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Merge pull request #192 from colour-science/feature/cvd
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PR: Simulation of CVD - Machado (2009)
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@KelSolaar
KelSolaar authored Mar 13, 2018
2 parents adf97a6 + dca90c2 commit 423f01e
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1,042 changes: 521 additions & 521 deletions BIBLIOGRAPHY.bib
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16 changes: 16 additions & 0 deletions README.rst
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Expand Up @@ -654,6 +654,22 @@ Munsell Colour
>>> colour.munsell_colour_to_xyY('4.2YR 8.1/5.3')
array([ 0.38736945, 0.35751656, 0.59362 ])
Colour Blindness
^^^^^^^^^^^^^^^^

.. code-block:: python
>>> import colour
>>> cmfs = colour.LMS_CMFS['Stockman & Sharpe 2 Degree Cone Fundamentals']
>>> colour.anomalous_trichromacy_cmfs_Machado2009(cmfs, np.array([15, 0, 0]))[450]
array([ 0.08912884, 0.0870524 , 0.955393 ])
>>> primaries = colour.DISPLAYS_RGB_PRIMARIES['Apple Studio Display']
>>> d_LMS = (15, 0, 0)
>>> colour.anomalous_trichromacy_matrix_Machado2009(cmfs, primaries, d_LMS)
array([[-0.27774652, 2.65150084, -1.37375432],
[ 0.27189369, 0.20047862, 0.52762768],
[ 0.00644047, 0.25921579, 0.73434374]])
Optical Phenomena
^^^^^^^^^^^^^^^^^

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8 changes: 8 additions & 0 deletions colour/__init__.py
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Expand Up @@ -12,6 +12,7 @@
- algebra: Algebra utilities.
- appearance: Colour appearance models.
- biochemistry: Biochemistry computations.
- blindness: Colour vision deficiency models.
- continuous: Base objects for continuous data representation.
- characterisation: Colour fitting and camera characterisation.
- colorimetry: Core objects for colour computations.
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luminous_efficacy, luminous_efficiency, luminous_flux,
mesopic_luminous_efficiency_function, ones_spd, spectral_to_XYZ,
wavelength_to_XYZ, whiteness, yellowness, zeros_spd)
from .blindness import (
CVD_MATRICES_MACHADO2010, anomalous_trichromacy_cmfs_Machado2009,
anomalous_trichromacy_matrix_Machado2009, cvd_matrix_Machado2009)
from .appearance import (
ATD95_Specification, CAM16_Specification, CAM16_VIEWING_CONDITIONS,
CAM16_to_XYZ, CIECAM02_Specification, CIECAM02_VIEWING_CONDITIONS,
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'mesopic_luminous_efficiency_function', 'ones_spd', 'spectral_to_XYZ',
'wavelength_to_XYZ', 'whiteness', 'yellowness', 'zeros_spd'
]
__all__ += [
'CVD_MATRICES_MACHADO2010', 'anomalous_trichromacy_cmfs_Machado2009',
'anomalous_trichromacy_matrix_Machado2009', 'cvd_matrix_Machado2009'
]
__all__ += [
'ATD95_Specification', 'CAM16_Specification', 'CAM16_VIEWING_CONDITIONS',
'CAM16_to_XYZ', 'CIECAM02_Specification', 'CIECAM02_VIEWING_CONDITIONS',
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17 changes: 17 additions & 0 deletions colour/blindness/__init__.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import absolute_import

from .dataset import * # noqa
from . import dataset
from .machado2009 import (
anomalous_trichromacy_cmfs_Machado2009,
anomalous_trichromacy_matrix_Machado2009,
cvd_matrix_Machado2009)

__all__ = []
__all__ += dataset.__all__
__all__ += ['anomalous_trichromacy_cmfs_Machado2009',
'anomalous_trichromacy_matrix_Machado2009',
'cvd_matrix_Machado2009']
8 changes: 8 additions & 0 deletions colour/blindness/dataset/__init__.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import absolute_import

from .machado2010 import CVD_MATRICES_MACHADO2010

__all__ = ['CVD_MATRICES_MACHADO2010']
248 changes: 248 additions & 0 deletions colour/blindness/dataset/machado2010.py
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# -*- coding: utf-8 -*-
"""
Pre-Computed Matrices for simulation of CVD - Machado (2009)
============================================================
Defines the pre-computed matrices for simulation of colour vision deficiency
from Machado (2010).
See Also
--------
`Machado (2009) - CVD IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/\
blob/master/notebooks/cvd/Machado2009.ipynb>`_
References
----------
- :cite:`Machado2010a` : Machado, G. M. (2010). A model for simulation of
color vision deficiency and a color contrast enhancement technique for
dichromats. Retrieved from http://www.lume.ufrgs.br/handle/10183/26950
"""

from __future__ import division, unicode_literals

import numpy as np

from colour.utilities import CaseInsensitiveMapping

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2018 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = '[email protected]'
__status__ = 'Production'

