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Module « scipy.ndimage »

Fonction maximum_filter - module scipy.ndimage

Signature de la fonction maximum_filter

def maximum_filter(input, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0, *, axes=None) 

Description

help(scipy.ndimage.maximum_filter)

Calculate a multidimensional maximum filter.

Parameters
----------
input : array_like
    The input array.
size : scalar or tuple, optional
    See footprint, below. Ignored if footprint is given.
footprint : array, optional
    Either `size` or `footprint` must be defined. `size` gives
    the shape that is taken from the input array, at every element
    position, to define the input to the filter function.
    `footprint` is a boolean array that specifies (implicitly) a
    shape, but also which of the elements within this shape will get
    passed to the filter function. Thus ``size=(n,m)`` is equivalent
    to ``footprint=np.ones((n,m))``.  We adjust `size` to the number
    of dimensions of the input array, so that, if the input array is
    shape (10,10,10), and `size` is 2, then the actual size used is
    (2,2,2). When `footprint` is given, `size` is ignored.
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.
origin : int or sequence, optional
    Controls the placement of the filter on the input array's pixels.
    A value of 0 (the default) centers the filter over the pixel, with
    positive values shifting the filter to the left, and negative ones
    to the right. By passing a sequence of origins with length equal to
    the number of dimensions of the input array, different shifts can
    be specified along each axis.
axes : tuple of int or None, optional
    If None, `input` is filtered along all axes. Otherwise,
    `input` is filtered along the specified axes. When `axes` is
    specified, any tuples used for `size`, `origin`, and/or `mode`
    must match the length of `axes`. The ith entry in any of these tuples
    corresponds to the ith entry in `axes`.

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

Notes
-----
A sequence of modes (one per axis) is only supported when the footprint is
separable. Otherwise, a single mode string must be provided.

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.maximum_filter(ascent, size=20)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()

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