Source code Image reconstruction:

[python]
import cv2
import numpy as np

img = cv2.imread("hand.jpg")

# Gaussian Pyramid
layer = img.copy()
gaussian_pyramid = [layer]
for i in range(6):
layer = cv2.pyrDown(layer)
gaussian_pyramid.append(layer)

# Laplacian Pyramid
layer = gaussian_pyramid[5]
laplacian_pyramid = [layer]
for i in range(5, 0, -1):
size = (gaussian_pyramid[i – 1].shape[1], gaussian_pyramid[i – 1].shape[0])
gaussian_expanded = cv2.pyrUp(gaussian_pyramid[i], dstsize=size)
laplacian = cv2.subtract(gaussian_pyramid[i – 1], gaussian_expanded)
laplacian_pyramid.append(laplacian)

reconstructed_image = laplacian_pyramid[0]
for i in range(1, 6):
size = (laplacian_pyramid[i].shape[1], laplacian_pyramid[i].shape[0])
reconstructed_image = cv2.pyrUp(reconstructed_image, dstsize=size)
reconstructed_image = cv2.add(reconstructed_image, laplacian_pyramid[i])
cv2.imshow(str(i), reconstructed_image)

cv2.imshow("original", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
[/python]

 

Source code Images blending:

[python]
import cv2
import numpy as np

img1 = cv2.imread("baseball_ball.png")
img1 = cv2.resize(img1, (1000, 1000))
img2 = cv2.imread("football_ball.jpg")
img2 = cv2.resize(img2, (1000, 1000))

footbase_ball = np.hstack((img1[:, :500], img2[:, 500:]))

# Gaussian Pyramid 1
layer = img1.copy()
gaussian_pyramid = [layer]
for i in range(6):
layer = cv2.pyrDown(layer)
gaussian_pyramid.append(layer)

# Laplacian Pyramid 1
layer = gaussian_pyramid[5]
laplacian_pyramid = [layer]
for i in range(5, 0, -1):
size = (gaussian_pyramid[i – 1].shape[1], gaussian_pyramid[i – 1].shape[0])
gaussian_expanded = cv2.pyrUp(gaussian_pyramid[i], dstsize=size)
laplacian = cv2.subtract(gaussian_pyramid[i – 1], gaussian_expanded)
laplacian_pyramid.append(laplacian)

# Gaussian Pyramid 2
layer = img2.copy()
gaussian_pyramid2 = [layer]
for i in range(6):
layer = cv2.pyrDown(layer)
gaussian_pyramid2.append(layer)

# Laplacian Pyramid 2
layer = gaussian_pyramid2[5]
laplacian_pyramid2 = [layer]
for i in range(5, 0, -1):
size = (gaussian_pyramid2[i – 1].shape[1], gaussian_pyramid2[i – 1].shape[0])
gaussian_expanded = cv2.pyrUp(gaussian_pyramid2[i], dstsize=size)
laplacian = cv2.subtract(gaussian_pyramid2[i – 1], gaussian_expanded)
laplacian_pyramid2.append(laplacian)

# Laplacian Pyramid Footbase_ball
footbase_ball_pyramid = []
n = 0
for img1_lap, img2_lap in zip(laplacian_pyramid, laplacian_pyramid2):
n += 1
cols, rows, ch = img1_lap.shape
laplacian = np.hstack((img1_lap[:, 0:int(cols/2)], img2_lap[:, int(cols/2):]))
footbase_ball_pyramid.append(laplacian)

# Reconstructed Footbase_ball
footbase_ball_reconstructed = footbase_ball_pyramid[0]
for i in range(1, 6):
size = (footbase_ball_pyramid[i].shape[1], footbase_ball_pyramid[i].shape[0])
footbase_ball_reconstructed = cv2.pyrUp(footbase_ball_reconstructed, dstsize=size)
footbase_ball_reconstructed = cv2.add(footbase_ball_pyramid[i], footbase_ball_reconstructed)

cv2.imshow("Footbase ball reconstructed", footbase_ball_reconstructed)
cv2.imshow("Footbase ball", footbase_ball)
#cv2.imshow("img1", img1)
#cv2.imshow("img2", img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
[/python]

 

Files:

  1. hand.jpg
  2. football_ball.jpg
  3. baseball_ball.png