"""
Smits (1999) - Reflectance Recovery
===================================
Define objects for reflectance recovery using the *Smits (1999)* method.
References
----------
- :cite:`Smits1999a` : Smits, B. (1999). An RGB-to-Spectrum Conversion for
Reflectances. Journal of Graphics Tools, 4(4), 11-22.
doi:10.1080/10867651.1999.10487511
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from colour.colorimetry import (
CCS_ILLUMINANTS,
MultiSpectralDistributions,
SpectralDistribution,
)
from colour.models import RGB_Colourspace, RGB_COLOURSPACE_sRGB, XYZ_to_RGB
from colour.recovery import MSDS_SMITS1999
from colour.utilities import (
as_float_array,
optional,
to_domain_1,
tsplit,
)
if TYPE_CHECKING:
from colour.hints import ArrayLike, Domain1, NDArrayFloat, Range1
__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "colour-developers@colour-science.org"
__status__ = "Production"
__all__ = [
"PRIMARIES_SMITS1999",
"WHITEPOINT_NAME_SMITS1999",
"CCS_WHITEPOINT_SMITS1999",
"RGB_COLOURSPACE_SMITS1999",
"XYZ_to_RGB_Smits1999",
"RGB_to_msds_Smits1999",
"RGB_to_sd_Smits1999",
]
PRIMARIES_SMITS1999: NDArrayFloat = RGB_COLOURSPACE_sRGB.primaries
"""*Smits (1999)* method implementation colourspace primaries."""
WHITEPOINT_NAME_SMITS1999 = "E"
"""*Smits (1999)* method implementation colourspace whitepoint name."""
CCS_WHITEPOINT_SMITS1999: NDArrayFloat = CCS_ILLUMINANTS[
"CIE 1931 2 Degree Standard Observer"
][WHITEPOINT_NAME_SMITS1999]
"""*Smits (1999)* method implementation colourspace whitepoint."""
RGB_COLOURSPACE_SMITS1999 = RGB_Colourspace(
"Smits 1999",
PRIMARIES_SMITS1999,
CCS_WHITEPOINT_SMITS1999,
WHITEPOINT_NAME_SMITS1999,
)
RGB_COLOURSPACE_SMITS1999.__doc__ = """
*Smits (1999)* colourspace.
References
----------
:cite:`Smits1999a`,
"""
def XYZ_to_RGB_Smits1999(XYZ: Domain1) -> Range1:
"""
Convert from *CIE XYZ* tristimulus values to *RGB* colourspace using
the conditions required by the current *Smits (1999)* method
implementation.
Parameters
----------
XYZ
*CIE XYZ* tristimulus values.
Returns
-------
:class:`numpy.ndarray`
*RGB* colour array.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ`` | 1 | 1 |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``RGB`` | 1 | 1 |
+------------+-----------------------+---------------+
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.21781186, 0.12541048, 0.04697113])
>>> XYZ_to_RGB_Smits1999(XYZ) # doctest: +ELLIPSIS
array([0.4063959..., 0.0275289..., 0.0398219...])
"""
return XYZ_to_RGB(XYZ, RGB_COLOURSPACE_SMITS1999)
[docs]
def RGB_to_msds_Smits1999(
RGB: ArrayLike,
basis: MultiSpectralDistributions | None = None,
) -> NDArrayFloat:
"""
Recover spectral values from *RGB* colourspace array using the
*Smits (1999)* decomposition algorithm.
This is a vectorised implementation supporting multi-dimensional arrays.
Parameters
----------
RGB
*RGB* colourspace array to recover spectral values from. The last
dimension must be size 3.
basis
Multi-spectral distributions basis with signals: white, cyan, magenta,
yellow, red, green, blue. Defaults to :attr:`MSDS_SMITS1999`.
Returns
-------
:class:`numpy.ndarray`
Recovered spectral values with shape ``(*RGB.shape[:-1], wavelengths)``.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``RGB`` | 1 | 1 |
+------------+-----------------------+---------------+
References
----------
:cite:`Smits1999a`
Examples
--------
>>> import numpy as np
>>> RGB = np.array(
... [
... [0.45623196, 0.03080455, 0.04093343],
... [0.05438271, 0.29877169, 0.07188444],
... [0.01863137, 0.05139773, 0.28887675],
... ]
... )
>>> RGB_to_msds_Smits1999(RGB).shape
(3, 10)
>>> float(RGB_to_msds_Smits1999(RGB)[0, 0]) # doctest: +ELLIPSIS
0.0829...
