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opencv/modules/calib3d/test/test_fundam.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
using namespace cv;
using namespace std;
int cvTsRodrigues( const CvMat* src, CvMat* dst, CvMat* jacobian )
{
int depth;
int i;
float Jf[27];
double J[27];
CvMat _Jf, matJ = cvMat( 3, 9, CV_64F, J );
depth = CV_MAT_DEPTH(src->type);
if( jacobian )
{
assert( (jacobian->rows == 9 && jacobian->cols == 3) ||
(jacobian->rows == 3 && jacobian->cols == 9) );
}
if( src->cols == 1 || src->rows == 1 )
{
double r[3], theta;
CvMat _r = cvMat( src->rows, src->cols, CV_MAKETYPE(CV_64F,CV_MAT_CN(src->type)), r);
assert( dst->rows == 3 && dst->cols == 3 );
cvConvert( src, &_r );
theta = sqrt(r[0]*r[0] + r[1]*r[1] + r[2]*r[2]);
if( theta < DBL_EPSILON )
{
cvSetIdentity( dst );
if( jacobian )
{
memset( J, 0, sizeof(J) );
J[5] = J[15] = J[19] = 1;
J[7] = J[11] = J[21] = -1;
}
}
else
{
// omega = r/theta (~[w1, w2, w3])
double itheta = 1./theta;
double w1 = r[0]*itheta, w2 = r[1]*itheta, w3 = r[2]*itheta;
double alpha = cos(theta);
double beta = sin(theta);
double gamma = 1 - alpha;
double omegav[] =
{
0, -w3, w2,
w3, 0, -w1,
-w2, w1, 0
};
double A[] =
{
w1*w1, w1*w2, w1*w3,
w2*w1, w2*w2, w2*w3,
w3*w1, w3*w2, w3*w3
};
double R[9];
CvMat _omegav = cvMat(3, 3, CV_64F, omegav);
CvMat matA = cvMat(3, 3, CV_64F, A);
CvMat matR = cvMat(3, 3, CV_64F, R);
cvSetIdentity( &matR, cvRealScalar(alpha) );
cvScaleAdd( &_omegav, cvRealScalar(beta), &matR, &matR );
cvScaleAdd( &matA, cvRealScalar(gamma), &matR, &matR );
cvConvert( &matR, dst );
if( jacobian )
{
// m3 = [r, theta]
double dm3din[] =
{
1, 0, 0,
0, 1, 0,
0, 0, 1,
w1, w2, w3
};
// m2 = [omega, theta]
double dm2dm3[] =
{
itheta, 0, 0, -w1*itheta,
0, itheta, 0, -w2*itheta,
0, 0, itheta, -w3*itheta,
0, 0, 0, 1
};
double t0[9*4];
double dm1dm2[21*4];
double dRdm1[9*21];
CvMat _dm3din = cvMat( 4, 3, CV_64FC1, dm3din );
CvMat _dm2dm3 = cvMat( 4, 4, CV_64FC1, dm2dm3 );
CvMat _dm1dm2 = cvMat( 21, 4, CV_64FC1, dm1dm2 );
CvMat _dRdm1 = cvMat( 9, 21, CV_64FC1, dRdm1 );
CvMat _dRdm1_part;
CvMat _t0 = cvMat( 9, 4, CV_64FC1, t0 );
CvMat _t1 = cvMat( 9, 4, CV_64FC1, dRdm1 );
// m1 = [alpha, beta, gamma, omegav; A]
memset( dm1dm2, 0, sizeof(dm1dm2) );
dm1dm2[3] = -beta;
dm1dm2[7] = alpha;
dm1dm2[11] = beta;
// dm1dm2(4:12,1:3) = [0 0 0 0 0 1 0 -1 0;
// 0 0 -1 0 0 0 1 0 0;
// 0 1 0 -1 0 0 0 0 0]'
// -------------------
// 0 0 0 0 0 0 0 0 0
dm1dm2[12 + 6] = dm1dm2[12 + 20] = dm1dm2[12 + 25] = 1;
dm1dm2[12 + 9] = dm1dm2[12 + 14] = dm1dm2[12 + 28] = -1;
double dm1dw[] =
{
2*w1, w2, w3, w2, 0, 0, w3, 0, 0,
0, w1, 0, w1, 2*w2, w3, 0, w3, 0,
0, 0, w1, 0, 0, w2, w1, w2, 2*w3
};
CvMat _dm1dw = cvMat( 3, 9, CV_64FC1, dm1dw );
CvMat _dm1dm2_part;
cvGetSubRect( &_dm1dm2, &_dm1dm2_part, cvRect(0,12,3,9) );
cvTranspose( &_dm1dw, &_dm1dm2_part );
memset( dRdm1, 0, sizeof(dRdm1) );
dRdm1[0*21] = dRdm1[4*21] = dRdm1[8*21] = 1;
cvGetCol( &_dRdm1, &_dRdm1_part, 1 );
cvTranspose( &_omegav, &_omegav );
cvReshape( &_omegav, &_omegav, 1, 1 );
cvTranspose( &_omegav, &_dRdm1_part );
cvGetCol( &_dRdm1, &_dRdm1_part, 2 );
cvReshape( &matA, &matA, 1, 1 );
cvTranspose( &matA, &_dRdm1_part );
cvGetSubRect( &_dRdm1, &_dRdm1_part, cvRect(3,0,9,9) );
cvSetIdentity( &_dRdm1_part, cvScalarAll(beta) );
cvGetSubRect( &_dRdm1, &_dRdm1_part, cvRect(12,0,9,9) );
cvSetIdentity( &_dRdm1_part, cvScalarAll(gamma) );
matJ = cvMat( 9, 3, CV_64FC1, J );
cvMatMul( &_dRdm1, &_dm1dm2, &_t0 );
cvMatMul( &_t0, &_dm2dm3, &_t1 );
cvMatMul( &_t1, &_dm3din, &matJ );
_t0 = cvMat( 3, 9, CV_64FC1, t0 );
cvTranspose( &matJ, &_t0 );
for( i = 0; i < 3; i++ )
{
_t1 = cvMat( 3, 3, CV_64FC1, t0 + i*9 );
cvTranspose( &_t1, &_t1 );
}
cvTranspose( &_t0, &matJ );
}
}
}
else if( src->cols == 3 && src->rows == 3 )
{
double R[9], A[9], I[9], r[3], W[3], U[9], V[9];
double tr, alpha, beta, theta;
CvMat matR = cvMat( 3, 3, CV_64F, R );
CvMat matA = cvMat( 3, 3, CV_64F, A );
CvMat matI = cvMat( 3, 3, CV_64F, I );
CvMat _r = cvMat( dst->rows, dst->cols, CV_MAKETYPE(CV_64F, CV_MAT_CN(dst->type)), r );
CvMat matW = cvMat( 1, 3, CV_64F, W );
CvMat matU = cvMat( 3, 3, CV_64F, U );
CvMat matV = cvMat( 3, 3, CV_64F, V );
cvConvert( src, &matR );
cvSVD( &matR, &matW, &matU, &matV, CV_SVD_MODIFY_A + CV_SVD_U_T + CV_SVD_V_T );
cvGEMM( &matU, &matV, 1, 0, 0, &matR, CV_GEMM_A_T );
cvMulTransposed( &matR, &matA, 0 );
cvSetIdentity( &matI );
if( cvNorm( &matA, &matI, CV_C ) > 1e-3 ||
fabs( cvDet(&matR) - 1 ) > 1e-3 )
return 0;
tr = (cvTrace(&matR).val[0] - 1.)*0.5;
tr = tr > 1. ? 1. : tr < -1. ? -1. : tr;
theta = acos(tr);
