vm_placement_problem_result<TraitsT> make_vm_placement_problem_result(ProblemDescrT problem_descr, ProblemResultT problem_res) { typedef TraitsT traits_type; typedef vm_placement_problem_result<traits_type> result_type; typedef typename result_type::real_type real_type; typedef typename result_type::resource_share_container resource_share_container; typedef typename result_type::physical_machine_identifier_type physical_machine_identifier_type; typedef typename result_type::virtual_machine_identifier_type virtual_machine_identifier_type; result_type res; res.cost(problem_res.cost()); typename ProblemResultT::smallint_matrix_type placement_flags(problem_res.virtual_machine_placement()); typename ProblemResultT::real_matrix_type placement_shares(problem_res.virtual_machine_shares()); ::std::size_t npm(::boost::numeric::ublasx::num_rows(placement_flags)); ::std::size_t nvm(::boost::numeric::ublasx::num_columns(placement_flags)); for (::std::size_t i = 0; i < npm; ++i) { physical_machine_identifier_type pm_id(problem_descr.pm_ids[i]); real_type share_sum(0); // Normalization factor for (::std::size_t k = 0; k < 2; ++k) { for (::std::size_t j = 0; j < nvm; ++j) { if (placement_flags(i,j)) { if (k == 0) { share_sum += placement_shares(i,j); } else { virtual_machine_identifier_type vm_id(problem_descr.vm_ids[j]); real_type share(placement_shares(i,j)); if (share_sum > 1) { share /= share_sum; } //FIXME: CPU resource category is hard-coded. resource_share_container shares(1, ::std::make_pair(cpu_resource_category, share)); res.placement()[::std::make_pair(pm_id, vm_id)] = shares; } } } } } return res; }
int main(int argc, char *argv[]) { // Info: reads only the _first_ 3D model in the NVM file! TCLAP::CmdLine cmd("LINE3D++"); TCLAP::ValueArg<std::string> inputArg("i", "input_folder", "folder containing the images (if not specified, path in .nvm file is expected to be correct)", false, "", "string"); cmd.add(inputArg); TCLAP::ValueArg<std::string> nvmArg("m", "nvm_file", "full path to the VisualSfM result file (.nvm)", true, ".", "string"); cmd.add(nvmArg); TCLAP::ValueArg<std::string> outputArg("o", "output_folder", "folder where result and temporary files are stored (if not specified --> input_folder+'/Line3D++/')", false, "", "string"); cmd.add(outputArg); TCLAP::ValueArg<int> scaleArg("w", "max_image_width", "scale image down to fixed max width for line segment detection", false, L3D_DEF_MAX_IMG_WIDTH, "int"); cmd.add(scaleArg); TCLAP::ValueArg<int> neighborArg("n", "num_matching_neighbors", "number of neighbors for matching", false, L3D_DEF_MATCHING_NEIGHBORS, "int"); cmd.add(neighborArg); TCLAP::ValueArg<float> sigma_A_Arg("a", "sigma_a", "angle regularizer", false, L3D_DEF_SCORING_ANG_REGULARIZER, "float"); cmd.add(sigma_A_Arg); TCLAP::ValueArg<float> sigma_P_Arg("p", "sigma_p", "position regularizer (if negative: fixed sigma_p in world-coordinates)", false, L3D_DEF_SCORING_POS_REGULARIZER, "float"); cmd.add(sigma_P_Arg); TCLAP::ValueArg<float> epipolarArg("e", "min_epipolar_overlap", "minimum epipolar overlap for matching", false, L3D_DEF_EPIPOLAR_OVERLAP, "float"); cmd.add(epipolarArg); TCLAP::ValueArg<int> knnArg("k", "knn_matches", "number of matches to be kept (<= 0 --> use all that fulfill overlap)", false, L3D_DEF_KNN, "int"); cmd.add(knnArg); TCLAP::ValueArg<int> segNumArg("y", "num_segments_per_image", "maximum number of 2D segments per image (longest)", false, L3D_DEF_MAX_NUM_SEGMENTS, "int"); cmd.add(segNumArg); TCLAP::ValueArg<int> visibilityArg("v", "visibility_t", "minimum number of cameras to see a valid 3D