Source code for colour.appearance.ciecam02

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
CIECAM02 Colour Appearance Model
================================

Defines CIECAM02 colour appearance model objects:

-   :class:`CIECAM02_InductionFactors`
-   :attr:`CIECAM02_VIEWING_CONDITIONS`
-   :class:`CIECAM02_Specification`
-   :func:`XYZ_to_CIECAM02`
-   :func:`CIECAM02_to_XYZ`

See Also
--------
`CIECAM02 Colour Appearance Model IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/appearance/ciecam02.ipynb>`_  # noqa

References
----------
.. [1]  Wikipedia. (n.d.). CIECAM02. Retrieved August 14, 2014, from
        http://en.wikipedia.org/wiki/CIECAM02
.. [2]  Fairchild, M. D. (2004). CIECAM02. In Color Appearance Models
        (2nd ed., pp. 289–301). Wiley. ISBN:978-0470012161
.. [3]  Westland, S., Ripamonti, C., & Cheung, V. (2012). Extrapolation
        Methods. Computational Colour Science Using MATLAB (2nd ed., p. 38).
        ISBN:978-0-470-66569-5
.. [4]  Moroney, N., Fairchild, M. D., Hunt, R. W. G., Li, C., Luo, M. R., &
        Newman, T. (n.d.). The CIECAM02 Color Appearance Model. Color and
        Imaging Conference, 2002(1), 23–27. Retrieved from
        http://www.ingentaconnect.com/content/ist/cic/2002/00002002/00000001/art00006  # noqa
"""

from __future__ import division, unicode_literals

import bisect

try:
    from functools import lru_cache
except ImportError:
    from backports.functools_lru_cache import lru_cache

import numpy as np
from collections import namedtuple

from colour.adaptation import CAT02_CAT
from colour.appearance.hunt import (XYZ_TO_HPE_MATRIX,
                                    HPE_TO_XYZ_MATRIX,
                                    luminance_level_adaptation_factor)
from colour.utilities import CaseInsensitiveMapping

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2014 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'

__all__ = ['CAT02_INVERSE_CAT',
           'CIECAM02_InductionFactors',
           'CIECAM02_VIEWING_CONDITIONS',
           'HUE_DATA_FOR_HUE_QUADRATURE',
           'CIECAM02_Specification',
           'XYZ_to_CIECAM02',
           'CIECAM02_to_XYZ',
           'chromatic_induction_factors',
           'base_exponential_non_linearity',
           'viewing_condition_dependent_parameters',
           'degree_of_adaptation',
           'full_chromatic_adaptation_forward',
           'full_chromatic_adaptation_reverse',
           'RGB_to_rgb',
           'rgb_to_RGB',
           'post_adaptation_non_linear_response_compression_forward',
           'post_adaptation_non_linear_response_compression_reverse',
           'opponent_colour_dimensions_forward',
           'opponent_colour_dimensions_reverse',
           'hue_angle',
           'hue_quadrature',
           'eccentricity_factor',
           'achromatic_response_forward',
           'achromatic_response_reverse',
           'lightness_correlate',
           'brightness_correlate',
           'temporary_magnitude_quantity_forward',
           'temporary_magnitude_quantity_reverse',
           'chroma_correlate',
           'colourfulness_correlate',
           'saturation_correlate',
           'P',
           'post_adaptation_non_linear_response_compression_matrix']

CAT02_INVERSE_CAT = np.linalg.inv(CAT02_CAT)
"""
Inverse CAT02 chromatic adaptation transform.

CAT02_INVERSE_CAT : array_like, (3, 3)
"""


