NARF關鍵點提取

1、背景

關鍵點也稱爲興趣點，是通過定義檢測標準來獲取的具有穩定性、區別性的點集。從技術上來說，關鍵點的數量要比原始點雲的數目少很多，與局部特徵描述子結合在一起，組成關鍵點描述子常用來形成原始數據的緊湊表示，而且不失代表性與描述性，從而加快識別、追蹤有等對數據的處理速度。故而，關鍵點提取就成了2D和3D信息處理中不可或缺的關鍵技術。

本例程將點雲數據生成深度圖數據，然後對深度圖數據進行關鍵點提取，最後將關鍵點在點雲視圖中進行可視化。

2、關鍵點提取代碼

//提取NARF關鍵點
pcl::RangeImageBorderExtractor range_image_border_extractor;//創建深度圖像邊界提取對象
pcl::NarfKeypoint narf_keypoint_detector(&range_image_border_extractor);//創建Narf關鍵點提取器，輸入爲深度圖像邊緣提取器
narf_keypoint_detector.setRangeImage(&range_image);//關鍵點提取設置輸入深度圖像
narf_keypoint_detector.getParameters().support_size=support_size;//關鍵點提取的參數：搜索空間球體的半徑
narf_keypoint_detector.getParameters ().add_points_on_straight_edges = true;//對豎直邊是否感興趣
//narf_keypoint_detector.getParameters ().distance_for_additional_points = 10;//與support_size一起作用
 
pcl::PointCloud<int> keypoint_indices;//關鍵點索引
narf_keypoint_detector.compute(keypoint_indices);//關鍵點計算,結果放置到keypoint_indices
std::cerr<<"Found "<<keypoint_indices.points.size()<<" key points.\n";//輸出得到的NARF關鍵點數目

3、全部代碼

/* \author Bastian Steder */
 
#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>//特徵點提取頭文件
#include <pcl/console/parse.h>//命令行解析
 
#include <pcl/visualization/common/float_image_utils.h>//保存深度圖像相關頭文件
#include <pcl/io/png_io.h>//保存深度圖像相關頭文件
 
typedef pcl::PointXYZ PointType;
//參數
float angular_resolution=0.5f;
float support_size=0.2f;//默認關鍵點提取的參數（關鍵點提取器所支持的範圍：搜索空間球體的半徑，指定了計算感興趣值的測度時所使用的鄰域範圍）
pcl::RangeImage::CoordinateFrame coordinate_frame=pcl::RangeImage::CAMERA_FRAME;
bool setUnseenToMaxRange=false;
//打印幫助
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 as maximum range readings\n"
<<"-s <float>   support size for the interest points (diameter of the used sphere - "
<<"default "<<support_size<<")\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]);
/*
viewer.camera_.pos[0]=pos_vector[0];
viewer.camera_.pos[1]=pos_vector[1];
viewer.camera_.pos[2]=pos_vector[2];
viewer.camera_.focal[0]=look_at_vector[0];
viewer.camera_.focal[1]=look_at_vector[1];
viewer.camera_.focal[2]=look_at_vector[2];
viewer.camera_.view[0]=up_vector[0];
viewer.camera_.view[1]=up_vector[1];
viewer.camera_.view[2]=up_vector[2];
viewer.updateCamera();
 */
}
 
// -----Main-----
int
main(int argc,char** argv)
{
//解析命令行參數
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";
}
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);
//讀取給定的pcd文件或者自行創建隨機點雲
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)//打開失敗
{
cerr<<"Was not able to open file \""<<filename<<"\".\n";
printUsage(argv[0]);
return 0;
}
//打開pcd文件成功
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)//如果打開far_ranges文件失敗
std::cout<<"Far ranges file \""<<far_ranges_filename<<"\" does not exists.\n";
}
 
//沒有給的輸入點雲，則生成一個點雲
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;//height = 1爲無序點雲
}
//從點雲創建距離圖像
float noise_level=0.0;
float min_range=0.0f;
int border_size=1;//深度圖邊界大小
boost::shared_ptr<pcl::RangeImage> range_image_ptr(new pcl::RangeImage);
pcl::RangeImage&range_image=*range_image_ptr;
//生成深度圖像range_image
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);//不是太懂
if(setUnseenToMaxRange)
range_image.setUnseenToMaxRange();
// 創建3D點雲可視化窗口，並顯示點雲
pcl::visualization::PCLVisualizer viewer("3D Viewer");//3D窗口
//第一個窗口，點雲和關鍵點都顯示
int v1(0);
viewer.createViewPort(0,0,0.5,1,v1);
viewer.setBackgroundColor(255,1,1,v1);//背景顏色
pcl::visualization::PointCloudColorHandlerCustom<PointType> color1(point_cloud_ptr,200,10,10);//輸入點雲的顏色
//pcl::visualization::PointCloudColorHandlerCustom<pcl::PointWithRange>range_image_color_handler(range_image_ptr,0,0,0);//輸入點雲的顏色,賦值兼容規則
viewer.addPointCloud(point_cloud_ptr,color1,"point_cloud",v1);//添加點雲
//viewer.addPointCloud(range_image_ptr,range_image_color_handler,"range image",v1);//添加點雲
viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE,1,"range image",v1);//設置點的大小
 
