Colour Science for Python
Project description
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.
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 L*a*b* Colourspace
colour.XYZ_to_Lab([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529, 52.63858304, 26.92317922])
3.12.3 CIE L*u*v* 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 U*V*W* 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})
3.18 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")
3.20.2 Spectral Distribution
plot_single_illuminant_sd("FL1")
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),
)
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),
)
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),
)
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"),
)
plot_single_colour_checker("ColorChecker 2005", text_kwargs={"visible": False})
3.20.7 Chromaticities Prediction
plot_corresponding_chromaticities_prediction(2, "Von Kries", "Bianco 2010")
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"],
)
3.20.9 Colour Rendering Index Bars
plot_single_sd_colour_rendering_index_bars(colour.SDS_ILLUMINANTS["FL2"])
3.20.10 ANSI/IES TM-30-18 Colour Rendition Report
plot_single_sd_colour_rendition_report(colour.SDS_ILLUMINANTS["FL2"])
3.20.11 Gamut Section
plot_visible_spectrum_section(section_colours="RGB", section_opacity=0.15)
plot_RGB_colourspace_section("sRGB", section_colours="RGB", section_opacity=0.15)
3.20.12 Colour Temperature
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(["A", "B", "C"])
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
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
Project details
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