[docs]class CIECAM02_InductionFactors( namedtuple('CIECAM02_InductionFactors', ('F', 'c', 'N_c'))): """ CIECAM02 colour appearance model induction factors. Parameters ---------- F : numeric Maximum degree of adaptation :math:`F`. c : numeric Exponential non linearity :math:`c`. N_c : numeric Chromatic induction factor :math:`N_c`. """
CIECAM02_VIEWING_CONDITIONS = CaseInsensitiveMapping( {'Average': CIECAM02_InductionFactors(1, 0.69, 1), 'Dim': CIECAM02_InductionFactors(0.9, 0.59, 0.95), 'Dark': CIECAM02_InductionFactors(0.8, 0.525, 0.8)}) """ Reference CIECAM02 colour appearance model viewing conditions. CIECAM02_VIEWING_CONDITIONS : CaseInsensitiveMapping {'Average', 'Dim', 'Dark'} """ HUE_DATA_FOR_HUE_QUADRATURE = { 'h_i': np.array([20.14, 90.00, 164.25, 237.53, 380.14]), 'e_i': np.array([0.8, 0.7, 1.0, 1.2, 0.8]), 'H_i': np.array([0.0, 100.0, 200.0, 300.0, 400.0])}
[docs]class CIECAM02_Specification( namedtuple('CIECAM02_Specification', ('J', 'C', 'h', 's', 'Q', 'M', 'H', 'HC'))): """ Defines the CIECAM02 colour appearance model specification. Parameters ---------- J : numeric Correlate of *Lightness* :math:`J`. C : numeric Correlate of *chroma* :math:`C`. h : numeric *Hue* angle :math:`h` in degrees. s : numeric Correlate of *saturation* :math:`s`. Q : numeric Correlate of *brightness* :math:`Q`. M : numeric Correlate of *colourfulness* :math:`M`. H : numeric *Hue* :math:`h` quadrature :math:`H`. HC : numeric *Hue* :math:`h` composition :math:`H^C`. """
[docs]def XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround=CIECAM02_VIEWING_CONDITIONS.get('Average'), discount_illuminant=False): """ Computes the CIECAM02 colour appearance model correlates from given *CIE XYZ* colourspace matrix. This is the *forward* implementation. Parameters ---------- XYZ : array_like, (3,) *CIE XYZ* colourspace matrix of test sample / stimulus in domain [0, 100]. XYZ_w : array_like, (3,) *CIE XYZ* colourspace matrix of reference white in domain [0, 100]. L_A : numeric Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. Y_b : numeric Adapting field *Y* tristimulus value :math:`Y_b`. surround : CIECAM02_InductionFactors, optional Surround viewing conditions induction factors. discount_illuminant : bool, optional Truth value indicating if the illuminant should be discounted. Returns ------- CIECAM02_Specification CIECAM02 colour appearance model specification. Warning ------- The input domain of that definition is non standard! Notes ----- - Input *CIE XYZ* colourspace matrix is in domain [0, 100]. - Input *CIE XYZ_w* colourspace matrix is in domain [0, 100]. Examples -------- >>> XYZ = np.array([19.01, 20.00, 21.78]) >>> XYZ_w = np.array([95.05, 100.00, 108.88]) >>> L_A = 318.31 >>> Y_b = 20.0 >>> surround = CIECAM02_VIEWING_CONDITIONS['Average'] >>> XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround) # doctest: +ELLIPSIS CIECAM02_Specification(J=41.7310911..., C=0.1047077..., h=219.0484326..., s=2.3603053..., Q=195.3713259..., M=0.1088421..., H=278.0607358..., HC=None) """ XYZ = np.array(XYZ).reshape((3, 1)) XYZ_w = np.array(XYZ_w).reshape((3, 1)) X_w, Y_w, Z_w = np.ravel(XYZ_w) n, F_L, N_bb, N_cb, z = viewing_condition_dependent_parameters(Y_b, Y_w, L_A) # Converting *CIE XYZ* colourspace matrices to CMCCAT2000 transform # sharpened *RGB* values. RGB = np.dot(CAT02_CAT, XYZ) RGB_w = np.dot(CAT02_CAT, XYZ_w) # Computing degree of adaptation :math:`D`. D = degree_of_adaptation(surround.F, L_A) if not discount_illuminant else 1 # Computing full chromatic adaptation. RGB_c = full_chromatic_adaptation_forward(RGB, RGB_w, Y_w, D) RGB_wc = full_chromatic_adaptation_forward(RGB_w, RGB_w, Y_w, D) # Converting to *Hunt-Pointer-Estevez* colourspace. RGB_p = RGB_to_rgb(RGB_c) RGB_pw = RGB_to_rgb(RGB_wc) # Applying forward post-adaptation non linear response compression. RGB_a = post_adaptation_non_linear_response_compression_forward( RGB_p, F_L) RGB_aw = post_adaptation_non_linear_response_compression_forward( RGB_pw, F_L) # Converting to preliminary cartesian coordinates. a, b = opponent_colour_dimensions_forward(RGB_a) # ------------------------------------------------------------------------- # Computing the *hue* angle :math:`h`. h = hue_angle(a, b) # ------------------------------------------------------------------------- # Computing hue :math:`h` quadrature :math:`H`. H = hue_quadrature(h) # TODO: Compute hue composition. # Computing eccentricity factor *e_t*. e_t = eccentricity_factor(h) # Computing achromatic responses for the stimulus and the whitepoint. A = achromatic_response_forward(RGB_a, N_bb) A_w = achromatic_response_forward(RGB_aw, N_bb) # ------------------------------------------------------------------------- # Computing the correlate of *Lightness* :math:`J`. # ------------------------------------------------------------------------- J = lightness_correlate(A, A_w, surround.c, z) # ------------------------------------------------------------------------- # Computing the correlate of *brightness* :math:`Q`. # ------------------------------------------------------------------------- Q = brightness_correlate(surround.c, J, A_w, F_L) # ------------------------------------------------------------------------- # Computing the correlate of *chroma* :math:`C`. # ------------------------------------------------------------------------- C = chroma_correlate(J, n, surround.N_c, N_cb, e_t, a, b, RGB_a) # ------------------------------------------------------------------------- # Computing the correlate of *colourfulness* :math:`M`. # ------------------------------------------------------------------------- M = colourfulness_correlate(C, F_L) # ------------------------------------------------------------------------- # Computing the correlate of *saturation* :math:`s`. # ------------------------------------------------------------------------- s = saturation_correlate(M, Q) return CIECAM02_Specification(J, C, h, s, Q, M, H, None)
[docs]def CIECAM02_to_XYZ(J, C, h, XYZ_w, L_A, Y_b, surround=CIECAM02_VIEWING_CONDITIONS.get( 'Average'), discount_illuminant=False): """ Converts CIECAM02 specification to *CIE XYZ* colourspace matrix. This is the *reverse* implementation. Parameters ---------- CIECAM02_Specification : CIECAM02_Specification CIECAM02 specification. XYZ_w : array_like *CIE XYZ* colourspace matrix of reference white. L_A : numeric Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. Y_b : numeric Adapting field *Y* tristimulus value :math:`Y_b`. surround : CIECAM02_Surround, optional Surround viewing conditions. discount_illuminant : bool, optional Discount the illuminant. Returns ------- XYZ : ndarray *CIE XYZ* colourspace matrix. Warning ------- The output domain of that definition is non standard! Notes ----- - Input *CIE XYZ_w* colourspace matrix is in domain [0, 100]. - Output *CIE XYZ* colourspace matrix is in domain [0, 100]. Examples -------- >>> J = 41.731091132513917 >>> C = 0.1047077571711053 >>> h = 219.0484326582719 >>> XYZ_w = np.array([95.05, 100.00, 108.88]) >>> L_A = 318.31 >>> Y_b = 20.0 >>> CIECAM02_to_XYZ(J, C, h, XYZ_w, L_A, Y_b) # doctest: +ELLIPSIS array([ 19.01..., 20... , 21.78...]) """ XYZ_w = np.array(XYZ_w).reshape((3, 1)) X_w, Y_w, Zw = np.ravel(XYZ_w) n, F_L, N_bb, N_cb, z = viewing_condition_dependent_parameters(Y_b, Y_w, L_A) # Converting *CIE XYZ* colourspace matrices to CMCCAT2000 transform # sharpened *RGB* values. RGB_w = np.dot(CAT02_CAT, XYZ_w) # Computing degree of adaptation :math:`D`. D = degree_of_adaptation(surround.F, L_A) if not discount_illuminant else 1 # Computation full chromatic adaptation. RGB_wc = full_chromatic_adaptation_forward(RGB_w, RGB_w, Y_w, D) # Converting to *Hunt-Pointer-Estevez* colourspace. RGB_pw = RGB_to_rgb(RGB_wc) # Applying post-adaptation non linear response compression. RGB_aw = post_adaptation_non_linear_response_compression_forward( RGB_pw, F_L) # Computing