__all__ = ['CVD_MATRICES_MACHADO2010']

CVD_MATRICES_MACHADO2010 = CaseInsensitiveMapping({
'Protanomaly': {
0.0:
np.array([
[1.000000, 0.000000, -0.000000],
[0.000000, 1.000000, 0.000000],
[-0.000000, -0.000000, 1.000000],
]),
0.1:
np.array([
[0.856167, 0.182038, -0.038205],
[0.029342, 0.955115, 0.015544],
[-0.002880, -0.001563, 1.004443],
]),
0.2:
np.array([
[0.734766, 0.334872, -0.069637],
[0.051840, 0.919198, 0.028963],
[-0.004928, -0.004209, 1.009137],
]),
0.3:
np.array([
[0.630323, 0.465641, -0.095964],
[0.069181, 0.890046, 0.040773],
[-0.006308, -0.007724, 1.014032],
]),
0.4:
np.array([
[0.539009, 0.579343, -0.118352],
[0.082546, 0.866121, 0.051332],
[-0.007136, -0.011959, 1.019095],
]),
0.5:
np.array([
[0.458064, 0.679578, -0.137642],
[0.092785, 0.846313, 0.060902],
[-0.007494, -0.016807, 1.024301],
]),
0.6:
np.array([
[0.385450, 0.769005, -0.154455],
[0.100526, 0.829802, 0.069673],
[-0.007442, -0.022190, 1.029632],
]),
0.7:
np.array([
[0.319627, 0.849633, -0.169261],
[0.106241, 0.815969, 0.077790],
[-0.007025, -0.028051, 1.035076],
]),
0.8:
np.array([
[0.259411, 0.923008, -0.182420],
[0.110296, 0.804340, 0.085364],
[-0.006276, -0.034346, 1.040622],
]),
0.9:
np.array([
[0.203876, 0.990338, -0.194214],
[0.112975, 0.794542, 0.092483],
[-0.005222, -0.041043, 1.046265],
]),
1.0:
np.array([
[0.152286, 1.052583, -0.204868],
[0.114503, 0.786281, 0.099216],
[-0.003882, -0.048116, 1.051998],
])
},
'Deuteranomaly': {
0.0:
np.array([
[1.000000, 0.000000, -0.000000],
[0.000000, 1.000000, 0.000000],
[-0.000000, -0.000000, 1.000000],
]),
0.1:
np.array([
[0.866435, 0.177704, -0.044139],
[0.049567, 0.939063, 0.011370],
[-0.003453, 0.007233, 0.996220],
]),
0.2:
np.array([
[0.760729, 0.319078, -0.079807],
[0.090568, 0.889315, 0.020117],
[-0.006027, 0.013325, 0.992702],
]),
0.3:
np.array([
[0.675425, 0.433850, -0.109275],
[0.125303, 0.847755, 0.026942],
[-0.007950, 0.018572, 0.989378],
]),
0.4:
np.array([
[0.605511, 0.528560, -0.134071],
[0.155318, 0.812366, 0.032316],
[-0.009376, 0.023176, 0.986200],
]),
0.5:
np.array([
[0.547494, 0.607765, -0.155259],
[0.181692, 0.781742, 0.036566],
[-0.010410, 0.027275, 0.983136],
]),
0.6:
np.array([
[0.498864, 0.674741, -0.173604],
[0.205199, 0.754872, 0.039929],
[-0.011131, 0.030969, 0.980162],
]),
0.7:
np.array([
[0.457771, 0.731899, -0.189670],
[0.226409, 0.731012, 0.042579],
[-0.011595, 0.034333, 0.977261],
]),
0.8:
np.array([
[0.422823, 0.781057, -0.203881],
[0.245752, 0.709602, 0.044646],
[-0.011843, 0.037423, 0.974421],
]),
0.9:
np.array([
[0.392952, 0.823610, -0.216562],
[0.263559, 0.690210, 0.046232],
[-0.011910, 0.040281, 0.971630],
]),
1.0:
np.array([
[0.367322, 0.860646, -0.227968],
[0.280085, 0.672501, 0.047413],
[-0.011820, 0.042940, 0.968881],
])
},
'Tritanomaly': {
0.0:
np.array([
[1.000000, 0.000000, -0.000000],
[0.000000, 1.000000, 0.000000],
[-0.000000, -0.000000, 1.000000],
]),
0.1:
np.array([
[0.926670, 0.092514, -0.019184],
[0.021191, 0.964503, 0.014306],
[0.008437, 0.054813, 0.936750],
]),
0.2:
np.array([
[0.895720, 0.133330, -0.029050],
[0.029997, 0.945400, 0.024603],
[0.013027, 0.104707, 0.882266],
]),
0.3:
np.array([
[0.905871, 0.127791, -0.033662],
[0.026856, 0.941251, 0.031893],
[0.013410, 0.148296, 0.838294],
]),
0.4:
np.array([
[0.948035, 0.089490, -0.037526],
[0.014364, 0.946792, 0.038844],
[0.010853, 0.193991, 0.795156],
]),
0.5:
np.array([
[1.017277, 0.027029, -0.044306],
[-0.006113, 0.958479, 0.047634],
[0.006379, 0.248708, 0.744913],
]),
0.6:
np.array([
[1.104996, -0.046633, -0.058363],
[-0.032137, 0.971635, 0.060503],
[0.001336, 0.317922, 0.680742],
]),
0.7:
np.array([
[1.193214, -0.109812, -0.083402],
[-0.058496, 0.979410, 0.079086],
[-0.002346, 0.403492, 0.598854],
]),
0.8:
np.array([
[1.257728, -0.139648, -0.118081],
[-0.078003, 0.975409, 0.102594],
[-0.003316, 0.501214, 0.502102],
]),
0.9:
np.array([
[1.278864, -0.125333, -0.153531],
[-0.084748, 0.957674, 0.127074],
[-0.000989, 0.601151, 0.399838],
]),
1.0:
np.array([
[1.255528, -0.076749, -0.178779],
[-0.078411, 0.930809, 0.147602],
[0.004733, 0.691367, 0.303900],
])
}
})
CVD_MATRICES_MACHADO2010.__doc__ = """
Machado (2010) Simulation matrices :math:`\Phi_{CVD}`.
CVD_MATRICES_MACHADO2010 : CaseInsensitiveMapping
{'Protanomaly', 'Deuteranomaly', 'Tritanomaly'}
"""
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