This example shows how Otsu’s threshold [1] method can be applied locally. For each pixel, an “optimal” threshold is determined by maximizing the variance between two classes of pixels of the local neighborhood defined by a structuring element.
The example compares the local threshold with the global threshold.
[1] | http://en.wikipedia.org/wiki/Otsu’s_method |
import matplotlib.pyplot as plt
from skimage import data
from skimage.morphology.selem import disk
import skimage.filter.rank as rank
from skimage.filter import threshold_otsu
p8 = data.page()
radius = 10
selem = disk(radius)
loc_otsu = rank.otsu(p8, selem)
t_glob_otsu = threshold_otsu(p8)
glob_otsu = p8 >= t_glob_otsu
plt.figure()
plt.subplot(2, 2, 1)
plt.imshow(p8, cmap=plt.cm.gray)
plt.xlabel('original')
plt.colorbar()
plt.subplot(2, 2, 2)
plt.imshow(loc_otsu, cmap=plt.cm.gray)
plt.xlabel('local Otsu ($radius=%d$)' % radius)
plt.colorbar()
plt.subplot(2, 2, 3)
plt.imshow(p8 >= loc_otsu, cmap=plt.cm.gray)
plt.xlabel('original>=local Otsu' % t_glob_otsu)
plt.subplot(2, 2, 4)
plt.imshow(glob_otsu, cmap=plt.cm.gray)
plt.xlabel('global Otsu ($t=%d$)' % t_glob_otsu)
plt.show()
Python source code: download (generated using skimage 0.8.2)