achromatic responses for the stimulus and the whitepoint. A_w = achromatic_response_forward(RGB_aw, N_bb) # Computing temporary magnitude quantity :math:`t`. t = temporary_magnitude_quantity_reverse(C, J, n) # Computing eccentricity factor *e_t*. e_t = eccentricity_factor(h) # Computing achromatic response :math:`A` for the stimulus. A = achromatic_response_reverse(A_w, J, surround.c, z) # Computing *P_1* to *P_3*. P_1, P_2, P_3 = P(surround.N_c, N_cb, e_t, t, A, N_bb) # Computing opponent colour dimensions :math:`a` and :math:`b`. a, b = opponent_colour_dimensions_reverse((P_1, P_2, P_3), h) # Computing post-adaptation non linear response compression matrix. RGB_a = post_adaptation_non_linear_response_compression_matrix(P_2, a, b) # Applying reverse post-adaptation non linear response compression. RGB_p = post_adaptation_non_linear_response_compression_reverse(RGB_a, F_L) # Converting to *Hunt-Pointer-Estevez* colourspace. RGB_c = rgb_to_RGB(RGB_p) # Applying reverse full chromatic adaptation. RGB = full_chromatic_adaptation_reverse(RGB_c, RGB_w, Y_w, D) # Converting CMCCAT2000 transform sharpened *RGB* values to *CIE XYZ* # colourspace matrices. XYZ = np.dot(CAT02_INVERSE_CAT, RGB) return XYZ
[docs]def chromatic_induction_factors(n): """ Returns the chromatic induction factors :math:`N_{bb}` and :math:`N_{cb}`. Parameters ---------- n : numeric Function of the luminance factor of the background :math:`n`. Returns ------- tuple Chromatic induction factors :math:`N_{bb}` and :math:`N_{cb}`. Examples -------- >>> chromatic_induction_factors(0.2) # doctest: +ELLIPSIS (1.0003040..., 1.0003040...) """ N_bb = N_cb = 0.725 * (1 / n) ** 0.2 return N_bb, N_cb
[docs]def base_exponential_non_linearity(n): """ Returns the base exponential non linearity :math:`n`. Parameters ---------- n : numeric Function of the luminance factor of the background :math:`n`. Returns ------- numeric Base exponential non linearity :math:`z`. Examples -------- >>> base_exponential_non_linearity(0.2) # doctest: +ELLIPSIS 1.9272135... """ z = 1.48 + np.sqrt(n) return z
@lru_cache(maxsize=8192)
[docs]def viewing_condition_dependent_parameters(Y_b, Y_w, L_A): """ Returns the viewing condition dependent parameters. Parameters ---------- Y_b : numeric Adapting field *Y* tristimulus value :math:`Y_b`. Y_w : numeric Whitepoint *Y* tristimulus value :math:`Y_w`. L_A : numeric Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. Returns ------- tuple Viewing condition dependent parameters. Examples -------- >>> viewing_condition_dependent_parameters(20.0, 100.0, 318.31) # noqa # doctest: +ELLIPSIS (0.2000000..., 1.1675444..., 1.0003040..., 1.0003040..., 1.9272135...) """ n = Y_b / Y_w F_L = luminance_level_adaptation_factor(L_A) N_bb, N_cb = chromatic_induction_factors(n) z = base_exponential_non_linearity(n) return n, F_L, N_bb, N_cb, z
[docs]def degree_of_adaptation(F, L_A): """ Returns the degree of adaptation :math:`D` from given surround maximum degree of adaptation :math:`F` and Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. Parameters ---------- F : numeric Surround maximum degree of adaptation :math:`F`. L_A : numeric Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. Returns ------- numeric Degree of adaptation :math:`D`. Examples -------- >>> degree_of_adaptation(1.0, 318.31) # doctest: +ELLIPSIS 0.9944687... """ D = F * (1 - (1 / 3.6) * np.exp((-L_A - 42) / 92)) return D
