""" :math:`LLAB(l:c)` Colour Appearance Model ========================================= Define the *:math:`LLAB(l:c)`* colour appearance model for predicting perceptual colour attributes under varying viewing conditions. - :class:`colour.appearance.InductionFactors_LLAB` - :attr:`colour.VIEWING_CONDITIONS_LLAB` - :class:`colour.CAM_Specification_LLAB` - :func:`colour.XYZ_to_LLAB` References ---------- - :cite:`Fairchild2013x` : Fairchild, M. D. (2013). LLAB Model. In Color Appearance Models (3rd ed., pp. 6025-6178). Wiley. ISBN:B00DAYO8E2 - :cite:`Luo1996b` : Luo, Ming Ronnier, Lo, M.-C., & Kuo, W.-G. (1996). The LLAB (l:c) colour model. Color Research & Application, 21(6), 412-429. doi:10.1002/(SICI)1520-6378(199612)21:6<412::AID-COL4>3.0.CO;2-Z - :cite:`Luo1996c` : Luo, Ming Ronnier, & Morovic, J. (1996). Two Unsolved Issues in Colour Management - Colour Appearance and Gamut Mapping. Conference: 5th International Conference on High Technology: Imaging Science and Technology - Evolution & Promise, 136-147. http://www.researchgate.net/publication/\ 236348295_Two_Unsolved_Issues_in_Colour_Management__\ Colour_Appearance_and_Gamut_Mapping """ from __future__ import annotations from dataclasses import dataclass, field import numpy as np from colour.algebra import polar_to_cartesian, sdiv, sdiv_mode, spow, vecmul from colour.hints import Annotated, ArrayLike, Domain100, NDArrayFloat # noqa: TC001 from colour.utilities import ( CanonicalMapping, MixinDataclassArithmetic, MixinDataclassIterable, as_float, as_float_array, from_range_degrees, to_domain_100, tsplit, tstack, ) __author__ = "Colour Developers" __copyright__ = "Copyright 2013 Colour Developers" __license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause" __maintainer__ = "Colour Developers" __email__ = "colour-developers@colour-science.org" __status__ = "Production" __all__ = [ "InductionFactors_LLAB", "VIEWING_CONDITIONS_LLAB", "MATRIX_XYZ_TO_RGB_LLAB", "MATRIX_RGB_TO_XYZ_LLAB", "CAM_ReferenceSpecification_LLAB", "CAM_Specification_LLAB", "XYZ_to_LLAB", "XYZ_to_RGB_LLAB", "chromatic_adaptation", "f", "opponent_colour_dimensions", "hue_angle", "chroma_correlate", "colourfulness_correlate", "saturation_correlate", "final_opponent_signals", ] @dataclass(frozen=True) class InductionFactors_LLAB(MixinDataclassIterable): """ Define the *:math:`LLAB(l:c)`* colour appearance model induction factors. Parameters ---------- D *Discounting-the-Illuminant* factor :math:`D`. F_S Surround induction factor :math:`F_S`. F_L *Lightness* induction factor :math:`F_L`. F_C *Chroma* induction factor :math:`F_C`. References ---------- :cite:`Fairchild2013x`, :cite:`Luo1996b`, :cite:`Luo1996c` """ D: float F_S: float F_L: float F_C: float VIEWING_CONDITIONS_LLAB: CanonicalMapping = CanonicalMapping( { "Reference Samples & Images, Average Surround, Subtending > 4": ( InductionFactors_LLAB(1, 3, 0, 1) ), "Reference Samples & Images, Average Surround, Subtending < 4": ( InductionFactors_LLAB(1, 3, 1, 1) ), "Television & VDU Displays, Dim Surround": ( InductionFactors_LLAB(0.7, 3.5, 1, 1) ), "Cut Sheet Transparency, Dim Surround": (InductionFactors_LLAB(1, 5, 1, 1.1)), "35mm Projection Transparency, Dark Surround": ( InductionFactors_LLAB(0.7, 4, 1, 1) ), } ) VIEWING_CONDITIONS_LLAB.