line", false, L3D_DEF_MIN_VISIBILITY_T, "int"); cmd.add(visibilityArg); TCLAP::ValueArg<bool> diffusionArg("d", "diffusion", "perform Replicator Dynamics Diffusion before clustering", false, L3D_DEF_PERFORM_RDD, "bool"); cmd.add(diffusionArg); TCLAP::ValueArg<bool> loadArg("l", "load_and_store_flag", "load/store segments (recommended for big images)", false, L3D_DEF_LOAD_AND_STORE_SEGMENTS, "bool"); cmd.add(loadArg); TCLAP::ValueArg<float> collinArg("r", "collinearity_t", "threshold for collinearity", false, L3D_DEF_COLLINEARITY_T, "float"); cmd.add(collinArg); TCLAP::ValueArg<bool> cudaArg("g", "use_cuda", "use the GPU (CUDA)", false, true, "bool"); cmd.add(cudaArg); TCLAP::ValueArg<bool> ceresArg("c", "use_ceres", "use CERES (for 3D line optimization)", false, L3D_DEF_USE_CERES, "bool"); cmd.add(ceresArg); TCLAP::ValueArg<float> minBaselineArg("x", "min_image_baseline", "minimum baseline between matching images (world space)", false, L3D_DEF_MIN_BASELINE, "float"); cmd.add(minBaselineArg); TCLAP::ValueArg<float> constRegDepthArg("z", "const_reg_depth", "use a constant regularization depth (only when sigma_p is metric!)", false, -1.0f, "float"); cmd.add(constRegDepthArg); // read arguments cmd.parse(argc,argv); std::string inputFolder = inputArg.getValue().c_str(); std::string nvmFile = nvmArg.getValue().c_str(); // check if NVM file exists boost::filesystem::path nvm(nvmFile); if(!boost::filesystem::exists(nvm)) { std::cerr << "NVM file " << nvmFile << " does not exist!" << std::endl; return -1; } bool use_full_image_path = false; if(inputFolder.length() == 0) { // parse input folder from .nvm file use_full_image_path = true; inputFolder = nvm.parent_path().string(); } std::string outputFolder = outputArg.getValue().c_str(); if(outputFolder.length() == 0) outputFolder = inputFolder+"/Line3D++/"; int maxWidth = scaleArg.getValue(); unsigned int neighbors = std::max(neighborArg.getValue(),2); bool diffusion = diffusionArg.getValue(); bool loadAndStore = loadArg.getValue(); float collinearity = collinArg.getValue(); bool useGPU = cudaArg.getValue(); bool useCERES = ceresArg.getValue(); float epipolarOverlap = fmin(fabs(epipolarArg.getValue()),0.99f); float sigmaA = fabs(sigma_A_Arg.getValue()); float sigmaP = sigma_P_Arg.getValue(); float minBaseline = fabs(minBaselineArg.getValue()); int kNN = knnArg.getValue(); unsigned int maxNumSegments = segNumArg.getValue(); unsigned int visibility_t = visibilityArg.getValue(); float constRegDepth = constRegDepthArg.getValue(); // create output directory boost::filesystem::path dir(outputFolder); boost::filesystem::create_directory(dir); // create Line3D++ object L3DPP::Line3D* Line3D = new L3DPP::Line3D(outputFolder,loadAndStore,maxWidth, maxNumSegments,true,useGPU); // read NVM file std::ifstream nvm_file; nvm_file.open(nvmFile.c_str()); std::string nvm_line; std::getline(nvm_file,nvm_line); // ignore first line... std::getline(nvm_file,nvm_line); // ignore second line... // read number of images std::getline(nvm_file,nvm_line); std::stringstream nvm_stream(nvm_line); unsigned int num_cams; nvm_stream >> num_cams; if(num_cams == 0) { std::cerr << "No aligned cameras in NVM file!" << std::endl; return -1; } // read camera data (sequentially) std::vector<std::string> cams_imgFilenames(num_cams); std::vector<float> cams_focals(num_cams); std::vector<Eigen::Matrix3d> cams_rotation(num_cams); std::vector<Eigen::Vector3d> cams_translation(num_cams); std::vector<Eigen::Vector3d> cams_centers(num_cams); std::vector<float> cams_distortion(num_cams); for(unsigned int i=0; i<num_cams; ++i) { std::getline(nvm_file,nvm_line); // image filename std::string filename; // focal_length,quaternion,center,distortion