[docs]def full_chromatic_adaptation_forward(RGB, RGB_w, Y_w, D): """ Applies full chromatic adaptation to given CMCCAT2000 transform sharpened *RGB* matrix using given CMCCAT2000 transform sharpened whitepoint *RGB_w* matrix. Parameters ---------- RGB : array_like CMCCAT2000 transform sharpened *RGB* matrix. RGB_w : array_like CMCCAT2000 transform sharpened whitepoint *RGB_w* matrix. Y_w : numeric Whitepoint *Y* tristimulus value :math:`Y_w`. D : numeric Degree of adaptation :math:`D`. Returns ------- ndarray, (3,) Adapted *RGB* matrix. Examples -------- >>> RGB = np.array([18.985456, 20.707422, 21.747482]) >>> RGB_w = np.array([94.930528, 103.536988, 108.717742]) >>> Y_w = 100.0 >>> D = 0.994468780088 >>> full_chromatic_adaptation_forward(RGB, RGB_w, Y_w, D) # noqa # doctest: +ELLIPSIS array([ 19.9937078..., 20.0039363..., 20.0132638...]) """ R, G, B = np.ravel(RGB) R_w, G_w, B_w = np.ravel(RGB_w) equation = lambda x, y: ((Y_w * D / y) + 1 - D) * x R_c = equation(R, R_w) G_c = equation(G, G_w) B_c = equation(B, B_w) return np.array([R_c, G_c, B_c])
[docs]def full_chromatic_adaptation_reverse(RGB, RGB_w, Y_w, D): """ Reverts full chromatic adaptation of given CMCCAT2000 transform sharpened *RGB* matrix using given CMCCAT2000 transform sharpened whitepoint *RGB_w* matrix. Parameters ---------- RGB : array_like CMCCAT2000 transform sharpened *RGB* matrix. RGB_w : array_like CMCCAT2000 transform sharpened whitepoint *RGB_w* matrix. Y_w : numeric Whitepoint *Y* tristimulus value :math:`Y_w`. D : numeric Degree of adaptation :math:`D`. Returns ------- ndarray, (3,) Adapted *RGB* matrix. Examples -------- >>> RGB = np.array([19.99370783, 20.00393634, 20.01326387]) >>> RGB_w = np.array([94.930528, 103.536988, 108.717742]) >>> Y_w = 100.0 >>> D = 0.994468780088 >>> full_chromatic_adaptation_reverse(RGB, RGB_w, Y_w, D) array([ 18.985456, 20.707422, 21.747482]) """ R, G, B = np.ravel(RGB) R_w, G_w, B_w = np.ravel(RGB_w) equation = lambda x, y: x / (Y_w * (D / y) + 1 - D) R_c = equation(R, R_w) G_c = equation(G, G_w) B_c = equation(B, B_w) return np.array([R_c, G_c, B_c])
[docs]def RGB_to_rgb(RGB): """ Converts given *RGB* matrix to *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace. Parameters ---------- RGB : array_like, (3,) *RGB* matrix. Returns ------- ndarray, (3,) *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace matrix. Examples -------- >>> RGB = np.array([19.99370783, 20.00393634, 20.01326387]) >>> RGB_to_rgb(RGB) # doctest: +ELLIPSIS array([ 19.9969397..., 20.0018612..., 20.0135053...]) """ rgb = np.dot(np.dot(XYZ_TO_HPE_MATRIX, CAT02_INVERSE_CAT), RGB) return rgb
[docs]def rgb_to_RGB(rgb): """ Converts given *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace matrix to *RGB* matrix. Parameters ---------- rgb : array_like, (3,) *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace matrix. Returns ------- ndarray, (3,) *RGB* matrix. Examples -------- >>> rgb = np.array([19.99693975, 20.00186123, 20.0135053]) >>> rgb_to_RGB(rgb) # doctest: +ELLIPSIS array([ 19.9937078..., 20.0039363..., 20.0132638...]) """ RGB = np.dot(np.dot(CAT02_CAT, HPE_TO_XYZ_MATRIX), rgb) return RGB
[docs]def post_adaptation_non_linear_response_compression_forward(RGB, F_L): """ Returns given CMCCAT2000 transform sharpened *RGB* matrix with post adaptation non linear response compression. Parameters ---------- RGB : array_like CMCCAT2000 transform sharpened *RGB* matrix. Returns ------- ndarray, (3,) Compressed CMCCAT2000 transform sharpened *RGB* matrix. Examples -------- >>> RGB = np.array([19.99693975, 20.00186123, 20.0135053]) >>> F_L = 1.16754446415 >>> post_adaptation_non_linear_response_compression_forward(RGB, F_L) # noqa # doctest: +ELLIPSIS array([ 7.9463202..., 7.9471152..., 7.9489959...]) """ # TODO: Check for negative values and their handling. RGB_c = ((((400 * (F_L * RGB / 100) ** 0.42) / (27.13 + (F_L * RGB / 100) ** 0.42))) + 0.1) return RGB_c
[docs]def post_adaptation_non_linear_response_compression_reverse(RGB, F_L): """ Returns given CMCCAT2000 transform sharpened *RGB* matrix without post adaptation non linear response compression. Parameters ---------- RGB : array_like CMCCAT2000 transform sharpened *RGB* matrix. Returns ------- ndarray, (3,) Uncompressed CMCCAT2000 transform sharpened *RGB* matrix. Examples -------- >>> RGB = np.array([7.9463202, 7.94711528, 7.94899595]) >>> F_L = 1.16754446415 >>> post_adaptation_non_linear_response_compression_reverse(RGB, F_L) # noqa # doctest: +ELLIPSIS array([ 19.9969397..., 20.0018612..., 20.0135052...]) """ RGB_p = ((np.sign(RGB - 0.1) * (100 / F_L) * ((27.13 * np.abs(RGB - 0.1)) / (400 - np.abs(RGB - 0.1))) ** (1 / 0.42))) return RGB_p
