colour_hdri.characterise_vignette

colour_hdri.characterise_vignette(image: ArrayLike, method: Literal['2D Function', 'Bivariate Spline', 'RBF'] | str = 'RBF', **kwargs: Any) → DataVignetteCharacterisation

Characterise the vignette of given image using given method.

Parameters:

• image (ArrayLike) – Image to characterise the vignette of.

• method (Literal['2D Function', 'Bivariate Spline', 'RBF'] | str) – Vignette characterisation method.

• function – {colour_hdri.distortion.characterise_vignette_2D_function()}, Characterisation function.

• pre_denoise_sigma – {colour_hdri.distortion.characterise_vignette_bivariate_spline()}, Standard deviation of the gaussian filtering kernel applied on the image.

• post_denoise_sigma – {colour_hdri.distortion.characterise_vignette_bivariate_spline()}, Standard deviation of the gaussian filtering kernel applied on the resampled image at given samples count.

• samples – {colour_hdri.distortion.characterise_vignette_bivariate_spline()}, Sample count of the resampled image on the long edge.

• degree – {colour_hdri.distortion.characterise_vignette_bivariate_spline()}, Degree of the bivariate spline.

• denoise_sigma – {colour_hdri.distortion.characterise_vignette_RBF()}, Standard deviation of the gaussian filtering kernel applied on the image.

• kwargs (Any)

Returns:

Vignette characterisation.

Return type:

DataVignetteCharacterisation

Examples

>>> image = apply_radial_gradient(np.ones([300, 400]))
>>> parameters, principal_point = characterise_vignette(image).values
>>> parameters.shape
(180, 1)
>>> principal_point  
array([ 0.4983333...,  0.49875   ])
>>> parameters, principal_point = characterise_vignette(image, method="RBF").values
>>> parameters.shape
(180, 1)
>>> principal_point  
array([ 0.4983333...,  0.49875   ])
>>> parameters, principal_point = characterise_vignette(
...     image, method="2D Function"
... ).values
>>> parameters.shape
(1, 6)
>>> principal_point  
array([ 0.4983333...,  0.49875   ])
>>> parameters, principal_point = characterise_vignette(
...     image, method="Bivariate Spline"
... ).values
>>> parameters.shape
(37, 50, 1)
>>> principal_point  
array([ 0.4983333...,  0.49875   ])