skimage2.morphology.local_maxima#

skimage2.morphology.local_maxima(image, footprint=None, connectivity=None, indices=False, allow_borders=True)[source]#

Find local maxima of n-dimensional array.

The local maxima are defined as connected sets of pixels with equal gray level (plateaus) strictly greater than the gray levels of all pixels in the neighborhood.

Parameters:
imagendarray

An n-dimensional array.

footprintndarray, optional

The footprint (structuring element) used to determine the neighborhood of each evaluated pixel (True denotes a connected pixel). It must be a boolean array and have the same number of dimensions as image. If neither footprint nor connectivity are given, all adjacent pixels are considered as part of the neighborhood.

connectivityint, optional

A number used to determine the neighborhood of each evaluated pixel. Adjacent pixels whose squared distance from the center is less than or equal to connectivity are considered neighbors. Ignored if footprint is not None.

indicesbool, optional

If True, the output will be a tuple of one-dimensional arrays representing the indices of local maxima in each dimension. If False, the output will be a boolean array with the same shape as image.

allow_bordersbool, optional

If true, plateaus that touch the image border are valid maxima.

Returns:
maximandarray or tuple[ndarray]

If indices is false, a boolean array with the same shape as image is returned with True indicating the position of local maxima (False otherwise). If indices is true, a tuple of one-dimensional arrays containing the coordinates (indices) of all found maxima.

Warns:
UserWarning

If allow_borders is false and any dimension of the given image is shorter than 3 samples, maxima can’t exist and a warning is shown.

See also

skimage.morphology.local_minima
skimage.morphology.h_maxima
skimage.morphology.h_minima

Notes

This function operates on the following ideas:

  1. Make a first pass over the image’s last dimension and flag candidates for local maxima by comparing pixels in only one direction. If the pixels aren’t connected in the last dimension all pixels are flagged as candidates instead.

For each candidate:

  1. Perform a flood-fill to find all connected pixels that have the same gray value and are part of the plateau.

  2. Consider the connected neighborhood of a plateau: if no bordering sample has a higher gray level, mark the plateau as a definite local maximum.

Examples

>>> from _skimage2.morphology import local_maxima
>>> image = np.zeros((4, 7), dtype=int)
>>> image[1:3, 1:3] = 1
>>> image[3, 0] = 1
>>> image[1:3, 4:6] = 2
>>> image[3, 6] = 3
>>> image
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 1, 1, 0, 2, 2, 0],
       [0, 1, 1, 0, 2, 2, 0],
       [1, 0, 0, 0, 0, 0, 3]])

Find local maxima by comparing to all neighboring pixels (maximal connectivity):

>>> local_maxima(image)
array([[False, False, False, False, False, False, False],
       [False,  True,  True, False, False, False, False],
       [False,  True,  True, False, False, False, False],
       [ True, False, False, False, False, False,  True]])
>>> local_maxima(image, indices=True)
(array([1, 1, 2, 2, 3, 3]), array([1, 2, 1, 2, 0, 6]))

Find local maxima without comparing to diagonal pixels (connectivity 1):

>>> local_maxima(image, connectivity=1)
array([[False, False, False, False, False, False, False],
       [False,  True,  True, False,  True,  True, False],
       [False,  True,  True, False,  True,  True, False],
       [ True, False, False, False, False, False,  True]])

and exclude maxima that border the image edge:

>>> local_maxima(image, connectivity=1, allow_borders=False)
array([[False, False, False, False, False, False, False],
       [False,  True,  True, False,  True,  True, False],
       [False,  True,  True, False,  True,  True, False],
       [False, False, False, False, False, False, False]])