alpha = cos(theta);
beta = sin(theta);
if( beta >= 1e-5 )
{
double dtheta_dtr = -1./sqrt(1 - tr*tr);
double vth = 1/(2*beta);
// om1 = [R(3,2) - R(2,3), R(1,3) - R(3,1), R(2,1) - R(1,2)]'
double om1[] = { R[7] - R[5], R[2] - R[6], R[3] - R[1] };
// om = om1*vth
// r = om*theta
double d3 = vth*theta;
r[0] = om1[0]*d3; r[1] = om1[1]*d3; r[2] = om1[2]*d3;
cvConvert( &_r, dst );
if( jacobian )
{
// var1 = [vth;theta]
// var = [om1;var1] = [om1;vth;theta]
double dvth_dtheta = -vth*alpha/beta;
double d1 = 0.5*dvth_dtheta*dtheta_dtr;
double d2 = 0.5*dtheta_dtr;
// dvar1/dR = dvar1/dtheta*dtheta/dR = [dvth/dtheta; 1] * dtheta/dtr * dtr/dR
double dvardR[5*9] =
{
0, 0, 0, 0, 0, 1, 0, -1, 0,
0, 0, -1, 0, 0, 0, 1, 0, 0,
0, 1, 0, -1, 0, 0, 0, 0, 0,
d1, 0, 0, 0, d1, 0, 0, 0, d1,
d2, 0, 0, 0, d2, 0, 0, 0, d2
};
// var2 = [om;theta]
double dvar2dvar[] =
{
vth, 0, 0, om1[0], 0,
0, vth, 0, om1[1], 0,
0, 0, vth, om1[2], 0,
0, 0, 0, 0, 1
};
double domegadvar2[] =
{
theta, 0, 0, om1[0]*vth,
0, theta, 0, om1[1]*vth,
0, 0, theta, om1[2]*vth
};
CvMat _dvardR = cvMat( 5, 9, CV_64FC1, dvardR );
CvMat _dvar2dvar = cvMat( 4, 5, CV_64FC1, dvar2dvar );
CvMat _domegadvar2 = cvMat( 3, 4, CV_64FC1, domegadvar2 );
double t0[3*5];
CvMat _t0 = cvMat( 3, 5, CV_64FC1, t0 );
cvMatMul( &_domegadvar2, &_dvar2dvar, &_t0 );
cvMatMul( &_t0, &_dvardR, &matJ );
}
}
else if( tr > 0 )
{
cvZero( dst );
if( jacobian )
{
memset( J, 0, sizeof(J) );
J[5] = J[15] = J[19] = 0.5;
J[7] = J[11] = J[21] = -0.5;
}
}
else
{
r[0] = theta*sqrt((R[0] + 1)*0.5);
r[1] = theta*sqrt((R[4] + 1)*0.5)*(R[1] >= 0 ? 1 : -1);
r[2] = theta*sqrt((R[8] + 1)*0.5)*(R[2] >= 0 ? 1 : -1);
cvConvert( &_r, dst );
if( jacobian )
memset( J, 0, sizeof(J) );
}
if( jacobian )
{
for( i = 0; i < 3; i++ )
{
CvMat t = cvMat( 3, 3, CV_64F, J + i*9 );
cvTranspose( &t, &t );
}
}
}
else
{
assert(0);
return 0;
}
if( jacobian )
{
if( depth == CV_32F )
{
if( jacobian->rows == matJ.rows )
cvConvert( &matJ, jacobian );
else
{
_Jf = cvMat( matJ.rows, matJ.cols, CV_32FC1, Jf );
cvConvert( &matJ, &_Jf );
cvTranspose( &_Jf, jacobian );
}
}
else if( jacobian->rows == matJ.rows )
cvCopy( &matJ, jacobian );
else
cvTranspose( &matJ, jacobian );
}
return 1;
}
void cvtest::Rodrigues(const Mat& src, Mat& dst, Mat* jac)
{
CvMat _src = src, _dst = dst, _jac;
if( jac )
_jac = *jac;
cvTsRodrigues(&_src, &_dst, jac ? &_jac : 0);
}
static void test_convertHomogeneous( const Mat& _src, Mat& _dst )
{
Mat src = _src, dst = _dst;
int i, count, sdims, ddims;
int sstep1, sstep2, dstep1, dstep2;
if( src.depth() != CV_64F )
_src.convertTo(src, CV_64F);
if( dst.depth() != CV_64F )
dst.create(dst.size(), CV_MAKETYPE(CV_64F, _dst.channels()));
if( src.rows > src.cols )
{
count = src.rows;
sdims = src.channels()*src.cols;
sstep1 = (int)(src.step/sizeof(double));
sstep2 = 1;
}
else
{
count = src.cols;
sdims = src.channels()*src.rows;
if( src.rows == 1 )
{
sstep1 = sdims;
sstep2 = 1;
}
else
{
sstep1 = 1;
sstep2 = (int)(src.step/sizeof(double));
}
}
if( dst.rows > dst.cols )
{
CV_Assert( count == dst.rows );
ddims = dst.channels()*dst.cols;
dstep1 = (int)(dst.step/sizeof(double));
dstep2 = 1;
}
else
{
assert( count == dst.cols );
ddims = dst.channels()*dst.rows;
if( dst.rows == 1 )
{
dstep1 = ddims;
dstep2 = 1;
}
else
{
dstep1 = 1;
dstep2 = (int)(dst.step/sizeof(double));
}
}
double* s = src.ptr<double>();
double* d = dst.ptr<double>();
if( sdims <= ddims )
{
int wstep = dstep2*(ddims - 1);
for( i = 0; i < count; i++, s += sstep1, d += dstep1 )
{
double x = s[0];
double y = s[sstep2];
d[wstep] = 1;
d[0] = x;
d[dstep2] = y;
if( sdims >= 3 )
{
d[dstep2*2] = s[sstep2*2];
if( sdims == 4 )
d[dstep2*3] = s[sstep2*3];
}
}
}
else
{
int wstep = sstep2*(sdims - 1);
for( i = 0; i < count; i++, s += sstep1, d += dstep1 )
{
double w = s[wstep];
double x = s[0];
double y = s[sstep2];
w = w ? 1./w : 1;
d[0] = x*w;
d[dstep2] = y*w;
if( ddims == 3 )
d[dstep2*2] = s[sstep2*2]*w;
}
}
if( dst.data != _dst.data )
dst.convertTo(_dst, _dst.depth());
}
void
test_projectPoints( const Mat& _3d, const Mat& Rt, const Mat& A, Mat& _2d, RNG* rng, double sigma )
{
CV_Assert( _3d.isContinuous() );
double p[12];
Mat P( 3, 4, CV_64F, p );
gemm(A, Rt, 1, Mat(), 0, P);
int i, count = _3d.cols;
Mat noise;
if( rng )
{
if( sigma == 0 )
rng = 0;
else
{
noise.create( 1, _3d.cols, CV_64FC2 );
rng->fill(noise, RNG::NORMAL, Scalar::all(0), Scalar::all(sigma) );
}
}
Mat temp( 1, count, CV_64FC3 );
for( i = 0; i < count; i++ )
{
const double* M = _3d.ptr<double>() + i*3;
double* m = temp.ptr<double>() + i*3;
double X = M[0], Y = M[1], Z = M[2];
double u = p[0]*X + p[1]*Y + p[2]*Z + p[3];
