scipy.ndimage.measurements.label

scipy.ndimage.measurements.label(input, structure=None, output=None)

Label features in an array.

Parameters :

input : array_like

An array-like object to be labeled. Any non-zero values in input are counted as features and zero values are considered the background.

structure : array_like, optional

A structuring element that defines feature connections. structure must be symmetric. If no structuring element is provided, one is automatically generated with a squared connectivity equal to one. That is, for a 2-D input array, the default structuring element is:

[[0,1,0],
 [1,1,1],
 [0,1,0]]

output : (None, data-type, array_like), optional

If output is a data type, it specifies the type of the resulting labeled feature array If output is an array-like object, then output will be updated with the labeled features from this function. This function can operate in-place, by passing output=input. Note that the output must be able to store the largest label, or this function will raise an Exception.

Returns :

label : ndarray or int

An integer ndarray where each unique feature in input has a unique label in the returned array.

num_features : int

How many objects were found.

If output is None, this function returns a tuple of (labeled_array, num_features).

If output is a ndarray, then it will be updated with values in labeled_array and only num_features will be returned by this function.

See also

find_objects

generate a list of slices for the labeled features (or objects); useful for finding features’ position or dimensions

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]])