Point Cloud Library (PCL)
1.3.1
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IN NO EVENT SHALL THE 00025 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00026 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00027 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00028 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00029 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00030 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00031 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00032 * POSSIBILITY OF SUCH DAMAGE. 00033 * 00034 * $Id: vfh_nn_classifier.h 3039 2011-11-01 04:43:33Z rusu $ 00035 * 00036 */ 00037 00038 #ifndef VFHCLASSIFICATION_H_ 00039 #define VFHCLASSIFICATION_H_ 00040 00041 #include <fstream> 00042 #include <pcl/point_types.h> 00043 #include <pcl/io/pcd_io.h> 00044 #include <pcl/features/vfh.h> 00045 #include <pcl/features/normal_3d.h> 00046 #include <pcl/apps/nn_classification.h> 00047 #include <sensor_msgs/PointCloud2.h> 00048 00049 namespace pcl 00050 { 00056 template <typename PointT> pcl::PointCloud<pcl::VFHSignature308>::Ptr 00057 computeVFH (typename PointCloud<PointT>::ConstPtr cloud, double radius) 00058 { 00059 using namespace pcl; 00060 00061 // Create an empty kdtree representation, and pass it to the objects. 00062 // Its content will be filled inside the object, based on the given input dataset (as no other search surface is given). 00063 typename pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ()); 00064 00065 // Create the normal estimation class, and pass the input dataset to it 00066 NormalEstimation<PointT, Normal> ne; 00067 ne.setInputCloud (cloud); 00068 ne.setSearchMethod (tree); 00069 00070 // Use all neighbors in a sphere of given radius to compute the normals 00071 PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ()); 00072 ne.setRadiusSearch (radius); 00073 ne.compute (*normals); 00074 00075 // Create the VFH estimation class, and pass the input dataset+normals to it 00076 VFHEstimation<PointT, Normal, VFHSignature308> vfh; 00077 vfh.setInputCloud (cloud); 00078 vfh.setInputNormals (normals); 00079 vfh.setSearchMethod (tree); 00080 00081 // Output datasets 00082 PointCloud<VFHSignature308>::Ptr vfhs (new PointCloud<VFHSignature308>); 00083 00084 // Compute the features and return 00085 vfh.compute (*vfhs); 00086 return vfhs; 00087 } 00088 00093 class VFHClassifierNN 00094 { 00095 public: 00096 00097 typedef pcl::PointCloud<pcl::VFHSignature308> FeatureCloud; 00098 typedef pcl::PointCloud<pcl::VFHSignature308>::Ptr FeatureCloudPtr; 00099 typedef pcl::PointCloud<pcl::VFHSignature308>::ConstPtr FeatureCloudConstPtr; 00100 typedef NNClassification<pcl::VFHSignature308>::Result Result; 00101 typedef NNClassification<pcl::VFHSignature308>::ResultPtr ResultPtr; 00102 00103 private: 00104 00106 FeatureCloudPtr training_features_; 00108 std::vector<std::string> labels_; 00110 NNClassification<pcl::VFHSignature308> classifier_; 00111 00112 public: 00113 00114 VFHClassifierNN () 00115 { 00116 reset (); 00117 } 00118 00119 void reset () 00120 { 00121 training_features_.reset (new FeatureCloud); 00122 labels_.clear (); 00123 classifier_ = NNClassification<pcl::VFHSignature308> (); 00124 } 00125 00127 void finalizeTraining () 00128 { 00129 finalizeTree (); 00130 finalizeLabels (); 00131 } 00132 00134 void finalizeTree () 00135 { 00136 classifier_.setTrainingFeatures(training_features_); 00137 } 00138 00140 void finalizeLabels () 00141 { 00142 classifier_.setTrainingLabels(labels_); 00143 } 00144 00150 bool saveTrainingFeatures(std::string file_name, std::string labels_file_name) 00151 { 00152 if (labels_.size () == training_features_->points.size ()) 00153 { 00154 if (pcl::io::savePCDFile (file_name.c_str (), *training_features_) != 0) 00155 return false; 00156 std::ofstream f (labels_file_name.c_str ()); 00157 BOOST_FOREACH (std::string s, labels_) 00158 f << s << "\n"; 00159 return true; 00160 } 00161 return false; 00162 } 00163 00170 bool addTrainingFeatures (const FeatureCloudPtr training_features, const std::vector<std::string> &labels) 00171 { 00172 if (labels.size () == training_features->points.size ()) 00173 { 00174 labels_.insert (labels_.end (), labels.begin (), labels.end ()); 00175 training_features_->points.insert (training_features_->points.end (), training_features->points.begin (), training_features->points.end ()); 00176 training_features_->header = training_features->header; 00177 training_features_->height = 1; 00178 training_features_->width = training_features_->points.size (); 00179 training_features_->is_dense &= training_features->is_dense; 00180 training_features_->sensor_origin_ = training_features->sensor_origin_; 00181 training_features_->sensor_orientation_ = training_features->sensor_orientation_; 00182 return true; 00183 } 00184 return false; 00185 } 00186 00193 bool loadTrainingFeatures(std::string file_name, std::string labels_file_name) 00194 { 00195 FeatureCloudPtr cloud (new FeatureCloud); 00196 if (pcl::io::loadPCDFile (file_name.c_str (), *cloud) != 0) 00197 return false; 00198 std::vector<std::string> labels; 00199 std::ifstream f (labels_file_name.c_str ()); 00200 std::string label; 00201 while (getline (f, label)) 00202 if (label.size () > 0) 00203 labels.push_back(label); 00204 return addTrainingFeatures (cloud, labels); 00205 } 00206 00214 bool loadTrainingData (std::string file_name, std::string label) 00215 { 00216 sensor_msgs::PointCloud2 cloud_blob; 00217 if (pcl::io::loadPCDFile (file_name.c_str (), cloud_blob) != 0) 00218 return false; 00219 return addTrainingData (cloud_blob, label); 00220 } 00221 00228 bool addTrainingData (const sensor_msgs::PointCloud2 &training_data, std::string &label) 00229 { 00230 // Create label list containing the single label 00231 std::vector<std::string> labels; 00232 labels.push_back (label); 00233 00234 // Compute the feature from the cloud and add it as a training example 00235 FeatureCloudPtr vfhs = computeFeature (training_data); 00236 return addTrainingFeatures(vfhs, labels); 00237 } 00238 00245 ResultPtr classify (const sensor_msgs::PointCloud2 &testing_data, double radius = 300, double min_score = 0.002) 00246 { 00247 // compute the VFH feature for this point cloud 00248 FeatureCloudPtr vfhs = computeFeature (testing_data); 00249 // compute gaussian parameter producing the desired minimum score (around 50 for the default values) 00250 double gaussian_param = -radius / log (min_score); 00251 // TODO accept result to be filled in by reference 00252 return classifier_.classify(vfhs->points.at (0), radius, gaussian_param); 00253 } 00254 00260 FeatureCloudPtr computeFeature (const sensor_msgs::PointCloud2 &points, double radius = 0.03) 00261 { 00262 pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ> ()); 00263 pcl::fromROSMsg (points, *cloud); 00264 return pcl::computeVFH<pcl::PointXYZ> (cloud, radius); 00265 } 00266 }; 00267 } 00268 00269 #endif /* VFHCLASSIFICATION_H_ */