OpenCV Python - 使用Pyramid进行图像混合

通过使用图像Pyramid可以最小化图像的不连续性。这会产生无缝混合图像。

采取以下步骤来实现最终结果 -

首先加载图像并找到两者的高斯Pyramid。其程序如下 -

import cv2
import numpy as np,sys

kalam = cv2.imread('kalam.jpg')
einst = cv2.imread('einstein.jpg')
### generate Gaussian pyramid for first
G = kalam.copy()
gpk = [G]
for i in range(6):
   G = cv2.pyrDown(G)
   gpk.append(G)
# generate Gaussian pyramid for second
G = einst.copy()
gpe = [G]
for i in range(6):
   G = cv2.pyrDown(G)
   gpe.append(G)

从高斯Pyramid中，获得相应的拉普拉斯Pyramid。其程序如下 -

# generate Laplacian Pyramid for first
lpk = [gpk[5]]
for i in range(5,0,-1):
   GE = cv2.pyrUp(gpk[i])
   L = cv2.subtract(gpk[i-1],GE)
   lpk.append(L)

# generate Laplacian Pyramid for second
lpe = [gpe[5]]
for i in range(5,0,-1):
   GE = cv2.pyrUp(gpe[i])
   L = cv2.subtract(gpe[i-1],GE)
   lpe.append(L)

然后，在每一层Pyramid中将第一张图像的左半部分与第二张图像的右半部分连接起来。其程序如下 -

# Now add left and right halves of images in each level
LS = []
for la,lb in zip(lpk,lpe):
   rows,cols,dpt = la.shape
   ls = np.hstack((la[:,0:int(cols/2)], lb[:,int(cols/2):]))
   LS.append(ls)

最后，从这个联合Pyramid重建图像。下面给出了相同的程序 -

ls_ = LS[0]
for i in range(1,6):
   ls_ = cv2.pyrUp(ls_)
   ls_ = cv2.add(ls_, LS[i])
   cv2.imshow('RESULT',ls_)

输出

混合结果应如下 -

混合Pyramid