PCL学习(二)三维模型转点云 obj转pcd----PCL实现
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | #include <pcl/io/io.h> #include <pcl/io/pcd_io.h> #include <pcl/io/obj_io.h> #include <pcl/PolygonMesh.h> //#include <pcl/ros/conversions.h>//formROSMsg所属头文件; #include <pcl/point_cloud.h> #include <pcl/io/vtk_lib_io.h>//loadPolygonFileOBJ所属头文件; //#include <pcl/visualization/pcl_visualizer.h> using namespace std; using namespace pcl; int main() { pcl::PolygonMesh mesh; pcl::io::loadPolygonFile( "sofa.obj" , mesh); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud( new pcl::PointCloud<pcl::PointXYZ>); pcl::fromPCLPointCloud2(mesh.cloud, *cloud); pcl::io::savePCDFileASCII( "result.pcd" , *cloud); cout << cloud->size() << endl; cout << "OK!" ; cin.get(); return 0; } |
- 转换前的obj模型
- 转换成pcd点云后
提取3D模型的meshes的顶点(Vertex)坐标,对于点云来说点数不够,而且在3D模型存在平面或者是简单立方体的情况下几乎没有点。
所以又需要PCL库了,pcl_mesh_sampling可以轻松解决这个问题。
它是通过调用VTK(Visualization ToolKit)读取模型,在3D模型平面均匀地采样点然后生成点云,并且你可以选择需要的点数, 以及voxel grid的采样距离。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 | #include <pcl/visualization/pcl_visualizer.h> #include <pcl/io/pcd_io.h> #include <pcl/io/vtk_lib_io.h> #include <pcl/common/transforms.h> #include <vtkVersion.h> #include <vtkPLYReader.h> #include <vtkOBJReader.h> #include <vtkTriangle.h> #include <vtkTriangleFilter.h> #include <vtkPolyDataMapper.h> #include <pcl/filters/voxel_grid.h> #include <pcl/console/print.h> #include <pcl/console/parse.h> inline double uniform_deviate ( int seed) { double ran = seed * (1.0 / (RAND_MAX + 1.0)); return ran; } inline void randomPointTriangle ( float a1, float a2, float a3, float b1, float b2, float b3, float c1, float c2, float c3, Eigen::Vector4f& p) { float r1 = static_cast < float > (uniform_deviate ( rand ())); float r2 = static_cast < float > (uniform_deviate ( rand ())); float r1sqr = std:: sqrt (r1); float OneMinR1Sqr = (1 - r1sqr); float OneMinR2 = (1 - r2); a1 *= OneMinR1Sqr; a2 *= OneMinR1Sqr; a3 *= OneMinR1Sqr; b1 *= OneMinR2; b2 *= OneMinR2; b3 *= OneMinR2; c1 = r1sqr * (r2 * c1 + b1) + a1; c2 = r1sqr * (r2 * c2 + b2) + a2; c3 = r1sqr * (r2 * c3 + b3) + a3; p[0] = c1; p[1] = c2; p[2] = c3; p[3] = 0; } inline void randPSurface (vtkPolyData * polydata, std::vector< double > * cumulativeAreas, double totalArea, Eigen::Vector4f& p, bool calcNormal, Eigen::Vector3f& n) { float r = static_cast < float > (uniform_deviate ( rand ()) * totalArea); std::vector< double >::iterator low = std::lower_bound (cumulativeAreas->begin (), cumulativeAreas->end (), r); vtkIdType el = vtkIdType (low - cumulativeAreas->begin ()); double A[3], B[3], C[3]; vtkIdType npts = 0; vtkIdType *ptIds = NULL; polydata->GetCellPoints (el, npts, ptIds); polydata->GetPoint (ptIds[0], A); polydata->GetPoint (ptIds[1], B); polydata->GetPoint (ptIds[2], C); if (calcNormal) { // OBJ: Vertices are stored in a counter-clockwise order by default Eigen::Vector3f v1 = Eigen::Vector3f (A[0], A[1], A[2]) - Eigen::Vector3f (C[0], C[1], C[2]); Eigen::Vector3f v2 = Eigen::Vector3f (B[0], B[1], B[2]) - Eigen::Vector3f (C[0], C[1], C[2]); n = v1.cross (v2); n.normalize (); } randomPointTriangle ( float (A[0]), float (A[1]), float (A[2]), float (B[0]), float (B[1]), float (B[2]), float (C[0]), float (C[1]), float (C[2]), p); } void uniform_sampling (vtkSmartPointer<vtkPolyData> polydata, size_t n_samples, bool calc_normal, pcl::PointCloud<pcl::PointNormal> & cloud_out) { polydata->BuildCells (); vtkSmartPointer<vtkCellArray> cells = polydata->GetPolys (); double p1[3], p2[3], p3[3], totalArea = 0; std::vector< double > cumulativeAreas (cells->GetNumberOfCells (), 0); size_t i = 0; vtkIdType npts = 0, *ptIds = NULL; for (cells->InitTraversal (); cells->GetNextCell (npts, ptIds); i++) { polydata->GetPoint (ptIds[0], p1); polydata->GetPoint (ptIds[1], p2); polydata->GetPoint (ptIds[2], p3); totalArea += vtkTriangle::TriangleArea (p1, p2, p3); cumulativeAreas[i] = totalArea; } cloud_out.points.resize (n_samples); cloud_out.width = static_cast <pcl::uint32_t> (n_samples); cloud_out.height = 1; for (i = 0; i < n_samples; i++) { Eigen::Vector4f p; Eigen::Vector3f n; randPSurface (polydata, &cumulativeAreas, totalArea, p, calc_normal, n); cloud_out.points[i].x = p[0]; cloud_out.points[i].y = p[1]; cloud_out.points[i].z = p[2]; if (calc_normal) { cloud_out.points[i].normal_x = n[0]; cloud_out.points[i].normal_y = n[1]; cloud_out.points[i].normal_z = n[2]; } } } using namespace pcl; using namespace pcl::io; using namespace pcl::console; const int default_number_samples = 100000; const float default_leaf_size = 0.01f; void printHelp ( int , char **argv) { print_error ( "Syntax is: %s input.{ply,obj} output.pcd <options>\n" , argv[0]); print_info ( " where options are:\n" ); print_info ( " -n_samples X = number of samples (default: " ); print_value ( "%d" , default_number_samples); print_info ( ")\n" ); print_info ( " -leaf_size X = the XYZ leaf size for the VoxelGrid -- for data reduction (default: " ); print_value ( "%f" , default_leaf_size); print_info ( " m)\n" ); print_info ( " -write_normals = flag to write normals to the output pcd\n" ); print_info ( " -no_vis_result = flag to stop visualizing the generated pcd\n" ); } /* ---[ */ int main ( int argc, char **argv) { print_info ( "Convert a CAD model to a point cloud using uniform sampling. For more information, use: %s -h\n" , argv[0]); if (argc < 3) { printHelp (argc, argv); return (-1); } // Parse command line arguments int SAMPLE_POINTS_ = default_number_samples; parse_argument (argc, argv, "-n_samples" , SAMPLE_POINTS_); float leaf_size = default_leaf_size; parse_argument (argc, argv, "-leaf_size" , leaf_size); bool vis_result = ! find_switch (argc, argv, "-no_vis_result" ); const bool write_normals = find_switch (argc, argv, "-write_normals" ); // Parse the command line arguments for .ply and PCD files std::vector< int > pcd_file_indices = parse_file_extension_argument (argc, argv, ".pcd" ); if (pcd_file_indices.size () != 1) { print_error ( "Need a single output PCD file to continue.