I am working on an object detection code and I chose the edge orientation histogram as a descriptor for the matching.
I am facing a problem in the back projected histogram as i don't seem to have a good matching , the back projected image is mostly white, which means that i cannot use meanshift or so for detection of the object.
Please help me regarding this matter:
here is what i've done so far:
Take an initial ROI (the target needed to be detected in the video stream).
convert ROI to grayscale
apply sobel operator for both x, y derivatives.
calculate orientation using opencv phase function (from derivative x and derivative y)
make a histogram of the generated orientation. with the following specs:
(range : 0 to 2 PI) , (single channel) , (256 bins)
normalize the histogram
the code for doing these steps is the following:
Mat ROI_grad_x, ROI_grad_y , ROI_grad , ROI_gray;
Mat ROI_abs_grad_x, ROI_abs_grad_y;
cvtColor(ROI, ROI_gray, CV_BGR2GRAY);
/// Gradient X
Sobel( ROI_gray, ROI_grad_x, CV_16S, 1, 0, 3 );
/// Gradient Y
Sobel( ROI_gray, ROI_grad_y, CV_16S, 0, 1, 3 );
convertScaleAbs( ROI_grad_x, ROI_abs_grad_x );
convertScaleAbs( ROI_grad_y, ROI_abs_grad_y );
addWeighted( ROI_abs_grad_x, 0.5, ROI_abs_grad_y, 0.5, 0, ROI_grad );
Mat ROI_orientation = Mat::zeros(ROI_abs_grad_x.rows, ROI_abs_grad_y.cols, CV_32F); //to store the gradients
Mat ROI_orientation_norm ;
ROI_grad_x.convertTo(ROI_grad_x,CV_32F);
ROI_grad_y.convertTo(ROI_grad_y,CV_32F);
phase(ROI_grad_x, ROI_grad_y, ROI_orientation , false);
Mat ROI_orientation_hist ;
float ROI_orientation_range[] = {0 , CV_PI};
const float *ROI_orientation_histRange[] = {ROI_orientation_range};
int ROI_orientation_histSize =256;
//calcHist( &ROI_orientation, 1, 0, Mat(), ROI_orientation_hist, 1, &ROI_orientation_histSize, &ROI_orientation_histRange , true, false);
calcHist( &ROI_orientation, 1, 0, Mat(), ROI_orientation_hist, 1, &ROI_orientation_histSize, ROI_orientation_histRange , true, false);
normalize( ROI_orientation_hist, ROI_orientation_hist, 0, 255, NORM_MINMAX, -1, Mat() );
then , and for each camera frame captured , I do the following steps:
convert to grayscale
apply sobel operator for both x derivative and y derivative.
compute orientation using phase opencv function (using the 2 derivatives mentioned above)
back project the histogram onto the orientation frame matrix to get the matches.
the code used for this part is the following :
Mat grad_x, grad_y , grad;
Mat abs_grad_x, abs_grad_y;
/// Gradient X
Sobel( frame_gray, grad_x, CV_16S, 1, 0, 3 );
/// Gradient Y
Sobel( frame_gray, grad_y, CV_16S, 0, 1, 3 );
convertScaleAbs( grad_x, abs_grad_x );
convertScaleAbs( grad_y, abs_grad_y );
addWeighted( abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad );
///======================
Mat orientation = Mat::zeros(abs_grad_x.rows, abs_grad_y.cols, CV_32F); //to store the gradients
Mat orientation_norm ;
grad_x.convertTo(grad_x,CV_32F);
grad_y.convertTo(grad_y,CV_32F);
phase(grad_x, grad_y, orientation , false);
Mat EOH_backProj ;
calcBackProject( &orientation, 1, 0, ROI_orientation_hist, EOH_backProj, ROI_orientation_histRange, 1, true );
So , what seems to be the problem in my approach ?!
Thanks alot.
