/*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 "_cv.h" /* Evaluation of Fundamental Matrix from point correspondences. The original code has been written by Valery Mosyagin */ /* The algorithms (except for RANSAC) and the notation have been taken from Zhengyou Zhang's research report "Determining the Epipolar Geometry and its Uncertainty: A Review" that can be found at http://www-sop.inria.fr/robotvis/personnel/zzhang/zzhang-eng.html */ /************************************** 7-point algorithm *******************************/ static int icvFMatrix_7Point( const CvPoint2D64f* m0, const CvPoint2D64f* m1, double* fmatrix ) { double a[7*9], w[7], v[9*9], c[4], r[3]; double* f1, *f2; double t0, t1, t2; CvMat A = cvMat( 7, 9, CV_64F, a ); CvMat V = cvMat( 9, 9, CV_64F, v ); CvMat W = cvMat( 7, 1, CV_64F, w ); CvMat coeffs = cvMat( 1, 4, CV_64F, c ); CvMat roots = cvMat( 1, 3, CV_64F, r ); int i, k, n; assert( m0 && m1 && fmatrix ); // form a linear system: i-th row of A(=a) represents // the equation: (m1[i], 1)'*F*(m0[i], 1) = 0 for( i = 0; i < 7; i++ ) { double x0 = m0[i].x, y0 = m0[i].y; double x1 = m1[i].x, y1 = m1[i].y; a[i*9+0] = x1*x0; a[i*9+1] = x1*y0; a[i*9+2] = x1; a[i*9+3] = y1*x0; a[i*9+4] = y1*y0; a[i*9+5] = y1; a[i*9+6] = x0; a[i*9+7] = y0; a[i*9+8] = 1; } // A*(f11 f12 ... f33)' = 0 is singular (7 equations for 9 variables), so // the solution is linear subspace of dimensionality 2. // => use the last two singular vectors as a basis of the space // (according to SVD properties) cvSVD( &A, &W, 0, &V, CV_SVD_MODIFY_A + CV_SVD_V_T ); f1 = v + 7*9; f2 = v + 8*9; // f1, f2 is a basis => lambda*f1 + mu*f2 is an arbitrary f. matrix. // as it is determined up to a scale, normalize lambda & mu (lambda + mu = 1), // so f ~ lambda*f1 + (1 - lambda)*f2. // use the additional constraint det(f) = det(lambda*f1 + (1-lambda)*f2) to find lambda. // it will be a cubic equation. // find c - polynomial coefficients. for( i = 0; i < 9; i++ ) f1[i] -= f2[i]; t0 = f2[4]*f2[8] - f2[5]*f2[7]; t1 = f2[3]*f2[8] - f2[5]*f2[6]; t2 = f2[3]*f2[7] - f2[4]*f2[6]; c[3] = f2[0]*t0 - f2[1]*t1 + f2[2]*t2; c[2] = f1[0]*t0 - f1[1]*t1 + f1[2]*t2 - f1[3]*(f2[1]*f2[8] - f2[2]*f2[7]) + f1[4]*(f2[0]*f2[8] - f2[2]*f2[6]) - f1[5]*(f2[0]*f2[7] - f2[1]*f2[6]) + f1[6]*(f2[1]*f2[5] - f2[2]*f2[4]) - f1[7]*(f2[0]*f2[5] - f2[2]*f2[3]) + f1[8]*(f2[0]*f2[4] - f2[1]*f2[3]); t0 = f1[4]*f1[8] - f1[5]*f1[7]; t1 = f1[3]*f1[8] - f1[5]*f1[6]; t2 = f1[3]*f1[7] - f1[4]*f1[6]; c[1] = f2[0]*t0 - f2[1]*t1 + f2[2]*t2 - f2[3]*(f1[1]*f1[8] - f1[2]*f1[7]) + f2[4]*(f1[0]*f1[8] - f1[2]*f1[6]) - f2[5]*(f1[0]*f1[7] - f1[1]*f1[6]) + f2[6]*(f1[1]*f1[5] - f1[2]*f1[4]) - f2[7]*(f1[0]*f1[5] - f1[2]*f1[3]) + f2[8]*(f1[0]*f1[4] - f1[1]*f1[3]); c[0] = f1[0]*t0 - f1[1]*t1 + f1[2]*t2; // solve the cubic equation; there can be 1 to 3 roots ... n = cvSolveCubic( &coeffs, &roots ); if( n < 1 || n > 3 ) return n; for( k = 0; k < n; k++, fmatrix += 9 ) { // for each root form the fundamental matrix double lambda = r[k], mu = 1.; double