skimage.morphology.local_minima(image, selem=None, connectivity=None, indices=False, allow_borders=True) [source]
 
Find local minima of n-dimensional array. The local minima are defined as connected sets of pixels with equal gray level (plateaus) strictly smaller than the gray levels of all pixels in the neighborhood.  Parameters 
 
imagendarray 

An n-dimensional array.  
selemndarray, optional 

A 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 selem 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 selem is not None.  
indicesbool, optional 

If True, the output will be a tuple of one-dimensional arrays representing the indices of local minima 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 minima.    Returns 
 
minimandarray 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 minima (False otherwise). If indices is true, a tuple of one-dimensional arrays containing the coordinates (indices) of all found minima.      See also  
skimage.morphology.local_maxima


skimage.morphology.h_maxima


skimage.morphology.h_minima

  Notes This function operates on the following ideas:  Make a first pass over the image’s last dimension and flag candidates for local minima 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:  Perform a flood-fill to find all connected pixels that have the same gray value and are part of the plateau. Consider the connected neighborhood of a plateau: if no bordering sample has a smaller gray level, mark the plateau as a definite local minimum.  Examples >>> from skimage.morphology import local_minima
>>> 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 minima by comparing to all neighboring pixels (maximal connectivity): >>> local_minima(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_minima(image, indices=True)
(array([1, 1, 2, 2, 3, 3]), array([1, 2, 1, 2, 0, 6]))
 Find local minima without comparing to diagonal pixels (connectivity 1): >>> local_minima(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 minima that border the image edge: >>> local_minima(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]])