"""
basis = optional(basis, MSDS_SMITS1999)
RGB = to_domain_1(as_float_array(RGB))
shape = RGB.shape
RGB = np.atleast_2d(RGB.reshape(-1, 3))
R, G, B = tsplit(RGB)
labels = list(basis.labels)
white = basis.values[:, labels.index("white")]
cyan = basis.values[:, labels.index("cyan")]
magenta = basis.values[:, labels.index("magenta")]
yellow = basis.values[:, labels.index("yellow")]
red = basis.values[:, labels.index("red")]
green = basis.values[:, labels.index("green")]
blue = basis.values[:, labels.index("blue")]
R = R[..., np.newaxis]
G = G[..., np.newaxis]
B = B[..., np.newaxis]
# if R <= G and R <= B:
# sd += white * R
# if G <= B:
# sd += cyan * (G - R) + blue * (B - G)
# else:
# sd += cyan * (B - R) + green * (G - B)
R_le_G_le_B = (R <= G) & (R <= B) & (G <= B)
R_le_B_lt_G = (R <= G) & (R <= B) & (G > B)
# elif G <= R and G <= B:
# sd += white * G
# if R <= B:
# sd += magenta * (R - G) + blue * (B - R)
# else:
# sd += magenta * (B - G) + red * (R - B)
G_lt_R_le_B = (G < R) & (G <= B) & (R <= B)
G_le_B_lt_R = (G < R) & (G <= B) & (R > B)
# else: # B < R and B < G
# sd += white * B
# if R <= G:
# sd += yellow * (R - B) + green * (G - R)
# else:
# sd += yellow * (G - B) + red * (R - G)
B_lt_R_le_G = (B < R) & (B < G) & (R <= G)
B_lt_G_lt_R = (B < R) & (B < G) & (R > G)
spectra = np.select(
[R_le_G_le_B, R_le_B_lt_G, G_lt_R_le_B, G_le_B_lt_R, B_lt_R_le_G, B_lt_G_lt_R],
[
white * R + cyan * (G - R) + blue * (B - G),
white * R + cyan * (B - R) + green * (G - B),
white * G + magenta * (R - G) + blue * (B - R),
white * G + magenta * (B - G) + red * (R - B),
white * B + yellow * (R - B) + green * (G - R),
white * B + yellow * (G - B) + red * (R - G),
],
)
return np.reshape(spectra, [*list(shape[:-1]), len(white)])
[docs]
def RGB_to_sd_Smits1999(
RGB: Domain1,
basis: MultiSpectralDistributions | None = None,
name: str | None = None,
) -> SpectralDistribution:
"""
Generate a spectral distribution from *RGB* values using the
*Smits (1999)* decomposition algorithm.
Parameters
----------
RGB
*RGB* colourspace array to recover the spectral distribution from.
basis
Multi-spectral distributions basis with signals: white, cyan, magenta,
yellow, red, green, blue. Defaults to :attr:`MSDS_SMITS1999`.
name
Name for the resulting spectral distribution.
Returns
-------
:class:`colour.SpectralDistribution`
Recovered spectral distribution.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``RGB`` | 1 | 1 |
+------------+-----------------------+---------------+
References
----------
:cite:`Smits1999a`
Examples
--------
>>> import numpy as np
>>> from colour import MSDS_CMFS, SDS_ILLUMINANTS, SpectralShape
>>> from colour.colorimetry import sd_to_XYZ_integration
>>> from colour.utilities import numpy_print_options
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> RGB = XYZ_to_RGB_Smits1999(XYZ)
>>> cmfs = (
... MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]
... .copy()
... .align(SpectralShape(360, 780, 10))
... )
>>> illuminant = SDS_ILLUMINANTS["E"].copy().align(cmfs.shape)
>>> sd = RGB_to_sd_Smits1999(RGB)
>>> with numpy_print_options(suppress=True):
... sd # doctest: +ELLIPSIS
SpectralDistribution([[380. , 0.0787830...],
[417.7778 , 0.0622018...],
[455.5556 , 0.0446206...],
[493.3333 , 0.0352220...],
[531.1111 , 0.0324149...],
[568.8889 , 0.0330105...],
[606.6667 , 0.3207115...],
[644.4444 , 0.3836164...],
[682.2222 , 0.3836164...],
[720. , 0.3835649...]],
LinearInterpolator,
{},
Extrapolator,
{'method': 'Constant', 'left': None, 'right': None})
>>> sd_to_XYZ_integration(sd, cmfs, illuminant) / 100 # doctest: +ELLIPSIS
array([0.1894770..., 0.1126470..., 0.0474420...])
"""
basis = optional(basis, MSDS_SMITS1999)
name = optional(name, f"Smits (1999) - {RGB!r}")
values = RGB_to_msds_Smits1999(RGB, basis)
return SpectralDistribution(
values,
basis.wavelengths,
name=name,
interpolator=basis.interpolator,
interpolator_kwargs=basis.interpolator_kwargs,
)