//第二個窗口，只顯示關鍵點
int v2(0);
viewer.createViewPort(0.5,0,1,1,v2);
viewer.setBackgroundColor(128,0,190,v2);
 
//viewer.addCoordinateSystem (1.0f);
//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());
// 顯示距離圖像
pcl::visualization::RangeImageVisualizer range_image_widget("Range image");
range_image_widget.showRangeImage(range_image);
//保存深度圖像
float* ranges = range_image.getRangesArray();
unsigned char* rgb_image = pcl::visualization::FloatImageUtils::getVisualImage(ranges,range_image.width,range_image.height);
pcl::io::saveRgbPNGFile("range_image.png",rgb_image,range_image.width,range_image.height);//保存深度圖像至range_image.png
std::cerr << "range_image has been saved!" << std::endl;
 
 
//提取NARF關鍵點
pcl::RangeImageBorderExtractor range_image_border_extractor;//創建深度圖像邊界提取對象
pcl::NarfKeypoint narf_keypoint_detector(&range_image_border_extractor);//創建Narf關鍵點提取器，輸入爲深度圖像邊緣提取器
narf_keypoint_detector.setRangeImage(&range_image);//關鍵點提取設置輸入深度圖像
narf_keypoint_detector.getParameters().support_size=support_size;//關鍵點提取的參數：搜索空間球體的半徑
narf_keypoint_detector.getParameters ().add_points_on_straight_edges = true;//對豎直邊是否感興趣
//narf_keypoint_detector.getParameters ().distance_for_additional_points = 10;//與support_size一起作用
 
pcl::PointCloud<int> keypoint_indices;//關鍵點索引
narf_keypoint_detector.compute(keypoint_indices);//關鍵點計算,結果放置到keypoint_indices
std::cerr<<"Found "<<keypoint_indices.points.size()<<" key points.\n";//輸出得到的NARF關鍵點數目
//保存關鍵點
//轉換關鍵點類型
pcl::PointCloud<PointType>::Ptr narf_points_ptr(new pcl::PointCloud<PointType>);
narf_points_ptr->width = keypoint_indices.points.size();
narf_points_ptr->height = 1;
narf_points_ptr->resize(keypoint_indices.points.size());
narf_points_ptr->is_dense = false;
for (size_t i = 0;i<keypoint_indices.points.size();i++)
{
    narf_points_ptr->points[i].getVector3fMap() = range_image.points[keypoint_indices.points[i]].getVector3fMap();
}
//輸出narf轉換後的關鍵點
std::cerr<<"narf_points: "<<narf_points_ptr->points.size()<<std::endl;
//for (size_t i = 0;i<narf_points_ptr->points.size();i++)
//   std::cerr<<"   ["<<i<<"]   "<<narf_points_ptr->points[i].x<<", "<<narf_points_ptr->points[i].y<<", "<<narf_points_ptr->points[i].z<<std::endl;
//保存narf關鍵點
pcl::io::savePCDFileASCII("narf_points.pcd",*narf_points_ptr);
std::cerr<<"narf_points save succeed!"<<std::endl;
//在距離圖像顯示組件內顯示關鍵點
//for (size_ti=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);
//在3D窗口中顯示關鍵點
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();//將得到的關鍵點轉換給keypoints(pcl::PointCloud<pcl::PointXYZ>類型)
//設置關鍵點在3D Viewer中的顏色大小屬性
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>keypoints_color_handler(keypoints_ptr,0,255,0);//顏色
viewer.addPointCloud<pcl::PointXYZ>(keypoints_ptr,keypoints_color_handler,"keypoints");//顯示
viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE,7,"keypoints");//關鍵點的大小
// 主循環
while(!viewer.wasStopped())
{
range_image_widget.spinOnce();// process GUI events
viewer.spinOnce();
pcl_sleep(0.01);
}
}

代碼運行效果：

將” narf_keypoint_detector.getParameters ().add_points_on_straight_edges = true;//對豎直邊是否感興趣”設置爲感興趣(true)，此時關鍵點多。

參考博文：

1、PCL NARF關鍵點提取、SIFT關鍵點提取、HARRIS關鍵點提取

PCL NARF關鍵點提取

NARF關鍵點提取

1、背景

2、關鍵點提取代碼

3、全部代碼

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