colour-science

Colour Science for Python

These details have not been verified by PyPI

Project links

Project description

https://raw.githubusercontent.com/colour-science/colour-branding/master/images/Colour_Logo_001.png

Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science.

It is freely available under the BSD-3-Clause terms.

Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States.

1 Draft Release Notes

The draft release notes of the develop branch are available at this url.

2 Sponsors

We are grateful 💖 for the support of our sponsors. If you’d like to join them, please consider becoming a sponsor on OpenCollective.

3 Features

Most of the objects are available from the colour namespace:

import colour

3.1 Automatic Colour Conversion Graph - colour.graph

Starting with version 0.3.14, Colour implements an automatic colour conversion graph enabling easier colour conversions.

https://colour.readthedocs.io/en/develop/_static/Examples_Colour_Automatic_Conversion_Graph.png

sd = colour.SDS_COLOURCHECKERS["ColorChecker N Ohta"]["dark skin"]
colour.convert(sd, "Spectral Distribution", "sRGB", verbose={"mode": "Short"})

===============================================================================
*                                                                             *
*   [ Conversion Path ]                                                       *
*                                                                             *
*   "sd_to_XYZ" --> "XYZ_to_sRGB"                                             *
*                                                                             *
===============================================================================
array([ 0.45675795,  0.30986982,  0.24861924])

illuminant = colour.SDS_ILLUMINANTS["FL2"]
colour.convert(
    sd,
    "Spectral Distribution",
    "sRGB",
    sd_to_XYZ={"illuminant": illuminant},
)

array([ 0.47924575,  0.31676968,  0.17362725])

3.2 Chromatic Adaptation - colour.adaptation

XYZ = [0.20654008, 0.12197225, 0.05136952]
D65 = colour.CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["D65"]
A = colour.CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["A"]
colour.chromatic_adaptation(XYZ, colour.xy_to_XYZ(D65), colour.xy_to_XYZ(A))

array([ 0.2533053 ,  0.13765138,  0.01543307])

sorted(colour.CHROMATIC_ADAPTATION_METHODS)

['CIE 1994', 'CMCCAT2000', 'Fairchild 1990', 'Von Kries', 'Zhai 2018', 'vK20']

3.3 Algebra - colour.algebra

3.3.1 Kernel Interpolation

y = [5.9200, 9.3700, 10.8135, 4.5100, 69.5900, 27.8007, 86.0500]
x = range(len(y))
colour.KernelInterpolator(x, y)([0.25, 0.75, 5.50])

array([  6.18062083,   8.08238488,  57.85783403])

3.3.2 Sprague (1880) Interpolation

y = [5.9200, 9.3700, 10.8135, 4.5100, 69.5900, 27.8007, 86.0500]
x = range(len(y))
colour.SpragueInterpolator(x, y)([0.25, 0.75, 5.50])

array([  6.72951612,   7.81406251,  43.77379185])

3.4 Colour Appearance Models - colour.appearance

XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
XYZ_w = [95.05, 100.00, 108.88]
L_A = 318.31
Y_b = 20.0
colour.XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b)

CAM_Specification_CIECAM02(J=34.434525727858997, C=67.365010921125943, h=22.279164147957065, s=62.81485585332716, Q=177.47124941102123, M=70.024939419291414, H=2.6896085344238898, HC=None)

colour.XYZ_to_CIECAM16(XYZ, XYZ_w, L_A, Y_b)

CAM_Specification_CIECAM16(J=34.434525727858997, C=67.365010921125943, h=22.279164147957065, s=62.81485585332716, Q=177.47124941102123, M=70.024939419291414, H=2.6896085344238898, HC=None)

colour.XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b)

CAM_Specification_CAM16(J=33.880368498111686, C=69.444353357408033, h=19.510887327451748, s=64.03612114840314, Q=176.03752758512178, M=72.18638534116765, H=399.52975599115319, HC=None)

colour.XYZ_to_Hellwig2022(XYZ, XYZ_w, L_A)

CAM_Specification_Hellwig2022(J=33.880368498111686, C=40.347043294550311, h=19.510887327451748, s=117.38555017188679, Q=45.34489577734751, M=53.228355383108031, H=399.52975599115319, HC=None)

colour.XYZ_to_Kim2009(XYZ, XYZ_w, L_A)

CAM_Specification_Kim2009(J=19.879918542450902, C=55.839055250876946, h=22.013388165090046, s=112.97979354939129, Q=36.309026130161449, M=46.346415858227864, H=2.3543198369639931, HC=None)

colour.XYZ_to_ZCAM(XYZ, XYZ_w, L_A, Y_b)

CAM_Specification_ZCAM(J=38.347186278956357, C=21.12138989208518, h=33.711578931095197, s=81.444585609489536, Q=76.986725284523772, M=42.403805833900506, H=0.45779200212219573, HC=None, V=43.623590687423544, K=43.20894953152817, W=34.829588380192149)

3.5 Colour Blindness - colour.blindness

import numpy as np

cmfs = colour.LMS_CMFS["Stockman & Sharpe 2 Degree Cone Fundamentals"]
colour.msds_cmfs_anomalous_trichromacy_Machado2009(cmfs, np.array([15, 0, 0]))[450]

array([ 0.08912884,  0.0870524 ,  0.955393  ])

primaries = colour.MSDS_DISPLAY_PRIMARIES["Apple Studio Display"]
d_LMS = (15, 0, 0)
colour.matrix_anomalous_trichromacy_Machado2009(cmfs, primaries, d_LMS)

array([[-0.27774652,  2.65150084, -1.37375432],
       [ 0.27189369,  0.20047862,  0.52762768],
       [ 0.00644047,  0.25921579,  0.73434374]])

3.6 Colour Correction - colour characterisation

import numpy as np

RGB = [0.17224810, 0.09170660, 0.06416938]
M_T = np.random.random((24, 3))
M_R = M_T + (np.random.random((24, 3)) - 0.5) * 0.5
colour.colour_correction(RGB, M_T, M_R)

array([ 0.1806237 ,  0.07234791,  0.07848845])

sorted(colour.COLOUR_CORRECTION_METHODS)

['Cheung 2004', 'Finlayson 2015', 'Vandermonde']