double v = p[4]*X + p[5]*Y + p[6]*Z + p[7];
double s = p[8]*X + p[9]*Y + p[10]*Z + p[11];
if( !noise.empty() )
{
u += noise.at<Point2d>(i).x*s;
v += noise.at<Point2d>(i).y*s;
}
m[0] = u;
m[1] = v;
m[2] = s;
}
test_convertHomogeneous( temp, _2d );
}
/********************************** Rodrigues transform ********************************/
class CV_RodriguesTest : public cvtest::ArrayTest
{
public:
CV_RodriguesTest();
protected:
int read_params( CvFileStorage* fs );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
int prepare_test_case( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void prepare_to_validation( int );
bool calc_jacobians;
bool test_cpp;
};
CV_RodriguesTest::CV_RodriguesTest()
{
test_array[INPUT].push_back(NULL); // rotation vector
test_array[OUTPUT].push_back(NULL); // rotation matrix
test_array[OUTPUT].push_back(NULL); // jacobian (J)
test_array[OUTPUT].push_back(NULL); // rotation vector (backward transform result)
test_array[OUTPUT].push_back(NULL); // inverse transform jacobian (J1)
test_array[OUTPUT].push_back(NULL); // J*J1 (or J1*J) == I(3x3)
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
element_wise_relative_error = false;
calc_jacobians = false;
test_cpp = false;
}
int CV_RodriguesTest::read_params( CvFileStorage* fs )
{
int code = cvtest::ArrayTest::read_params( fs );
return code;
}
void CV_RodriguesTest::get_test_array_types_and_sizes(
int /*test_case_idx*/, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int depth = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
int i, code;
code = cvtest::randInt(rng) % 3;
types[INPUT][0] = CV_MAKETYPE(depth, 1);
if( code == 0 )
{
sizes[INPUT][0] = cvSize(1,1);
types[INPUT][0] = CV_MAKETYPE(depth, 3);
}
else if( code == 1 )
sizes[INPUT][0] = cvSize(3,1);
else
sizes[INPUT][0] = cvSize(1,3);
sizes[OUTPUT][0] = cvSize(3, 3);
types[OUTPUT][0] = CV_MAKETYPE(depth, 1);
types[OUTPUT][1] = CV_MAKETYPE(depth, 1);
if( cvtest::randInt(rng) % 2 )
sizes[OUTPUT][1] = cvSize(3,9);
else
sizes[OUTPUT][1] = cvSize(9,3);
types[OUTPUT][2] = types[INPUT][0];
sizes[OUTPUT][2] = sizes[INPUT][0];
types[OUTPUT][3] = types[OUTPUT][1];
sizes[OUTPUT][3] = cvSize(sizes[OUTPUT][1].height, sizes[OUTPUT][1].width);
types[OUTPUT][4] = types[OUTPUT][1];
sizes[OUTPUT][4] = cvSize(3,3);
calc_jacobians = cvtest::randInt(rng) % 3 != 0;
if( !calc_jacobians )
sizes[OUTPUT][1] = sizes[OUTPUT][3] = sizes[OUTPUT][4] = cvSize(0,0);
for( i = 0; i < 5; i++ )
{
types[REF_OUTPUT][i] = types[OUTPUT][i];
sizes[REF_OUTPUT][i] = sizes[OUTPUT][i];
}
test_cpp = (cvtest::randInt(rng) & 256) == 0;
}
double CV_RodriguesTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int j )
{
return j == 4 ? 1e-2 : 1e-2;
}
void CV_RodriguesTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i == INPUT && j == 0 )
{
double r[3], theta0, theta1, f;
Mat _r( arr.rows, arr.cols, CV_MAKETYPE(CV_64F,arr.channels()), r );
RNG& rng = ts->get_rng();
r[0] = cvtest::randReal(rng)*CV_PI*2;
r[1] = cvtest::randReal(rng)*CV_PI*2;
r[2] = cvtest::randReal(rng)*CV_PI*2;
theta0 = sqrt(r[0]*r[0] + r[1]*r[1] + r[2]*r[2]);
theta1 = fmod(theta0, CV_PI*2);
if( theta1 > CV_PI )
theta1 = -(CV_PI*2 - theta1);
f = theta1/(theta0 ? theta0 : 1);
r[0] *= f;
r[1] *= f;
r[2] *= f;
cvtest::convert( _r, arr, arr.type() );
}
else
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
}
int CV_RodriguesTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
return code;
}
void CV_RodriguesTest::run_func()
{
CvMat v2m_jac, m2v_jac;
if( calc_jacobians )
{
v2m_jac = test_mat[OUTPUT][1];
m2v_jac = test_mat[OUTPUT][3];
}
if( !test_cpp )
{
CvMat _input = test_mat[INPUT][0], _output = test_mat[OUTPUT][0], _output2 = test_mat[OUTPUT][2];
cvRodrigues2( &_input, &_output, calc_jacobians ? &v2m_jac : 0 );
cvRodrigues2( &_output, &_output2, calc_jacobians ? &m2v_jac : 0 );
}
else
{
cv::Mat v = test_mat[INPUT][0], M = test_mat[OUTPUT][0], v2 = test_mat[OUTPUT][2];
cv::Mat M0 = M, v2_0 = v2;
if( !calc_jacobians )
{
cv::Rodrigues(v, M);
cv::Rodrigues(M, v2);
}
else
{
cv::Mat J1 = test_mat[OUTPUT][1], J2 = test_mat[OUTPUT][3];
cv::Mat J1_0 = J1, J2_0 = J2;
cv::Rodrigues(v, M, J1);
cv::Rodrigues(M, v2, J2);
if( J1.data != J1_0.data )
{
if( J1.size() != J1_0.size() )
J1 = J1.t();
J1.convertTo(J1_0, J1_0.type());
}
if( J2.data != J2_0.data )
{
if( J2.size() != J2_0.size() )
J2 = J2.t();
J2.convertTo(J2_0, J2_0.type());
}
}
if( M.data != M0.data )
M.reshape(M0.channels(), M0.rows).convertTo(M0, M0.type());
if( v2.data != v2_0.data )
v2.reshape(v2_0.channels(), v2_0.rows).convertTo(v2_0, v2_0.type());
}
}