I use openCV function projectPoints() to rotate, translate and project a set of 3D points and solvePnp() to find this rotation and translation. This works well when the lens distortion coefficients are all zero but fails otherwise. It takes as little distortion as this to fail completely:
distCoeffs << 0.0, 0.01, 0.0, 0.0, 0.0;
The code is below:
#include <iostream>
#include "opencv.hpp"
using namespace std;
using namespace cv;
#define DEG2RAD (3.1415293/180.0)
#define RAD2DEG (1.0/DEG2RAD)
int main() {
const int npoints = 10; // number of points
// extrinsic
const Point3f tvec(10, 20, 30);
Point3f rvec(3, 5, 7);
cout << "Finding extrinsic parameters (PnP)" << endl;
cout<<"Test transformations: ";
cout<<"Rotation: "<<rvec<<"; translation: "<<tvec<<endl;
rvec*=DEG2RAD;
// intrinsic
Mat_ <double>cameraMatrix(3, 3);
cameraMatrix << 300., 0., 200., 0, 300., 100., 0., 0., 1.;
Mat_ <double>distCoeffs(1, 5); // (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements.
//distCoeffs << 1.2, 0.2, 0., 0., 0.; // non-zero distortion
distCoeffs << 0.0, 0.0, 0.0, 0.0, 0.0; // zero distortion
cout<<"distrotion coeff: "<<distCoeffs<<endl;
cout<<"============= Running PnP..."<<endl;
vector<Point3f> objPts(npoints);
vector<Point2f> imagePoints(npoints);
Mat rvec_est, tvec_est;
randu(Mat(objPts), 0.0f, 100.0f);
// project
projectPoints(Mat(objPts), Mat(rvec), Mat(tvec), cameraMatrix, distCoeffs, Mat(imagePoints));
// extrinsic
solvePnP(objPts, imagePoints, cameraMatrix, distCoeffs, rvec_est, tvec_est);
cout<<"Rotation: "<<rvec_est*RAD2DEG<<endl;
cout<<"Translation "<<tvec_est<<endl;
return 0;
}
When all distortion coefficients are 0 the result is OK:
Finding extrinsic parameters (PnP)
Test transformations: Rotation: [3, 5, 7]; translation: [10, 20, 30]
distrotion coeff: [0, 0, 0, 0, 0]
============= Running PnP...
Rotation: [2.999999581709123; 4.999997813985293; 6.999999826089725]
Translation [9.999999792663072; 19.99999648222693; 29.99999699621362]
However when they aren't zero the result is totally wrong:
Finding extrinsic parameters (PnP)
Test transformations: Rotation: [3, 5, 7]; translation: [10, 20, 30]
distrotion coeff: [1.2, 0.2, 0, 0, 0]
============= Running PnP...
Rotation: [-91.56479629305277; -124.3631985067845; -74.46486950666471]
Translation [-69.72473511009439; -117.7463271636532; -87.27777166027946]
Since people asked, I am adding intermediate input - some 3D points and their projections for non-zero distortion coefficients. My camera matrix was
cameraMatrix << 300., 0., 200., 0, 300., 100., 0., 0., 1.;
3d points [53.0283, 19.9259, 40.1059]; 2D projection [1060.34, 700.59]
3d points [81.4385, 43.7133, 24.879]; 2D projection [6553.88, 5344.22]
3d points [77.3105, 76.2094, 30.7794]; 2D projection [5143.32, 6497.12]
3d points [70.2432, 47.8447, 79.219]; 2D projection [771.497, 611.726]
Another interesting observation: applying undistort when distCoeff are non zero doesn’t really works (but it does produce identical 2D points when distortion coefficients are all 0):
cout<<"applying undistort..."<<endl;
vector<Point2f> imagePointsUndistort(npoints);
undistortPoints(Mat(imagePoints), Mat(imagePointsUndistort), cameraMatrix, distCoeffs);
for (int i=0; i<4; i++)
cout<<"2d original "<<imagePoints[i]<<"; 2d undistort "<<imagePointsUndistort[i]<<endl;
applying undistort...
2d original [1060.34, 700.59]; 2d undistort [0, 0]
2d original [6553.88, 5344.22]; 2d undistort [0, 0]
2d original [5143.32, 6497.12]; 2d undistort [0, 0]
2d original [771.497, 611.726]; 2d undistort [0, 0]
The reason why I tried undistort() is because if one undoes the effect of known intrinsic parameters PnP becomes just a minimum direction problem of the form Ax=0. It needs min. 6 points for an approximate linear solution which is probably further improved with LMA (flags=CV_ITERATIVE). Technically there are only 6DOF and thus 3 points required so other methods (flags=CV_P3P, CV_EPNP) take less points. Anyways, regardless of a method or number of points the result is still invalid with non-zero distortion coefficients. The last thing I will try is to put all points on a 3D plane. It still fails:
for (int i=0; i<npoints; i++)
objPts[i].z=0.0f;
Finding extrinsic parameters (PnP)
Test transformations: Rotation: [3, 5, 7]; translation: [10, 20, 30]
distrotion coeff: [1.2, 0.2, 0, 0, 0]
============= Running PnP...
