`scipy.ndimage._measurements:watershed_ift`

source: /scipy/ndimage/_measurements.py :1621

Signature

def watershed_ift ( input , markers , structure = None , output = None )

Summary

Apply watershed from markers using image foresting transform algorithm.

Parameters

input : array_like: Input.
markers : array_like: Markers are points within each watershed that form the beginning of the process. Negative markers are considered background markers which are processed after the other markers.
structure : structure element, optional: A structuring element defining the connectivity of the object can be provided. If None, an element is generated with a squared connectivity equal to one.
output : ndarray, optional: An output array can optionally be provided. The same shape as input.

Returns

watershed_ift : ndarray: Output. Same shape as input.

Notes

Array API Standard Support

watershed_ift 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.

Aliases

scipy.ndimage.watershed_ift