# scipy.ndimage.measurements.find_objects¶

scipy.ndimage.measurements.find_objects(input, max_label=0)[source]

Find objects in a labeled array.

Parameters : input : ndarray of ints Array containing objects defined by different labels. 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. 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.

See also

Notes

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

Examples

```>>> a = np.zeros((6,6), dtype=np.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)
[(slice(2, 5, None), slice(2, 5, None)), (slice(0, 2, None), slice(0, 3, None))]
>>> 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]])
```

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