scipy.ndimage.

binary_hit_or_miss#

scipy.ndimage.binary_hit_or_miss(input, structure1=None, structure2=None, output=None, origin1=0, origin2=None, *, axes=None)[source]#

Multidimensional binary hit-or-miss transform.

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

Parameters:
inputarray_like (cast to booleans)

Binary image where a pattern is to be detected.

structure1array_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.

structure2array_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.

outputndarray, optional

Array of the same shape as input, into which the output is placed. By default, a new array is created.

origin1int or tuple of ints, optional

Placement of the first part of the structuring element structure1, by default 0 for a centered structure.

origin2int 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.

axestuple 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_missndarray

Hit-or-miss transform of input with the given structuring element (structure1, structure2).

See also

binary_erosion

References

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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