colour.models.derivation Module¶

RGB Colourspace Derivation¶

Defines objects related to RGB colourspace derivation, essentially calculating the normalised primary matrix for given RGB colourspace primaries and whitepoint.

See also

RGB Colourspaces IPython Notebook

References

[1]	SMPTE. (1993). Derivation of Basic Television Color Equations. In RP 177:1993 (Vol. RP 177:199). doi:10.5594/S9781614821915

colour.models.derivation.xy_to_z(xy)[source]¶

Returns the z coordinate using given xy chromaticity coordinates.

Parameters:	xy (array_like) – xy chromaticity coordinates.
Returns:	z coordinate.
Return type:	numeric

Examples

>>> xy_to_z(np.array([0.25, 0.25]))
0.5

colour.models.derivation.normalised_primary_matrix(primaries, whitepoint)[source]¶

Returns the normalised primary matrix using given primaries and whitepoint.

Parameters:	primaries (array_like, (3, 2)) – Primaries chromaticity coordinates. whitepoint (array_like) – Illuminant / whitepoint chromaticity coordinates.
Returns:	Normalised primary matrix.
Return type:	ndarray, (3, 3)

Examples

>>> pms = np.array([0.73470, 0.26530, 0.00000, 1.00000, 0.00010, -0.07700])
>>> whitepoint = np.array([0.32168, 0.33767])
>>> normalised_primary_matrix(pms, whitepoint)  
array([[  9.5255239...e-01,   0.0000000...e+00,   9.3678631...e-05],
       [  3.4396645...e-01,   7.2816609...e-01,  -7.2132546...e-02],
       [  0.0000000...e+00,   0.0000000...e+00,   1.0088251...e+00]])

colour.models.derivation.primaries_whitepoint(npm)[source]¶

Returns primaries and whitepoint using given normalised primary matrix.

Parameters:	npm (array_like, (3, 3)) – Normalised primary matrix.
Returns:	Primaries and whitepoint.
Return type:	tuple

References

[2]	Trieu, T. (2015). Private Discussion with Mansencal, T.

Examples

>>> npm = np.array([[9.52552396e-01, 0.00000000e+00, 9.36786317e-05],
...                 [3.43966450e-01, 7.28166097e-01, -7.21325464e-02],
...                 [0.00000000e+00, 0.00000000e+00, 1.00882518e+00]])
>>> p, w = primaries_whitepoint(npm)
>>> p  
array([[  7.3470000...e-01,   2.6530000...e-01],
       [  0.0000000...e+00,   1.0000000...e+00],
       [  1.0000000...e-04,  -7.7000000...e-02]])
>>> w 
array([ 0.32168,  0.33767])

colour.models.derivation.RGB_luminance_equation(primaries, whitepoint)[source]¶

Returns the luminance equation from given primaries and whitepoint.

Parameters:	primaries (array_like, (3, 2)) – Primaries chromaticity coordinates. whitepoint (array_like) – Illuminant / whitepoint chromaticity coordinates.
Returns:	Luminance equation.
Return type:	unicode

Examples

>>> pms = np.array([0.73470, 0.26530, 0.00000, 1.00000, 0.00010, -0.07700])
>>> whitepoint = np.array([0.32168, 0.33767])
>>> # Doctests skip for Python 2.x compatibility.
>>> RGB_luminance_equation(pms, whitepoint)  
'Y = 0.3439664...(R) + 0.7281660...(G) + -0.0721325...(B)'

colour.models.derivation.RGB_luminance(RGB, primaries, whitepoint)[source]¶

Returns the luminance \(Y\) of given RGB components from given primaries and whitepoint.

Parameters:	RGB (array_like) – RGB chromaticity coordinate matrix. primaries (array_like, (3, 2)) – Primaries chromaticity coordinate matrix. whitepoint (array_like) – Illuminant / whitepoint chromaticity coordinates.
Returns:	Luminance \(Y\).
Return type:	numeric or ndarray

Examples

>>> RGB = np.array([40.6, 4.2, 67.4])
>>> pms = np.array([0.73470, 0.26530, 0.00000, 1.00000, 0.00010, -0.07700])
>>> whitepoint = np.array([0.32168, 0.33767])
>>> RGB_luminance(RGB, pms, whitepoint)  
12.1616018...