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bundles / skimage 0.26.1rc0.dev0+git20260530.b607368ff / skimage / filters / _gaussian / difference_of_gaussians

function

skimage.filters._gaussian:difference_of_gaussians

source: /dev/scikit-image/src/skimage/filters/_gaussian.py :147

Signature

def   difference_of_gaussians ( image ,  low_sigma ,  high_sigma = None ,  * ,  mode = nearest ,  cval = 0 ,  channel_axis = None ,  truncate = 4.0 )

Summary

Find features between low_sigma and high_sigma in size.

Extended Summary

This function uses the Difference of Gaussians method for applying band-pass filters to multi-dimensional arrays. The input array is blurred with two Gaussian kernels of differing sigmas to produce two intermediate, filtered images. The more-blurred image is then subtracted from the less-blurred image. The final output image will therefore have had high-frequency components attenuated by the smaller-sigma Gaussian, and low frequency components will have been removed due to their presence in the more-blurred intermediate.

Parameters

image : ndarray

Input array to filter.

low_sigma : scalar or sequence of scalars

Standard deviation(s) for the Gaussian kernel with the smaller sigmas across all axes. The standard deviations are given for each axis as a sequence, or as a single number, in which case the single number is used as the standard deviation value for all axes.

high_sigma : scalar or sequence of scalars, optional (default is None)

Standard deviation(s) for the Gaussian kernel with the larger sigmas across all axes. The standard deviations are given for each axis as a sequence, or as a single number, in which case the single number is used as the standard deviation value for all axes. If None is given (default), sigmas for all axes are calculated as 1.6 * low_sigma.

mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional

The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'. Default is 'nearest'.

cval : scalar, optional

Value to fill past edges of input if mode is 'constant'. Default is 0.0

channel_axis : int or None, optional

If None, the image is assumed to be a grayscale (single channel) image. Otherwise, this parameter indicates which axis of the array corresponds to channels.

truncate : float, optional (default is 4.0)

Truncate the filter at this many standard deviations.

Returns

filtered_image : ndarray

the filtered array.

Notes

This function will subtract an array filtered with a Gaussian kernel with sigmas given by high_sigma from an array filtered with a Gaussian kernel with sigmas provided by low_sigma. The values for high_sigma must always be greater than or equal to the corresponding values in low_sigma, or a ValueError will be raised.

When high_sigma is none, the values for high_sigma will be calculated as 1.6x the corresponding values in low_sigma. This ratio was originally proposed by Marr and Hildreth (1980) [1] and is commonly used when approximating the inverted Laplacian of Gaussian, which is used in edge and blob detection.

Input image is converted according to the conventions of img_as_float.

Except for sigma values, all parameters are used for both filters.

Examples

Apply a simple Difference of Gaussians filter to a color image: 
from skimage.data import astronaut
from skimage.filters import difference_of_gaussians
filtered_image = difference_of_gaussians(astronaut(), 2, 10,
                                         channel_axis=-1)
✓
Apply a Laplacian of Gaussian filter as approximated by the Difference of Gaussians filter: 
filtered_image = difference_of_gaussians(astronaut(), 2,
                                         channel_axis=-1)
✓
Apply a Difference of Gaussians filter to a grayscale image using different sigma values for each axis: 
from skimage.data import camera
filtered_image = difference_of_gaussians(camera(), (2,5), (3,20))
✓

See also

skimage.feature.blob_dog

Aliases

  • skimage.filters.difference_of_gaussians