3.7 ACES Input Transform - colour characterisation

sensitivities = colour.MSDS_CAMERA_SENSITIVITIES["Nikon 5100 (NPL)"]
illuminant = colour.SDS_ILLUMINANTS["D55"]
colour.matrix_idt(sensitivities, illuminant)

(array([[ 0.59368175,  0.30418371,  0.10213454],
       [ 0.00457979,  1.14946003, -0.15403982],
       [ 0.03552213, -0.16312291,  1.12760077]]), array([ 1.58214188,  1.        ,  1.28910346]))

3.8 Colorimetry - colour.colorimetry

3.8.1 Spectral Computations

colour.sd_to_XYZ(colour.SDS_LIGHT_SOURCES["Neodimium Incandescent"])

array([ 36.94726204,  32.62076174,  13.0143849 ])

sorted(colour.SPECTRAL_TO_XYZ_METHODS)

['ASTM E308', 'Integration', 'astm2015']

3.8.2 Multi-Spectral Computations

msds = np.array(
    [
        [
            [
                0.01367208,
                0.09127947,
                0.01524376,
                0.02810712,
                0.19176012,
                0.04299992,
            ],
            [
                0.00959792,
                0.25822842,
                0.41388571,
                0.22275120,
                0.00407416,
                0.37439537,
            ],
            [
                0.01791409,
                0.29707789,
                0.56295109,
                0.23752193,
                0.00236515,
                0.58190280,
            ],
        ],
        [
            [
                0.01492332,
                0.10421912,
                0.02240025,
                0.03735409,
                0.57663846,
                0.32416266,
            ],
            [
                0.04180972,
                0.26402685,
                0.03572137,
                0.00413520,
                0.41808194,
                0.24696727,
            ],
            [
                0.00628672,
                0.11454948,
                0.02198825,
                0.39906919,
                0.63640803,
                0.01139849,
            ],
        ],
        [
            [
                0.04325933,
                0.26825359,
                0.23732357,
                0.05175860,
                0.01181048,
                0.08233768,
            ],
            [
                0.02484169,
                0.12027161,
                0.00541695,
                0.00654612,
                0.18603799,
                0.36247808,
            ],
            [
                0.03102159,
                0.16815442,
                0.37186235,
                0.08610666,
                0.00413520,
                0.78492409,
            ],
        ],
        [
            [
                0.11682307,
                0.78883040,
                0.74468607,
                0.83375293,
                0.90571451,
                0.70054168,
            ],
            [
                0.06321812,
                0.41898224,
                0.15190357,
                0.24591440,
                0.55301750,
                0.00657664,
            ],
            [
                0.00305180,
                0.11288624,
                0.11357290,
                0.12924391,
                0.00195315,
                0.21771573,
            ],
        ],
    ]
)
colour.msds_to_XYZ(
    msds,
    method="Integration",
    shape=colour.SpectralShape(400, 700, 60),
)

array([[[  7.68544647,   4.09414317,   8.49324254],
        [ 17.12567298,  27.77681821,  25.52573685],
        [ 19.10280411,  34.45851476,  29.76319628]],
       [[ 18.03375827,   8.62340812,   9.71702574],
        [ 15.03110867,   6.54001068,  24.53208465],
        [ 37.68269495,  26.4411103 ,  10.66361816]],
       [[  8.09532373,  12.75333339,  25.79613956],
        [  7.09620297,   2.79257389,  11.15039854],
        [  8.933163  ,  19.39985815,  17.14915636]],
       [[ 80.00969553,  80.39810464,  76.08184429],
        [ 33.27611427,  24.38947838,  39.34919287],
        [  8.89425686,  11.05185138,  10.86767594]]])

sorted(colour.MSDS_TO_XYZ_METHODS)

['ASTM E308', 'Integration', 'astm2015']

3.8.3 Blackbody Spectral Radiance Computation

colour.sd_blackbody(5000)

SpectralDistribution([[  3.60000000e+02,   6.65427827e+12],
                      [  3.61000000e+02,   6.70960528e+12],
                      [  3.62000000e+02,   6.76482512e+12],
                      ...
                      [  7.78000000e+02,   1.06068004e+13],
                      [  7.79000000e+02,   1.05903327e+13],
                      [  7.80000000e+02,   1.05738520e+13]],
                     interpolator=SpragueInterpolator,
                     interpolator_args={},
                     extrapolator=Extrapolator,
                     extrapolator_args={'right': None, 'method': 'Constant', 'left': None})

3.8.4 Dominant, Complementary Wavelength & Colour Purity Computation

xy = [0.54369557, 0.32107944]
xy_n = [0.31270000, 0.32900000]
colour.dominant_wavelength(xy, xy_n)

(array(616.0),
 array([ 0.68354746,  0.31628409]),
 array([ 0.68354746,  0.31628409]))

3.8.5 Lightness Computation

colour.lightness(12.19722535)

41.527875844653451

sorted(colour.LIGHTNESS_METHODS)

['Abebe 2017',
 'CIE 1976',
 'Fairchild 2010',
 'Fairchild 2011',
 'Glasser 1958',
 'Lstar1976',
 'Wyszecki 1963']

3.8.6 Luminance Computation

colour.luminance(41.52787585)

12.197225353400775

sorted(colour.LUMINANCE_METHODS)

['ASTM D1535',
 'CIE 1976',
 'Fairchild 2010',
 'Fairchild 2011',
 'Newhall 1943',
 'astm2008',
 'cie1976']

3.8.7 Whiteness Computation

XYZ = [95.00000000, 100.00000000, 105.00000000]
XYZ_0 = [94.80966767, 100.00000000, 107.30513595]
colour.whiteness(XYZ, XYZ_0)

array([ 93.756     ,  -1.33000001])

sorted(colour.WHITENESS_METHODS)

['ASTM E313',
 'Berger 1959',
 'CIE 2004',
 'Ganz 1979',
 'Stensby 1968',
 'Taube 1960',
 'cie2004']

3.8.8 Yellowness Computation

XYZ = [95.00000000, 100.00000000, 105.00000000]
colour.yellowness(XYZ)

4.3400000000000034

sorted(colour.YELLOWNESS_METHODS)

['ASTM D1925', 'ASTM E313', 'ASTM E313 Alternative']