Rotation: [-1830.321574903016; 2542.206083947917; 2532.255948350521]
Translation [1407.918216894239; 1391.373407846455; 556.7108606094299]
How to make your code work?
I am able to reproduce the described behavior using the code you provided, however, either one of the two following options solve the problem:
Replace const Point3f tvec(10, 20, 30); by const Point3f tvec(10, 20, N); where N is much lower than 0 (e.g. -300) or much larger than 100 (e.g. 300).
Replace your call to solvePnP by a call to solvePnPRansac.
Why does each of these changes fix the undesired behavior?
First, consider what your original code requests from the solvePnP function. You are using a rotation of rather small magnitude, hence for simplicity of the explanation, I will assume that the rotation is identity. Then, the camera is positionned at world coordinates X=10, Y=20 and Z=30 and you generate object points randomly with world coordinates (X,Y,Z) uniformly drawn in [0,100]3. Hence, the camera is in the middle of the possible range for the object points, as illustrated on the following picture:
This means that object points may be generated very close to the focal plane (i.e. the plane going through the optical center and perpendicularly with respect to the optical axis). The projection in the camera image for such object points is undefined. However, in practice the non-linear optimization algorithm for undistortPoints is unstable even for object points close to the focal plane. This unstability causes the iterative algorithm for undistortPoints to diverge, except when the coefficients are all zero since in that case the initial values remain strictly constant during the estimation.
Hence, the two possible solutions to avoid this behavior are the following:
Avoid generating object points near the focal plane of the camera, i.e. change the translation vector or the range of the coordinates of the object points.
Eliminate the object points too close to the focal plane of the camera, whose undistorted estimation diverged (outliers), before the PnP estimation for example using solvePnPRansac.
Details about why undistortPoints fails:
NB: As we know the 3D world points, I used the following call to obtain the true undistorted coordinates, independently from the result of undistortPoints:
cv::projectPoints(obj_pts, rvec, tvec, cv::Mat_<double>::eye(3,3), cv::Mat_<double>::zeros(5,1), true_norm_pts);
The following function is a simplified version of what undistortPoints is doing:
void simple_undistort_point(const cv::Mat &img_pt,
const cv::Mat_<double> &K,
const cv::Mat_<double> &D,
cv::Mat &norm_pt)
{
// Define temporary variables
double k[8]={D.at<double>(0),
D.at<double>(1),
D.at<double>(2),
D.at<double>(3),
D.at<double>(4)},
fx, fy, ifx, ify, cx, cy;
fx = K.at<double>(0,0);
fy = K.at<double>(1,1);
ifx = 1./fx;
ify = 1./fy;
cx = K.at<double>(0,2);
cy = K.at<double>(1,2);
// Cancel distortion iteratively
const int iters = 5;
double x, y, x0, y0;
x0=x=(img_pt.at<double>(0)-cx)*ifx;
y0=y=(img_pt.at<double>(1)-cy)*ify;
for(int j = 0; j < iters; ++j)
{
double r2 = x*x + y*y;
double icdist = 1/(1 + ((k[4]*r2 + k[1])*r2 + k[0])*r2);
double deltaX = 2*k[2]*x*y + k[3]*(r2 + 2*x*x);
double deltaY = k[2]*(r2 + 2*y*y) + 2*k[3]*x*y;
x = (x0 - deltaX)*icdist;
y = (y0 - deltaY)*icdist;
}
// Store result
norm_pt.create(1,2,CV_64F);
norm_pt.at<double>(0) = x;
norm_pt.at<double>(1) = y;
}
If you add code to check how x and y change with each iteration, you'll see that the iterative optimization diverges due to r2 being very large at the beginning. Here is a log example:
#0: [2.6383300, 1.7651500] r2=10.0766000, icdist=0.0299408, deltaX=0, deltaY=0
#1: [0.0789937, 0.0528501] r2=0.00903313, icdist=0.9892610, deltaX=0, deltaY=0
#2: [2.6100000, 1.7462000] r2=9.86128000, icdist=0.0309765, deltaX=0, deltaY=0
#3: [0.0817263, 0.0546783] r2=0.00966890, icdist=0.9885120, deltaX=0, deltaY=0
#4: [2.6080200, 1.7448800] r2=9.84637000, icdist=0.0310503, deltaX=0, deltaY=0
end: [0.0819209, 0.0548085]
true: [0.9327440, 0.6240440]
When r2 is large, r2*r2*r2 is huge hence icdist is very small, hence the next iteration starts with a very small r2. When r2 is very small, icdist is close to 1, hence x and y are respectively set to x0 and y0 and we are back with a large r2, etc.
