拉普拉斯金字塔加权融合代码

以下是拉普拉斯金字塔加权融合的Python代码：

import cv2
import numpy as np

# 生成高斯金字塔
def generate_gaussian_pyramid(image, levels):
    pyramid = [image]
    for i in range(levels - 1):
        image = cv2.pyrDown(image)
        pyramid.append(image)
    return pyramid

# 生成拉普拉斯金字塔
def generate_laplacian_pyramid(image, levels):
    gaussian_pyramid = generate_gaussian_pyramid(image, levels)
    pyramid = []
    for i in range(levels - 1):
        expanded = cv2.pyrUp(gaussian_pyramid[i + 1])
        laplacian = cv2.subtract(gaussian_pyramid[i], expanded)
        pyramid.append(laplacian)
    pyramid.append(gaussian_pyramid[-1])
    return pyramid

# 加权融合
def weighted_fusion(laplacian_pyramid_1, laplacian_pyramid_2, mask_pyramid):
    fused_pyramid = []
    for i in range(len(mask_pyramid)):
        weight1 = mask_pyramid[i] / 255.0
        weight2 = 1.0 - weight1
        fused = cv2.addWeighted(laplacian_pyramid_1[i], weight1, laplacian_pyramid_2[i], weight2, 0)
        fused_pyramid.append(fused)
    return fused_pyramid

# 重建图像
def reconstruct_image(pyramid):
    image = pyramid[-1]
    for i in range(len(pyramid) - 2, -1, -1):
        image = cv2.pyrUp(image)
        image = cv2.add(image, pyramid[i])
    return image

# 加载两张图片和掩膜
image1 = cv2.imread('image1.jpg')
image2 = cv2.imread('image2.jpg')
mask = cv2.imread('mask.jpg', 0)

# 将掩膜转换为三通道
mask = cv2.merge((mask, mask, mask))

# 生成拉普拉斯金字塔和掩膜金字塔
levels = 6
laplacian_pyramid_1 = generate_laplacian_pyramid(image1, levels)
laplacian_pyramid_2 = generate_laplacian_pyramid(image2, levels)
mask_pyramid = generate_gaussian_pyramid(mask, levels)

# 加权融合
fused_pyramid = weighted_fusion(laplacian_pyramid_1, laplacian_pyramid_2, mask_pyramid)

# 重建图像
fused_image = reconstruct_image(fused_pyramid)

# 显示结果
cv2.imshow('Fused Image', fused_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

其中，`generate_gaussian_pyramid` 用于生成高斯金字塔，`generate_laplacian_pyramid` 用于生成拉普拉斯金字塔，`weighted_fusion` 用于加权融合，`reconstruct_image` 用于重建图像。在代码中，我们首先加载了两张图片和一个掩膜，然后将掩膜转换为三通道，接着生成拉普拉斯金字塔和掩膜金字塔，并使用 `weighted_fusion` 函数进行加权融合，最后使用 `reconstruct_image` 函数重建图像，并显示结果。