3.8.9 Luminous Flux, Efficiency & Efficacy Computation

sd = colour.SDS_LIGHT_SOURCES["Neodimium Incandescent"]
colour.luminous_flux(sd)

23807.655527367202

sd = colour.SDS_LIGHT_SOURCES["Neodimium Incandescent"]
colour.luminous_efficiency(sd)

0.19943935624521045

sd = colour.SDS_LIGHT_SOURCES["Neodimium Incandescent"]
colour.luminous_efficacy(sd)

136.21708031547874

3.9 Contrast Sensitivity Function - colour.contrast

colour.contrast_sensitivity_function(u=4, X_0=60, E=65)

358.51180789884984

sorted(colour.CONTRAST_SENSITIVITY_METHODS)

['Barten 1999']

3.10 Colour Difference - colour.difference

Lab_1 = [100.00000000, 21.57210357, 272.22819350]
Lab_2 = [100.00000000, 426.67945353, 72.39590835]
colour.delta_E(Lab_1, Lab_2)

94.035649026659485

sorted(colour.DELTA_E_METHODS)

['CAM02-LCD',
 'CAM02-SCD',
 'CAM02-UCS',
 'CAM16-LCD',
 'CAM16-SCD',
 'CAM16-UCS',
 'CIE 1976',
 'CIE 1994',
 'CIE 2000',
 'CMC',
 'DIN99',
 'ITP',
 'cie1976',
 'cie1994',
 'cie2000']

3.11 IO - colour.io

3.11.1 Images

RGB = colour.read_image("Ishihara_Colour_Blindness_Test_Plate_3.png")
RGB.shape

(276, 281, 3)

3.11.2 Spectral Images - Fichet et al. (2021)

components = colour.read_spectral_image_Fichet2021("Polarised.exr")
list(components.keys())

['S0', 'S1', 'S2', 'S3']

3.11.3 Look Up Table (LUT) Data

LUT = colour.read_LUT("ACES_Proxy_10_to_ACES.cube")
print(LUT)

LUT3x1D - ACES Proxy 10 to ACES
-------------------------------
Dimensions : 2
Domain     : [[0 0 0]
              [1 1 1]]
Size       : (32, 3)

RGB = [0.17224810, 0.09170660, 0.06416938]
LUT.apply(RGB)

array([ 0.00575674,  0.00181493,  0.00121419])

3.12 Colour Models - colour.models

3.12.1 CIE xyY Colourspace

colour.XYZ_to_xyY([0.20654008, 0.12197225, 0.05136952])

array([ 0.54369557,  0.32107944,  0.12197225])

3.12.2 CIE Lab* Colourspace

colour.XYZ_to_Lab([0.20654008, 0.12197225, 0.05136952])

array([ 41.52787529,  52.63858304,  26.92317922])

3.12.3 CIE Luv* Colourspace

colour.XYZ_to_Luv([0.20654008, 0.12197225, 0.05136952])

array([ 41.52787529,  96.83626054,  17.75210149])

3.12.4 CIE 1960 UCS Colourspace

colour.XYZ_to_UCS([0.20654008, 0.12197225, 0.05136952])

array([ 0.13769339,  0.12197225,  0.1053731 ])

3.12.5 CIE 1964 UVW* Colourspace

XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
colour.XYZ_to_UVW(XYZ)

array([ 94.55035725,  11.55536523,  40.54757405])

3.12.6 CAM02-LCD, CAM02-SCD, and CAM02-UCS Colourspaces - Luo, Cui and Li (2006)

XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
XYZ_w = [95.05, 100.00, 108.88]
L_A = 318.31
Y_b = 20.0
surround = colour.VIEWING_CONDITIONS_CIECAM02["Average"]
specification = colour.XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b, surround)
JMh = (specification.J, specification.M, specification.h)
colour.JMh_CIECAM02_to_CAM02UCS(JMh)

array([ 47.16899898,  38.72623785,  15.8663383 ])

XYZ = [0.20654008, 0.12197225, 0.05136952]
XYZ_w = [95.05 / 100, 100.00 / 100, 108.88 / 100]
colour.XYZ_to_CAM02UCS(XYZ, XYZ_w=XYZ_w, L_A=L_A, Y_b=Y_b)

array([ 47.16899898,  38.72623785,  15.8663383 ])

3.12.7 CAM16-LCD, CAM16-SCD, and CAM16-UCS Colourspaces - Li et al. (2017)

XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
XYZ_w = [95.05, 100.00, 108.88]
L_A = 318.31
Y_b = 20.0
surround = colour.VIEWING_CONDITIONS_CAM16["Average"]
specification = colour.XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b, surround)
JMh = (specification.J, specification.M, specification.h)
colour.JMh_CAM16_to_CAM16UCS(JMh)

array([ 46.55542238,  40.22460974,  14.25288392])

XYZ = [0.20654008, 0.12197225, 0.05136952]
XYZ_w = [95.05 / 100, 100.00 / 100, 108.88 / 100]
colour.XYZ_to_CAM16UCS(XYZ, XYZ_w=XYZ_w, L_A=L_A, Y_b=Y_b)

array([ 46.55542238,  40.22460974,  14.25288392])

3.12.8 DIN99 Colourspace and DIN99b, DIN99c, DIN99d Refined Formulas

Lab = [41.52787529, 52.63858304, 26.92317922]
colour.Lab_to_DIN99(Lab)

array([ 53.22821988,  28.41634656,   3.89839552])

3.12.9 ICaCb Colourspace

XYZ_to_ICaCb(np.array([0.20654008, 0.12197225, 0.05136952]))

array([ 0.06875297,  0.05753352,  0.02081548])

3.12.10 IgPgTg Colourspace

colour.XYZ_to_IgPgTg([0.20654008, 0.12197225, 0.05136952])

array([ 0.42421258,  0.18632491,  0.10689223])

3.12.11 IPT Colourspace

colour.XYZ_to_IPT([0.20654008, 0.12197225, 0.05136952])

array([ 0.38426191,  0.38487306,  0.18886838])

3.12.12 Jzazbz Colourspace

colour.XYZ_to_Jzazbz([0.20654008, 0.12197225, 0.05136952])

array([ 0.00535048,  0.00924302,  0.00526007])

