"""
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.sd_to_ACES2065_1`
- :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`
- :func:`colour.camera_RGB_to_ACES2065_1`
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 annotations
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, vector_dot
from colour.colorimetry import (
MultiSpectralDistributions,
SDS_ILLUMINANTS,
SpectralDistribution,
SpectralShape,
handle_spectral_arguments,
reshape_msds,
reshape_sd,
sds_and_msds_to_msds,
sd_CIE_illuminant_D_series,
sd_blackbody,
sd_to_XYZ,
)
from colour.characterisation import MSDS_ACES_RICD, RGB_CameraSensitivities
from colour.hints import (
ArrayLike,
Boolean,
Callable,
Dict,
Floating,
FloatingOrNDArray,
Literal,
Mapping,
NDArray,
Optional,
Tuple,
Union,
cast,
)
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,
as_float_array,
from_range_1,
optional,
runtime_warning,
suppress_warnings,
tsplit,
)
__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 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",
"sd_to_ACES2065_1",
"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",
"camera_RGB_to_ACES2065_1",
]
FLARE_PERCENTAGE: Floating = 0.00500
"""Flare percentage in the *ACES* system."""
S_FLARE_FACTOR: Floating = 0.18000 / (0.18000 + FLARE_PERCENTAGE)
"""Flare modulation factor in the *ACES* system."""
[docs]def sd_to_aces_relative_exposure_values(
sd: SpectralDistribution,
illuminant: Optional[SpectralDistribution] = None,
apply_chromatic_adaptation: Boolean = False,
chromatic_adaptation_transform: Union[
Literal[
"Bianco 2010",
"Bianco PC 2010",
"Bradford",
"CAT02 Brill 2008",
"CAT02",
"CAT16",
"CMCCAT2000",
"CMCCAT97",
"Fairchild",
"Sharp",
"Von Kries",
"XYZ Scaling",
],
str,
] = "CAT02",
) -> NDArray:
"""
Convert given spectral distribution to *ACES2065-1* colourspace relative
exposure values.
Parameters
----------
sd
Spectral distribution.
illuminant
*Illuminant* spectral distribution, default to
*CIE Standard Illuminant D65*.
apply_chromatic_adaptation
Whether to apply chromatic adaptation using given transform.
chromatic_adaptation_transform
*Chromatic adaptation* transform.
Returns
-------
:class:`numpy.ndarray`
*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...])
"""
illuminant = cast(
SpectralDistribution, optional(illuminant, SDS_ILLUMINANTS["D65"])
)
shape = MSDS_ACES_RICD.shape
if sd.shape != MSDS_ACES_RICD.shape:
sd = reshape_sd(sd, shape)
if illuminant.shape != MSDS_ACES_RICD.shape:
illuminant = reshape_sd(illuminant, shape)
s_v = sd.values
i_v = illuminant.values
r_bar, g_bar, b_bar = tsplit(MSDS_ACES_RICD.values)
def k(x: NDArray, y: NDArray) -> NDArray:
"""Compute 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)
sd_to_ACES2065_1 = sd_to_aces_relative_exposure_values
SPECTRAL_SHAPE_RAWTOACES: SpectralShape = SpectralShape(380, 780, 5)
"""Default spectral shape according to *RAW to ACES* v1."""
RESOURCES_DIRECTORY_RAWTOACES: str = 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*.
"""
_TRAINING_DATA_RAWTOACES_V1: Optional[MultiSpectralDistributions] = None
[docs]def read_training_data_rawtoaces_v1() -> MultiSpectralDistributions:
"""
Read the *RAW to ACES* v1 190 patches.
Returns
-------
:class:`colour.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(
list(read_sds_from_csv_file(path).values())
)
_TRAINING_DATA_RAWTOACES_V1 = training_data
return training_data
_ILLUMINANTS_RAWTOACES_V1: Optional[CaseInsensitiveMapping] = None
[docs]def generate_illuminants_rawtoaces_v1() -> CaseInsensitiveMapping:
"""
Generate 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
-------
:class:`colour.utilities.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
--------
>>> list(sorted(generate_illuminants_rawtoaces_v1().keys()))
['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 = f"D{int(CCT / 100):d}"
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: RGB_CameraSensitivities, illuminant: SpectralDistribution
) -> NDArray:
"""
Compute the *RGB* white balance multipliers for given camera *RGB*
spectral sensitivities and illuminant.
Parameters
----------
sensitivities
Camera *RGB* spectral sensitivities.
illuminant
Illuminant spectral distribution.
Returns
-------
:class:`numpy.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(
f'Aligning "{illuminant.name}" illuminant shape to "{shape}".'
)
illuminant = reshape_sd(illuminant, 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: ArrayLike,
sensitivities: RGB_CameraSensitivities,
illuminants: Mapping,
) -> SpectralDistribution:
"""
Select the best illuminant for given *RGB* white balance multipliers, and
sensitivities in given series of illuminants.
Parameters
----------
RGB_w
*RGB* white balance multipliers.
sensitivities
Camera *RGB* spectral sensitivities.
illuminants
Illuminant spectral distributions to choose the best illuminant from.