s = f1[8]*r[k] + f2[8]; // normalize each matrix, so that F(3,3) (~fmatrix[8]) == 1 if( fabs(s) > DBL_EPSILON ) { mu = 1./s; lambda *= mu; fmatrix[8] = 1.; } else fmatrix[8] = 0.; for( i = 0; i < 8; i++ ) fmatrix[i] = f1[i]*lambda + f2[i]*mu; } return n; } /*************************************** 8-point algorithm ******************************/ static int icvFMatrix_8Point( const CvPoint2D64f* m0, const CvPoint2D64f* m1, const uchar* mask, int count, double* fmatrix ) { int result = 0; CvMat* A = 0; double w[9], v[9*9]; CvMat W = cvMat( 1, 9, CV_64F, w); CvMat V = cvMat( 9, 9, CV_64F, v); CvMat U, F0, TF; int i, good_count = 0; CvPoint2D64f m0c = {0,0}, m1c = {0,0}; double t, scale0 = 0, scale1 = 0; double* a; int a_step; CV_FUNCNAME( "icvFMatrix_8Point" ); __BEGIN__; assert( m0 && m1 && fmatrix ); // compute centers and average distances for each of the two point sets for( i = 0; i < count; i++ ) if( !mask || mask[i] ) { double x = m0[i].x, y = m0[i].y; m0c.x += x; m0c.y += y; x = m1[i].x, y = m1[i].y; m1c.x += x; m1c.y += y; good_count++; } if( good_count < 8 ) EXIT; // calculate the normalizing transformations for each of the point sets: // after the transformation each set will have the mass center at the coordinate origin // and the average distance from the origin will be ~sqrt(2). t = 1./good_count; m0c.x *= t; m0c.y *= t; m1c.x *= t; m1c.y *= t; for( i = 0; i < count; i++ ) if( !mask || mask[i] ) { double x = m0[i].x - m0c.x, y = m0[i].y - m0c.y; scale0 += sqrt(x*x + y*y); x = fabs(m1[i].x - m1c.x), y = fabs(m1[i].y - m1c.y); scale1 += sqrt(x*x + y*y); } scale0 *= t; scale1 *= t; if( scale0 < FLT_EPSILON || scale1 < FLT_EPSILON ) EXIT; scale0 = sqrt(2.)/scale0; scale1 = sqrt(2.)/scale1; CV_CALL( A = cvCreateMat( good_count, 9, CV_64F )); a = A->data.db; a_step = A->step / sizeof(a[0]); // form a linear system: for each selected pair of points m0 & m1, // the row of A(=a) represents the equation: (m1, 1)'*F*(m0, 1) = 0 for( i = 0; i < count; i++ ) { if( !mask || mask[i] ) { double x0 = (m0[i].x - m0c.x)*scale0; double y0 = (m0[i].y - m0c.y)*scale0; double x1 = (m1[i].x - m1c.x)*scale1; double y1 = (m1[i].y - m1c.y)*scale1; a[0] = x1*x0; a[1] = x1*y0; a[2] = x1; a[3] = y1*x0; a[4] = y1*y0; a[5] = y1; a[6] = x0; a[7] = y0; a[8] = 1; a += a_step; } } cvSVD( A, &W, 0, &V, CV_SVD_MODIFY_A + CV_SVD_V_T ); for( i = 0; i < 8; i++ ) { if( fabs(w[i]) < FLT_EPSILON ) break; } if( i < 7 ) EXIT; F0 = cvMat( 3, 3, CV_64F, v + 9*8 ); // take the last column of v as a solution of Af = 0 // make F0 singular (of rank 2) by decomposing it with SVD, // zeroing the last diagonal element of W and then composing the matrices back. // use v as a temporary storage for different 3x3 matrices W = U = V = TF = F0; W.data.db = v; U.data.db = v + 9; V.data.db = v + 18; TF.data.db = v + 27; cvSVD( &F0, &W, &U, &V, CV_SVD_MODIFY_A + CV_SVD_U_T + CV_SVD_V_T ); W.data.db[8] = 0.; // F0 <- U*diag([W(1), W(2), 0])*V' cvGEMM( &U, &W, 1., 0, 0., &TF, CV_GEMM_A_T ); cvGEMM( &TF, &V, 1., 