这周学习目录
1、可视化
2、深度图像

一、可视化Visualization

1.1 可视化的基本概念

可视化(Visualization)是利用计算机图形学和图像处理技术，将数据转换成图形或图像在屏幕上显示出来，并进行交互处理的理论、方法和技术。

1.2 PCLVisualizer介绍

class pcl: : visualization: : PCL Visualizer
类PCLVisuafizer为PCL可视化3D点云的主要类。其内部实现了添加各种3D对象以及交互实现等，比其他类实现的功能更齐全。

类PCLVisualizer的关键函数说明：

• PCL Visualizer (int &.argc, char **argv, canst std: : string &.name="",PCLVisualizerlnteractorStyle *style= PCLVisualizerlnteractorStyle:: New (),const bool create_mteractor true):重构函数，其中name为创建窗口名，style为交互类实现，默认为PCLVisualizerlnteractorStyle对象，create_interactor设置是否重建 交互对象，默认是创建的。
• void spin () 调用内部渲染函数，重新渲染输出。
• void spinOnce (int time 1, bool force_redraw false):调用内部渲染函数一次，重新渲染输出时间最大不超过time,单位ms,force_ re­draw设置是否强制 重新绘制。
• void addCoordinateSystem (double scale =1.0 , int viewport 0):在坐标原点(0,0,0)添加指示坐标轴，viewport为需要添加的视口，默认在所有视口中都添加，scale设置指示坐标轴的放大系数。
• void setBackgroundColor (const double &.r, const double &.g, canst double &.b, int viewport = O) 设置指定viewport 视口的背景色。
  等等还有很多函数。

1.3 CloudViewer介绍

class pcl: : visualization: : CloudViewer
类CloudViewer实现创建点云可视化的窗口，以及相关的可视化功能。

CloudViewer的关键成员函数

• CloudViewer (const std: :string &.window_name) ：构建可视化点云窗口，窗口名为window_name。
• void showCloud (const ColorCloud: : ConstPtr &.cloud, const std: : string&. cloudname = “cloud”)： 在可视化点云窗口中显示 cloud对应的点云，考虑到多个点云用键值cloudname 来限定具体是哪个点云。
• bool wasStopped (int millis_to_ wait= 1) ：判断用户是否已关闭窗口，如果是则需要注销窗口，millis_to_wait为在注销窗口之前的等待。
• void runOnVisualizationThread (VizCallable x, const std: : string & key = " callable") :在窗口运行期间处理x 回调函数，key为键值标识此回调函数，直到窗口关闭,可以多次使用回调函数。**重点：**此X为回调函数（返回值为空，参数是pcl::visualization::PCLVisualizer &viewer）
• void run On VisualizationThreadOnce (VizCallable x) 同上，但只调用回询函数一次。

