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Module « scipy.ndimage »

Fonction binary_opening - module scipy.ndimage

Signature de la fonction binary_opening

def binary_opening(input, structure=None, iterations=1, output=None, origin=0, mask=None, border_value=0, brute_force=False, *, axes=None) 

Description

help(scipy.ndimage.binary_opening)

Multidimensional binary opening with the given structuring element.

The *opening* of an input image by a structuring element is the
*dilation* of the *erosion* of the image by the structuring element.

Parameters
----------
input : array_like
    Binary array_like to be opened. Non-zero (True) elements form
    the subset to be opened.
structure : array_like, optional
    Structuring element used for the opening. Non-zero elements are
    considered True. If no structuring element is provided an element
    is generated with a square connectivity equal to one (i.e., only
    nearest neighbors are connected to the center, diagonally-connected
    elements are not considered neighbors).
iterations : int, optional
    The erosion step of the opening, then the dilation step are each
    repeated `iterations` times (one, by default). If `iterations` is
    less than 1, each operation is repeated until the result does
    not change anymore. Only an integer of iterations is accepted.
output : ndarray, optional
    Array of the same shape as input, into which the output is placed.
    By default, a new array is created.
origin : int or tuple of ints, optional
    Placement of the filter, by default 0.
mask : array_like, optional
    If a mask is given, only those elements with a True value at
    the corresponding mask element are modified at each iteration.

    .. versionadded:: 1.1.0
border_value : int (cast to 0 or 1), optional
    Value at the border in the output array.

    .. versionadded:: 1.1.0
brute_force : boolean, optional
    Memory condition: if False, only the pixels whose value was changed in
    the last iteration are tracked as candidates to be updated in the
    current iteration; if true all pixels are considered as candidates for
    update, regardless of what happened in the previous iteration.
    False by default.

    .. versionadded:: 1.1.0
axes : tuple of int or None
    The axes over which to apply the filter. If None, `input` is filtered
    along all axes. If an `origin` tuple is provided, its length must match
    the number of axes.

Returns
-------
binary_opening : ndarray of bools
    Opening of the input by the structuring element.

See Also
--------
grey_opening, binary_closing, binary_erosion, binary_dilation,
generate_binary_structure

Notes
-----
*Opening* [1]_ is a mathematical morphology operation [2]_ that
consists in the succession of an erosion and a dilation of the
input with the same structuring element. Opening, therefore, removes
objects smaller than the structuring element.

Together with *closing* (`binary_closing`), opening can be used for
noise removal.

References
----------
.. [1] https://en.wikipedia.org/wiki/Opening_%28morphology%29
.. [2] https://en.wikipedia.org/wiki/Mathematical_morphology

Examples
--------
>>> from scipy import ndimage
>>> import numpy as np
>>> a = np.zeros((5,5), dtype=int)
>>> a[1:4, 1:4] = 1; a[4, 4] = 1
>>> a
array([[0, 0, 0, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 0, 0, 1]])
>>> # Opening removes small objects
>>> ndimage.binary_opening(a, structure=np.ones((3,3))).astype(int)
array([[0, 0, 0, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 0, 0, 0]])
>>> # Opening can also smooth corners
>>> ndimage.binary_opening(a).astype(int)
array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0]])
>>> # Opening is the dilation of the erosion of the input
>>> ndimage.binary_erosion(a).astype(int)
array([[0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]])
>>> ndimage.binary_dilation(ndimage.binary_erosion(a)).astype(int)
array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0]])

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