3.12.13 hdr-CIELAB Colourspace

colour.XYZ_to_hdr_CIELab([0.20654008, 0.12197225, 0.05136952])

array([ 51.87002062,  60.4763385 ,  32.14551912])

3.12.14 hdr-IPT Colourspace

colour.XYZ_to_hdr_IPT([0.20654008, 0.12197225, 0.05136952])

array([ 25.18261761, -22.62111297,   3.18511729])

3.12.15 Hunter L,a,b Colour Scale

XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
colour.XYZ_to_Hunter_Lab(XYZ)

array([ 34.92452577,  47.06189858,  14.38615107])

3.12.16 Hunter Rd,a,b Colour Scale

XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
colour.XYZ_to_Hunter_Rdab(XYZ)

array([ 12.197225  ,  57.12537874,  17.46241341])

3.12.17 Oklab Colourspace

colour.XYZ_to_Oklab([0.20654008, 0.12197225, 0.05136952])

array([ 0.51634019,  0.154695  ,  0.06289579])

3.12.18 OSA UCS Colourspace

XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
colour.XYZ_to_OSA_UCS(XYZ)

array([-3.0049979 ,  2.99713697, -9.66784231])

3.12.19 ProLab Colourspace

colour.XYZ_to_ProLab([0.51634019, 0.15469500, 0.06289579])

array([1.24610688, 2.39525236, 0.41902126])

3.12.20 Ragoo and Farup (2021) Optimised IPT Colourspace

colour.XYZ_to_IPT_Ragoo2021([0.20654008, 0.12197225, 0.05136952])

array([ 0.42248243,  0.2910514 ,  0.20410663])

3.12.21 Yrg Colourspace - Kirk (2019)

colour.XYZ_to_Yrg([0.20654008, 0.12197225, 0.05136952])

array([ 0.13137801,  0.49037645,  0.37777388])

3.12.22 Y’CbCr Colour Encoding

colour.RGB_to_YCbCr([1.0, 1.0, 1.0])

array([ 0.92156863,  0.50196078,  0.50196078])

3.12.23 YCoCg Colour Encoding

colour.RGB_to_YCoCg([0.75, 0.75, 0.0])

array([ 0.5625,  0.375 ,  0.1875])

3.12.24 ICtCp Colour Encoding

colour.RGB_to_ICtCp([0.45620519, 0.03081071, 0.04091952])

array([ 0.07351364,  0.00475253,  0.09351596])

3.12.25 HSV Colourspace

colour.RGB_to_HSV([0.45620519, 0.03081071, 0.04091952])

array([ 0.99603944,  0.93246304,  0.45620519])

3.12.26 IHLS Colourspace

colour.RGB_to_IHLS([0.45620519, 0.03081071, 0.04091952])

array([ 6.26236117,  0.12197943,  0.42539448])

3.12.27 Prismatic Colourspace

colour.RGB_to_Prismatic([0.25, 0.50, 0.75])

array([ 0.75      ,  0.16666667,  0.33333333,  0.5       ])

3.12.28 RGB Colourspace and Transformations

XYZ = [0.21638819, 0.12570000, 0.03847493]
illuminant_XYZ = [0.34570, 0.35850]
illuminant_RGB = [0.31270, 0.32900]
chromatic_adaptation_transform = "Bradford"
matrix_XYZ_to_RGB = [
    [3.24062548, -1.53720797, -0.49862860],
    [-0.96893071, 1.87575606, 0.04151752],
    [0.05571012, -0.20402105, 1.05699594],
]
colour.XYZ_to_RGB(
    XYZ,
    illuminant_XYZ,
    illuminant_RGB,
    matrix_XYZ_to_RGB,
    chromatic_adaptation_transform,
)

array([ 0.45595571,  0.03039702,  0.04087245])

3.12.29 RGB Colourspace Derivation

p = [0.73470, 0.26530, 0.00000, 1.00000, 0.00010, -0.07700]
w = [0.32168, 0.33767]
colour.normalised_primary_matrix(p, w)

array([[  9.52552396e-01,   0.00000000e+00,   9.36786317e-05],
       [  3.43966450e-01,   7.28166097e-01,  -7.21325464e-02],
       [  0.00000000e+00,   0.00000000e+00,   1.00882518e+00]])

3.12.30 RGB Colourspaces

sorted(colour.RGB_COLOURSPACES)

['ACES2065-1',
 'ACEScc',
 'ACEScct',
 'ACEScg',
 'ACESproxy',
 'ARRI Wide Gamut 3',
 'ARRI Wide Gamut 4',
 'Adobe RGB (1998)',
 'Adobe Wide Gamut RGB',
 'Apple RGB',
 'Best RGB',
 'Beta RGB',
 'Blackmagic Wide Gamut',
 'CIE RGB',
 'Cinema Gamut',
 'ColorMatch RGB',
 'DCDM XYZ',
 'DCI-P3',
 'DCI-P3-P',
 'DJI D-Gamut',
 'DRAGONcolor',
 'DRAGONcolor2',
 'DaVinci Wide Gamut',
 'Display P3',
 'Don RGB 4',
 'EBU Tech. 3213-E',
 'ECI RGB v2',
 'ERIMM RGB',
 'Ekta Space PS 5',
 'F-Gamut',
 'FilmLight E-Gamut',
 'ITU-R BT.2020',
 'ITU-R BT.470 - 525',
 'ITU-R BT.470 - 625',
 'ITU-R BT.709',
 'ITU-T H.273 - 22 Unspecified',
 'ITU-T H.273 - Generic Film',
 'Max RGB',
 'N-Gamut',
 'NTSC (1953)',
 'NTSC (1987)',
 'P3-D65',
 'PLASA ANSI E1.54',
 'Pal/Secam',
 'ProPhoto RGB',
 'Protune Native',
 'REDWideGamutRGB',
 'REDcolor',
 'REDcolor2',
 'REDcolor3',
 'REDcolor4',
 'RIMM RGB',
 'ROMM RGB',
 'Russell RGB',
 'S-Gamut',
 'S-Gamut3',
 'S-Gamut3.Cine',
 'SMPTE 240M',
 'SMPTE C',
 'Sharp RGB',
 'V-Gamut',
 'Venice S-Gamut3',
 'Venice S-Gamut3.Cine',
 'Xtreme RGB',
 'aces',
 'adobe1998',
 'prophoto',
 'sRGB']

