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Arithmetic operations using OpenCV | Python

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Prerequisite: Arithmetic Operations on Images using OpenCV | Basics We can perform different Arithmetic operations on images e.g. Addition, Subtraction, etc. This is possible because images are actually stored as arrays (3 Dimensional for RGB images and 1 dimensional for the grayscale images). Importance of Arithmetic Operations on images:
  • Image Blending: Addition of Images is used for image blending where images are multiplied with different weights and added together to give a blending effect.
  • WaterMarking: It is also based on the principle of addition of very low weight image addition to the original image.
  • Detecting changes in image: Image subtraction can help in identifying the changes in two images as well as to level uneven sections of the image e.g. to handle half part of image which has shadow on it.
Code for Image Addition –
import  cv2
import matplotlib.pyplot as plt % matplotlib inline
# matplotlib can be used to plot the images as subplot
  
first_img = cv2.imread("C://gfg//image_processing//players.jpg")
second_img = cv2.imread("C://gfg//image_processing//tomatoes.jpg")
  
print(first_img.shape)
print(second_img.shape)
  
# we need to resize, as they differ in shape
dim =(544, 363)
resized_second_img = cv2.resize(second_img, dim, interpolation = cv2.INTER_AREA)
print("shape after resizing", resized_second_img.shape)
  
added_img = cv2.add(first_img, resized_second_img)
  
cv2.imshow("first_img", first_img)
cv2.waitKey(0)
cv2.imshow("second_img", resized_second_img)
cv2.waitKey(0)
cv2.imshow("Added image", added_img)
cv2.waitKey(0)
  
cv2.destroyAllWindows()

                    
Output: (363, 544, 3) (500, 753, 3) shape after resizing (363, 544, 3)   Code for image Subtraction –
import  cv2
import matplotlib.pyplot as plt % matplotlib inline
  
  
first_img = cv2.imread("C://gfg//image_processing//players.jpg")
second_img = cv2.imread("C://gfg//image_processing//tomatoes.jpg")
  
print(first_img.shape)
print(second_img.shape)
  
# we need to resize, as they differ in shape
dim =(544, 363)
resized_second_img = cv2.resize(second_img, dim, interpolation = cv2.INTER_AREA)
print("shape after resizing", resized_second_img.shape)
  
subtracted = cv2.subtract(first_img, resized_second_img)
cv2.imshow("first_img", first_img)
cv2.waitKey(0)
cv2.imshow("second_img", resized_second_img)
cv2.waitKey(0)
cv2.imshow("subtracted image", subtracted)
cv2.waitKey(0)
  
cv2.destroyAllWindows()

                    
Output: (363, 544, 3) (500, 753, 3) shape after resizing (363, 544, 3)

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Last Updated : 04 Jan, 2023
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