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Commit 9c7e0bfd authored by Ilya Krylov's avatar Ilya Krylov
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Added fisheye camera model

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modules/calib3d/include/opencv2/calib3d/calib3d.hpp
View file @ 9c7e0bfd
......@@ -45,6 +45,7 @@
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/core/affine.hpp"
#ifdef __cplusplus
extern "C" {
......@@ -744,6 +745,132 @@ CV_EXPORTS_W int estimateAffine3D(InputArray src, InputArray dst,
OutputArray out, OutputArray inliers,
double ransacThreshold=3, double confidence=0.99);
class Fisheye
{
public:
//Definitions:
// Let P be a point in 3D of coordinates X in the world reference frame (stored in the matrix X)
// The coordinate vector of P in the camera reference frame is: Xc = R*X + T
// where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om);
// call x, y and z the 3 coordinates of Xc: x = Xc(1); y = Xc(2); z = Xc(3);
// The pinehole projection coordinates of P is [a;b] where a=x/z and b=y/z.
// call r^2 = a^2 + b^2,
// call theta = atan(r),
//
// Fisheye distortion -> theta_d = theta * (1 + k(1)*theta^2 + k(2)*theta^4 + k(3)*theta^6 + k(4)*theta^8)
//
// The distorted point coordinates are: xd = [xx;yy] where:
//
// xx = (theta_d / r) * x
// yy = (theta_d / r) * y
//
// Finally, convertion into pixel coordinates: The final pixel coordinates vector xp=[xxp;yyp] where:
//
// xxp = f(1)*(xx + alpha*yy) + c(1)
// yyp = f(2)*yy + c(2)
enum{
CALIB_USE_INTRINSIC_GUESS = 1,
CALIB_RECOMPUTE_EXTRINSIC = 2,
CALIB_CHECK_COND = 4,
CALIB_FIX_SKEW = 8,
CALIB_FIX_K1 = 16,
CALIB_FIX_K2 = 32,
CALIB_FIX_K3 = 64,
CALIB_FIX_K4 = 128
};
//! projects 3D points using fisheye model
static void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine,
InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray());
//! projects points using fisheye model
static void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec,
InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray());
//! distorts 2D points using fisheye model
static void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0);
//! undistorts 2D points using fisheye model
static void undistortPoints(InputArray distorted, OutputArray undistorted,
InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray());
//! computing undistortion and rectification maps for image transform by cv::remap()
//! If D is empty zero distortion is used, if R or P is empty identity matrixes are used
static void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P,
const cv::Size& size, int m1type, OutputArray map1, OutputArray map2);
//! undistorts image, optinally chanes resolution and camera matrix. If Knew zero identity matrix is used
static void undistortImage(InputArray distorted, OutputArray undistorted,
InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size());
//! estimates new camera matrix for undistortion or rectification
static void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R,
OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0);
//! stereo rectification for fisheye camera model
static void stereoRectify( InputArray K1, InputArray D1, InputArray K2, InputArray D2, const cv::Size& imageSize,
InputArray rotaion, InputArray tvec, OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q,
int flags = cv::CALIB_ZERO_DISPARITY, double alpha = -1, const Size& newImageSize = Size(), Rect* roi1 = 0, Rect* roi2 = 0 );
//! performs camera calibaration
static double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size,
InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON));
//! stereo rectification estimation
static void stereoRectify(InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec,
OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize = Size(),
double balance = 0.0, double fov_scale = 1.0);
};
namespace internal {
struct IntrinsicParams
{
Vec2d f;
Vec2d c;
Vec4d k;
double alpha;
std::vector<int> isEstimate;
IntrinsicParams();
IntrinsicParams(Vec2d f, Vec2d c, Vec4d k, double alpha = 0);
IntrinsicParams operator+(const Mat& a);
IntrinsicParams& operator =(const Mat& a);
void Init(const cv::Vec2d& f, const cv::Vec2d& c, const cv::Vec4d& k = Vec4d(0,0,0,0), const double& alpha = 0);
};
void projectPoints(cv::InputArray objectPoints, cv::OutputArray imagePoints,
cv::InputArray _rvec,cv::InputArray _tvec,
const IntrinsicParams& param, cv::OutputArray jacobian);
void ComputeExtrinsicRefine(const Mat& imagePoints, const Mat& objectPoints, Mat& rvec,
Mat& tvec, Mat& J, const int MaxIter,
const IntrinsicParams& param, const double thresh_cond);
Mat ComputeHomography(Mat m, Mat M);
Mat NormalizePixels(const Mat& imagePoints, const IntrinsicParams& param);
void InitExtrinsics(const Mat& _imagePoints, const Mat& _objectPoints, const IntrinsicParams& param, Mat& omckk, Mat& Tckk);
void CalibrateExtrinsics(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
const IntrinsicParams& param, const int check_cond,
const double thresh_cond, InputOutputArray omc, InputOutputArray Tc);
void ComputeJacobians(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
const IntrinsicParams& param, InputArray omc, InputArray Tc,
const int& check_cond, const double& thresh_cond, Mat& JJ2_inv, Mat& ex3);
void EstimateUncertainties(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
const IntrinsicParams& params, InputArray omc, InputArray Tc,
IntrinsicParams& errors, Vec2d& std_err, double thresh_cond, int check_cond, double& rms);
}
}
#endif
......
modules/calib3d/src/fisheye.cpp 0 → 100644
View file @ 9c7e0bfd
#include "opencv2/opencv.hpp"
#include "opencv2/core/affine.hpp"
#include "opencv2/core/affine.hpp"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
/// cv::Fisheye::projectPoints
namespace cv { namespace
{
struct JacobianRow
{
Vec2d df, dc;
Vec4d dk;
Vec3d dom, dT;
double dalpha;
};
}}
void cv::Fisheye::projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine,
InputArray K, InputArray D, double alpha, OutputArray jacobian)
{
projectPoints(objectPoints, imagePoints, affine.rvec(), affine.translation(), K, D, alpha, jacobian);
}
void cv::Fisheye::projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray _rvec,
InputArray _tvec, InputArray _K, InputArray _D, double alpha, OutputArray jacobian)
{
// will support only 3-channel data now for points
CV_Assert(objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3);
imagePoints.create(objectPoints.size(), CV_MAKETYPE(objectPoints.depth(), 2));
size_t n = objectPoints.total();
CV_Assert(_rvec.total() * _rvec.channels() == 3 && (_rvec.depth() == CV_32F || _rvec.depth() == CV_64F));
CV_Assert(_tvec.total() * _tvec.channels() == 3 && (_tvec.depth() == CV_32F || _tvec.depth() == CV_64F));
CV_Assert(_tvec.getMat().isContinuous() && _rvec.getMat().isContinuous());
Vec3d om = _rvec.depth() == CV_32F ? (Vec3d)*_rvec.getMat().ptr<Vec3f>() : *_rvec.getMat().ptr<Vec3d>();
Vec3d T = _tvec.depth() == CV_32F ? (Vec3d)*_tvec.getMat().ptr<Vec3f>() : *_tvec.getMat().ptr<Vec3d>();
CV_Assert(_K.size() == Size(3,3) && (_K.type() == CV_32F || _K.type() == CV_64F) && _D.type() == _K.type() && _D.total() == 4);
cv::Vec2d f, c;
if (_K.depth() == CV_32F)
{
Matx33f K = _K.getMat();
f = Vec2f(K(0, 0), K(1, 1));
c = Vec2f(K(0, 2), K(1, 2));
}
else
{
Matx33d K = _K.getMat();
f = Vec2d(K(0, 0), K(1, 1));
c = Vec2d(K(0, 2), K(1, 2));