a、CloudViewer实现：

// 使用CloudViewer，CloudViewer是PCLVisualizer的子类，所以可以传参，进行设置背景颜色，
// CloudViewer是没有setBackground属性
#include <vtkAutoInit.h>
VTK_MODULE_INIT(vtkRenderingOpenGL);
VTK_MODULE_INIT(vtkInteractionStyle);#include<thread>
#include<pcl/visualization/cloud_viewer.h>
#include<pcl/point_cloud.h>
#include<pcl/io/pcd_io.h>
#include<pcl/point_types.h>using namespace std;
static int num = 0;
//int user_data;
// 设置背景
void viewerOneOff(pcl::visualization::PCLVisualizer &viewer) {viewer.setBackgroundColor(1, 0.5, 1);  // 设置背景颜色// 设置点云尺寸大小viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3);
}//设置文字
void viewerText(pcl::visualization::PCLVisualizer &viewer) {stringstream s;  // 定义字符串s << " viewer loop " << num++ ; // 输出文字viewer.removeShape("Text", 0);  // 删除“Text”形状//viewer.addText(s.str(), 100, 200, "Text", 0);  // 定义文字框viewer.setPosition(100, 200);pcl::PointXYZ position;position.x = 1.0;position.y = 1.0;position.z = 1.0;viewer.addText3D(s.str(), position, 0.3, 0.3, 0.3,0.4,"Text",0);  // 将Text3D添加到“Text”形状里// user_data++;
}void getCube(pcl::visualization::PCLVisualizer& viewer) {num++;//cout << "num : "<< num << endl;string cube = "cube" + num;float r = rand() + 0.1;float b = rand() + 0.2;float g = rand() + 0.3;viewer.addCube(0, 1, 0, 1, 0, 1, r, g, b, "cube" + num , 0);//std::cout <<cube<<"  run !!!" << std::endl;
}int main() {pcl::visualization::CloudViewer viewer("可视化");//pcl::PointCloud<pcl::PointXYZ> cloud;  // 在viewer.showCloud()作为参数是会报错的，因为其接受Ptr参数// 当参数为PointXYZRGB就会加载点云颜色数据，如果为PointXYZ，则会输出白色数据。pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGB>());pcl::io::loadPCDFile("maize.pcd", *cloud);//if (pcl::io::loadPCDFile("maize.pcd", *cloud)) {//	cout << "w文件不存在" << endl;//	return -1;//}// 加载点云数据viewer.showCloud(cloud);// 一次性线程，加载一次viewerOneOffviewer.runOnVisualizationThreadOnce(viewerOneOff);// 多次加载viewer.runOnVisualizationThread(viewerText);while (!viewer.wasStopped()) {//user_data++;// 可以加一些其他的活动std::this_thread::sleep_for(100ms);viewer.runOnVisualizationThreadOnce(getCube);}return 0;
}

运行结果：

1.3 Range_image_visualizer的介绍

可视化深度图像的两种方法，在3D视窗中以点云形式进行可视化（深度图像来源于点云），另一种是将深度值映射为颜色，从而以彩色图像方式可视化深度图像。

#include <iostream>
#include <boost/thread/thread.hpp>  // boost中的线程
#include <pcl/common/common_headers.h>
#include <pcl/pcl_macros.h>
#include <pcl/range_image/range_image.h>  // 深度图像
#include <pcl/io/pcd_io.h>  // 读取pcd数据
#include <pcl/visualization/range_image_visualizer.h>  // 深度图像可视化要通过这个头文件
#include <pcl/visualization/pcl_visualizer.h>  // 可视化
#include <pcl/console/parse.h>typedef pcl::PointXYZ PointType;  // 重定义
// 全局参数
float angular_resolution = 0.5f;  // 分辨率
pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;  // 定义深度图像坐标系为照相机坐标系
pcl::RangeImage::CoordinateFrame coordinate_frame1 = pcl::RangeImage::LASER_FRAME;  // 定义深度图像坐标系为照相机坐标系
bool live_update = false;  // 设置更新为false// -----打印帮助-----
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"<< "-l           live update - update the range image according to the selected view in the 3D viewer.\n"<< "-h           this help\n"<< "\n\n";
}// 深度图像位置，Eigen::Affine3f&可以保存摄像头位置
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);// 设置摄像头位置，前三个参数是摄像头的位置，中间三个参数是视向，最后三个参数是向上方向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
main03(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, "-l") >= 0){live_update = true;std::cout << "Live update is on.\n";}if (pcl::console::parse(argc, argv, "-r", angular_resolution) >= 0)std::cout << "Setting angular resolution to " << angular_resolution << "deg.\n";int tmp_coordinate_frame;  // 坐标系// 命令行解析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 = pcl::deg2rad(angular_resolution);// 读取给定的pcd点云文件或者自行创建随机点云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());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;}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{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;}//从点云创建深度图像对象float noise_level = 0.0;float min_range = 0.0f;int border_size = 1;// boost::shared_ptr共享指针，代表该指针在整个程序全局中都可以使用，不用担心内存错误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);//创建3D视图并且添加点云进行显示pcl::visualization::PCLVisualizer viewer("3D Viewer");viewer.setBackgroundColor(1, 1, 1);// 自定义点云颜色(白色)pcl::visualization::PointCloudColorHandlerCustom<pcl::PointWithRange> range_image_color_handler(range_image_ptr, 0, 0, 0);// 添加点云数据到viewer中，第一个参数是图像指针，第二个参数是颜色属性，第三个参数是ID号viewer.addPointCloud(range_image_ptr, range_image_color_handler, "range image");// 设置点云尺寸viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "range image");viewer.addCoordinateSystem (1.0f);  // 添加坐标系pcl::visualization::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();  // 初始化摄像机参数（按照默认方向）setViewerPose(viewer, range_image.getTransformationToWorldSystem());  // 设置深度图像的位置//显示深度图像pcl::visualization::RangeImageVisualizer range_image_widget("Range image");range_image_widget.showRangeImage(range_image);//主循环while (!viewer.wasStopped()){// 深度图像渲染一次range_image_widget.spinOnce();// 3D viewer渲染一次viewer.spinOnce();// 更新间隔10mspcl_sleep(0.01);if (live_update){scene_sensor_pose = viewer.getViewerPose();range_image.createFromPointCloud(point_cloud, angular_resolution, 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);}}
}

