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";
}

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);
  }
}

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();
	}
}