void CV_RodriguesTest::prepare_to_validation( int /*test_case_idx*/ )
{
const Mat& vec = test_mat[INPUT][0];
Mat& m = test_mat[REF_OUTPUT][0];
Mat& vec2 = test_mat[REF_OUTPUT][2];
Mat* v2m_jac = 0, *m2v_jac = 0;
double theta0, theta1;
if( calc_jacobians )
{
v2m_jac = &test_mat[REF_OUTPUT][1];
m2v_jac = &test_mat[REF_OUTPUT][3];
}
cvtest::Rodrigues( vec, m, v2m_jac );
cvtest::Rodrigues( m, vec2, m2v_jac );
cvtest::copy( vec, vec2 );
theta0 = norm( vec2, CV_L2 );
theta1 = fmod( theta0, CV_PI*2 );
if( theta1 > CV_PI )
theta1 = -(CV_PI*2 - theta1);
vec2 *= theta1/(theta0 ? theta0 : 1);
if( calc_jacobians )
{
//cvInvert( v2m_jac, m2v_jac, CV_SVD );
double nrm = norm(test_mat[REF_OUTPUT][3],CV_C);
if( FLT_EPSILON < nrm && nrm < 1000 )
{
gemm( test_mat[OUTPUT][1], test_mat[OUTPUT][3],
1, Mat(), 0, test_mat[OUTPUT][4],
v2m_jac->rows == 3 ? 0 : CV_GEMM_A_T + CV_GEMM_B_T );
}
else
{
setIdentity(test_mat[OUTPUT][4], Scalar::all(1.));
cvtest::copy( test_mat[REF_OUTPUT][2], test_mat[OUTPUT][2] );
}
setIdentity(test_mat[REF_OUTPUT][4], Scalar::all(1.));
}
}
/********************************** fundamental matrix *********************************/
class CV_FundamentalMatTest : public cvtest::ArrayTest
{
public:
CV_FundamentalMatTest();
protected:
int read_params( CvFileStorage* fs );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
int prepare_test_case( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void prepare_to_validation( int );
int method;
int img_size;
int cube_size;
int dims;
int f_result;
double min_f, max_f;
double sigma;
bool test_cpp;
};
CV_FundamentalMatTest::CV_FundamentalMatTest()
{
// input arrays:
// 0, 1 - arrays of 2d points that are passed to %func%.
// Can have different data type, layout, be stored in homogeneous coordinates or not.
// 2 - array of 3d points that are projected to both view planes
// 3 - [R|t] matrix for the second view plane (for the first one it is [I|0]
// 4, 5 - intrinsic matrices
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[TEMP].push_back(NULL);
test_array[TEMP].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
element_wise_relative_error = false;
method = 0;
img_size = 10;
cube_size = 10;
min_f = 1;
max_f = 3;
sigma = 0;//0.1;
f_result = 0;
test_cpp = false;
}
int CV_FundamentalMatTest::read_params( CvFileStorage* fs )
{
int code = cvtest::ArrayTest::read_params( fs );
return code;
}
void CV_FundamentalMatTest::get_test_array_types_and_sizes( int /*test_case_idx*/,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int pt_depth = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
double pt_count_exp = cvtest::randReal(rng)*6 + 1;
int pt_count = cvRound(exp(pt_count_exp));
dims = cvtest::randInt(rng) % 2 + 2;
method = 1 << (cvtest::randInt(rng) % 4);
if( method == CV_FM_7POINT )
pt_count = 7;
else
{
pt_count = MAX( pt_count, 8 + (method == CV_FM_8POINT) );
if( pt_count >= 8 && cvtest::randInt(rng) % 2 )
method |= CV_FM_8POINT;
}
types[INPUT][0] = CV_MAKETYPE(pt_depth, 1);
if( cvtest::randInt(rng) % 2 )
sizes[INPUT][0] = cvSize(pt_count, dims);
else
{
sizes[INPUT][0] = cvSize(dims, pt_count);
if( cvtest::randInt(rng) % 2 )
{
types[INPUT][0] = CV_MAKETYPE(pt_depth, dims);
if( cvtest::randInt(rng) % 2 )
sizes[INPUT][0] = cvSize(pt_count, 1);
else
sizes[INPUT][0] = cvSize(1, pt_count);
}
}
sizes[INPUT][1] = sizes[INPUT][0];
types[INPUT][1] = types[INPUT][0];
sizes[INPUT][2] = cvSize(pt_count, 1 );
types[INPUT][2] = CV_64FC3;
sizes[INPUT][3] = cvSize(4,3);
types[INPUT][3] = CV_64FC1;
sizes[INPUT][4] = sizes[INPUT][5] = cvSize(3,3);
types[INPUT][4] = types[INPUT][5] = CV_MAKETYPE(CV_64F, 1);
sizes[TEMP][0] = cvSize(3,3);
types[TEMP][0] = CV_64FC1;
sizes[TEMP][1] = cvSize(pt_count,1);
types[TEMP][1] = CV_8UC1;
sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(3,1);
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1;
sizes[OUTPUT][1] = sizes[REF_OUTPUT][1] = cvSize(pt_count,1);
types[OUTPUT][1] = types[REF_OUTPUT][1] = CV_8UC1;
test_cpp = (cvtest::randInt(rng) & 256) == 0;
}
double CV_FundamentalMatTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 1e-2;
}
void CV_FundamentalMatTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
double t[12]={0};
RNG& rng = ts->get_rng();
if( i != INPUT )
{
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
return;
}
switch( j )
{
case 0:
case 1:
return; // fill them later in prepare_test_case
case 2:
{
double* p = arr.ptr<double>();
for( i = 0; i < arr.cols*3; i += 3 )
{
p[i] = cvtest::randReal(rng)*cube_size;
p[i+1] = cvtest::randReal(rng)*cube_size;
p[i+2] = cvtest::randReal(rng)*cube_size + cube_size;
}
}
break;
case 3:
