# colour.difference.delta_e Module¶

## $$\Delta E_{ab}$$ - Delta E Colour Difference¶

Defines $$\Delta E_{ab}$$ colour difference computation objects:

The following methods are available:

References

  Wikipedia. (n.d.). Color difference. Retrieved August 29, 2014, from http://en.wikipedia.org/wiki/Color_difference
colour.difference.delta_e.delta_E_CIE1976(Lab_1, Lab_2)[source]

Returns the difference $$\Delta E_{ab}$$ between two given CIE Lab colourspace arrays using CIE 1976 recommendation.

Parameters: Lab_1 (array_like) – CIE Lab colourspace array 1. Lab_2 (array_like) – CIE Lab colourspace array 2. Colour difference $$\Delta E_{ab}$$. numeric or ndarray

References

  Lindbloom, B. (2003). Delta E (CIE 1976). Retrieved February 24, 2014, from http://brucelindbloom.com/Eqn_DeltaE_CIE76.html

Examples

>>> Lab_1 = np.array([100.00000000, 21.57210357, 272.22819350])
>>> Lab_2 = np.array([100.00000000, 426.67945353, 72.39590835])
>>> delta_E_CIE1976(Lab_1, Lab_2)
451.7133019...

colour.difference.delta_e.delta_E_CIE1994(Lab_1, Lab_2, textiles=False)[source]

Returns the difference $$\Delta E_{ab}$$ between two given CIE Lab colourspace arrays using CIE 1994 recommendation.

Parameters: Lab_1 (array_like) – CIE Lab colourspace array 1. Lab_2 (array_like) – CIE Lab colourspace array 2. textiles (bool, optional) – 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 $$k_L=k_C=k_H=1,\ k_1=0.045,\ k_2=0.015$$. Colour difference $$\Delta E_{ab}$$. numeric or ndarray

Notes

CIE 1994 colour differences are not symmetrical: difference between Lab_1 and Lab_2 may not be the same as difference between Lab_2 and Lab_1 thus one colour must be understood to be the reference against which a sample colour is compared.

References

  Lindbloom, B. (2011). Delta E (CIE 1994). Retrieved February 24, 2014, from http://brucelindbloom.com/Eqn_DeltaE_CIE94.html

Examples

>>> Lab_1 = np.array([100.00000000, 21.57210357, 272.22819350])
>>> Lab_2 = np.array([100.00000000, 426.67945353, 72.39590835])
>>> delta_E_CIE1994(Lab_1, Lab_2)
83.7792255...
>>> delta_E_CIE1994(Lab_1, Lab_2, textiles=True)
88.3355530...

colour.difference.delta_e.delta_E_CIE2000(Lab_1, Lab_2, textiles=False)[source]

Returns the difference $$\Delta E_{ab}$$ between two given CIE Lab colourspace arrays using CIE 2000 recommendation.

Parameters: Lab_1 (array_like) – CIE Lab colourspace array 1. Lab_2 (array_like) – CIE Lab colourspace array 2. textiles (bool, optional) – Textiles application specific parametric factors $$k_L=2,\ k_C=k_H=1$$ weights are used instead of $$k_L=k_C=k_H=1$$. Colour difference $$\Delta E_{ab}$$. numeric or ndarray

Notes

• CIE 2000 colour differences are not symmetrical: difference between Lab_1 and Lab_2 may not be the same as difference between Lab_2 and Lab_1 thus one colour must be understood to be the reference against which a sample colour is compared.
• 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 $$\Delta E_{ab}$$
• Sample structure: Homogeneous (without texture)

References

  Lindbloom, B. (2009). Delta E (CIE 2000). Retrieved February 24, 2014, from http://brucelindbloom.com/Eqn_DeltaE_CIE2000.html
  Melgosa, M. (2013). CIE / ISO new standard: CIEDE2000, 2013(July). Retrieved from http://www.color.org/events/colorimetry/Melgosa_CIEDE2000_Workshop-July4.pdf