程序运行：首先通过cmake把cpp文件和CmakeLists文件进行编译生成Debug文件，然后再命令行中输入命令运行程序。
./range_image_visualization.exe -h :是帮助的意思，可以看一下命令的含义所在。
./range_image_visualization.exe rabbit.pcd -r:是当分辨率为0.5时的深度图像。但是不是动态的。
./range_image_visualization.exe rabbit.pcd -l:是动态深度图像，当移动3D Viewer中的模型位置时，深度图像也会发生变化。

深度的描述：图中不可区视域（代表无限远）用浅绿光表示，远距离区域（范围无限远激光，扫描时可获取信息）用淡蓝色表示，注意两者虽然都是无穷远，但前者是在激光扫描不可见区域，后者是在可见区域的无穷远。

1.4 实例三

ModelCoefficients 介绍：可以定义ModelCoefficients 得到各种各样的图形。前提是知道图形的函数方程，如下ax+by+cz+d = 0是一个平面方程，所以pcl::ModelCoefficients coeffs;coeffs.values.push_back(0.0);  // 是a的值coeffs.values.push_back(0.0);  // b的值coeffs.values.push_back(1.0);  // c的值coeffs.values.push_back(0.0);  // d的值

分屏的介绍：意思就是在把一个Viewer分成n块。这个就要利用到viewportint v1(0);  // 定义一个viewport为0viewer->createViewPort(0.0, 0.0, 0.5, 1.0, v1);   // 创建一个视口，索引为v1，（0.0，0.0）可以看成一个窗口左上角的位置，（0.5，1.0）代表窗口x轴的一半（宽度），1.0代表窗口的高度viewer->setBackgroundColor(0, 0, 0, v1);  / /定义背景颜色int v2(0);  // 定义另一个viewport为1viewer->createViewPort(0.5, 0.0, 1.0, 1.0, v2);viewer->setBackgroundColor(0.3, 0.3, 0.3, v2);  // 定义背景颜色
上面几行代码创建新的视口，所需的4个参数是视口在X轴的最小值、最大值、 Y轴的最小值、最大值，取值在0~1。我们创建的视口分布于窗口的左半部分，最后 一个字符串参数，用来唯一标志该视口，在其他改变该视口内容的调用中需要以该唯 一标志为参数

