• PCL 点云转深度图像


    点云转深度图像

    #include <pcl/range_image/range_image.h>    //深度图像的头文件
    
    int main(int argc, char** argv) {
    	pcl::PointCloud<pcl::PointXYZ> pointCloud;   //定义点云的对象
    
    	// 循环产生点云的数据
    	for (float y = -0.5f; y <= 0.5f; y += 0.01f) {
    		for (float z = -0.5f; z <= 0.5f; z += 0.01f) {
    			pcl::PointXYZ point;
    			point.x = 2.0f - y;
    			point.y = y;
    			point.z = z;
    			pointCloud.points.push_back(point); //循环添加点数据到点云对象
    		}
    	}
    	pointCloud.width = (uint32_t)pointCloud.points.size();
    	pointCloud.height = 1;                                        //设置点云对象的头信息
    	  //实现一个呈矩形形状的点云
    	// We now want to create a range image from the above point cloud, with a 1deg angular resolution
    	 //angular_resolution为模拟的深度传感器的角度分辨率,即深度图像中一个像素对应的角度大小
    	float angularResolution = (float)(1.0f * (M_PI / 180.0f));  //   1.0 degree in radians
    	 //max_angle_width为模拟的深度传感器的水平最大采样角度,
    	float maxAngleWidth = (float)(360.0f * (M_PI / 180.0f));  // 360.0 degree in radians
    	//max_angle_height为模拟传感器的垂直方向最大采样角度  都转为弧度
    	float maxAngleHeight = (float)(180.0f * (M_PI / 180.0f));  // 180.0 degree in radians
    	 //传感器的采集位置
    	Eigen::Affine3f sensorPose = (Eigen::Affine3f)Eigen::Translation3f(0.0f, 0.0f, 0.0f);
    	//深度图像遵循坐标系统
    	pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;
    	float noiseLevel = 0.00;    //noise_level获取深度图像深度时,近邻点对查询点距离值的影响水平
    	float minRange = 0.0f;     //min_range设置最小的获取距离,小于最小获取距离的位置为传感器的盲区
    	int borderSize = 1;        //border_size获得深度图像的边缘的宽度
    
    	pcl::RangeImage rangeImage;
    	rangeImage.createFromPointCloud(pointCloud, angularResolution, maxAngleWidth, maxAngleHeight,
    		sensorPose, coordinate_frame, noiseLevel, minRange, borderSize);
    
