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Module « scipy.ndimage »

Fonction binary_hit_or_miss - module scipy.ndimage

Signature de la fonction binary_hit_or_miss

def binary_hit_or_miss(input, structure1=None, structure2=None, output=None, origin1=0, origin2=None, *, axes=None) 

Description

help(scipy.ndimage.binary_hit_or_miss)

Multidimensional binary hit-or-miss transform.

The hit-or-miss transform finds the locations of a given pattern
inside the input image.

Parameters
----------
input : array_like (cast to booleans)
    Binary image where a pattern is to be detected.
structure1 : array_like (cast to booleans), optional
    Part of the structuring element to be fitted to the foreground
    (non-zero elements) of `input`. If no value is provided, a
    structure of square connectivity 1 is chosen.
structure2 : array_like (cast to booleans), optional
    Second part of the structuring element that has to miss completely
    the foreground. If no value is provided, the complementary of
    `structure1` is taken.
output : ndarray, optional
    Array of the same shape as input, into which the output is placed.
    By default, a new array is created.
origin1 : int or tuple of ints, optional
    Placement of the first part of the structuring element `structure1`,
    by default 0 for a centered structure.
origin2 : int or tuple of ints, optional
    Placement of the second part of the structuring element `structure2`,
    by default 0 for a centered structure. If a value is provided for
    `origin1` and not for `origin2`, then `origin2` is set to `origin1`.
axes : tuple of int or None
    The axes over which to apply the filter. If None, `input` is filtered
    along all axes. If `origin1` or `origin2` tuples are provided, their
    length must match the number of axes.

Returns
-------
binary_hit_or_miss : ndarray
    Hit-or-miss transform of `input` with the given structuring
    element (`structure1`, `structure2`).

See Also
--------
binary_erosion

References
----------
.. [1] https://en.wikipedia.org/wiki/Hit-or-miss_transform

Examples
--------
>>> from scipy import ndimage
>>> import numpy as np
>>> a = np.zeros((7,7), dtype=int)
>>> a[1, 1] = 1; a[2:4, 2:4] = 1; a[4:6, 4:6] = 1
>>> a
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0, 0],
       [0, 0, 1, 1, 0, 0, 0],
       [0, 0, 1, 1, 0, 0, 0],
       [0, 0, 0, 0, 1, 1, 0],
       [0, 0, 0, 0, 1, 1, 0],
       [0, 0, 0, 0, 0, 0, 0]])
>>> structure1 = np.array([[1, 0, 0], [0, 1, 1], [0, 1, 1]])
>>> structure1
array([[1, 0, 0],
       [0, 1, 1],
       [0, 1, 1]])
>>> # Find the matches of structure1 in the array a
>>> ndimage.binary_hit_or_miss(a, structure1=structure1).astype(int)
array([[0, 0, 0, 0, 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, 0, 0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0]])
>>> # Change the origin of the filter
>>> # origin1=1 is equivalent to origin1=(1,1) here
>>> ndimage.binary_hit_or_miss(a, structure1=structure1,\
... origin1=1).astype(int)
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, 1, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0]])

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