{
double r[3];
Mat rot_vec( 3, 1, CV_64F, r );
Mat rot_mat( 3, 3, CV_64F, t, 4*sizeof(t[0]) );
r[0] = cvtest::randReal(rng)*CV_PI*2;
r[1] = cvtest::randReal(rng)*CV_PI*2;
r[2] = cvtest::randReal(rng)*CV_PI*2;
cvtest::Rodrigues( rot_vec, rot_mat );
t[3] = cvtest::randReal(rng)*cube_size;
t[7] = cvtest::randReal(rng)*cube_size;
t[11] = cvtest::randReal(rng)*cube_size;
Mat( 3, 4, CV_64F, t ).convertTo(arr, arr.type());
}
break;
case 4:
case 5:
t[0] = t[4] = cvtest::randReal(rng)*(max_f - min_f) + min_f;
t[2] = (img_size*0.5 + cvtest::randReal(rng)*4. - 2.)*t[0];
t[5] = (img_size*0.5 + cvtest::randReal(rng)*4. - 2.)*t[4];
t[8] = 1.;
Mat( 3, 3, CV_64F, t ).convertTo( arr, arr.type() );
break;
}
}
int CV_FundamentalMatTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
const Mat& _3d = test_mat[INPUT][2];
RNG& rng = ts->get_rng();
double Idata[] = { 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0 };
Mat I( 3, 4, CV_64F, Idata );
int k;
for( k = 0; k < 2; k++ )
{
const Mat& Rt = k == 0 ? I : test_mat[INPUT][3];
const Mat& A = test_mat[INPUT][k == 0 ? 4 : 5];
Mat& _2d = test_mat[INPUT][k];
test_projectPoints( _3d, Rt, A, _2d, &rng, sigma );
}
}
return code;
}
void CV_FundamentalMatTest::run_func()
{
//if(!test_cpp)
{
CvMat _input0 = test_mat[INPUT][0], _input1 = test_mat[INPUT][1];
CvMat F = test_mat[TEMP][0], mask = test_mat[TEMP][1];
f_result = cvFindFundamentalMat( &_input0, &_input1, &F, method, MAX(sigma*3, 0.01), 0, &mask );
}
/*else
{
cv::findFundamentalMat(const Mat& points1, const Mat& points2,
vector<uchar>& mask, int method=FM_RANSAC,
double param1=3., double param2=0.99 );
CV_EXPORTS Mat findFundamentalMat( const Mat& points1, const Mat& points2,
int method=FM_RANSAC,
double param1=3., double param2=0.99 );
}*/
}
void CV_FundamentalMatTest::prepare_to_validation( int test_case_idx )
{
const Mat& Rt = test_mat[INPUT][3];
const Mat& A1 = test_mat[INPUT][4];
const Mat& A2 = test_mat[INPUT][5];
double f0[9], f[9];
Mat F0(3, 3, CV_64FC1, f0), F(3, 3, CV_64F, f);
Mat invA1, invA2, R=Rt.colRange(0, 3), T;
cv::invert(A1, invA1, CV_SVD);
cv::invert(A2, invA2, CV_SVD);
double tx = Rt.at<double>(0, 3);
double ty = Rt.at<double>(1, 3);
double tz = Rt.at<double>(2, 3);
double _t_x[] = { 0, -tz, ty, tz, 0, -tx, -ty, tx, 0 };
// F = (A2^-T)*[t]_x*R*(A1^-1)
cv::gemm( invA2, Mat( 3, 3, CV_64F, _t_x ), 1, Mat(), 0, T, CV_GEMM_A_T );
cv::gemm( R, invA1, 1, Mat(), 0, invA2 );
cv::gemm( T, invA2, 1, Mat(), 0, F0 );
F0 *= 1./f0[8];
uchar* status = test_mat[TEMP][1].data;
double err_level = get_success_error_level( test_case_idx, OUTPUT, 1 );
uchar* mtfm1 = test_mat[REF_OUTPUT][1].data;
uchar* mtfm2 = test_mat[OUTPUT][1].data;
double* f_prop1 = (double*)test_mat[REF_OUTPUT][0].data;
double* f_prop2 = (double*)test_mat[OUTPUT][0].data;
int i, pt_count = test_mat[INPUT][2].cols;
Mat p1( 1, pt_count, CV_64FC2 );
Mat p2( 1, pt_count, CV_64FC2 );
test_convertHomogeneous( test_mat[INPUT][0], p1 );
test_convertHomogeneous( test_mat[INPUT][1], p2 );
cvtest::convert(test_mat[TEMP][0], F, F.type());
if( method <= CV_FM_8POINT )
memset( status, 1, pt_count );
for( i = 0; i < pt_count; i++ )
{
double x1 = p1.at<Point2d>(i).x;
double y1 = p1.at<Point2d>(i).y;
double x2 = p2.at<Point2d>(i).x;
double y2 = p2.at<Point2d>(i).y;
double n1 = 1./sqrt(x1*x1 + y1*y1 + 1);
double n2 = 1./sqrt(x2*x2 + y2*y2 + 1);
double t0 = fabs(f0[0]*x2*x1 + f0[1]*x2*y1 + f0[2]*x2 +
f0[3]*y2*x1 + f0[4]*y2*y1 + f0[5]*y2 +
f0[6]*x1 + f0[7]*y1 + f0[8])*n1*n2;
double t = fabs(f[0]*x2*x1 + f[1]*x2*y1 + f[2]*x2 +
f[3]*y2*x1 + f[4]*y2*y1 + f[5]*y2 +
f[6]*x1 + f[7]*y1 + f[8])*n1*n2;
mtfm1[i] = 1;
mtfm2[i] = !status[i] || t0 > err_level || t < err_level;
}
f_prop1[0] = 1;
f_prop1[1] = 1;
f_prop1[2] = 0;
f_prop2[0] = f_result != 0;
f_prop2[1] = f[8];
f_prop2[2] = cv::determinant( F );
}
/******************************* find essential matrix ***********************************/
class CV_EssentialMatTest : public cvtest::ArrayTest
{
public:
CV_EssentialMatTest();
protected:
int read_params( CvFileStorage* fs );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
int prepare_test_case( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void prepare_to_validation( int );
double sampson_error(const double* f, double x1, double y1, double x2, double y2);
int method;
int img_size;
int cube_size;
int dims;
int e_result;
double min_f, max_f;
double sigma;
};
CV_EssentialMatTest::CV_EssentialMatTest()
{
// input arrays:
// 0, 1 - arrays of 2d points that are passed to %func%.
// Can have different data type, layout, be stored in homogeneous coordinates or not.
