`scipy.ndimage._morphology:binary_propagation`

source: /scipy/ndimage/_morphology.py :947

Signature

   def     binary_propagation (    input    ,    structure    =  None   ,    mask    =  None   ,    output    =  None   ,    border_value    =  0   ,    origin    =  0   ,  * ,     axes    =  None     )

Summary

Multidimensional binary propagation with the given structuring element.

Parameters

input : array_like: Binary image to be propagated inside mask.
structure : array_like, optional: Structuring element used in the successive dilations. The output may depend on the structuring element, especially if mask has several connex components. If no structuring element is provided, an element is generated with a squared connectivity equal to one.
mask : array_like, optional: Binary mask defining the region into which input is allowed to propagate.
output : ndarray, optional: Array of the same shape as input, into which the output is placed. By default, a new array is created.
border_value : int (cast to 0 or 1), optional: Value at the border in the output array.
origin : int or tuple of ints, optional: Placement of the filter, by default 0.
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

binary_propagation : ndarray: Binary propagation of input inside mask.

Notes

This function is functionally equivalent to calling binary_dilation with the number of iterations less than one: iterative dilation until the result does not change anymore.

The succession of an erosion and propagation inside the original image can be used instead of an opening for deleting small objects while keeping the contours of larger objects untouched.

Array API Standard Support

binary_propagation 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
input = np.zeros((8, 8), dtype=int)
input[2, 2] = 1
mask = np.zeros((8, 8), dtype=int)
mask[1:4, 1:4] = mask[4, 4]  = mask[6:8, 6:8] = 1
input
mask
ndimage.binary_propagation(input, mask=mask).astype(int)
ndimage.binary_propagation(input, mask=mask,\
structure=np.ones((3,3))).astype(int)

✓

a = np.zeros((6,6), dtype=int)
a[2:5, 2:5] = 1; a[0, 0] = 1; a[5, 5] = 1
a
ndimage.binary_opening(a).astype(int)
b = ndimage.binary_erosion(a)
b.astype(int)
ndimage.binary_propagation(b, mask=a).astype(int)

✓

Aliases

scipy.ndimage.binary_propagation