__doc__ = """ Define the reference :math:`LLAB(l:c)` colour appearance model viewing conditions. References ---------- :cite:`Fairchild2013x`, :cite:`Luo1996b`, :cite:`Luo1996c` Aliases: - 'ref_average_4_plus': 'Reference Samples & Images, Average Surround, Subtending > 4' - 'ref_average_4_minus': 'Reference Samples & Images, Average Surround, Subtending < 4' - 'tv_dim': 'Television & VDU Displays, Dim Surround' - 'sheet_dim': 'Cut Sheet Transparency, Dim Surround' - 'projected_dark': '35mm Projection Transparency, Dark Surround' """ VIEWING_CONDITIONS_LLAB["ref_average_4_plus"] = VIEWING_CONDITIONS_LLAB[ "Reference Samples & Images, Average Surround, Subtending > 4" ] VIEWING_CONDITIONS_LLAB["ref_average_4_minus"] = VIEWING_CONDITIONS_LLAB[ "Reference Samples & Images, Average Surround, Subtending < 4" ] VIEWING_CONDITIONS_LLAB["tv_dim"] = VIEWING_CONDITIONS_LLAB[ "Television & VDU Displays, Dim Surround" ] VIEWING_CONDITIONS_LLAB["sheet_dim"] = VIEWING_CONDITIONS_LLAB[ "Cut Sheet Transparency, Dim Surround" ] VIEWING_CONDITIONS_LLAB["projected_dark"] = VIEWING_CONDITIONS_LLAB[ "35mm Projection Transparency, Dark Surround" ] MATRIX_XYZ_TO_RGB_LLAB: NDArrayFloat = np.array( [ [0.8951, 0.2664, -0.1614], [-0.7502, 1.7135, 0.0367], [0.0389, -0.0685, 1.0296], ] ) """ LLAB(l:c) colour appearance model *CIE XYZ* tristimulus values to normalised cone responses matrix. """ MATRIX_RGB_TO_XYZ_LLAB: NDArrayFloat = np.linalg.inv(MATRIX_XYZ_TO_RGB_LLAB) """ LLAB(l:c) colour appearance model normalised cone responses to *CIE XYZ* tristimulus values matrix. """ @dataclass class CAM_ReferenceSpecification_LLAB(MixinDataclassArithmetic): """ Define the *:math:`LLAB(l:c)`* colour appearance model reference specification. This specification contains field names consistent with the *Fairchild (2013)* reference. Parameters ---------- L_L Correlate of *Lightness* :math:`L_L`. Ch_L Correlate of *chroma* :math:`Ch_L`. h_L *Hue* angle :math:`h_L` in degrees. s_L Correlate of *saturation* :math:`s_L`. C_L Correlate of *colourfulness* :math:`C_L`. HC *Hue* :math:`h` composition :math:`H^C`. A_L Opponent signal :math:`A_L`. B_L Opponent signal :math:`B_L`. References ---------- :cite:`Fairchild2013x`, :cite:`Luo1996b`, :cite:`Luo1996c` """ L_L: float | NDArrayFloat | None = field(default_factory=lambda: None) Ch_L: float | NDArrayFloat | None = field(default_factory=lambda: None) h_L: float | NDArrayFloat | None = field(default_factory=lambda: None) s_L: float | NDArrayFloat | None = field(default_factory=lambda: None) C_L: float | NDArrayFloat | None = field(default_factory=lambda: None) HC: float | NDArrayFloat | None = field(default_factory=lambda: None) A_L: float | NDArrayFloat | None = field(default_factory=lambda: None) B_L: float | NDArrayFloat | None = field(default_factory=lambda: None) @dataclass class CAM_Specification_LLAB(MixinDataclassArithmetic): """ Define the *:math:`LLAB(l:c)`* colour appearance model specification. This specification provides a standardized interface for the *LLAB(l:c)* model with field names consistent across all colour appearance models in :mod:`colour.appearance`. While the field names differ from the original *Fairchild (2013)* reference notation, they map directly to the model's perceptual correlates. Parameters ---------- J Correlate of *lightness* :math:`L_L`. C Correlate of *chroma* :math:`Ch_L`. h *Hue* angle :math:`h_L` in degrees. s Correlate of *saturation* :math:`s_L`. M Correlate of *colourfulness* :math:`C_L`. HC *Hue* :math:`h` composition :math:`H^C`. a Opponent signal :math:`A_L`. b Opponent signal :math:`B_L`. Notes ----- - This specification is the one used in the current model implementation. References ---------- :cite:`Fairchild2013x`, :cite:`Luo1996b`, :cite:`Luo1996c` """ J: float | NDArrayFloat | None = field(default_factory=lambda: None) C: float | NDArrayFloat | None = field(default_factory=lambda: None) h: float | NDArrayFloat | None = field(default_factory=lambda: None) s: float | NDArrayFloat | None = field(default_factory=lambda: None) M: float | NDArrayFloat | None = field(default_factory=lambda: None) HC: float | NDArrayFloat | None = field(default_factory=lambda: None) a: float | NDArrayFloat | None = field(default_factory=lambda: None) b: float | NDArrayFloat | None = field(default_factory=lambda: None) def XYZ_to_LLAB( XYZ: Domain100, XYZ_0: Domain100, Y_b: ArrayLike, L: ArrayLike, surround: InductionFactors_LLAB = VIEWING_CONDITIONS_LLAB[ "Reference Samples & Images, Average Surround, Subtending < 4" ], ) -> Annotated[CAM_Specification_LLAB, 360]: """ Compute the *:math:`LLAB(l:c)`* colour appearance model correlates from the specified *CIE XYZ* tristimulus values. Parameters ---------- XYZ *CIE XYZ* tristimulus values of test sample / stimulus. XYZ_0 *CIE XYZ* tristimulus values of reference white. Y_b Luminance factor of the background in :math:`cd/m^2`. L Absolute luminance :math:`L` of reference white in :math:`cd/m^2`. surround Surround viewing conditions induction factors. Returns ------- :class:`colour.CAM_Specification_LLAB` *:math:`LLAB(l:c)`* colour appearance model specification. Notes ----- +---------------------+-----------------------+---------------+ | **Domain** | **Scale - Reference** | **Scale - 1** | +=====================+=======================+===============+ | ``XYZ`` | 100 | 1 | +---------------------+-----------------------+---------------+ | ``XYZ_0`` | 100 | 1 | +---------------------+-----------------------+---------------+ +---------------------+-----------------------+---------------+ | **Range** | **Scale - Reference** | **Scale - 1** | +=====================+=======================+===============+ | ``specification.h`` | 360 | 1 | +---------------------+-----------------------+---------------+ References ---------- :cite:`Fairchild2013x`, :cite:`Luo1996b`, :cite:`Luo1996c` Examples -------- >>> XYZ = np.array([19.01, 20.00, 21.78]) >>> XYZ_0 = np.array([95.05, 100.00, 108.88]) >>> Y_b = 20.0 >>> L = 318.31 >>> surround = VIEWING_CONDITIONS_LLAB["ref_average_4_minus"] >>> XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround) # doctest: +ELLIPSIS CAM_Specification_LLAB(J=37.3668650..., C=0.0089496..., h=270..., \ s=0.0002395..., M=0.0190185..., HC=None, a=..., b=-0.0190185...) """ _X, Y, _Z = tsplit(to_domain_100(XYZ)) RGB = XYZ_to_RGB_LLAB(to_domain_100(XYZ)) RGB_0 = XYZ_to_RGB_LLAB(to_domain_100(XYZ_0)) # Reference illuminant *CIE Standard Illuminant D Series* *D65*. XYZ_0r = np.array([95.05, 100.00, 108.88]) RGB_0r = XYZ_to_RGB_LLAB(XYZ_0r) # Computing chromatic adaptation. XYZ_r = chromatic_adaptation(RGB, RGB_0, RGB_0r, Y, surround.D) # ------------------------------------------------------------------------- # Computing the correlate of *Lightness* :math:`L_L`. # ------------------------------------------------------------------------- # Computing opponent colour dimensions. L_L, a, b = tsplit( opponent_colour_dimensions(XYZ_r, Y_b, surround.F_S, surround.F_L) ) # Computing perceptual correlates. # ------------------------------------------------------------------------- # Computing the correlate of *chroma* :math:`Ch_L`. # ------------------------------------------------------------------------- Ch_L = chroma_correlate(a, b) # ------------------------------------------------------------------------- # Computing the correlate of *colourfulness* :math:`C_L`. # ------------------------------------------------------------------------- C_L = colourfulness_correlate(L, L_L, Ch_L, surround.F_C) # ------------------------------------------------------------------------- # Computing the correlate of *saturation* :math:`s_L`. # ------------------------------------------------------------------------- s_L = saturation_correlate(Ch_L, L_L) # ------------------------------------------------------------------------- # Computing the *hue* angle :math:`h_L`. # ------------------------------------------------------------------------- h_L = hue_angle(a, b) # TODO: Implement hue composition computation. # ------------------------------------------------------------------------- # Computing final opponent signals. # ------------------------------------------------------------------------- A_L, B_L = tsplit(final_opponent_signals(C_L, h_L)) return CAM_Specification_LLAB( J=L_L, C=Ch_L, h=as_float(from_range_degrees(h_L)), s=s_L, M=C_L, HC=None, a=A_L, b=B_L, ) def XYZ_to_RGB_LLAB(XYZ: ArrayLike) -> NDArrayFloat: """ Convert from *CIE XYZ* tristimulus values to normalised cone responses. Parameters ---------- XYZ *CIE XYZ* tristimulus values. Returns ------- :class:`numpy.ndarray` Normalised cone responses. Examples -------- >>> XYZ = np.array([19.01, 20.00, 21.78]) >>> XYZ_to_RGB_LLAB(XYZ) # doctest: +ELLIPSIS array([ 0.9414279..., 1.0404012..., 1.0897088...]) """ XYZ = as_float_array(XYZ) with sdiv_mode(): return vecmul(MATRIX_XYZ_TO_RGB_LLAB, sdiv(XYZ, XYZ[..., 1, None])) def chromatic_adaptation( RGB: ArrayLike, RGB_0: ArrayLike, RGB_0r: ArrayLike, Y: ArrayLike, D: ArrayLike = 1, ) -> NDArrayFloat: """ Apply chromatic adaptation to the specified *RGB* normalised cone responses array. Parameters ---------- RGB *RGB* normalised cone responses array of the test sample / stimulus. RGB_0 *RGB* normalised cone responses array of the reference white. RGB_0r *RGB* normalised cone responses array of the reference illuminant *CIE Standard Illuminant D Series* *D65*. Y Tristimulus value :math:`Y` of the stimulus. D *Discounting-the-Illuminant* factor normalised to domain [0, 1]. Default is 1. Returns ------- :class:`numpy.ndarray` Adapted *CIE XYZ* tristimulus values. Examples -------- >>> RGB = np.array([0.94142795, 1.04040120, 1.08970885]) >>> RGB_0 = np.array([0.94146023, 1.04039386, 1.08950293]) >>> RGB_0r = np.array([0.94146023, 1.04039386, 1.08950293]) >>> Y = 20.0 >>> chromatic_adaptation(RGB, RGB_0, RGB_0r, Y) # doctest: +ELLIPSIS array([ 19.01, 20. , 21.78]) """ R, G, B = tsplit(RGB) R_0, G_0, B_0 = tsplit(RGB_0) R_0r, G_0r, B_0r = tsplit(RGB_0r) Y = as_float_array(Y) D = as_float_array(D) beta = spow(B_0 / B_0r, 0.0834) R_r = (D * R_0r / R_0 + 1 - D) * R G_r = (D * G_0r / G_0 + 1 - D) * G B_r = (D * B_0r / spow(B_0, beta) + 1 - D) * spow(B, beta) RGB_r = tstack([R_r, G_r, B_r]) Y = tstack([Y, Y, Y]) return vecmul(MATRIX_RGB_TO_XYZ_LLAB, RGB_r * Y) def f(x: ArrayLike, F_S: ArrayLike) -> NDArrayFloat: """ Model the nonlinear response function of the *:math:`LLAB(l:c)`* colour appearance model to simulate the nonlinear behaviour of various visual responses. Parameters ---------- x Visual response variable :math:`x`. F_S Surround induction factor :math:`F_S`. Returns ------- :class:`numpy.ndarray` Modeled visual response variable :math:`x`. Examples -------- >>> x = np.array([0.23350512, 0.23351103, 0.23355179]) >>> f(0.200009186234000, 3) # doctest: +ELLIPSIS 0.5848125... """ x = as_float_array(x) F_S = as_float_array(F_S) one_F_s = 1 / F_S x_m = np.where( x > 0.008856, spow(x, one_F_s), ((spow(0.008856, one_F_s) - (16 / 116)) / 0.008856) * x + (16 / 116), ) return as_float(x_m) def opponent_colour_dimensions( XYZ: ArrayLike, Y_b: ArrayLike, F_S: ArrayLike, F_L: ArrayLike, ) -> NDArrayFloat: """ Compute opponent colour dimensions from the specified adapted *CIE XYZ* tristimulus values. The opponent colour dimensions are based on a modified *CIE L\\*a\\*b\\** colourspace formulae. Parameters ---------- XYZ Adapted *CIE XYZ* tristimulus values. Y_b Luminance factor of the background in :math:`cd/m^2`. F_S Surround induction factor :math:`F_S`. F_L Lightness induction factor :math:`F_L`. Returns ------- :class:`numpy.ndarray` Opponent colour dimensions. Examples -------- >>> XYZ = np.array([19.00999572, 20.00091862, 21.77993863]) >>> Y_b = 