_to_RGB(training_data, sensitivities, illuminant):
"""
Converts given training data to *RGB* tristimulus values using given
illuminant and given camera *RGB* spectral sensitivities.
Parameters
----------
training_data : MultiSpectralDistributions
Training data multi-spectral distributions.
sensitivities : RGB_CameraSensitivities
Camera *RGB* spectral sensitivities.
illuminant : SpectralDistribution
Illuminant spectral distribution.
Returns
-------
ndarray
Training data *RGB* tristimulus values.
Examples
--------
>>> path = os.path.join(
... RESOURCES_DIRECTORY_RAWTOACES,
... 'CANON_EOS_5DMark_II_RGB_Sensitivities.csv')
>>> sensitivities = sds_and_msds_to_msds(
... read_sds_from_csv_file(path).values())
>>> illuminant = normalise_illuminant(
... SDS_ILLUMINANTS['D55'], sensitivities)
>>> training_data = read_training_data_rawtoaces_v1()
>>> training_data_sds_to_RGB(training_data, sensitivities, illuminant)[:5]
... # doctest: +ELLIPSIS
array([[ 0.0207582..., 0.0196857..., 0.0213935...],
[ 0.0895775..., 0.0891922..., 0.0891091...],
[ 0.7810230..., 0.7801938..., 0.7764302...],
[ 0.1995 ..., 0.1995 ..., 0.1995 ...],
[ 0.5898478..., 0.5904015..., 0.5851076...]])
"""
shape = sensitivities.shape
if illuminant.shape != shape:
runtime_warning('Aligning "{0}" illuminant shape to "{1}".'.format(
illuminant.name, shape))
illuminant = illuminant.copy().align(shape)
if training_data.shape != shape:
runtime_warning('Aligning "{0}" training data shape to "{1}".'.format(
training_data.name, shape))
training_data = training_data.copy().align(shape)
RGB_w = white_balance_multipliers(sensitivities, illuminant)
RGB = np.dot(
np.transpose(
illuminant.values[..., np.newaxis] * training_data.values),
sensitivities.values)
RGB *= RGB_w
return RGB
[docs]def training_data_sds_to_XYZ(training_data, cmfs, illuminant):
"""
Converts given training data to *CIE XYZ* tristimulus values using given
illuminant and given standard observer colour matching functions.
Parameters
----------
training_data : MultiSpectralDistributions
Training data multi-spectral distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
illuminant : SpectralDistribution
Illuminant spectral distribution.
Returns
-------
ndarray
Training data *CIE XYZ* tristimulus values.
Examples
--------
>>> path = os.path.join(
... RESOURCES_DIRECTORY_RAWTOACES,
... 'CANON_EOS_5DMark_II_RGB_Sensitivities.csv')
>>> cmfs = MSDS_CMFS['CIE 1931 2 Degree Standard Observer']
>>> sensitivities = sds_and_msds_to_msds(
... read_sds_from_csv_file(path).values())
>>> illuminant = normalise_illuminant(
... SDS_ILLUMINANTS['D55'], sensitivities)
>>> training_data = read_training_data_rawtoaces_v1()
>>> training_data_sds_to_XYZ(training_data, cmfs, illuminant)[:5]
... # doctest: +ELLIPSIS
array([[ 0.0174353..., 0.0179504..., 0.0196109...],
[ 0.0855607..., 0.0895735..., 0.0901703...],
[ 0.7455880..., 0.7817549..., 0.7834356...],
[ 0.1900528..., 0.1995 ..., 0.2012606...],
[ 0.5626319..., 0.5914544..., 0.5894500...]])