So why is r2 so large in the first place? Because the points may be generated close to the focal plane, in which case they are far from the optical axis (hence a very large r2). See the following log example:
img_pt#0=[991.4992804037340, 629.5460091483255], r2=10.07660, norm(cv_undist-true)=1.0236800
img_pt#1=[5802.666489402056, 4402.387472311543], r2=554.4490, norm(cv_undist-true)=2.1568300
img_pt#2=[5040.551339386630, 5943.173381042060], r2=639.7070, norm(cv_undist-true)=2.1998700
img_pt#3=[741.9742544382640, 572.9513930063181], r2=5.749100, norm(cv_undist-true)=0.8158670
img_pt#4=[406.9101658356062, 403.0152736214052], r2=1.495890, norm(cv_undist-true)=0.1792810
img_pt#5=[516.2079583447821, 1038.026553216831], r2=10.88760, norm(cv_undist-true)=1.0494500
img_pt#6=[1876.220394606081, 8129.280202695572], r2=747.5450, norm(cv_undist-true)=2.2472900
img_pt#7=[236.9935231831764, 329.3418854620716], r2=0.599625, norm(cv_undist-true)=0.0147487
img_pt#8=[1037.586015858139, 1346.494838992490], r2=25.05890, norm(cv_undist-true)=1.2998400
img_pt#9=[499.9808133105154, 715.6213031242644], r2=5.210870, norm(cv_undist-true)=0.7747020
You can see that for most points, r2 is very large, except for a few (#3, #4 & #7) which are also those associated with the best undistortion accuracy.
This problem is due to the particular undistortion algorithm implemented in OpenCV, which has been chosen for its efficiency. Other non-linear optimization algorithm (e.g. Levenberg-Marquardt) would be more accurate but also much slower, and would definitely be an overkill in most applications.
Let me go through opencv sources. But first I present "pure" opencv function that works as in the sources (please read below how I got this point) merged with your code to show it works as the library one:
#include <iostream>
#include <opencv2\opencv.hpp>
using namespace std;
using namespace cv;
#define DEG2RAD (3.1415293/180.0)
#define RAD2DEG (1.0/DEG2RAD)
Point2f Project(Point3f p, double R[], double t[], double k[], double fx, double fy, double cx, double cy) {
double X = p.x, Y = p.y, Z = p.z;
double x = R[0]*X + R[1]*Y + R[2]*Z + t[0];
double y = R[3]*X + R[4]*Y + R[5]*Z + t[1];
double z = R[6]*X + R[7]*Y + R[8]*Z + t[2];
double r2, r4, r6, a1, a2, a3, cdist, icdist2;
double xd, yd;
z = z ? 1./z : 1;
x *= z; y *= z;
r2 = x*x + y*y;
r4 = r2*r2;
r6 = r4*r2;
a1 = 2*x*y;
a2 = r2 + 2*x*x;
a3 = r2 + 2*y*y;
cdist = 1 + k[0]*r2 + k[1]*r4 + k[4]*r6;
icdist2 = 1./(1 + k[5]*r2 + k[6]*r4 + k[7]*r6);
xd = x*cdist*icdist2 + k[2]*a1 + k[3]*a2;
yd = y*cdist*icdist2 + k[2]*a3 + k[3]*a1;
double xRet = xd*fx + cx;
double yRet = yd*fy + cy;
return Point2f(xRet, yRet);
}
int main() {
const int npoints = 10; // number of points
// extrinsic
const Point3f tvec(10, 20, 30);
Point3f rvec(3, 5, 7);
cout << "Finding extrinsic parameters (PnP)" << endl;
cout<<"Test transformations: ";
cout<<"Rotation: "<<rvec<<"; translation: "<<tvec<<endl;
rvec*=DEG2RAD;
// intrinsic
Mat_ <double>cameraMatrix(3, 3);
cameraMatrix << 300., 0., 200., 0, 300., 100., 0., 0., 1.;
Mat_ <double>distCoeffs(1, 5); // (k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]]) of 4, 5, or 8 elements.
