colour.adaptation.cie1994 Module¶

CIE 1994 Chromatic Adaptation Model¶

Defines CIE 1994 chromatic adaptation model objects:

• chromatic_adaptation_CIE1994()

References

colour.adaptation.cie1994.CIE1994_XYZ_TO_RGB_MATRIX = array([[ 0.40024, 0.7076 , -0.08081], [-0.2263 , 1.16532, 0.0457 ], [ 0. , 0. , 0.91822]])¶

CIE 1994 colour appearance model CIE XYZ colourspace to cone responses matrix.

CIE1994_XYZ_TO_RGB_MATRIX : array_like, (3, 3)

colour.adaptation.cie1994.CIE1994_RGB_TO_XYZ_MATRIX = array([[ 1.85993639e+00, -1.12938162e+00, 2.19897410e-01], [ 3.61191436e-01, 6.38812463e-01, -6.37059684e-06], [ 0.00000000e+00, 0.00000000e+00, 1.08906362e+00]])¶

CIE 1994 colour appearance model cone responses to CIE XYZ colourspace matrix.

CIE1994_RGB_TO_XYZ_MATRIX : array_like, (3, 3)

colour.adaptation.cie1994.chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2, n=1)[source]¶

Adapts given CIE XYZ_1 colourspace stimulus from test viewing conditions to reference viewing conditions using CIE 1994 chromatic adaptation model.

Parameters:	XYZ (array_like, (3,)) – CIE XYZ colourspace matrix of test sample / stimulus in domain [0, 100]. xy_o1 (array_like, (2,)) – Chromaticity coordinates $x_{o1}$ and $y_{o1}$ of test illuminant and background. xy_o2 (array_like, (2,)) – Chromaticity coordinates $x_{o2}$ and $y_{o2}$ of reference illuminant and background. Y_o (numeric) – Luminance factor $Y_o$ of achromatic background as percentage in domain [18, 100]. E_o1 (numeric) – Test illuminance $E_{o1}$ in $cd/m^2$. E_o2 (numeric) – Reference illuminance $E_{o2}$ in $cd/m^2$. n (numeric, optional) – Noise component in fundamental primary system.
Returns:	Adapted CIE XYZ_2 colourspace test stimulus.
Return type:	ndarray, (3,)

Warning

The input domain of that definition is non standard!

Notes

• Input CIE XYZ_1 colourspace matrix is in domain [0, 100].
• Output CIE XYZ_2 colourspace matrix is in domain [0, 100].

Examples

>>> XYZ_1 = np.array([28.0, 21.26, 5.27])
>>> xy_o1 = (0.4476, 0.4074)
>>> xy_o2 = (0.3127, 0.3290)
>>> Y_o = 20
>>> E_o1 = 1000
>>> E_o2 = 1000
>>> chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)    
array([ 24.0337952...,  21.1562121...,  17.6430119...])

colour.adaptation.cie1994.XYZ_to_RGB_cie1994(XYZ)[source]¶

Converts from CIE XYZ colourspace to cone responses.

Parameters:	XYZ (array_like, (3,)) – CIE XYZ colourspace matrix.
Returns:	Cone responses.
Return type:	ndarray, (3,)

Examples

>>> XYZ = np.array([28.0, 21.26, 5.27])
>>> XYZ_to_RGB_cie1994(XYZ)  
array([ 25.8244273...,  18.6791422...,   4.8390194...])

colour.adaptation.cie1994.RGB_to_XYZ_cie1994(RGB)[source]¶

Converts from cone responses to CIE XYZ colourspace.

Parameters:	RGB (array_like, (3,)) – Cone responses.
Returns:	CIE XYZ colourspace matrix.
Return type:	ndarray, (3,)

Examples

>>> RGB = np.array([25.8244273, 18.6791422, 4.8390194])
>>> RGB_to_XYZ_cie1994(RGB)  
array([ 28.  ,  21.26,   5.27])

colour.adaptation.cie1994.intermediate_values(xy_o)[source]¶

Returns the intermediate values $\xi$, $\eta$, $\zeta$.

Parameters:	xy_o (array_like, (2,)) – Chromaticity coordinates $x_o$ and $y_o$ of whitepoint.
Returns:	Intermediate values $\xi$, $\eta$, $\zeta$.
Return type:	ndarray, (3,)

Examples

>>> xy_o = (0.4476, 0.4074)
>>> intermediate_values(xy_o)  
array([ 1.1185719...,  0.9329553...,  0.3268087...])

colour.adaptation.cie1994.effective_adapting_responses(Y_o, E_o, xez)[source]¶

Derives the effective adapting responses in the fundamental primary system of the test or reference field.

Parameters:	Y_o (numeric) – Luminance factor $Y_o$ of achromatic background as percentage in domain [18, 100]. E_o (numeric) – Test or reference illuminance $E_{o}$ in lux. xez (ndarray, (3,)) – Intermediate values $\xi$, $\eta$, $\zeta$.
Returns:	Effective adapting responses.
Return type:	ndarray, (3,)

Examples

>>> Y_o = 20
>>> E_o = 1000
>>> xez = np.array([1.11857195, 0.9329553, 0.32680879])
>>> effective_adapting_responses(Y_o, E_o, xez)  
array([ 71.2105020...,  59.3937790...,  20.8052937...])

colour.adaptation.cie1994.beta_1(x)[source]¶

Computes the exponent $\beta_1$ for the middle and long-wavelength sensitive cones.

