THRESHOLDING

Aim

To segment the image using global thresholding, adaptive thresholding and Otsu's thresholding using python and OpenCV.

Software Required

Anaconda - Python 3.7
OpenCV

Algorithm

Step1:

Import necessary packages

Step2:

Read the Image and convert to grayscale.

Step3:

Use Global thresholding to segment the image.

Step4:

Use Adaptive thresholding to segment the image.

Step5:

Use Otsu's method to segment the image and display the results.

Program

Load the necessary packages:

import numpy as np
import matplotlib.pyplot as plt
import cv2

Read the Image and convert to grayscale:

img = cv2.imread("300.webp",1)
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
img_gry = cv2.imread("300.webp",0)

Use Global thresholding to segment the image:

r,t_img1=cv2.threshold(img_gry,86,255,cv2.THRESH_BINARY)
r,t_img2=cv2.threshold(img_gry,86,255,cv2.THRESH_BINARY_INV)
r,t_img3=cv2.threshold(img_gry,86,255,cv2.THRESH_TOZERO)
r,t_img4=cv2.threshold(img_gry,86,255,cv2.THRESH_TOZERO_INV)
r,t_img5=cv2.threshold(img_gry,100,255,cv2.THRESH_TRUNC)

Use Adaptive thresholding to segment the image:

t_img7=cv2.adaptiveThreshold(img_gry,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
t_img8=cv2.adaptiveThreshold(img_gry,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)

Use Otsu's method to segment the image:

ret,t_img6=cv2.threshold(img_gry,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

Display the results:

titles=["Gray Image","Threshold Image (Binary)","Threshold Image (Binary Inverse)","Threshold Image (To Zero)"
       ,"Threshold Image (To Zero-Inverse)","Threshold Image (Truncate)","Otsu","Adaptive Threshold (Mean)","Adaptive Threshold (Gaussian)"]
imgs=[img_gry,t_img1,t_img2,t_img3,t_img4,t_img5,t_img6,t_img7,t_img8]
for i in range(0,9):
    plt.figure(figsize=(10,10))
    plt.subplot(1,2,1)
    plt.title("Original Image")
    plt.imshow(img)
    plt.axis("off")
    plt.subplot(1,2,2)
    plt.title(titles[i])
    plt.imshow(cv2.cvtColor(imgs[i],cv2.COLOR_BGR2RGB))
    plt.axis("off")
    plt.show()