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The updated implementation uses the @ operator for matrix multiplication, uses the `illuminant_D65` constant from the `colour` library for the reference white point, and includes a more detailed function signature.
andreped
requested changes
Apr 4, 2023
| _rgb2xyz = np.array([[0.412453, 0.357580, 0.180423], | ||
| [0.212671, 0.715160, 0.072169], | ||
| [0.019334, 0.119193, 0.950227]]) | ||
| from colour.models import illuminants |
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For this project, we wish to use pure numpy, torch, or tensorflow without introducing any new libraries. Hence, using the colour library is not ideal.
| # scale by CIE XYZ tristimulus values of the reference white point | ||
| arr = xyz.copy() | ||
| arr = arr / np.asarray((0.95047, 1., 1.08883), dtype=xyz.dtype) | ||
| xyz = xyz / illuminants.illuminant_D65()[np.newaxis, :] |
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Due to the beforementioned comment, I would keep the old solution for this transform.
The old copy() can still likely be removed, and the variable renaming is fine.
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The updated implementation uses the @ operator for matrix multiplication, uses the
illuminant_D65constant from thecolourlibrary for the reference white point, and includes a more detailed function signature.