`skimage.morphology.extrema:h_minima`

source: /dev/scikit-image/src/skimage/morphology/extrema.py :178

Signature

def h_minima ( image , h , footprint = None )

Summary

Determine all minima of the image with depth >= h.

Extended Summary

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

A local minimum M of depth h is a local minimum for which there is at least one path joining M with an equal or lower local minimum on which the maximal value is f(M) + h (i.e. the values along the path are not increasing by more than h with respect to the minimum's value) and no path to an equal or lower local minimum for which the maximal value is smaller.

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

Parameters

image : ndarray: The input image for which the minima are to be calculated.
h : unsigned integer: The minimal depth of all extracted minima.
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_min : ndarray: The local minima of depth >= h and the global minima. The resulting image is a binary image, where pixels belonging to the determined minima 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 minimum in the center and 4 additional constant maxima. The depth of the minima are: 1, 21, 41, 61, 81

w = 10
x, y = np.mgrid[0:w,0:w]
f = 180 + 0.2*((x - w/2)**2 + (y-w/2)**2)
f[2:4,2:4] = 160; f[2:4,7:9] = 140; f[7:9,2:4] = 120; f[7:9,7:9] = 100
f = f.astype(int)

✓

We can calculate all minima with a depth of at least 40:

minima = extrema.h_minima(f, 40)

✓

The resulting image will contain 3 local minima.

Aliases

skimage.morphology.h_minima