\n" ); return (-1); } std::vector< int > ply_file_indices = parse_file_extension_argument (argc, argv, ".ply" ); std::vector< int > obj_file_indices = parse_file_extension_argument (argc, argv, ".obj" ); if (ply_file_indices.size () != 1 && obj_file_indices.size () != 1) { print_error ( "Need a single input PLY/OBJ file to continue.\n" ); return (-1); } vtkSmartPointer<vtkPolyData> polydata1 = vtkSmartPointer<vtkPolyData>::New (); if (ply_file_indices.size () == 1) { pcl::PolygonMesh mesh; pcl::io::loadPolygonFilePLY (argv[ply_file_indices[0]], mesh); pcl::io::mesh2vtk (mesh, polydata1); } else if (obj_file_indices.size () == 1) { vtkSmartPointer<vtkOBJReader> readerQuery = vtkSmartPointer<vtkOBJReader>::New (); readerQuery->SetFileName (argv[obj_file_indices[0]]); readerQuery->Update (); polydata1 = readerQuery->GetOutput (); } //make sure that the polygons are triangles! vtkSmartPointer<vtkTriangleFilter> triangleFilter = vtkSmartPointer<vtkTriangleFilter>::New (); #if VTK_MAJOR_VERSION < 6 triangleFilter->SetInput (polydata1); #else triangleFilter->SetInputData (polydata1); #endif triangleFilter->Update (); vtkSmartPointer<vtkPolyDataMapper> triangleMapper = vtkSmartPointer<vtkPolyDataMapper>::New (); triangleMapper->SetInputConnection (triangleFilter->GetOutputPort ()); triangleMapper->Update (); polydata1 = triangleMapper->GetInput (); bool INTER_VIS = false ; if (INTER_VIS) { visualization::PCLVisualizer vis; vis.addModelFromPolyData (polydata1, "mesh1" , 0); vis.setRepresentationToSurfaceForAllActors (); vis.spin (); } pcl::PointCloud<pcl::PointNormal>::Ptr cloud_1 ( new pcl::PointCloud<pcl::PointNormal>); uniform_sampling (polydata1, SAMPLE_POINTS_, write_normals, *cloud_1); if (INTER_VIS) { visualization::PCLVisualizer vis_sampled; vis_sampled.addPointCloud<pcl::PointNormal> (cloud_1); if (write_normals) vis_sampled.addPointCloudNormals<pcl::PointNormal> (cloud_1, 1, 0.02f, "cloud_normals" ); vis_sampled.spin (); } // Voxelgrid VoxelGrid<PointNormal> grid_; grid_.setInputCloud (cloud_1); grid_.setLeafSize (leaf_size, leaf_size, leaf_size); pcl::PointCloud<pcl::PointNormal>::Ptr voxel_cloud ( new pcl::PointCloud<pcl::PointNormal>); grid_.filter (*voxel_cloud); if (vis_result) { visualization::PCLVisualizer vis3 ( "VOXELIZED SAMPLES CLOUD" ); vis3.addPointCloud<pcl::PointNormal> (voxel_cloud); if (write_normals) vis3.addPointCloudNormals<pcl::PointNormal> (voxel_cloud, 1, 0.02f, "cloud_normals" ); vis3.spin (); } if (!write_normals) { pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xyz ( new pcl::PointCloud<pcl::PointXYZ>); // Strip uninitialized normals from cloud: pcl::copyPointCloud (*voxel_cloud, *cloud_xyz); savePCDFileASCII (argv[pcd_file_indices[0]], *cloud_xyz); } else { savePCDFileASCII (argv[pcd_file_indices[0]], *voxel_cloud); } } |
【推荐】编程新体验,更懂你的AI,立即体验豆包MarsCode编程助手
【推荐】凌霞软件回馈社区,博客园 & 1Panel & Halo 联合会员上线
【推荐】抖音旗下AI助手豆包,你的智能百科全书,全免费不限次数
【推荐】博客园社区专享云产品让利特惠,阿里云新客6.5折上折
【推荐】轻量又高性能的 SSH 工具 IShell:AI 加持,快人一步