"""
shape = cmfs.shape
if illuminant.shape != shape:
runtime_warning('Aligning "{0}" illuminant shape to "{1}".'.format(
illuminant.name, shape))
illuminant = illuminant.copy().align(shape)
if training_data.shape != shape:
runtime_warning('Aligning "{0}" training data shape to "{1}".'.format(
training_data.name, shape))
training_data = training_data.copy().align(shape)
XYZ = np.dot(
np.transpose(
illuminant.values[..., np.newaxis] * training_data.values),
cmfs.values)
XYZ *= 1 / np.sum(cmfs.values[..., 1] * illuminant.values)
XYZ_w = np.dot(np.transpose(cmfs.values), illuminant.values)
XYZ_w *= 1 / XYZ_w[1]
M_CAT = matrix_chromatic_adaptation_VonKries(
XYZ_w, xy_to_XYZ(RGB_COLOURSPACE_ACES2065_1.whitepoint))
XYZ = vector_dot(M_CAT, XYZ)
return XYZ
[docs]def optimisation_factory_rawtoaces_v1():
"""
Factory that returns the objective function and *CIE XYZ* colourspace to
optimisation colourspace/colour model function according to *RAW to ACES*
v1.
The objective function returns the euclidean distance between the training
data *RGB* tristimulus values and the training data *CIE XYZ* tristimulus
values** in *CIE L\\*a\\*b\\** colourspace.
Returns
-------
tuple
Objective function and *CIE XYZ* colourspace to *CIE L\\*a\\*b\\**
colourspace function.
Examples
--------
>>> # Doctests skip for Python 2.x compatibility.
>>> optimisation_factory_rawtoaces_v1() # doctest: +SKIP
(<function optimisation_factory_rawtoaces_v1.<locals>\
.objective_function at 0x...>, \
<function optimisation_factory_rawtoaces_v1.<locals>\
.XYZ_to_optimization_colour_model at 0x...>)
"""
def objective_function(M, RGB, Lab):
"""
Objective function according to *RAW to ACES* v1.
"""
M = np.reshape(M, [3, 3])
XYZ_t = vector_dot(RGB_COLOURSPACE_ACES2065_1.matrix_RGB_to_XYZ,
vector_dot(M, RGB))
Lab_t = XYZ_to_Lab(XYZ_t, RGB_COLOURSPACE_ACES2065_1.whitepoint)
return np.linalg.norm(Lab_t - Lab)
def XYZ_to_optimization_colour_model(XYZ):
"""
*CIE XYZ* colourspace to *CIE L\\*a\\*b\\** colourspace function.
"""
return XYZ_to_Lab(XYZ, RGB_COLOURSPACE_ACES2065_1.whitepoint)
return objective_function, XYZ_to_optimization_colour_model
[docs]def optimisation_factory_JzAzBz():
"""
Factory that returns the objective function and *CIE XYZ* colourspace to
optimisation colourspace/colour model function based on the
:math:`J_zA_zB_z` colourspace.
The objective function returns the euclidean distance between the training
data *RGB* tristimulus values and the training data *CIE XYZ* tristimulus
values** in the :math:`J_zA_zB_z` colourspace.
Returns
-------
tuple
Objective function and *CIE XYZ* colourspace to :math:`J_zA_zB_z`
colourspace function.
Examples
--------
>>> # Doctests skip for Python 2.x compatibility.
>>> optimisation_factory_JzAzBz() # doctest: +SKIP
(<function optimisation_factory_JzAzBz.<locals>\
.objective_function at 0x...>, \
<function optimisation_factory_JzAzBz.<locals>\
.XYZ_to_optimization_colour_model at 0x...>)
"""
def objective_function(M, RGB, Jab):
"""
:math:`J_zA_zB_z` colourspace based objective function.
"""
M = np.reshape(M, [3, 3])
XYZ_t = vector_dot(RGB_COLOURSPACE_ACES2065_1.matrix_RGB_to_XYZ,
vector_dot(M, RGB))
Jab_t = XYZ_to_JzAzBz(XYZ_t)
return np.sum(euclidean_distance(Jab, Jab_t))
def XYZ_to_optimization_colour_model(XYZ):
"""
*CIE XYZ* colourspace to :math:`J_zA_zB_z` colourspace function.