distCoeffs << 1.2, 0.2, 0., 0., 0.; // non-zero distortion
//distCoeffs << 0.0, 0.0, 0.0, 0.0, 0.0; // zero distortion
//distCoeffs << 1.8130418031666484e+000, -1.3285019729932657e+001, -1.6921715019797313e-002, -1.3327183367510961e-001, -5.2725832482783389e+001;
cout<<"distrotion coeff: "<<distCoeffs<<endl;
cout<<"============= Running PnP..."<<endl;
vector<Point3f> objPts(npoints);
vector<Point2f> imagePoints(npoints);
Mat rvec_est, tvec_est;
randu(Mat(objPts), 0.0f, 100.0f);
// project
projectPoints(Mat(objPts), Mat(rvec), Mat(tvec), cameraMatrix, distCoeffs, Mat(imagePoints));
std::cout << objPts << std::endl;
std::cout << imagePoints << std::endl;
double R[9];
Mat matR( 3, 3, CV_64F, R);
Mat_<double> m(1,3);
m << (double)rvec.x, (double)rvec.y, (double)rvec.z;
Rodrigues(m, matR);
std::cout << matR << std::endl;
double t[3] = {tvec.x, tvec.y, tvec.z};
double k[8] = {1.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
double fx = 300, fy = 300, cx = 200, cy = 100;
for(int i=0;i<objPts.size();i++)
std::cout << Project(objPts[i], R, t, k, fx, fy, cx, cy) << "; ";
std::cout << std::endl;
// extrinsic
solvePnP(objPts, imagePoints, cameraMatrix, distCoeffs, rvec_est, tvec_est);
cout<<"Rotation: "<<rvec_est*RAD2DEG<<endl;
cout<<"Translation "<<tvec_est<<endl;
return 0;
}
R is rotation, t translation, k distortion. Look at the 'r2' computation - it is x*x + y*y, but x,y is the position (scaled by z though) just after applying translation and rotation. And this r stands for (as wikpedia says) for "square distance in image projected by ideal pinhole model". We can say projectPoints implementation is OK.
How I got this result:
I'm digging up version 2.4.8. If you go to the calibration.cpp in the calib3d module, start with
void cv::projectPoints( InputArray _opoints,
InputArray _rvec,
InputArray _tvec,
InputArray _cameraMatrix,
InputArray _distCoeffs,
OutputArray _ipoints,
OutputArray _jacobian,
double aspectRatio )
{
Mat opoints = _opoints.getMat();
int npoints = opoints.checkVector(3), depth = opoints.depth();
CV_Assert(npoints >= 0 && (depth == CV_32F || depth == CV_64F));
CvMat dpdrot, dpdt, dpdf, dpdc, dpddist;
CvMat *pdpdrot=0, *pdpdt=0, *pdpdf=0, *pdpdc=0, *pdpddist=0;
_ipoints.create(npoints, 1, CV_MAKETYPE(depth, 2), -1, true);
CvMat c_imagePoints = _ipoints.getMat();
CvMat c_objectPoints = opoints;
Mat cameraMatrix = _cameraMatrix.getMat();
Mat rvec = _rvec.getMat(), tvec = _tvec.getMat();
CvMat c_cameraMatrix = cameraMatrix;
CvMat c_rvec = rvec, c_tvec = tvec;
double dc0buf[5]={0};
Mat dc0(5,1,CV_64F,dc0buf);
Mat distCoeffs = _distCoeffs.getMat();
if( distCoeffs.empty() )
distCoeffs = dc0;
CvMat c_distCoeffs = distCoeffs;
int ndistCoeffs = distCoeffs.rows + distCoeffs.cols - 1;
if( _jacobian.needed() )
{
// cut out, we dont use this part
}
cvProjectPoints2( &c_objectPoints, &c_rvec, &c_tvec, &c_cameraMatrix, &c_distCoeffs,
&c_imagePoints, pdpdrot, pdpdt, pdpdf, pdpdc, pdpddist, aspectRatio );
}
Nothing special, right? No content manipulation at all. Let's go deeper:
CV_IMPL void cvProjectPoints2( const CvMat* objectPoints,
const CvMat* r_vec,
const CvMat* t_vec,
const CvMat* A,
const CvMat* distCoeffs,
CvMat* imagePoints, CvMat* dpdr,
