EXP.8 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: KANISHKA V S
REGISTER NO: 212222230061

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("Downloads/puppies.png",1)
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
image_gray = cv2.imread("Downloads/puppies.png",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()

Output

Original Image

Global Thresholding

Adaptive Thresholding

Optimum Global Thesholding using Otsu's Method

Result

Thus the images are segmented using global thresholding, adaptive thresholding and optimum global thresholding using python and OpenCV.

kanishka2305 / thresholding- Goto Github PK

thresholding-'s Introduction