- scipy.ndimage.measurements.label(input, structure=None, output=None)¶
Label features in an array.
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.
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.
- generate a list of slices for the labeled features (or objects); useful for finding features’ position or dimensions
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)
>>> 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]])