#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Hunt Colour Appearance Model
============================
Defines Hunt colour appearance model objects:
- :class:`Hunt_InductionFactors`
- :attr:`HUNT_VIEWING_CONDITIONS`
- :class:`Hunt_Specification`
- :func:`XYZ_to_Hunt`
See Also
--------
`Hunt Colour Appearance Model IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/appearance/hunt.ipynb>`_ # noqa
References
----------
.. [1] Fairchild, M. D. (2013). The Hunt Model. In Color Appearance Models
(3rd ed., pp. 5094–5556). Wiley. ASIN:B00DAYO8E2
.. [2] Hunt, R. W. G. (2004). The Reproduction of Colour (6th ed.). Wiley.
ISBN:978-0-470-02425-6
"""
from __future__ import division, unicode_literals
import numpy as np
from collections import namedtuple
from colour.utilities import (
CaseInsensitiveMapping,
dot_vector,
tsplit,
tstack,
warning)
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2015 - Colour Developers'
__license__ = 'GPL V3.0 - http://www.gnu.org/licenses/'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['Hunt_InductionFactors',
'HUNT_VIEWING_CONDITIONS',
'HUE_DATA_FOR_HUE_QUADRATURE',
'XYZ_TO_HPE_MATRIX',
'HPE_TO_XYZ_MATRIX',
'Hunt_ReferenceSpecification',
'Hunt_Specification',
'XYZ_to_Hunt',
'luminance_level_adaptation_factor',
'illuminant_scotopic_luminance',
'XYZ_to_rgb',
'f_n',
'chromatic_adaptation',
'adjusted_reference_white_signals',
'achromatic_post_adaptation_signal',
'colour_difference_signals',
'hue_angle',
'eccentricity_factor',
'low_luminance_tritanopia_factor',
'yellowness_blueness_response',
'redness_greenness_response',
'overall_chromatic_response',
'saturation_correlate',
'achromatic_signal',
'brightness_correlate',
'lightness_correlate',
'chroma_correlate',
'colourfulness_correlate']
[docs]class Hunt_InductionFactors(
namedtuple('Hunt_InductionFactors',
('N_c', 'N_b', 'N_cb', 'N_bb'))):
"""
Hunt colour appearance model induction factors.
Parameters
----------
N_c : numeric or array_like
Chromatic surround induction factor :math:`N_c`.
N_b : numeric or array_like
*Brightness* surround induction factor :math:`N_b`.
N_cb : numeric or array_like, optional
Chromatic background induction factor :math:`N_{cb}`, approximated
using tristimulus values :math:`Y_w` and :math:`Y_b` of
respectively the reference white and the background if not specified.
N_bb : numeric or array_like, optional
*Brightness* background induction factor :math:`N_{bb}`, approximated
using tristimulus values :math:`Y_w` and :math:`Y_b` of
respectively the reference white and the background if not specified.
"""
def __new__(cls, N_c, N_b, N_cb=None, N_bb=None):
"""
Returns a new instance of the :class:`Hunt_InductionFactors` class.
"""
return super(Hunt_InductionFactors, cls).__new__(
cls, N_c, N_b, N_cb, N_bb)
HUNT_VIEWING_CONDITIONS = CaseInsensitiveMapping(
{'Small Areas, Uniform Background & Surrounds': (
Hunt_InductionFactors(1, 300)),
'Normal Scenes': (
Hunt_InductionFactors(1, 75)),
'Television & CRT, Dim Surrounds': (
Hunt_InductionFactors(1, 25)),
'Large Transparencies On Light Boxes': (
Hunt_InductionFactors(0.7, 25)),
'Projected Transparencies, Dark Surrounds': (
Hunt_InductionFactors(0.7, 10))})
"""
Reference Hunt colour appearance model viewing conditions.
HUNT_VIEWING_CONDITIONS : CaseInsensitiveMapping
**{'Small Areas, Uniform Background & Surrounds',
'Normal Scenes',
'Television & CRT, Dim Surrounds',
'Large Transparencies On Light Boxes',
'Projected Transparencies, Dark Surrounds'}**
Aliases:
- 'small_uniform': 'Small Areas, Uniform Background & Surrounds'
- 'normal': 'Normal Scenes'
- 'tv_dim': 'Television & CRT, Dim Surrounds'
- 'light_boxes': 'Large Transparencies On Light Boxes'
- 'projected_dark': 'Projected Transparencies, Dark Surrounds'
"""
HUNT_VIEWING_CONDITIONS['small_uniform'] = (
HUNT_VIEWING_CONDITIONS['Small Areas, Uniform Background & Surrounds'])
HUNT_VIEWING_CONDITIONS['normal'] = (
HUNT_VIEWING_CONDITIONS['Normal Scenes'])
HUNT_VIEWING_CONDITIONS['tv_dim'] = (
HUNT_VIEWING_CONDITIONS['Television & CRT, Dim Surrounds'])
HUNT_VIEWING_CONDITIONS['light_boxes'] = (
HUNT_VIEWING_CONDITIONS['Large Transparencies On Light Boxes'])
HUNT_VIEWING_CONDITIONS['projected_dark'] = (
HUNT_VIEWING_CONDITIONS['Projected Transparencies, Dark Surrounds'])
HUE_DATA_FOR_HUE_QUADRATURE = {
'h_s': np.array([20.14, 90.00, 164.25, 237.53]),
'e_s': np.array([0.8, 0.7, 1.0, 1.2])}
XYZ_TO_HPE_MATRIX = np.array(
[[0.38971, 0.68898, -0.07868],
[-0.22981, 1.18340, 0.04641],
[0.00000, 0.00000, 1.00000]])
"""
Hunt colour appearance model *CIE XYZ* tristimulus values to
*Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace matrix.
