def find_objects(input, max_label=0):
"""
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.
Returns
-------
object_slices : list of slices
A list of slices, one for the extent of each labeled object.
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
--------
>>> 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]
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
if max_label < 1:
max_label = input.max()
return _nd_image.find_objects(input, max_label)
def find_objects(input, max_label=0):
"""
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.
Returns
-------
object_slices : list of slices
A list of slices, one for the extent of each labeled object.
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
--------
>>> 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]
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
if max_label < 1:
max_label = input.max()
return _nd_image.find_objects(input, max_label)
def find_objects(input, max_label=0):
"""Find objects in a labeled array.
The input must be an array with labeled objects. A list of slices
into the array is returned that contain the objects. The list
represents a sequence of the numbered objects. If a number is
missing, None is returned instead of a slice. If max_label > 0, it
gives the largest object number that is searched for, otherwise
all are returned.
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
if max_label < 1:
max_label = input.max()
return _nd_image.find_objects(input, max_label)
def find_objects(input, max_label = 0):
"""Find objects in a labeled array.
The input must be an array with labeled objects. A list of slices
into the array is returned that contain the objects. The list
represents a sequence of the numbered objects. If a number is
missing, None is returned instead of a slice. If max_label > 0, it
gives the largest object number that is searched for, otherwise
all are returned.
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
if max_label < 1:
max_label = input.max()
return _nd_image.find_objects(input, max_label)
