基于金字塔的图像融合

金字塔底部最大的图像是level 0，pyup表示金字塔向上采样，向上采样表示像素变大；pydown表示金字塔向下采样，向下采样表示像素变小。

基于金字塔的图像融合

Load the two images of apple and orange
Find the Gaussian Pyramids for apple and orange (in this particular example, number of levels is 6)
From Gaussian Pyramids, find their Laplacian Pyramids
Now join the left half of apple and right half of orange in each levels of Laplacian Pyramids
Finally from this joint image pyramids, reconstruct the original image.

import cv2

import numpy as np,sys

A = cv2.imread('apple.jpg')

B = cv2.imread('orange.jpg')

# generate Gaussian pyramid for A

G = A.copy()

gpA = [G]

for i in xrange(6):

G = cv2.pyrDown(G)

gpA.append(G)

# generate Gaussian pyramid for B

G = B.copy()

gpB = [G]

for i in xrange(6):

G = cv2.pyrDown(G)

gpB.append(G)

# generate Laplacian Pyramid for A

lpA = [gpA[5]]

for i in xrange(5,0,-1):

GE = cv2.pyrUp(gpA[i])

L = cv2.subtract(gpA[i-1],GE)

lpA.append(L)

# generate Laplacian Pyramid for B

lpB = [gpB[5]]

for i in xrange(5,0,-1):

GE = cv2.pyrUp(gpB[i])

L = cv2.subtract(gpB[i-1],GE)

lpB.append(L)

# Now add left and right halves of images in each level

LS = []

for la,lb in zip(lpA,lpB):

rows,cols,dpt = la.shape

ls = np.hstack((la[:,0:cols/2], lb[:,cols/2:]))

LS.append(ls)

# now reconstruct

ls_ = LS[0]

for i in xrange(1,6):

ls_ = cv2.pyrUp(ls_)

ls_ = cv2.add(ls_, LS[i])

# image with direct connecting each half

real = np.hstack((A[:,:cols/2],B[:,cols/2:]))

cv2.imwrite('Pyramid_blending2.jpg',ls_)

cv2.imwrite('Direct_blending.jpg',real)