写一个代码，使用LIFT算法进行图像匹配，用CNN网络

这里是一个使用LIFT算法进行图像匹配的代码，其中使用了CNN网络：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import cv2
import matplotlib.pyplot as plt
from torchvision import transforms
from PIL import Image
from lift import LIFT

# 定义CNN网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2)
        self.fc1 = nn.Linear(32 * 28 * 28, 1000)
        self.fc2 = nn.Linear(1000, 2)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 32 * 28 * 28)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 加载图像
img1 = cv2.imread('image1.jpg', 0)
img2 = cv2.imread('image2.jpg', 0)

# 初始化LIFT算法
detector = LIFT()

# 提取特征
kp1, des1 = detector.detectAndCompute(img1, None)
kp2, des2 = detector.detectAndCompute(img2, None)

# 初始化CNN网络
net = Net()

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

# 将特征转换为张量
des1_tensor = torch.tensor(des1).float()
des2_tensor = torch.tensor(des2).float()

# 将图像转换为PIL格式
img1_pil = Image.fromarray(img1)
img2_pil = Image.fromarray(img2)

# 图像预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

img1_tensor = transform(img1_pil).unsqueeze(0)
img2_tensor = transform(img2_pil).unsqueeze(0)

# 训练CNN网络
for epoch in range(100):
    optimizer.zero_grad()

    output1 = net(des1_tensor)
    output2 = net(des2_tensor)

    loss = criterion(output1, output2)
    loss.backward()
    optimizer.step()

    print("Epoch: {}, Loss: {}".format(epoch, loss.item()))

# 将特征嵌入到空间中
des1_embedded = net(des1_tensor).detach().numpy()
des2_embedded = net(des2_tensor).detach().numpy()

# 计算匹配矩阵
match_matrix = np.dot(des1_embedded, des2_embedded.T)

# 对匹配矩阵进行归一化
norms1 = np.linalg.norm(des1_embedded, axis=1)
norms2 = np.linalg.norm(des2_embedded, axis=1)
normalized_match_matrix = match_matrix / np.outer(norms1, norms2)

# 求取最佳匹配对
max_indices = np.argmax(normalized_match_matrix, axis=1)
matches = []
for i, idx in enumerate(max_indices):
    matches.append(cv2.DMatch(i, idx, 0))

# 绘制匹配图像
img_matches = cv2.drawMatches(img1, kp1, img2, kp2, matches, None)
plt.imshow(img_matches)
plt.show()

需要注意的是，这里的`lift`和`cv2`是需要提前安装的Python库，且该代码仅为演示用途，不一定能够完美匹配所有图像。