/* \author Geoffrey Biggs */#include <iostream>
#include <boost/thread/thread.hpp>
#include <pcl/common/common_headers.h>
#include <pcl/common/common_headers.h>
#include <pcl/features/normal_3d.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/console/parse.h>
// 帮助
void
printUsage1(const char* progName)
{std::cout << "\n\nUsage: " << progName << " [options]\n\n"<< "Options:\n"<< "-------------------------------------------\n"<< "-h           this help\n"<< "-s           Simple visualisation example\n"<< "-r           RGB colour visualisation example\n"<< "-c           Custom colour visualisation example\n"<< "-n           Normals visualisation example\n"<< "-a           Shapes visualisation example\n"<< "-v           Viewports example\n"<< "-i           Interaction Customization example\n"<< "\n\n";
}boost::shared_ptr<pcl::visualization::PCLVisualizer> simpleVis(pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
{//创建3D窗口并添加点云boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));viewer->setBackgroundColor(0, 0, 0);viewer->addPointCloud<pcl::PointXYZ>(cloud, "sample cloud");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "sample cloud");viewer->addCoordinateSystem(1.0);viewer->initCameraParameters();  // 初始化所有参数return (viewer);
}boost::shared_ptr<pcl::visualization::PCLVisualizer> rgbVis(pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr cloud)
{//创建3D窗口并添加点云	boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));viewer->setBackgroundColor(0, 0, 0);pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> rgb(cloud);viewer->addPointCloud<pcl::PointXYZRGB>(cloud, rgb, "sample cloud");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");viewer->addCoordinateSystem(1.0);viewer->initCameraParameters();return (viewer);
}boost::shared_ptr<pcl::visualization::PCLVisualizer> customColourVis(pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
{//创建3D窗口并添加点云boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));viewer->setBackgroundColor(0, 0, 0);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> single_color(cloud, 0, 255, 0);viewer->addPointCloud<pcl::PointXYZ>(cloud, single_color, "sample cloud");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");viewer->addCoordinateSystem(1.0);viewer->initCameraParameters();return (viewer);
}boost::shared_ptr<pcl::visualization::PCLVisualizer> normalsVis(pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr cloud, pcl::PointCloud<pcl::Normal>::ConstPtr normals)
{//创建3D窗口并添加点云其包括法线  boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));viewer->setBackgroundColor(0, 0, 0);pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> rgb(cloud);viewer->addPointCloud<pcl::PointXYZRGB>(cloud, rgb, "sample cloud");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");viewer->addPointCloudNormals<pcl::PointXYZRGB, pcl::Normal>(cloud, normals, 10, 0.05, "normals");viewer->addCoordinateSystem(1.0);viewer->initCameraParameters();return (viewer);
}boost::shared_ptr<pcl::visualization::PCLVisualizer> shapesVis(pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr cloud)
