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; }
int main(int argc, char *argv[]) { TCLAP::CmdLine cmd("LINE3D++"); TCLAP::ValueArg<std::string> inputArg("i", "input_folder", "folder containing the original images", true, ".", "string"); cmd.add(inputArg); TCLAP::ValueArg<std::string> jsonArg("j", "sfm_json_file", "full path to the OpenMVG result file (sfm_data.json)", true, ".", "string"); cmd.add(jsonArg); 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 (-1 --> use all)", 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> 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 jsonFile = jsonArg.getValue().c_str(); 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(); int kNN = knnArg.getValue(); unsigned int maxNumSegments = segNumArg.getValue(); unsigned int visibility_t = visibilityArg.getValue(); float constRegDepth = constRegDepthArg.getValue(); // check if json file exists boost::filesystem::path json(jsonFile); if(!boost::filesystem::exists(json)) { std::cerr << "OpenMVG json file " << jsonFile << " does not exist!" << std::endl; return -1; } // 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); // parse json file std::ifstream jsonFileIFS(jsonFile.c_str()); std::string str((std::istreambuf_iterator<char>(jsonFileIFS)), std::istreambuf_iterator<char>()); rapidjson::Document d; d.Parse(str.c_str()); rapidjson::Value& s = d["views"]; size_t num_cams = s.Size(); if(num_cams == 0) { std::cerr << "No aligned cameras in json file!" << std::endl; return -1; } // read image IDs and filename (sequentially) std::vector<std::string> cams_imgFilenames(num_cams); std::vector<unsigned int> cams_intrinsic_IDs(num_cams); std::vector<unsigned int> cams_view_IDs(num_cams); std::vector<unsigned int> cams_pose_IDs(num_cams); std::vector<bool> img_found(num_cams); std::map<unsigned int,unsigned int> pose2view; for(rapidjson::SizeType i=0; i<s.Size(); ++i) { rapidjson::Value& array_element = s[i]; rapidjson::Value& view_data = array_element["value"]["ptr_wrapper"]["data"]; std::string filename = view_data["filename"].GetString(); unsigned int view_id = view_data["id_view"].GetUint(); unsigned int intrinsic_id = view_data["id_intrinsic"].GetUint(); unsigned int pose_id = view_data["id_pose"].GetUint(); std::string full_path = inputFolder+"/"+filename; boost::filesystem::path full_path_check(full_path); if(boost::filesystem::exists(full_path_check)) { // image exists cams_imgFilenames[i] = full_path; cams_view_IDs[i] = view_id; cams_intrinsic_IDs[i] = intrinsic_id; cams_pose_IDs[i] = pose_id; img_found[i] = true; pose2view[pose_id] = view_id; } else { // image not found... img_found[i] = false; std::cerr << "WARNING: image '" << filename << "' not found (ID=" << view_id << ")" << std::endl; } } // read intrinsics (sequentially) std::map<unsigned int,Eigen::Vector3d> radial_dist; std::map<unsigned int,Eigen::Vector2d> tangential_dist; std::map<unsigned int,Eigen::Matrix3d> K_matrices; std::map<unsigned int,bool> is_distorted; rapidjson::Value& intr = d["intrinsics"]; size_t num_intrinsics = intr.Size(); if(num_intrinsics == 0) { std::cerr << "No intrinsics in json file!" << std::endl; return -1; } std::string cam_model; for(rapidjson::SizeType i=0; i<intr.Size(); ++i) { rapidjson::Value& array_element = intr[i]; rapidjson::Value& intr_data = array_element["value"]["ptr_wrapper"]["data"]; if (array_element["value"].HasMember("polymorphic_name")) cam_model = array_element["value"]["polymorphic_name"].GetString(); unsigned int groupID = array_element["key"].GetUint(); bool distorted = false; Eigen::Vector3d radial_d(0,0,0); Eigen::Vector2d tangential_d(0,0); double focal_length = intr_data["focal_length"].GetDouble();; Eigen::Vector2d principle_p; principle_p(0) = intr_data["principal_point"][0].GetDouble(); principle_p(1) = intr_data["principal_point"][1].GetDouble(); // check camera model for distortion if(cam_model.compare("pinhole_radial_k3") == 0) { // 3 radial radial_d(0) = intr_data["disto_k3"][0].GetDouble(); radial_d(1) = intr_data["disto_k3"][1].GetDouble(); radial_d(2) = intr_data["disto_k3"][2].GetDouble(); } else if(cam_model.compare("pinhole_radial_k1") == 0) { // 1 radial radial_d(0) = intr_data["disto_k1"][0].GetDouble(); } else if(cam_model.compare("pinhole_brown_t2") == 0) { // 3 radial radial_d(0) = intr_data["disto_t2"][0].GetDouble(); radial_d(1) = intr_data["disto_t2"][1].GetDouble(); radial_d(2) = intr_data["disto_t2"][2].GetDouble(); // 2 tangential