Thresholding of Images

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:

Step 1:

Load the necessary packages.

Step 2:

Read the Image and convert to grayscale.

Step 3:

Use Global thresholding to segment the image.

Step 4:

Use Adaptive thresholding to segment the image.

Step 5:

Use Otsu's method to segment the image.

Step 6:

Display the results.

Program:

Developed by : H.Syed Abdul Wasih
Register Number : 212221240057

Load the necessary packages:

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

Read the Image and convert to grayscale:

image=cv2.imread("image.jpg",1)
image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
image_gray=cv2.imread("image.jpg",0)

Use Global thresholding to segment the image:

ret,thresh_img1=cv2.threshold(image_gray,86,255,cv2.THRESH_BINARY)
ret,thresh_img2=cv2.threshold(image_gray,86,255,cv2.THRESH_BINARY_INV)
ret,thresh_img3=cv2.threshold(image_gray,86,255,cv2.THRESH_TOZERO)
ret,thresh_img4=cv2.threshold(image_gray,86,255,cv2.THRESH_TOZERO_INV)
ret,thresh_img5=cv2.threshold(image_gray,100,255,cv2.THRESH_TRUNC)

Use Adaptive thresholding to segment the image:

thresh_img7=cv2.adaptiveThreshold(image_gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
thresh_img8=cv2.adaptiveThreshold(image_gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)

Use Otsu's method to segment the image:

ret,thresh_img6=cv2.threshold(image_gray,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)"]
images=[image_gray,thresh_img1,thresh_img2,thresh_img3,thresh_img4,thresh_img5,thresh_img6,thresh_img7,thresh_img8]
for i in range(0,9):
    plt.figure(figsize=(10,10))
    plt.subplot(1,2,1)
    plt.title("Original Image")
    plt.imshow(image)
    plt.axis("off")
    plt.subplot(1,2,2)
    plt.title(titles[i])
    plt.imshow(cv2.cvtColor(images[i],cv2.COLOR_BGR2RGB))
    plt.axis("off")
    plt.show()