Source code for colour.adaptation.vonkries

# -*- coding: utf-8 -*-
"""
Von Kries Chromatic Adaptation Model
====================================

Defines *Von Kries* chromatic adaptation model objects:

-   :func:`colour.adaptation.chromatic_adaptation_matrix_VonKries`
-   :func:`colour.adaptation.chromatic_adaptation_VonKries`

References
----------
-   :cite:`Fairchild2013t` : Fairchild, M. D. (2013). Chromatic Adaptation
    Models. In Color Appearance Models (3rd ed., pp. 4179-4252). Wiley.
    ISBN:B00DAYO8E2
"""

from __future__ import division, unicode_literals

import numpy as np

from colour.adaptation import CHROMATIC_ADAPTATION_TRANSFORMS
from colour.utilities import (dot_matrix, dot_vector, from_range_1,
                              row_as_diagonal, to_domain_1)

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2020 - 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__ = [
    'chromatic_adaptation_matrix_VonKries', 'chromatic_adaptation_VonKries'
]


[docs]def chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform='CAT02'): """ Computes the *chromatic adaptation* matrix from test viewing conditions to reference viewing conditions. Parameters ---------- XYZ_w : array_like Test viewing condition *CIE XYZ* tristimulus values of whitepoint. XYZ_wr : array_like Reference viewing condition *CIE XYZ* tristimulus values of whitepoint. transform : unicode, optional **{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp', 'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco', 'Bianco PC'}**, Chromatic adaptation transform. Returns ------- ndarray Chromatic adaptation matrix :math:`M_{cat}`. Raises ------ KeyError If chromatic adaptation method is not defined. Notes ----- +------------+-----------------------+---------------+ | **Domain** | **Scale - Reference** | **Scale - 1** | +============+=======================+===============+ | ``XYZ_w`` | [0, 1] | [0, 1] | +------------+-----------------------+---------------+ | ``XYZ_wr`` | [0, 1] | [0, 1] | +------------+-----------------------+---------------+ References ---------- :cite:`Fairchild2013t` Examples -------- >>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775]) >>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460]) >>> chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr) ... # doctest: +ELLIPSIS array([[ 1.0425738..., 0.0308910..., -0.0528125...], [ 0.0221934..., 1.0018566..., -0.0210737...], [-0.0011648..., -0.0034205..., 0.7617890...]]) Using Bradford method: >>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775]) >>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460]) >>> method = 'Bradford' >>> chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, method) ... # doctest: +ELLIPSIS array([[ 1.0479297..., 0.0229468..., -0.0501922...], [ 0.0296278..., 0.9904344..., -0.0170738...], [-0.0092430..., 0.0150551..., 0.7518742...]]) """ XYZ_w = to_domain_1(XYZ_w) XYZ_wr = to_domain_1(XYZ_wr) M = CHROMATIC_ADAPTATION_TRANSFORMS.get(transform) if M is None: raise KeyError( '"{0}" chromatic adaptation transform is not defined! Supported ' 'methods: "{1}".'.format(transform, CHROMATIC_ADAPTATION_TRANSFORMS.keys())) rgb_w = np.einsum('...i,...ij->...j', XYZ_w, np.transpose(M)) rgb_wr = np.einsum('...i,...ij->...j', XYZ_wr, np.transpose(M)) D = rgb_wr / rgb_w D = row_as_diagonal(D) M_CAT = dot_matrix(np.linalg.inv(M), D) M_CAT = dot_matrix(M_CAT, M) return M_CAT
[docs]def chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr, transform='CAT02'): """ Adapts given stimulus from test viewing conditions to reference viewing conditions. Parameters ---------- XYZ : array_like *CIE XYZ* tristimulus values of stimulus to adapt. XYZ_w : array_like Test viewing condition *CIE XYZ* tristimulus values of whitepoint. XYZ_wr : array_like Reference viewing condition *CIE XYZ* tristimulus values of whitepoint. transform : unicode, optional **{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp', 'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco', 'Bianco PC'}**, Chromatic adaptation transform. Returns ------- ndarray *CIE XYZ_c* tristimulus values of the stimulus corresponding colour. Notes ----- +------------+-----------------------+---------------+ | **Domain** | **Scale - Reference** | **Scale - 1** | +============+=======================+===============+ | ``XYZ`` | [0, 1] | [0, 1] | +------------+-----------------------+---------------+ | ``XYZ_n`` | [0, 1] | [0, 1] | +------------+-----------------------+---------------+ | ``XYZ_r`` | [0, 1] | [0, 1] | +------------+-----------------------+---------------+ +------------+-----------------------+---------------+ | **Range** | **Scale - Reference** | **Scale - 1** | +============+=======================+===============+ | ``XYZ_c`` | [0, 1] | [0, 1] | +------------+-----------------------+---------------+ References ---------- :cite:`Fairchild2013t` Examples -------- >>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) >>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775]) >>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460]) >>> chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr) # doctest: +ELLIPSIS array([ 0.2163881..., 0.1257 , 0.0384749...]) Using Bradford method: >>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) >>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775]) >>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460]) >>> transform = 'Bradford' >>> chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr, transform) ... # doctest: +ELLIPSIS array([ 0.2166600..., 0.1260477..., 0.0385506...]) """ XYZ = to_domain_1(XYZ) M_CAT = chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform) XYZ_a = dot_vector(M_CAT, XYZ) return from_range_1(XYZ_a)