double focal_length,qx,qy,qz,qw; double Cx,Cy,Cz,dist; nvm_stream.str(""); nvm_stream.clear(); nvm_stream.str(nvm_line); nvm_stream >> filename >> focal_length >> qw >> qx >> qy >> qz; nvm_stream >> Cx >> Cy >> Cz >> dist; cams_imgFilenames[i] = filename; cams_focals[i] = focal_length; cams_distortion[i] = dist; // rotation amd translation Eigen::Matrix3d R; R(0,0) = 1.0-2.0*qy*qy-2.0*qz*qz; R(0,1) = 2.0*qx*qy-2.0*qz*qw; R(0,2) = 2.0*qx*qz+2.0*qy*qw; R(1,0) = 2.0*qx*qy+2.0*qz*qw; R(1,1) = 1.0-2.0*qx*qx-2.0*qz*qz; R(1,2) = 2.0*qy*qz-2.0*qx*qw; R(2,0) = 2.0*qx*qz-2.0*qy*qw; R(2,1) = 2.0*qy*qz+2.0*qx*qw; R(2,2) = 1.0-2.0*qx*qx-2.0*qy*qy; Eigen::Vector3d C(Cx,Cy,Cz); cams_centers[i] = C; Eigen::Vector3d t = -R*C; cams_translation[i] = t; cams_rotation[i] = R; } // read number of images std::getline(nvm_file,nvm_line); // ignore line... std::getline(nvm_file,nvm_line); nvm_stream.str(""); nvm_stream.clear(); nvm_stream.str(nvm_line); unsigned int num_points; nvm_stream >> num_points; // read features (for image similarity calculation) std::vector<std::list<unsigned int> > cams_worldpointIDs(num_cams); std::vector<std::vector<float> > cams_worldpointDepths(num_cams); for(unsigned int i=0; i<num_points; ++i) { // 3D position std::getline(nvm_file,nvm_line); std::istringstream iss_point3D(nvm_line); double px,py,pz,colR,colG,colB; iss_point3D >> px >> py >> pz; iss_point3D >> colR >> colG >> colB; Eigen::Vector3d pos3D(px,py,pz); // measurements unsigned int num_views; iss_point3D >> num_views; unsigned int camID,siftID; float posX,posY; for(unsigned int j=0; j<num_views; ++j) { iss_point3D >> camID >> siftID; iss_point3D >> posX >> posY; cams_worldpointIDs[camID].push_back(i); cams_worldpointDepths[camID].push_back((pos3D-cams_centers[camID]).norm()); } } nvm_file.close(); // load images (parallel) #ifdef L3DPP_OPENMP #pragma omp parallel for #endif //L3DPP_OPENMP for(unsigned int i=0; i<num_cams; ++i) { if(cams_worldpointDepths[i].size() > 0) { // parse filename std::string fname = cams_imgFilenames[i]; boost::filesystem::path img_path(fname); // load image cv::Mat image; if(use_full_image_path) image = cv::imread(inputFolder+"/"+fname,CV_LOAD_IMAGE_GRAYSCALE); else image = cv::imread(inputFolder+"/"+img_path.filename().string(),CV_LOAD_IMAGE_GRAYSCALE); // setup intrinsics float px = float(image.cols)/2.0f; float py = float(image.rows)/2.0f; float f = cams_focals[i]; Eigen::Matrix3d K = Eigen::Matrix3d::Zero(); K(0,0) = f; K(1,1) = f; K(0,2) = px; K(1,2) = py; K(2,2) = 1.0; // undistort (if necessary) float d = cams_distortion[i]; cv::Mat img_undist; if(fabs(d) > L3D_EPS) { // undistorting Eigen::Vector3d radial(-d,0.0,0.0); Eigen::Vector2d tangential(0.0,0.0); Line3D->undistortImage(image,img_undist,radial,tangential,K); } else { // already undistorted img_undist = image; } // median point depth std::sort(cams_worldpointDepths[i].begin(),cams_worldpointDepths[i].end()); size_t med_pos = cams_worldpointDepths[i].size()/2; float med_depth = cams_worldpointDepths[i].at(med_pos); // add to system Line3D->addImage(i,img_undist,K,cams_rotation[i], cams_translation[i], med_depth,cams_worldpointIDs[i]); } } // match images Line3D->matchImages(sigmaP,sigmaA,neighbors,epipolarOverlap, minBaseline,kNN,constRegDepth); // compute result Line3D->reconstruct3Dlines(visibility_t,diffusion,collinearity,useCERES); // save end result std::vector<L3DPP::FinalLine3D> result; Line3D->get3Dlines(result); // save as STL Line3D->saveResultAsSTL(outputFolder); // save as OBJ Line3D->saveResultAsOBJ(outputFolder); // save as TXT Line3D->save3DLinesAsTXT(outputFolder); // cleanup delete Line3D; }