XYZ_TO_HPE_MATRIX : array_like, (3, 3)
"""
HPE_TO_XYZ_MATRIX = np.linalg.inv(XYZ_TO_HPE_MATRIX)
"""
Hunt colour appearance model *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta`
colourspace to *CIE XYZ* tristimulus values matrix.
HPE_TO_XYZ_MATRIX : array_like, (3, 3)
"""
[docs]class Hunt_ReferenceSpecification(
namedtuple('Hunt_ReferenceSpecification',
('J', 'C_94', 'h_S', 's', 'Q', 'M_94', 'H', 'H_C'))):
"""
Defines the Hunt colour appearance model reference specification.
This specification has field names consistent with Fairchild (2013)
reference.
Parameters
----------
J : numeric or array_like
Correlate of *Lightness* :math:`J`.
C_94 : numeric or array_like
Correlate of *chroma* :math:`C_94`.
h_S : numeric or array_like
*Hue* angle :math:`h_S` in degrees.
s : numeric or array_like
Correlate of *saturation* :math:`s`.
Q : numeric or array_like
Correlate of *brightness* :math:`Q`.
M_94 : numeric or array_like
Correlate of *colourfulness* :math:`M_94`.
H : numeric or array_like
*Hue* :math:`h` quadrature :math:`H`.
H_C : numeric or array_like
*Hue* :math:`h` composition :math:`H_C`.
"""
[docs]class Hunt_Specification(
namedtuple('Hunt_Specification',
('J', 'C', 'h', 's', 'Q', 'M', 'H', 'HC'))):
"""
Defines the Hunt colour appearance model specification.
This specification has field names consistent with the remaining colour
appearance models in :mod:`colour.appearance` but diverge from Fairchild
(2013) reference.
Parameters
----------
J : numeric or array_like
Correlate of *Lightness* :math:`J`.
C : numeric or array_like
Correlate of *chroma* :math:`C_94`.
h : numeric or array_like
*Hue* angle :math:`h_S` in degrees.
s : numeric or array_like
Correlate of *saturation* :math:`s`.
Q : numeric or array_like
Correlate of *brightness* :math:`Q`.
M : numeric or array_like
Correlate of *colourfulness* :math:`M_94`.
H : numeric or array_like
*Hue* :math:`h` quadrature :math:`H`.
HC : numeric or array_like
*Hue* :math:`h` composition :math:`H_C`.
"""
[docs]def XYZ_to_Hunt(XYZ,
XYZ_w,
XYZ_b,
L_A,
surround=HUNT_VIEWING_CONDITIONS.get('Normal Scenes'),
L_AS=None,
CCT_w=None,
XYZ_p=None,
p=None,
S=None,
S_W=None,
helson_judd_effect=False,
discount_illuminant=True):
"""
Computes the Hunt colour appearance model correlates.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of test sample / stimulus in domain
[0, 100].
XYZ_w : array_like
*CIE XYZ* tristimulus values of reference white in domain [0, 100].
XYZ_b : array_like
*CIE XYZ* tristimulus values of background in domain [0, 100].
L_A : numeric or array_like
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
surround : Hunt_InductionFactors, optional
Surround viewing conditions induction factors.
L_AS : numeric or array_like, optional
Scotopic luminance :math:`L_{AS}` of the illuminant, approximated if
not specified.
CCT_w : numeric or array_like, optional
Correlated color temperature :math:`T_{cp}`: of the illuminant, needed
to approximate :math:`L_{AS}`.
XYZ_p : array_like, optional
*CIE XYZ* tristimulus values of proximal field in domain [0, 100],
assumed to be equal to background if not specified.
p : numeric or array_like, optional
Simultaneous contrast / assimilation factor :math:`p` with value in
domain [-1, 0] when simultaneous contrast occurs and domain [0, 1]
when assimilation occurs.
S : numeric or array_like, optional
Scotopic response :math:`S` to the stimulus, approximated using
tristimulus values :math:`Y` of the stimulus if not specified.
