1 从点云创建深度图像
#include <pcl/range_image/range_image.h>
int main (int argc, char** argv) {
pcl::PointCloud<pcl::PointXYZ> pointCloud;//定义点云对象
//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); //添加点云数据到点云对象
}
}
pointCloud.width = (uint32_t) pointCloud.points.size();
pointCloud.height = 1;//设置点云对象的头信息
/************************************************************************************
这段程序首先创建一组数据作为点云的数据内容，在设置文件头文件的信息，整个实现生成一个呈现矩形形状的点云。
***************************************************************************/
//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);//采集位置
pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;//深度图像遵循的坐标系统
float noiseLevel=0.00;
float minRange = 0.0f;
int borderSize = 1;
/************************************************************************************
其余代码使用用户给定的参数进行从点云创建深度图像，并且在终端下打印出一些深度图像的信息
***************************************************************************/
pcl::RangeImage rangeImage;
rangeImage.createFromPointCloud(pointCloud, angularResolution, maxAngleWidth, maxAngleHeight, sensorPose, coordinate_frame, noiseLevel, minRange, borderSize);
std::cout << rangeImage << "\n";
}
这部分定义了创建深度图像时需要的设置参数，将角分辩率定义为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，我们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:当传感器后面没有可以观测的点时，一个水平视角为180°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的圆内包含的点以平均计算而得到的。如果minRange>0,则所有模拟器所在位置半minRange内的邻近点都将被忽略，即为盲区。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。
2 从深度图像中提取边界
/* \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);
}
}3 深度图像精通级实例解析：点云到深度图的变换与曲面重建
#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/vi**sualization/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 ();
}
}终端输入：
//1.pcd为要输入的点云，第二个用于控制重建曲面时的精细程度
./greedy_projection ../1.pcd pcd-size 2输出：
原始点云：
