Image synthesis - Detailed explanation of OpenCV-Python image fusion
In image processing, image synthesis is an important task. OpenCV provides many methods to achieve image composition. Among them, the cv::addWeighted() function is a commonly used image fusion method. It can add two images with a certain weight to generate a new fusion image.
Below we will explain the cv::addWeighted() function in OpenCV in detail and give the corresponding source code.
Function prototype:
Dst(I)=alpha×Img1(I)+beta×Img2(I)+gamma
Among them, alpha and beta are weight coefficients, and gamma is the offset. For color images, the above formula is performed independently for each channel.
Sample code:
import cv2 as cv
import numpy as np
Read in image
img1 = cv.imread('img1.png')
img2 = cv.imread('img2.png')
image fusion
result = cv.addWeighted(img1, 0.7, img2, 0.3, 0)
Display the fused image
cv.imshow(‘result’, result)
cv.waitKey(0)
cv.destroyAllWindows()
In the above example code, we first read the two images that need to be fused, and then use the cv::addWeighted() function to linearly add the two images according to the specified weight coefficient, and finally obtain a fused image. image. Finally, we display the result using the cv::imshow() function.
Summarize:
Image synthesis is an important task in image processing. OpenCV provides many methods to achieve image composition. Among them, the cv::addWeighted() function is a commonly used image fusion method. It can add two images with a certain weight to generate a new fusion image. Through the explanation of this article and the demonstration of the sample code, I believe that everyone has a deeper understanding of the image fusion method in OpenCV.