nn_classification_example.cpp
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00001 /*
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00034  * $Id: nn_classification_example.cpp 6218 2012-07-06 21:46:51Z aichim $
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00036  */
00037 
00038 #include <pcl/point_types.h>
00039 #include <pcl/io/pcd_io.h>
00040 #include <pcl/apps/vfh_nn_classifier.h>
00041 
00042 int 
00043 main (int, char* argv[])
00044 {
00045   // Load input file
00046   char* file_name = argv[1];
00047   sensor_msgs::PointCloud2 cloud_blob;
00048   pcl::io::loadPCDFile (file_name, cloud_blob);
00049 
00050   // Declare variable to hold result
00051   pcl::NNClassification<pcl::VFHSignature308>::ResultPtr result;
00052   // same as: pcl::VFHClassifierNN::ResultPtr result;
00053 
00054   // Do general classification using NNClassification or use the VHClassiierNN helper class
00055   if (false)
00056   {
00057     // Estimate your favorite feature
00058     pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ> ());
00059     pcl::fromROSMsg (cloud_blob, *cloud);
00061     pcl::PointCloud<pcl::VFHSignature308>::Ptr feature = pcl::computeVFH<pcl::PointXYZ> (cloud, 0.03);
00062 
00063     // Nearest neighbors classification
00064     pcl::NNClassification<pcl::VFHSignature308> nn;
00065     //nn.setTrainingFeatures(cloud);
00066     //nn.setTrainingLabels(std::vector<std::string>(cloud->points.size(), "bla"));
00067     nn.loadTrainingFeatures (argv[2], argv[3]);
00068     result = nn.classify(feature->points[0], 300, 50);
00069   }
00070   else
00071   {
00072     pcl::VFHClassifierNN vfh_classifier;
00073     //vfh_classifier.loadTrainingData ("/home/marton/ros/pcl/trunk/apps/data/can.pcd", "can");
00074     //vfh_classifier.loadTrainingData ("/home/marton/ros/pcl/trunk/apps/data/salt.pcd", "salt");
00075     //vfh_classifier.loadTrainingData ("/home/marton/ros/pcl/trunk/apps/data/sugar.pcd", "sugar");
00076     //vfh_classifier.saveTrainingFeatures ("/tmp/vfhs.pcd", "/tmp/vfhs.labels");
00077     vfh_classifier.loadTrainingFeatures (argv[2], argv[3]);
00078     vfh_classifier.finalizeTraining ();
00079     result = vfh_classifier.classify(cloud_blob);
00080   }
00081 
00082   // Print results
00083   for (unsigned i = 0; i < result->first.size(); ++i)
00084     std::cerr << result->first.at (i) << ": " << result->second.at (i) << std::endl;
00085 
00086   return 0;
00087 }


pcl
Author(s): Open Perception
autogenerated on Mon Oct 6 2014 03:15:48