skimage 0.17.2

NotesParametersReturnsBackRef
gaussian(image, sigma=1, output=None, mode='nearest', cval=0, multichannel=None, preserve_range=False, truncate=4.0)

Notes

This function is a wrapper around scipy.ndi.gaussian_filter .

Integer arrays are converted to float.

The output should be floating point data type since gaussian converts to float provided image . If output is not provided, another array will be allocated and returned as the result.

The multi-dimensional filter is implemented as a sequence of one-dimensional convolution filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.

Parameters

image : array-like

Input image (grayscale or color) to filter.

sigma : scalar or sequence of scalars, optional

Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.

output : array, optional

The output parameter passes an array in which to store the filter output.

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

multichannel : bool, optional (default: None)

Whether the last axis of the image is to be interpreted as multiple channels. If True, each channel is filtered separately (channels are not mixed together). Only 3 channels are supported. If None , the function will attempt to guess this, and raise a warning if ambiguous, when the array has shape (M, N, 3).

preserve_range : bool, optional

Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float . Also see https://scikit-image.org/docs/dev/user_guide/data_types.html

truncate : float, optional

Truncate the filter at this many standard deviations.

Returns

filtered_image : ndarray

the filtered array

Multi-dimensional Gaussian filter.

Examples

This example is valid syntax, but we were not able to check execution
>>> a = np.zeros((3, 3))
... a[1, 1] = 1
... a array([[0., 0., 0.], [0., 1., 0.], [0., 0., 0.]])
This example is valid syntax, but we were not able to check execution
>>> gaussian(a, sigma=0.4)  # mild smoothing
array([[0.00163116, 0.03712502, 0.00163116],
       [0.03712502, 0.84496158, 0.03712502],
       [0.00163116, 0.03712502, 0.00163116]])
This example is valid syntax, but we were not able to check execution
>>> gaussian(a, sigma=1)  # more smoothing
array([[0.05855018, 0.09653293, 0.05855018],
       [0.09653293, 0.15915589, 0.09653293],
       [0.05855018, 0.09653293, 0.05855018]])
This example is valid syntax, but we were not able to check execution
>>> # Several modes are possible for handling boundaries
... gaussian(a, sigma=1, mode='reflect') array([[0.08767308, 0.12075024, 0.08767308], [0.12075024, 0.16630671, 0.12075024], [0.08767308, 0.12075024, 0.08767308]])
This example is valid syntax, but we were not able to check execution
>>> # For RGB images, each is filtered separately
... from skimage.data import astronaut
... image = astronaut()
... filtered_img = gaussian(image, sigma=1, multichannel=True)
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.segmentation.active_contour_model.active_contour skimage.filters._gaussian.gaussian

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