写一下基于自适应分割的方法的代码

基于自适应分割的方法是一种常用的图像融合方法，可以根据图像的特征自适应地确定分割的结果，然后对每个分割区域内的像素进行加权平均，以确定权重。下面是基于自适应分割的图像融合的示例代码：

import cv2
import numpy as np

# 读取图像
img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')

# 转换为Lab色彩空间
lab1 = cv2.cvtColor(img1, cv2.COLOR_BGR2Lab)
lab2 = cv2.cvtColor(img2, cv2.COLOR_BGR2Lab)

# 分割图像
numSegments = 100
seg1 = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()
seg1.setBaseImage(img1)
seg1.switchToSelectiveSearchFast()
rects1 = seg1.process()
seg2 = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()
seg2.setBaseImage(img2)
seg2.switchToSelectiveSearchFast()
rects2 = seg2.process()

# 计算每个区域的像素平均值
meanColors1 = np.zeros((numSegments, 3))
for i in range(numSegments):
    x, y, w, h = rects1[i]
    roi = lab1[y:y+h, x:x+w, :]
    meanColors1[i, :] = [np.mean(roi[:, :, 0]), np.mean(roi[:, :, 1]), np.mean(roi[:, :, 2])]
meanColors2 = np.zeros((numSegments, 3))
for i in range(numSegments):
    x, y, w, h = rects2[i]
    roi = lab2[y:y+h, x:x+w, :]
    meanColors2[i, :] = [np.mean(roi[:, :, 0]), np.mean(roi[:, :, 1]), np.mean(roi[:, :, 2])]

# 计算权重
weights = np.zeros(numSegments)
for i in range(numSegments):
    diff = np.abs(meanColors1[i, :] - meanColors2[i, :])
    weights[i] = 1 - np.mean(diff)

# 融合图像
output = np.zeros_like(img1)
for i in range(numSegments):
    x, y, w, h = rects1[i]
    output[y:y+h, x:x+w, :] = output[y:y+h, x:x+w, :] + weights[i] * img1[y:y+h, x:x+w, :]
    x, y, w, h = rects2[i]
    output[y:y+h, x:x+w, :] = output[y:y+h, x:x+w, :] + (1 - weights[i]) * img2[y:y+h, x:x+w, :]

# 显示结果
cv2.imshow('Output', output)
cv2.waitKey(0)
cv2.destroyAllWindows()

在这个示例代码中，我们首先读取了两张图像，然后将它们转换为Lab色彩空间。接着，我们使用Selective Search算法对图像进行分割，得到若干个区域。然后，我们计算每个区域内像素的平均值，并计算每个区域的权重。最后，我们将两张图像按照确定的权重进行融合，得到最终的融合图像。