{//创建3D窗口并添加点云    boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));viewer->setBackgroundColor(0, 0, 0);pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> rgb(cloud);viewer->addPointCloud<pcl::PointXYZRGB>(cloud, rgb, "sample cloud");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");viewer->addCoordinateSystem(1.0);viewer->initCameraParameters();//在点云上添加直线和球体模型viewer->addLine<pcl::PointXYZRGB>(cloud->points[0],cloud->points[cloud->size() - 1], "line");viewer->addSphere(cloud->points[0], 0.2, 0.5, 0.5, 0.0, "sphere");//在其他位置添加基于模型参数的平面及圆锥体pcl::ModelCoefficients coeffs;coeffs.values.push_back(0.0);coeffs.values.push_back(0.0);coeffs.values.push_back(1.0);coeffs.values.push_back(0.0);viewer->addPlane(coeffs, "plane");coeffs.values.clear();coeffs.values.push_back(0.3);coeffs.values.push_back(0.3);coeffs.values.push_back(0.0);coeffs.values.push_back(0.0);coeffs.values.push_back(1.0);coeffs.values.push_back(0.0);coeffs.values.push_back(5.0);viewer->addCone(coeffs, "cone");return (viewer);
}boost::shared_ptr<pcl::visualization::PCLVisualizer> viewportsVis(pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr cloud, pcl::PointCloud<pcl::Normal>::ConstPtr normals1, pcl::PointCloud<pcl::Normal>::ConstPtr normals2)
{// 创建3D窗口并添加显示点云其包括法线boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));viewer->initCameraParameters();int v1(0);viewer->createViewPort(0.0, 0.0, 0.5, 1.0, v1);viewer->setBackgroundColor(0, 0, 0, v1);viewer->addText("Radius: 0.01", 10, 10, "v1 text", v1);pcl::visualization::PointCloudColorHandlerRGBField<pcl::PointXYZRGB> rgb(cloud);viewer->addPointCloud<pcl::PointXYZRGB>(cloud, rgb, "sample cloud1", v1);int v2(0);viewer->createViewPort(0.5, 0.0, 1.0, 1.0, v2);viewer->setBackgroundColor(0.3, 0.3, 0.3, v2);viewer->addText("Radius: 0.1", 10, 10, "v2 text", v2);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGB> single_color(cloud, 0, 255, 0);viewer->addPointCloud<pcl::PointXYZRGB>(cloud, single_color, "sample cloud2", v2);viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud1");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud2");viewer->addCoordinateSystem(1.0);viewer->addPointCloudNormals<pcl::PointXYZRGB, pcl::Normal>(cloud, normals1, 10, 0.05, "normals1", v1);viewer->addPointCloudNormals<pcl::PointXYZRGB, pcl::Normal>(cloud, normals2, 10, 0.05, "normals2", v2);return (viewer);
}unsigned int text_id = 0;
void keyboardEventOccurred(const pcl::visualization::KeyboardEvent &event,void* viewer_void)
{boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer = *static_cast<boost::shared_ptr<pcl::visualization::PCLVisualizer> *> (viewer_void);if (event.getKeySym() == "r" && event.keyDown()){std::cout << "r was pressed => removing all text" << std::endl;char str[512];for (unsigned int i = 0; i < text_id; ++i){sprintf(str, "text#%03d", i);viewer->removeShape(str);}text_id = 0;}
}void mouseEventOccurred(const pcl::visualization::MouseEvent &event,void* viewer_void)
{boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer = *static_cast<boost::shared_ptr<pcl::visualization::PCLVisualizer> *> (viewer_void);if (event.getButton() == pcl::visualization::MouseEvent::LeftButton &&event.getType() == pcl::visualization::MouseEvent::MouseButtonRelease){std::cout << "Left mouse button released at position (" << event.getX() << ", " << event.getY() << ")" << std::endl;char str[512];sprintf(str, "text#%03d", text_id++);viewer->addText("clicked here", event.getX(), event.getY(), str);}
}boost::shared_ptr<pcl::visualization::PCLVisualizer> interactionCustomizationVis()
{cout << "Interaction...."<<endl;boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));viewer->setBackgroundColor(0, 0, 0);viewer->addCoordinateSystem(1.0);viewer->registerKeyboardCallback(keyboardEventOccurred, (void*)&viewer);viewer->registerMouseCallback (mouseEventOccurred, (void*)&viewer);return (viewer);
