Otsu's method
lastmod 2023-10-25created 2023-10-252 edits
In computer vision and image processing, Otsu's method, named after Nobuyuki Otsu (大津展之, Ōtsu Nobuyuki), is used to perform automatic image thresholding.
About
Applicable to a bimodal image, whose histogram consists of two peaks.
Otsu_method_-_Bimodal_image_histogram.png
Example
import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
img = cv.imread('noisy2.png',0)
# global thresholding
ret1,th1 = cv.threshold(img,127,255,cv.THRESH_BINARY)
# Otsu's thresholding
ret2,th2 = cv.threshold(img,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
# Otsu's thresholding after Gaussian filtering
blur = cv.GaussianBlur(img,(5,5),0)
ret3,th3 = cv.threshold(blur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
# plot all the images and their histograms
images = [img, 0, th1, img, 0, th2, blur, 0, th3]
titles = [
'Original Noisy Image',
'Histogram',
'Global Thresholding (v=127)',
'Original Noisy Image',
'Histogram',
"Otsu's Thresholding",
'Gaussian filtered Image',
'Histogram',
"Otsu's Thresholding"
]
for i in range(3):
plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])
plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
plt.title(titles[i*3+1]), plt.xticks([]), plt.yticks([])
plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray')
plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([])
plt.show()
Otsu_method_-_result.png
The last image, which went through Gaussian filtering, is processed better.