virtual void run(InputArrayOfArrays _points2d)
{
std::vector<Mat> points2d;
_points2d.getMatVector(points2d);
CV_Assert( _points2d.total() >= 2 );
// Parse 2d points to Tracks
Tracks tracks;
parser_2D_tracks(points2d, tracks);
// Set libmv logs level
libmv_initLogging("");
if (libmv_reconstruction_options_.verbosity_level >= 0)
{
libmv_startDebugLogging();
libmv_setLoggingVerbosity(
libmv_reconstruction_options_.verbosity_level);
}
// Perform reconstruction
libmv_reconstruction_ =
*libmv_solveReconstruction(tracks,
&libmv_camera_intrinsics_options_,
&libmv_reconstruction_options_);
}
libmv_Reconstruction *libmv_solveReconstructionImpl(
const std::vector<std::string> &images,
const libmv_CameraIntrinsicsOptions* libmv_camera_intrinsics_options,
libmv_ReconstructionOptions* libmv_reconstruction_options)
{
Ptr<Feature2D> edetector = ORB::create(10000);
Ptr<Feature2D> edescriber = xfeatures2d::DAISY::create();
//Ptr<Feature2D> edescriber = xfeatures2d::LATCH::create(64, true, 4);
cout << "Initialize nViewMatcher ... ";
libmv::correspondence::nRobustViewMatching nViewMatcher(edetector, edescriber);
cout << "OK" << endl << "Performing Cross Matching ... ";
nViewMatcher.computeCrossMatch(images); cout << "OK" << endl;
// Building tracks
libmv::Tracks tracks;
libmv::Matches matches = nViewMatcher.getMatches();
parser_2D_tracks( matches, tracks );
// Perform reconstruction
return libmv_solveReconstruction(tracks,
libmv_camera_intrinsics_options,
libmv_reconstruction_options);
}
//--------------------------------------------------------------
void ofApp::setup(){
camera_pov = false;
// Read input parameters
// Read 2D points from text file
tracks_filename = "data/desktop_tracks.txt";
f = 1914.0;
cx = 640;
cy = 360;
std::vector<cv::Mat> points2d;
parser_2D_tracks(tracks_filename, points2d);
// Set the camera calibration matrix
cv::Matx33d K = cv::Matx33d(f, 0, cx,
0, f, cy,
0, 0, 1);
/// Reconstruct the scene using the 2d correspondences
is_projective = true;
cv::sfm::reconstruct(points2d, Rs_est, ts_est, K, points3d_estimated, is_projective);
// Print output
cout << "\n----------------------------\n" << endl;
cout << "Reconstruction: " << endl;
cout << "============================" << endl;
cout << "Estimated 3D points: " << points3d_estimated.size() << endl;
cout << "Estimated cameras: " << Rs_est.size() << endl;
cout << "Refined intrinsics: " << endl << K << endl << endl;
cout << "3D Visualization: " << endl;
cout << "============================" << endl;
/// Create 3D windows
ofSetWindowTitle("Estimation Coordinate Frame");
ofSetBackgroundColor(ofColor::black);
// Create the pointcloud
cout << "Recovering points ... ";
// recover estimated points3d
for (int i = 0; i < points3d_estimated.size(); ++i)
point_cloud_est.push_back(cv::Vec3f(points3d_estimated[i]));
cout << "[DONE]" << endl;
/// Recovering cameras
cout << "Recovering cameras ... ";
for (size_t i = 0; i < Rs_est.size(); ++i)
path_est.push_back(cv::Affine3d(Rs_est[i], ts_est[i]));
cout << "[DONE]" << endl;
/// Add cameras
cout << "Rendering Trajectory ... ";
/// Wait for key 'q' to close the window
cout << endl << "Press: " << endl;
cout << " 's' to switch the camera pov" << endl;
cout << " 'q' to close the windows " << endl;
if (path_est.size() > 0)
{
// animated trajectory
idx = 0;
forw = -1;
n = static_cast<int>(path_est.size());
}
cam.setNearClip(0.001f);
cam.setDistance(5.0f);
}