如何求取叶片点云前后缘与叶盆叶背的分界点（c++）

求取叶片点云前后缘与叶盆叶背的分界点需要进行以下步骤：

1. 对点云进行预处理，包括去除离群点、降采样等操作。

2. 确定叶片的主轴方向，可以采用PCA等方法求解。

3. 将点云沿主轴方向进行投影，得到一维的点云分布。

4. 对点云分布进行平滑滤波，得到分布的拐点。

5. 将点云沿法向方向进行投影，得到叶片截面的点云。

6. 对叶片截面的点云进行聚类，得到叶片前后两侧点云。

7. 对前后两侧点云进行分别拟合，得到拟合曲线。

8. 拟合曲线的交点即为前后缘的分界点，拟合曲线与点云的交点即为叶盆叶背的分界点。

以下是C++代码的示例：

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/visualization/cloud_viewer.h>

typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloudT;

int main(int argc, char** argv)
{
    // Load point cloud
    PointCloudT::Ptr cloud(new PointCloudT);
    pcl::io::loadPCDFile<PointT>("leaf.pcd", *cloud);

    // Voxel grid filtering
    pcl::VoxelGrid<PointT> vg;
    vg.setInputCloud(cloud);
    vg.setLeafSize(0.01f, 0.01f, 0.01f);
    PointCloudT::Ptr cloud_filtered(new PointCloudT);
    vg.filter(*cloud_filtered);

    // Statistical outlier removal
    pcl::StatisticalOutlierRemoval<PointT> sor;
    sor.setInputCloud(cloud_filtered);
    sor.setMeanK(50);
    sor.setStddevMulThresh(1.0);
    PointCloudT::Ptr cloud_sor(new PointCloudT);
    sor.filter(*cloud_sor);

    // Estimate normals
    pcl::NormalEstimation<PointT, pcl::Normal> ne;
    ne.setInputCloud(cloud_sor);
    pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>());
    ne.setSearchMethod(tree);
    pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
    ne.setRadiusSearch(0.03);
    ne.compute(*normals);

    // Segment plane
    pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg;
    seg.setOptimizeCoefficients(true);
    seg.setModelType(pcl::SACMODEL_NORMAL_PLANE);
    seg.setNormalDistanceWeight(0.1);
    seg.setMethodType(pcl::SAC_RANSAC);
    seg.setMaxIterations(100);
    seg.setDistanceThreshold(0.02);
    seg.setInputCloud(cloud_sor);
    seg.setInputNormals(normals);
    pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
    pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
    seg.segment(*inliers, *coefficients);

    // Extract inliers
    pcl::ExtractIndices<PointT> extract;
    extract.setInputCloud(cloud_sor);
    extract.setIndices(inliers);
    extract.setNegative(false);
    PointCloudT::Ptr cloud_plane(new PointCloudT);
    extract.filter(*cloud_plane);

    // Project to axis
    Eigen::Vector4f centroid;
    pcl::compute3DCentroid(*cloud_plane, centroid);
    Eigen::Matrix3f covariance_matrix;
    pcl::computeCovarianceMatrixNormalized(*cloud_plane, centroid, covariance_matrix);
    Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eigen_solver(covariance_matrix, Eigen::ComputeEigenvectors);
    Eigen::Matrix3f eigD = eigen_solver.eigenvalues().asDiagonal();
    Eigen::Matrix3f eigV = eigen_solver.eigenvectors();
    int idx = 0;
    if (eigD(1, 1) > eigD(0, 0))
        idx = 1;
    if (eigD(2, 2) > eigD(idx, idx))
        idx = 2;
    Eigen::Vector3f axis = eigV.col(idx);

    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_projected(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::ProjectInliers<PointT> proj;
    proj.setModelType(pcl::SACMODEL_PLANE);
    proj.setInputCloud(cloud_plane);
    proj.setModelCoefficients(coefficients);
    proj.setInputCloud(cloud_plane);
    proj.filter(*cloud_projected);