// 2 - array of 3d points that are projected to both view planes
// 3 - [R|t] matrix for the second view plane (for the first one it is [I|0]
// 4 - intrinsic matrix for both camera
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[TEMP].push_back(NULL);
test_array[TEMP].push_back(NULL);
test_array[TEMP].push_back(NULL);
test_array[TEMP].push_back(NULL);
test_array[TEMP].push_back(NULL);
test_array[OUTPUT].push_back(NULL); // Essential Matrix singularity
test_array[OUTPUT].push_back(NULL); // Inliers mask
test_array[OUTPUT].push_back(NULL); // Translation error
test_array[OUTPUT].push_back(NULL); // Positive depth count
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
element_wise_relative_error = false;
method = 0;
img_size = 10;
cube_size = 10;
min_f = 1;
max_f = 3;
}
int CV_EssentialMatTest::read_params( CvFileStorage* fs )
{
int code = cvtest::ArrayTest::read_params( fs );
return code;
}
void CV_EssentialMatTest::get_test_array_types_and_sizes( int /*test_case_idx*/,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int pt_depth = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
double pt_count_exp = cvtest::randReal(rng)*6 + 1;
int pt_count = MAX(5, cvRound(exp(pt_count_exp)));
dims = cvtest::randInt(rng) % 2 + 2;
dims = 2;
method = CV_LMEDS << (cvtest::randInt(rng) % 2);
types[INPUT][0] = CV_MAKETYPE(pt_depth, 1);
if( 0 && cvtest::randInt(rng) % 2 )
sizes[INPUT][0] = cvSize(pt_count, dims);
else
{
sizes[INPUT][0] = cvSize(dims, pt_count);
if( cvtest::randInt(rng) % 2 )
{
types[INPUT][0] = CV_MAKETYPE(pt_depth, dims);
if( cvtest::randInt(rng) % 2 )
sizes[INPUT][0] = cvSize(pt_count, 1);
else
sizes[INPUT][0] = cvSize(1, pt_count);
}
}
sizes[INPUT][1] = sizes[INPUT][0];
types[INPUT][1] = types[INPUT][0];
sizes[INPUT][2] = cvSize(pt_count, 1 );
types[INPUT][2] = CV_64FC3;
sizes[INPUT][3] = cvSize(4,3);
types[INPUT][3] = CV_64FC1;
sizes[INPUT][4] = cvSize(3,3);
types[INPUT][4] = CV_MAKETYPE(CV_64F, 1);
sizes[TEMP][0] = cvSize(3,3);
types[TEMP][0] = CV_64FC1;
sizes[TEMP][1] = cvSize(pt_count,1);
types[TEMP][1] = CV_8UC1;
sizes[TEMP][2] = cvSize(3,3);
types[TEMP][2] = CV_64FC1;
sizes[TEMP][3] = cvSize(3, 1);
types[TEMP][3] = CV_64FC1;
sizes[TEMP][4] = cvSize(pt_count,1);
types[TEMP][4] = CV_8UC1;
sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(3,1);
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1;
sizes[OUTPUT][1] = sizes[REF_OUTPUT][1] = cvSize(pt_count,1);
types[OUTPUT][1] = types[REF_OUTPUT][1] = CV_8UC1;
sizes[OUTPUT][2] = sizes[REF_OUTPUT][2] = cvSize(1,1);
types[OUTPUT][2] = types[REF_OUTPUT][2] = CV_64FC1;
sizes[OUTPUT][3] = sizes[REF_OUTPUT][3] = cvSize(1,1);
types[OUTPUT][3] = types[REF_OUTPUT][3] = CV_8UC1;
}
double CV_EssentialMatTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 1e-2;
}
void CV_EssentialMatTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
double t[12]={0};
RNG& rng = ts->get_rng();
if( i != INPUT )
{
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
return;
}
switch( j )
{
case 0:
case 1:
return; // fill them later in prepare_test_case
case 2:
{
double* p = arr.ptr<double>();
for( i = 0; i < arr.cols*3; i += 3 )
{
p[i] = cvtest::randReal(rng)*cube_size;
p[i+1] = cvtest::randReal(rng)*cube_size;
p[i+2] = cvtest::randReal(rng)*cube_size + cube_size;
}
}
break;
case 3:
{
double r[3];
Mat rot_vec( 3, 1, CV_64F, r );
Mat rot_mat( 3, 3, CV_64F, t, 4*sizeof(t[0]) );
r[0] = cvtest::randReal(rng)*CV_PI*2;
r[1] = cvtest::randReal(rng)*CV_PI*2;
r[2] = cvtest::randReal(rng)*CV_PI*2;
cvtest::Rodrigues( rot_vec, rot_mat );
t[3] = cvtest::randReal(rng)*cube_size;
t[7] = cvtest::randReal(rng)*cube_size;
t[11] = cvtest::randReal(rng)*cube_size;
Mat( 3, 4, CV_64F, t ).convertTo(arr, arr.type());
}
break;
case 4:
t[0] = t[4] = cvtest::randReal(rng)*(max_f - min_f) + min_f;
t[2] = (img_size*0.5 + cvtest::randReal(rng)*4. - 2.)*t[0];
t[5] = (img_size*0.5 + cvtest::randReal(rng)*4. - 2.)*t[4];
t[8] = 1.;
Mat( 3, 3, CV_64F, t ).convertTo( arr, arr.type() );
break;
}
}
int CV_EssentialMatTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
const Mat& _3d = test_mat[INPUT][2];
RNG& rng = ts->get_rng();
double Idata[] = { 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0 };
Mat I( 3, 4, CV_64F, Idata );
int k;
for( k = 0; k < 2; k++ )
{
const Mat& Rt = k == 0 ? I : test_mat[INPUT][3];
const Mat& A = test_mat[INPUT][4];
Mat& _2d = test_mat[INPUT][k];
test_projectPoints( _3d, Rt, A, _2d, &rng, sigma );
}
}
return code;
}
void CV_EssentialMatTest::run_func()
{
Mat _input0(test_mat[INPUT][0]), _input1(test_mat[INPUT][1]);
Mat K(test_mat[INPUT][4]);
double focal(K.at<double>(0, 0));
cv::Point2d pp(K.at<double>(0, 2), K.at<double>(1, 2));
RNG& rng = ts->get_rng();
Mat E, mask1(test_mat[TEMP][1]);
E = cv::findEssentialMat( _input0, _input1, focal, pp, method, 0.99, MAX(sigma*3, 0.0001), mask1 );
if (E.rows > 3)
{
int count = E.rows / 3;
int row = (cvtest::randInt(rng) % count) * 3;
E = E.rowRange(row, row + 3) * 1.0;
}
E.copyTo(test_mat[TEMP][0]);
Mat R, t, mask2;
recoverPose( E, _input0, _input1, R, t, focal, pp, mask2 );
R.copyTo(test_mat[TEMP][2]);
t.copyTo(test_mat[TEMP][3]);
mask2.copyTo(test_mat[TEMP][4]);
}