}
Vec4d k = _D.depth() == CV_32F ? (Vec4d)*_D.getMat().ptr<Vec4f>(): *_D.getMat().ptr<Vec4d>();
JacobianRow *Jn = 0;
if (jacobian.needed())
{
int nvars = 2 + 2 + 1 + 4 + 3 + 3; // f, c, alpha, k, om, T,
jacobian.create(2*(int)n, nvars, CV_64F);
Jn = jacobian.getMat().ptr<JacobianRow>(0);
}
Matx33d R;
Matx<double, 3, 9> dRdom;
Rodrigues(om, R, dRdom);
Affine3d aff(om, T);
const Vec3f* Xf = objectPoints.getMat().ptr<Vec3f>();
const Vec3d* Xd = objectPoints.getMat().ptr<Vec3d>();
Vec2f *xpf = imagePoints.getMat().ptr<Vec2f>();
Vec2d *xpd = imagePoints.getMat().ptr<Vec2d>();
for(size_t i = 0; i < n; ++i)
{
Vec3d Xi = objectPoints.depth() == CV_32F ? (Vec3d)Xf[i] : Xd[i];
Vec3d Y = aff*Xi;
Vec2d x(Y[0]/Y[2], Y[1]/Y[2]);
double r2 = x.dot(x);
double r = std::sqrt(r2);
// Angle of the incoming ray:
double theta = atan(r);
double theta2 = theta*theta, theta3 = theta2*theta, theta4 = theta2*theta2, theta5 = theta4*theta,
theta6 = theta3*theta3, theta7 = theta6*theta, theta8 = theta4*theta4, theta9 = theta8*theta;
double theta_d = theta + k[0]*theta3 + k[1]*theta5 + k[2]*theta7 + k[3]*theta9;
double inv_r = r > 1e-8 ? 1.0/r : 1;
double cdist = r > 1e-8 ? theta_d * inv_r : 1;
Vec2d xd1 = x * cdist;
Vec2d xd3(xd1[0] + alpha*xd1[1], xd1[1]);
Vec2d final_point(xd3[0] * f[0] + c[0], xd3[1] * f[1] + c[1]);
if (objectPoints.depth() == CV_32F)
xpf[i] = final_point;
else
xpd[i] = final_point;
if (jacobian.needed())
{
//Vec3d Xi = pdepth == CV_32F ? (Vec3d)Xf[i] : Xd[i];
//Vec3d Y = aff*Xi;
double dYdR[] = { Xi[0], Xi[1], Xi[2], 0, 0, 0, 0, 0, 0,
0, 0, 0, Xi[0], Xi[1], Xi[2], 0, 0, 0,
0, 0, 0, 0, 0, 0, Xi[0], Xi[1], Xi[2] };
Matx33d dYdom_data = Matx<double, 3, 9>(dYdR) * dRdom.t();
const Vec3d *dYdom = (Vec3d*)dYdom_data.val;
Matx33d dYdT_data = Matx33d::eye();
const Vec3d *dYdT = (Vec3d*)dYdT_data.val;
//Vec2d x(Y[0]/Y[2], Y[1]/Y[2]);
Vec3d dxdom[2];
dxdom[0] = (1.0/Y[2]) * dYdom[0] - x[0]/Y[2] * dYdom[2];
dxdom[1] = (1.0/Y[2]) * dYdom[1] - x[1]/Y[2] * dYdom[2];
Vec3d dxdT[2];
dxdT[0] = (1.0/Y[2]) * dYdT[0] - x[0]/Y[2] * dYdT[2];
dxdT[1] = (1.0/Y[2]) * dYdT[1] - x[1]/Y[2] * dYdT[2];
//double r2 = x.dot(x);
Vec3d dr2dom = 2 * x[0] * dxdom[0] + 2 * x[1] * dxdom[1];
Vec3d dr2dT = 2 * x[0] * dxdT[0] + 2 * x[1] * dxdT[1];
//double r = std::sqrt(r2);
double drdr2 = r > 1e-8 ? 1.0/(2*r) : 1;
Vec3d drdom = drdr2 * dr2dom;
Vec3d drdT = drdr2 * dr2dT;
// Angle of the incoming ray:
//double theta = atan(r);
double dthetadr = 1.0/(1+r2);
Vec3d dthetadom = dthetadr * drdom;
Vec3d dthetadT = dthetadr * drdT;
//double theta_d = theta + k[0]*theta3 + k[1]*theta5 + k[2]*theta7 + k[3]*theta9;
double dtheta_ddtheta = 1 + 3*k[0]*theta2 + 5*k[1]*theta4 + 7*k[2]*theta6 + 9*k[3]*theta8;
Vec3d dtheta_ddom = dtheta_ddtheta * dthetadom;
Vec3d dtheta_ddT = dtheta_ddtheta * dthetadT;
Vec4d dtheta_ddk = Vec4d(theta3, theta5, theta7, theta9);
//double inv_r = r > 1e-8 ? 1.0/r : 1;
//double cdist = r > 1e-8 ? theta_d / r : 1;
Vec3d dcdistdom = inv_r * (dtheta_ddom - cdist*drdom);
Vec3d dcdistdT = inv_r * (dtheta_ddT - cdist*drdT);
Vec4d dcdistdk = inv_r * dtheta_ddk;
//Vec2d xd1 = x * cdist;
Vec4d dxd1dk[2];
Vec3d dxd1dom[2], dxd1dT[2];
dxd1dom[0] = x[0] * dcdistdom + cdist * dxdom[0];
dxd1dom[1] = x[1] * dcdistdom + cdist * dxdom[1];
dxd1dT[0] = x[0] * dcdistdT + cdist * dxdT[0];
dxd1dT[1] = x[1] * dcdistdT + cdist * dxdT[1];
dxd1dk[0] = x[0] * dcdistdk;
dxd1dk[1] = x[1] * dcdistdk;
//Vec2d xd3(xd1[0] + alpha*xd1[1], xd1[1]);
Vec4d dxd3dk[2];
Vec3d dxd3dom[2], dxd3dT[2];
dxd3dom[0] = dxd1dom[0] + alpha * dxd1dom[1];
dxd3dom[1] = dxd1dom[1];
dxd3dT[0] = dxd1dT[0] + alpha * dxd1dT[1];
dxd3dT[1] = dxd1dT[1];
dxd3dk[0] = dxd1dk[0] + alpha * dxd1dk[1];
dxd3dk[1] = dxd1dk[1];
Vec2d dxd3dalpha(xd1[1], 0);
//final jacobian
Jn[0].dom = f[0] * dxd3dom[0];
Jn[1].dom = f[1] * dxd3dom[1];
Jn[0].dT = f[0] * dxd3dT[0];
Jn[1].dT = f[1] * dxd3dT[1];
Jn[0].dk = f[0] * dxd3dk[0];
Jn[1].dk = f[1] * dxd3dk[1];
Jn[0].dalpha = f[0] * dxd3dalpha[0];
Jn[1].dalpha = 0; //f[1] * dxd3dalpha[1];
Jn[0].df = Vec2d(xd3[0], 0);
Jn[1].df = Vec2d(0, xd3[1]);
Jn[0].dc = Vec2d(1, 0);
Jn[1].dc = Vec2d(0, 1);
//step to jacobian rows for next point
Jn += 2;
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
/// cv::Fisheye::distortPoints
void cv::Fisheye::distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha)
{
// will support only 2-channel data now for points
CV_Assert(undistorted.type() == CV_32FC2 || undistorted.type() == CV_64FC2);
distorted.create(undistorted.size(), undistorted.type());
size_t n = undistorted.total();
CV_Assert(K.size() == Size(3,3) && (K.type() == CV_32F || K.type() == CV_64F) && D.total() == 4);
cv::Vec2d f, c;
if (K.depth() == CV_32F)
{
Matx33f camMat = K.getMat();
f = Vec2f(camMat(0, 0), camMat(1, 1));
c = Vec2f(camMat(0, 2), camMat(1, 2));
}
else
{
Matx33d camMat = K.getMat();
f = Vec2d(camMat(0, 0), camMat(1, 1));
c = Vec2d(camMat(0 ,2), camMat(1, 2));
}
Vec4d k = D.depth() == CV_32F ? (Vec4d)*D.getMat().ptr<Vec4f>(): *D.getMat().ptr<Vec4d>();
const Vec2f* Xf = undistorted.getMat().ptr<Vec2f>();
const Vec2d* Xd = undistorted.getMat().ptr<Vec2d>();
Vec2f *xpf = distorted.getMat().ptr<Vec2f>();
Vec2d *xpd = distorted.getMat().ptr<Vec2d>();
for(size_t i = 0; i < n; ++i)
{
Vec2d x = undistorted.depth() == CV_32F ? (Vec2d)Xf[i] : Xd[i];
double r2 = x.dot(x);
double r = std::sqrt(r2);
// Angle of the incoming ray:
double theta = atan(r);
double theta2 = theta*theta, theta3 = theta2*theta, theta4 = theta2*theta2, theta5 = theta4*theta,
theta6 = theta3*theta3, theta7 = theta6*theta, theta8 = theta4*theta4, theta9 = theta8*theta;
double theta_d = theta + k[0]*theta3 + k[1]*theta5 + k[2]*theta7 + k[3]*theta9;
double inv_r = r > 1e-8 ? 1.0/r : 1;
double cdist = r > 1e-8 ? theta_d * inv_r : 1;
Vec2d xd1 = x * cdist;
Vec2d xd3(xd1[0] + alpha*xd1[1], xd1[1]);
Vec2d final_point(xd3[0] * f[0] + c[0], xd3[1] * f[1] + c[1]);
if (undistorted.depth() == CV_32F)
xpf[i] = final_point;
else
xpd[i] = final_point;
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
/// cv::Fisheye::undistortPoints
void cv::Fisheye::undistortPoints( InputArray distorted, OutputArray undistorted, InputArray _K, InputArray _D, InputArray _R, InputArray _P)
{
// will support only 2-channel data now for points
CV_Assert(distorted.type() == CV_32FC2 || distorted.type() == CV_64FC2);
undistorted.create(distorted.size(), distorted.type());
CV_Assert(_P.empty() || _P.size() == Size(3, 3) || _P.size() == Size(4, 3));
CV_Assert(_R.empty() || _R.size() == Size(3, 3) || _R.total() * _R.channels() == 3);
CV_Assert(_D.total() == 4 && _K.size() == Size(3, 3) && (_K.depth() == CV_32F || _K.depth() == CV_64F));
cv::Vec2d f, c;
if (_K.depth() == CV_32F)
{
Matx33f camMat = _K.getMat();
f = Vec2f(camMat(0, 0), camMat(1, 1));
c = Vec2f(camMat(0, 2), camMat(1, 2));
}
else
{
Matx33d camMat = _K.getMat();
f = Vec2d(camMat(0, 0), camMat(1, 1));
c = Vec2d(camMat(0, 2), camMat(1, 2));
}
Vec4d k = _D.depth() == CV_32F ? (Vec4d)*_D.getMat().ptr<Vec4f>(): *_D.getMat().ptr<Vec4d>();
cv::Matx33d RR = cv::Matx33d::eye();
if (!_R.empty() && _R.total() * _R.channels() == 3)
{
cv::Vec3d rvec;
_R.getMat().convertTo(rvec, CV_64F);
RR = cv::Affine3d(rvec).rotation();
}
else if (!_R.empty() && _R.size() == Size(3, 3))
_R.getMat().convertTo(RR, CV_64F);
if(!_P.empty())
{
cv::Matx33d P;
_P.getMat().colRange(0, 3).convertTo(P, CV_64F);
RR = P * RR;
}
// start undistorting
