| /*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>(); |
| 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 = 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<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; |
| 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<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() |
| { |
| // cvFindFundamentalMat calls cv::findFundamentalMat |
| 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 ); |
| } |
| |
| |
| 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].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* f_prop1 = test_mat[REF_OUTPUT][0].ptr<double>(); |
| double* f_prop2 = test_mat[OUTPUT][0].ptr<double>(); |
| |
| 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; |
| 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<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].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>(); |
| double* e_prop2 = test_mat[OUTPUT][0].ptr<double>(); |
| 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 = test_mat[REF_OUTPUT][2].ptr<double>(); |
| double* pose_prop2 = test_mat[OUTPUT][2].ptr<double>(); |
| 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<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(); } |
| |
| TEST(Calib3d_FindFundamentalMat, correctMatches) |
| { |
| double fdata[] = {0, 0, 0, 0, 0, -1, 0, 1, 0}; |
| double p1data[] = {200, 0, 1}; |
| double p2data[] = {170, 0, 1}; |
| |
| Mat F(3, 3, CV_64F, fdata); |
| Mat p1(1, 1, CV_64FC2, p1data); |
| Mat p2(1, 1, CV_64FC2, p2data); |
| Mat np1, np2; |
| |
| correctMatches(F, p1, p2, np1, np2); |
| |
| cout << np1 << endl; |
| cout << np2 << endl; |
| } |
| |
| /* End of file. */ |
