Source code for colour.quality.ssi

"""
Academy Spectral Similarity Index (SSI)
=======================================

Defines the *Academy Spectral Similarity Index* (SSI) computation objects:

-   :func:`colour.spectral_similarity_index`

References
----------
-   :cite:`TheAcademyofMotionPictureArtsandSciences2019` : The Academy of
    Motion Picture Arts and Sciences. (2019). Academy Spectral Similarity Index
    (SSI): Overview (pp. 1-7).
"""

from __future__ import annotations

import numpy as np
from scipy.ndimage.filters import convolve1d

from colour.algebra import LinearInterpolator
from colour.colorimetry import SpectralDistribution, SpectralShape, reshape_sd
from colour.hints import NDArray, Optional
from colour.utilities import zeros

__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__ = [
    "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: Optional[NDArray] = None


[docs]def spectral_similarity_index( sd_test: SpectralDistribution, sd_reference: SpectralDistribution ) -> NDArray: """ 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. Returns ------- :class:`numpy.ndarray` *Academy Spectral Similarity Index* (SSI). References ---------- :cite:`TheAcademyofMotionPictureArtsandSciences2019` 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 if _MATRIX_INTEGRATION is None: _MATRIX_INTEGRATION = zeros( ( len(_SPECTRAL_SHAPE_SSI_LARGE.range()), len(SPECTRAL_SHAPE_SSI.range()), ) ) 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", **settings) sd_reference = reshape_sd( sd_reference, SPECTRAL_SHAPE_SSI, "Align", **settings ) test_i = np.dot(_MATRIX_INTEGRATION, sd_test.values) reference_i = np.dot(_MATRIX_INTEGRATION, sd_reference.values) test_i /= np.sum(test_i) reference_i /= np.sum(reference_i) d_i = test_i - reference_i dr_i = d_i / (reference_i + np.mean(reference_i)) wdr_i = dr_i * [ 12 / 45, 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(np.hstack([0, wdr_i, 0]), [0.22, 0.56, 0.22]) m_v = np.sum(c_wdr_i**2) SSI = np.around(100 - 32 * np.sqrt(m_v)) return SSI