S_w : numeric or array_like, optional
Scotopic response :math:`S_w` for the reference white, approximated
using the tristimulus values :math:`Y_w` of the reference white if not
specified.
helson_judd_effect : bool, optional
Truth value indicating whether the *Helson-Judd* effect should be
accounted for.
discount_illuminant : bool, optional
Truth value indicating if the illuminant should be discounted.
Warning
-------
The input domain of that definition is non standard!
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 100].
- Input *CIE XYZ_b* tristimulus values are in domain [0, 100].
- Input *CIE XYZ_w* tristimulus values are in domain [0, 100].
- Input *CIE XYZ_p* tristimulus values are in domain [0, 100].
Returns
-------
Hunt_Specification
Hunt colour appearance model specification.
Raises
------
ValueError
If an illegal arguments combination is specified.
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_w = np.array([95.05, 100.00, 108.88])
>>> XYZ_b = np.array([95.05, 100.00, 108.88])
>>> L_A = 318.31
>>> surround = HUNT_VIEWING_CONDITIONS['Normal Scenes']
>>> CCT_w = 6504.0
>>> XYZ_to_Hunt( # doctest: +ELLIPSIS
... XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w)
Hunt_Specification(J=30.0462678..., C=0.1210508..., h=269.2737594..., \
s=0.0199093..., Q=22.2097654..., M=0.1238964..., H=None, HC=None)
"""
_X, Y, Z = tsplit(XYZ)
X_b, Y_b, _Z_b = tsplit(XYZ_b)
_X_w, Y_w, Z_w = tsplit(XYZ_w)
# Arguments handling.
if XYZ_p is not None:
X_p, Y_p, Z_p = tsplit(XYZ_p)
else:
X_p = X_b
Y_p = Y_b
Z_p = Y_b
warning('Unspecified proximal field "XYZ_p" argument, using '
'background "XYZ_b" as approximation!')
if surround.N_cb is None:
N_cb = 0.725 * (Y_w / Y_b) ** 0.2
warning('Unspecified "N_cb" argument, using approximation: '
'"{0}"'.format(N_cb))
if surround.N_bb is None:
N_bb = 0.725 * (Y_w / Y_b) ** 0.2
warning('Unspecified "N_bb" argument, using approximation: '
'"{0}"'.format(N_bb))
if L_AS is None and CCT_w is None:
raise ValueError('Either the scotopic luminance "L_AS" of the '
'illuminant or its correlated colour temperature '
'"CCT_w" must be specified!')
if L_AS is None:
L_AS = illuminant_scotopic_luminance(L_A, CCT_w)
warning('Unspecified "L_AS" argument, using approximation from "CCT": '
'"{0}"'.format(L_AS))
if S is None != S_W is None:
raise ValueError('Either both stimulus scotopic response "S" and '
'reference white scotopic response "S_w" arguments '
'need to be specified or none of them!')
elif S is None and S_W is None:
S = Y
S_W = Y_w
warning('Unspecified stimulus scotopic response "S" and reference '
'white scotopic response "S_w" arguments, using '
'approximation: "{0}", "{1}"'.format(S, S_W))
if p is None:
warning('Unspecified simultaneous contrast / assimilation "p" '
'argument, model will not account for simultaneous chromatic '
'contrast!')
XYZ_p = tstack((X_p, Y_p, Z_p))
# Computing luminance level adaptation factor :math:`F_L`.
F_L = luminance_level_adaptation_factor(L_A)
# Computing test sample chromatic adaptation.
rgb_a = chromatic_adaptation(XYZ,
XYZ_w,
XYZ_b,
L_A,
F_L,
XYZ_p,
p,
helson_judd_effect,
discount_illuminant)
# Computing reference white chromatic adaptation.
rgb_aw = chromatic_adaptation(XYZ_w,
XYZ_w,
XYZ_b,
L_A,
F_L,
XYZ_p,
p,
helson_judd_effect,
discount_illuminant)
# Computing opponent colour dimensions.
# Computing achromatic post adaptation signals.
A_a = achromatic_post_adaptation_signal(rgb_a)
A_aw = achromatic_post_adaptation_signal(rgb_aw)
# Computing colour difference signals.
C = colour_difference_signals(rgb_a)
C_w = colour_difference_signals(rgb_aw)
# -------------------------------------------------------------------------
# Computing the *hue* angle :math:`h_s`.
# -------------------------------------------------------------------------
h = hue_angle(C)
# hue_w = hue_angle(C_w)
# TODO: Implement hue quadrature & composition computation.
# -------------------------------------------------------------------------
# Computing the correlate of *saturation* :math:`s`.
# -------------------------------------------------------------------------
# Computing eccentricity factors.
e_s = eccentricity_factor(h)
