PCL点云库学习笔记（5）——深度图像

1.深度图像简介

深度图像 (Depth Images), 也被称为距离影像 (Range Images), 是指将图像采集器到 场景中各点的距离(深度)值作为像素值的图像，它直接反映了景物可见表面的几何形状, 利用它可以很方便地解决 3D 目标描述中的许多问题。深度图像经过坐标转换可以计算为点云数据，有规则及必要信息的点云数据也可以反算为深度图像数据。

2.PCL中RangeImage类

1）从点云中创建深度图像
下面的程序，首先是生成一个矩形点云，然后基于该点云创建深度图像。

#include 
int main (int argc, char** argv) {
  pcl::PointCloud<pcl::PointXYZ> pointCloud;
 //生成数据
  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);
    }
  }
  pointCloud.width = (uint32_t) pointCloud.points.size();
  pointCloud.height = 1;
//以1度为角分辨率，从上面创建的点云创建深度图像。
  float angularResolution = (float) (  1.0f * (M_PI/180.0f));  
// 1度转弧度 分辨率
  float maxAngleWidth     = (float) (360.0f * (M_PI/180.0f));  
// 360.0度转弧度 最大视角宽度
  float maxAngleHeight    = (float) (180.0f * (M_PI/180.0f)); 
// 180.0度转弧度 最大视角高度
  Eigen::Affine3f sensorPose = (Eigen::Affine3f)Eigen::Translation3f(0.0f, 0.0f, 0.0f);//深度图像坐标系中心
  pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;//设定深度图像遵循的坐标系，X向右，Y向下，Z向前
																				//此外还有LASER_FRAME：X朝前，Y向左，Z向上
  float noiseLevel=0.00;//想让临近点集都落入一个像素单元，可以将noiseLevel设置的大一些
  //例如 noiseLevel =0.05 可以理解为，深度距离值是通过查询 点半径为 5cm 的圆内包含的点以平均计算而得到的
  float minRange = 0.0f;//如果 minRange>0, 则所有模拟传感器所在位 置半径 minRange 内的邻近点都将被忽略，即为盲区。
  int borderSize = 1;//在裁剪图像时，如果 borderSize >0, 将在图像周围留下当前视点不可见点的边界
  pcl::RangeImage rangeImage;
  rangeImage.createFromPointCloud(pointCloud, angularResolution, maxAngleWidth, maxAngleHeight, sensorPose, coordinate_frame, noiseLevel, minRange, borderSize);
std::cout << rangeImage << "\n";
}

2)从深度图像中提取边界
提取边界信息时很重要的一点是区分深度图像中的当前视点不可见点集合和应该可见但处于传感器获取距离范围外的点集，后者可以标记为典型边界，然而当前视点不可见点则不能成为边界。

/* \author Bastian Steder */
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
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] \n\n"
            << "Options:\n"
            << "-------------------------------------------\n"
            << "-r    angular resolution in degrees (default "<<angular_resolution<<")\n"
            << "-c      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   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);//边界提取器
  pcl::PointCloud<pcl::BorderDescription> border_descriptions;//存储边界信息
  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);
  }
}

3）点云转换为深度图像并进行曲面重建
将点云数据转换为深度图像，进而使用 PCL 内部适用于深度图的算法来进行曲面重建等。

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include  
#include 
#include 
#include 
#include 
#include 

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];
	int width=640,height=480,size=2,type=0;
	float fx=525,fy=525,cx=320,cy=240;
	//参数配置
	parse_argument (argc, argv, "-w", width);
	parse_argument (argc, argv, "-h", height);
	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
	//convert unorignized point cloud to orginized point cloud begin
	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;//相机位姿
	sensorPose.setIdentity(); 
	pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;//遵循的坐标系
	float noiseLevel=0.00;
	float minRange = 0.0f;
	pcl::RangeImagePlanar::Ptr rangeImage(new pcl::RangeImagePlanar);
	//生成深度图像
	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
	//viusalization of range image
	pcl::visualization::RangeImageVisualizer range_image_widget ("点云库PCL从入门到精通");
	range_image_widget.showRangeImage (*rangeImage);
	range_image_widget.setWindowTitle("点云库PCL从入门到精通");
	//曲面重建
	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);//设置重建方法，此处设置的是三角形方法
	tri->reconstruct(triangles);
	boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("点云库PCL从入门到精通"));
	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 ();
	}
}