本节将显示如何提取出NARF关键点通过NARF描述器从一个深度图里面。
以下是一段代码
#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 type keypoint_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 events viewer.spinOnce (); pcl_sleep(0.01); } }
一开始我们做的是命令行解析,从磁盘中读取点云文件,创建一个深度图,把NARF特征点导出。
我们感兴趣的部分从下面开始:
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 type keypoint_indices2[i]=keypoint_indices.points[i];
这里我们拷贝向量的下标作为特征的输入:
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";
这个代码是描述器里面的计算部分。它先第一步创造了NarfDescriptor这个对象,然后把它作为输入值,然后有两个很重要的参数被设置了。支持的尺寸,决定了描述器计算的面积,如果NARF描述器里面的旋转不变量会被使用的话。接下去我们创造了输出点云然后做实际的计算。最后,我们输出了关键点的数量和导出描述器的数量。这个数量将会改变。有可能,它会发生计算失败的情况,因为没有足够的点在深度图像里面。或者可能会有多重描述器在同一个地方,虽然属于不同的方向域。
最终结果的点云包含了Narf26的类型。下面的代码把关键点的位置在深度图控件里面可视化出来,还有一个是在3D viewer里面可视化出来。
然后我们运行
./narf_feature_extraction -m
这将自动生成矩形浮动的点云。关键点会在角上被察觉。参数-m是必要的,因为矩形周围的区域是看不到的因此系统是不会把它看做是一个角。-m的选项改变不可见的区域扩大深度的读取范围,从而使系统可以用到那些角
你也可以让这个程序读取一个点云文件
./narf_feature_extraction <point_cloud.pcd>