[docs]def opponent_colour_dimensions_forward(RGB): """ Returns opponent colour dimensions from given compressed CMCCAT2000 transform sharpened *RGB* matrix for forward CIECAM02 implementation Parameters ---------- RGB : array_like Compressed CMCCAT2000 transform sharpened *RGB* matrix. Returns ------- tuple Opponent colour dimensions. Examples -------- >>> RGB = np.array([7.9463202, 7.94711528, 7.94899595]) >>> opponent_colour_dimensions_forward(RGB) # doctest: +ELLIPSIS (-0.0006241..., -0.0005062...) """ R, G, B = np.ravel(RGB) a = R - 12 * G / 11 + B / 11 b = (R + G - 2 * B) / 9 return a, b
[docs]def opponent_colour_dimensions_reverse(P, h): """ Returns opponent colour dimensions from given points :math:`P` and hue :math:`h` in degrees for reverse CIECAM02 implementation. Parameters ---------- p : array_like Points :math:`P`. h : numeric Hue :math:`h` in degrees. Returns ------- tuple Opponent colour dimensions. Examples -------- >>> p = (30162.890815335879, 24.237205467134817, 1.05) >>> h = -140.9515673417281 >>> opponent_colour_dimensions_reverse(p, h) # doctest: +ELLIPSIS (-0.0006241..., -0.0005062...) """ P_1, P_2, P_3 = P hr = np.radians(h) sin_hr, cos_hr = np.sin(hr), np.cos(hr) P_4 = P_1 / sin_hr P_5 = P_1 / cos_hr n = P_2 * (2 + P_3) * (460 / 1403) if abs(sin_hr) >= abs(cos_hr): b = n / (P_4 + (2 + P_3) * (220 / 1403) * (cos_hr / sin_hr) - ( 27 / 1403) + P_3 * (6300 / 1403)) a = b * (cos_hr / sin_hr) else: a = n / (P_5 + (2 + P_3) * (220 / 1403) - ( (27 / 1403) - P_3 * (6300 / 1403)) * (sin_hr / cos_hr)) b = a * (sin_hr / cos_hr) return a, b
[docs]def hue_angle(a, b): """ Returns the *hue* angle :math:`h` in degrees. Parameters ---------- a : numeric Opponent colour dimension :math:`a`. b : numeric Opponent colour dimension :math:`b`. Returns ------- numeric *Hue* angle :math:`h` in degrees. Examples -------- >>> a = -0.0006241120682426434 >>> b = -0.0005062701067729668 >>> hue_angle(a, b) # doctest: +ELLIPSIS 219.0484326... """ h = np.degrees(np.arctan2(b, a)) % 360 return h
[docs]def hue_quadrature(h): """ Returns the hue quadrature from given hue :math:`h` angle in degrees. Parameters ---------- h : numeric Hue :math:`h` angle in degrees. Returns ------- numeric Hue quadrature. Examples -------- >>> hue_quadrature(219.0484326582719) # doctest: +ELLIPSIS 278.0607358... """ h_i = HUE_DATA_FOR_HUE_QUADRATURE.get('h_i') e_i = HUE_DATA_FOR_HUE_QUADRATURE.get('e_i') H_i = HUE_DATA_FOR_HUE_QUADRATURE.get('H_i') i = bisect.bisect_left(h_i, h) - 1 h_ii = h_i[i] e_ii = e_i[i] H_ii = H_i[i] h_ii1 = h_i[i + 1] e_ii1 = e_i[i + 1] if h < 20.14: H = 385.9 H += (14.1 * h / 0.856) / (h / 0.856 + (20.14 - h) / 0.8) elif h >= 237.53: H = H_ii H += ((85.9 * (h - h_ii) / e_ii) / ((h - h_ii) / e_ii + (360 - h) / 0.856)) else: H = H_ii H += ((100 * (h - h_ii) / e_ii) / ((h - h_ii) / e_ii + (h_ii1 - h) / e_ii1)) return H
[docs]def eccentricity_factor(h): """ Returns the eccentricity factor :math:`e_t` from given hue :math:`h` angle for forward CIECAM02 implementation. Parameters ---------- h : numeric Hue :math:`h` angle in degrees. Returns ------- numeric Eccentricity factor :math:`e_t`. Examples -------- >>> eccentricity_factor(-140.951567342) # doctest: +ELLIPSIS 1.1740054... """ e_t = 1 / 4 * (np.cos(2 + h * np.pi / 180) + 3.8) return e_t
