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Arithmetic Operations on Images using OpenCV¶
Arithmetic Operations like Addition, Subtraction, and Bitwise Operations(AND, OR, NOT, XOR) can be applied to the input images
- Addition
- Subtraction
Bitwise operations are used in image manipulation and used for extracting essential parts in the image. In this article, Bitwise operations used are:
- Bitwise AND
- Bitwise OR
- Bitwise XOR
- Bitwise NOT
1. Addition¶
- Syntax: cv2.add(img1, img2)
But adding the pixels is not an ideal situation. So, we use cv2.addweighted(). Remember, both images should be of equal size and depth.
Image 1¶
Image 2¶
In [2]:
import cv2
import numpy as np
image1 = cv2.imread('ocv3.jpg')
image2 = cv2.imread('ocv4.jpg')
weightedSum = cv2.add(image1,image2)
cv2.imshow('Weighted Image', weightedSum)
cv2.waitKey(0)
cv2.destroyAllWindows()
After Addition¶
Syntax: cv2.addWeighted(img1, wt1, img2, wt2, gammaValue)
Parameters:
- img1: First Input Image array(Single-channel, 8-bit or floating-point)
- wt1: Weight of the first input image elements to be applied to the final image
- img2: Second Input Image array(Single-channel, 8-bit or floating-point)
- wt2: Weight of the second input image elements to be applied to the final image
- gammaValue: Measurement of light
In [3]:
import cv2
import numpy as np
image1 = cv2.imread('ocv3.jpg')
image2 = cv2.imread('ocv4.jpg')
weightedSum = cv2.addWeighted(image1, 0.5, image2, 0.5, 0)
cv2.imshow('Weighted Image', weightedSum)
cv2.waitKey(0)
cv2.destroyAllWindows()
2.Subtraction of Image:¶
Just like addition, we can subtract the pixel values in two images and merge them with the help of cv2.subtract(). The images should be of equal size and depth.
Syntax: cv2.subtract(image1, image2)
In [4]:
import cv2
import numpy as np
image1 = cv2.imread('ocv3.jpg')
image2 = cv2.imread('ocv4.jpg')
weightedSum = cv2.subtract(image1,image2)
cv2.imshow('Weighted Image', weightedSum)
cv2.waitKey(0)
cv2.destroyAllWindows()
