写一个点云骨架提取代码

点云骨架提取是一个比较复杂的问题，需要利用计算机视觉和机器学习等技术进行处理。下面给出一个基于深度学习的点云骨架提取代码实现，供参考：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.optim import Adam
import numpy as np
from sklearn.neighbors import NearestNeighbors
from plyfile import PlyData, PlyElement
import open3d as o3d

class PointNet(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(PointNet, self).__init__()
        self.conv1 = nn.Conv1d(in_channels, 64, kernel_size=1)
        self.conv2 = nn.Conv1d(64, 128, kernel_size=1)
        self.conv3 = nn.Conv1d(128, 1024, kernel_size=1)
        self.fc1 = nn.Linear(1024, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, out_channels)

    def forward(self, x):
        batch_size, _, _ = x.size()
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = torch.max(x, dim=2)[0]
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

class PointCloudDataset(torch.utils.data.Dataset):
    def __init__(self, data_path):
        self.data_path = data_path
        self.plydata = PlyData.read(data_path)
        self.points = np.array([list(point) for point in self.plydata.elements[0].data])
        self.normals = np.array([list(normal) for normal in self.plydata.elements[1].data])
        self.tree = NearestNeighbors(n_neighbors=10).fit(self.points)

    def __getitem__(self, index):
        point = self.points[index]
        normal = self.normals[index]
        _, indices = self.tree.kneighbors([point])
        neighbors = self.points[indices[0]]
        relative_position = neighbors - point
        relative_position = np.transpose(relative_position)
        return relative_position, normal

    def __len__(self):
        return len(self.points)

def train(model, optimizer, data_loader, device):
    model.train()
    for data, target in data_loader:
        data, target = data.to(device).float(), target.to(device).float().view(-1, 3)
        optimizer.zero_grad()
        output = model(data)
        loss = F.mse_loss(output, target)
        loss.backward()
        optimizer.step()

def test(model, data_loader, device):
    model.eval()
    with torch.no_grad():
        for data, target in data_loader:
            data, target = data.to(device).float(), target.to(device).float().view(-1, 3)
            output = model(data)
            mse_loss = F.mse_loss(output, target)
            print('MSE loss:', mse_loss)

def main():
    data_path = 'point_cloud.ply'
    dataset = PointCloudDataset(data_path)
    data_loader = DataLoader(dataset, batch_size=32, shuffle=True)
    model = PointNet(9, 3)
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.to(device)
    optimizer = Adam(model.parameters(), lr=0.001)
    for epoch in range(100):
        train(model, optimizer, data_loader, device)
        test(model, data_loader, device)

if __name__ == '__main__':
    main()

其中，`PointCloudDataset` 类用于读取点云数据并提取相邻点之间的相对位置，`PointNet` 类是一个基于 PointNet 的神经网络模型，用于学习相对位置和法向量之间的关系。`train` 函数用于训练模型，`test` 函数用于测试模型，`main` 函数用于整个流程的控制。