example_sift_z_keypoint_estimation.cpp
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00001 /*
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00036  * $Id: example_sift_z_keypoint_estimation.cpp 6062 2012-06-29 08:53:59Z svn $
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00039  */
00040 
00041 // STL
00042 #include <iostream>
00043 
00044 // PCL
00045 #include <pcl/io/pcd_io.h>
00046 #include <pcl/point_types.h>
00047 #include <pcl/common/io.h>
00048 #include <pcl/keypoints/sift_keypoint.h>
00049 #include <pcl/features/normal_3d.h>
00050 // #include <pcl/visualization/pcl_visualizer.h>
00051 
00052 /* This examples shows how to estimate the SIFT points based on the 
00053  * z gradient of the 3D points than using the Intensity gradient as
00054  * usually used for SIFT keypoint estimation.
00055  */
00056 
00057 namespace pcl
00058 {
00059   template<>
00060     struct SIFTKeypointFieldSelector<PointXYZ>
00061     {
00062       inline float
00063       operator () (const PointXYZ &p) const
00064       {
00065         return p.z;
00066       }
00067     };
00068 }
00069 
00070 int
00071 main(int, char** argv)
00072 {
00073   std::string filename = argv[1];
00074   std::cout << "Reading " << filename << std::endl;
00075   pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xyz (new pcl::PointCloud<pcl::PointXYZ>);
00076   if(pcl::io::loadPCDFile<pcl::PointXYZ> (filename, *cloud_xyz) == -1) // load the file
00077   {
00078     PCL_ERROR ("Couldn't read file");
00079     return -1;
00080   }
00081   std::cout << "points: " << cloud_xyz->points.size () <<std::endl;
00082   
00083   // Parameters for sift computation
00084   const float min_scale = 0.005f;
00085   const int n_octaves = 6;
00086   const int n_scales_per_octave = 4;
00087   const float min_contrast = 0.005f;
00088   
00089   // Estimate the sift interest points using z values from xyz as the Intensity variants
00090   pcl::SIFTKeypoint<pcl::PointXYZ, pcl::PointWithScale> sift;
00091   pcl::PointCloud<pcl::PointWithScale> result;
00092   pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ> ());
00093   sift.setSearchMethod(tree);
00094   sift.setScales(min_scale, n_octaves, n_scales_per_octave);
00095   sift.setMinimumContrast(min_contrast);
00096   sift.setInputCloud(cloud_xyz);
00097   sift.compute(result);
00098 
00099   std::cout << "No of SIFT points in the result are " << result.points.size () << std::endl;
00100 
00101 /*
00102   // Copying the pointwithscale to pointxyz so as visualize the cloud
00103   pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_temp (new pcl::PointCloud<pcl::PointXYZ>);
00104   copyPointCloud(result, *cloud_temp);
00105   std::cout << "SIFT points in the result are " << cloud_temp->points.size () << std::endl;
00106   // Visualization of keypoints along with the original cloud
00107   pcl::visualization::PCLVisualizer viewer("PCL Viewer");
00108   pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> keypoints_color_handler (cloud_temp, 0, 255, 0);
00109   pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> cloud_color_handler (cloud_xyz, 255, 0, 0);
00110   viewer.setBackgroundColor( 0.0, 0.0, 0.0 );
00111   viewer.addPointCloud(cloud_xyz, cloud_color_handler, "cloud");
00112   viewer.addPointCloud(cloud_temp, keypoints_color_handler, "keypoints");
00113   viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "keypoints");
00114   
00115   while(!viewer.wasStopped ())
00116   {
00117     viewer.spinOnce ();
00118   }
00119 */
00120 
00121   return 0;
00122   
00123 }


pcl
Author(s): Open Perception
autogenerated on Mon Oct 6 2014 03:14:54