本節將顯示如何提取出NARF關鍵點通過NARF描述器從一個深度圖里面。

以下是一段代碼

#include <iostream>#include <boost/thread/thread.hpp> #include <pcl/range_image/range_image.h> #include <pcl/io/pcd_io.h> #include <pcl/visualization/range_image_visualizer.h> #include <pcl/visualization/pcl_visualizer.h> #include <pcl/features/range_image_border_extractor.h> #include <pcl/keypoints/narf_keypoint.h> #include <pcl/features/narf_descriptor.h> #include <pcl/console/parse.h>typedef pcl::PointXYZ PointType;// -------------------- // -----Parameters----- // -------------------- float angular_resolution = 0.5f; float support_size = 0.2f; pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME; bool setUnseenToMaxRange = false; bool rotation_invariant = true;// -------------- // -----Help----- // -------------- void printUsage (const char* progName) {std::cout << "\n\nUsage: "<<progName<<" [options] <scene.pcd>\n\n"<< "Options:\n"<< "-------------------------------------------\n"<< "-r <float> angular resolution in degrees (default "<<angular_resolution<<")\n"<< "-c <int> coordinate frame (default "<< (int)coordinate_frame<<")\n"<< "-m Treat all unseen points to max range\n"<< "-s <float> support size for the interest points (diameter of the used sphere - ""default "<<support_size<<")\n"<< "-o <0/1> switch rotational invariant version of the feature on/off"<< " (default "<< (int)rotation_invariant<<")\n"<< "-h this help\n"<< "\n\n"; }void setViewerPose (pcl::visualization::PCLVisualizer& viewer, const Eigen::Affine3f& viewer_pose) {Eigen::Vector3f pos_vector = viewer_pose * Eigen::Vector3f (0, 0, 0);Eigen::Vector3f look_at_vector = viewer_pose.rotation () * Eigen::Vector3f (0, 0, 1) + pos_vector;Eigen::Vector3f up_vector = viewer_pose.rotation () * Eigen::Vector3f (0, -1, 0);viewer.setCameraPosition (pos_vector[0], pos_vector[1], pos_vector[2],look_at_vector[0], look_at_vector[1], look_at_vector[2],up_vector[0], up_vector[1], up_vector[2]); }// -------------- // -----Main----- // -------------- int main (int argc, char** argv) {// --------------------------------------// -----Parse Command Line Arguments-----// --------------------------------------if (pcl::console::find_argument (argc, argv, "-h") >= 0){printUsage (argv[0]);return 0;}if (pcl::console::find_argument (argc, argv, "-m") >= 0){setUnseenToMaxRange = true;cout << "Setting unseen values in range image to maximum range readings.\n";}if (pcl::console::parse (argc, argv, "-o", rotation_invariant) >= 0)cout << "Switching rotation invariant feature version "<< (rotation_invariant ? "on" : "off")<<".\n";int tmp_coordinate_frame;if (pcl::console::parse (argc, argv, "-c", tmp_coordinate_frame) >= 0){coordinate_frame = pcl::RangeImage::CoordinateFrame (tmp_coordinate_frame);cout << "Using coordinate frame "<< (int)coordinate_frame<<".\n";}if (pcl::console::parse (argc, argv, "-s", support_size) >= 0)cout << "Setting support size to "<<support_size<<".\n";if (pcl::console::parse (argc, argv, "-r", angular_resolution) >= 0)cout << "Setting angular resolution to "<<angular_resolution<<"deg.\n";angular_resolution = pcl::deg2rad (angular_resolution);// ------------------------------------------------------------------// -----Read pcd file or create example point cloud if not given-----// ------------------------------------------------------------------pcl::PointCloud<PointType>::Ptr point_cloud_ptr (new pcl::PointCloud<PointType>);pcl::PointCloud<PointType>& point_cloud = *point_cloud_ptr;pcl::PointCloud<pcl::PointWithViewpoint> far_ranges;Eigen::Affine3f scene_sensor_pose (Eigen::Affine3f::Identity ());std::vector<int> pcd_filename_indices = pcl::console::parse_file_extension_argument (argc, argv, "pcd");if (!pcd_filename_indices.empty ()){std::string filename = argv[pcd_filename_indices[0]];if (pcl::io::loadPCDFile (filename, point_cloud) == -1){cerr << "Was not able to open file \""<<filename<<"\".\n";printUsage (argv[0]);return 0;}scene_sensor_pose = Eigen::Affine3f (Eigen::Translation3f (point_cloud.sensor_origin_[0],point_cloud.sensor_origin_[1],point_cloud.sensor_origin_[2])) *Eigen::Affine3f (point_cloud.sensor_orientation_);std::string far_ranges_filename = pcl::getFilenameWithoutExtension (filename)+"_far_ranges.pcd";if (pcl::io::loadPCDFile (far_ranges_filename.c_str (), far_ranges) == -1)std::cout << "Far ranges file \""<<far_ranges_filename<<"\" does not exists.\n";}else{setUnseenToMaxRange = true;cout << "\nNo *.pcd file given => Genarating example point cloud.