Image Blending using Pyramids in OpenCV Python

In this tutorial on OpenCV Python Tutorial For Beginners, I am going to show How to Image Blending using Pyramids in OpenCV.
There are two kinds of Image Pyramids. 1) Gaussian Pyramid and 2) Laplacian Pyramids
Gaussian pyramids use cv.pyrDown() and cv.pyrUp() functions.

OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python.
it is Open Source and free. opencv is easy to use and install.

Starting with an overview of what the course will be covering, we move on to discussing morphological operations and practically learn how they work on images. We will then learn contrast enhancement using equalization and contrast limiting. Finally we will learn 3 methods to subtract the background from the video and implement them using OpenCV.

At the end of this tutorial, you will have a firm grasp of Computer Vision techniques using OpenCV libraries. This course will be your gateway to the world of data science.

import cv2
	import numpy as np
	apple = cv2.imread('apple.jpg')
	orange = cv2.imread('orange.jpg')
	print(apple.shape)
	print(orange.shape)
	apple_orange = np.hstack((apple[:, :256], orange[:, 256:]))

	## generate Gaussian pyramid for apple
	apple_copy = apple.copy()
	gp_apple = [apple_copy]
	for i in range(6):
	    apple_copy = cv2.pyrDown(apple_copy)
	    gp_apple.append(apple_copy)

	## generate Gaussian pyramid for orange
	orange_copy = orange.copy()
	gp_orange = [orange_copy]
	for i in range(6):
	    orange_copy = cv2.pyrDown(orange_copy)
	    gp_orange.append(orange_copy)

	## generate Laplacian Pyramid for apple
	apple_copy = gp_apple[5]
	lp_apple = [apple_copy]
	for i in range(5, 0, -1):
	    gaussian_expanded = cv2.pyrUp(gp_apple[i])
	    laplacian = cv2.subtract(gp_apple[i-1], gaussian_expanded)
	    lp_apple.append(laplacian)

	## generate Laplacian Pyramid for orange
	orange_copy = gp_orange[5]
	lp_orange = [orange_copy]
	for i in range(5, 0, -1):
	    gaussian_expanded = cv2.pyrUp(gp_orange[i])
	    laplacian = cv2.subtract(gp_orange[i-1], gaussian_expanded)
	    lp_orange.append(laplacian)

	## Now add left and right halves of images in each level
	apple_orange_pyramid = []
	n = 0
	for apple_lap, orange_lap in zip(lp_apple, lp_orange):
	    n += 1
	    cols, rows, ch = apple_lap.shape
	    laplacian = np.hstack((apple_lap[:, 0:int(cols/2)], orange_lap[:, int(cols/2):]))
	    apple_orange_pyramid.append(laplacian)
	## now reconstruct
	apple_orange_reconstruct = apple_orange_pyramid[0]
	for i in range(1, 6):
	    apple_orange_reconstruct = cv2.pyrUp(apple_orange_reconstruct)
	    apple_orange_reconstruct = cv2.add(apple_orange_pyramid[i], apple_orange_reconstruct)

	cv2.imshow("apple", apple)
	cv2.imshow("orange", orange)
	cv2.imshow("apple_orange", apple_orange)
	cv2.imshow("apple_orange_reconstruct", apple_orange_reconstruct)
	cv2.waitKey(0)
	cv2.destroyAllWindows()

#python #opencv #image