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bundles / scipy 2.0.0.dev0+git20260907.41eeb59 / scipy / ndimage / _morphology / binary_hit_or_miss

function

scipy.ndimage._morphology:binary_hit_or_miss

source: build-install/usr/lib/python3.14/site-packages/scipy/ndimage/_morphology.py:829

Signature

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

Summary

Multidimensional binary hit-or-miss transform.

Extended Summary

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).

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
structure1 = np.array([[1, 0, 0], [0, 1, 1], [0, 1, 1]])
structure1
ndimage.binary_hit_or_miss(a, structure1=structure1).astype(int)
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]])

See also

binary_erosion

Aliases

  • scipy.ndimage.binary_hit_or_miss