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  • ransc_circle

    #include <iostream>
    #include <pcl/console/parse.h>
    #include <pcl/filters/extract_indices.h>
    #include <pcl/io/pcd_io.h>
    #include <pcl/point_types.h>
    #include <pcl/sample_consensus/ransac.h>
    #include <pcl/sample_consensus/sac_model_plane.h>
    #include <pcl/sample_consensus/sac_model_sphere.h>
    #include <pcl/visualization/pcl_visualizer.h>
    #include <boost/thread/thread.hpp>
    
    #include <pcl/sample_consensus/sac_model_circle.h>
    
    
    boost::shared_ptr<pcl::visualization::PCLVisualizer>
    simpleVis(pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
    {
        // --------------------------------------------
        // -----Open 3D viewer and add point cloud-----
        // --------------------------------------------
        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, 3, "sample cloud");
        //viewer->addCoordinateSystem (1.0, "global");
        viewer->initCameraParameters();
        return (viewer);
    }
    
    int
    main(int argc, char** argv)
    {
        // initialize PointClouds
        pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
        pcl::PointCloud<pcl::PointXYZ>::Ptr final(new pcl::PointCloud<pcl::PointXYZ>);
    
        // populate our PointCloud with points
        cloud->width = 500;
        cloud->height = 1;
        cloud->is_dense = false;
        cloud->points.resize(cloud->width * cloud->height);
        for (size_t i = 0; i < cloud->points.size(); ++i)
        {
            if (pcl::console::find_argument(argc, argv, "-s") >= 0 || pcl::console::find_argument(argc, argv, "-sf") >= 0)
            {
                cloud->points[i].x = 1024 * rand() / (RAND_MAX + 1.0);
                cloud->points[i].y = 1024 * rand() / (RAND_MAX + 1.0);
                if (i % 5 == 0)
                    cloud->points[i].z = 1024 * rand() / (RAND_MAX + 1.0);
                else if (i % 2 == 0)
                    cloud->points[i].z = sqrt(1 - (cloud->points[i].x * cloud->points[i].x)
                        - (cloud->points[i].y * cloud->points[i].y));
                else
                    cloud->points[i].z = -sqrt(1 - (cloud->points[i].x * cloud->points[i].x)
                        - (cloud->points[i].y * cloud->points[i].y));
            }
            else
            {
                cloud->points[i].x = 1024 * rand() / (RAND_MAX + 1.0);
                cloud->points[i].y = 1024 * rand() / (RAND_MAX + 1.0);
                if (i % 2 == 0)
                    cloud->points[i].z = 1024 * rand() / (RAND_MAX + 1.0);
                else
                    cloud->points[i].z = -1 * (cloud->points[i].x + cloud->points[i].y);
            }
        }
    
        std::vector<int> inliers;
    
        // created RandomSampleConsensus object and compute the appropriated model
        pcl::SampleConsensusModelSphere<pcl::PointXYZ>::Ptr
            model_s(new pcl::SampleConsensusModelSphere<pcl::PointXYZ>(cloud));
    
    
        pcl::SampleConsensusModelPlane<pcl::PointXYZ>::Ptr
            model_p(new pcl::SampleConsensusModelPlane<pcl::PointXYZ>(cloud));
        if (pcl::console::find_argument(argc, argv, "-f") >= 0)
        {
            pcl::RandomSampleConsensus<pcl::PointXYZ> ransac(model_p);
            ransac.setDistanceThreshold(.01);
            ransac.computeModel();
            ransac.getInliers(inliers);
        }
    
    
        else if (pcl::console::find_argument(argc, argv, "-sf") >= 0)
        {
            pcl::RandomSampleConsensus<pcl::PointXYZ> ransac(model_s);
            ransac.setDistanceThreshold(.01);
            ransac.computeModel();
            ransac.getInliers(inliers);
        }
    
        // copies all inliers of the model computed to another PointCloud
        pcl::copyPointCloud<pcl::PointXYZ>(*cloud, inliers, *final);
    
    
    
    
        // creates the visualization object and adds either our original cloud or all of the inliers
        // depending on the command line arguments specified.
        boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer;
        if (pcl::console::find_argument(argc, argv, "-f") >= 0 || pcl::console::find_argument(argc, argv, "-sf") >= 0)
            viewer = simpleVis(final);
        else
            viewer = simpleVis(cloud);
    
    
    
    
    
        pcl::SampleConsensusModelCircle2D<pcl::PointXYZ>::Ptr
            model_circle2D(new pcl::SampleConsensusModelCircle2D<pcl::PointXYZ>(cloud));
    
    
    
        pcl::RandomSampleConsensus<pcl::PointXYZ> ransac(model_circle2D);
        ransac.setDistanceThreshold(.01);
        ransac.computeModel();
        ransac.getInliers(inliers);
    
        Eigen::VectorXf modelParas;
        ransac.getModelCoefficients(modelParas);
        std::cout << modelParas << "
    
    ";
    
    
    
    
    
    
    
    
    
    
    
    
        while (!viewer->wasStopped())
        {
            viewer->spinOnce(100);
            boost::this_thread::sleep(boost::posix_time::microseconds(100000));
        }
        return 0;
    }
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  • 原文地址:https://www.cnblogs.com/codeAndlearn/p/14119186.html
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