    // Smooth filtering
    std::vector<float> data(cloud_projected->points.size());
    for (int i = 0; i < cloud_projected->points.size(); i++)
        data[i] = cloud_projected->points[i].x;
    std::vector<float> kernel = { 1, 5, 8, 5, 1 };
    std::vector<float> data_smooth(cloud_projected->points.size());
    for (int i = 2; i < cloud_projected->points.size() - 2; i++)
    {
        for (int j = -2; j <= 2; j++)
            data_smooth[i] += data[i + j] * kernel[j + 2];
        data_smooth[i] /= 20;
    }

    // Find inflection points
    std::vector<int> inflections;
    for (int i = 2; i < cloud_projected->points.size() - 2; i++)
    {
        if (data_smooth[i] > data_smooth[i - 1] && data_smooth[i] > data_smooth[i + 1])
            inflections.push_back(i);
        if (data_smooth[i] < data_smooth[i - 1] && data_smooth[i] < data_smooth[i + 1])
            inflections.push_back(i);
    }

    // Extract cross section
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_section(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::ExtractIndices<PointT> extract_indices;
    extract_indices.setInputCloud(cloud);
    pcl::PointIndices::Ptr section_indices(new pcl::PointIndices);
    for (int i = 0; i < cloud_projected->points.size(); i++)
    {
        if (abs(cloud_projected->points[i].x - data_smooth[inflections[0]]) < 0.002)
            section_indices->indices.push_back(i);
    }
    extract_indices.setIndices(section_indices);
    extract_indices.setNegative(false);
    extract_indices.filter(*cloud_section);

    // Cluster front and back sides
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree_section(new pcl::search::KdTree<pcl::PointXYZ>);
    tree_section->setInputCloud(cloud_section);
    std::vector<pcl::PointIndices> cluster_indices;
    pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
    ec.setClusterTolerance(0.02);
    ec.setMinClusterSize(100);
    ec.setMaxClusterSize(10000);
    ec.setSearchMethod(tree_section);
    ec.setInputCloud(cloud_section);
    ec.extract(cluster_indices);

    // Fit curves
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_front(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_back(new pcl::PointCloud<pcl::PointXYZ>);
    for (int i = 0; i < cluster_indices.size(); i++)
    {
        pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster(new pcl::PointCloud<pcl::PointXYZ>);
        pcl::PointIndices::Ptr cluster_indices_ptr(new pcl::PointIndices(cluster_indices[i]));
        extract_indices.setInputCloud(cloud_section);
        extract_indices.setIndices(cluster_indices_ptr);
        extract_indices.setNegative(false);
        extract_indices.filter(*cloud_cluster);

        pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
        pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
        pcl::SACSegmentation<pcl::PointXYZ> seg;
        seg.setOptimizeCoefficients(true);
        seg.setModelType(pcl::SACMODEL_LINE);
        seg.setMethodType(pcl::SAC_RANSAC);
        seg.setMaxIterations(1000);
        seg.setDistanceThreshold(0.02);
        seg.setInputCloud(cloud_cluster);
        seg.segment(*inliers, *coefficients);

        if (coefficients->values[3] > 0)
            *cloud_front += *cloud_cluster;
        else
            *cloud_back += *cloud_cluster;
    }

    // Show results
    pcl::visualization::PCLVisualizer viewer("Leaf");
    viewer.setBackgroundColor(0.0, 0.0, 0.0);
    viewer.addPointCloud(cloud, "cloud");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "cloud");
    viewer.addPointCloud(cloud_projected, "projected");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 1.0, 0.0, 0.0, "projected");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "projected");
    viewer.addPointCloud(cloud_section, "section");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0.0, 1.0, 0.0, "section");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "section");
    viewer.addPointCloud(cloud_front, "front");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0.0, 0.0, 1.0, "front");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "front");
    viewer.addPointCloud(cloud_back, "back");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 1.0, 1.0, 0.0, "back");
    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "back");
    viewer.spin();

    return 0;
}