CvMat* dpdt, CvMat* dpdf,
CvMat* dpdc, CvMat* dpdk,
double aspectRatio )
{
Ptr<CvMat> matM, _m;
Ptr<CvMat> _dpdr, _dpdt, _dpdc, _dpdf, _dpdk;
int i, j, count;
int calc_derivatives;
const CvPoint3D64f* M;
CvPoint2D64f* m;
double r[3], R[9], dRdr[27], t[3], a[9], k[8] = {0,0,0,0,0,0,0,0}, fx, fy, cx, cy;
CvMat _r, _t, _a = cvMat( 3, 3, CV_64F, a ), _k;
CvMat matR = cvMat( 3, 3, CV_64F, R ), _dRdr = cvMat( 3, 9, CV_64F, dRdr );
// some code not important ...
if( r_vec->rows == 3 && r_vec->cols == 3 )
{
_r = cvMat( 3, 1, CV_64FC1, r );
cvRodrigues2( r_vec, &_r );
cvRodrigues2( &_r, &matR, &_dRdr );
cvCopy( r_vec, &matR );
}
else
{
_r = cvMat( r_vec->rows, r_vec->cols, CV_MAKETYPE(CV_64F,CV_MAT_CN(r_vec->type)), r );
cvConvert( r_vec, &_r );
cvRodrigues2( &_r, &matR, &_dRdr );
}
Last part is important, because we use cv::Rodriguez to create an rotation matrix from rotation vector. And later in the function we also create translation matrix, but still no data manipulation. Going further in the ProjectPoints2:
fx = a[0]; fy = a[4];
cx = a[2]; cy = a[5];
if( fixedAspectRatio )
fx = fy*aspectRatio;
if( distCoeffs )
{
if( !CV_IS_MAT(distCoeffs) ||
(CV_MAT_DEPTH(distCoeffs->type) != CV_64F &&
CV_MAT_DEPTH(distCoeffs->type) != CV_32F) ||
(distCoeffs->rows != 1 && distCoeffs->cols != 1) ||
(distCoeffs->rows*distCoeffs->cols*CV_MAT_CN(distCoeffs->type) != 4 &&
distCoeffs->rows*distCoeffs->cols*CV_MAT_CN(distCoeffs->type) != 5 &&
distCoeffs->rows*distCoeffs->cols*CV_MAT_CN(distCoeffs->type) != 8) )
CV_Error( CV_StsBadArg, cvDistCoeffErr );
_k = cvMat( distCoeffs->rows, distCoeffs->cols,
CV_MAKETYPE(CV_64F,CV_MAT_CN(distCoeffs->type)), k );
cvConvert( distCoeffs, &_k );
}
Here we set focal lengths from camera matrix and principal point coords. Also we set array k which contains distortion coefs. Now we finished setting up variables. Let's go to the computations:
double X = M[i].x, Y = M[i].y, Z = M[i].z;
double x = R[0]*X + R[1]*Y + R[2]*Z + t[0];
double y = R[3]*X + R[4]*Y + R[5]*Z + t[1];
double z = R[6]*X + R[7]*Y + R[8]*Z + t[2];
double r2, r4, r6, a1, a2, a3, cdist, icdist2;
double xd, yd;
z = z ? 1./z : 1;
x *= z; y *= z;
r2 = x*x + y*y;
r4 = r2*r2;
r6 = r4*r2;
a1 = 2*x*y;
a2 = r2 + 2*x*x;
a3 = r2 + 2*y*y;
cdist = 1 + k[0]*r2 + k[1]*r4 + k[4]*r6;
icdist2 = 1./(1 + k[5]*r2 + k[6]*r4 + k[7]*r6);
xd = x*cdist*icdist2 + k[2]*a1 + k[3]*a2;
yd = y*cdist*icdist2 + k[2]*a3 + k[3]*a1;
m[i].x = xd*fx + cx; // here projection
m[i].y = yd*fy + cy;
And we have the function exactly as the one I presented on the top/
I have a piece of code for rotating and translating image:
Point2f pt(0, in.rows);
double angle = atan(trans.c / trans.b) * 180 / M_PI;
Mat r = getRotationMatrix2D(pt, -angle, 1.0);
warpAffine(in, out, r, in.size(), interpolation); /* rotation */
Mat t = (Mat_<double>(2, 3) << 1, 0, trans.a, 0, 1, -trans.d);
warpAffine(out, out, t, in.size(), interpolation); /* translation */
The problem is that I'm doing this in two times. So if I have an angle of 90degree for example, the first "out" variable will be empty because all data are out of bounds. Is there a way to do it in one pass ? In order to avoid loosing my data and having black image.
