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# scipy.ndimage.find_objects

`scipy.ndimage.``find_objects`(input, max_label=0)

  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

  `label`, `center_of_mass`

  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]])