# Otsu’s Binarization

For global thresholding methods we gather the threshold values by trial and error. However suppose the image is Bimodal(Basically a bimodal image has two peaks in its histogram). For this the threshold value is gained by taking the value in between the peaks. This is what is done by Otsu’s Binarization. To use it we simply pass an extra flag cv2.THRESH_OTSU to the cv2.threshold function. Pass the maxVal as 0.

``` import cv2 import numpy as np from matplotlib import pyplot as plt```

``` img = cv2.imread('image.jpg',0) # global thresholding ret1,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) # Otsu's thresholding ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) # Otsu's thresholding after Gaussian filtering blur = cv2.GaussianBlur(img,(5,5),0) ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.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 xrange(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() ```