Returns
-------
:class:`colour.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'])
>>> best_illuminant(RGB_w, sensitivities, illuminants).name
'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 # type: ignore[return-value]
[docs]def normalise_illuminant(
illuminant: SpectralDistribution, sensitivities: RGB_CameraSensitivities
) -> SpectralDistribution:
"""
Normalise 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
Illuminant spectral distribution.
sensitivities
Camera *RGB* spectral sensitivities.
Returns
-------
:class:`colour.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(
f'Aligning "{illuminant.name}" illuminant shape to "{shape}".'
)
illuminant = reshape_sd(illuminant, 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: MultiSpectralDistributions,
sensitivities: RGB_CameraSensitivities,
illuminant: SpectralDistribution,
) -> Tuple[NDArray, NDArray]:
"""
Convert given training data to *RGB* tristimulus values using given
illuminant and given camera *RGB* spectral sensitivities.
Parameters
----------
training_data
Training data multi-spectral distributions.
sensitivities
Camera *RGB* spectral sensitivities.
illuminant
Illuminant spectral distribution.
Returns
-------
:class:`tuple`
Tuple of training data *RGB* tristimulus values and white balance
multipliers.
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()
>>> RGB, RGB_w = training_data_sds_to_RGB(
... training_data, sensitivities, illuminant)
>>> RGB[: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...]])
>>> RGB_w # doctest: +ELLIPSIS
array([ 2.3414154..., 1. , 1.5163375...])
"""
shape = sensitivities.shape
if illuminant.shape != shape:
runtime_warning(
f'Aligning "{illuminant.name}" illuminant shape to "{shape}".'
)
illuminant = reshape_sd(illuminant, shape)
if training_data.shape != shape:
runtime_warning(
f'Aligning "{training_data.name}" training data shape to "{shape}".'
)
# pylint: disable=E1102
training_data = reshape_msds(training_data, 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, RGB_w
[docs]def training_data_sds_to_XYZ(
training_data: MultiSpectralDistributions,
cmfs: MultiSpectralDistributions,
illuminant: SpectralDistribution,
chromatic_adaptation_transform: Union[
Literal[
"Bianco 2010",
"Bianco PC 2010",
"Bradford",
"CAT02 Brill 2008",
"CAT02",
"CAT16",
"CMCCAT2000",
"CMCCAT97",
"Fairchild",
"Sharp",
"Von Kries",
"XYZ Scaling",
],
str,
] = "CAT02",
) -> NDArray:
"""
Convert given training data to *CIE XYZ* tristimulus values using given
illuminant and given standard observer colour matching functions.
Parameters
----------
training_data
Training data multi-spectral distributions.
cmfs
Standard observer colour matching functions.
illuminant
Illuminant spectral distribution.
chromatic_adaptation_transform
*Chromatic adaptation* transform, if *None* no chromatic adaptation is
performed.
Returns
-------
:class:`numpy.ndarray`
Training data *CIE XYZ* tristimulus values.
Examples
--------
>>> from colour import MSDS_CMFS
>>> 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(
f'Aligning "{illuminant.name}" illuminant shape to "{shape}".'
)
illuminant = reshape_sd(illuminant, shape)
if training_data.shape != shape:
runtime_warning(
f'Aligning "{training_data.name}" training data shape to "{shape}".'
)
# pylint: disable=E1102
training_data = reshape_msds(training_data, 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]
if chromatic_adaptation_transform is not None:
M_CAT = matrix_chromatic_adaptation_VonKries(
XYZ_w,
xy_to_XYZ(RGB_COLOURSPACE_ACES2065_1.whitepoint),
chromatic_adaptation_transform,
)
XYZ = vector_dot(M_CAT, XYZ)
return XYZ
[docs]def optimisation_factory_rawtoaces_v1() -> Tuple[Callable, Callable]:
"""
Produce 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
-------
:class:`tuple`
Objective function and *CIE XYZ* colourspace to *CIE L\\*a\\*b\\**
colourspace function.
Examples
--------
>>> 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: ArrayLike, RGB: ArrayLike, Lab: ArrayLike
) -> FloatingOrNDArray:
"""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 as_float(np.linalg.norm(Lab_t - Lab))
def XYZ_to_optimization_colour_model(XYZ: ArrayLike) -> NDArray:
"""*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() -> Tuple[Callable, Callable]:
"""
Produce 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
-------
:class:`tuple`
Objective function and *CIE XYZ* colourspace to :math:`J_za_zb_z`
colourspace function.
