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Module « scipy.ndimage »

Fonction gaussian_gradient_magnitude - module scipy.ndimage

Signature de la fonction gaussian_gradient_magnitude

def gaussian_gradient_magnitude(input, sigma, output=None, mode='reflect', cval=0.0, *, axes=None, **kwargs) 

Description

help(scipy.ndimage.gaussian_gradient_magnitude)

Multidimensional gradient magnitude using Gaussian derivatives.

Parameters
----------
input : array_like
    The input array.
sigma : scalar or sequence of scalars
    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 or dtype, optional
    The array in which to place the output, or the dtype of the
    returned array. By default an array of the same dtype as input
    will be created.
mode : str or sequence, optional
    The `mode` parameter determines how the input array is extended
    when the filter overlaps a border. By passing a sequence of modes
    with length equal to the number of dimensions of the input array,
    different modes can be specified along each axis. Default value is
    'reflect'. The valid values and their behavior is as follows:

    'reflect' (`d c b a | a b c d | d c b a`)
        The input is extended by reflecting about the edge of the last
        pixel. This mode is also sometimes referred to as half-sample
        symmetric.

    'constant' (`k k k k | a b c d | k k k k`)
        The input is extended by filling all values beyond the edge with
        the same constant value, defined by the `cval` parameter.

    'nearest' (`a a a a | a b c d | d d d d`)
        The input is extended by replicating the last pixel.

    'mirror' (`d c b | a b c d | c b a`)
        The input is extended by reflecting about the center of the last
        pixel. This mode is also sometimes referred to as whole-sample
        symmetric.

    'wrap' (`a b c d | a b c d | a b c d`)
        The input is extended by wrapping around to the opposite edge.

    For consistency with the interpolation functions, the following mode
    names can also be used:

    'grid-constant'
        This is a synonym for 'constant'.

    'grid-mirror'
        This is a synonym for 'reflect'.

    'grid-wrap'
        This is a synonym for 'wrap'.
cval : scalar, optional
    Value to fill past edges of input if `mode` is 'constant'. Default
    is 0.0.
axes : tuple of int or None
    The axes over which to apply the filter. If `sigma` or `mode` tuples
    are provided, their length must match the number of axes.
Extra keyword arguments will be passed to gaussian_filter().

Returns
-------
gaussian_gradient_magnitude : ndarray
    Filtered array. Has the same shape as `input`.

Examples
--------
>>> from scipy import ndimage, datasets
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray()  # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121)  # left side
>>> ax2 = fig.add_subplot(122)  # right side
>>> ascent = datasets.ascent()
>>> result = ndimage.gaussian_gradient_magnitude(ascent, sigma=5)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()

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