3.12.31 OETFs

sorted(colour.OETFS)

['ARIB STD-B67',
 'Blackmagic Film Generation 5',
 'DaVinci Intermediate',
 'ITU-R BT.2020',
 'ITU-R BT.2100 HLG',
 'ITU-R BT.2100 PQ',
 'ITU-R BT.601',
 'ITU-R BT.709',
 'ITU-T H.273 IEC 61966-2',
 'ITU-T H.273 Log',
 'ITU-T H.273 Log Sqrt',
 'SMPTE 240M']

3.12.32 EOTFs

sorted(colour.EOTFS)

['DCDM',
 'DICOM GSDF',
 'ITU-R BT.1886',
 'ITU-R BT.2100 HLG',
 'ITU-R BT.2100 PQ',
 'ITU-T H.273 ST.428-1',
 'SMPTE 240M',
 'ST 2084',
 'sRGB']

3.12.33 OOTFs

sorted(colour.OOTFS)

['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']

3.12.34 Log Encoding / Decoding

sorted(colour.LOG_ENCODINGS)

['ACEScc',
 'ACEScct',
 'ACESproxy',
 'Apple Log Profile',
 'ARRI LogC3',
 'ARRI LogC4',
 'Canon Log',
 'Canon Log 2',
 'Canon Log 3',
 'Cineon',
 'D-Log',
 'ERIMM RGB',
 'F-Log',
 'F-Log2',
 'Filmic Pro 6',
 'L-Log',
 'Log2',
 'Log3G10',
 'Log3G12',
 'N-Log',
 'PLog',
 'Panalog',
 'Protune',
 'REDLog',
 'REDLogFilm',
 'S-Log',
 'S-Log2',
 'S-Log3',
 'T-Log',
 'V-Log',
 'ViperLog']

3.12.35 CCTFs Encoding / Decoding

sorted(colour.CCTF_ENCODINGS)

['ACEScc',
 'ACEScct',
 'ACESproxy',
 'Apple Log Profile',
 'ARRI LogC3',
 'ARRI LogC4',
 'ARIB STD-B67',
 'Canon Log',
 'Canon Log 2',
 'Canon Log 3',
 'Cineon',
 'D-Log',
 'DCDM',
 'DICOM GSDF',
 'ERIMM RGB',
 'F-Log',
 'F-Log2',
 'Filmic Pro 6',
 'Gamma 2.2',
 'Gamma 2.4',
 'Gamma 2.6',
 'ITU-R BT.1886',
 'ITU-R BT.2020',
 'ITU-R BT.2100 HLG',
 'ITU-R BT.2100 PQ',
 'ITU-R BT.601',
 'ITU-R BT.709',
 'Log2',
 'Log3G10',
 'Log3G12',
 'PLog',
 'Panalog',
 'ProPhoto RGB',
 'Protune',
 'REDLog',
 'REDLogFilm',
 'RIMM RGB',
 'ROMM RGB',
 'S-Log',
 'S-Log2',
 'S-Log3',
 'SMPTE 240M',
 'ST 2084',
 'T-Log',
 'V-Log',
 'ViperLog',
 'sRGB']

3.12.36 Recommendation ITU-T H.273 Code points for Video Signal Type Identification

colour.COLOUR_PRIMARIES_ITUTH273.keys()

dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 22, 23])

colour.models.describe_video_signal_colour_primaries(1)

===============================================================================
*                                                                             *
*   Colour Primaries: 1                                                       *
*   -------------------                                                       *
*                                                                             *
*   Primaries        : [[ 0.64  0.33]                                         *
*                       [ 0.3   0.6 ]                                         *
*                       [ 0.15  0.06]]                                        *
*   Whitepoint       : [ 0.3127  0.329 ]                                      *
*   Whitepoint Name  : D65                                                    *
*   NPM              : [[ 0.4123908   0.35758434  0.18048079]                 *
*                       [ 0.21263901  0.71516868  0.07219232]                 *
*                       [ 0.01933082  0.11919478  0.95053215]]                *
*   NPM -1           : [[ 3.24096994 -1.53738318 -0.49861076]                 *
*                       [-0.96924364  1.8759675   0.04155506]                 *
*                       [ 0.05563008 -0.20397696  1.05697151]]                *
*   FFmpeg Constants : ['AVCOL_PRI_BT709', 'BT709']                           *
*                                                                             *
===============================================================================

colour.TRANSFER_CHARACTERISTICS_ITUTH273.keys()

dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])

colour.models.describe_video_signal_transfer_characteristics(1)

===============================================================================
*                                                                             *
*   Transfer Characteristics: 1                                               *
*   ---------------------------                                               *
*                                                                             *
*   Function         : <function oetf_BT709 at 0x165bb3550>                   *
*   FFmpeg Constants : ['AVCOL_TRC_BT709', 'BT709']                           *
*                                                                             *
===============================================================================

colour.MATRIX_COEFFICIENTS_ITUTH273.keys()

dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])

colour.models.describe_video_signal_matrix_coefficients(1)

===============================================================================
*                                                                             *
*   Matrix Coefficients: 1                                                    *
*   ----------------------                                                    *
*                                                                             *
*   Matrix Coefficients : [ 0.2126  0.0722]                                   *
*   FFmpeg Constants    : ['AVCOL_SPC_BT709', 'BT709']                        *
*                                                                             *
===============================================================================

3.13 Colour Notation Systems - colour.notation

3.13.1 Munsell Value

colour.munsell_value(12.23634268)

4.0824437076525664

sorted(colour.MUNSELL_VALUE_METHODS)

['ASTM D1535',
 'Ladd 1955',
 'McCamy 1987',
 'Moon 1943',
 'Munsell 1933',
 'Priest 1920',
 'Saunderson 1944',
 'astm2008']

3.13.2 Munsell Colour

colour.xyY_to_munsell_colour([0.38736945, 0.35751656, 0.59362000])

'4.2YR 8.1/5.3'

colour.munsell_colour_to_xyY("4.2YR 8.1/5.3")

array([ 0.38736945,  0.35751656,  0.59362   ])