Examples
--------
>>> 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: ArrayLike, RGB: ArrayLike, Jab: ArrayLike
) -> FloatingOrNDArray:
""":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: ArrayLike) -> NDArray:
"""*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: RGB_CameraSensitivities,
illuminant: SpectralDistribution,
training_data: Optional[MultiSpectralDistributions] = None,
cmfs: Optional[MultiSpectralDistributions] = None,
optimisation_factory: Callable = optimisation_factory_rawtoaces_v1,
optimisation_kwargs: Optional[Dict] = None,
chromatic_adaptation_transform: Union[
Literal[
"Bianco 2010",
"Bianco PC 2010",
"Bradford",
"CAT02 Brill 2008",
"CAT02",
"CAT16",
"CMCCAT2000",
"CMCCAT97",
"Fairchild",
"Sharp",
"Von Kries",
"XYZ Scaling",
],
str,
] = "CAT02",
additional_data: Boolean = False,
) -> Union[Tuple[NDArray, NDArray, NDArray, NDArray], Tuple[NDArray, NDArray]]:
"""
Compute 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
Camera *RGB* spectral sensitivities.
illuminant
Illuminant spectral distribution.
training_data
Training data multi-spectral distributions, defaults to using the
*RAW to ACES* v1 190 patches.
cmfs
Standard observer colour matching functions, default to the
*CIE 1931 2 Degree Standard Observer*.
optimisation_factory
Callable producing the objective function and the *CIE XYZ* to
optimisation colour model function.
optimisation_kwargs
Parameters for :func:`scipy.optimize.minimize` definition.
chromatic_adaptation_transform
*Chromatic adaptation* transform, if *None* no chromatic adaptation is
performed.
additional_data
If *True*, the *XYZ* and *RGB* tristimulus values are also returned.
Returns
-------
:class:`tuple`
Tuple of *Input Device Transform* (IDT) matrix and white balance
multipliers or tuple of *Input Device Transform* (IDT) matrix, white
balance multipliers, *XYZ* and *RGB* tristimulus values.
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']
>>> M, RGB_w = matrix_idt(sensitivities, illuminant)
>>> np.around(M, 3)
array([[ 0.85 , -0.016, 0.151],
[ 0.051, 1.126, -0.185],
[ 0.02 , -0.194, 1.162]])
>>> RGB_w # doctest: +ELLIPSIS
array([ 2.3414154..., 1. , 1.5163375...])
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
>>> M, RGB_w = matrix_idt(
... sensitivities, illuminant,
... optimisation_factory=optimisation_factory_Jzazbz)
>>> np.around(M, 3)
array([[ 0.848, -0.016, 0.158],
[ 0.053, 1.114, -0.175],
[ 0.023, -0.225, 1.196]])
>>> RGB_w # doctest: +ELLIPSIS
array([ 2.3414154..., 1. , 1.5163375...])
"""
training_data = optional(training_data, read_training_data_rawtoaces_v1())
cmfs, illuminant = handle_spectral_arguments(
cmfs, illuminant, shape_default=SPECTRAL_SHAPE_RAWTOACES
)
shape = cmfs.shape
if sensitivities.shape != shape:
runtime_warning(
f'Aligning "{sensitivities.name}" sensitivities shape to "{shape}".'
)
# pylint: disable=E1102
sensitivities = reshape_msds(sensitivities, shape) # type: ignore[assignment]
if training_data.shape != shape:
runtime_warning(
f'Aligning "{training_data.name}" training data shape to "{shape}".'
)
# pylint: disable=E1102
training_data = reshape_msds(training_data, shape)
illuminant = normalise_illuminant(illuminant, sensitivities)
RGB, RGB_w = training_data_sds_to_RGB(
training_data, sensitivities, illuminant
)
XYZ = training_data_sds_to_XYZ(
training_data, cmfs, illuminant, chromatic_adaptation_transform
)
(
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])
if additional_data:
return M, RGB_w, XYZ, RGB
else:
return M, RGB_w
[docs]def camera_RGB_to_ACES2065_1(
RGB: ArrayLike,
B: ArrayLike,
b: ArrayLike,
k: ArrayLike = np.ones(3),
clip: Boolean = False,
) -> NDArray:
"""
Convert given camera *RGB* colourspace array to *ACES2065-1* colourspace
using the *Input Device Transform* (IDT) matrix :math:`B`, the white
balance multipliers :math:`b` and the exposure factor :math:`k` according
to *P-2013-001* procedure.
Parameters
----------
RGB
Camera *RGB* colourspace array.
B
*Input Device Transform* (IDT) matrix :math:`B`.
b
White balance multipliers :math:`b`.
k
Exposure factor :math:`k` that results in a nominally "18% gray" object
in the scene producing ACES values [0.18, 0.18, 0.18].
clip
Whether to clip the white balanced camera *RGB* colourspace array
between :math:`-\\infty` and 1. The intent is to keep sensor saturated
values achromatic after white balancing.
Returns
-------
:class:`numpy.ndarray`
*ACES2065-1* colourspace relative exposure values array.
References
----------
:cite:`TheAcademyofMotionPictureArtsandSciences2015c`
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']
>>> B, b = matrix_idt(sensitivities, illuminant)
>>> camera_RGB_to_ACES2065_1(np.array([0.1, 0.2, 0.3]), B, b)
... # doctest: +ELLIPSIS
array([ 0.2646811..., 0.1528898..., 0.4944335...])
"""
RGB = as_float_array(RGB)
B = as_float_array(B)
b = as_float_array(b)
k = as_float_array(k)
RGB_r = b * RGB / np.min(b)
RGB_r = np.clip(RGB_r, -np.inf, 1) if clip else RGB_r
return k * vector_dot(B, RGB_r)