double CV_EssentialMatTest::sampson_error(const double * f, double x1, double y1, double x2, double y2)
{
double Fx1[3] = {
f[0] * x1 + f[1] * y1 + f[2],
f[3] * x1 + f[4] * y1 + f[5],
f[6] * x1 + f[7] * y1 + f[8]
};
double Ftx2[3] = {
f[0] * x2 + f[3] * y2 + f[6],
f[1] * x2 + f[4] * y2 + f[7],
f[2] * x2 + f[5] * y2 + f[8]
};
double x2tFx1 = Fx1[0] * x2 + Fx1[1] * y2 + Fx1[2];
double error = x2tFx1 * x2tFx1 / (Fx1[0] * Fx1[0] + Fx1[1] * Fx1[1] + Ftx2[0] * Ftx2[0] + Ftx2[1] * Ftx2[1]);
error = sqrt(error);
return error;
}
void CV_EssentialMatTest::prepare_to_validation( int test_case_idx )
{
const Mat& Rt0 = test_mat[INPUT][3];
const Mat& A = test_mat[INPUT][4];
double f0[9], f[9], e[9];
Mat F0(3, 3, CV_64FC1, f0), F(3, 3, CV_64F, f);
Mat E(3, 3, CV_64F, e);
Mat invA, R=Rt0.colRange(0, 3), T1, T2;
cv::invert(A, invA, CV_SVD);
double tx = Rt0.at<double>(0, 3);
double ty = Rt0.at<double>(1, 3);
double tz = Rt0.at<double>(2, 3);
double _t_x[] = { 0, -tz, ty, tz, 0, -tx, -ty, tx, 0 };
// F = (A2^-T)*[t]_x*R*(A1^-1)
cv::gemm( invA, Mat( 3, 3, CV_64F, _t_x ), 1, Mat(), 0, T1, CV_GEMM_A_T );
cv::gemm( R, invA, 1, Mat(), 0, T2 );
cv::gemm( T1, T2, 1, Mat(), 0, F0 );
F0 *= 1./f0[8];
uchar* status = test_mat[TEMP][1].data;
double err_level = get_success_error_level( test_case_idx, OUTPUT, 1 );
uchar* mtfm1 = test_mat[REF_OUTPUT][1].data;
uchar* mtfm2 = test_mat[OUTPUT][1].data;
double* e_prop1 = (double*)test_mat[REF_OUTPUT][0].data;
double* e_prop2 = (double*)test_mat[OUTPUT][0].data;
Mat E_prop2 = Mat(3, 1, CV_64F, e_prop2);
int i, pt_count = test_mat[INPUT][2].cols;
Mat p1( 1, pt_count, CV_64FC2 );
Mat p2( 1, pt_count, CV_64FC2 );
test_convertHomogeneous( test_mat[INPUT][0], p1 );
test_convertHomogeneous( test_mat[INPUT][1], p2 );
cvtest::convert(test_mat[TEMP][0], E, E.type());
cv::gemm( invA, E, 1, Mat(), 0, T1, CV_GEMM_A_T );
cv::gemm( T1, invA, 1, Mat(), 0, F );
for( i = 0; i < pt_count; i++ )
{
double x1 = p1.at<Point2d>(i).x;
double y1 = p1.at<Point2d>(i).y;
double x2 = p2.at<Point2d>(i).x;
double y2 = p2.at<Point2d>(i).y;
// double t0 = sampson_error(f0, x1, y1, x2, y2);
// double t = sampson_error(f, x1, y1, x2, y2);
double n1 = 1./sqrt(x1*x1 + y1*y1 + 1);
double n2 = 1./sqrt(x2*x2 + y2*y2 + 1);
double t0 = fabs(f0[0]*x2*x1 + f0[1]*x2*y1 + f0[2]*x2 +
f0[3]*y2*x1 + f0[4]*y2*y1 + f0[5]*y2 +
f0[6]*x1 + f0[7]*y1 + f0[8])*n1*n2;
double t = fabs(f[0]*x2*x1 + f[1]*x2*y1 + f[2]*x2 +
f[3]*y2*x1 + f[4]*y2*y1 + f[5]*y2 +
f[6]*x1 + f[7]*y1 + f[8])*n1*n2;
mtfm1[i] = 1;
mtfm2[i] = !status[i] || t0 > err_level || t < err_level;
}
e_prop1[0] = sqrt(0.5);
e_prop1[1] = sqrt(0.5);
e_prop1[2] = 0;
e_prop2[0] = 0;
e_prop2[1] = 0;
e_prop2[2] = 0;
SVD::compute(E, E_prop2);
double* pose_prop1 = (double*)test_mat[REF_OUTPUT][2].data;
double* pose_prop2 = (double*)test_mat[OUTPUT][2].data;
double terr1 = norm(Rt0.col(3) / norm(Rt0.col(3)) + test_mat[TEMP][3]);
double terr2 = norm(Rt0.col(3) / norm(Rt0.col(3)) - test_mat[TEMP][3]);
Mat rvec;
Rodrigues(Rt0.colRange(0, 3), rvec);
pose_prop1[0] = 0;
// No check for CV_LMeDS on translation. Since it
// involves with some degraded problem, when data is exact inliers.
pose_prop2[0] = method == CV_LMEDS || pt_count == 5 ? 0 : MIN(terr1, terr2);
// int inliers_count = countNonZero(test_mat[TEMP][1]);
// int good_count = countNonZero(test_mat[TEMP][4]);
test_mat[OUTPUT][3] = true; //good_count >= inliers_count / 2;
test_mat[REF_OUTPUT][3] = true;
}
/********************************** convert homogeneous *********************************/
class CV_ConvertHomogeneousTest : public cvtest::ArrayTest
{
public:
CV_ConvertHomogeneousTest();
protected:
int read_params( CvFileStorage* fs );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void prepare_to_validation( int );
int dims1, dims2;
int pt_count;
};
CV_ConvertHomogeneousTest::CV_ConvertHomogeneousTest()
{
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
element_wise_relative_error = false;
pt_count = dims1 = dims2 = 0;
}
int CV_ConvertHomogeneousTest::read_params( CvFileStorage* fs )
{
int code = cvtest::ArrayTest::read_params( fs );
return code;
}
void CV_ConvertHomogeneousTest::get_test_array_types_and_sizes( int /*test_case_idx*/,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int pt_depth1 = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
int pt_depth2 = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
double pt_count_exp = cvtest::randReal(rng)*6 + 1;
int t;
pt_count = cvRound(exp(pt_count_exp));
pt_count = MAX( pt_count, 5 );
dims1 = 2 + (cvtest::randInt(rng) % 3);
dims2 = 2 + (cvtest::randInt(rng) % 3);
if( dims1 == dims2 + 2 )
dims1--;
else if( dims1 == dims2 - 2 )
dims1++;
if( cvtest::randInt(rng) % 2 )
CV_SWAP( dims1, dims2, t );
types[INPUT][0] = CV_MAKETYPE(pt_depth1, 1);
if( cvtest::randInt(rng) % 2 )
sizes[INPUT][0] = cvSize(pt_count, dims1);
else
{
sizes[INPUT][0] = cvSize(dims1, pt_count);
if( cvtest::randInt(rng) % 2 )
{
types[INPUT][0] = CV_MAKETYPE(pt_depth1, dims1);
if( cvtest::randInt(rng) % 2 )
sizes[INPUT][0] = cvSize(pt_count, 1);
else
sizes[INPUT][0] = cvSize(1, pt_count);
}
}
types[OUTPUT][0] = CV_MAKETYPE(pt_depth2, 1);
if( cvtest::randInt(rng) % 2 )
sizes[OUTPUT][0] = cvSize(pt_count, dims2);
else
{
sizes[OUTPUT][0] = cvSize(dims2, pt_count);
