gaussian_filter
-
skimage.filters.gaussian_filter(*args, **kwargs)
[source] -
Deprecated function. Use
skimage.filters.gaussian
instead.Multi-dimensional Gaussian filter
Parameters: image : array-like
input image (grayscale or color) to filter.
sigma : scalar or sequence of scalars
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, wherecval
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 0.0multichannel : 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).Returns: filtered_image : ndarray
the filtered array
Notes
This function is a wrapper around
scipy.ndi.gaussian_filter()
.Integer arrays are converted to float.
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
>>> a = np.zeros((3, 3)) >>> a[1, 1] = 1 >>> a array([[ 0., 0., 0.], [ 0., 1., 0.], [ 0., 0., 0.]]) >>> 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]]) >>> gaussian(a, sigma=1) # more smooting 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 >>> 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 >>> from skimage.data import astronaut >>> image = astronaut() >>> filtered_img = gaussian(image, sigma=1, multichannel=True)
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