The support in LibWeb is quite easy as the previous commits introduced
helpers to support lab-like colors.
Now for the methods in Color:
- The formulas in `from_lab()` are derived from the CIEXYZ to CIELAB
formulas the "Colorimetry" paper published by the CIE.
- The conversion in `from_xyz50()` can be decomposed in multiple steps
XYZ D50 -> XYZ D65 -> Linear sRGB -> sRGB. The two first conversion
are done with a singular matrix operation. This matrix was generated
with a Python script [1].
This commit makes us pass all the `css/css-color/lab-00*.html` WPT
tests (0 to 7 at the time of writing).
[1] Python script used to generate the XYZ D50 -> Linear sRGB
conversion:
```python
import numpy as np
# http://www.brucelindbloom.com/index.html?Eqn_ChromAdapt.html
# First let's convert from D50 to D65 using the Bradford method.
m_a = np.array([
[0.8951000, 0.2664000, -0.1614000],
[-0.7502000, 1.7135000, 0.0367000],
[0.0389000, -0.0685000, 1.0296000]
])
# D50
chromaticities_source = np.array([0.96422, 1, 0.82521])
# D65
chromaticities_destination = np.array([0.9505, 1, 1.0890])
cone_response_source = m_a @ chromaticities_source
cone_response_destination = m_a @ chromaticities_destination
cone_response_ratio = cone_response_destination / cone_response_source
m = np.linalg.inv(m_a) @ np.diagflat(cone_response_ratio) @ m_a
D50_to_D65 = m
# https://en.wikipedia.org/wiki/SRGB#From_CIE_XYZ_to_sRGB
# Then, the matrix to convert to linear sRGB.
xyz_65_to_srgb = np.array([
[3.2406, - 1.5372, - 0.4986],
[-0.9689, + 1.8758, 0.0415],
[0.0557, - 0.2040, 1.0570]
])
# Finally, let's retrieve the final transformation.
xyz_50_to_srgb = xyz_65_to_srgb @ D50_to_D65
print(xyz_50_to_srgb)
```
