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Module « scipy.ndimage »

Fonction label - module scipy.ndimage

Signature de la fonction label

def label(input, structure=None, output=None) 

Description

help(scipy.ndimage.label)

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 centrosymmetric
    (see Notes).
    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

Notes
-----
A centrosymmetric matrix is a matrix that is symmetric about the center.
See [1]_ for more information.

The `structure` matrix must be centrosymmetric to ensure
two-way connections.
For instance, if the `structure` matrix is not centrosymmetric
and is defined as::

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

and the `input` is::

    [[1,2],
     [0,3]]

then the structure matrix would indicate the
entry 2 in the input is connected to 1,
but 1 is not connected to 2.

References
----------
.. [1] James R. Weaver, "Centrosymmetric (cross-symmetric)
   matrices, their basic properties, eigenvalues, and
   eigenvectors." The American Mathematical Monthly 92.10
   (1985): 711-717.

Examples
--------
Create an image with some features, then label it using the default
(cross-shaped) structuring element:

>>> from scipy.ndimage import label, generate_binary_structure
>>> import numpy as np
>>> 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:

>>> num_features
4
>>> 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]], dtype=int32)

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):

>>> num_features
2
>>> 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]], dtype=int32)

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