colour.difference.delta_E_CIE2000#
- colour.difference.delta_E_CIE2000(Lab_1: ArrayLike, Lab_2: ArrayLike, textiles: bool = False) NDArrayFloat [source]#
Return the difference \(\Delta E_{00}\) between two given CIE L*a*b* colourspace arrays using CIE 2000 recommendation.
- Parameters:
Lab_1 (ArrayLike) – CIE L*a*b* colourspace array 1.
Lab_2 (ArrayLike) – CIE L*a*b* colourspace array 2.
textiles (bool) – Textiles application specific parametric factors. \(k_L=2,\ k_C=k_H=1\) weights are used instead of \(k_L=k_C=k_H=1\).
- Returns:
Colour difference \(\Delta E_{00}\).
- Return type:
Notes
Domain
Scale - Reference
Scale - 1
Lab_1
L_1
: [0, 100]a_1
: [-100, 100]b_1
: [-100, 100]L_1
: [0, 1]a_1
: [-1, 1]b_1
: [-1, 1]Lab_2
L_2
: [0, 100]a_2
: [-100, 100]b_2
: [-100, 100]L_2
: [0, 1]a_2
: [-1, 1]b_2
: [-1, 1]Parametric factors \(k_L=k_C=k_H=1\) weights under reference conditions:
Illumination: D65 source
Illuminance: 1000 lx
Observer: Normal colour vision
Background field: Uniform, neutral gray with \(L^*=50\)
Viewing mode: Object
Sample size: Greater than 4 degrees
Sample separation: Direct edge contact
Sample colour-difference magnitude: Lower than 5.0 \(\Delta E_{00}\)
Sample structure: Homogeneous (without texture)
References
Examples
>>> Lab_1 = np.array([48.99183622, -0.10561667, 400.65619925]) >>> Lab_2 = np.array([50.65907324, -0.11671910, 402.82235718]) >>> delta_E_CIE2000(Lab_1, Lab_2) 1.6709303... >>> delta_E_CIE2000(Lab_1, Lab_2, textiles=True) 0.8412338...