const cv::Vec2f* srcf = distorted.getMat().ptr<cv::Vec2f>();
const cv::Vec2d* srcd = distorted.getMat().ptr<cv::Vec2d>();
cv::Vec2f* dstf = undistorted.getMat().ptr<cv::Vec2f>();
cv::Vec2d* dstd = undistorted.getMat().ptr<cv::Vec2d>();
size_t n = distorted.total();
int sdepth = distorted.depth();
for(size_t i = 0; i < n; i++ )
{
Vec2d pi = sdepth == CV_32F ? (Vec2d)srcf[i] : srcd[i]; // image point
Vec2d pw((pi[0] - c[0])/f[0], (pi[1] - c[1])/f[1]); // world point
double scale = 1.0;
double theta_d = sqrt(pw[0]*pw[0] + pw[1]*pw[1]);
if (theta_d > 1e-8)
{
// compensate distortion iteratively
double theta = theta_d;
for(int j = 0; j < 10; j++ )
{
double theta2 = theta*theta, theta4 = theta2*theta2, theta6 = theta4*theta2, theta8 = theta6*theta2;
theta = theta_d / (1 + k[0] * theta2 + k[1] * theta4 + k[2] * theta6 + k[3] * theta8);
}
scale = std::tan(theta) / theta_d;
}
Vec2d pu = pw * scale; //undistorted point
// reproject
Vec3d pr = RR * Vec3d(pu[0], pu[1], 1.0); // rotated point optionally multiplied by new camera matrix
Vec2d fi(pr[0]/pr[2], pr[1]/pr[2]); // final
if( sdepth == CV_32F )
dstf[i] = fi;
else
dstd[i] = fi;
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
/// cv::Fisheye::undistortPoints
void cv::Fisheye::initUndistortRectifyMap( InputArray _K, InputArray _D, InputArray _R, InputArray _P,
const cv::Size& size, int m1type, OutputArray map1, OutputArray map2 )
{
CV_Assert( m1type == CV_16SC2 || m1type == CV_32F || m1type <=0 );
map1.create( size, m1type <= 0 ? CV_16SC2 : m1type );
map2.create( size, map1.type() == CV_16SC2 ? CV_16UC1 : CV_32F );
CV_Assert((_K.depth() == CV_32F || _K.depth() == CV_64F) && (_D.depth() == CV_32F || _D.depth() == CV_64F));
CV_Assert((_P.depth() == CV_32F || _P.depth() == CV_64F) && (_R.depth() == CV_32F || _R.depth() == CV_64F));
CV_Assert(_K.size() == Size(3, 3) && (_D.empty() || _D.total() == 4));
CV_Assert(_R.empty() || _R.size() == Size(3, 3) || _R.total() * _R.channels() == 3);
CV_Assert(_P.empty() || _P.size() == Size(3, 3) || _P.size() == Size(4, 3));
cv::Vec2d f, c;
if (_K.depth() == CV_32F)
{
Matx33f camMat = _K.getMat();
f = Vec2f(camMat(0, 0), camMat(1, 1));
c = Vec2f(camMat(0, 2), camMat(1, 2));
}
else
{
Matx33d camMat = _K.getMat();
f = Vec2d(camMat(0, 0), camMat(1, 1));
c = Vec2d(camMat(0, 2), camMat(1, 2));
}
Vec4d k = Vec4d::all(0);
if (!_D.empty())
k = _D.depth() == CV_32F ? (Vec4d)*_D.getMat().ptr<Vec4f>(): *_D.getMat().ptr<Vec4d>();
cv::Matx33d R = cv::Matx33d::eye();
if (!_R.empty() && _R.total() * _R.channels() == 3)
{
cv::Vec3d rvec;
_R.getMat().convertTo(rvec, CV_64F);
R = Affine3d(rvec).rotation();
}
else if (!_R.empty() && _R.size() == Size(3, 3))
_R.getMat().convertTo(R, CV_64F);
cv::Matx33d P = cv::Matx33d::eye();
if (!_P.empty())
_P.getMat().colRange(0, 3).convertTo(P, CV_64F);
cv::Matx33d iR = (P * R).inv(cv::DECOMP_SVD);
for( int i = 0; i < size.height; ++i)
{
float* m1f = map1.getMat().ptr<float>(i);
float* m2f = map2.getMat().ptr<float>(i);
short* m1 = (short*)m1f;
ushort* m2 = (ushort*)m2f;
double _x = i*iR(0, 1) + iR(0, 2),
_y = i*iR(1, 1) + iR(1, 2),
_w = i*iR(2, 1) + iR(2, 2);
for( int j = 0; j < size.width; ++j)
{
double x = _x/_w, y = _y/_w;
double r = sqrt(x*x + y*y);
double theta = atan(r);
double theta2 = theta*theta, theta4 = theta2*theta2, theta6 = theta4*theta2, theta8 = theta4*theta4;
double theta_d = theta * (1 + k[0]*theta2 + k[1]*theta4 + k[2]*theta6 + k[3]*theta8);
double scale = (r == 0) ? 1.0 : theta_d / r;
double u = f[0]*x*scale + c[0];
double v = f[1]*y*scale + c[1];
if( m1type == CV_16SC2 )
{
int iu = cv::saturate_cast<int>(u*cv::INTER_TAB_SIZE);
int iv = cv::saturate_cast<int>(v*cv::INTER_TAB_SIZE);
m1[j*2+0] = (short)(iu >> cv::INTER_BITS);
m1[j*2+1] = (short)(iv >> cv::INTER_BITS);
m2[j] = (ushort)((iv & (cv::INTER_TAB_SIZE-1))*cv::INTER_TAB_SIZE + (iu & (cv::INTER_TAB_SIZE-1)));
}
else if( m1type == CV_32FC1 )
{
m1f[j] = (float)u;
m2f[j] = (float)v;
}
_x += iR(0, 0);
_y += iR(1, 0);
_w += iR(2, 0);
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
/// cv::Fisheye::undistortImage
void cv::Fisheye::undistortImage(InputArray distorted, OutputArray undistorted,
InputArray K, InputArray D, InputArray Knew, const Size& new_size)
{
Size size = new_size.area() != 0 ? new_size : distorted.size();
cv::Mat map1, map2;
initUndistortRectifyMap(K, D, cv::Matx33d::eye(), Knew, size, CV_16SC2, map1, map2 );
cv::remap(distorted, undistorted, map1, map2, INTER_LINEAR, BORDER_CONSTANT);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
/// cv::Fisheye::estimateNewCameraMatrixForUndistortRectify
void cv::Fisheye::estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R,
OutputArray P, double balance, const Size& new_size, double fov_scale)
{
CV_Assert( K.size() == Size(3, 3) && (K.depth() == CV_32F || K.depth() == CV_64F));
CV_Assert((D.empty() || D.total() == 4) && (D.depth() == CV_32F || D.depth() == CV_64F || D.empty()));
int w = image_size.width, h = image_size.height;
balance = std::min(std::max(balance, 0.0), 1.0);
cv::Mat points(1, 4, CV_64FC2);
Vec2d* pptr = points.ptr<Vec2d>();
pptr[0] = Vec2d(w/2, 0);
pptr[1] = Vec2d(w, h/2);
pptr[2] = Vec2d(w/2, h);
pptr[3] = Vec2d(0, h/2);
#if 0
const int N = 10;
cv::Mat points(1, N * 4, CV_64FC2);
Vec2d* pptr = points.ptr<Vec2d>();
for(int i = 0, k = 0; i < 10; ++i)
{
pptr[k++] = Vec2d(w/2, 0) - Vec2d(w/8, 0) + Vec2d(w/4/N*i, 0);
pptr[k++] = Vec2d(w/2, h-1) - Vec2d(w/8, h-1) + Vec2d(w/4/N*i, h-1);
pptr[k++] = Vec2d(0, h/2) - Vec2d(0, h/8) + Vec2d(0, h/4/N*i);
pptr[k++] = Vec2d(w-1, h/2) - Vec2d(w-1, h/8) + Vec2d(w-1, h/4/N*i);
}
#endif
undistortPoints(points, points, K, D, R);
cv::Scalar center_mass = mean(points);
cv::Vec2d cn(center_mass.val);
double aspect_ratio = (K.depth() == CV_32F) ? K.getMat().at<float >(0,0)/K.getMat().at<float> (1,1)
: K.getMat().at<double>(0,0)/K.getMat().at<double>(1,1);
// convert to identity ratio
cn[0] *= aspect_ratio;
for(size_t i = 0; i < points.total(); ++i)
pptr[i][1] *= aspect_ratio;
double minx = DBL_MAX, miny = DBL_MAX, maxx = -DBL_MAX, maxy = -DBL_MAX;
for(size_t i = 0; i < points.total(); ++i)
{
miny = std::min(miny, pptr[i][1]);
maxy = std::max(maxy, pptr[i][1]);
minx = std::min(minx, pptr[i][0]);
maxx = std::max(maxx, pptr[i][0]);
}
#if 0
double minx = -DBL_MAX, miny = -DBL_MAX, maxx = DBL_MAX, maxy = DBL_MAX;
for(size_t i = 0; i < points.total(); ++i)
{
if (i % 4 == 0) miny = std::max(miny, pptr[i][1]);
if (i % 4 == 1) maxy = std::min(maxy, pptr[i][1]);
if (i % 4 == 2) minx = std::max(minx, pptr[i][0]);
if (i % 4 == 3) maxx = std::min(maxx, pptr[i][0]);
}
#endif
double f1 = w * 0.5/(cn[0] - minx);
double f2 = w * 0.5/(maxx - cn[0]);
double f3 = h * 0.5 * aspect_ratio/(cn[1] - miny);
double f4 = h * 0.5 * aspect_ratio/(maxy - cn[1]);
double fmin = std::min(f1, std::min(f2, std::min(f3, f4)));
double fmax = std::max(f1, std::max(f2, std::max(f3, f4)));
double f = balance * fmin + (1.0 - balance) * fmax;
f *= fov_scale > 0 ? 1.0/fov_scale : 1.0;
cv::Vec2d new_f(f, f), new_c = -cn * f + Vec2d(w, h * aspect_ratio) * 0.5;
// restore aspect ratio
new_f[1] /= aspect_ratio;
new_c[1] /= aspect_ratio;
if (new_size.area() > 0)
{
double rx = new_size.width /(double)image_size.width;
double ry = new_size.height/(double)image_size.height;
new_f[0] *= rx; new_f[1] *= ry;
new_c[0] *= rx; new_c[1] *= ry;
}
Mat(Matx33d(new_f[0], 0, new_c[0],
0, new_f[1], new_c[1],
