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# 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]])