    	std::cout << rangeImage << "\n";
    }
    
    
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    从深度图像提取边界

    /* \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/console/parse.h>
    typedef pcl::PointXYZ PointType;
    // --------------------
    // -----参数-----
    // --------------------
    float angular_resolution = 0.5f;
    pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;
    bool setUnseenToMaxRange = false;
    // --------------
    // -----帮助-----
    // --------------
    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 to max range\n"
                << "-h           this help\n"
                << "\n\n";
    }
    // --------------
    // -----主函数-----
    // --------------
    int 
    main (int argc, char** argv)
    {
      // --------------------------------------
      // -----解析命令行参数-----
      // --------------------------------------
      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, "-r", angular_resolution) >= 0)
        cout << "Setting angular resolution to "<<angular_resolution<<"deg.\n";
      angular_resolution = pcl::deg2rad (angular_resolution);
      // ------------------------------------------------------------------
      // -----读取pcd文件,如果没有给出pcd文件则创建一个示例点云-----
      // ------------------------------------------------------------------
      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)
        {
          cout << "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
      {
        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;
      }
      // -----------------------------------------------
      // -----从点云创建深度图像-----
      // -----------------------------------------------
      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 ();
      // --------------------------------------------
      // -----打开三维浏览器并添加点云-----
      // --------------------------------------------
      pcl::visualization::PCLVisualizer viewer ("3D Viewer");
      viewer.setBackgroundColor (1, 1, 1);
      viewer.addCoordinateSystem (1.0f);
      pcl::visualization::PointCloudColorHandlerCustom<PointType> point_cloud_color_handler (point_cloud_ptr, 0, 0, 0);
      viewer.addPointCloud (point_cloud_ptr, point_cloud_color_handler, "original point cloud");
      //PointCloudColorHandlerCustom<pcl::PointWithRange>   range_image_color_handler (range_image_ptr, 150, 150, 150);
      //viewer.addPointCloud (range_image_ptr, range_image_color_handler, "range image");
      //viewer.setPointCloudRenderingProperties (PCL_VISUALIZER_POINT_SIZE, 2, "range image");
      // -------------------------
      // -----提取边界-----
      // -------------------------
      pcl::RangeImageBorderExtractor border_extractor (&range_image);
      pcl::PointCloud<pcl::BorderDescription> border_descriptions;
      border_extractor.compute (border_descriptions);
      // ----------------------------------
      // -----在三维浏览器中显示点集-----
      // ----------------------------------
      pcl::PointCloud<pcl::PointWithRange>::Ptr border_points_ptr(new pcl::PointCloud<pcl::PointWithRange>), veil_points_ptr(new pcl::PointCloud<pcl::PointWithRange>), shadow_points_ptr(new pcl::PointCloud<pcl::PointWithRange>);
      pcl::PointCloud<pcl::PointWithRange>& border_points = *border_points_ptr, & veil_points = * veil_points_ptr, & shadow_points = *shadow_points_ptr;
      for (int y=0; y< (int)range_image.height; ++y)
      {
        for (int x=0; x< (int)range_image.width; ++x)
        {
          if (border_descriptions.points[y*range_image.width + x].traits[pcl::BORDER_TRAIT__OBSTACLE_BORDER])
            border_points.points.push_back (range_image.points[y*range_image.width + x]);
          if (border_descriptions.points[y*range_image.width + x].traits[pcl::BORDER_TRAIT__VEIL_POINT])
            veil_points.points.push_back (range_image.points[y*range_image.width + x]);
          if (border_descriptions.points[y*range_image.width + x].traits[pcl::BORDER_TRAIT__SHADOW_BORDER])
            shadow_points.points.push_back (range_image.points[y*range_image.width + x]);
        }
      }
      pcl::visualization::PointCloudColorHandlerCustom<pcl::PointWithRange> border_points_color_handler (border_points_ptr, 0, 255, 0);
      viewer.addPointCloud<pcl::PointWithRange> (border_points_ptr, border_points_color_handler, "border points");
      viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "border points");
      pcl::visualization::PointCloudColorHandlerCustom<pcl::PointWithRange> veil_points_color_handler (veil_points_ptr, 255, 0, 0);
      viewer.addPointCloud<pcl::PointWithRange> (veil_points_ptr, veil_points_color_handler, "veil points");
      viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "veil points");
      pcl::visualization::PointCloudColorHandlerCustom<pcl::PointWithRange> shadow_points_color_handler (shadow_points_ptr, 0, 255, 255);
      viewer.addPointCloud<pcl::PointWithRange> (shadow_points_ptr, shadow_points_color_handler, "shadow points");
      viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 7, "shadow points");
      //-------------------------------------
      // -----在深度图像中显示点集-----
      // ------------------------------------
      pcl::visualization::RangeImageVisualizer* range_image_borders_widget = NULL;
      range_image_borders_widget =
        pcl::visualization::RangeImageVisualizer::getRangeImageBordersWidget (range_image, -std::numeric_limits<float>::infinity (), std::numeric_limits<float>::infinity (), false, border_descriptions, "Range image with borders" );                     
      // -------------------------------------
      //--------------------
      // -----主循环-----
      //--------------------
      while (!viewer.wasStopped ())
      {
        range_image_borders_widget->spinOnce ();
        viewer.spinOnce ();
        pcl_sleep(0.01);
      }
    }
    
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    实例解析

    #include <pcl/range_image/range_image.h>
    #include <pcl/range_image/range_image_planar.h>
    #include <pcl/io/io.h>
    #include <pcl/io/pcd_io.h>
    #include <pcl/features/integral_image_normal.h>
    #include <pcl/visualization/cloud_viewer.h>
    #include <pcl/point_types.h>
    #include <pcl/features/normal_3d.h>
    #include <pcl/console/print.h>
    #include <pcl/surface/organized_fast_mesh.h>
    #include <pcl/console/time.h>
    #include <Eigen/StdVector>
    #include <Eigen/Geometry>
    #include <iostream>
    #include <pcl/surface/impl/organized_fast_mesh.hpp> 
    #include <boost/thread/thread.hpp>
    #include <pcl/common/common_headers.h>
    #include <pcl/visualization/range_image_visualizer.h>
    #include <pcl/visualization/pcl_visualizer.h>
    #include <pcl/console/parse.h>
    
    using namespace pcl::console;
    
    int main(int argc, char** argv) {
    	// Generate the data
    	if (argc < 2)
    	{
    		print_error("Syntax is: %s input.pcd -w 640 -h 480 -cx 320 -cy 240 -fx 525 -fy 525 -type 0 -size 2\n", argv[0]);
    		print_info("  where options are:\n");
    		print_info("                     -w X = width of detph iamge ");
    		return -1;
    	}
    	std::string filename = argv[1];
    