\n\n";for (float x=-0.5f; x<=0.5f; x+=0.01f){for (float y=-0.5f; y<=0.5f; y+=0.01f){PointType point; point.x = x; point.y = y; point.z = 2.0f - y;point_cloud.points.push_back (point);}}point_cloud.width = (int) point_cloud.points.size (); point_cloud.height = 1;}// -----------------------------------------------// -----Create RangeImage from the PointCloud-----// -----------------------------------------------float noise_level = 0.0;float min_range = 0.0f;int border_size = 1;boost::shared_ptr<pcl::RangeImage> range_image_ptr (new pcl::RangeImage);pcl::RangeImage& range_image = *range_image_ptr; range_image.createFromPointCloud (point_cloud, angular_resolution, pcl::deg2rad (360.0f), pcl::deg2rad (180.0f),scene_sensor_pose, coordinate_frame, noise_level, min_range, border_size);range_image.integrateFarRanges (far_ranges);if (setUnseenToMaxRange)range_image.setUnseenToMaxRange ();// --------------------------------------------// -----Open 3D viewer and add point cloud-----// --------------------------------------------pcl::visualization::PCLVisualizer viewer ("3D Viewer");viewer.setBackgroundColor (1, 1, 1);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointWithRange> range_image_color_handler (range_image_ptr, 0, 0, 0);viewer.addPointCloud (range_image_ptr, range_image_color_handler, "range image");viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "range image");//viewer.addCoordinateSystem (1.0f, "global");//PointCloudColorHandlerCustom<PointType> point_cloud_color_handler (point_cloud_ptr, 150, 150, 150);//viewer.addPointCloud (point_cloud_ptr, point_cloud_color_handler, "original point cloud");viewer.initCameraParameters ();setViewerPose (viewer, range_image.getTransformationToWorldSystem ());// --------------------------// -----Show range image-----// --------------------------pcl::visualization::RangeImageVisualizer range_image_widget ("Range image");range_image_widget.showRangeImage (range_image);// --------------------------------// -----Extract NARF keypoints-----// --------------------------------pcl::RangeImageBorderExtractor range_image_border_extractor;pcl::NarfKeypoint narf_keypoint_detector;narf_keypoint_detector.setRangeImageBorderExtractor (&range_image_border_extractor);narf_keypoint_detector.setRangeImage (&range_image);narf_keypoint_detector.getParameters ().support_size = support_size;pcl::PointCloud<int> keypoint_indices;narf_keypoint_detector.compute (keypoint_indices);std::cout << "Found "<<keypoint_indices.points.size ()<<" key points.\n";// ----------------------------------------------// -----Show keypoints in range image widget-----// ----------------------------------------------//for (size_t i=0; i<keypoint_indices.points.size (); ++i)//range_image_widget.markPoint (keypoint_indices.points[i]%range_image.width,//keypoint_indices.points[i]/range_image.width);// -------------------------------------// -----Show keypoints in 3D viewer-----// -------------------------------------pcl::PointCloud<pcl::PointXYZ>::Ptr keypoints_ptr (new pcl::PointCloud<pcl::PointXYZ>);pcl::PointCloud<pcl::PointXYZ>& keypoints = *keypoints_ptr;keypoints.points.resize (keypoint_indices.points.size ());for (size_t i=0; i<keypoint_indices.points.size (); ++i)keypoints.points[i].getVector3fMap () = range_image.points[keypoint_indices.points[i]].getVector3fMap ();pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> keypoints_color_handler (keypoints_ptr, 0, 255, 0);viewer.addPointCloud<pcl::PointXYZ> (keypoints_ptr, keypoints_color_handler, "keypoints");viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "keypoints");// ------------------------------------------------------// -----Extract NARF descriptors for interest points-----// ------------------------------------------------------std::vector<int> keypoint_indices2;keypoint_indices2.resize (keypoint_indices.points.size ());for (unsigned int i=0; i<keypoint_indices.size (); ++i) // This step is necessary to get the right vector typekeypoint_indices2[i]=keypoint_indices.points[i];pcl::NarfDescriptor narf_descriptor (&range_image, &keypoint_indices2);narf_descriptor.getParameters ().support_size = support_size;narf_descriptor.getParameters ().rotation_invariant = rotation_invariant;pcl::PointCloud<pcl::Narf36> narf_descriptors;narf_descriptor.compute (narf_descriptors);cout << "Extracted "<<narf_descriptors.size ()<<" descriptors for "<<keypoint_indices.points.size ()<< " keypoints.\n";//--------------------// -----Main loop-----//--------------------while (!viewer.wasStopped ()){range_image_widget.spinOnce (); // process GUI eventsviewer.spinOnce ();pcl_sleep(0.01);} }

