Image Blending with OpenCV’s addWeighted Function

We’ll explore how to use OpenCV’s addWeighted function to blend images with different weights.

Understanding Image Blending

Image blending, also known as image compositing, involves combining two or more images by assigning different weights to each pixel. This technique is useful for various applications, including:

  • Image Merging: Combining two or more images into a single image.
  • Image Overlay: Overlaying one image on top of another, often with adjustable transparency.
  • Transition Effects: Creating smooth transitions between images or frames.
  • HDR Imaging: Merging multiple exposures of the same scene to capture a wider dynamic range.
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Using the addWeighted Function

OpenCV’s addWeighted function provides a simple way to blend two images by specifying their weights. Here’s a step-by-step guide on how to use it:

1. Import OpenCV:

import cv2

2. Read the Images:

image1 = cv2.imread('image1.jpg')
image2 = cv2.imread('image2.jpg')

3. Define the Weights:

alpha = 0.7
beta = 0.3

The weights determine the contribution of each image to the final result. You can adjust these values to control the blending effect.

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4. Perform Image Blending:

blended_image = cv2.addWeighted(image1, alpha, image2, beta, gamma=0)

The gamma parameter (default value is 0) is an optional scalar added to the weighted sum. It can be used to adjust the brightness of the result.

5. Save or Display the Blended Image:

cv2.imwrite('blended_image.jpg', blended_image)

This saves the blended image to a file. Alternatively, you can use cv2.imshow() to display the result if you are working in a graphical environment.

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Experimenting with Blending

Image blending allows for creative experimentation. By adjusting the weights and combining different images, you can achieve various visual effects, such as:

  • Simple Blending: Equal weights for a balanced blend.
  • Image Overlay: Varying alpha and beta for different levels of transparency.
  • Image Transitions: Creating smooth transitions between images.
  • HDR Imaging: Merging multiple exposures for enhanced dynamic range.