#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Fairchild (1990) Chromatic Adaptation Model
===========================================
Defines Fairchild (1990) chromatic adaptation model objects:
- :func:`chromatic_adaptation_Fairchild1990`
See Also
--------
`Fairchild (1990) Chromatic Adaptation Model IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/adaptation/fairchild1990.ipynb>`_ # noqa
References
----------
.. [1] Fairchild, M. D. (1991). Formulation and testing of an
incomplete-chromatic-adaptation model. Color Research & Application,
16(4), 243–250. doi:10.1002/col.5080160406
.. [2] Fairchild, M. D. (2013). FAIRCHILD’S 1990 MODEL. In Color Appearance
Models (3rd ed., pp. 4418–4495). Wiley. ASIN:B00DAYO8E2
"""
from __future__ import division, unicode_literals
import numpy as np
from colour.adaptation import VON_KRIES_CAT
from colour.utilities import dot_vector, row_as_diagonal, tsplit, tstack
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2015 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['FAIRCHILD1990_XYZ_TO_RGB_MATRIX',
'FAIRCHILD1990_RGB_TO_XYZ_MATRIX',
'chromatic_adaptation_Fairchild1990',
'XYZ_to_RGB_fairchild1990',
'RGB_to_XYZ_fairchild1990',
'degrees_of_adaptation']
FAIRCHILD1990_XYZ_TO_RGB_MATRIX = VON_KRIES_CAT
"""
Fairchild (1990) colour appearance model *CIE XYZ* tristimulus values to cone
responses matrix.
FAIRCHILD1990_XYZ_TO_RGB_MATRIX : array_like, (3, 3)
"""
FAIRCHILD1990_RGB_TO_XYZ_MATRIX = np.linalg.inv(VON_KRIES_CAT)
"""
Fairchild (1990) colour appearance model cone responses to *CIE XYZ*
tristimulus values matrix.
FAIRCHILD1990_RGB_TO_XYZ_MATRIX : array_like, (3, 3)
"""
[docs]def chromatic_adaptation_Fairchild1990(XYZ_1,
XYZ_n,
XYZ_r,
Y_n,
discount_illuminant=False):
"""
Adapts given stimulus *CIE XYZ_1* tristimulus values from test viewing
conditions to reference viewing conditions using Fairchild (1990) chromatic
adaptation model.
Parameters
----------
XYZ_1 : array_like
*CIE XYZ_1* tristimulus values of test sample / stimulus in domain
[0, 100].
XYZ_n : array_like
Test viewing condition *CIE XYZ_n* tristimulus values of whitepoint.
XYZ_r : array_like
Reference viewing condition *CIE XYZ_r* tristimulus values of
whitepoint.
Y_n : numeric or array_like
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
discount_illuminant : bool, optional
Truth value indicating if the illuminant should be discounted.
Returns
-------
ndarray
Adapted *CIE XYZ_2* tristimulus values of stimulus.
Warning
-------
The input domain of that definition is non standard!
Notes
-----
- Input *CIE XYZ_1*, *CIE XYZ_n* and *CIE XYZ_r* tristimulus values are
in domain [0, 100].
- Output *CIE XYZ_2* tristimulus values are in domain [0, 100].
Examples
--------
>>> XYZ_1 = np.array([19.53, 23.07, 24.97])
>>> XYZ_n = np.array([111.15, 100.00, 35.20])
>>> XYZ_r = np.array([94.81, 100.00, 107.30])
>>> Y_n = 200
>>> chromatic_adaptation_Fairchild1990( # doctest: +ELLIPSIS
... XYZ_1, XYZ_n, XYZ_r, Y_n)
array([ 23.3252634..., 23.3245581..., 76.1159375...])