3.14 Optical Phenomena - colour.phenomena

colour.rayleigh_scattering_sd()

SpectralDistribution([[  3.60000000e+02,   5.99101337e-01],
                      [  3.61000000e+02,   5.92170690e-01],
                      [  3.62000000e+02,   5.85341006e-01],
                      ...
                      [  7.78000000e+02,   2.55208377e-02],
                      [  7.79000000e+02,   2.53887969e-02],
                      [  7.80000000e+02,   2.52576106e-02]],
                     interpolator=SpragueInterpolator,
                     interpolator_args={},
                     extrapolator=Extrapolator,
                     extrapolator_args={'right': None, 'method': 'Constant', 'left': None})

3.15 Light Quality - colour.quality

3.15.1 Colour Fidelity Index

colour.colour_fidelity_index(colour.SDS_ILLUMINANTS["FL2"])

70.120825477833037

sorted(colour.COLOUR_FIDELITY_INDEX_METHODS)

['ANSI/IES TM-30-18', 'CIE 2017']

3.15.2 Colour Quality Scale

colour.colour_quality_scale(colour.SDS_ILLUMINANTS["FL2"])

64.111703163816699

sorted(colour.COLOUR_QUALITY_SCALE_METHODS)

['NIST CQS 7.4', 'NIST CQS 9.0']

3.15.3 Colour Rendering Index

colour.colour_rendering_index(colour.SDS_ILLUMINANTS["FL2"])

64.233724121664807

3.15.4 Academy Spectral Similarity Index (SSI)

colour.spectral_similarity_index(
    colour.SDS_ILLUMINANTS["C"], colour.SDS_ILLUMINANTS["D65"]
)

94.0

3.16 Spectral Up-Sampling & Recovery - colour.recovery

3.16.1 Reflectance Recovery

colour.XYZ_to_sd([0.20654008, 0.12197225, 0.05136952])

SpectralDistribution([[  3.60000000e+02,   8.40144095e-02],
                      [  3.65000000e+02,   8.41264236e-02],
                      [  3.70000000e+02,   8.40057597e-02],
                      ...
                      [  7.70000000e+02,   4.46743012e-01],
                      [  7.75000000e+02,   4.46817187e-01],
                      [  7.80000000e+02,   4.46857696e-01]],
                     SpragueInterpolator,
                     {},
                     Extrapolator,
                     {'method': 'Constant', 'left': None, 'right': None})

sorted(colour.REFLECTANCE_RECOVERY_METHODS)

['Jakob 2019', 'Mallett 2019', 'Meng 2015', 'Otsu 2018', 'Smits 1999']

3.16.2 Camera RGB Sensitivities Recovery

illuminant = colour.colorimetry.SDS_ILLUMINANTS["D65"]
sensitivities = colour.characterisation.MSDS_CAMERA_SENSITIVITIES["Nikon 5100 (NPL)"]
reflectances = [
    sd.copy().align(colour.recovery.SPECTRAL_SHAPE_BASIS_FUNCTIONS_DYER2017)
    for sd in colour.characterisation.SDS_COLOURCHECKERS["BabelColor Average"].values()
]
reflectances = colour.colorimetry.sds_and_msds_to_msds(reflectances)
RGB = colour.colorimetry.msds_to_XYZ(
    reflectances,
    method="Integration",
    cmfs=sensitivities,
    illuminant=illuminant,
    k=0.01,
    shape=colour.recovery.SPECTRAL_SHAPE_BASIS_FUNCTIONS_DYER2017,
)
colour.recovery.RGB_to_msds_camera_sensitivities_Jiang2013(
    RGB,
    illuminant,
    reflectances,
    colour.recovery.BASIS_FUNCTIONS_DYER2017,
    colour.recovery.SPECTRAL_SHAPE_BASIS_FUNCTIONS_DYER2017,
)

RGB_CameraSensitivities([[  4.00000000e+02,   7.22815777e-03,   9.22506480e-03,
                           -9.88368972e-03],
                         [  4.10000000e+02,  -8.50457609e-03,   1.12777480e-02,
                            3.86248655e-03],
                         [  4.20000000e+02,   4.58191132e-02,   7.15520948e-02,
                            4.04068293e-01],
                         ...
                         [  6.80000000e+02,   4.08276173e-02,   5.55290476e-03,
                            1.39907862e-03],
                         [  6.90000000e+02,  -3.71437574e-03,   2.50935640e-03,
                            3.97652622e-04],
                         [  7.00000000e+02,  -5.62256563e-03,   1.56433970e-03,
                            5.84726936e-04]],
                        ['red', 'green', 'blue'],
                        SpragueInterpolator,
                        {},
                        Extrapolator,
                        {'method': 'Constant', 'left': None, 'right': None})

3.17 Correlated Colour Temperature Computation Methods - colour.temperature

colour.uv_to_CCT([0.1978, 0.3122])

array([  6.50751282e+03,   3.22335875e-03])

sorted(colour.UV_TO_CCT_METHODS)

['Krystek 1985', 'Ohno 2013', 'Planck 1900', 'Robertson 1968', 'ohno2013', 'robertson1968']

sorted(colour.XY_TO_CCT_METHODS)

['CIE Illuminant D Series',
 'Hernandez 1999',
 'Kang 2002',
 'McCamy 1992',
 'daylight',
 'hernandez1999',
 'kang2002',
 'mccamy1992']

3.18 Colour Volume - colour.volume

colour.RGB_colourspace_volume_MonteCarlo(colour.RGB_COLOURSPACE_RGB["sRGB"])

821958.30000000005

3.19 Geometry Primitives Generation - colour.geometry

colour.primitive("Grid")

(array([ ([-0.5,  0.5,  0. ], [ 0.,  1.], [ 0.,  0.,  1.], [ 0.,  1.,  0.,  1.]),
          ([ 0.5,  0.5,  0. ], [ 1.,  1.], [ 0.,  0.,  1.], [ 1.,  1.,  0.,  1.]),
          ([-0.5, -0.5,  0. ], [ 0.,  0.], [ 0.,  0.,  1.], [ 0.,  0.,  0.,  1.]),
          ([ 0.5, -0.5,  0. ], [ 1.,  0.], [ 0.,  0.,  1.], [ 1.,  0.,  0.,  1.])],
         dtype=[('position', '<f4', (3,)), ('uv', '<f4', (2,)), ('normal', '<f4', (3,)), ('colour', '<f4', (4,))]), array([[0, 2, 1],
          [2, 3, 1]], dtype=uint32), array([[0, 2],
          [2, 3],
          [3, 1],
          [1, 0]], dtype=uint32))

sorted(colour.PRIMITIVE_METHODS)

