colour.recovery.spectral_primary_decomposition_Mallett2019¶
- colour.recovery.spectral_primary_decomposition_Mallett2019(colourspace, cmfs=XYZ_ColourMatchingFunctions(name='CIE 1931 2 Degree Standard Observer', ...), illuminant=SpectralDistribution(name='D65', ...), metric=<function norm>, metric_args=(), optimisation_kwargs=None)[source]¶
Performs the spectral primary decomposition as described in Mallett and Yuksel (2019) for given RGB colourspace.
- Parameters
colourspace (RGB_Colourspace) – RGB colourspace.
cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
illuminant (SpectralDistribution, optional) – Illuminant spectral distribution.
metric (unicode, optional) –
Function to be minimised, i.e. the objective function.
metric(basis, *metric_args) -> floatwhere
basisis three reflectances concatenated together, each with a shape matchingshape.metric_args (tuple, optional) – Additional arguments passed to
metric.optimisation_kwargs (dict_like, optional) – Parameters for
scipy.optimize.minimize()definition.
- Returns
Basis functions for given RGB colourspace.
- Return type
References
[]
Notes
In-addition to the BT.709 primaries used by the sRGB colourspace, [] tried BT.2020, P3 D65, Adobe RGB 1998, NTSC (1987), Pal/Secam, ProPhoto RGB, and Adobe Wide Gamut RGB primaries, every one of which encompasses a larger (albeit not-always-enveloping) set of CIE L*a*b* colours than BT.709. Of these, only Pal/Secam produces a feasible basis, which is relatively unsurprising since it is very similar to BT.709, whereas the others are significantly larger.
Examples
>>> from colour.colorimetry import SpectralShape >>> from colour.models import RGB_COLOURSPACE_PAL_SECAM >>> from colour.utilities import numpy_print_options >>> cmfs = ( ... MSDS_CMFS_STANDARD_OBSERVER['CIE 1931 2 Degree Standard Observer']. ... copy().align(SpectralShape(360, 780, 10)) ... ) >>> illuminant = SDS_ILLUMINANTS['D65'].copy().align(cmfs.shape) >>> msds = spectral_primary_decomposition_Mallett2019( ... RGB_COLOURSPACE_PAL_SECAM, cmfs, illuminant, optimisation_kwargs={ ... 'options': {'ftol': 1e-5} ... } ... ) >>> with numpy_print_options(suppress=True): ... # Doctests skip for Python 2.x compatibility. ... print(msds) [[ 360. 0.3324728... 0.3332663... 0.3342608...] [ 370. 0.3323307... 0.3327746... 0.3348946...] [ 380. 0.3341115... 0.3323995... 0.3334889...] [ 390. 0.3337570... 0.3298092... 0.3364336...] [ 400. 0.3209352... 0.3218213... 0.3572433...] [ 410. 0.2881025... 0.2837628... 0.4281346...] [ 420. 0.1836749... 0.1838893... 0.6324357...] [ 430. 0.0187212... 0.0529655... 0.9283132...] [ 440. 0. ... 0. ... 1. ...] [ 450. 0. ... 0. ... 1. ...] [ 460. 0. ... 0. ... 1. ...] [ 470. 0. ... 0.0509556... 0.9490443...] [ 480. 0. ... 0.2933996... 0.7066003...] [ 490. 0. ... 0.5001396... 0.4998603...] [ 500. 0. ... 0.6734805... 0.3265195...] [ 510. 0. ... 0.8555213... 0.1444786...] [ 520. 0. ... 0.9999985... 0.0000014...] [ 530. 0. ... 1. ... 0. ...] [ 540. 0. ... 1. ... 0. ...] [ 550. 0. ... 1. ... 0. ...] [ 560. 0. ... 0.9924229... 0. ...] [ 570. 0. ... 0.9913344... 0.0083032...] [ 580. 0.0370289... 0.9145168... 0.0484542...] [ 590. 0.7100075... 0.2898477... 0.0001446...] [ 600. 1. ... 0. ... 0. ...] [ 610. 1. ... 0. ... 0. ...] [ 620. 1. ... 0. ... 0. ...] [ 630. 1. ... 0. ... 0. ...] [ 640. 0.9711347... 0.0137659... 0.0150993...] [ 650. 0.7996619... 0.1119379... 0.0884001...] [ 660. 0.6064640... 0.202815 ... 0.1907209...] [ 670. 0.4662959... 0.2675037... 0.2662005...] [ 680. 0.4010958... 0.2998989... 0.2990052...] [ 690. 0.3617485... 0.3208921... 0.3173592...] [ 700. 0.3496691... 0.3247855... 0.3255453...] [ 710. 0.3433979... 0.3273540... 0.329248 ...] [ 720. 0.3358860... 0.3345583... 0.3295556...] [ 730. 0.3349498... 0.3314232... 0.3336269...] [ 740. 0.3359954... 0.3340147... 0.3299897...] [ 750. 0.3310392... 0.3327595... 0.3362012...] [ 760. 0.3346883... 0.3314158... 0.3338957...] [ 770. 0.3332167... 0.333371 ... 0.3334122...] [ 780. 0.3319670... 0.3325476... 0.3354852...]]