"""
return XYZ_to_JzAzBz(XYZ)
return objective_function, XYZ_to_optimization_colour_model
[docs]def matrix_idt(sensitivities,
illuminant,
training_data=None,
cmfs=MSDS_CMFS['CIE 1931 2 Degree Standard Observer'].copy()
.align(SPECTRAL_SHAPE_RAWTOACES),
optimisation_factory=optimisation_factory_rawtoaces_v1,
optimisation_kwargs=None):
"""
Computes an *Input Device Transform* (IDT) matrix for given camera *RGB*
spectral sensitivities, illuminant, training data, standard observer colour
matching functions and optimization settings according to *RAW to ACES* v1
and *P-2013-001* procedures.
Parameters
----------
sensitivities : RGB_CameraSensitivities
Camera *RGB* spectral sensitivities.
illuminant : SpectralDistribution
Illuminant spectral distribution.
training_data : MultiSpectralDistributions, optional
Training data multi-spectral distributions, defaults to using the
*RAW to ACES* v1 190 patches.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
optimisation_factory : callable, optional
Callable producing the objective function and the *CIE XYZ* to
optimisation colour model function.
optimisation_kwargs : dict_like, optional
Parameters for :func:`scipy.optimize.minimize` definition.
Returns
-------
ndarray
*Input Device Transform* (IDT) matrix.
References
----------
:cite:`Dyer2017`, :cite:`TheAcademyofMotionPictureArtsandSciences2015c`
Examples
--------
Computing the *Input Device Transform* (IDT) matrix for a
*CANON EOS 5DMark II* and *CIE Illuminant D Series* *D55* using
the method given in *RAW to ACES* v1:
>>> path = os.path.join(
... RESOURCES_DIRECTORY_RAWTOACES,
... 'CANON_EOS_5DMark_II_RGB_Sensitivities.csv')
>>> sensitivities = sds_and_msds_to_msds(
... read_sds_from_csv_file(path).values())
>>> illuminant = SDS_ILLUMINANTS['D55']
>>> np.around(
... matrix_idt(sensitivities, illuminant), 3)
array([[ 0.85 , -0.016, 0.151],
[ 0.051, 1.126, -0.185],
[ 0.02 , -0.194, 1.162]])
The *RAW to ACES* v1 matrix for the same camera and optimized by
`Ceres Solver <http://ceres-solver.org/>`__ is as follows:
0.864994 -0.026302 0.161308
0.056527 1.122997 -0.179524
0.023683 -0.202547 1.178864
>>> np.around(matrix_idt(
... sensitivities, illuminant,
... optimisation_factory=optimisation_factory_JzAzBz), 3)
array([[ 0.848, -0.016, 0.158],
[ 0.053, 1.114, -0.175],
[ 0.023, -0.225, 1.196]])
"""
if training_data is None:
training_data = read_training_data_rawtoaces_v1()
shape = cmfs.shape
if sensitivities.shape != shape:
runtime_warning('Aligning "{0}" sensitivities shape to "{1}".'.format(
sensitivities.name, shape))
sensitivities = sensitivities.copy().align(shape)
if illuminant.shape != shape:
runtime_warning('Aligning "{0}" illuminant shape to "{1}".'.format(
illuminant.name, shape))
illuminant = illuminant.copy().align(shape)
if training_data.shape != shape:
runtime_warning('Aligning "{0}" training data shape to "{1}".'.format(
training_data.name, shape))
training_data = training_data.copy().align(shape)
illuminant = normalise_illuminant(illuminant, sensitivities)
RGB = training_data_sds_to_RGB(training_data, sensitivities, illuminant)
XYZ = training_data_sds_to_XYZ(training_data, cmfs, illuminant)
objective_function, XYZ_to_optimization_colour_model = (
optimisation_factory())
optimisation_settings = {
'method': 'BFGS',
'jac': '2-point',
}
if optimisation_kwargs is not None:
optimisation_settings.update(optimisation_kwargs)
M = minimize(objective_function, np.ravel(np.identity(3)),
(RGB, XYZ_to_optimization_colour_model(XYZ)),
**optimisation_settings).x.reshape([3, 3])
return M