# Computing low luminance tritanopia factor :math:`F_t`.
F_t = low_luminance_tritanopia_factor(L_A)
M_yb = yellowness_blueness_response(C, e_s, surround.N_c, N_cb, F_t)
M_rg = redness_greenness_response(C, e_s, surround.N_c, N_cb)
M_yb_w = yellowness_blueness_response(C_w, e_s, surround.N_c, N_cb, F_t)
M_rg_w = redness_greenness_response(C_w, e_s, surround.N_c, N_cb)
# Computing overall chromatic response.
M = overall_chromatic_response(M_yb, M_rg)
M_w = overall_chromatic_response(M_yb_w, M_rg_w)
s = saturation_correlate(M, rgb_a)
# -------------------------------------------------------------------------
# Computing the correlate of *brightness* :math:`Q`.
# -------------------------------------------------------------------------
# Computing achromatic signal :math:`A`.
A = achromatic_signal(L_AS, S, S_W, N_bb, A_a)
A_w = achromatic_signal(L_AS, S_W, S_W, N_bb, A_aw)
Q = brightness_correlate(A, A_w, M, surround.N_b)
brightness_w = brightness_correlate(A_w, A_w, M_w, surround.N_b)
# TODO: Implement whiteness-blackness :math:`Q_{wb}` computation.
# -------------------------------------------------------------------------
# Computing the correlate of *Lightness* :math:`J`.
# -------------------------------------------------------------------------
J = lightness_correlate(Y_b, Y_w, Q, brightness_w)
# -------------------------------------------------------------------------
# Computing the correlate of *chroma* :math:`C_{94}`.
# -------------------------------------------------------------------------
C_94 = chroma_correlate(s, Y_b, Y_w, Q, brightness_w)
# -------------------------------------------------------------------------
# Computing the correlate of *colourfulness* :math:`M_{94}`.
# -------------------------------------------------------------------------
M_94 = colourfulness_correlate(F_L, C_94)
return Hunt_Specification(J, C_94, h, s, Q, M_94, None, None)
[docs]def luminance_level_adaptation_factor(L_A):
"""
Returns the *luminance* level adaptation factor :math:`F_L`.
Parameters
----------
L_A : numeric or array_like
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
Returns
-------
numeric or ndarray
*Luminance* level adaptation factor :math:`F_L`
Examples
--------
>>> luminance_level_adaptation_factor(318.31) # doctest: +ELLIPSIS
1.1675444...
"""
L_A = np.asarray(L_A)
k = 1 / (5 * L_A + 1)
k4 = k ** 4
F_L = 0.2 * k4 * (5 * L_A) + 0.1 * (1 - k4) ** 2 * (5 * L_A) ** (1 / 3)
return F_L
[docs]def illuminant_scotopic_luminance(L_A, CCT):
"""
Returns the approximate scotopic luminance :math:`L_{AS}` of the
illuminant.
Parameters
----------
L_A : numeric or array_like
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
CCT : numeric or array_like
Correlated color temperature :math:`T_{cp}` of the illuminant.
Returns
-------
numeric or ndarray
Approximate scotopic luminance :math:`L_{AS}`.
Examples
--------
>>> illuminant_scotopic_luminance(318.31, 6504.0) # doctest: +ELLIPSIS
769.9376286...
"""
L_A = np.asarray(L_A)
CCT = np.asarray(CCT)
CCT = 2.26 * L_A * ((CCT / 4000) - 0.4) ** (1 / 3)
return CCT
[docs]def XYZ_to_rgb(XYZ):
"""
Converts from *CIE XYZ* tristimulus values to *Hunt-Pointer-Estevez*
:math:`\\rho\gamma\\beta` colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
Returns
-------
ndarray
*Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace.
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_to_rgb(XYZ) # doctest: +ELLIPSIS
array([ 19.4743367..., 20.3101217..., 21.78 ])
"""
return dot_vector(XYZ_TO_HPE_MATRIX, XYZ)
[docs]def f_n(x):
"""
Defines the nonlinear response function of the Hunt colour appearance
model used to model the nonlinear behavior of various visual responses.
Parameters
----------
x : numeric or array_like or array_like
Visual response variable :math:`x`.
Returns
-------
numeric or array_like
Modeled visual response variable :math:`x`.
Examples
--------
>>> x = np.array([0.23350512, 0.23351103, 0.23355179])
>>> f_n(x) # doctest: +ELLIPSIS
array([ 5.8968592..., 5.8969521..., 5.8975927...])
"""
x = np.asarray(x)
x_m = 40 * ((x ** 0.73) / (x ** 0.73 + 2))
return x_m
[docs]def chromatic_adaptation(XYZ,
XYZ_w,
XYZ_b,
L_A,
F_L,
XYZ_p=None,
p=None,
helson_judd_effect=False,
discount_illuminant=True):
"""
Applies chromatic adaptation to given *CIE XYZ* tristimulus values.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of test sample in domain [0, 100].
