colour_hdri.correct_vignette#

colour_hdri.correct_vignette(image: ArrayLike, characterisation_data: DataVignetteCharacterisation, method: Literal['2D Function', 'Bivariate Spline', 'RBF'] | str = 'RBF', **kwargs) → NDArrayFloat[source]#

Correct the vignette of given image using given method.

Parameters:

image (ArrayLike) – Image to correct the vignette of.
characterisation_data (DataVignetteCharacterisation) – Vignette characterisation data for given function.
method (Literal['2D Function', 'Bivariate Spline', 'RBF'] | str) – Vignette characterisation method.
function – {colour_hdri.distortion.correct_vignette_2D_function()}, Characterisation function.
degree – {colour_hdri.distortion.correct_vignette_bivariate_spline()}, Degree of the bivariate spline.
smoothing – {colour_hdri.distortion.correct_vignette_RBF()}, Smoothing parameter, see scipy.interpolate.RBFInterpolator class.
kernel –

{colour_hdri.distortion.correct_vignette_RBF()},
Type of RBF, see scipy.interpolate.RBFInterpolator class.
epsilon – {colour_hdri.distortion.correct_vignette_RBF()}, Shape parameter that scales the input to the RBF, see scipy.interpolate.RBFInterpolator class.

Returns:

Vignette corrected image.

Return type:

numpy.ndarray

Examples

>>> image = apply_radial_gradient(np.ones([5, 7]))
>>> characterisation_data = characterise_vignette(image)
>>> np.around(correct_vignette_RBF(image, characterisation_data), 3)
array([[ 0.   ,  0.091,  0.841,  1.134,  0.841,  0.091,  0.   ],
       [ 0.   ,  0.967,  2.03 ,  2.552,  2.03 ,  0.967,  0.   ],
       [ 0.   ,  1.323,  2.647,  3.97 ,  2.647,  1.323,  0.   ],
       [ 0.   ,  0.967,  2.03 ,  2.552,  2.03 ,  0.967,  0.   ],
       [ 0.   ,  0.091,  0.841,  1.134,  0.841,  0.091,  0.   ]])
>>> characterisation_data = characterise_vignette(image, method="RBF")
>>> np.around(correct_vignette(image, characterisation_data, method="RBF"), 3)
array([[ 0.   ,  0.091,  0.841,  1.134,  0.841,  0.091,  0.   ],
       [ 0.   ,  0.967,  2.03 ,  2.552,  2.03 ,  0.967,  0.   ],
       [ 0.   ,  1.323,  2.647,  3.97 ,  2.647,  1.323,  0.   ],
       [ 0.   ,  0.967,  2.03 ,  2.552,  2.03 ,  0.967,  0.   ],
       [ 0.   ,  0.091,  0.841,  1.134,  0.841,  0.091,  0.   ]])
>>> characterisation_data = characterise_vignette(image, method="2D Function")
>>> np.around(
...     correct_vignette(image, characterisation_data, method="2D Function"),
...     3,
... )
array([[-0.   ,  0.122,  0.597,  0.747,  0.781,  1.08 , -0.   ],
       [ 0.   ,  0.413,  0.676,  0.82 ,  0.76 ,  0.576,  0.   ],
       [ 0.   ,  0.468,  0.759,  1.103,  0.838,  0.611,  0.   ],
       [ 0.   ,  0.439,  0.709,  0.858,  0.801,  0.628, -0.   ],
       [-0.   ,  0.193,  0.742,  0.913,  1.049, -0.477, -0.   ]])
>>> characterisation_data = characterise_vignette(image, method="Bivariate Spline")
>>> np.around(
...     correct_vignette(image, characterisation_data, method="Bivariate Spline"),
...     3,
... )
array([[  0.   ,   0.345,   3.059,   4.072,   3.059,   0.345,   0.   ],
       [  0.   ,   3.624,   7.304,   9.058,   7.304,   3.624,   0.   ],
       [  0.   ,   4.936,   9.481,  14.032,   9.481,   4.936,   0.   ],
       [  0.   ,   3.624,   7.304,   9.058,   7.304,   3.624,   0.   ],
       [  0.   ,   0.345,   3.059,   4.072,   3.059,   0.345,   0.   ]])