I think that the best thing would be to combine r and t in one matrix but I'm a little lost.
Best regards,
Here is an example on how to combine 2 homographies by simple multiplication and how to extract an affine transformation from a 3x3 homography.
int main(int argc, char* argv[])
{
cv::Mat input = cv::imread("C:/StackOverflow/Input/Lenna.png");
// create to 3x3 identity homography matrices
cv::Mat homography1 = cv::Mat::eye(3, 3, CV_64FC1);
cv::Mat homography2 = cv::Mat::eye(3, 3, CV_64FC1);
double alpha1 = -13; // degrees
double t1_x = -86; // pixel
double t1_y = -86; // pixel
double alpha2 = 21; // degrees
double t2_x = 86; // pixel
double t2_y = 86; // pixel
// hope there is no error in the signs:
// combine homography1
homography1.at<double>(0, 0) = cos(CV_PI*alpha1 / 180);
homography1.at<double>(0, 1) = -sin(CV_PI*alpha1 / 180);
homography1.at<double>(1, 0) = sin(CV_PI*alpha1 / 180);
homography1.at<double>(1, 1) = cos(CV_PI*alpha1 / 180);
homography1.at<double>(0, 2) = t1_x;
homography1.at<double>(1, 2) = t1_y;
// compose homography2
homography2.at<double>(0, 0) = cos(CV_PI*alpha2 / 180);
homography2.at<double>(0, 1) = -sin(CV_PI*alpha2 / 180);
homography2.at<double>(1, 0) = sin(CV_PI*alpha2 / 180);
homography2.at<double>(1, 1) = cos(CV_PI*alpha2 / 180);
homography2.at<double>(0, 2) = t2_x;
homography2.at<double>(1, 2) = t2_y;
cv::Mat affine1 = homography1(cv::Rect(0, 0, 3, 2));
cv::Mat affine2 = homography2(cv::Rect(0, 0, 3, 2));
cv::Mat dst1;
cv::Mat dst2;
cv::warpAffine(input, dst1, affine1, input.size());
cv::warpAffine(input, dst2, affine2, input.size());
cv::Mat combined_homog = homography1*homography2;
cv::Mat combined_affine = combined_homog(cv::Rect(0, 0, 3, 2));
cv::Mat dst_combined;
cv::warpAffine(input, dst_combined, combined_affine, input.size());
cv::imshow("input", input);
cv::imshow("dst1", dst1);
cv::imshow("dst2", dst2);
cv::imshow("combined", dst_combined);
cv::waitKey(0);
return 0;
}
In this example, an image is first rotated and translated to the left, later to the right. If the two transformations are performed after each other, significant image areas would get lost. Instead if they are combined by homograhy multiplication, it is like the full operation done in a single step without losing image parts in the intemediate step.
input:
if image was first transformed with H1, later with H2:
if the image is transformed with the combination of H1*H2 directly:
One typical application of this homography combination is to first translate the image center to the origin, then rotate, then translate back to original position. This has the effect as if the image was rotated around its center of gravity.