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;
}
