colour.recovery.RGB_to_sd_Mallett2019#
- colour.recovery.RGB_to_sd_Mallett2019(RGB: ArrayLike, basis_functions: MultiSpectralDistributions = MSDS_BASIS_FUNCTIONS_sRGB_MALLETT2019) SpectralDistribution [source]#
Recover the spectral distribution of given RGB colourspace array using Mallett and Yuksel (2019) method.
- Parameters:
RGB (ArrayLike) – RGB colourspace array.
basis_functions (MultiSpectralDistributions) – Basis functions for the method. The default is to use the built-in sRGB basis functions, i.e.
colour.recovery.MSDS_BASIS_FUNCTIONS_sRGB_MALLETT2019
.
- Returns:
Recovered reflectance.
- Return type:
References
[MY19]
Notes
In-addition to the BT.709 primaries used by the sRGB colourspace, [MY19] 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 import MSDS_CMFS, SDS_ILLUMINANTS, XYZ_to_sRGB >>> from colour.colorimetry import sd_to_XYZ_integration >>> from colour.recovery import SPECTRAL_SHAPE_sRGB_MALLETT2019 >>> from colour.utilities import numpy_print_options >>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) >>> RGB = XYZ_to_sRGB(XYZ, apply_cctf_encoding=False) >>> cmfs = ( ... MSDS_CMFS["CIE 1931 2 Degree Standard Observer"] ... .copy() ... .align(SPECTRAL_SHAPE_sRGB_MALLETT2019) ... ) >>> illuminant = SDS_ILLUMINANTS["D65"].copy().align(cmfs.shape) >>> sd = RGB_to_sd_Mallett2019(RGB) >>> with numpy_print_options(suppress=True): ... sd ... SpectralDistribution([[ 380. , 0.1735531...], [ 385. , 0.1720357...], [ 390. , 0.1677721...], [ 395. , 0.1576605...], [ 400. , 0.1372829...], [ 405. , 0.1170849...], [ 410. , 0.0895694...], [ 415. , 0.0706232...], [ 420. , 0.0585765...], [ 425. , 0.0523959...], [ 430. , 0.0497598...], [ 435. , 0.0476057...], [ 440. , 0.0465079...], [ 445. , 0.0460337...], [ 450. , 0.0455839...], [ 455. , 0.0452872...], [ 460. , 0.0450981...], [ 465. , 0.0448895...], [ 470. , 0.0449257...], [ 475. , 0.0448987...], [ 480. , 0.0446834...], [ 485. , 0.0441372...], [ 490. , 0.0417137...], [ 495. , 0.0373832...], [ 500. , 0.0357657...], [ 505. , 0.0348263...], [ 510. , 0.0341953...], [ 515. , 0.0337683...], [ 520. , 0.0334979...], [ 525. , 0.0332991...], [ 530. , 0.0331909...], [ 535. , 0.0332181...], [ 540. , 0.0333387...], [ 545. , 0.0334970...], [ 550. , 0.0337381...], [ 555. , 0.0341847...], [ 560. , 0.0346447...], [ 565. , 0.0353993...], [ 570. , 0.0367367...], [ 575. , 0.0392007...], [ 580. , 0.0445902...], [ 585. , 0.0625633...], [ 590. , 0.2965381...], [ 595. , 0.4215576...], [ 600. , 0.4347139...], [ 605. , 0.4385134...], [ 610. , 0.4385184...], [ 615. , 0.4385249...], [ 620. , 0.4374694...], [ 625. , 0.4384672...], [ 630. , 0.4368251...], [ 635. , 0.4340867...], [ 640. , 0.4303219...], [ 645. , 0.4243257...], [ 650. , 0.4159482...], [ 655. , 0.4057443...], [ 660. , 0.3919874...], [ 665. , 0.3742784...], [ 670. , 0.3518421...], [ 675. , 0.3240127...], [ 680. , 0.2955145...], [ 685. , 0.2625658...], [ 690. , 0.2343423...], [ 695. , 0.2174830...], [ 700. , 0.2060461...], [ 705. , 0.1977437...], [ 710. , 0.1916846...], [ 715. , 0.1861020...], [ 720. , 0.1823908...], [ 725. , 0.1807923...], [ 730. , 0.1795571...], [ 735. , 0.1785623...], [ 740. , 0.1775758...], [ 745. , 0.1771614...], [ 750. , 0.1767431...], [ 755. , 0.1764319...], [ 760. , 0.1762597...], [ 765. , 0.1762209...], [ 770. , 0.1761803...], [ 775. , 0.1761195...], [ 780. , 0.1760763...]], SpragueInterpolator, {}, Extrapolator, {'method': 'Constant', 'left': None, 'right': None}) >>> sd_to_XYZ_integration(sd, cmfs, illuminant) / 100 ... array([ 0.2065436..., 0.1219996..., 0.0513764...])