tangential_d(0) = intr_data["disto_t2"][3].GetDouble(); tangential_d(1) = intr_data["disto_t2"][4].GetDouble(); } else if(cam_model.compare("pinhole") != 0) { std::cerr << "WARNING: camera model '" << cam_model << "' for group " << groupID << " unknown! No distortion assumed..." << std::endl; } // check if distortion actually occured if(fabs(radial_d(0)) > L3D_EPS || fabs(radial_d(1)) > L3D_EPS || fabs(radial_d(2)) > L3D_EPS || fabs(tangential_d(0)) > L3D_EPS || fabs(tangential_d(1)) > L3D_EPS) { distorted = true; } // create K Eigen::Matrix3d K = Eigen::Matrix3d::Zero(); K(0,0) = focal_length; K(1,1) = focal_length; K(0,2) = principle_p(0); K(1,2) = principle_p(1); K(2,2) = 1.0; // store radial_dist[groupID] = radial_d; tangential_dist[groupID] = tangential_d; K_matrices[groupID] = K; is_distorted[groupID] = distorted; } // read extrinsics (sequentially) std::map<unsigned int,Eigen::Vector3d> translations; std::map<unsigned int,Eigen::Vector3d> centers; std::map<unsigned int,Eigen::Matrix3d> rotations; rapidjson::Value& extr = d["extrinsics"]; size_t num_extrinsics = extr.Size(); if(num_extrinsics == 0) { std::cerr << "No extrinsics in json file!" << std::endl; return -1; } for(rapidjson::SizeType i=0; i<extr.Size(); ++i) { rapidjson::Value& array_element = extr[i]; unsigned int poseID = array_element["key"].GetUint(); if(pose2view.find(poseID) != pose2view.end()) { unsigned int viewID = pose2view[poseID]; // rotation rapidjson::Value& _R = array_element["value"]["rotation"]; Eigen::Matrix3d R = Eigen::Matrix3d::Zero(); R(0,0) = _R[0][0].GetDouble(); R(0,1) = _R[0][1].GetDouble(); R(0,2) = _R[0][2].GetDouble(); R(1,0) = _R[1][0].GetDouble(); R(1,1) = _R[1][1].GetDouble(); R(1,2) = _R[1][2].GetDouble(); R(2,0) = _R[2][0].GetDouble(); R(2,1) = _R[2][1].GetDouble(); R(2,2) = _R[2][2].GetDouble(); // center rapidjson::Value& _C = array_element["value"]["center"]; Eigen::Vector3d C; C(0) = _C[0].GetDouble(); C(1) = _C[1].GetDouble(); C(2) = _C[2].GetDouble(); // translation Eigen::Vector3d t = -R*C; // store translations[viewID] = t; centers[viewID] = C; rotations[viewID] = R; } else { std::cerr << "WARNING: pose with ID " << poseID << " does not map to an image!" << std::endl; } } // read worldpoint data (sequentially) std::map<unsigned int,std::list<unsigned int> > views2wps; std::map<unsigned int,std::vector<float> > views2depths; rapidjson::Value& wps = d["structure"]; size_t num_wps = wps.Size(); if(num_wps == 0) { std::cerr << "No worldpoints in json file!" << std::endl; return -1; } for(rapidjson::SizeType i=0; i<wps.Size(); ++i) { rapidjson::Value& array_element = wps[i]; rapidjson::Value& wp_data = array_element["value"]; // id and position unsigned int wpID = array_element["key"].GetUint(); Eigen::Vector3d X; X(0) = wp_data["X"][0].GetDouble(); X(1) = wp_data["X"][1].GetDouble(); X(2) = wp_data["X"][2].GetDouble(); // observations size_t num_obs = wp_data["observations"].Size(); for(size_t j=0; j<num_obs; ++j) { unsigned int viewID = wp_data["observations"][j]["key"].GetUint(); if(centers.find(viewID) != centers.end()) { float depth = (centers[viewID]-X).norm(); // store in list views2wps[viewID].push_back(wpID); views2depths[viewID].push_back(depth); } } } // load images (parallel) #ifdef L3DPP_OPENMP #pragma omp parallel for #endif //L3DPP_OPENMP for(unsigned int i=0; i<num_cams; ++i) { unsigned int camID = cams_view_IDs[i]; unsigned int intID = cams_intrinsic_IDs[i]; if(views2wps.find(camID) != views2wps.end() && img_found[i] && K_matrices.find(intID) != K_matrices.end()) { // load image cv::Mat image = cv::imread(cams_imgFilenames[i],CV_LOAD_IMAGE_GRAYSCALE); // intrinsics Eigen::Matrix3d K = K_matrices[intID]; // undistort (if necessary) bool distorted = is_distorted[intID]; Eigen::Vector3d radial = radial_dist[intID]; Eigen::Vector2d tangential = tangential_dist[intID]; cv::Mat img_undist; if(distorted) { // undistorting Line3D->undistortImage(image,img_undist,radial,tangential,K); } else { // already undistorted img_undist = image; } // median point depth std::sort(views2depths[camID].begin(),views2depths[camID].end()); size_t med_pos = views2depths[camID].size()/2; float med_depth = views2depths[camID].at(med_pos); // add to system Line3D->addImage(camID,img_undist,K,rotations[camID], translations[camID], med_depth,views2wps[camID]); } } // match images Line3D->matchImages(sigmaP,sigmaA,neighbors,epipolarOverlap, 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); // save as BIN Line3D->save3DLinesAsBIN(outputFolder); // cleanup delete Line3D; }