}
// -----Main-----
int
main04(int argc, char** argv)
{// 解析命令行参数if (pcl::console::find_argument(argc, argv, "-h") >= 0){printUsage1(argv[0]);return 0;}bool simple(false), rgb(false), custom_c(false), normals(false),shapes(false), viewports(false), interaction_customization(false);if (pcl::console::find_argument(argc, argv, "-s") >= 0){simple = true;std::cout << "Simple visualisation example\n";}else if (pcl::console::find_argument(argc, argv, "-c") >= 0){custom_c = true;std::cout << "Custom colour visualisation example\n";}else if (pcl::console::find_argument(argc, argv, "-r") >= 0){rgb = true;std::cout << "RGB colour visualisation example\n";}else if (pcl::console::find_argument(argc, argv, "-n") >= 0){normals = true;std::cout << "Normals visualisation example\n";}else if (pcl::console::find_argument(argc, argv, "-a") >= 0){shapes = true;std::cout << "Shapes visualisation example\n";}else if (pcl::console::find_argument(argc, argv, "-v") >= 0){viewports = true;std::cout << "Viewports example\n";}else if (pcl::console::find_argument(argc, argv, "-i") >= 0){interaction_customization = true;std::cout << "Interaction Customization example\n";}else{printUsage1(argv[0]);return 0;}// 自行创建一随机点云pcl::PointCloud<pcl::PointXYZ>::Ptr basic_cloud_ptr(new pcl::PointCloud<pcl::PointXYZ>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr point_cloud_ptr(new pcl::PointCloud<pcl::PointXYZRGB>);std::cout << "Genarating example point clouds.\n\n";// 以椭圆为边线沿z轴拉伸获取其点云，并赋予红绿蓝渐变色。uint8_t r(255), g(15), b(15);for (float z(-1.0); z <= 1.0; z += 0.05){for (float angle(0.0); angle <= 360.0; angle += 5.0){pcl::PointXYZ basic_point;basic_point.x = 0.5 * cosf(pcl::deg2rad(angle));basic_point.y = sinf(pcl::deg2rad(angle));basic_point.z = z;basic_cloud_ptr->points.push_back(basic_point);pcl::PointXYZRGB point;point.x = basic_point.x;point.y = basic_point.y;point.z = basic_point.z;uint32_t rgb = (static_cast<uint32_t>(r) << 16 |static_cast<uint32_t>(g) << 8 | static_cast<uint32_t>(b));point.rgb = *reinterpret_cast<float*>(&rgb);point_cloud_ptr->points.push_back(point);}if (z < 0.0){r -= 12;g += 12;}else{g -= 12;b += 12;}}basic_cloud_ptr->width = (int)basic_cloud_ptr->points.size();basic_cloud_ptr->height = 1;point_cloud_ptr->width = (int)point_cloud_ptr->points.size();point_cloud_ptr->height = 1;// 0.05为搜索半径获取点云法线pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> ne;ne.setInputCloud(point_cloud_ptr);pcl::search::KdTree<pcl::PointXYZRGB>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZRGB>());ne.setSearchMethod(tree);pcl::PointCloud<pcl::Normal>::Ptr cloud_normals1(new pcl::PointCloud<pcl::Normal>);ne.setRadiusSearch(0.05);ne.compute(*cloud_normals1);//  0.1为搜索半径获取点云法线pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2(new pcl::PointCloud<pcl::Normal>);ne.setRadiusSearch(0.1);ne.compute(*cloud_normals2);boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer;if (simple){viewer = simpleVis(basic_cloud_ptr);}else if (rgb){viewer = rgbVis(point_cloud_ptr);}else if (custom_c){viewer = customColourVis(basic_cloud_ptr);}else if (normals){viewer = normalsVis(point_cloud_ptr, cloud_normals2);}else if (shapes){viewer = shapesVis(point_cloud_ptr);}else if (viewports){viewer = viewportsVis(point_cloud_ptr, cloud_normals1, cloud_normals2);}else if (interaction_customization){viewer = interactionCustomizationVis();}// 主循环while (!viewer->wasStopped()){viewer->spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}
}

