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Module « scipy.ndimage »

Fonction binary_erosion - module scipy.ndimage

Signature de la fonction binary_erosion

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

Description

help(scipy.ndimage.binary_erosion)

Multidimensional binary erosion with a given structuring element.

Binary erosion is a mathematical morphology operation used for image
processing.

Parameters
----------
input : array_like
    Binary image to be eroded. Non-zero (True) elements form
    the subset to be eroded.
structure : array_like, optional
    Structuring element used for the erosion. Non-zero elements are
    considered True. If no structuring element is provided, an element
    is generated with a square connectivity equal to one.
iterations : int, optional
    The erosion is repeated `iterations` times (one, by default).
    If iterations is less than 1, the erosion is repeated until the
    result does not change anymore.
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.
output : ndarray, optional
    Array of the same shape as input, into which the output is placed.
    By default, a new array is created.
border_value : int (cast to 0 or 1), optional
    Value at the border in the output array.
origin : int or tuple of ints, optional
    Placement of the filter, by default 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 (eroded) in
    the current iteration; if True all pixels are considered as candidates
    for erosion, regardless of what happened in the previous iteration.
    False by default.
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_erosion : ndarray of bools
    Erosion of the input by the structuring element.

See Also
--------
grey_erosion, binary_dilation, binary_closing, binary_opening,
generate_binary_structure

Notes
-----
Erosion [1]_ is a mathematical morphology operation [2]_ that uses a
structuring element for shrinking the shapes in an image. The binary
erosion of an image by a structuring element is the locus of the points
where a superimposition of the structuring element centered on the point
is entirely contained in the set of non-zero elements of the image.

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

Examples
--------
>>> from scipy import ndimage
>>> import numpy as np
>>> a = np.zeros((7,7), dtype=int)
>>> a[1:6, 2:5] = 1
>>> a
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 0, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 0, 0],
       [0, 0, 0, 0, 0, 0, 0]])
>>> ndimage.binary_erosion(a).astype(a.dtype)
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 1, 0, 0, 0],
       [0, 0, 0, 1, 0, 0, 0],
       [0, 0, 0, 1, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0]])
>>> #Erosion removes objects smaller than the structure
>>> ndimage.binary_erosion(a, structure=np.ones((5,5))).astype(a.dtype)
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0]])

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