一開始我們做的是命令行解析，從磁盤中讀取點云文件，創建一個深度圖，把NARF特征點導出。

我們感興趣的部分從下面開始:

std::vector<int> keypoint_indices2; keypoint_indices2.resize(keypoint_indices.points.size()); for (unsigned int i=0; i<keypoint_indices.size(); ++i) // This step is necessary to get the right vector typekeypoint_indices2[i]=keypoint_indices.points[i];

這里我們拷貝向量的下標作為特征的輸入:

pcl::NarfDescriptor narf_descriptor(&range_image, &keypoint_indices2); narf_descriptor.getParameters().support_size = support_size; narf_descriptor.getParameters().rotation_invariant = rotation_invariant; pcl::PointCloud<pcl::Narf36> narf_descriptors; narf_descriptor.compute(narf_descriptors); cout << "Extracted "<<narf_descriptors.size()<<" descriptors for "<<keypoint_indices.points.size()<< " keypoints.\n";

這個代碼是描述器里面的計算部分。它先第一步創造了NarfDescriptor這個對象，然后把它作為輸入值，然后有兩個很重要的參數被設置了。支持的尺寸，決定了描述器計算的面積，如果NARF描述器里面的旋轉不變量會被使用的話。接下去我們創造了輸出點云然后做實際的計算。最后，我們輸出了關鍵點的數量和導出描述器的數量。這個數量將會改變。有可能，它會發生計算失敗的情況，因為沒有足夠的點在深度圖像里面?；蛘呖赡軙卸嘀孛枋銎髟谕粋€地方，雖然屬于不同的方向域。

最終結果的點云包含了Narf26的類型。下面的代碼把關鍵點的位置在深度圖控件里面可視化出來，還有一個是在3D viewer里面可視化出來。

然后我們運行

./narf_feature_extraction -m

這將自動生成矩形浮動的點云。關鍵點會在角上被察覺。參數-m是必要的，因為矩形周圍的區域是看不到的因此系統是不會把它看做是一個角。-m的選項改變不可見的區域擴大深度的讀取范圍，從而使系統可以用到那些角

你也可以讓這個程序讀取一個點云文件

./narf_feature_extraction <point_cloud.pcd>



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