['Cube', 'Grid']

colour.primitive_vertices("Quad MPL")

array([[ 0.,  0.,  0.],
       [ 1.,  0.,  0.],
       [ 1.,  1.,  0.],
       [ 0.,  1.,  0.]])
sorted(colour.PRIMITIVE_VERTICES_METHODS)
['Cube MPL', 'Grid MPL', 'Quad MPL', 'Sphere']

3.20 Plotting - colour.plotting

Most of the objects are available from the colour.plotting namespace:

from colour.plotting import *

colour_style()

3.20.1 Visible Spectrum

plot_visible_spectrum("CIE 1931 2 Degree Standard Observer")

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Visible_Spectrum.png

3.20.2 Spectral Distribution

plot_single_illuminant_sd("FL1")

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Illuminant_F1_SD.png

3.20.3 Blackbody

blackbody_sds = [
    colour.sd_blackbody(i, colour.SpectralShape(0, 10000, 10))
    for i in range(1000, 15000, 1000)
]
plot_multi_sds(
    blackbody_sds,
    y_label="W / (sr m$^2$) / m",
    plot_kwargs={"use_sd_colours": True, "normalise_sd_colours": True},
    legend_location="upper right",
    bounding_box=(0, 1250, 0, 2.5e6),
)

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Blackbodies.png

3.20.4 Colour Matching Functions

plot_single_cmfs(
    "Stockman & Sharpe 2 Degree Cone Fundamentals",
    y_label="Sensitivity",
    bounding_box=(390, 870, 0, 1.1),
)

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Cone_Fundamentals.png

3.20.5 Luminous Efficiency

sd_mesopic_luminous_efficiency_function = (
    colour.sd_mesopic_luminous_efficiency_function(0.2)
)
plot_multi_sds(
    (
        sd_mesopic_luminous_efficiency_function,
        colour.PHOTOPIC_LEFS["CIE 1924 Photopic Standard Observer"],
        colour.SCOTOPIC_LEFS["CIE 1951 Scotopic Standard Observer"],
    ),
    y_label="Luminous Efficiency",
    legend_location="upper right",
    y_tighten=True,
    margins=(0, 0, 0, 0.1),
)

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Luminous_Efficiency.png

3.20.6 Colour Checker

from colour.characterisation.dataset.colour_checkers.sds import (
    COLOURCHECKER_INDEXES_TO_NAMES_MAPPING,
)

plot_multi_sds(
    [
        colour.SDS_COLOURCHECKERS["BabelColor Average"][value]
        for key, value in sorted(COLOURCHECKER_INDEXES_TO_NAMES_MAPPING.items())
    ],
    plot_kwargs={
        "use_sd_colours": True,
    },
    title=("BabelColor Average - " "Spectral Distributions"),
)

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_BabelColor_Average.png

plot_single_colour_checker("ColorChecker 2005", text_kwargs={"visible": False})

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_ColorChecker_2005.png

3.20.7 Chromaticities Prediction

plot_corresponding_chromaticities_prediction(2, "Von Kries", "Bianco 2010")

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Chromaticities_Prediction.png

3.20.8 Chromaticities

import numpy as np

RGB = np.random.random((32, 32, 3))
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
    RGB,
    "ITU-R BT.709",
    colourspaces=["ACEScg", "S-Gamut", "Pointer Gamut"],
)

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png

3.20.9 Colour Rendering Index Bars

plot_single_sd_colour_rendering_index_bars(colour.SDS_ILLUMINANTS["FL2"])

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_CRI.png

3.20.10 ANSI/IES TM-30-18 Colour Rendition Report

plot_single_sd_colour_rendition_report(colour.SDS_ILLUMINANTS["FL2"])

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Colour_Rendition_Report.png

3.20.11 Gamut Section

plot_visible_spectrum_section(section_colours="RGB", section_opacity=0.15)

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Plot_Visible_Spectrum_Section.png

plot_RGB_colourspace_section("sRGB", section_colours="RGB", section_opacity=0.15)

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Plot_RGB_Colourspace_Section.png

3.20.12 Colour Temperature

plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(["A", "B", "C"])

https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png

4 User Guide

4.1 Installation

Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:

$ pip install --user colour-science

The detailed installation procedure for the secondary dependencies is described in the Installation Guide.

Colour is also available for Anaconda from Continuum Analytics via conda-forge:

$ conda install -c conda-forge colour-science

4.2 Tutorial

The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.

4.3 How-To

The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases.

4.4 Contributing

If you would like to contribute to Colour, please refer to the following Contributing guide.

4.5 Changes

The changes are viewable on the Releases page.

4.6 Bibliography

The bibliography is available on the Bibliography page.

It is also viewable directly from the repository in BibTeX format.

5 API Reference

The main technical reference for Colour is the API Reference:

6 See Also

Software

Python

ColorAide by Muse, I.
ColorPy by Kness, M.
Colorspacious by Smith, N. J., et al.
python-colormath by Taylor, G., et al.

go-colorful by Beyer, L., et al.

.NET

Colourful by Pažourek, T., et al.

Julia

Colors.jl by Holy, T., et al.

Matlab & Octave

COLORLAB by Malo, J., et al.
Psychtoolbox by Brainard, D., et al.
The Munsell and Kubelka-Munk Toolbox by Centore, P.

7 Code of Conduct

The Code of Conduct, adapted from the Contributor Covenant 1.4, is available on the Code of Conduct page.

