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mmedit.evaluation.functional.gaussian_funcs

Module Contents

Functions

gaussian(x, sigma)

Gaussian function.

dgaussian(x, sigma)

Gradient of gaussian.

gauss_filter(sigma[, epsilon])

Gradient of gaussian.

gauss_gradient(img, sigma)

Gaussian gradient.

mmedit.evaluation.functional.gaussian_funcs.gaussian(x, sigma)[source]

Gaussian function.

Parameters
  • x (array_like) – The independent variable.

  • sigma (float) – Standard deviation of the gaussian function.

Returns

Gaussian value of x.

Return type

np.ndarray or scalar

mmedit.evaluation.functional.gaussian_funcs.dgaussian(x, sigma)[source]

Gradient of gaussian.

Parameters
  • x (array_like) – The independent variable.

  • sigma (float) – Standard deviation of the gaussian function.

Returns

Gradient of gaussian of x.

Return type

np.ndarray or scalar

mmedit.evaluation.functional.gaussian_funcs.gauss_filter(sigma, epsilon=0.01)[source]

Gradient of gaussian.

Parameters
  • sigma (float) – Standard deviation of the gaussian kernel.

  • epsilon (float) – Small value used when calculating kernel size. Default: 1e-2.

Returns

Gaussian filter along x axis. filter_y (np.ndarray): Gaussian filter along y axis.

Return type

filter_x (np.ndarray)

mmedit.evaluation.functional.gaussian_funcs.gauss_gradient(img, sigma)[source]

Gaussian gradient.

From https://www.mathworks.com/matlabcentral/mlc-downloads/downloads/ submissions/8060/versions/2/previews/gaussgradient/gaussgradient.m/ index.html

Parameters
  • img (np.ndarray) – Input image.

  • sigma (float) – Standard deviation of the gaussian kernel.

Returns

Gaussian gradient of input img.

Return type

np.ndarray

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