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

Step3:

If the read image is a color image, convert it into a grayscale image

Step4:

perform the threshold operation you want to do(global thresholding or adaptive thresholding or otsu's thresholding)

Step5:

Display the Results

Program

Load the necessary packages

import cv2

Read the Image and convert it to grayscale

img = cv2.imread('cat.jpeg',-1)
cv2.imshow('original_image',img)
cv2.waitKey(0)
cv2.destroyAllWindows
gray =cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('gray_image',gray)
cv2.waitKey(0)
cv2.destroyAllWindows

Use Global thresholding to segment the image

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

Use Adaptive thresholding to segment the image

thresh_img6=cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
thresh_img7=cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)

Use Otsu's method to segment the image

ret,thresh_img8=cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

Display the results

image =[thresh_img1,thresh_img2,thresh_img3,thresh_img4,thresh_img5,thresh_img6,thresh_img7,thresh_img8]
for i in range(0,8):
    cv2.imshow('threshold_image',image[i])
    cv2.waitKey(0)
    cv2.destroyAllWindows