9 About

Colour by Colour Developers
Copyright 2013 Colour Developers – colour-developers@colour-science.org
This software is released under terms of BSD-3-Clause: https://opensource.org/licenses/BSD-3-Clause
https://github.com/colour-science/colour

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

This version

0.4.6

Oct 11, 2024

0.4.5

Oct 10, 2024

0.4.4

Dec 17, 2023

0.4.3

Aug 26, 2023

0.4.2

Nov 29, 2022

0.4.1

Feb 25, 2022

0.4.0

Feb 20, 2022

0.3.16

Nov 27, 2020

0.3.15

Jan 25, 2020

0.3.14

Oct 26, 2019

0.3.13

Apr 20, 2019

0.3.12

Mar 23, 2019

0.3.11

Feb 18, 2018

0.3.10

Jul 12, 2017

0.3.9

Mar 12, 2017

0.3.8

Jul 9, 2016

0.3.7

Dec 13, 2015

0.3.6

Aug 9, 2015

0.3.5

May 6, 2015

0.3.4

Jan 27, 2015

0.3.3

Oct 28, 2014

0.3.2

Sep 3, 2014

0.3.1

Sep 2, 2014

0.3.0

Sep 2, 2014

0.2.1

Aug 9, 2014

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

colour_science-0.4.6.tar.gz (2.2 MB view details)

Uploaded Oct 11, 2024 Source

Built Distribution

colour_science-0.4.6-py3-none-any.whl (2.5 MB view details)

Uploaded Oct 11, 2024 Python 3

File details

Details for the file colour_science-0.4.6.tar.gz.

File metadata

Download URL: colour_science-0.4.6.tar.gz
Upload date: Oct 11, 2024
Size: 2.2 MB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.1.1 CPython/3.13.0

File hashes

Hashes for colour_science-0.4.6.tar.gz
Algorithm	Hash digest
SHA256	`be98c2c9b2a5caf0c443431f402599ca9e1cc7d944bb804156803bcc97af4cf0`
MD5	`f244e69376dbde7f584f09204c26783d`
BLAKE2b-256	`5a294ea0082b8ad8c5e18b9ac7cf7ad5f21b70e10976ed53b877773ead3c268d`

See more details on using hashes here.

File details

Details for the file colour_science-0.4.6-py3-none-any.whl.

File metadata

Download URL: colour_science-0.4.6-py3-none-any.whl
Upload date: Oct 11, 2024
Size: 2.5 MB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.1.1 CPython/3.13.0

File hashes

Hashes for colour_science-0.4.6-py3-none-any.whl
Algorithm	Hash digest
SHA256	`4cd90e6d500c16f3c24225da57031e1944de52fec6b484f5bb3d4ea7d0cfee08`
MD5	`b92017592ee04476ff4f8765f47ddbab`
BLAKE2b-256	`6e3e7a39e00d11a58ab6aa75985433ad4b8001eabf965c20280ed22ed1512887`

See more details on using hashes here.

colour-science 0.4.6

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

1 Draft Release Notes

2 Sponsors

3 Features

3.1 Automatic Colour Conversion Graph - colour.graph

3.2 Chromatic Adaptation - colour.adaptation

3.3 Algebra - colour.algebra

3.3.1 Kernel Interpolation

3.3.2 Sprague (1880) Interpolation

3.4 Colour Appearance Models - colour.appearance

3.5 Colour Blindness - colour.blindness

3.6 Colour Correction - colour characterisation

3.7 ACES Input Transform - colour characterisation

3.8 Colorimetry - colour.colorimetry

3.8.1 Spectral Computations

3.8.2 Multi-Spectral Computations

3.8.3 Blackbody Spectral Radiance Computation

3.8.4 Dominant, Complementary Wavelength & Colour Purity Computation

3.8.5 Lightness Computation

3.8.6 Luminance Computation

3.8.7 Whiteness Computation

3.8.8 Yellowness Computation

3.8.9 Luminous Flux, Efficiency & Efficacy Computation

3.9 Contrast Sensitivity Function - colour.contrast

3.10 Colour Difference - colour.difference

3.11 IO - colour.io

3.11.1 Images

3.11.2 Spectral Images - Fichet et al. (2021)

3.11.3 Look Up Table (LUT) Data

3.12 Colour Models - colour.models

3.12.1 CIE xyY Colourspace

3.12.2 CIE L*a*b* Colourspace

3.12.3 CIE L*u*v* Colourspace

3.12.4 CIE 1960 UCS Colourspace

3.12.5 CIE 1964 U*V*W* Colourspace

3.12.6 CAM02-LCD, CAM02-SCD, and CAM02-UCS Colourspaces - Luo, Cui and Li (2006)

3.12.7 CAM16-LCD, CAM16-SCD, and CAM16-UCS Colourspaces - Li et al. (2017)

3.12.8 DIN99 Colourspace and DIN99b, DIN99c, DIN99d Refined Formulas

3.12.9 ICaCb Colourspace

3.12.10 IgPgTg Colourspace

3.12.11 IPT Colourspace

3.12.12 Jzazbz Colourspace

3.12.13 hdr-CIELAB Colourspace

3.12.14 hdr-IPT Colourspace

3.12.15 Hunter L,a,b Colour Scale

3.12.16 Hunter Rd,a,b Colour Scale

3.12.17 Oklab Colourspace

3.12.18 OSA UCS Colourspace

3.12.19 ProLab Colourspace

3.12.20 Ragoo and Farup (2021) Optimised IPT Colourspace

3.12.21 Yrg Colourspace - Kirk (2019)

3.12.22 Y’CbCr Colour Encoding

3.12.23 YCoCg Colour Encoding

3.12.24 ICtCp Colour Encoding

3.12.25 HSV Colourspace

3.12.26 IHLS Colourspace

3.12.27 Prismatic Colourspace

3.12.28 RGB Colourspace and Transformations

3.12.29 RGB Colourspace Derivation

3.12.30 RGB Colourspaces

3.12.31 OETFs

3.12.32 EOTFs

3.12.33 OOTFs

3.12.34 Log Encoding / Decoding

3.12.35 CCTFs Encoding / Decoding

3.12.36 Recommendation ITU-T H.273 Code points for Video Signal Type Identification

3.13 Colour Notation Systems - colour.notation

3.13.1 Munsell Value

3.13.2 Munsell Colour

3.14 Optical Phenomena - colour.phenomena

3.15 Light Quality - colour.quality

3.15.1 Colour Fidelity Index

3.12.2 CIE Lab* Colourspace

3.12.3 CIE Luv* Colourspace

3.12.5 CIE 1964 UVW* Colourspace