20.0 >>> F_S = 3.0 >>> F_L = 1.0 >>> opponent_colour_dimensions(XYZ, Y_b, F_S, F_L) # doctest: +ELLIPSIS array([ 3.7368047...e+01, -4.4986443...e-03, -5.2604647...e-03]) """ X, Y, Z = tsplit(XYZ) Y_b = as_float_array(Y_b) F_S = as_float_array(F_S) F_L = as_float_array(F_L) # Account for background lightness contrast. z = 1 + F_L * spow(Y_b / 100, 0.5) # Computing modified *CIE L\\*a\\*b\\** colourspace array. L = 116 * spow(f(Y / 100, F_S), z) - 16 a = 500 * (f(X / 95.05, F_S) - f(Y / 100, F_S)) b = 200 * (f(Y / 100, F_S) - f(Z / 108.88, F_S)) return tstack([L, a, b]) def hue_angle(a: ArrayLike, b: ArrayLike) -> NDArrayFloat: """ Compute the *hue* angle :math:`h_L` in degrees from the specified opponent colour dimensions. Parameters ---------- a Opponent colour dimension :math:`a`. b Opponent colour dimension :math:`b`. Returns ------- :class:`numpy.ndarray` *Hue* angle :math:`h_L` in degrees. Examples -------- >>> hue_angle(-4.49864756e-03, -5.26046353e-03) # doctest: +ELLIPSIS 229.4635727... """ a = as_float_array(a) b = as_float_array(b) h_L = np.degrees(np.arctan2(b, a)) % 360 return as_float(h_L) def chroma_correlate(a: ArrayLike, b: ArrayLike) -> NDArrayFloat: """ Compute the correlate of *chroma* :math:`Ch_L` from the specified opponent colour dimensions. Parameters ---------- a Opponent colour dimension :math:`a`. b Opponent colour dimension :math:`b`. Returns ------- :class:`numpy.ndarray` Correlate of *chroma* :math:`Ch_L`. Examples -------- >>> a = -4.49864756e-03 >>> b = -5.26046353e-03 >>> chroma_correlate(a, b) # doctest: +ELLIPSIS 0.0086506... """ a = as_float_array(a) b = as_float_array(b) c = spow(a**2 + b**2, 0.5) Ch_L = 25 * np.log1p(0.05 * c) return as_float(Ch_L) def colourfulness_correlate( L: ArrayLike, L_L: ArrayLike, Ch_L: ArrayLike, F_C: ArrayLike, ) -> NDArrayFloat: """ Compute the correlate of *colourfulness* :math:`C_L`. Parameters ---------- L Absolute luminance :math:`L` of the reference white in :math:`cd/m^2`. L_L Correlate of *Lightness* :math:`L_L`. Ch_L Correlate of *chroma* :math:`Ch_L`. F_C Chroma induction factor :math:`F_C`. Returns ------- :class:`numpy.ndarray` Correlate of *colourfulness* :math:`C_L`. Examples -------- >>> L = 318.31 >>> L_L = 37.368047493928195 >>> Ch_L = 0.008650662051714 >>> F_C = 1.0 >>> colourfulness_correlate(L, L_L, Ch_L, F_C) # doctest: +ELLIPSIS 0.0183832... """ L = as_float_array(L) L_L = as_float_array(L_L) Ch_L = as_float_array(Ch_L) F_C = as_float_array(F_C) S_C = 1 + 0.47 * np.log10(L) - 0.057 * np.log10(L) ** 2 S_M = 0.7 + 0.02 * L_L - 0.0002 * L_L**2 C_L = Ch_L * S_M * S_C * F_C return as_float(C_L) def saturation_correlate(Ch_L: ArrayLike, L_L: ArrayLike) -> NDArrayFloat: """ Compute the correlate of *saturation* :math:`S_L`. Parameters ---------- Ch_L Correlate of *chroma* :math:`Ch_L`. L_L Correlate of *lightness* :math:`L_L`. Returns ------- :class:`numpy.ndarray` Correlate of *saturation* :math:`S_L`. Examples -------- >>> Ch_L = 0.008650662051714 >>> L_L = 37.368047493928195 >>> saturation_correlate(Ch_L, L_L) # doctest: +ELLIPSIS 0.0002314... """ Ch_L = as_float_array(Ch_L) L_L = as_float_array(L_L) return Ch_L / L_L def final_opponent_signals(C_L: ArrayLike, h_L: ArrayLike) -> NDArrayFloat: """ Compute the final opponent signals :math:`A_L` and :math:`B_L`. Parameters ---------- C_L Correlate of *colourfulness* :math:`C_L`. h_L Correlate of *hue* :math:`h_L` in degrees. Returns ------- :class:`numpy.ndarray` Final opponent signals :math:`A_L` and :math:`B_L`. Examples -------- >>> C_L = 0.0183832899143 >>> h_L = 229.46357270858391 >>> final_opponent_signals(C_L, h_L) # doctest: +ELLIPSIS array([-0.0119478..., -0.0139711...]) """ return polar_to_cartesian(tstack([as_float_array(C_L), np.radians(h_L)]))