0, 0, 1)).convertTo(P, P.empty() ? K.type() : P.type());
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
/// cv::Fisheye::stereoRectify
void cv::Fisheye::stereoRectify( InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size& imageSize,
InputArray _R, InputArray _tvec, OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2,
OutputArray Q, int flags, const Size& newImageSize, double balance, double fov_scale)
{
CV_Assert((_R.size() == Size(3, 3) || _R.total() * _R.channels() == 3) && (_R.depth() == CV_32F || _R.depth() == CV_64F));
CV_Assert(_tvec.total() * _tvec.channels() == 3 && (_tvec.depth() == CV_32F || _tvec.depth() == CV_64F));
cv::Mat aaa = _tvec.getMat().reshape(3, 1);
Vec3d rvec; // Rodrigues vector
if (_R.size() == Size(3, 3))
{
cv::Matx33d rmat;
_R.getMat().convertTo(rmat, CV_64F);
rvec = Affine3d(rmat).rvec();
}
else if (_R.total() * _R.channels() == 3)
_R.getMat().convertTo(rvec, CV_64F);
Vec3d tvec;
_tvec.getMat().convertTo(tvec, CV_64F);
// rectification algorithm
rvec *= -0.5; // get average rotation
Matx33d r_r;
Rodrigues(rvec, r_r); // rotate cameras to same orientation by averaging
Vec3d t = r_r * tvec;
Vec3d uu(t[0] > 0 ? 1 : -1, 0, 0);
// calculate global Z rotation
Vec3d ww = t.cross(uu);
double nw = norm(ww);
if (nw > 0.0)
ww *= acos(fabs(t[0])/cv::norm(t))/nw;
Matx33d wr;
Rodrigues(ww, wr);
// apply to both views
Matx33d ri1 = wr * r_r.t();
Mat(ri1, false).convertTo(R1, R1.empty() ? CV_64F : R1.type());
Matx33d ri2 = wr * r_r;
Mat(ri2, false).convertTo(R2, R2.empty() ? CV_64F : R2.type());
Vec3d tnew = ri2 * tvec;
// calculate projection/camera matrices. these contain the relevant rectified image internal params (fx, fy=fx, cx, cy)
Matx33d newK1, newK2;
estimateNewCameraMatrixForUndistortRectify(K1, D1, imageSize, R1, newK1, balance, newImageSize, fov_scale);
estimateNewCameraMatrixForUndistortRectify(K2, D2, imageSize, R2, newK2, balance, newImageSize, fov_scale);
double fc_new = std::min(newK1(1,1), newK2(1,1));
Point2d cc_new[2] = { Vec2d(newK1(0, 2), newK1(1, 2)), Vec2d(newK2(0, 2), newK2(1, 2)) };
// Vertical focal length must be the same for both images to keep the epipolar constraint use fy for fx also.
// For simplicity, set the principal points for both cameras to be the average
// of the two principal points (either one of or both x- and y- coordinates)
if( flags & cv::CALIB_ZERO_DISPARITY )
cc_new[0] = cc_new[1] = (cc_new[0] + cc_new[1]) * 0.5;
else
cc_new[0].y = cc_new[1].y = (cc_new[0].y + cc_new[1].y)*0.5;
Mat(Matx34d(fc_new, 0, cc_new[0].x, 0,
0, fc_new, cc_new[0].y, 0,
0, 0, 1, 0), false).convertTo(P1, P1.empty() ? CV_64F : P1.type());
Mat(Matx34d(fc_new, 0, cc_new[1].x, tnew[0]*fc_new, // baseline * focal length;,
0, fc_new, cc_new[1].y, 0,
0, 0, 1, 0), false).convertTo(P2, P2.empty() ? CV_64F : P2.type());
if (Q.needed())
Mat(Matx44d(1, 0, 0, -cc_new[0].x,
0, 1, 0, -cc_new[0].y,
0, 0, 0, fc_new,
0, 0, -1./tnew[0], (cc_new[0].x - cc_new[1].x)/tnew[0]), false).convertTo(Q, Q.empty() ? CV_64F : Q.depth());
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
/// cv::Fisheye::calibrate
double cv::Fisheye::calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size,
InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
int flags , cv::TermCriteria criteria)
{
CV_Assert(!objectPoints.empty() && !imagePoints.empty() && objectPoints.total() == imagePoints.total());
CV_Assert(objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3);
CV_Assert(imagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2);
CV_Assert((!K.empty() && K.size() == Size(3,3)) || K.empty());
CV_Assert((!D.empty() && D.total() == 4) || D.empty());
CV_Assert((!rvecs.empty() && rvecs.channels() == 3) || rvecs.empty());
CV_Assert((!tvecs.empty() && tvecs.channels() == 3) || tvecs.empty());
CV_Assert(((flags & CALIB_USE_INTRINSIC_GUESS) && !K.empty() && !D.empty()) || !(flags & CALIB_USE_INTRINSIC_GUESS));
using namespace cv::internal;
//-------------------------------Initialization
IntrinsicParams finalParam;
IntrinsicParams currentParam;
IntrinsicParams errors;
finalParam.isEstimate[0] = 1;
finalParam.isEstimate[1] = 1;
finalParam.isEstimate[2] = 1;
finalParam.isEstimate[3] = 1;
finalParam.isEstimate[4] = flags & CALIB_FIX_SKEW ? 0 : 1;
finalParam.isEstimate[5] = flags & CALIB_FIX_K1 ? 0 : 1;
finalParam.isEstimate[6] = flags & CALIB_FIX_K2 ? 0 : 1;
finalParam.isEstimate[7] = flags & CALIB_FIX_K3 ? 0 : 1;
finalParam.isEstimate[8] = flags & CALIB_FIX_K4 ? 0 : 1;
const int recompute_extrinsic = flags & CALIB_RECOMPUTE_EXTRINSIC ? 1: 0;
const int check_cond = flags & CALIB_CHECK_COND ? 1 : 0;
const double alpha_smooth = 0.4;
const double thresh_cond = 1e6;
double change = 1;
Vec2d err_std;
Matx33d _K;
Vec4d _D;
if (flags & CALIB_USE_INTRINSIC_GUESS)
{
K.getMat().convertTo(_K, CV_64FC1);
D.getMat().convertTo(_D, CV_64FC1);
finalParam.Init(Vec2d(_K(0,0), _K(1, 1)),
Vec2d(_K(0,2), _K(1, 2)),
Vec4d(flags & CALIB_FIX_K1 ? 0 : _D[0],
flags & CALIB_FIX_K2 ? 0 : _D[1],
flags & CALIB_FIX_K3 ? 0 : _D[2],
flags & CALIB_FIX_K4 ? 0 : _D[3]),
_K(0, 1) / _K(0, 0));
}
else
{
finalParam.Init(Vec2d(max(image_size.width, image_size.height) / CV_PI, max(image_size.width, image_size.height) / CV_PI),
Vec2d(image_size.width / 2.0 - 0.5, image_size.height / 2.0 - 0.5));
}
errors.isEstimate = finalParam.isEstimate;
std::vector<Vec3d> omc(objectPoints.total()), Tc(objectPoints.total());
CalibrateExtrinsics(objectPoints, imagePoints, finalParam, check_cond, thresh_cond, omc, Tc);
//-------------------------------Optimization
for(int iter = 0; ; ++iter)
{
if ((criteria.type == 1 && iter >= criteria.maxCount) ||
(criteria.type == 2 && change <= criteria.epsilon) ||
(criteria.type == 3 && (change <= criteria.epsilon || iter >= criteria.maxCount)))
break;
double alpha_smooth2 = 1 - std::pow(1 - alpha_smooth, iter + 1.0);
Mat JJ2_inv, ex3;
ComputeJacobians(objectPoints, imagePoints, finalParam, omc, Tc, check_cond,thresh_cond, JJ2_inv, ex3);
Mat G = alpha_smooth2 * JJ2_inv * ex3;
currentParam = finalParam + G;
change = norm(Vec4d(currentParam.f[0], currentParam.f[1], currentParam.c[0], currentParam.c[1]) -
Vec4d(finalParam.f[0], finalParam.f[1], finalParam.c[0], finalParam.c[1]))
/ norm(Vec4d(currentParam.f[0], currentParam.f[1], currentParam.c[0], currentParam.c[1]));
finalParam = currentParam;
if (recompute_extrinsic)
{
CalibrateExtrinsics(objectPoints, imagePoints, finalParam, check_cond,
thresh_cond, omc, Tc);
}
}
//-------------------------------Validation
double rms;
EstimateUncertainties(objectPoints, imagePoints, finalParam, omc, Tc, errors, err_std, thresh_cond,
check_cond, rms);
//-------------------------------
_K = Matx33d(finalParam.f[0], finalParam.f[0] * finalParam.alpha, finalParam.c[0],
0, finalParam.f[1], finalParam.c[1],
0, 0, 1);
if (K.needed()) cv::Mat(_K).convertTo(K, K.empty() ? CV_64FC1 : K.type());
if (D.needed()) cv::Mat(finalParam.k).convertTo(D, D.empty() ? CV_64FC1 : D.type());
if (rvecs.needed()) cv::Mat(omc).convertTo(rvecs, rvecs.empty() ? CV_64FC3 : rvecs.type());
if (tvecs.needed()) cv::Mat(Tc).convertTo(tvecs, tvecs.empty() ? CV_64FC3 : tvecs.type());
return rms;
}
namespace cv{ namespace {
void subMatrix(const Mat& src, Mat& dst, const vector<int>& cols, const vector<int>& rows)
{
CV_Assert(src.type() == CV_64FC1);
int nonzeros_cols = cv::countNonZero(cols);
Mat tmp(src.rows, nonzeros_cols, CV_64FC1);