XYZ_b : array_like
*CIE XYZ* tristimulus values of background in domain [0, 100].
XYZ_w : array_like
*CIE XYZ* tristimulus values of reference white in domain [0, 100].
L_A : numeric or array_like
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
F_L : numeric or array_like
Luminance adaptation factor :math:`F_L`.
XYZ_p : array_like, optional
*CIE XYZ* tristimulus values of proximal field in domain [0, 100],
assumed to be equal to background if not specified.
p : numeric or array_like, optional
Simultaneous contrast / assimilation factor :math:`p` with value in
domain [-1, 0] when simultaneous contrast occurs and domain [0, 1]
when assimilation occurs.
helson_judd_effect : bool, optional
Truth value indicating whether the *Helson-Judd* effect should be
accounted for.
discount_illuminant : bool, optional
Truth value indicating if the illuminant should be discounted.
Returns
-------
ndarray
Adapted *CIE XYZ* tristimulus values.
Examples
--------
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_b = np.array([95.05, 100.00, 108.88])
>>> XYZ_w = np.array([95.05, 100.00, 108.88])
>>> L_A = 318.31
>>> F_L = 1.16754446415
>>> chromatic_adaptation(XYZ, XYZ_w, XYZ_b, L_A, F_L) # doctest: +ELLIPSIS
array([ 6.8959454..., 6.8959991..., 6.8965708...])
"""
XYZ_w = np.asarray(XYZ_w)
XYZ_b = np.asarray(XYZ_b)
L_A = np.asarray(L_A)
F_L = np.asarray(F_L)
rgb = XYZ_to_rgb(XYZ)
rgb_w = XYZ_to_rgb(XYZ_w)
Y_w = XYZ_w[..., 1]
Y_b = XYZ_b[..., 1]
h_rgb = 3 * rgb_w / np.sum(rgb_w, axis=-1)[..., np.newaxis]
# Computing chromatic adaptation factors.
if not discount_illuminant:
F_rgb = ((1 + (L_A ** (1 / 3)) + h_rgb) /
(1 + (L_A ** (1 / 3)) + (1 / h_rgb)))
else:
F_rgb = np.ones(h_rgb.shape)
# Computing Helson-Judd effect parameters.
if helson_judd_effect:
D_rgb = (f_n((Y_b / Y_w) * F_L * F_rgb[..., 1]) -
f_n((Y_b / Y_w) * F_L * F_rgb))
else:
D_rgb = np.zeros(F_rgb.shape)
# Computing cone bleach factors.
B_rgb = (10 ** 7) / ((10 ** 7) + 5 * L_A[..., np.newaxis] * (rgb_w / 100))
# Computing adjusted reference white signals.
if XYZ_p is not None and p is not None:
rgb_p = XYZ_to_rgb(XYZ_p)
rgb_w = adjusted_reference_white_signals(rgb_p, B_rgb, rgb_w,
p)
# Computing adapted cone responses.
rgb_a = 1
rgb_a += B_rgb * (f_n(F_L[..., np.newaxis] * F_rgb * rgb / rgb_w) + D_rgb)
return rgb_a
[docs]def adjusted_reference_white_signals(rgb_p, rgb_b, rgb_w, p):
"""
Adjusts the white point for simultaneous chromatic contrast.
Parameters
----------
rgb_p : array_like
Cone signals *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta`
colourspace array of the proximal field.
rgb_b : array_like
Cone signals *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta`
colourspace array of the background.
rgb_w : array_like
Cone signals array *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta`
colourspace array of the reference white.
p : numeric or array_like
Simultaneous contrast / assimilation factor :math:`p` with value in
domain [-1, 0] when simultaneous contrast occurs and domain [0, 1]
when assimilation occurs.
Returns
-------
ndarray
Adjusted cone signals *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta`
colourspace array of the reference white.
Examples
--------
>>> rgb_p = np.array([98.07193550, 101.13755950, 100.00000000])
>>> rgb_b = np.array([0.99984505, 0.99983840, 0.99982674])
>>> rgb_w = np.array([97.37325710, 101.54968030, 108.88000000])
>>> p = 0.1
>>> adjusted_reference_white_signals( # doctest: +ELLIPSIS
... rgb_p, rgb_b, rgb_w, p)
array([ 88.0792742..., 91.8569553..., 98.4876543...])
"""
rgb_p = np.asarray(rgb_p)
rgb_b = np.asarray(rgb_b)
rgb_w = np.asarray(rgb_w)
p = np.asarray(p)
p_rgb = rgb_p / rgb_b
rgb_w = (rgb_w * (((1 - p) * p_rgb + (1 + p) / p_rgb) ** 0.5) /
(((1 + p) * p_rgb + (1 - p) / p_rgb) ** 0.5))
return rgb_w
[docs]def achromatic_post_adaptation_signal(rgb):
"""
Returns the achromatic post adaptation signal :math:`A` from given
*Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array.
