`scipy.ndimage._morphology:binary_hit_or_miss`

source: /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).

Notes

Array API Standard Support

binary_hit_or_miss has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ⛔                   
JAX                   ⚠️ no JIT             ⛔                   
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

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)

✓

Aliases

scipy.ndimage.binary_hit_or_miss