Label features in an array.
Parameters: | input : array_like
structure : array_like, optional
output : (None, data-type, array_like), optional
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Returns: | label : ndarray or int
num_features : int
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See also
Examples
Create an image with some features, then label it using the default (cross-shaped) structuring element:
>>> a = np.array([[0,0,1,1,0,0],
... [0,0,0,1,0,0],
... [1,1,0,0,1,0],
... [0,0,0,1,0,0]])
>>> labeled_array, num_features = label(a)
Each of the 4 features are labeled with a different integer:
>>> print(num_features)
4
>>> print(labeled_array)
array([[0, 0, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 0],
[2, 2, 0, 0, 3, 0],
[0, 0, 0, 4, 0, 0]])
Generate a structuring element that will consider features connected even if they touch diagonally:
>>> s = generate_binary_structure(2,2)
or,
>>> s = [[1,1,1],
[1,1,1],
[1,1,1]]
Label the image using the new structuring element:
>>> labeled_array, num_features = label(a, structure=s)
Show the 2 labeled features (note that features 1, 3, and 4 from above are now considered a single feature):
>>> print(num_features)
2
>>> print(labeled_array)
array([[0, 0, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 0],
[2, 2, 0, 0, 1, 0],
[0, 0, 0, 1, 0, 0]])