Parameters
----------
rgb : array_like
*Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array.
Returns
-------
numeric or ndarray
Achromatic post adaptation signal :math:`A`.
Examples
--------
>>> rgb = np.array([6.89594549, 6.89599915, 6.89657085])
>>> achromatic_post_adaptation_signal(rgb) # doctest: +ELLIPSIS
18.9827186...
"""
r, g, b = tsplit(rgb)
A = 2 * r + g + (1 / 20) * b - 3.05 + 1
return A
[docs]def colour_difference_signals(rgb):
"""
Returns the colour difference signals :math:`C_1`, :math:`C_2` and
:math:`C_3` from given *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta`
colourspace array.
Parameters
----------
rgb : array_like
*Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array.
Returns
-------
ndarray
Colour difference signals :math:`C_1`, :math:`C_2` and :math:`C_3`.
Examples
--------
>>> rgb = np.array([6.89594549, 6.89599915, 6.89657085])
>>> colour_difference_signals(rgb) # doctest: +ELLIPSIS
array([ -5.3660000...e-05, -5.7170000...e-04, 6.2536000...e-04])
"""
r, g, b = tsplit(rgb)
C_1 = r - g
C_2 = g - b
C_3 = b - r
C = tstack((C_1, C_2, C_3))
return C
[docs]def hue_angle(C):
"""
Returns the *hue* angle :math:`h` from given colour difference signals
:math:`C`.
Parameters
----------
C : array_like
Colour difference signals :math:`C`.
Returns
-------
numeric or ndarray
*Hue* angle :math:`h`.
Examples
--------
>>> C = np.array([-5.3658655819965873e-05,
... -0.00057169938364687312,
... 0.00062535803946683899])
>>> hue_angle(C) # doctest: +ELLIPSIS
269.2737594...
"""
C_1, C_2, C_3 = tsplit(C)
hue = (180 * np.arctan2(0.5 * (C_2 - C_3) / 4.5,
C_1 - (C_2 / 11)) / np.pi) % 360
return hue
[docs]def eccentricity_factor(hue):
"""
Returns eccentricity factor :math:`e_s` from given hue angle :math:`h`.
Parameters
----------
hue : numeric or array_like
Hue angle :math:`h`.
Returns
-------
numeric or ndarray
Eccentricity factor :math:`e_s`.
Examples
--------
>>> eccentricity_factor(269.273759) # doctest: +ELLIPSIS
array(1.1108365...)
"""
hue = np.asarray(hue)
h_s = HUE_DATA_FOR_HUE_QUADRATURE.get('h_s')
e_s = HUE_DATA_FOR_HUE_QUADRATURE.get('e_s')
x = np.interp(hue, h_s, e_s)
x = np.where(hue < 20.14, 0.856 - (hue / 20.14) * 0.056, x)
x = np.where(hue > 237.53, 0.856 + 0.344 * (360 - hue) / (360 - 237.53), x)
return x
[docs]def low_luminance_tritanopia_factor(L_A):
"""
Returns the low luminance tritanopia factor :math:`F_t` from given adapting
field *luminance* :math:`L_A` in :math:`cd/m^2`.
Parameters
----------
L_A : numeric or array_like
Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`.
Returns
-------
numeric or ndarray
Low luminance tritanopia factor :math:`F_t`.
Examples
--------
>>> low_luminance_tritanopia_factor(318.31) # doctest: +ELLIPSIS
0.9996859...
"""
L_A = np.asarray(L_A)
F_t = L_A / (L_A + 0.1)
return F_t
[docs]def yellowness_blueness_response(C, e_s, N_c, N_cb, F_t):
"""
Returns the yellowness / blueness response :math:`M_{yb}`.
Parameters
----------
C : array_like
Colour difference signals :math:`C`.
e_s : numeric or array_like
Eccentricity factor :math:`e_s`.
N_c : numeric or array_like
Chromatic surround induction factor :math:`N_c`.
N_b : numeric or array_like
Brightness surround induction factor :math:`N_b`.
F_t : numeric or array_like
Low luminance tritanopia factor :math:`F_t`.
Returns
-------
numeric or ndarray
Yellowness / blueness response :math:`M_{yb}`.
Examples
--------
>>> C = np.array([-5.3658655819965873e-05,
... -0.00057169938364687312,
... 0.00062535803946683899])
>>> e_s = 1.1108365048626296
>>> N_c = 1.0
>>> N_cb = 0.72499999999999998
>>> F_t = 0.99968593951195
>>> yellowness_blueness_response( # doctest: +ELLIPSIS
... C, e_s, N_c, N_cb, F_t)
-0.0082372...
