Source code for colour.quality.ssi

# -*- coding: utf-8 -*-
"""
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).
"""

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

from colour.algebra import LinearInterpolator
from colour.colorimetry import SpectralShape
from colour.utilities import zeros

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2021 - 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(375, 675, 1)
"""
*Academy Spectral Similarity Index* (SSI) spectral shape.

SPECTRAL_SHAPE_SSI : SpectralShape
"""

_SPECTRAL_SHAPE_SSI_LARGE = SpectralShape(380, 670, 10)

_MATRIX_INTEGRATION = None


[docs]def spectral_similarity_index(sd_test, sd_reference): """ Returns the *Academy Spectral Similarity Index* (SSI) of given test spectral distribution with given reference spectral distribution. Parameters ---------- sd_test : SpectralDistribution Test spectral distribution. sd_reference : SpectralDistribution Reference spectral distribution. Returns ------- numeric *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 = sd_test.copy().align(SPECTRAL_SHAPE_SSI, **settings) sd_reference = sd_reference.copy().align(SPECTRAL_SHAPE_SSI, **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