"""
XYZ_1 = np.asarray(XYZ_1)
XYZ_n = np.asarray(XYZ_n)
XYZ_r = np.asarray(XYZ_r)
Y_n = np.asarray(Y_n)
LMS_1 = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_1)
LMS_n = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_n)
LMS_r = dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ_r)
p_LMS = degrees_of_adaptation(LMS_1,
Y_n,
discount_illuminant=discount_illuminant)
a_LMS_1 = p_LMS / LMS_n
a_LMS_2 = p_LMS / LMS_r
A_1 = row_as_diagonal(a_LMS_1)
A_2 = row_as_diagonal(a_LMS_2)
LMSp_1 = dot_vector(A_1, LMS_1)
c = 0.219 - 0.0784 * np.log10(Y_n)
C = row_as_diagonal(tstack((c, c, c)))
LMS_a = dot_vector(C, LMSp_1)
LMSp_2 = dot_vector(np.linalg.inv(C), LMS_a)
LMS_c = dot_vector(np.linalg.inv(A_2), LMSp_2)
XYZ_c = dot_vector(FAIRCHILD1990_RGB_TO_XYZ_MATRIX, LMS_c)
return XYZ_c
[docs]def XYZ_to_RGB_fairchild1990(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to cone responses.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
Cone responses.
Examples
--------
>>> XYZ = np.array([19.53, 23.07, 24.97])
>>> XYZ_to_RGB_fairchild1990(XYZ) # doctest: +ELLIPSIS
array([ 22.1231935..., 23.6054224..., 22.9279534...])
"""
return dot_vector(FAIRCHILD1990_XYZ_TO_RGB_MATRIX, XYZ)
[docs]def RGB_to_XYZ_fairchild1990(RGB):
"""
Converts from cone responses to *CIE XYZ* tristimulus values.
Parameters
----------
RGB : array_like
Cone responses.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Examples
--------
>>> RGB = np.array([22.12319350, 23.60542240, 22.92795340])
>>> RGB_to_XYZ_fairchild1990(RGB) # doctest: +ELLIPSIS
array([ 19.53, 23.07, 24.97])
"""
return dot_vector(FAIRCHILD1990_RGB_TO_XYZ_MATRIX, RGB)
[docs]def degrees_of_adaptation(LMS, Y_n, v=1 / 3, discount_illuminant=False):
"""
Computes the degrees of adaptation :math:`p_L`, :math:`p_M` and
:math:`p_S`.
Parameters
----------
LMS : array_like
Cone responses.
Y_n : numeric or array_like
Luminance :math:`Y_n` of test adapting stimulus in :math:`cd/m^2`.
v : numeric or array_like, optional
Exponent :math:`v`.
discount_illuminant : bool, optional
Truth value indicating if the illuminant should be discounted.
Returns
-------
ndarray
Degrees of adaptation :math:`p_L`, :math:`p_M` and :math:`p_S`.
Examples
--------
>>> LMS = np.array([20.00052060, 19.99978300, 19.99883160])
>>> Y_n = 31.83
>>> degrees_of_adaptation(LMS, Y_n) # doctest: +ELLIPSIS
array([ 0.9799324..., 0.9960035..., 1.0233041...])
>>> degrees_of_adaptation(LMS, Y_n, 1 / 3, True)
array([ 1., 1., 1.])
"""
LMS = np.asarray(LMS)
if discount_illuminant:
return np.ones(LMS.shape)
Y_n = np.asarray(Y_n)
v = np.asarray(v)
L, M, S = tsplit(LMS)
LMS_E = dot_vector(VON_KRIES_CAT, np.ones(LMS.shape)) # E illuminant.
L_E, M_E, S_E = tsplit(LMS_E)
Ye_n = Y_n ** v
f_E = lambda x, y: (3 * (x / y)) / (L / L_E + M / M_E + S / S_E)
f_P = lambda x: (1 + Ye_n + x) / (1 + Ye_n + 1 / x)
p_L = f_P(f_E(L, L_E))
p_M = f_P(f_E(M, M_E))
p_S = f_P(f_E(S, S_E))
p_LMS = tstack((p_L, p_M, p_S))
return p_LMS
Colour 0.3