"""
_C_1, C_2, C_3 = tsplit(C)
e_s = np.asarray(e_s)
N_c = np.asarray(N_c)
N_cb = np.asarray(N_cb)
F_t = np.asarray(F_t)
M_yb = (100 * (0.5 * (C_2 - C_3) / 4.5) *
(e_s * (10 / 13) * N_c * N_cb * F_t))
return M_yb
[docs]def redness_greenness_response(C, e_s, N_c, N_cb):
"""
Returns the redness / greenness response :math:`M_{yb}`.
Parameters
----------
C : array_like
Colour difference signals :math:`C`.
e_s : numeric or array_like
Eccentricity factor :math:`e_s`.
N_c : numeric or array_like
Chromatic surround induction factor :math:`N_c`.
N_b : numeric or array_like
Brightness surround induction factor :math:`N_b`.
Returns
-------
numeric or ndarray
Redness / greenness response :math:`M_{rg}`.
Examples
--------
>>> C = np.array([-5.3658655819965873e-05,
... -0.00057169938364687312,
... 0.00062535803946683899])
>>> e_s = 1.1108365048626296
>>> N_c = 1.0
>>> N_cb = 0.72499999999999998
>>> redness_greenness_response(C, e_s, N_c, N_cb) # doctest: +ELLIPSIS
-0.0001044...
"""
C_1, C_2, _C_3 = tsplit(C)
e_s = np.asarray(e_s)
N_c = np.asarray(N_c)
N_cb = np.asarray(N_cb)
M_rg = 100 * (C_1 - (C_2 / 11)) * (e_s * (10 / 13) * N_c * N_cb)
return M_rg
[docs]def overall_chromatic_response(M_yb, M_rg):
"""
Returns the overall chromatic response :math:`M`.
Parameters
----------
M_yb : numeric or array_like
Yellowness / blueness response :math:`M_{yb}`.
M_rg : numeric or array_like
Redness / greenness response :math:`M_{rg}`.
Returns
-------
numeric or ndarray
Overall chromatic response :math:`M`.
Examples
--------
>>> M_yb = -0.008237223618824608
>>> M_rg = -0.00010444758327626432
>>> overall_chromatic_response(M_yb, M_rg) # doctest: +ELLIPSIS
0.0082378...
"""
M_yb = np.asarray(M_yb)
M_rg = np.asarray(M_rg)
M = ((M_yb ** 2) + (M_rg ** 2)) ** 0.5
return M
[docs]def saturation_correlate(M, rgb_a):
"""
Returns the *saturation* correlate :math:`s`.
Parameters
----------
M : numeric or array_like
Overall chromatic response :math:`M`.
rgb_a : array_like
Adapted *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace
array.
Returns
-------
numeric or ndarray
*Saturation* correlate :math:`s`.
Examples
--------
>>> M = 0.008237885787274198
>>> rgb_a = np.array([6.89594549, 6.89599915, 6.89657085])
>>> saturation_correlate(M, rgb_a) # doctest: +ELLIPSIS
0.0199093...
"""
M = np.asarray(M)
rgb_a = np.asarray(rgb_a)
s = 50 * M / np.sum(rgb_a, axis=-1)
return s
[docs]def achromatic_signal(L_AS, S, S_W, N_bb, A_a):
"""
Returns the achromatic signal :math:`A`.
Parameters
----------
L_AS : numeric or array_like
Scotopic luminance :math:`L_{AS}` of the illuminant.
S : numeric or array_like
Scotopic response :math:`S` to the stimulus.
S_w : numeric or array_like
Scotopic response :math:`S_w` for the reference white.
N_bb : numeric or array_like
Brightness background induction factor :math:`N_{bb}`.
A_a: numeric or array_like
Achromatic post adaptation signal of the stimulus :math:`A_a`.
Returns
-------
numeric or ndarray
Achromatic signal :math:`A`.
Examples
--------
>>> L_AS = 769.9376286541402
>>> S = 20.0
>>> S_W = 100.0
>>> N_bb = 0.72499999999999998
>>> A_a = 18.982718664838487
>>> achromatic_signal(L_AS, S, S_W, N_bb, A_a) # doctest: +ELLIPSIS
15.5068546...
"""
L_AS = np.asarray(L_AS)
S = np.asarray(S)
S_W = np.asarray(S_W)
N_bb = np.asarray(N_bb)
A_a = np.asarray(A_a)
j = 0.00001 / ((5 * L_AS / 2.26) + 0.00001)
# Computing scotopic luminance level adaptation factor :math:`F_{LS}`.
F_LS = 3800 * (j ** 2) * (5 * L_AS / 2.26)
F_LS += 0.2 * ((1 - (j ** 2)) ** 0.4) * ((5 * L_AS / 2.26) ** (1 / 6))
# Computing cone bleach factors :math:`B_S`.
B_S = 0.5 / (1 + 0.3 * ((5 * L_AS / 2.26) * (S / S_W)) ** 0.3)
B_S += 0.5 / (1 + 5 * (5 * L_AS / 2.26))
# Computing adapted scotopic signal :math:`A_S`.
A_S = (f_n(F_LS * S / S_W) * 3.05 * B_S) + 0.3
# Computing achromatic signal :math:`A`.
A = N_bb * (A_a - 1 + A_S - 0.3 + np.sqrt((1 + (0.3 ** 2))))
return A
[docs]def brightness_correlate(A, A_w, M, N_b):
"""
Returns the *brightness* correlate :math:`Q`.
