int
main (int, char** argv)
{
std::string filename = argv[1];
std::cout << "Reading " << filename << std::endl;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPCDFile<pcl::PointXYZ> (filename, *cloud) == -1) //* load the file
{
PCL_ERROR ("Couldn't read file");
return (-1);
}
std::cout << "Loaded " << cloud->points.size () << " points." << std::endl;
// Compute the normals
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimation;
normal_estimation.setInputCloud (cloud);
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
normal_estimation.setSearchMethod (tree);
pcl::PointCloud<pcl::Normal>::Ptr cloud_with_normals (new pcl::PointCloud<pcl::Normal>);
normal_estimation.setRadiusSearch (0.03);
normal_estimation.compute (*cloud_with_normals);
// Setup the feature computation
pcl::PFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::PFHSignature125> pfh_estimation;
// Provide the original point cloud (without normals)
pfh_estimation.setInputCloud (cloud);
// Provide the point cloud with normals
pfh_estimation.setInputNormals (cloud_with_normals);
// pfh_estimation.setInputWithNormals (cloud, cloud_with_normals); PFHEstimation does not have this function
// Use the same KdTree from the normal estimation
pfh_estimation.setSearchMethod (tree);
pcl::PointCloud<pcl::PFHSignature125>::Ptr pfh_features (new pcl::PointCloud<pcl::PFHSignature125>);
pfh_estimation.setRadiusSearch (0.2);
// Actually compute the spin images
pfh_estimation.compute (*pfh_features);
std::cout << "output points.size (): " << pfh_features->points.size () << std::endl;
// Display and retrieve the shape context descriptor vector for the 0th point.
pcl::PFHSignature125 descriptor = pfh_features->points[0];
std::cout << descriptor << std::endl;
return 0;
}
void Get_ToTal_FPFH(const char *path_pcd,float normal_search_radius, float fpfh_search_radius, float a[33])
{
std::string fileName = path_pcd;
// std::cout << "Reading " << fileName << std::endl;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPCDFile<pcl::PointXYZ> (fileName, *cloud) == -1) // load the file
{
PCL_ERROR ("Couldn't read file");
exit(0);
}
// std::cout << "Loaded " << cloud->points.size () << " points." << std::endl;
// Compute the normals
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimation;
normal_estimation.setInputCloud (cloud);
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
normal_estimation.setSearchMethod (tree);
pcl::PointCloud<pcl::Normal>::Ptr cloud_with_normals (new pcl::PointCloud<pcl::Normal>);
normal_estimation.setRadiusSearch (0.05);
normal_estimation.compute (*cloud_with_normals);
// Setup the feature computation
pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh_estimation;
// Provide the original point cloud (without normals)
fpfh_estimation.setInputCloud (cloud);
// Provide the point cloud with normals
fpfh_estimation.setInputNormals (cloud_with_normals);
// fpfhEstimation.setInputWithNormals(cloud, cloudWithNormals); PFHEstimation does not have this function
// Use the same KdTree from the normal estimation
fpfh_estimation.setSearchMethod (tree);
pcl::PointCloud<pcl::FPFHSignature33>::Ptr pfh_features (new pcl::PointCloud<pcl::FPFHSignature33>);
fpfh_estimation.setRadiusSearch (0.25);
// Actually compute the spin images
fpfh_estimation.compute (*pfh_features);
// std::cout << "output points.size (): " << pfh_features->points.size () << std::endl;
// float a[33] = {0.0f};
// Display and retrieve the shape context descriptor vector for the 0th point.
for (std::size_t i = 0; i < pfh_features->points.size(); i++)
{
pcl::FPFHSignature33 descriptor = pfh_features->points[i];
for (int j = 0; j < 33; j++)
a[j] = descriptor.histogram[j];
}
}
