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:

Load the 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

Developed By : Paul Samson.S

Register Number : 212222230104

# Load the necessary packages

import numpy as np
import matplotlib.pyplot as plt
import cv2
# Read the Image and convert to grayscale

image = cv2.imread('lap.jpeg',1)
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
image_gray = cv2.imread('lap.jpeg',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()