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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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