colour_hdri.distortion.characterise_vignette_bivariate_spline#

colour_hdri.distortion.characterise_vignette_bivariate_spline(image: ArrayLike, pre_denoise_sigma: float = 6, post_denoise_sigma: float = 1, samples: int = 50, degree: int = 3) → DataVignetteCharacterisation[source]#

Characterise the vignette of given image using a bivariate spline.

Parameters:

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

  • pre_denoise_sigma (float) – Standard deviation of the gaussian filtering kernel applied on the image.

  • post_denoise_sigma (float) – Standard deviation of the gaussian filtering kernel applied on the resampled image at given samples count.

  • samples (int) – Sample count of the resampled image on the long edge.

  • degree (int) – Degree of the bivariate spline.

Returns:

Vignette characterisation.

Return type:

DataVignetteCharacterisation

Examples

>>> parameters, principal_point = characterise_vignette_bivariate_spline(
...     apply_radial_gradient(np.ones([300, 400]))
... ).values
>>> parameters.shape
(37, 50, 1)
>>> principal_point  
array([ 0.4983333...,  0.49875   ])