for (size_t i = 0, j = 0; i < cols.size(); i++)
{
if (cols[i])
{
src.col(i).copyTo(tmp.col(j++));
}
}
int nonzeros_rows = cv::countNonZero(rows);
Mat tmp1(nonzeros_rows, nonzeros_cols, CV_64FC1);
for (size_t i = 0, j = 0; i < rows.size(); i++)
{
if (rows[i])
{
tmp.row(i).copyTo(tmp1.row(j++));
}
}
dst = tmp1.clone();
}
}}
cv::internal::IntrinsicParams::IntrinsicParams():
f(Vec2d::all(0)), c(Vec2d::all(0)), k(Vec4d::all(0)), alpha(0), isEstimate(9,0)
{
}
cv::internal::IntrinsicParams::IntrinsicParams(Vec2d _f, Vec2d _c, Vec4d _k, double _alpha):
f(_f), c(_c), k(_k), alpha(_alpha), isEstimate(9,0)
{
}
cv::internal::IntrinsicParams cv::internal::IntrinsicParams::operator+(const Mat& a)
{
CV_Assert(a.type() == CV_64FC1);
IntrinsicParams tmp;
const double* ptr = a.ptr<double>();
int j = 0;
tmp.f[0] = this->f[0] + (isEstimate[0] ? ptr[j++] : 0);
tmp.f[1] = this->f[1] + (isEstimate[1] ? ptr[j++] : 0);
tmp.c[0] = this->c[0] + (isEstimate[2] ? ptr[j++] : 0);
tmp.alpha = this->alpha + (isEstimate[4] ? ptr[j++] : 0);
tmp.c[1] = this->c[1] + (isEstimate[3] ? ptr[j++] : 0);
tmp.k[0] = this->k[0] + (isEstimate[5] ? ptr[j++] : 0);
tmp.k[1] = this->k[1] + (isEstimate[6] ? ptr[j++] : 0);
tmp.k[2] = this->k[2] + (isEstimate[7] ? ptr[j++] : 0);
tmp.k[3] = this->k[3] + (isEstimate[8] ? ptr[j++] : 0);
tmp.isEstimate = isEstimate;
return tmp;
}
cv::internal::IntrinsicParams& cv::internal::IntrinsicParams::operator =(const Mat& a)
{
CV_Assert(a.type() == CV_64FC1);
const double* ptr = a.ptr<double>();
int j = 0;
this->f[0] = isEstimate[0] ? ptr[j++] : 0;
this->f[1] = isEstimate[1] ? ptr[j++] : 0;
this->c[0] = isEstimate[2] ? ptr[j++] : 0;
this->c[1] = isEstimate[3] ? ptr[j++] : 0;
this->alpha = isEstimate[4] ? ptr[j++] : 0;
this->k[0] = isEstimate[5] ? ptr[j++] : 0;
this->k[1] = isEstimate[6] ? ptr[j++] : 0;
this->k[2] = isEstimate[7] ? ptr[j++] : 0;
this->k[3] = isEstimate[8] ? ptr[j++] : 0;
return *this;
}
void cv::internal::IntrinsicParams::Init(const cv::Vec2d& _f, const cv::Vec2d& _c, const cv::Vec4d& _k, const double& _alpha)
{
this->c = _c;
this->f = _f;
this->k = _k;
this->alpha = _alpha;
}
void cv::internal::projectPoints(cv::InputArray objectPoints, cv::OutputArray imagePoints,
cv::InputArray _rvec,cv::InputArray _tvec,
const IntrinsicParams& param, cv::OutputArray jacobian)
{
CV_Assert(!objectPoints.empty() && objectPoints.type() == CV_64FC3);
Matx33d K(param.f[0], param.f[0] * param.alpha, param.c[0],
0, param.f[1], param.c[1],
0, 0, 1);
Fisheye::projectPoints(objectPoints, imagePoints, _rvec, _tvec, K, param.k, param.alpha, jacobian);
}
void cv::internal::ComputeExtrinsicRefine(const Mat& imagePoints, const Mat& objectPoints, Mat& rvec,
Mat& tvec, Mat& J, const int MaxIter,
const IntrinsicParams& param, const double thresh_cond)
{
CV_Assert(!objectPoints.empty() && objectPoints.type() == CV_64FC3);
CV_Assert(!imagePoints.empty() && imagePoints.type() == CV_64FC2);
Vec6d extrinsics(rvec.at<double>(0), rvec.at<double>(1), rvec.at<double>(2),
tvec.at<double>(0), tvec.at<double>(1), tvec.at<double>(2));
double change = 1;
int iter = 0;
while (change > 1e-10 && iter < MaxIter)
{
std::vector<Point2d> x;
Mat jacobians;
projectPoints(objectPoints, x, rvec, tvec, param, jacobians);
Mat ex = imagePoints - Mat(x).t();
ex = ex.reshape(1, 2);
J = jacobians.colRange(8, 14).clone();
SVD svd(J, SVD::NO_UV);
double condJJ = svd.w.at<double>(0)/svd.w.at<double>(5);
if (condJJ > thresh_cond)
change = 0;
else
{
Vec6d param_innov;
solve(J, ex.reshape(1, (int)ex.total()), param_innov, DECOMP_SVD + DECOMP_NORMAL);
Vec6d param_up = extrinsics + param_innov;
change = norm(param_innov)/norm(param_up);
extrinsics = param_up;
iter = iter + 1;
rvec = Mat(Vec3d(extrinsics.val));
tvec = Mat(Vec3d(extrinsics.val+3));
}
}
}
cv::Mat cv::internal::ComputeHomography(Mat m, Mat M)
{
int Np = m.cols;
if (m.rows < 3)
{
vconcat(m, Mat::ones(1, Np, CV_64FC1), m);
}
if (M.rows < 3)
{
vconcat(M, Mat::ones(1, Np, CV_64FC1), M);
}
divide(m, Mat::ones(3, 1, CV_64FC1) * m.row(2), m);
divide(M, Mat::ones(3, 1, CV_64FC1) * M.row(2), M);
Mat ax = m.row(0).clone();
Mat ay = m.row(1).clone();
double mxx = mean(ax)[0];
double myy = mean(ay)[0];
ax = ax - mxx;
ay = ay - myy;
double scxx = mean(abs(ax))[0];
double scyy = mean(abs(ay))[0];
Mat Hnorm (Matx33d( 1/scxx, 0.0, -mxx/scxx,
0.0, 1/scyy, -myy/scyy,
0.0, 0.0, 1.0 ));
Mat inv_Hnorm (Matx33d( scxx, 0, mxx,
0, scyy, myy,
0, 0, 1 ));
Mat mn = Hnorm * m;
Mat L = Mat::zeros(2*Np, 9, CV_64FC1);
for (int i = 0; i < Np; ++i)
{
for (int j = 0; j < 3; j++)
{
L.at<double>(2 * i, j) = M.at<double>(j, i);
L.at<double>(2 * i + 1, j + 3) = M.at<double>(j, i);
L.at<double>(2 * i, j + 6) = -mn.at<double>(0,i) * M.at<double>(j, i);
L.at<double>(2 * i + 1, j + 6) = -mn.at<double>(1,i) * M.at<double>(j, i);
}
}
if (Np > 4) L = L.t() * L;
SVD svd(L);
Mat hh = svd.vt.row(8) / svd.vt.row(8).at<double>(8);
Mat Hrem = hh.reshape(1, 3);
Mat H = inv_Hnorm * Hrem;
if (Np > 4)
{
Mat hhv = H.reshape(1, 9)(Rect(0, 0, 1, 8)).clone();
for (int iter = 0; iter < 10; iter++)
{
Mat mrep = H * M;
Mat J = Mat::zeros(2 * Np, 8, CV_64FC1);
Mat MMM;
divide(M, Mat::ones(3, 1, CV_64FC1) * mrep(Rect(0, 2, mrep.cols, 1)), MMM);
divide(mrep, Mat::ones(3, 1, CV_64FC1) * mrep(Rect(0, 2, mrep.cols, 1)), mrep);
Mat m_err = m(Rect(0,0, m.cols, 2)) - mrep(Rect(0,0, mrep.cols, 2));
m_err = Mat(m_err.t()).reshape(1, m_err.cols * m_err.rows);
Mat MMM2, MMM3;
multiply(Mat::ones(3, 1, CV_64FC1) * mrep(Rect(0, 0, mrep.cols, 1)), MMM, MMM2);
multiply(Mat::ones(3, 1, CV_64FC1) * mrep(Rect(0, 1, mrep.cols, 1)), MMM, MMM3);
for (int i = 0; i < Np; ++i)
{
for (int j = 0; j < 3; ++j)
{
J.at<double>(2 * i, j) = -MMM.at<double>(j, i);
J.at<double>(2 * i + 1, j + 3) = -MMM.at<double>(j, i);
}
for (int j = 0; j < 2; ++j)
{
J.at<double>(2 * i, j + 6) = MMM2.at<double>(j, i);
J.at<double>(2 * i + 1, j + 6) = MMM3.at<double>(j, i);
}
}
divide(M, Mat::ones(3, 1, CV_64FC1) * mrep(Rect(0,2,mrep.cols,1)), MMM);
Mat hh_innov = (J.t() * J).inv() * (J.t()) * m_err;
Mat hhv_up = hhv - hh_innov;
Mat tmp;
vconcat(hhv_up, Mat::ones(1,1,CV_64FC1), tmp);
Mat H_up = tmp.reshape(1,3);
hhv = hhv_up;
H = H_up;
}
}
return H;
}
cv::Mat cv::internal::NormalizePixels(const Mat& imagePoints, const IntrinsicParams& param)
{
CV_Assert(!imagePoints.empty() && imagePoints.type() == CV_64FC2);
Mat distorted((int)imagePoints.total(), 1, CV_64FC2), undistorted;
const Vec2d* ptr = imagePoints.ptr<Vec2d>(0);
Vec2d* ptr_d = distorted.ptr<Vec2d>(0);
for (size_t i = 0; i < imagePoints.total(); ++i)
{
ptr_d[i] = (ptr[i] - param.c).mul(Vec2d(1.0 / param.f[0], 1.0 / param.f[1]));
ptr_d[i][0] = ptr_d[i][0] - param.alpha * ptr_d[i][1];
}
cv::Fisheye::undistortPoints(distorted, undistorted, Matx33d::eye(), param.k);
return undistorted;
}
void cv::internal::InitExtrinsics(const Mat& _imagePoints, const Mat& _objectPoints, const IntrinsicParams& param, Mat& omckk, Mat& Tckk)
{
CV_Assert(!_objectPoints.empty() && _objectPoints.type() == CV_64FC3);
CV_Assert(!_imagePoints.empty() && _imagePoints.type() == CV_64FC2);
Mat imagePointsNormalized = NormalizePixels(_imagePoints.t(), param).reshape(1).t();
Mat objectPoints = Mat(_objectPoints.t()).reshape(1).t();
Mat objectPointsMean, covObjectPoints;
Mat Rckk;
int Np = imagePointsNormalized.cols;
calcCovarMatrix(objectPoints, covObjectPoints, objectPointsMean, CV_COVAR_NORMAL | CV_COVAR_COLS);
SVD svd(covObjectPoints);
Mat R(svd.vt);
