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 : T MOUNISH

Register Number : 212223240098

Load the necessary packages

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

Read the Image and convert to grayscale

python image = cv2.imread('pandaa.jpg',1) image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB) image_gray = cv2.imread('pandaa.jpg',0)

Use Global thresholding to segment the image

python 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

python 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

python ret,thresh_img6=cv2.threshold(image_gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

Display the results

python 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()