https://www.cnblogs.com/li-yao7758258/p/6476046.html
所以我们知道有规则及必要信息就可以反算为深度图像。那么我们就可以直接创建一个有序的规则的点云，比如一张平面，或者我们直接使用Kinect获取的点云来可视化深度的图，所以首先分析程序中是如果实现的点云到深度图的转变的，（程序的注释是我自己的理解，注释的比较详细）
#include <iostream>
#include <boost/thread/thread.hpp>
#include <pcl/common/common_headers.h>
#include <pcl/range_image/range_image.h> //关于深度图像的头文件
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/range_image_visualizer.h> //深度图可视化的头文件
#include <pcl/visualization/pcl_visualizer.h> //PCL可视化的头文件
#include <pcl/console/parse.h>
typedef pcl::PointXYZ PointType;
//参数
float angular_resolution_x = 0.5f,//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
angular_resolution_y = angular_resolution_x;
pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;//深度图像遵循坐标系统
bool live_update = false;
//命令帮助提示
void
printUsage (const char* progName)
{
std::cout << "\n\nUsage: "<<progName<<" [options] <scene.pcd>\n\n"
<< "Options:\n"
<< "-------------------------------------------\n"
<< "-rx <float> angular resolution in degrees (default "<<angular_resolution_x<<")\n"
<< "-ry <float> angular resolution in degrees (default "<<angular_resolution_y<<")\n"
<< "-c <int> coordinate frame (default "<< (int)coordinate_frame<<")\n"
<< "-l live update - update the range image according to the selected view in the 3D viewer.\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]);
}
//主函数
int
main (int argc, char** argv)
{
//输入命令分析
if (pcl::console::find_argument (argc, argv, "-h") >= 0)
{
printUsage (argv[0]);
return 0;
}
if (pcl::console::find_argument (argc, argv, "-l") >= 0)
{
live_update = true;
std::cout << "Live update is on.\n";
}
if (pcl::console::parse (argc, argv, "-rx", angular_resolution_x) >= 0)
std::cout << "Setting angular resolution in x-direction to "<<angular_resolution_x<<"deg.\n";
if (pcl::console::parse (argc, argv, "-ry", angular_resolution_y) >= 0)
std::cout << "Setting angular resolution in y-direction to "<<angular_resolution_y<<"deg.\n";
int tmp_coordinate_frame;
if (pcl::console::parse (argc, argv, "-c", tmp_coordinate_frame) >= 0)
{
coordinate_frame = pcl::RangeImage::CoordinateFrame (tmp_coordinate_frame);
std::cout << "Using coordinate frame "<< (int)coordinate_frame<<".\n";
}
angular_resolution_x = pcl::deg2rad (angular_resolution_x);
angular_resolution_y = pcl::deg2rad (angular_resolution_y);
//读取点云PCD文件 如果没有输入PCD文件就生成一个点云
pcl::PointCloud<PointType>::Ptr point_cloud_ptr (new pcl::PointCloud<PointType>);
pcl::PointCloud<PointType>& point_cloud = *point_cloud_ptr;
Eigen::Affine3f scene_sensor_pose (Eigen::Affine3f::Identity ()); //申明传感器的位置是一个4*4的仿射变换
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)
{
std::cout << "Was not able to open file \""<<filename<<"\".\n";
printUsage (argv[0]);
return 0;
}
//给传感器的位姿赋值 就是获取点云的传感器的的平移与旋转的向量
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_);
}
else
{ //如果没有给点云，则我们要自己生成点云
std::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;
}
// -----从创建的点云中获取深度图--//
//设置基本参数
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;
/*
关于range_image.createFromPointCloud（）参数的解释 （涉及的角度都为弧度为单位） ：
point_cloud为创建深度图像所需要的点云
angular_resolution_x深度传感器X方向的角度分辨率
angular_resolution_y深度传感器Y方向的角度分辨率
pcl::deg2rad (360.0f)深度传感器的水平最大采样角度
pcl::deg2rad (180.0f)垂直最大采样角度
scene_sensor_pose设置的模拟传感器的位姿是一个仿射变换矩阵，默认为4*4的单位矩阵变换
coordinate_frame定义按照那种坐标系统的习惯 默认为CAMERA_FRAME
noise_level 获取深度图像深度时，邻近点对查询点距离值的影响水平
min_range 设置最小的获取距离，小于最小的获取距离的位置为传感器的盲区
border_size 设置获取深度图像边缘的宽度 默认为0
*/
range_image.createFromPointCloud (point_cloud, angular_resolution_x, angular_resolution_y,pcl::deg2rad (360.0f), pcl::deg2rad (180.0f),scene_sensor_pose, coordinate_frame, noise_level, min_range, border_size);
//可视化点云
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 ();
//range_image.getTransformationToWorldSystem ()的作用是获取从深度图像坐标系统（应该就是传感器的坐标）转换为世界坐标系统的转换矩阵
setViewerPose(viewer, range_image.getTransformationToWorldSystem ()); //设置视点的位置
//可视化深度图
pcl::visualization::RangeImageVisualizer range_image_widget ("Range image");
range_image_widget.showRangeImage (range_image);
while (!viewer.wasStopped ())
{
range_image_widget.spinOnce ();
viewer.spinOnce ();
pcl_sleep (0.01);
if (live_update)
{
//如果选择的是——l的参数说明就是要根据自己选择的视点来创建深度图。
// live update - update the range image according to the selected view in the 3D viewer.
scene_sensor_pose = viewer.getViewerPose();
range_image.createFromPointCloud (point_cloud, angular_resolution_x, angular_resolution_y,
pcl::deg2rad (360.0f), pcl::deg2rad (180.0f),
scene_sensor_pose, pcl::RangeImage::LASER_FRAME, noise_level, min_range, border_size);
range_image_widget.showRangeImage (range_image);
}
}
}