scipy.ndimage.find_objects¶
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scipy.ndimage.
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. 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.
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
>>> from scipy import ndimage >>> 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) [(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]])