skimage2.filters.gaussian#

skimage2.filters.gaussian(image, sigma=1.0, *, mode='nearest', cval=0, truncate=4.0, channel_axis=None, out=None)[source]#

Filter with Multi-dimensional Gaussian kernel.

Parameters:

imagendarray: Input image (grayscale or color) to filter.
sigmascalar 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.
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’.
cvalscalar, optional: Value to fill past edges of input if mode is ‘constant’. Default is 0.0.
truncatefloat, optional: Truncate the filter at this many standard deviations.
channel_axisint 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.
outndarray, optional: If given, the filtered image will be stored in this array.

Returns:

filtered_imagendarray: the filtered array

Notes

This function is a wrapper around scipy.ndimage.gaussian_filter().

Integer arrays are converted to float.

out should be of floating-point data type since gaussian converts the input image to float. If out 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.

Examples

>>> import _skimage2 as ski2
>>> import _skimage2 as ski2
>>> a = np.zeros((3, 3))
>>> a[1, 1] = 1
>>> a
array([[0., 0., 0.],
       [0., 1., 0.],
       [0., 0., 0.]])
>>> ski2.filters.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]])
>>> ski2.filters.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]])
>>> # Several modes are possible for handling boundaries
>>> ski2.filters.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]])
>>> # For RGB images, each is filtered separately
>>> image = ski2.data.astronaut()
>>> filtered_img = ski2.filters.gaussian(image, sigma=1, channel_axis=-1)