colour.difference.delta_E_CIE1994#
- colour.difference.delta_E_CIE1994(Lab_1: Domain100, Lab_2: Domain100, textiles: bool = False, *, additional_data: Literal[False] = False) NDArrayFloat[source]#
- colour.difference.delta_E_CIE1994(Lab_1: Domain100, Lab_2: Domain100, textiles: bool = False, *, additional_data: Literal[True]) DeltaE_Specification_CIE1994
Compute the colour difference \(\Delta E_{94}\) between two specified CIE L*a*b* colourspace arrays using the CIE 1994 recommendation.
- Parameters:
Lab_1 (Domain100) – CIE L*a*b* colourspace array 1.
Lab_2 (Domain100) – CIE L*a*b* colourspace array 2.
textiles (bool) – Textiles application specific parametric factors, \(k_L=2,\ k_C=k_H=1,\ k_1=0.048,\ k_2=0.014\) weights are used instead of \(k_L=k_C=k_H=1,\ k_1=0.045,\ k_2=0.015\).
additional_data (bool) – Whether to output additional data.
- Returns:
Colour difference \(\Delta E_{94}\).
- Return type:
numpy.ndarrayorDeltaE_Specification_CIE1994
Notes
Domain
Scale - Reference
Scale - 1
Lab_1100
1
Lab_2100
1
CIE 1994 colour differences are not symmetrical: difference between
Lab_1andLab_2may not be the same as difference betweenLab_2andLab_1thus one colour must be understood to be the reference against which a sample colour is compared.
References
[Lin11]
Examples
>>> Lab_1 = np.array([48.99183622, -0.10561667, 400.65619925]) >>> Lab_2 = np.array([50.65907324, -0.11671910, 402.82235718]) >>> delta_E_CIE1994(Lab_1, Lab_2) np.float64(1.6711191...) >>> delta_E_CIE1994( ... Lab_1, ... Lab_2, ... additional_data=True, ... ) DeltaE_Specification_CIE1994(dE=np.float64(1.6711191...), dL=np.float64(-1.6672370...), dC=np.float64(-0.1138315...), dH=np.float64(0.0014983...)) >>> delta_E_CIE1994(Lab_1, Lab_2, textiles=True) np.float64(0.8404677...) >>> delta_E_CIE1994( ... Lab_1, ... Lab_2, ... textiles=True, ... additional_data=True, ... ) DeltaE_Specification_CIE1994(dE=np.float64(0.8404677...), dL=np.float64(-0.8336185...), dC=np.float64(-0.1070687...), dH=np.float64(0.0015891...))