scipy.ndimage.

watershed_ift#

scipy.ndimage.watershed_ift(input, markers, structure=None, output=None)[source]#

Apply watershed from markers using image foresting transform algorithm.

Parameters:

inputarray_like: Input.
markersarray_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.
structurestructure 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.
outputndarray, optional: An output array can optionally be provided. The same shape as input.

Returns:

watershed_iftndarray: 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 Support for the array API standard for more information.

References

[1]

A.X. Falcao, J. Stolfi and R. de Alencar Lotufo, “The image foresting transform: theory, algorithms, and applications”, Pattern Analysis and Machine Intelligence, vol. 26, pp. 19-29, 2004.