`skimage.filters.thresholding:threshold_sauvola`

source: /dev/scikit-image/src/skimage/filters/thresholding.py :1123

Signature

def threshold_sauvola ( image , window_size = 15 , k = 0.2 , r = None )

Summary

Applies Sauvola local threshold to an array. Sauvola is a modification of Niblack technique.

Extended Summary

In the original method a threshold T is calculated for every pixel in the image using the following formula

T = m(x,y) * (1 + k * ((s(x,y) / R) - 1))

where m(x,y) and s(x,y) are the mean and standard deviation of pixel (x,y) neighborhood defined by a rectangular window with size w times w centered around the pixel. k is a configurable parameter that weights the effect of standard deviation. R is the maximum standard deviation of a grayscale image.

Parameters

image : (M, N[, ...]) ndarray: Grayscale input image.
window_size : int, or iterable of int, optional: Window size specified as a single odd integer (3, 5, 7, …), or an iterable of length image.ndim containing only odd integers (e.g. (1, 5, 5)).
k : float, optional: Value of the positive parameter k.
r : float, optional: Value of R, the dynamic range of standard deviation. If None, set to the half of the image dtype range.

Returns

threshold : (M, N[, ...]) ndarray: Threshold mask. All pixels with an intensity higher than this value are assumed to be foreground.

Notes

This algorithm is originally designed for text recognition.

Examples

from skimage import data
image = data.page()
t_sauvola = threshold_sauvola(image, window_size=15, k=0.2)
binary_image = image > t_sauvola

✓

Aliases

skimage.filters.threshold_sauvola