[docs]def achromatic_response_forward(RGB, N_bb): """ Returns the achromatic response :math:`A` from given compressed CMCCAT2000 transform sharpened *RGB* matrix and :math:`N_{bb}` chromatic induction factor for forward CIECAM02 implementation. Parameters ---------- RGB : array_like Compressed CMCCAT2000 transform sharpened *RGB* matrix. N_bb : numeric Chromatic induction factor :math:`N_{bb}`. Returns ------- numeric Achromatic response :math:`A`. Examples -------- >>> RGB = np.array([7.9463202, 7.94711528, 7.94899595]) >>> N_bb = 1.0003040045593807 >>> achromatic_response_forward(RGB, N_bb) # doctest: +ELLIPSIS 23.9394809... """ R, G, B = np.ravel(RGB) A = (2 * R + G + (1 / 20) * B - 0.305) * N_bb return A
[docs]def achromatic_response_reverse(A_w, J, c, z): """ Returns the achromatic response :math:`A` from given achromatic response :math:`A_w` for the whitepoint, *Lightness* correlate :math:`J`, surround exponential non linearity :math:`c` and base exponential non linearity :math:`z` for reverse CIECAM02 implementation. Parameters ---------- A_w : numeric Achromatic response :math:`A_w` for the whitepoint. J : numeric *Lightness* correlate :math:`J`. c : numeric Surround exponential non linearity :math:`c`. z : numeric Base exponential non linearity :math:`z`. Returns ------- numeric Achromatic response :math:`A`. Examples -------- >>> A_w = 46.1882087914 >>> J = 41.73109113251392 >>> c = 0.69 >>> z = 1.9272135954999579 >>> achromatic_response_reverse(A_w, J, c, z) # doctest: +ELLIPSIS 23.9394809... """ A = A_w * (J / 100) ** (1 / (c * z)) return A
[docs]def lightness_correlate(A, A_w, c, z): """ Returns the *Lightness* correlate :math:`J`. Parameters ---------- A : numeric Achromatic response :math:`A` for the stimulus. A_w : numeric Achromatic response :math:`A_w` for the whitepoint. c : numeric Surround exponential non linearity :math:`c`. z : numeric Base exponential non linearity :math:`z`. Returns ------- numeric *Lightness* correlate :math:`J`. Examples -------- >>> A = 23.9394809667 >>> A_w = 46.1882087914 >>> c = 0.69 >>> z = 1.9272135955 >>> lightness_correlate(A, A_w, c, z) # doctest: +ELLIPSIS 41.7310911... """ J = 100 * (A / A_w) ** (c * z) return J
[docs]def brightness_correlate(c, J, A_w, F_L): """ Returns the *brightness* correlate :math:`Q`. Parameters ---------- c : numeric Surround exponential non linearity :math:`c`. J : numeric *Lightness* correlate :math:`J`. A_w : numeric Achromatic response :math:`A_w` for the whitepoint. F_L : numeric *Luminance* level adaptation factor :math:`F_L`. Returns ------- numeric *Brightness* correlate :math:`Q`. Examples -------- >>> c = 0.69 >>> J = 41.7310911325 >>> A_w = 46.1882087914 >>> F_L = 1.16754446415 >>> brightness_correlate(c, J, A_w, F_L) # doctest: +ELLIPSIS 195.3713259... """ Q = (4 / c) * np.sqrt(J / 100) * (A_w + 4) * F_L ** 0.25 return Q
[docs]def temporary_magnitude_quantity_forward(N_c, N_cb, e_t, a, b, RGB_a): """ Returns the temporary magnitude quantity :math:`t`. for forward CIECAM02 implementation. Parameters ---------- N_c : numeric Surround chromatic induction factor :math:`N_{c}`. N_cb : numeric Chromatic induction factor :math:`N_{cb}`. e_t : numeric Eccentricity factor :math:`e_t`. a : numeric Opponent colour dimension :math:`a`. b : numeric Opponent colour dimension :math:`b`. RGB_a : array_like Compressed stimulus CMCCAT2000 transform sharpened *RGB* matrix. Returns ------- numeric Temporary magnitude quantity :math:`t`. Examples -------- >>> N_c = 1.0 >>> N_cb = 1.00030400456 >>> e_t = 1.1740054728519145 >>> a = -0.000624112068243 >>> b = -0.000506270106773 >>> RGB_a = np.array([7.9463202, 7.94711528, 7.94899595]) >>> temporary_magnitude_quantity_forward(N_c, N_cb, e_t, a, b, RGB_a) # noqa # doctest: +ELLIPSIS 0.1497462... """ Ra, Ga, Ba = np.ravel(RGB_a) t = ((50000 / 13) * N_c * N_cb) * (e_t * (a ** 2 + b ** 2) ** 0.5) / ( Ra + Ga + 21 * Ba / 20) return t
[docs]def temporary_magnitude_quantity_reverse(C, J, n): """ Returns the temporary magnitude quantity :math:`t`. for reverse CIECAM02 implementation. Parameters ---------- C : numeric *Chroma* correlate :math:`C`. J : numeric *Lightness* correlate :math:`J`. n : numeric Function of the luminance factor of the background :math:`n`. Returns ------- numeric Temporary magnitude quantity :math:`t`. Examples -------- >>> C = 68.8364136888275 >>> J = 41.749268505999 >>> n = 0.2 >>> temporary_magnitude_quantity_reverse(C, J, n) # doctest: +ELLIPSIS 202.3873619... """ t = (C / (np.sqrt(J / 100) * (1.64 - 0.29 ** n) ** 0.73)) ** (1 / 0.9) return t
