PFH-PCL-Cpp

#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/pfh.h>

int
main(int argc, char** argv)
{
	// Object for storing the point cloud.
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
	// Object for storing the normals.
	pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
	// Object for storing the PFH descriptors for each point.
	pcl::PointCloud<pcl::PFHSignature125>::Ptr descriptors(new pcl::PointCloud<pcl::PFHSignature125>());

	// Read a PCD file from disk.
	if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1], *cloud) != 0)
	{
		return -1;
	}

	// Note: you would usually perform downsampling now. It has been omitted here
	// for simplicity, but be aware that computation can take a long time.

	// Estimate the normals.
	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normalEstimation;
	normalEstimation.setInputCloud(cloud);
	normalEstimation.setRadiusSearch(0.03);
	pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
	normalEstimation.setSearchMethod(kdtree);
	normalEstimation.compute(*normals);

	// PFH estimation object.
  // Create the PFH estimation class, and pass the input dataset+normals to it
	pcl::PFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::PFHSignature125> pfh;
	pfh.setInputCloud(cloud);
	pfh.setInputNormals(normals);
  // alternatively, if cloud is of tpe PointNormal, do pfh.setInputNormals (cloud);

	pfh.setSearchMethod(kdtree);
	// Search radius, to look for neighbors. Note: the value given here has to be
	// larger than the radius used to estimate the normals.
	// Use all neighbors in a sphere of radius 5cm
  // IMPORTANT: the radius used here has to be larger than the radius used to estimate the surface normals!!!
  pfh.setRadiusSearch(0.05);

	pfh.compute(*descriptors);
  // pfhs->points.size () should have the same size as the input cloud->points.size ()*

  // 포인트수 출력  
  std::cout << "descriptors size" << descriptors->points.size ()  << std::endl;
}