    	/**************************************************************
    	定义从点云到深度图像转换时所需的参数,并可以接受用户命令行的参数设置。
    	***********************************************************/
    	int width = 640, height = 480, size = 2, type = 0;
    	float fx = 525, fy = 525, cx = 320, cy = 240;
    	parse_argument(argc, argv, "-w", width); //深度图像宽度
    	parse_argument(argc, argv, "-h", height);//深度图像高度
    	parse_argument(argc, argv, "-cx", cx);//光轴在深度图像上的x坐标
    	parse_argument(argc, argv, "-cy", cy);//光轴在深度图像上的y坐标
    	parse_argument(argc, argv, "-fx", fx);//水平方向焦距
    	parse_argument(argc, argv, "-fy", fy);//垂直方向焦距
    	parse_argument(argc, argv, "-type", type);//曲面重建时三角化的方式
    	parse_argument(argc, argv, "-size", size);//曲面重建时的面片大小
    
    	/**************************************************************
       加载原始点云,同时创建RangeImagePlanar对象,利用改对象的函数createFromPointCloudWithFixedSize()进行深
       度图像的生成,这里的参数除了上面用户设置的参数外,还需要提供相机成像的位姿,以及成像遵循的坐标系统。
       ***********************************************************/
    	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGB>);
    	pcl::io::loadPCDFile(filename, *cloud);
    	print_info("Read pcd file successfully\n");
    	Eigen::Affine3f sensorPose;
    	sensorPose.setIdentity();
    	pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;
    	float noiseLevel = 0.00;//成像时模拟噪声水平
    	float minRange = 0.0f;//成像时考虑阈值外的点
    	pcl::RangeImagePlanar::Ptr rangeImage(new pcl::RangeImagePlanar);
    	rangeImage->createFromPointCloudWithFixedSize(*cloud, width, height, cx, cy, fx, fy, sensorPose, coordinate_frame);
    
    	std::cout << rangeImage << "\n";
    	//convert unorignized point cloud to orginized point cloud end
    	//可视化深度图像
    	pcl::visualization::RangeImageVisualizer range_image_widget("range image");
    	range_image_widget.showRangeImage(*rangeImage);
    	range_image_widget.setWindowTitle("range image");
    
    	/**************************************************************
       完成从点云到深度图像的生成后,我们利用该深度图像作为输人,来使用曲面重建模块中的简单三角化类生成曲面模型。
       创建OrganizedFastMesh对象,该算法的输人参数有size,通过setTrianglePixelSize( )函数接口来改变,其控制重
       建曲面的精细程度。另外一个 参数是setTriangulationType( )方法设置的三角化的类型,是个枚举变量,包含三角
       形、四边形等。
       ***********************************************************/
       //triangulation based on range image
    	pcl::OrganizedFastMesh<pcl::PointWithRange>::Ptr tri(new pcl::OrganizedFastMesh<pcl::PointWithRange>);
    	pcl::search::KdTree<pcl::PointWithRange>::Ptr tree(new pcl::search::KdTree<pcl::PointWithRange>);
    	tree->setInputCloud(rangeImage);
    	pcl::PolygonMesh triangles;
    	tri->setTrianglePixelSize(size);
    	tri->setInputCloud(rangeImage);
    	tri->setSearchMethod(tree);
    	tri->setTriangulationType((pcl::OrganizedFastMesh<pcl::PointWithRange>::TriangulationType)type);
    	tri->reconstruct(triangles);
    
    	boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("range image"));
    	viewer->setBackgroundColor(0.5, 0.5, 0.5);
    	viewer->addPolygonMesh(triangles, "tin");
    	viewer->addCoordinateSystem();
    	while (!range_image_widget.wasStopped() && !viewer->wasStopped())
    	{
    		range_image_widget.spinOnce();
    		//pcl_sleep(0.01);
    		viewer->spinOnce();
    	}
    }
    
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  • 原文地址:https://blog.csdn.net/u011489887/article/details/125564940