结构运动-摄像机运动估计
目标
在本教程中,您将学习如何使用重建api的摄像机运动估计:
- 加载文件与跟踪二维点，并建立容器的所有帧。
- libmv重建传递途径运行。
- 使用即显示结果。
使用情况和结果
为了运行这个示例,我们需要指定的路径跟踪点文件,相机的焦距长度除了中心投影坐标(以像素为单位)。 你可以找到一个示例文件/数据/ desktop_trakcks.txt样品
。 / example_sfm_trajectory_reconstruction desktop_tracks。 txt 1914 640 360
下面的图片显示了获得相机运动从追踪获得2 d点:
Code
代码
#include <opencv2/core.hpp>
#include <opencv2/sfm.hpp>
#include <opencv2/viz.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 cameratrajectory(轨道) reconstruction capabilities\n"
<< " in the OpenCV Structure From Motion (SFM) module.\n"
<< " \n"
<< " Usage:\n"
<< " example_sfm_trajectory(轨道)_reconstruction <path_to_tracks_file> <f> <cx>
<cy>\n"
<< " where: is the tracks file absolute path into your system. \n"
<< " \n"
<< " The file must have the following format: \n"
<< " row1 : x1 y1 x2 y2 ... x36 y36 for track 1\n"
<< " row2 : x1 y1 x2 y2 ... x36 y36 for track 2\n"
<< " etc\n"
<< " \n"
<< " i.e. a row gives the 2D measured position of a point as it is tracked\n"
<< " through frames 1 to 36. If there is no match found in a view then x\n"
<< " and y are -1.\n"
<< " \n"
<< " Each row corresponds to a different point.\n"
<< " \n"
<< " f is the focal lenght 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;
}
/* Build the following structure data
*
* frame1 frame2 frameN
* track1 | (x11,y11) | -> | (x12,y12) | -> | (x1N,y1N) |
* track2 | (x21,y11) | -> | (x22,y22) | -> | (x2N,y2N) |
* trackN | (xN1,yN1) | -> | (xN2,yN2) | -> | (xNN,yNN) |
*
*
* In case a marker (x,y) does not appear in a frame its
* values will be (-1,-1).
*/
void
parser_2D_tracks(const
String &_filename, std::vector<Mat> &points2d )
{
ifstream myfile(_filename.c_str());
if (!myfile.is_open())
{
cout << "Unable to read file: " << _filename << endl;
exit(0);
} else {
double x, y;
string line_str;
int n_frames = 0, n_tracks = 0;
// extract data from text file
vector<vector<Vec2d> > tracks;
for ( ; getline(myfile,line_str); ++n_tracks)
{
istringstream
line(line_str);
vector<Vec2d> track;
for ( n_frames = 0; line >> x >> y; ++n_frames)
{
if ( x > 0 && y > 0)
track.push_back(Vec2d(x,y));
else
track.push_back(Vec2d(-1));
}
tracks.push_back(track);
}
// embed data in reconstruction api format
for (int i = 0; i < n_frames; ++i)
{
Mat_<double> frame(2, n_tracks);
for (int j = 0; j < n_tracks; ++j)
{
frame(0,j) = tracks[j][i][0];
frame(1,j) = tracks[j][i][1];
}
points2d.push_back(Mat(frame));
}
myfile.close();
}
}
/* Keyboard callback to control 3D visualization
*/
bool camera_pov =
false;
{
camera_pov = !camera_pov;
}
/* Sample main code
*/
int main(int argc,char** argv)
{
// Read input parameters
if (
argc(命令行参数个数) != 5 )
{
help();
exit(0);
}
// Read 2D points from text file
std::vector<Mat> points2d;
parser_2D_tracks( argv[1], points2d );
// Set the camera calibration matrix
const
double f = atof(argv[2]),
cx = atof(argv[3]), cy = atof(argv[4]);
0, f, cy,
0, 0, 1);
bool is_projective =true;
vector<Mat> Rs_est, ts_est, points3d_estimated;
// 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;
viz::Viz3d window_est("Estimation
Coordinate Frame");
window_est.setBackgroundColor();
// black by default
window_est.registerKeyboardCallback(&keyboard_callback);
// 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;
cout << "Recovering cameras ... ";
vector<Affine3d> path_est;
for (size_t i = 0; i < Rs_est.size(); ++i)
path_est.push_back(Affine3d(Rs_est[i],ts_est[i]));
cout << "[DONE]" << endl;
cout << "Rendering Trajectory ... ";
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
int idx = 0, forw = -1, n =static_cast<int>(path_est.size());
while(!window_est.wasStopped())
{
for (size_t i = 0; i < point_cloud_est.size(); ++i)
{
Vec3d point = point_cloud_est[i];
char buffer[50];
sprintf (buffer, "%d", static_cast<int>(i));
cube_widget.setRenderingProperty(viz::LINE_WIDTH,
2.0);
window_est.showWidget("Cube"+String(buffer),
cube_widget, point_pose);
}
Affine3d cam_pose = path_est[idx];
viz::WCameraPosition cpw(0.25);//
Coordinate axes
if ( camera_pov )
window_est.setViewerPose(cam_pose);
else
{
// render complete
trajectory(轨道)
window_est.showWidget("cameras_frames_and_lines_est",viz::WTrajectory(path_est,
viz::WTrajectory::PATH, 1.0, viz::Color::green()));
window_est.showWidget("CPW", cpw, cam_pose);
window_est.showWidget("CPW_FRUSTUM", cpw_frustum, cam_pose);
}
// update
trajectory(轨道) index (spring effect)
forw *= (idx==n || idx==0) ? -1: 1; idx += forw;
// frame rate 1s
window_est.spinOnce(1, true);
window_est.removeAllWidgets();
}
}
return 0;
}
Explanation
Firstly, we need to load the file containing the 2d points tracked over all the frames and construct the container to feed the reconstruction api. In this case the tracked 2d points will have the following structure, a vector of 2d points array, where each inner array represents a different frame. Every frame is composed by a list of 2d points which e.g. the first point in frame 1 is the same point in frame 2. If there is no point in a frame the assigned value will be (-1,-1):
解释
首先,我们需要加载这个文件包含2 d点跟踪所有帧和构造容器重建api。 在这种情况下,跟踪2 d点会有以下结构,2 d点的向量数组,每个数组内代表一个不同的框架。 每一帧由2 d点的列表,例如第一个点在坐标系1点在坐标系2相同。 如果没有在一帧分配的值将被(-1,-1):
/* Build the following structure data
*
* frame1 frame2 frameN
* track1 | (x11,y11) | -> | (x12,y12) | -> | (x1N,y1N) |
* track2 | (x21,y11) | -> | (x22,y22) | -> | (x2N,y2N) |
* trackN | (xN1,yN1) | -> | (xN2,yN2) | -> | (xNN,yNN) |
*
*
* In case a marker (x,y) does not appear in a frame its
* values will be (-1,-1).
*/
...
for (int i = 0; i < n_frames; ++i)
{
Mat_<double> frame(2, n_tracks);
for (int j = 0; j < n_tracks; ++j)
{
frame(0,j) = tracks[j][i][0];
frame(1,j) = tracks[j][i][1];
}
points2d.push_back(Mat(frame));
}
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<Mat>:
其次,建立集装箱将被用来喂重建api。 重要的是大纲,必须存储在一个向量的估计结果<<Mat>:
bool is_projective =true;
vector<Mat> Rs_est, ts_est, points3d_estimated;
// 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, in this case reproducing(复制) the camera with an oscillation(振荡) effect.
最后,获得的结果将即所示,在这种情况下繁殖相机与一个振荡的效果。