Parameters
----------
A : numeric or array_like
Achromatic signal :math:`A`.
A_a: numeric or array_like
Achromatic post adaptation signal of the reference white :math:`A_w`.
M : numeric or array_like
Overall chromatic response :math:`M`.
N_b : numeric or array_like
Brightness surround induction factor :math:`N_b`.
Returns
-------
numeric or ndarray
*Brightness* correlate :math:`Q`.
Examples
--------
>>> A = 15.506854623621885
>>> A_w = 35.718916676317086
>>> M = 0.0082378857872741976
>>> N_b = 75.0
>>> brightness_correlate(A, A_w, M, N_b) # doctest: +ELLIPSIS
22.2097654...
"""
A = np.asarray(A)
A_w = np.asarray(A_w)
M = np.asarray(M)
N_b = np.asarray(N_b)
N_1 = ((7 * A_w) ** 0.5) / (5.33 * N_b ** 0.13)
N_2 = (7 * A_w * N_b ** 0.362) / 200
Q = ((7 * (A + (M / 100))) ** 0.6) * N_1 - N_2
return Q
[docs]def lightness_correlate(Y_b, Y_w, Q, Q_w):
"""
Returns the *Lightness* correlate :math:`J`.
Parameters
----------
Y_b : numeric or array_like
Tristimulus values :math:`Y_b` the background.
Y_w : numeric or array_like
Tristimulus values :math:`Y_b` the reference white.
Q : numeric or array_like
*Brightness* correlate :math:`Q` of the stimulus.
Q_w : numeric or array_like
*Brightness* correlate :math:`Q` of the reference white.
Returns
-------
numeric or ndarray
*Lightness* correlate :math:`J`.
Examples
--------
>>> Y_b = 100.0
>>> Y_w = 100.0
>>> Q = 22.209765491265024
>>> Q_w = 40.518065821226081
>>> lightness_correlate(Y_b, Y_w, Q, Q_w) # doctest: +ELLIPSIS
30.0462678...
"""
Y_b = np.asarray(Y_b)
Y_w = np.asarray(Y_w)
Q = np.asarray(Q)
Q_w = np.asarray(Q_w)
Z = 1 + (Y_b / Y_w) ** 0.5
J = 100 * (Q / Q_w) ** Z
return J
[docs]def chroma_correlate(s, Y_b, Y_w, Q, Q_w):
"""
Returns the *chroma* correlate :math:`C_94`.
Parameters
----------
s : numeric or array_like
*Saturation* correlate :math:`s`.
Y_b : numeric or array_like
Tristimulus values :math:`Y_b` the background.
Y_w : numeric or array_like
Tristimulus values :math:`Y_b` the reference white.
Q : numeric or array_like
*Brightness* correlate :math:`Q` of the stimulus.
Q_w : numeric or array_like
*Brightness* correlate :math:`Q` of the reference white.
Returns
-------
numeric or ndarray
*Chroma* correlate :math:`C_94`.
Examples
--------
>>> s = 0.0199093206929
>>> Y_b = 100.0
>>> Y_w = 100.0
>>> Q = 22.209765491265024
>>> Q_w = 40.518065821226081
>>> chroma_correlate(s, Y_b, Y_w, Q, Q_w) # doctest: +ELLIPSIS
0.1210508...
"""
s = np.asarray(s)
Y_b = np.asarray(Y_b)
Y_w = np.asarray(Y_w)
Q = np.asarray(Q)
Q_w = np.asarray(Q_w)
C_94 = (2.44 * (s ** 0.69) *
((Q / Q_w) ** (Y_b / Y_w)) *
(1.64 - 0.29 ** (Y_b / Y_w)))
return C_94
[docs]def colourfulness_correlate(F_L, C_94):
"""
Returns the *colourfulness* correlate :math:`M_94`.
Parameters
----------
F_L : numeric or array_like
Luminance adaptation factor :math:`F_L`.
numeric
*Chroma* correlate :math:`C_94`.
Returns
-------
numeric
*Colourfulness* correlate :math:`M_94`.
Examples
--------
>>> F_L = 1.16754446414718
>>> C_94 = 0.12105083993617581
>>> colourfulness_correlate(F_L, C_94) # doctest: +ELLIPSIS
0.1238964...
"""
F_L = np.asarray(F_L)
C_94 = np.asarray(C_94)
M_94 = F_L ** 0.15 * C_94
return M_94
Colour 0.3