[docs]def chroma_correlate(J, n, N_c, N_cb, e_t, a, b, RGB_a): """ Returns the *chroma* correlate :math:`C`. Parameters ---------- J : numeric *Lightness* correlate :math:`J`. n : numeric Function of the luminance factor of the background :math:`n`. N_c : numeric Surround chromatic induction factor :math:`N_{c}`. N_cb : numeric Chromatic induction factor :math:`N_{cb}`. e_t : numeric Eccentricity factor :math:`e_t`. a : numeric Opponent colour dimension :math:`a`. b : numeric Opponent colour dimension :math:`b`. RGB_a : array_like Compressed stimulus CMCCAT2000 transform sharpened *RGB* matrix. Returns ------- numeric *Chroma* correlate :math:`C`. Examples -------- >>> J = 41.7310911325 >>> n = 0.2 >>> N_c = 1.0 >>> N_cb = 1.00030400456 >>> e_t = 1.17400547285 >>> a = -0.000624112068243 >>> b = -0.000506270106773 >>> RGB_a = np.array([7.9463202, 7.94711528, 7.94899595]) >>> chroma_correlate(J, n, N_c, N_cb, e_t, a, b, RGB_a) # noqa # doctest: +ELLIPSIS 0.1047077... """ t = temporary_magnitude_quantity_forward(N_c, N_cb, e_t, a, b, RGB_a) C = t ** 0.9 * (J / 100) ** 0.5 * (1.64 - 0.29 ** n) ** 0.73 return C
[docs]def colourfulness_correlate(C, F_L): """ Returns the *colourfulness* correlate :math:`M`. Parameters ---------- C : numeric *Chroma* correlate :math:`C`. F_L : numeric *Luminance* level adaptation factor :math:`F_L`. Returns ------- numeric *Colourfulness* correlate :math:`M`. Examples -------- >>> C = 0.104707757171 >>> F_L = 1.16754446415 >>> colourfulness_correlate(C, F_L) # doctest: +ELLIPSIS 0.1088421... """ M = C * F_L ** 0.25 return M
[docs]def saturation_correlate(M, Q): """ Returns the *saturation* correlate :math:`s`. Parameters ---------- M : numeric *Colourfulness* correlate :math:`M`. Q : numeric *Brightness* correlate :math:`C`. Returns ------- numeric *Saturation* correlate :math:`s`. Examples -------- >>> M = 0.108842175669 >>> Q = 195.371325966 >>> saturation_correlate(M, Q) # doctest: +ELLIPSIS 2.3603053... """ s = 100 * (M / Q) ** 0.5 return s
[docs]def P(N_c, N_cb, e_t, t, A, N_bb): """ Returns the points :math:`P_1`, :math:`P_2` and :math:`P_3`. Parameters ---------- N_c : numeric Surround chromatic induction factor :math:`N_{c}`. N_cb : numeric Chromatic induction factor :math:`N_{cb}`. e_t : numeric Eccentricity factor :math:`e_t`. t : numeric Temporary magnitude quantity :math:`t`. A : numeric Achromatic response :math:`A` for the stimulus. N_bb : numeric Chromatic induction factor :math:`N_{bb}`. Returns ------- tuple Points :math:`P`. Examples -------- >>> N_c = 1.0 >>> N_cb = 1.00030400456 >>> e_t = 1.1740054728519145 >>> t = 0.149746202921 >>> A = 23.9394809667 >>> N_bb = 1.00030400456 >>> P(N_c, N_cb, e_t, t, A, N_bb) # doctest: +ELLIPSIS (30162.8908154..., 24.2372054..., 1.05) """ P_1 = ((50000 / 13) * N_c * N_cb * e_t) / t P_2 = A / N_bb + 0.305 P_3 = 21 / 20 return P_1, P_2, P_3
[docs]def post_adaptation_non_linear_response_compression_matrix(P_2, a, b): """ Returns the post adaptation non linear response compression matrix. Parameters ---------- P_2 : numeric Point :math:`P_2`. a : numeric Opponent colour dimension :math:`a`. b : numeric Opponent colour dimension :math:`b`. Returns ------- ndarray, (3,) Points :math:`P`. Examples -------- >>> P_2 = 24.2372054671 >>> a = -0.000624112068243 >>> b = -0.000506270106773 >>> post_adaptation_non_linear_response_compression_matrix(P_2, a, b) # noqa # doctest: +ELLIPSIS array([ 7.9463202..., 7.9471152..., 7.9489959...]) """ R_a = (460 * P_2 + 451 * a + 288 * b) / 1403 G_a = (460 * P_2 - 891 * a - 261 * b) / 1403 B_a = (460 * P_2 - 220 * a - 6300 * b) / 1403 return np.array([R_a, G_a, B_a])