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.c_str (), *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 kdtree (new pcl::search::KdTree<pcl::PointXYZ>);
normal_estimation.setSearchMethod (kdtree);
pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud< pcl::Normal>);
normal_estimation.setRadiusSearch (100);
normal_estimation.compute (*normals);
// Setup spin iopenmage computation
pcl::SpinImageEstimation<pcl::PointXYZ, pcl::Normal, pcl::Histogram<153> > spin_image_descriptor(8, .05, 16);
spin_image_descriptor.setInputCloud (cloud);
spin_image_descriptor.setInputNormals (normals);
// Use the same KdTree from the normal estimation
spin_image_descriptor.setSearchMethod (kdtree);
pcl::PointCloud<pcl::Histogram<153> >::Ptr spin_images (new pcl::PointCloud<pcl::Histogram<153> >);
spin_image_descriptor.setRadiusSearch (100);
// Actually compute the spin images
spin_image_descriptor.compute (*spin_images);
std::cout << "SI output points.size (): " << spin_images->points.size () << std::endl;
// Display and retrieve the spin image descriptor vector for the first point.
FILE * pFile;
char * fileToWrite;
fileToWrite = strcat(argv[1], "spinImages.bin");
//pFile = fopen (fileToWrite,"w");
//pcl::Histogram<153> first_descriptor = spin_images->points[0];
std::ofstream myfile (fileToWrite);
for (int i = 0;i < spin_images-> points.size(); i ++) {
myfile << spin_images->points[i] << std::endl;
}
myfile.close();
//fclose(pFile);
return 0;
}
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.c_str (), *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 kdtree (new pcl::search::KdTree<pcl::PointXYZ>);
normal_estimation.setSearchMethod (kdtree);
pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud< pcl::Normal>);
normal_estimation.setRadiusSearch (0.03);
normal_estimation.compute (*normals);
// Setup spin image computation
pcl::SpinImageEstimation<pcl::PointXYZ, pcl::Normal, pcl::Histogram<153> > spin_image_descriptor(8, 0.5, 16);
spin_image_descriptor.setInputCloud (cloud);
spin_image_descriptor.setInputNormals (normals);
// Use the same KdTree from the normal estimation
spin_image_descriptor.setSearchMethod (kdtree);
pcl::PointCloud<pcl::Histogram<153> >::Ptr spin_images (new pcl::PointCloud<pcl::Histogram<153> >);
spin_image_descriptor.setRadiusSearch (0.2);
// Actually compute the spin images
spin_image_descriptor.compute (*spin_images);
std::cout << "SI output points.size (): " << spin_images->points.size () << std::endl;
// Display and retrieve the spin image descriptor vector for the first point.
pcl::Histogram<153> first_descriptor = spin_images->points[0];
std::cout << first_descriptor << std::endl;
return 0;
}
