/*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" #include "opencv2/calib3d/calib3d_c.h" 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* d = dst.ptr(); 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() + i*3; double* m = temp.ptr() + 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(i).x*s; v += noise.at(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 >& sizes, vector >& 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 >& sizes, vector >& 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 = cvtest::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 >& sizes, vector >& 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; dims = 0; 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 >& sizes, vector >& 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(); 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& 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(0, 3); double ty = Rt.at(1, 3); double tz = Rt.at(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].ptr(); double err_level = method <= CV_FM_8POINT ? 1 : get_success_error_level( test_case_idx, OUTPUT, 1 ); uchar* mtfm1 = test_mat[REF_OUTPUT][1].ptr(); uchar* mtfm2 = test_mat[OUTPUT][1].ptr(); double* f_prop1 = test_mat[REF_OUTPUT][0].ptr(); double* f_prop2 = test_mat[OUTPUT][0].ptr(); 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(i).x; double y1 = p1.at(i).y; double x2 = p2.at(i).x; double y2 = p2.at(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 >& sizes, vector >& 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; 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; dims = 0; min_f = 1; max_f = 3; sigma = 0; } 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 >& sizes, vector >& 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(); 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(0, 0)); cv::Point2d pp(K.at(0, 2), K.at(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(0, 3); double ty = Rt0.at(1, 3); double tz = Rt0.at(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].ptr(); double err_level = get_success_error_level( test_case_idx, OUTPUT, 1 ); uchar* mtfm1 = test_mat[REF_OUTPUT][1].ptr(); uchar* mtfm2 = test_mat[OUTPUT][1].ptr(); double* e_prop1 = test_mat[REF_OUTPUT][0].ptr(); double* e_prop2 = test_mat[OUTPUT][0].ptr(); 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(i).x; double y1 = p1.at(i).y; double x2 = p2.at(i).x; double y2 = p2.at(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 = test_mat[REF_OUTPUT][2].ptr(); double* pose_prop2 = test_mat[OUTPUT][2].ptr(); double terr1 = cvtest::norm(Rt0.col(3) / norm(Rt0.col(3)) + test_mat[TEMP][3], NORM_L2); double terr2 = cvtest::norm(Rt0.col(3) / norm(Rt0.col(3)) - test_mat[TEMP][3], NORM_L2); 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 >& sizes, vector >& 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 >& sizes, vector >& 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 >& sizes, vector >& 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 >& sizes, vector >& 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() + i*3; double* l = lines.ptr() + 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. */