if (norm(R(Rect(2, 0, 1, 2))) < 1e-6)
R = Mat::eye(3,3, CV_64FC1);
if (determinant(R) < 0)
R = -R;
Mat T = -R * objectPointsMean;
Mat X_new = R * objectPoints + T * Mat::ones(1, Np, CV_64FC1);
Mat H = ComputeHomography(imagePointsNormalized, X_new(Rect(0,0,X_new.cols,2)));
double sc = .5 * (norm(H.col(0)) + norm(H.col(1)));
H = H / sc;
Mat u1 = H.col(0).clone();
u1 = u1 / norm(u1);
Mat u2 = H.col(1).clone() - u1.dot(H.col(1).clone()) * u1;
u2 = u2 / norm(u2);
Mat u3 = u1.cross(u2);
Mat RRR;
hconcat(u1, u2, RRR);
hconcat(RRR, u3, RRR);
Rodrigues(RRR, omckk);
Rodrigues(omckk, Rckk);
Tckk = H.col(2).clone();
Tckk = Tckk + Rckk * T;
Rckk = Rckk * R;
Rodrigues(Rckk, omckk);
}
void cv::internal::CalibrateExtrinsics(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
const IntrinsicParams& param, const int check_cond,
const double thresh_cond, InputOutputArray omc, InputOutputArray Tc)
{
CV_Assert(!objectPoints.empty() && (objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3));
CV_Assert(!imagePoints.empty() && (imagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2));
CV_Assert(omc.type() == CV_64FC3 || Tc.type() == CV_64FC3);
if (omc.empty()) omc.create(1, (int)objectPoints.total(), CV_64FC3);
if (Tc.empty()) Tc.create(1, (int)objectPoints.total(), CV_64FC3);
const int maxIter = 20;
for(int image_idx = 0; image_idx < (int)imagePoints.total(); ++image_idx)
{
Mat omckk, Tckk, JJ_kk;
Mat image, object;
objectPoints.getMat(image_idx).convertTo(object, CV_64FC3);
imagePoints.getMat (image_idx).convertTo(image, CV_64FC2);
InitExtrinsics(image, object, param, omckk, Tckk);
ComputeExtrinsicRefine(image, object, omckk, Tckk, JJ_kk, maxIter, param, thresh_cond);
if (check_cond)
{
SVD svd(JJ_kk, SVD::NO_UV);
if (svd.w.at<double>(0) / svd.w.at<double>((int)svd.w.total() - 1) > thresh_cond)
{
CV_Assert(!"cond > thresh_cond");
}
}
omckk.reshape(3,1).copyTo(omc.getMat().col(image_idx));
Tckk.reshape(3,1).copyTo(Tc.getMat().col(image_idx));
}
}
void cv::internal::ComputeJacobians(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
const IntrinsicParams& param, InputArray omc, InputArray Tc,
const int& check_cond, const double& thresh_cond, Mat& JJ2_inv, Mat& ex3)
{
CV_Assert(!objectPoints.empty() && (objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3));
CV_Assert(!imagePoints.empty() && (imagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2));
CV_Assert(!omc.empty() && omc.type() == CV_64FC3);
CV_Assert(!Tc.empty() && Tc.type() == CV_64FC3);
int n = (int)objectPoints.total();
Mat JJ3 = Mat::zeros(9 + 6 * n, 9 + 6 * n, CV_64FC1);
ex3 = Mat::zeros(9 + 6 * n, 1, CV_64FC1 );
for (int image_idx = 0; image_idx < n; ++image_idx)
{
Mat image, object;
objectPoints.getMat(image_idx).convertTo(object, CV_64FC3);
imagePoints.getMat (image_idx).convertTo(image, CV_64FC2);
Mat om(omc.getMat().col(image_idx)), T(Tc.getMat().col(image_idx));
std::vector<Point2d> x;
Mat jacobians;
projectPoints(object, x, om, T, param, jacobians);
Mat exkk = image.t() - Mat(x);
Mat A(jacobians.rows, 9, CV_64FC1);
jacobians.colRange(0, 4).copyTo(A.colRange(0, 4));
jacobians.col(14).copyTo(A.col(4));
jacobians.colRange(4, 8).copyTo(A.colRange(5, 9));
A = A.t();
Mat B = jacobians.colRange(8, 14).clone();
B = B.t();
JJ3(Rect(0, 0, 9, 9)) = JJ3(Rect(0, 0, 9, 9)) + A * A.t();
JJ3(Rect(9 + 6 * image_idx, 9 + 6 * image_idx, 6, 6)) = B * B.t();
Mat AB = A * B.t();
AB.copyTo(JJ3(Rect(9 + 6 * image_idx, 0, 6, 9)));
JJ3(Rect(0, 9 + 6 * image_idx, 9, 6)) = AB.t();
ex3(Rect(0,0,1,9)) = ex3(Rect(0,0,1,9)) + A * exkk.reshape(1, 2 * exkk.rows);
ex3(Rect(0, 9 + 6 * image_idx, 1, 6)) = B * exkk.reshape(1, 2 * exkk.rows);
if (check_cond)
{
Mat JJ_kk = B.t();
SVD svd(JJ_kk, SVD::NO_UV);
double cond = svd.w.at<double>(0) / svd.w.at<double>(svd.w.rows - 1);
if (cond > thresh_cond)
{
CV_Assert(!"cond > thresh_cond");
}
}
}
vector<int> idxs(param.isEstimate);
idxs.insert(idxs.end(), 6 * n, 1);
subMatrix(JJ3, JJ3, idxs, idxs);
subMatrix(ex3, ex3, std::vector<int>(1, 1), idxs);
JJ2_inv = JJ3.inv();
}
void cv::internal::EstimateUncertainties(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
const IntrinsicParams& params, InputArray omc, InputArray Tc,
IntrinsicParams& errors, Vec2d& std_err, double thresh_cond, int check_cond, double& rms)
{
CV_Assert(!objectPoints.empty() && (objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3));
CV_Assert(!imagePoints.empty() && (imagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2));
CV_Assert(!omc.empty() && omc.type() == CV_64FC3);
CV_Assert(!Tc.empty() && Tc.type() == CV_64FC3);
Mat ex((int)(objectPoints.getMat(0).total() * objectPoints.total()), 1, CV_64FC2);
for (size_t image_idx = 0; image_idx < objectPoints.total(); ++image_idx)
{
Mat image, object;
objectPoints.getMat(image_idx).convertTo(object, CV_64FC3);
imagePoints.getMat (image_idx).convertTo(image, CV_64FC2);
Mat om(omc.getMat().col(image_idx)), T(Tc.getMat().col(image_idx));
std::vector<Point2d> x;
projectPoints(object, x, om, T, params, noArray());
Mat ex_ = image.t() - Mat(x);
ex_.copyTo(ex.rowRange(ex_.rows * image_idx, ex_.rows * (image_idx + 1)));
}
meanStdDev(ex, noArray(), std_err);
std_err *= sqrt(ex.total()/(ex.total() - 1.0));
Mat sigma_x;
meanStdDev(ex.reshape(1, 1), noArray(), sigma_x);
sigma_x *= sqrt(2 * ex.total()/(2 * ex.total() - 1.0));
Mat _JJ2_inv, ex3;
ComputeJacobians(objectPoints, imagePoints, params, omc, Tc, check_cond, thresh_cond, _JJ2_inv, ex3);
Mat_<double>& JJ2_inv = (Mat_<double>&)_JJ2_inv;
sqrt(JJ2_inv, JJ2_inv);
double s = sigma_x.at<double>(0);
Mat r = 3 * s * JJ2_inv.diag();
errors = r;
rms = 0;
const Vec2d* ptr_ex = ex.ptr<Vec2d>();
for (size_t i = 0; i < ex.total(); i++)
{
rms += ptr_ex[i][0] * ptr_ex[i][0] + ptr_ex[i][1] * ptr_ex[i][1];
}
rms /= ex.total();
rms = sqrt(rms);
}
modules/calib3d/test/test_fisheye.cpp 0 → 100644
View file @ 9c7e0bfd
#include "test_precomp.hpp"
#include <iostream>
#include <fstream>
#include <string>
#include <climits>
#include <algorithm>
#include <opencv2/ts/gpu_test.hpp>
#include <opencv2/ts/ts_perf.hpp>
#include <opencv2/ts/ts.hpp>
#define DEF_PARAM_TEST(name, ...) typedef ::perf::TestBaseWithParam< std::tr1::tuple< __VA_ARGS__ > > name
#define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< std::tr1::tuple< __VA_ARGS__ > >
namespace FishEye
{
const static cv::Size imageSize(1280, 800);
const static cv::Matx33d K(558.478087865323, 0, 620.458515360843,
0, 560.506767351568, 381.939424848348,
0, 0, 1);
const static cv::Vec4d D(-0.0014613319981768, -0.00329861110580401, 0.00605760088590183, -0.00374209380722371);
const static cv::Matx33d R ( 9.9756700084424932e-01, 6.9698277640183867e-02, 1.4929569991321144e-03,
-6.9711825162322980e-02, 9.9748249845531767e-01, 1.2997180766418455e-02,
-5.8331736398316541e-04,-1.3069635393884985e-02, 9.9991441852366736e-01);
const static cv::Vec3d T(-9.9217369356044638e-02, 3.1741831972356663e-03, 1.8551007952921010e-04);
}
namespace{
std::string combine(const std::string& _item1, const std::string& _item2)
{
std::string item1 = _item1, item2 = _item2;
std::replace(item1.begin(), item1.end(), '\\', '/');
std::replace(item2.begin(), item2.end(), '\\', '/');
if (item1.empty())
return item2;
if (item2.empty())
return item1;
char last = item1[item1.size()-1];
return item1 + (last != '/' ? "/" : "") + item2;
}
std::string combine_format(const std::string& item1, const std::string& item2, ...)