Parameters:	x (numeric) – Middle and long-wavelength sensitive cone response.
Returns:	Exponent $\beta_1$.
Return type:	numeric

Examples

>>> beta_1(318.323316315)  
4.6106222...

colour.adaptation.cie1994.beta_2(x)[source]¶

Computes the exponent $\beta_2$ for the short-wavelength sensitive cones.

Parameters:	x (numeric) – Short-wavelength sensitive cone response.
Returns:	Exponent $\beta_2$.
Return type:	numeric

Examples

>>> beta_2(318.323316315)  
4.6522416...

colour.adaptation.cie1994.exponential_factors(RGB_o)[source]¶

Returns the chromatic adaptation exponential factors $\beta_1(R_o)$, $\beta_1(G_o)$ and $\beta_2(B_o)$ of given cone responses.

Parameters:	RGB_o (ndarray, (3,)) – Cone responses.
Returns:	Chromatic adaptation exponential factors $\beta_1(R_o)$, $\beta_1(G_o)$ and $\beta_2(B_o)$.
Return type:	ndarray, (3,)

Examples

>>> RGB_o = np.array([318.32331631, 318.30352317, 318.23283482])
>>> exponential_factors(RGB_o)  
array([ 4.6106222...,  4.6105892...,  4.6520698...])

colour.adaptation.cie1994.K_coefficient(Y_o, xez_1, xez_2, bRGB_o1, bRGB_o2, n=1)[source]¶

Computes the coefficient $K$ for correcting the difference between the test and references illuminances.

Parameters:	Y_o (numeric) – Luminance factor $Y_o$ of achromatic background as percentage in domain [18, 100]. xez_1 (ndarray, (3,)) – Intermediate values $\xi_1$, $\eta_1$, $\zeta_1$ for the test illuminant and background. xez_2 (ndarray, (3,)) – Intermediate values $\xi_2$, $\eta_2$, $\zeta_2$ for the reference illuminant and background. bRGB_o1 (ndarray, (3,)) – Chromatic adaptation exponential factors $\beta_1(R_{o1})$, $\beta_1(G_{o1})$ and $\beta_2(B_{o1})$ of test sample. bRGB_o2 (ndarray, (3,)) – Chromatic adaptation exponential factors $\beta_1(R_{o2})$, $\beta_1(G_{o2})$ and $\beta_2(B_{o2})$ of reference sample. n (numeric, optional) – Noise component in fundamental primary system.
Returns:	Coefficient $K$.
Return type:	numeric

Examples

>>> Y_o = 20
>>> xez_1 = np.array([1.11857195, 0.9329553, 0.32680879])
>>> xez_2 = np.array([1.00000372, 1.00000176, 0.99999461])
>>> bRGB_o1 = np.array([3.74852518, 3.63920879, 2.78924811])
>>> bRGB_o2 = np.array([3.68102374, 3.68102256, 3.56557351])
>>> K_coefficient(Y_o, xez_1, xez_2, bRGB_o1, bRGB_o2)
1.0

colour.adaptation.cie1994.corresponding_colour(RGB_1, Y_o, xez_1, xez_2, bRGB_o1, bRGB_o2, K, n=1)[source]¶

Computes the corresponding colour cone responses of given test sample cone responses $RGB_1$.

Parameters:	RGB_1 (ndarray, (3,)) – Test sample cone responses $RGB_1$. Y_o (numeric) – Luminance factor $Y_o$ of achromatic background as percentage in domain [18, 100]. xez_1 (ndarray, (3,)) – Intermediate values $\xi_1$, $\eta_1$, $\zeta_1$ for the test illuminant and background. xez_2 (ndarray, (3,)) – Intermediate values $\xi_2$, $\eta_2$, $\zeta_2$ for the reference illuminant and background. bRGB_o1 (ndarray, (3,)) – Chromatic adaptation exponential factors $\beta_1(R_{o1})$, $\beta_1(G_{o1})$ and $\beta_2(B_{o1})$ of test sample. bRGB_o2 (ndarray, (3,)) – Chromatic adaptation exponential factors $\beta_1(R_{o2})$, $\beta_1(G_{o2})$ and $\beta_2(B_{o2})$ of reference sample. K (numeric) – Coefficient $K$. n (numeric, optional) – Noise component in fundamental primary system.
Returns:	Corresponding colour cone responses of given test sample cone responses.
Return type:	ndarray, (3,)

Examples

>>> RGB_1 = np.array([25.8244273, 18.6791422, 4.8390194])
>>> Y_o = 20
>>> xez_1 = np.array([1.11857195, 0.9329553, 0.32680879])
>>> xez_2 = np.array([1.00000372, 1.00000176, 0.99999461])
>>> bRGB_o1 = np.array([3.74852518, 3.63920879, 2.78924811])
>>> bRGB_o2 = np.array([3.68102374, 3.68102256, 3.56557351])
>>> K = 1.0
>>> corresponding_colour(RGB_1, Y_o, xez_1, xez_2, bRGB_o1, bRGB_o2, K)    
array([ 23.1636901...,  20.0211948...,  16.2001664...])