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Module « scipy.ndimage »

Fonction binary_fill_holes - module scipy.ndimage

Signature de la fonction binary_fill_holes

def binary_fill_holes(input, structure=None, output=None, origin=0, *, axes=None) 

Description

help(scipy.ndimage.binary_fill_holes)

Fill the holes in binary objects.


Parameters
----------
input : array_like
    N-D binary array with holes to be filled
structure : array_like, optional
    Structuring element used in the computation; large-size elements
    make computations faster but may miss holes separated from the
    background by thin regions. The default element (with a square
    connectivity equal to one) yields the intuitive result where all
    holes in the input have been filled.
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, tuple of ints, optional
    Position of the structuring element.
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
-------
out : ndarray
    Transformation of the initial image `input` where holes have been
    filled.

See Also
--------
binary_dilation, binary_propagation, label

Notes
-----
The algorithm used in this function consists in invading the complementary
of the shapes in `input` from the outer boundary of the image,
using binary dilations. Holes are not connected to the boundary and are
therefore not invaded. The result is the complementary subset of the
invaded region.

References
----------
.. [1] 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[2,2] = 0
>>> a
array([[0, 0, 0, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 0, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 0, 0, 0]])
>>> ndimage.binary_fill_holes(a).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]])
>>> # Too big structuring element
>>> ndimage.binary_fill_holes(a, structure=np.ones((5,5))).astype(int)
array([[0, 0, 0, 0, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 0, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 0, 0, 0]])

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