if( cvtest::randInt(rng) % 2 )
{
types[OUTPUT][0] = CV_MAKETYPE(pt_depth2, dims2);
if( cvtest::randInt(rng) % 2 )
sizes[OUTPUT][0] = cvSize(pt_count, 1);
else
sizes[OUTPUT][0] = cvSize(1, pt_count);
}
}
types[REF_OUTPUT][0] = types[OUTPUT][0];
sizes[REF_OUTPUT][0] = sizes[OUTPUT][0];
}
double CV_ConvertHomogeneousTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 1e-5;
}
void CV_ConvertHomogeneousTest::fill_array( int /*test_case_idx*/, int /*i*/, int /*j*/, Mat& arr )
{
Mat temp( 1, pt_count, CV_MAKETYPE(CV_64FC1,dims1) );
RNG& rng = ts->get_rng();
CvScalar low = cvScalarAll(0), high = cvScalarAll(10);
if( dims1 > dims2 )
low.val[dims1-1] = 1.;
cvtest::randUni( rng, temp, low, high );
test_convertHomogeneous( temp, arr );
}
void CV_ConvertHomogeneousTest::run_func()
{
CvMat _input = test_mat[INPUT][0], _output = test_mat[OUTPUT][0];
cvConvertPointsHomogeneous( &_input, &_output );
}
void CV_ConvertHomogeneousTest::prepare_to_validation( int /*test_case_idx*/ )
{
test_convertHomogeneous( test_mat[INPUT][0], test_mat[REF_OUTPUT][0] );
}
/************************** compute corresponding epipolar lines ************************/
class CV_ComputeEpilinesTest : public cvtest::ArrayTest
{
public:
CV_ComputeEpilinesTest();
protected:
int read_params( CvFileStorage* fs );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void prepare_to_validation( int );
int which_image;
int dims;
int pt_count;
};
CV_ComputeEpilinesTest::CV_ComputeEpilinesTest()
{
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
element_wise_relative_error = false;
pt_count = dims = which_image = 0;
}
int CV_ComputeEpilinesTest::read_params( CvFileStorage* fs )
{
int code = cvtest::ArrayTest::read_params( fs );
return code;
}
void CV_ComputeEpilinesTest::get_test_array_types_and_sizes( int /*test_case_idx*/,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int fm_depth = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
int pt_depth = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
int ln_depth = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
double pt_count_exp = cvtest::randReal(rng)*6;
which_image = 1 + (cvtest::randInt(rng) % 2);
pt_count = cvRound(exp(pt_count_exp));
pt_count = MAX( pt_count, 1 );
bool few_points = pt_count < 5;
dims = 2 + (cvtest::randInt(rng) % 2);
types[INPUT][0] = CV_MAKETYPE(pt_depth, 1);
if( cvtest::randInt(rng) % 2 && !few_points )
sizes[INPUT][0] = cvSize(pt_count, dims);
else
{
sizes[INPUT][0] = cvSize(dims, pt_count);
if( cvtest::randInt(rng) % 2 || few_points )
{
types[INPUT][0] = CV_MAKETYPE(pt_depth, dims);
if( cvtest::randInt(rng) % 2 )
sizes[INPUT][0] = cvSize(pt_count, 1);
else
sizes[INPUT][0] = cvSize(1, pt_count);
}
}
types[INPUT][1] = CV_MAKETYPE(fm_depth, 1);
sizes[INPUT][1] = cvSize(3, 3);
types[OUTPUT][0] = CV_MAKETYPE(ln_depth, 1);
if( cvtest::randInt(rng) % 2 && !few_points )
sizes[OUTPUT][0] = cvSize(pt_count, 3);
else
{
sizes[OUTPUT][0] = cvSize(3, pt_count);
if( cvtest::randInt(rng) % 2 || few_points )
{
types[OUTPUT][0] = CV_MAKETYPE(ln_depth, 3);
if( cvtest::randInt(rng) % 2 )
sizes[OUTPUT][0] = cvSize(pt_count, 1);
else
sizes[OUTPUT][0] = cvSize(1, pt_count);
}
}
types[REF_OUTPUT][0] = types[OUTPUT][0];
sizes[REF_OUTPUT][0] = sizes[OUTPUT][0];
}
double CV_ComputeEpilinesTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 1e-5;
}
void CV_ComputeEpilinesTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
RNG& rng = ts->get_rng();
if( i == INPUT && j == 0 )
{
Mat temp( 1, pt_count, CV_MAKETYPE(CV_64FC1,dims) );
cvtest::randUni( rng, temp, cvScalar(0,0,1), cvScalarAll(10) );
test_convertHomogeneous( temp, arr );
}
else if( i == INPUT && j == 1 )
cvtest::randUni( rng, arr, cvScalarAll(0), cvScalarAll(10) );
else
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
}
void CV_ComputeEpilinesTest::run_func()
{
CvMat _points = test_mat[INPUT][0], _F = test_mat[INPUT][1], _lines = test_mat[OUTPUT][0];
cvComputeCorrespondEpilines( &_points, which_image, &_F, &_lines );
}
void CV_ComputeEpilinesTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat pt( 1, pt_count, CV_MAKETYPE(CV_64F, 3) );
Mat lines( 1, pt_count, CV_MAKETYPE(CV_64F, 3) );
double f[9];
Mat F( 3, 3, CV_64F, f );
test_convertHomogeneous( test_mat[INPUT][0], pt );
test_mat[INPUT][1].convertTo(F, CV_64F);
if( which_image == 2 )
cv::transpose( F, F );
for( int i = 0; i < pt_count; i++ )
{
double* p = pt.ptr<double>() + i*3;
double* l = lines.ptr<double>() + i*3;
double t0 = f[0]*p[0] + f[1]*p[1] + f[2]*p[2];
double t1 = f[3]*p[0] + f[4]*p[1] + f[5]*p[2];
double t2 = f[6]*p[0] + f[7]*p[1] + f[8]*p[2];
double d = sqrt(t0*t0 + t1*t1);
d = d ? 1./d : 1.;
l[0] = t0*d; l[1] = t1*d; l[2] = t2*d;
}
test_convertHomogeneous( lines, test_mat[REF_OUTPUT][0] );
}
TEST(Calib3d_Rodrigues, accuracy) { CV_RodriguesTest test; test.safe_run(); }
TEST(Calib3d_FindFundamentalMat, accuracy) { CV_FundamentalMatTest test; test.safe_run(); }
TEST(Calib3d_ConvertHomogeneoous, accuracy) { CV_ConvertHomogeneousTest test; test.safe_run(); }
TEST(Calib3d_ComputeEpilines, accuracy) { CV_ComputeEpilinesTest test; test.safe_run(); }
TEST(Calib3d_FindEssentialMat, accuracy) { CV_EssentialMatTest test; test.safe_run(); }
/* End of file. */