{
std::string fmt = combine(item1, item2);
char buffer[1 << 16];
va_list args;
va_start( args, item2 );
vsprintf( buffer, fmt.c_str(), args );
va_end( args );
return std::string(buffer);
}
void readPoins(std::vector<std::vector<cv::Point3d> >& objectPoints,
std::vector<std::vector<cv::Point2d> >& imagePoints,
const std::string& path, const int n_images, const int n_points)
{
objectPoints.resize(n_images);
imagePoints.resize(n_images);
std::vector<cv::Point2d> image(n_points);
std::vector<cv::Point3d> object(n_points);
std::ifstream ipStream;
std::ifstream opStream;
for (int image_idx = 0; image_idx < n_images; image_idx++)
{
std::stringstream ss;
ss << image_idx;
std::string idxStr = ss.str();
ipStream.open(combine(path, std::string(std::string("x_") + idxStr + std::string(".csv"))).c_str(), std::ifstream::in);
opStream.open(combine(path, std::string(std::string("X_") + idxStr + std::string(".csv"))).c_str(), std::ifstream::in);
CV_Assert(ipStream.is_open() && opStream.is_open());
for (int point_idx = 0; point_idx < n_points; point_idx++)
{
double x, y, z;
char delim;
ipStream >> x >> delim >> y;
image[point_idx] = cv::Point2d(x, y);
opStream >> x >> delim >> y >> delim >> z;
object[point_idx] = cv::Point3d(x, y, z);
}
ipStream.close();
opStream.close();
imagePoints[image_idx] = image;
objectPoints[image_idx] = object;
}
}
void readExtrinsics(const std::string& file, cv::OutputArray _R, cv::OutputArray _T, cv::OutputArray _R1, cv::OutputArray _R2,
cv::OutputArray _P1, cv::OutputArray _P2, cv::OutputArray _Q)
{
cv::FileStorage fs(file, cv::FileStorage::READ);
CV_Assert(fs.isOpened());
cv::Mat R, T, R1, R2, P1, P2, Q;
fs["R"] >> R; fs["T"] >> T; fs["R1"] >> R1; fs["R2"] >> R2; fs["P1"] >> P1; fs["P2"] >> P2; fs["Q"] >> Q;
if (_R.needed()) R.copyTo(_R); if(_T.needed()) T.copyTo(_T); if (_R1.needed()) R1.copyTo(_R1); if (_R2.needed()) R2.copyTo(_R2);
if(_P1.needed()) P1.copyTo(_P1); if(_P2.needed()) P2.copyTo(_P2); if(_Q.needed()) Q.copyTo(_Q);
}
cv::Mat mergeRectification(const cv::Mat& l, const cv::Mat& r, double scale)
{
CV_Assert(l.type() == r.type() && l.size() == r.size());
cv::Mat merged(l.rows, l.cols * 2, l.type());
cv::Mat lpart = merged.colRange(0, l.cols);
cv::Mat rpart = merged.colRange(l.cols, merged.cols);
l.copyTo(lpart);
r.copyTo(rpart);
for(int i = 0; i < l.rows; i+=20)
cv::line(merged, cv::Point(0, i), cv::Point(merged.cols, i), CV_RGB(0, 255, 0));
return merged;
}
}
/// Change this parameter via CMake: cmake -DDATASETS_REPOSITORY_FOLDER=<path>
//const static std::string datasets_repository_path = "DATASETS_REPOSITORY_FOLDER";
const static std::string datasets_repository_path = "/home/krylov/data";
TEST(FisheyeTest, projectPoints)
{
double cols = FishEye::imageSize.width,
rows = FishEye::imageSize.height;
const int N = 20;
cv::Mat distorted0(1, N*N, CV_64FC2), undist1, undist2, distorted1, distorted2;
undist2.create(distorted0.size(), CV_MAKETYPE(distorted0.depth(), 3));
cv::Vec2d* pts = distorted0.ptr<cv::Vec2d>();
cv::Vec2d c(FishEye::K(0, 2), FishEye::K(1, 2));
for(int y = 0, k = 0; y < N; ++y)
for(int x = 0; x < N; ++x)
{
cv::Vec2d point(x*cols/(N-1.f), y*rows/(N-1.f));
pts[k++] = (point - c) * 0.85 + c;
}
cv::Fisheye::undistortPoints(distorted0, undist1, FishEye::K, FishEye::D);
cv::Vec2d* u1 = undist1.ptr<cv::Vec2d>();
cv::Vec3d* u2 = undist2.ptr<cv::Vec3d>();
for(int i = 0; i < (int)distorted0.total(); ++i)
u2[i] = cv::Vec3d(u1[i][0], u1[i][1], 1.0);
cv::Fisheye::distortPoints(undist1, distorted1, FishEye::K, FishEye::D);
cv::Fisheye::projectPoints(undist2, distorted2, cv::Vec3d::all(0), cv::Vec3d::all(0), FishEye::K, FishEye::D);
EXPECT_MAT_NEAR(distorted0, distorted1, 1e-5);
EXPECT_MAT_NEAR(distorted0, distorted2, 1e-5);
}
TEST(FisheyeTest, undistortImage)
{
cv::Matx33d K = FishEye::K;
cv::Mat D = cv::Mat(FishEye::D);
std::string file = combine(datasets_repository_path, "image000001.png");
cv::Matx33d newK = K;
cv::Mat distorted = cv::imread(file), undistorted;
{
newK(0, 0) = 100;
newK(1, 1) = 100;
cv::Fisheye::undistortImage(distorted, undistorted, K, D, newK);
cv::Mat correct = cv::imread(combine(datasets_repository_path, "test_undistortImage/new_f_100.png"));
if (correct.empty())
CV_Assert(cv::imwrite(combine(datasets_repository_path, "test_undistortImage/new_f_100.png"), undistorted));
else
EXPECT_MAT_NEAR(correct, undistorted, 1e-15);
}
{
double balance = 1.0;
cv::Fisheye::estimateNewCameraMatrixForUndistortRectify(K, D, distorted.size(), cv::noArray(), newK, balance);
cv::Fisheye::undistortImage(distorted, undistorted, K, D, newK);
cv::Mat correct = cv::imread(combine(datasets_repository_path, "test_undistortImage/balance_1.0.png"));
if (correct.empty())
CV_Assert(cv::imwrite(combine(datasets_repository_path, "test_undistortImage/balance_1.0.png"), undistorted));
else
EXPECT_MAT_NEAR(correct, undistorted, 1e-15);
}
{
double balance = 0.0;
cv::Fisheye::estimateNewCameraMatrixForUndistortRectify(K, D, distorted.size(), cv::noArray(), newK, balance);
cv::Fisheye::undistortImage(distorted, undistorted, K, D, newK);
cv::Mat correct = cv::imread(combine(datasets_repository_path, "test_undistortImage/balance_0.0.png"));
if (correct.empty())
CV_Assert(cv::imwrite(combine(datasets_repository_path, "test_undistortImage/balance_0.0.png"), undistorted));
else
EXPECT_MAT_NEAR(correct, undistorted, 1e-15);
}
cv::waitKey();
}
TEST(FisheyeTest, jacobians)
{
int n = 10;
cv::Mat X(1, n, CV_32FC4);
cv::Mat om(3, 1, CV_64F), T(3, 1, CV_64F);
cv::Mat f(2, 1, CV_64F), c(2, 1, CV_64F);
cv::Mat k(4, 1, CV_64F);
double alpha;
cv::RNG& r = cv::theRNG();
r.fill(X, cv::RNG::NORMAL, 0, 1);
X = cv::abs(X) * 10;
r.fill(om, cv::RNG::NORMAL, 0, 1);
om = cv::abs(om);
r.fill(T, cv::RNG::NORMAL, 0, 1);
T = cv::abs(T); T.at<double>(2) = 4; T *= 10;
r.fill(f, cv::RNG::NORMAL, 0, 1);
f = cv::abs(f) * 1000;
r.fill(c, cv::RNG::NORMAL, 0, 1);
c = cv::abs(c) * 1000;
r.fill(k, cv::RNG::NORMAL, 0, 1);
k*= 0.5;
alpha = 0.01*r.gaussian(1);
CV_Assert(!"/////////");
}
