data_source.hpp

/*
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 *     contributors may be used to endorse or promote products derived
 *     from this software without specific prior written permission.
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 *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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 *  Author: Anatoly Baskeheev, Itseez Ltd, (myname.mysurname@mycompany.com)
 */
 
 
#ifndef PCL_GPU_FEATURES_TEST_DATA_SOURCE_HPP_
#define PCL_GPU_FEATURES_TEST_DATA_SOURCE_HPP_
 
#include<string>
 
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/io/pcd_io.h>
#include <pcl/common/common.h>
#include <pcl/features/normal_3d.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/gpu/containers/kernel_containers.h>
#include <pcl/search/search.h>
 
#include <Eigen/StdVector>
 
#if defined (_WIN32) || defined(_WIN64)
    EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(pcl::PointXYZ)
    EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(pcl::Normal)
#endif
 
#include <algorithm>
 
namespace pcl
{
    namespace gpu
    {
        struct DataSource
        {               
            const static int k = 32;
            const static int max_elements = 500;
 
            PointCloud<PointXYZ>::Ptr cloud;
            PointCloud<PointXYZ>::Ptr surface;            
            IndicesPtr indices;
 
            PointCloud<Normal>::Ptr normals;
            PointCloud<Normal>::Ptr normals_surface;
            float radius;
 
            std::vector< std::vector<int> > neighbors_all;
            std::vector<int> sizes;
            int max_nn_size;
 
            DataSource(const std::string& file = "d:/office_chair_model.pcd") 
                : cloud(new PointCloud<PointXYZ>()), surface(new PointCloud<PointXYZ>()), indices( new std::vector<int>() ),
                normals(new PointCloud<Normal>()), normals_surface(new PointCloud<Normal>())
            {                
                PCDReader pcd;
                pcd.read(file, *cloud);
 
                PointXYZ minp, maxp;
                pcl::getMinMax3D(*cloud, minp, maxp);
                float sz = (maxp.x - minp.x + maxp.y - minp.y + maxp.z - minp.z) / 3;
                radius = sz / 15;
            }
 
            void generateColor()
            {
                for (auto& p: *cloud)
                {
                    int r = std::max(1, std::min(255, static_cast<int>((double(rand())/RAND_MAX)*255)));
                    int g = std::max(1, std::min(255, static_cast<int>((double(rand())/RAND_MAX)*255)));
                    int b = std::max(1, std::min(255, static_cast<int>((double(rand())/RAND_MAX)*255)));
 
                    *reinterpret_cast<int*>(&p.data[3]) = (b << 16) + (g << 8) + r;
                }
            }
 
            void estimateNormals()
            {
                pcl::NormalEstimation<PointXYZ, Normal> ne;
                ne.setInputCloud (cloud);
                ne.setSearchMethod (pcl::search::KdTree<PointXYZ>::Ptr (new pcl::search::KdTree<PointXYZ>));
                ne.setKSearch (k);
                //ne.setRadiusSearch (radius);
                
                ne.compute (*normals);                
            }
 
            void runCloudViewer() const
            {
                pcl::visualization::CloudViewer viewer ("Simple Cloud Viewer");
                viewer.showCloud (cloud);
                while (!viewer.wasStopped ()) {}
            }
 
            void findKNNeghbors()
            {                                
                KdTreeFLANN<PointXYZ>::Ptr kdtree(new KdTreeFLANN<PointXYZ>);
                kdtree->setInputCloud(cloud);                
                
                const auto cloud_size = cloud->size();
 
                std::vector<float> dists;
                neighbors_all.resize(cloud_size);
                for(std::size_t i = 0; i < cloud_size; ++i)
                {
                    kdtree->nearestKSearch((*cloud)[i], k, neighbors_all[i], dists);
                    sizes.push_back((int)neighbors_all[i].size());        
                }
                max_nn_size = *max_element(sizes.begin(), sizes.end());
            }
 
            void findRadiusNeghbors(float radius = -1)
            {
                radius = radius == -1 ? this->radius : radius;
 
                KdTreeFLANN<PointXYZ>::Ptr kdtree(new KdTreeFLANN<PointXYZ>);
                kdtree->setInputCloud(cloud);                
                
                const auto cloud_size = cloud->size();
 
                std::vector<float> dists;
                neighbors_all.resize(cloud_size);
                for(std::size_t i = 0; i < cloud_size; ++i)
                {
                    kdtree->radiusSearch((*cloud)[i], radius, neighbors_all[i], dists);
                    sizes.push_back((int)neighbors_all[i].size());        
                }
                max_nn_size = *max_element(sizes.begin(), sizes.end());
            }
 
            void getNeghborsArray(std::vector<int>& data)
            {   
                data.resize(max_nn_size * neighbors_all.size());
                pcl::gpu::PtrStep<int> ps(&data[0], max_nn_size * sizeof(int));    
                for(std::size_t i = 0; i < neighbors_all.size(); ++i)
                    copy(neighbors_all[i].begin(), neighbors_all[i].end(), ps.ptr(i));
            }
 
            void generateSurface()
            {
                surface->clear();
                for(std::size_t i = 0; i < cloud->size(); i+= 10)               
                    surface->push_back((*cloud)[i]);
                surface->width = surface->size();
                surface->height = 1;
                  
                if (!normals->empty())
                {
                    normals_surface->clear();
                    for(std::size_t i = 0; i < normals->size(); i+= 10)               
                        normals_surface->push_back((*normals)[i]);
 
                    normals_surface->width = surface->size();
                    normals_surface->height = 1;
                }                                
            }
 
            void generateIndices(std::size_t step = 100)
            {
                indices->clear();
                for(std::size_t i = 0; i < cloud->size(); i += step)
                    indices->push_back(i);                
            }
 
            struct Normal2PointXYZ
            {
                PointXYZ operator()(const Normal& n) const
                {
                    PointXYZ xyz;
                    xyz.x = n.normal[0];
                    xyz.y = n.normal[1];
                    xyz.z = n.normal[2];
                    return xyz;
                }
            };
        };
    }
}
 
#endif /* PCL_GPU_FEATURES_TEST_DATA_SOURCE_HPP_ */