`scipy.ndimage._measurements:find_objects`

source: /scipy/ndimage/_measurements.py:238

Signature

def find_objects(input, max_label = 0)

Summary

Find objects in a labeled array.

Parameters

input : ndarray of ints: Array containing objects defined by different labels. Labels with value 0 are ignored.
max_label : int, optional: Maximum label to be searched for in input. If max_label is not given, the positions of all objects are returned.

Returns

object_slices : list of tuples: A list of tuples, with each tuple containing N slices (with N the dimension of the input array). Slices correspond to the minimal parallelepiped that contains the object. If a number is missing, None is returned instead of a slice. The label l corresponds to the index l-1 in the returned list.

Notes

This function is very useful for isolating a volume of interest inside a 3-D array, that cannot be "seen through".

Array API Standard Support

find_objects 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((6,6), dtype=int)
a[2:4, 2:4] = 1
a[4, 4] = 1
a[:2, :3] = 2
a[0, 5] = 3
a

✓

array([[2, 2, 2, 0, 0, 3],
       [2, 2, 2, 0, 0, 0],
       [0, 0, 1, 1, 0, 0],
       [0, 0, 1, 1, 0, 0],
       [0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0]])

ndimage.find_objects(a)

✗

[(slice(2, 5, None), slice(2, 5, None)), (slice(0, 2, None), slice(0, 3, None)), (slice(0, 1, None), slice(5, 6, None))]

ndimage.find_objects(a, max_label=2)
ndimage.find_objects(a == 1, max_label=2)

✓

[(slice(2, 5, None), slice(2, 5, None)), None]

loc = ndimage.find_objects(a)[0]
a[loc]

✓

array([[1, 1, 0],
       [1, 1, 0],
       [0, 0, 1]])

Aliases

scipy.ndimage.find_objects