How to Blend Images Using Image Pyramids in OpenCV Python?

When working with images, sometimes we need to blend two images together to achieve a certain effect. One way to do this is to use image pyramids. Image pyramids are multi-scale representations of an image that can be used for various operations, including image blending.

In this tutorial, we will cover how to blend images using image pyramids in OpenCV Python. We will create two image pyramids, blend them together, and then reconstruct the blended image.

Importing Required Libraries

We will begin by importing the necessary libraries, which include numpy and OpenCV.

import cv2
import numpy as np

Loading Images

Next, we will load the two images that we want to blend. In this example, we will use a panda image and a flower image.

panda = cv2.imread('panda.jpg')
flower = cv2.imread('flower.jpg')

Creating Image Pyramids

Now, we will create image pyramids for both the panda and flower images. We will use the cv2.pyrDown() function to create the lower resolution versions of the images.

panda_gaussian_pyramid = [panda]
for i in range(6):
    gaussian = cv2.pyrDown(panda_gaussian_pyramid[i])
    panda_gaussian_pyramid.append(gaussian)

flower_gaussian_pyramid = [flower]
for i in range(6):
    gaussian = cv2.pyrDown(flower_gaussian_pyramid[i])
    flower_gaussian_pyramid.append(gaussian)

Blending Images

With the image pyramids created, we can now blend the two images together. We will do this by combining the corresponding Gaussian pyramid levels and Laplacian pyramid levels.

mixed_pyramid = []
for panda_gaussian, flower_gaussian in zip(panda_gaussian_pyramid, flower_gaussian_pyramid):
    rows, cols, channels = panda_gaussian.shape
    lap_panda = cv2.subtract(panda_gaussian, cv2.pyrUp(cv2.resize(panda_gaussian, (cols * 2, rows * 2))))
    lap_flower = cv2.subtract(flower_gaussian, cv2.pyrUp(cv2.resize(flower_gaussian, (cols * 2, rows * 2))))
    mixed = cv2.addWeighted(lap_panda, 0.5, lap_flower, 0.5, 0)
    mixed_pyramid.append(mixed)

Reconstructing the Blended Image

Finally, we will reconstruct the blended image by starting with the smallest Gaussian pyramid level and iteratively adding the corresponding Laplacian pyramid level until we reach the original size of the image.

blended = mixed_pyramid[5]
for i in range(4, -1, -1):
    blended = cv2.add(cv2.pyrUp(blended), mixed_pyramid[i])

Displaying the Result

We can now display the blended image using the cv2.imshow() function.

cv2.imshow('Blended Image', blended)
cv2.waitKey(0)
cv2.destroyAllWindows()

Conclusion

In this tutorial, we have covered how to blend images using image pyramids in OpenCV Python. By creating Gaussian and Laplacian pyramids of the images and blending the corresponding levels together, we can achieve a high-quality blend of the two images. By reconstructing the blended image from the pyramids, we can obtain the final result.