`skimage.morphology.extrema:h_maxima`

source: /dev/scikit-image/src/skimage/morphology/extrema.py :49

Signature

def h_maxima ( image , h , footprint = None )

Summary

Determine all maxima of the image with height >= h.

Extended Summary

The local maxima are defined as connected sets of pixels with equal gray level strictly greater than the gray level of all pixels in direct neighborhood of the set.

A local maximum M of height h is a local maximum for which there is at least one path joining M with an equal or higher local maximum on which the minimal value is f(M) - h (i.e. the values along the path are not decreasing by more than h with respect to the maximum's value) and no path to an equal or higher local maximum for which the minimal value is greater.

The global maxima of the image are also found by this function.

Parameters

image : ndarray: The input image for which the maxima are to be calculated.
h : unsigned integer: The minimal height of all extracted maxima.
footprint : ndarray, optional: The neighborhood expressed as an n-D array of 1's and 0's. Default is the ball of radius 1 according to the maximum norm (i.e. a 3x3 square for 2D images, a 3x3x3 cube for 3D images, etc.)

Returns

h_max : ndarray: The local maxima of height >= h and the global maxima. The resulting image is a binary image, where pixels belonging to the determined maxima take value 1, the others take value 0.

Examples

import numpy as np
from skimage.morphology import extrema

✓

We create an image (quadratic function with a maximum in the center and 4 additional constant maxima. The heights of the maxima are: 1, 21, 41, 61, 81

w = 10
x, y = np.mgrid[0:w,0:w]
f = 20 - 0.2*((x - w/2)**2 + (y-w/2)**2)
f[2:4,2:4] = 40; f[2:4,7:9] = 60; f[7:9,2:4] = 80; f[7:9,7:9] = 100
f = f.astype(int)

✓

We can calculate all maxima with a height of at least 40:

maxima = extrema.h_maxima(f, 40)

✓

The resulting image will contain 3 local maxima.

Aliases

skimage.morphology.h_maxima