"""
Academy Spectral Similarity Index (SSI)
=======================================
Defines the *Academy Spectral Similarity Index* (SSI) computation objects:
- :func:`colour.spectral_similarity_index`
References
----------
- :cite:`TheAcademyofMotionPictureArtsandSciences2020a` : The Academy of
Motion Picture Arts and Sciences. (2020). Academy Spectral Similarity Index
(SSI): Overview (pp. 1-7). Retrieved June 5, 2023, from
https://www.oscars.org/sites/oscars/files/ssi_overview_2020-09-16.pdf
"""
from __future__ import annotations
import numpy as np
from scipy.ndimage import convolve1d
from colour.algebra import LinearInterpolator, sdiv, sdiv_mode
from colour.colorimetry import SpectralDistribution, SpectralShape, reshape_sd
from colour.hints import NDArrayFloat
from colour.utilities import zeros
__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__ = [
"SPECTRAL_SHAPE_SSI",
"spectral_similarity_index",
]
SPECTRAL_SHAPE_SSI: SpectralShape = SpectralShape(375, 675, 1)
"""*Academy Spectral Similarity Index* (SSI) spectral shape."""
_SPECTRAL_SHAPE_SSI_LARGE: SpectralShape = SpectralShape(380, 670, 10)
_MATRIX_INTEGRATION: NDArrayFloat | None = None
[docs]
def spectral_similarity_index(
sd_test: SpectralDistribution,
sd_reference: SpectralDistribution,
round_result: bool = True,
) -> NDArrayFloat:
"""
Return the *Academy Spectral Similarity Index* (SSI) of given test
spectral distribution with given reference spectral distribution.
Parameters
----------
sd_test
Test spectral distribution.
sd_reference
Reference spectral distribution.
round_result
Whether to round the result/output. This is particularly useful when
using SSI in an optimisation routine. Default is *True*.
Returns
-------
:class:`numpy.ndarray`
*Academy Spectral Similarity Index* (SSI).
References
----------
:cite:`TheAcademyofMotionPictureArtsandSciences2020a`
Examples
--------
>>> from colour import SDS_ILLUMINANTS
>>> sd_test = SDS_ILLUMINANTS["C"]
>>> sd_reference = SDS_ILLUMINANTS["D65"]
>>> spectral_similarity_index(sd_test, sd_reference)
94.0
"""
global _MATRIX_INTEGRATION # noqa: PLW0603
if _MATRIX_INTEGRATION is None:
_MATRIX_INTEGRATION = zeros(
(
len(_SPECTRAL_SHAPE_SSI_LARGE.wavelengths),
len(SPECTRAL_SHAPE_SSI.wavelengths),
)
)
weights = np.array([0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5])
for i in range(_MATRIX_INTEGRATION.shape[0]):
_MATRIX_INTEGRATION[i, (10 * i) : (10 * i + 11)] = weights
settings = {
"interpolator": LinearInterpolator,
"extrapolator_kwargs": {"left": 0, "right": 0},
}
sd_test = reshape_sd(
sd_test, SPECTRAL_SHAPE_SSI, "Align", copy=False, **settings
)
sd_reference = reshape_sd(
sd_reference, SPECTRAL_SHAPE_SSI, "Align", copy=False, **settings
)
test_i = np.dot(_MATRIX_INTEGRATION, sd_test.values)
reference_i = np.dot(_MATRIX_INTEGRATION, sd_reference.values)
with sdiv_mode():
test_i = sdiv(test_i, np.sum(test_i))
reference_i = sdiv(reference_i, np.sum(reference_i))
dr_i = sdiv(test_i - reference_i, reference_i + 1 / 30)
wdr_i = dr_i * [
4 / 15,
22 / 45,
32 / 45,
40 / 45,
44 / 45,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
11 / 15,
3 / 15,
]
c_wdr_i = convolve1d(wdr_i, [0.22, 0.56, 0.22], mode="constant", cval=0)
m_v = np.sum(c_wdr_i**2)
SSI = 100 - 32 * np.sqrt(m_v)
return np.around(SSI) if round_result else SSI