TEST(FisheyeTest, Calibration)
{
const int n_images = 34;
const int n_points = 48;
cv::Size imageSize = cv::Size(1280, 800);
std::vector<std::vector<cv::Point2d> > imagePoints;
std::vector<std::vector<cv::Point3d> > objectPoints;
readPoins(objectPoints, imagePoints, combine(datasets_repository_path, "calib-3_stereo_from_JY/left"), n_images, n_points);
int flag = 0;
flag |= cv::Fisheye::CALIB_RECOMPUTE_EXTRINSIC;
flag |= cv::Fisheye::CALIB_CHECK_COND;
flag |= cv::Fisheye::CALIB_FIX_SKEW;
cv::Matx33d K;
cv::Vec4d D;
cv::Fisheye::calibrate(objectPoints, imagePoints, imageSize, K, D,
cv::noArray(), cv::noArray(), flag, cv::TermCriteria(3, 20, 1e-6));
EXPECT_MAT_NEAR(K, FishEye::K, 1e-11);
EXPECT_MAT_NEAR(D, FishEye::D, 1e-12);
}
TEST(FisheyeTest, Homography)
{
const int n_images = 1;
const int n_points = 48;
cv::Size imageSize = cv::Size(1280, 800);
std::vector<std::vector<cv::Point2d> > imagePoints;
std::vector<std::vector<cv::Point3d> > objectPoints;
readPoins(objectPoints, imagePoints, combine(datasets_repository_path, "calib-3_stereo_from_JY/left"), n_images, n_points);
cv::internal::IntrinsicParams param;
param.Init(cv::Vec2d(cv::max(imageSize.width, imageSize.height) / CV_PI, cv::max(imageSize.width, imageSize.height) / CV_PI),
cv::Vec2d(imageSize.width / 2.0 - 0.5, imageSize.height / 2.0 - 0.5));
cv::Mat _imagePoints (imagePoints[0]);
cv::Mat _objectPoints(objectPoints[0]);
cv::Mat imagePointsNormalized = NormalizePixels(_imagePoints, param).reshape(1).t();
_objectPoints = _objectPoints.reshape(1).t();
cv::Mat objectPointsMean, covObjectPoints;
int Np = imagePointsNormalized.cols;
cv::calcCovarMatrix(_objectPoints, covObjectPoints, objectPointsMean, CV_COVAR_NORMAL | CV_COVAR_COLS);
cv::SVD svd(covObjectPoints);
cv::Mat R(svd.vt);
if (cv::norm(R(cv::Rect(2, 0, 1, 2))) < 1e-6)
R = cv::Mat::eye(3,3, CV_64FC1);
if (cv::determinant(R) < 0)
R = -R;
cv::Mat T = -R * objectPointsMean;
cv::Mat X_new = R * _objectPoints + T * cv::Mat::ones(1, Np, CV_64FC1);
cv::Mat H = cv::internal::ComputeHomography(imagePointsNormalized, X_new.rowRange(0, 2));
cv::Mat M = cv::Mat::ones(3, X_new.cols, CV_64FC1);
X_new.rowRange(0, 2).copyTo(M.rowRange(0, 2));
cv::Mat mrep = H * M;
cv::divide(mrep, cv::Mat::ones(3,1, CV_64FC1) * mrep.row(2).clone(), mrep);
cv::Mat merr = (mrep.rowRange(0, 2) - imagePointsNormalized).t();
cv::Vec2d std_err;
cv::meanStdDev(merr.reshape(2), cv::noArray(), std_err);
std_err *= sqrt((double)merr.reshape(2).total() / (merr.reshape(2).total() - 1));
cv::Vec2d correct_std_err(0.00516740156010384, 0.00644205331553901);
EXPECT_MAT_NEAR(std_err, correct_std_err, 1e-16);
}
TEST(TestFisheye, EtimateUncertainties)
{
const int n_images = 34;
const int n_points = 48;
cv::Size imageSize = cv::Size(1280, 800);
std::vector<std::vector<cv::Point2d> > imagePoints;
std::vector<std::vector<cv::Point3d> > objectPoints;
readPoins(objectPoints, imagePoints, combine(datasets_repository_path, "calib-3_stereo_from_JY/left"), n_images, n_points);
int flag = 0;
flag |= cv::Fisheye::CALIB_RECOMPUTE_EXTRINSIC;
flag |= cv::Fisheye::CALIB_CHECK_COND;
flag |= cv::Fisheye::CALIB_FIX_SKEW;
cv::Matx33d K;
cv::Vec4d D;
std::vector<cv::Vec3d> rvec;
std::vector<cv::Vec3d> tvec;
cv::Fisheye::calibrate(objectPoints, imagePoints, imageSize, K, D,
cv::noArray(), cv::noArray(), flag, cv::TermCriteria(3, 20, 1e-6));
cv::internal::IntrinsicParams param, errors;
cv::Vec2d err_std;
double thresh_cond = 1e6;
int check_cond = 1;
param.Init(cv::Vec2d(K(0,0), K(1,1)), cv::Vec2d(K(0,2), K(1, 2)), D);
param.isEstimate = std::vector<int>(9, 1);
param.isEstimate[4] = 0;
errors.isEstimate = param.isEstimate;
double rms;
cv::internal::EstimateUncertainties(objectPoints, imagePoints, param, rvec, tvec,
errors, err_std, thresh_cond, check_cond, rms);
EXPECT_MAT_NEAR(errors.f, cv::Vec2d(1.29837104202046, 1.31565641071524), 1e-14);
EXPECT_MAT_NEAR(errors.c, cv::Vec2d(0.890439368129246, 0.816096854937896), 1e-15);
EXPECT_MAT_NEAR(errors.k, cv::Vec4d(0.00516248605191506, 0.0168181467500934, 0.0213118690274604, 0.00916010877545648), 1e-15);
EXPECT_MAT_NEAR(err_std, cv::Vec2d(0.187475975266883, 0.185678953263995), 1e-15);
CV_Assert(abs(rms - 0.263782587133546) < 1e-15);
CV_Assert(errors.alpha == 0);
}
TEST(FisheyeTest, rectify)
{
const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
cv::Size calibration_size = FishEye::imageSize, requested_size = calibration_size;
cv::Matx33d K1 = FishEye::K, K2 = K1;
cv::Mat D1 = cv::Mat(FishEye::D), D2 = D1;
cv::Vec3d T = FishEye::T;
cv::Matx33d R = FishEye::R;
double balance = 0.0, fov_scale = 1.1;
cv::Mat R1, R2, P1, P2, Q;
cv::Fisheye::stereoRectify(K1, D1, K2, D2, calibration_size, R, T, R1, R2, P1, P2, Q,
cv::CALIB_ZERO_DISPARITY, requested_size, balance, fov_scale);
cv::Mat lmapx, lmapy, rmapx, rmapy;
//rewrite for fisheye
cv::Fisheye::initUndistortRectifyMap(K1, D1, R1, P1, requested_size, CV_32F, lmapx, lmapy);
cv::Fisheye::initUndistortRectifyMap(K2, D2, R2, P2, requested_size, CV_32F, rmapx, rmapy);
cv::Mat l, r, lundist, rundist;
cv::VideoCapture lcap(combine(folder, "left/stereo_pair_%03d.jpg")),
rcap(combine(folder, "right/stereo_pair_%03d.jpg"));
for(int i = 0;; ++i)
{
lcap >> l; rcap >> r;
if (l.empty() || r.empty())
break;
int ndisp = 128;
cv::rectangle(l, cv::Rect(255, 0, 829, l.rows-1), CV_RGB(255, 0, 0));
cv::rectangle(r, cv::Rect(255, 0, 829, l.rows-1), CV_RGB(255, 0, 0));
cv::rectangle(r, cv::Rect(255-ndisp, 0, 829+ndisp ,l.rows-1), CV_RGB(255, 0, 0));
cv::remap(l, lundist, lmapx, lmapy, cv::INTER_LINEAR);
cv::remap(r, rundist, rmapx, rmapy, cv::INTER_LINEAR);
cv::Mat rectification = mergeRectification(lundist, rundist, 0.75);
cv::Mat correct = cv::imread(combine_format(folder, "test_rectify/rectification_AB_%03d.png", i));
if (correct.empty())
cv::imwrite(combine_format(folder, "test_rectify/rectification_AB_%03d.png", i), rectification);
else
EXPECT_MAT_NEAR(correct, rectification, 1e-15);
}
}
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