运行结果：一个是分窗口的结果，一个是添加图形的结果

二、深度图像

2.1 深度图像的概念

深度图像(Depth Images)也被称为距离影像(Range Images), 是指将从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，利用它可以很方便的解决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。

2.2 深度图像的创建

#include <pcl/range_image/range_image.h>
#include<pcl/visualization/range_image_visualizer.h>int main(int argc, char** argv) {pcl::PointCloud<pcl::PointXYZ> pointCloud;  // 定义点云//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);  // 将point添加到点云数据中}}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float maxAngleWidth = (float)(360.0f * (M_PI / 180.0f));// 360.0度转弧度float maxAngleHeight = (float)(180.0f * (M_PI / 180.0f));// 180.0度转弧度Eigen::Affine3f sensorPose = (Eigen::Affine3f)Eigen::Translation3f(0.0f, 0.0f, 0.0f);  // 观测角度pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;// 深度图像遵循的坐标系统float noiseLevel = 0.00;  // 归一化的的Z缓冲区来创建深度图像float minRange = 0.0f;  // 所有在这个半径范围内的数据被忽略int borderSize = 1;  // 将在图像周围留下当前视点不可见点的边界pcl::RangeImage rangeImage;  // 定义深度图像// 从点云数据来创建深度图像rangeImage.createFromPointCloud(pointCloud, angularResolution, maxAngleWidth, maxAngleHeight, sensorPose, coordinate_frame, noiseLevel, minRange, borderSize);std::cout << rangeImage << "\n";//显示深度图像pcl::visualization::RangeImageVisualizer range_image_widget("Range image");range_image_widget.showRangeImage(rangeImage);//主循环while (!range_image_widget.wasStopped()){// 深度图像渲染一次range_image_widget.spinOnce();// 更新间隔10mspcl_sleep(0.01);}
}

这部分定义了创建深度图像时需要的设置参数，将角度分辨率定义为1°意味着由邻近的像素点所对应的每个光束之间相差1°,
maxAngleWidth=360和maxAn­gleHeight= 180意味着，进行模拟的距离传感器对周围的环境拥有一个完整的360° 视角，
用户在任何数据集下都可以使用此设置，因为最终获取的深度图像将被裁剪到有空间物体存在的区域范围。但是，用户可以
通过减小数值来节省一些计算资源，例如：对于传感器后面没有可以观测的点时，一个水平视角为18矿的激光扫描仪，即
maxAngleWidth= 180 就足够了这样只需要观察距离传感器前面就可以了，因为后面没有需要观察的场景。sensorPose定义
了模拟深度图像获取传感器的6自由度位置，其原始值为横滚角roll,俯仰角pitch、偏航角yaw都为0,coordinate_ frame= CAMERA_FRAME
说明系统的X轴是向右的，Y轴是向下的，Z轴是向前的，另外一个选择是LASER_FRAME,其X轴向前，Y轴向左，Z轴向上。noiseLevel= 0是指使用
一个归一化的Z缓冲器来创建深度图像，但是如果想让邻近点集都落在同一 个像素单元，用户可以设置一个较高的值，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的圆内包含的点用来平均计算而得到的。如果minRange>O, 则所有模拟器所在位置半径minRange
内的邻近点都将被忽略，即为盲区。在栽剪图像时，如果borderSize>O,将在图像周围留下当前视点不可见点的边界。

2.3 深度图像边界的提取

#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;  // 设置角度分辨率
pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;  // 设置坐标系
bool setUnseenToMaxRange = false;  // 设置所有不能观察的点都是远距离的
// --------------
// -----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
main02(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);}
}

	pcl::RangeImageBorderExtractor border_extractor(&range_image);pcl::PointCloud<pcl::BorderDescription> border_descriptions;border_extractor.compute(border_descriptions);此部分创建了 RangelmageBorderExtractor 这个对象，给了它深度图像，并且计算后存储边界信息在 border_descriptions 中。

运行结果。
./range_image_border_extraction.exe rabbit.pcd -r ：设置角度分辨率为0.5时的结果
./range_image_border_extraction.exe rabbit.pcd -c：设置不同的坐标系
./range_image_border_extraction.exe rabbit.pcd -m：如果用户没有这些远距离的点云，则可采用命令行参数-m,这样设置所有不能观察到的点都为远距离的

./range_image_border_extraction.exe rabbit.pcd -r ：设置角度分辨率为0.5时的运行结果

./range_image_border_extraction.exe rabbit.pcd -m的运行结果如下

三、总结

	本周学习了可视化和深度图像的表示方法，明白了使用这些代码的意义所在，这些代码都来源于点云库PCL学习教程和PCL官方文档。