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GitHub Stars: 2.13K
Forks: 263
Contributors: 50

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Release Date: Oct 11, 2024
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License

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Open Issues: 78
Open PR: 8
Last Release: 1 month ago
Last Commit: 11 days ago

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Contributors: 50
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Package

Python Versions Compatibility: <3.14,>=3.10

Age: 10 years
Latest Release: 1 month ago
Dependencies: 0 Direct
Versions: 24
Maintainers: 1
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Readme

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.

Table of Contents

Draft Release Notes

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

Features

Most of the objects are available from the colour namespace:

import colour

Automatic Colour Conversion Graph - `colour.graph`

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

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

===============================================================================
*                                                                             *
*   [ Conversion Path ]                                                       *
*                                                                             *
*   "sd_to_XYZ" --&gt; "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])

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']

Algebra - `colour.algebra`

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])

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])

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)

Colour Blindness - `colour.blindness`

import numpy as np

cmfs = colour.LMS_CMFS["Stockman &amp; 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]])

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']

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]))

Colorimetry - `colour.colorimetry`

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']

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']

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})

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]))

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']

Luminance Computation

colour.luminance(41.52787585)

12.197225353400775

sorted(colour.LUMINANCE_METHODS)

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

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']

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']

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

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']

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']

IO - `colour.io`

Images

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

(276, 281, 3)

Spectral Images - Fichet et al. (2021)

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

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

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])

Colour Models - `colour.models`

CIE xyY Colourspace

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

array([ 0.54369557,  0.32107944,  0.12197225])

CIE Lab* Colourspace

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

array([ 41.52787529,  52.63858304,  26.92317922])

CIE Luv* Colourspace

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

array([ 41.52787529,  96.83626054,  17.75210149])

CIE 1960 UCS Colourspace

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

array([ 0.13769339,  0.12197225,  0.1053731 ])

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])

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 ])

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])

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])

ICaCb Colourspace

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

array([ 0.06875297,  0.05753352,  0.02081548])

IgPgTg Colourspace

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

array([ 0.42421258,  0.18632491,  0.10689223])

IPT Colourspace

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

array([ 0.38426191,  0.38487306,  0.18886838])

Jzazbz Colourspace

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

array([ 0.00535048,  0.00924302,  0.00526007])

hdr-CIELAB Colourspace

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

array([ 51.87002062,  60.4763385 ,  32.14551912])

hdr-IPT Colourspace

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

array([ 25.18261761, -22.62111297,   3.18511729])

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])

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])

Oklab Colourspace

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

array([ 0.51634019,  0.154695  ,  0.06289579])

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])

ProLab Colourspace

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

array([1.24610688, 2.39525236, 0.41902126])

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])

Yrg Colourspace - Kirk (2019)

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

array([ 0.13137801,  0.49037645,  0.37777388])

Y'CbCr Colour Encoding

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

array([ 0.92156863,  0.50196078,  0.50196078])

YCoCg Colour Encoding

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

array([ 0.5625,  0.375 ,  0.1875])

ICtCp Colour Encoding

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

array([ 0.07351364,  0.00475253,  0.09351596])

HSV Colourspace

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

array([ 0.99603944,  0.93246304,  0.45620519])

IHLS Colourspace

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

array([ 6.26236117,  0.12197943,  0.42539448])

Prismatic Colourspace

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

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

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])

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]])

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']

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']

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']

OOTFs

sorted(colour.OOTFS)

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

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']

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']

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         :                    *
*   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']                        *
*                                                                             *
===============================================================================

Colour Notation Systems - `colour.notation`

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']

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   ])

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})

Light Quality - `colour.quality`

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']

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']

Colour Rendering Index

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

64.233724121664807

Academy Spectral Similarity Index (SSI)

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

94.0

Spectral Up-Sampling & Recovery - `colour.recovery`

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']

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})

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']

Colour Volume - `colour.volume`

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

821958.30000000005

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', '

FAQs

What is colour-science?

Colour Science for Python. Visit Snyk Advisor to see a full health score report for colour-science, including popularity, security, maintenance & community analysis.

Is colour-science popular?

The python package colour-science receives a total of 20,806 weekly downloads. As such, colour-science popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is colour-science well maintained?

We found that colour-science demonstrated a healthy version release cadence and project activity. It has a community of 50 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is colour-science safe to use?

The python package colour-science was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 21 November-2024, at 05:23 (UTC).

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