Scene Reconstruction#
Goal#
In this tutorial you will learn how to use the reconstruction api for sparse reconstruction:
Load and file with a list of image paths.
Run libmv reconstruction pipeline.
Show obtained results using Viz.
Code#
#include <opencv2/sfm.hpp>
#include <opencv2/viz.hpp>
#include <opencv2/3d.hpp>
#include <opencv2/core.hpp>
#include <iostream>
#include <fstream>
using namespace std;
using namespace cv;
using namespace cv::sfm;
static void help() {
cout
<< "\n------------------------------------------------------------------------------------\n"
<< " This program shows the multiview reconstruction capabilities in the \n"
<< " OpenCV Structure From Motion (SFM) module.\n"
<< " It reconstruct a scene from a set of 2D images \n"
<< " Usage:\n"
<< " example_sfm_scene_reconstruction <path_to_file> <f> <cx> <cy>\n"
<< " where: path_to_file is the file absolute path into your system which contains\n"
<< " the list of images to use for reconstruction. \n"
<< " f is the focal length in pixels. \n"
<< " cx is the image principal point x coordinates in pixels. \n"
<< " cy is the image principal point y coordinates in pixels. \n"
<< "------------------------------------------------------------------------------------\n\n"
<< endl;
}
static int getdir(const string _filename, vector<String> &files)
{
ifstream myfile(_filename.c_str());
if (!myfile.is_open()) {
cout << "Unable to read file: " << _filename << endl;
exit(0);
} else {;
size_t found = _filename.find_last_of("/\\");
string line_str, path_to_file = _filename.substr(0, found);
while ( getline(myfile, line_str) )
files.push_back(path_to_file+string("/")+line_str);
}
return 1;
}
int main(int argc, char* argv[])
{
// Read input parameters
if ( argc != 5 )
{
help();
exit(0);
}
// Parse the image paths
vector<String> images_paths;
getdir( argv[1], images_paths );
// Build intrinsics
float f = atof(argv[2]),
cx = atof(argv[3]), cy = atof(argv[4]);
Matx33d K = Matx33d( f, 0, cx,
0, f, cy,
0, 0, 1);
/// Reconstruct the scene using the 2d images
bool is_projective = true;
vector<Mat> Rs_est, ts_est, points3d_estimated;
reconstruct(images_paths, 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
viz::Viz3d window("Coordinate Frame");
window.setWindowSize(Size(500,500));
window.setWindowPosition(Point(150,150));
window.setBackgroundColor(); // black by default
// Create the pointcloud
cout << "Recovering points ... ";
// recover estimated points3d
vector<Vec3f> point_cloud_est;
for (int i = 0; i < points3d_estimated.size(); ++i)
point_cloud_est.push_back(Vec3f(points3d_estimated[i]));
cout << "[DONE]" << endl;
/// Recovering cameras
cout << "Recovering cameras ... ";
vector<Affine3d> path;
for (size_t i = 0; i < Rs_est.size(); ++i)
path.push_back(Affine3d(Rs_est[i],ts_est[i]));
cout << "[DONE]" << endl;
/// Add the pointcloud
if ( point_cloud_est.size() > 0 )
{
cout << "Rendering points ... ";
viz::WCloud cloud_widget(point_cloud_est, viz::Color::green());
window.showWidget("point_cloud", cloud_widget);
cout << "[DONE]" << endl;
}
else
{
cout << "Cannot render points: Empty pointcloud" << endl;
}
/// Add cameras
if ( path.size() > 0 )
{
cout << "Rendering Cameras ... ";
window.showWidget("cameras_frames_and_lines", viz::WTrajectory(path, viz::WTrajectory::BOTH, 0.1, viz::Color::green()));
window.showWidget("cameras_frustums", viz::WTrajectoryFrustums(path, K, 0.1, viz::Color::yellow()));
window.setViewerPose(path[0]);
cout << "[DONE]" << endl;
}
else
{
cout << "Cannot render the cameras: Empty path" << endl;
}
/// Wait for key 'q' to close the window
cout << endl << "Press 'q' to close each windows ... " << endl;
window.spin();
return 0;
}
Explanation#
Firstly, we need to load the file containing list of image paths in order to feed the reconstruction api:
/home/eriba/software/opencv_contrib/modules/sfm/samples/data/images/resized_IMG_2889.jpg
/home/eriba/software/opencv_contrib/modules/sfm/samples/data/images/resized_IMG_2890.jpg
/home/eriba/software/opencv_contrib/modules/sfm/samples/data/images/resized_IMG_2891.jpg
/home/eriba/software/opencv_contrib/modules/sfm/samples/data/images/resized_IMG_2892.jpg
...
int getdir(const string _filename, vector<string> &files)
{
ifstream myfile(_filename.c_str());
if (!myfile.is_open()) {
cout << "Unable to read file: " << _filename << endl;
exit(0);
} else {
string line_str;
while ( getline(myfile, line_str) )
files.push_back(line_str);
}
return 1;
}
Secondly, the built container will be used to feed the reconstruction api. It is important outline that the estimated results must be stored in a vector
bool is_projective = true;
vector<Mat> Rs_est, ts_est, points3d_estimated;
reconstruct(images_paths, 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;
Finally, the obtained results will be shown in Viz.
Usage and Results#
In order to run this sample we need to specify the path to the image paths files, the focal length of the camera in addition to the center projection coordinates (in pixels).
1. Middlebury temple
Using following image sequence [1] and the followings camera parameters we can compute the sparse 3d reconstruction:
./example_sfm_scene_reconstruction image_paths_file.txt 800 400 225
The following picture shows the obtained camera motion in addition to the estimated sparse 3d reconstruction:
2. Sagrada Familia
Using following image sequence [2] and the followings camera parameters we can compute the sparse 3d reconstruction:
./example_sfm_scene_reconstruction image_paths_file.txt 350 240 360
The following picture shows the obtained camera motion in addition to the estimated sparse 3d reconstruction:
[1] http://vision.middlebury.edu/mview/data
[2] Penate Sanchez, A. and Moreno-Noguer, F. and Andrade Cetto, J. and Fleuret, F. (2014). LETHA: Learning from High Quality Inputs for 3D Pose Estimation in Low Quality Images. Proceedings of the International Conference on 3D vision (3DV). URL