Nepolo's profile - activity

Hi, thanks for the answers.

Do you have the (relative) 3d position of your points.

I don’t know the (relative) 3d position of the points. I know the two images, the focal length and the sensor size of the cameras.

I tested with Blender if that's possible. Blender motion tracking can compute the relative position and rotation of the two cameras. Blender needs 8 points in both images. The result is very exact.

It is possible. But how?

I have found the openCv function findFundamentalMat.

findFundamentalMat also requires min 8 points* in both images. This is the same rule as in Blender.

*CV_FM_8POINT for an 8-point algorithm. N>=8

CV_FM_RANSAC for the RANSAC algorithm. N>=8

CV_FM_LMEDS for the LMedS algorithm. N>=8

And I found the function stereoCalibrate.

UPDATE 2015.06.29 -----UPDATE 2015.06.29------- UPDATE 2015.06.29

Hi. thanks for the comments.

Why 8 Points? As LBerger said, every pair of points gives you a constraint in the form of p2'Fp1 = 0. Why you need 8 points is not that obvious.

The animation programm Blender can compute the points and position of the camera. Blender say that you needs min 8 points.

I take a look at the function computeCorrespondEpilines. Here is the result. Image 2 looks like very good.

Image 1

Image 2

I have copied the image into Blender.

Here is the result.

But what happens next? How can I get the following values? X, Y, Z, Roll, Pitch and Yaw

Here is my code.

#include <QCoreApplication>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <vector>
#include <iostream>
using namespace cv;
using namespace std;


Mat image1;
Mat image2;
Mat  F;

int main(int argc, char *argv[])
{
    QCoreApplication a(argc, argv);

    // Open image 1
    QString fileName = "cam1c.jpg";   
   image1= imread(fileName.toAscii().data());

   // Open image 2
   QString fileName2 = "cam2c.jpg";
  image2= imread(fileName2.toAscii().data());



  // this are the points
  // I added the point values manually (it is a test)
  vector<Point2f> image1_points;
  vector<Point2f> image2_points;
  image1_points.push_back(Point(403.83,299.63));
  image2_points.push_back(Point(401.38 ,300.03));

  image1_points.push_back(Point(311.5,388.5));
  image2_points.push_back(Point(310.45 ,378.28));

  image1_points.push_back(Point(741.9,72.08));
  image2_points.push_back(Point(567.58 ,160.20));

  image1_points.push_back(Point(488.45,211.58));
  image2_points.push_back(Point(397.43 ,237.73));

  image1_points.push_back(Point(250.6,200.43));
  image2_points.push_back(Point(314.95 ,229.7));

  image1_points.push_back(Point(171,529.08));
  image2_points.push_back(Point(359.9 ,477.5));

  image1_points.push_back(Point(400,227.75));
  image2_points.push_back(Point(272.78 ,251.90));

  image1_points.push_back(Point(513.95,414));
  image2_points.push_back(Point(508.15 ,361.03));

  image1_points.push_back(Point(280.68,140.9));
  image2_points.push_back(Point(223.55 ,178.93));

  image1_points.push_back(Point(479.58,220.48));
  image2_points.push_back(Point(355.98 ,244.63));

  image1_points.push_back(Point(621.95,122.48));
  image2_points.push_back(Point(454.78 ...