gaussian

gaussian

skimage.filters.gaussian(image, sigma, output=None, mode='nearest', cval=0, multichannel=None) [source]

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, 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).

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

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>>> 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)