Examples

>>> Lab_1 = np.array([100.00000000, 21.57210357, 272.22819350])
>>> Lab_2 = np.array([100.00000000, 426.67945353, 72.39590835])
>>> delta_E_CIE2000(Lab_1, Lab_2)
94.0356490...
>>> Lab_2 = np.array([50.00000000, 426.67945353, 72.39590835])
>>> delta_E_CIE2000(Lab_1, Lab_2)
100.8779470...
>>> delta_E_CIE2000(Lab_1, Lab_2, textiles=True)
95.7920535...

colour.difference.delta_e.delta_E_CMC(Lab_1, Lab_2, l=2, c=1)[source]

Returns the difference $$\Delta E_{ab}$$ between two given CIE Lab colourspace arrays using Colour Measurement Committee recommendation.

The quasimetric has two parameters: Lightness (l) and chroma (c), allowing the users to weight the difference based on the ratio of l:c. Commonly used values are 2:1 for acceptability and 1:1 for the threshold of imperceptibility.

Parameters: Lab_1 (array_like) – CIE Lab colourspace array 1. Lab_2 (array_like) – CIE Lab colourspace array 2. l (numeric, optional) – Lightness weighting factor. c (numeric, optional) – Chroma weighting factor. Colour difference $$\Delta E_{ab}$$. numeric or ndarray

References

  Lindbloom, B. (2009). Delta E (CMC). Retrieved February 24, 2014, from http://brucelindbloom.com/Eqn_DeltaE_CMC.html

Examples

>>> Lab_1 = np.array([100.00000000, 21.57210357, 272.22819350])
>>> Lab_2 = np.array([100.00000000, 426.67945353, 72.39590835])
>>> delta_E_CMC(Lab_1, Lab_2)
172.7047712...

colour.difference.delta_e.DELTA_E_METHODS = CaseInsensitiveMapping({u'cie1994': <function delta_E_CIE1994 at 0x7f5de099f050>, u'CIE 1994': <function delta_E_CIE1994 at 0x7f5de099f050>, u'cie1976': <function delta_E_CIE1976 at 0x7f5de0a06668>, u'CMC': <function delta_E_CMC at 0x7f5de099f140>, u'CIE 1976': <function delta_E_CIE1976 at 0x7f5de0a06668>, u'cie2000': <function delta_E_CIE2000 at 0x7f5de099f0c8>, u'CIE 2000': <function delta_E_CIE2000 at 0x7f5de099f0c8>})

Supported Delta E computations methods.

DELTA_E_METHODS : CaseInsensitiveMapping
{‘CIE 1976’, ‘CIE 1994’, ‘CIE 2000’, ‘CMC’}

Aliases:

• ‘cie1976’: ‘CIE 1976’
• ‘cie1994’: ‘CIE 1994’
• ‘cie2000’: ‘CIE 2000’
colour.difference.delta_e.delta_E(Lab_1, Lab_2, method=u'CMC', **kwargs)[source]

Returns the difference $$\Delta E_{ab}$$ between two given CIE Lab colourspace arrays using given method.

Parameters: Lab_1 (array_like) – CIE Lab colourspace array 1. Lab_2 (array_like) – CIE Lab colourspace array 2. method (unicode, optional) – {‘CMC’, ‘CIE 1976’, ‘CIE 1994’, ‘CIE 2000’}, Computation method. **kwargs (dict, optional) – Keywords arguments. Colour difference $$\Delta E_{ab}$$. numeric or ndarray

Examples

>>> Lab_1 = np.array([100.00000000, 21.57210357, 272.22819350])
>>> Lab_2 = np.array([100.00000000, 426.67945353, 72.39590835])
>>> delta_E(Lab_1, Lab_2)
172.7047712...
>>> delta_E(Lab_1, Lab_2, method='CIE 1976')
451.7133019...
>>> delta_E(Lab_1, Lab_2, method='CIE 1994')
83.7792255...
>>> delta_E(
...     Lab_1, Lab_2, method='CIE 1994', textiles=False)
83.7792255...
>>> delta_E(Lab_1, Lab_2, method='CIE 2000')
94.0356490...