0, 0., &F0, 0/*CV_GEMM_B_T*/ ); // apply the transformation that is inverse // to what we used to normalize the point coordinates { double tt0[] = { scale0, 0, -scale0*m0c.x, 0, scale0, -scale0*m0c.y, 0, 0, 1 }; double tt1[] = { scale1, 0, -scale1*m1c.x, 0, scale1, -scale1*m1c.y, 0, 0, 1 }; CvMat T0, T1; T0 = T1 = F0; T0.data.db = tt0; T1.data.db = tt1; // F0 <- T1'*F0*T0 cvGEMM( &T1, &F0, 1., 0, 0., &TF, CV_GEMM_A_T ); F0.data.db = fmatrix; cvGEMM( &TF, &T0, 1., 0, 0., &F0, 0 ); // make F(3,3) = 1 if( fabs(F0.data.db[8]) > FLT_EPSILON ) cvScale( &F0, &F0, 1./F0.data.db[8] ); } result = 1; __END__; cvReleaseMat( &A ); return result; } /************************************ RANSAC algorithm **********************************/ static int icvFMatrix_RANSAC( const CvPoint2D64f* m0, const CvPoint2D64f* m1, uchar* mask, int count, double* fmatrix, double threshold, double p, unsigned rng_seed, int use_8point ) { int result = 0; const int max_random_iters = 1000; const int sample_size = 7; uchar* curr_mask = 0; uchar* temp_mask = 0; CV_FUNCNAME( "icvFMatrix_RANSAC" ); __BEGIN__; double ff[9*3]; CvRNG rng = cvRNG(rng_seed); int i, j, k, sample_count, max_samples = 500; int best_good_count = 0; assert( m0 && m1 && fmatrix && 0 < p && p < 1 && threshold > 0 ); threshold *= threshold; CV_CALL( curr_mask = (uchar*)cvAlloc( count )); if( !mask && use_8point ) { CV_CALL( temp_mask = (uchar*)cvAlloc( count )); mask = temp_mask; } // find the best fundamental matrix (giving the least backprojection error) // by picking at most 7-tuples of corresponding points // may be updated (decreased) within the loop based on statistics of outliers for( sample_count = 0; sample_count < max_samples; sample_count++ ) { int idx[sample_size], n; CvPoint2D64f ms0[sample_size], ms1[sample_size]; // choose random (=7) points for( i = 0; i < sample_size; i++ ) { for( k = 0; k < max_random_iters; k++ ) { idx[i] = cvRandInt(&rng) % count; for( j = 0; j < i; j++ ) if( idx[j] == idx[i] ) break; if( j == i ) { ms0[i] = m0[idx[i]]; ms1[i] = m1[idx[i]]; break; } } if( k >= max_random_iters ) break; } if( i < sample_size ) continue; // find 1 or 3 fundamental matrices out of the 7 point correspondences n = icvFMatrix_7Point( ms0, ms1, ff ); if( n < 1 || n > 3 ) continue; // for each matrix calculate the backprojection error // (distance to the corresponding epipolar lines) for each point and thus find // the number of in-liers. for( k = 0; k < n; k++ ) { const double* f = ff + k*9; int good_count = 0; for( i = 0; i < count; i++ ) { double d0, d1, s0, s1; double a = f[0]*m0[i].x + f[1]*m0[i].y + f[2]; double b = f[3]*m0[i].x + f[4]*m0[i].y + f[5]; double c = f[6]*m0[i].x + f[7]*m0[i].y + f[8]; s1 = a*a + b*b; d1 = m1[i].x*a + m1[i].y*b + c; a = f[0]*m1[i].x + f[3]*m1[i].y + f[6]; b = f[1]*m1[i].x + f[4]*m1[i].y + f[7]; c = f[2]*m1[i].x + f[5]*m1[i].y + f[8]; s0 = a*a + b*b; d0 = m0[i].x*a + m0[i].y*b + c; curr_mask[i] = d1*d1 < threshold*s1 && d0*d0 < threshold*s0; good_count += curr_mask[i]; } if( good_count > MAX( best_good_count, 6 ) ) { double ep, lp, lep; int new_max_samples; // update the current best fundamental matrix and "goodness" flags if( mask ) memcpy( mask, curr_mask, count ); memcpy( fmatrix, f, 9*sizeof(f[0])); best_good_count = good_count; // try to update (decrease) ep = (double)(count - good_count)/count; lp = log(1. - p); lep = log(1. - pow(ep,7.)); if( lp < lep || lep >= 0 ) break; else { new_max_samples = cvRound(lp/lep); max_samples = MIN( new_max_samples, max_samples ); } } } } if( best_good_count < 7 ) EXIT; result = 1; // optionally, use 8-point algorithm to compute fundamental matrix using only the in-liers if( best_good_count >= 8 && use_8point ) result = icvFMatrix_8Point( m0, m1, mask, count, fmatrix ); __END__; cvFree( &temp_mask ); cvFree( &curr_mask ); return result; } /***************************** Least Median of Squares algorithm ************************/ static CV_IMPLEMENT_QSORT( icvSortDistances, int, CV_LT ) /* the algorithm is quite similar to RANSAC, but here we choose the matrix that gives the least median of d(m0[i], F'*m1[i])^2 + d(m1[i], F*m0[i])^2 (0<=i 0 ); threshold *= threshold; CV_CALL( curr_mask = (uchar*)cvAlloc( count )); CV_CALL( dist = (float*)cvAlloc( count*sizeof(dist[0]) )); if( !mask && use_8point ) { CV_CALL( temp_mask = (uchar*)cvAlloc( count )); mask = temp_mask; } // find the best fundamental matrix (giving the least backprojection error) // by picking at most 7-tuples of corresponding points // may be updated (decreased) within the loop based on statistics of outliers for( sample_count = 0; sample_count < max_samples; sample_count++ ) { int idx[sample_size], n; CvPoint2D64f ms0[sample_size], ms1[sample_size]; // choose random (=7) points for( i = 0; i < sample_size; i++ ) { for( k = 0; k < max_random_iters; k++ ) { idx[i] = cvRandInt(&rng) % count; for( j = 0; j < i; j++ ) if( idx[j] == idx[i] ) break; if( j == i ) { ms0[i] = m0[idx[i]]; ms1[i] = m1[idx[i]]; break; } } if( k >= max_random_iters ) break; } if( i < sample_size ) continue; // find 1 or 3 fundamental matrix out of the 7 point correspondences n = icvFMatrix_7Point( ms0, ms1, ff ); if( n < 1 || n > 3 ) continue; // for each matrix calculate the backprojection error // (distance to the corresponding epipolar lines) for each point and thus find // the number of in-liers. for( k = 0; k < n; k++ ) { const double* f = ff + k*9; int good_count = 0; for( i = 0; i < count; i++ ) { double d0, d1, s; double a = f[0]*m0[i].x + f[1]*m0[i].y + f[2]; double b = f[3]*m0[i].x + f[4]*m0[i].y + f[5]; double c = f[6]*m0[i].x + f[7]*m0[i].y + f[8]; s = 1./(a*a + b*b); d1 = m1[i].x*a + m1[i].y*b + c; d1 = s*d1*d1; a = f[0]*m1[i].x + f[3]*m1[i].y + f[6]; b = f[1]*m1[i].x + f[4]*m1[i].y + f[7]; c = f[2]*m1[i].x + f[5]*m1[i].y + f[8]; s = 1./(a*a + b*b); d0 = m0[i].x*a + m0[i].y*b + c; d0 = s*d0*d0; curr_mask[i] = d1 < threshold && d0 < threshold; good_count += curr_mask[i]; dist[i] = (float)(d0 + d1); } icvSortDistances( (int*)dist, count, 0 ); median = (double)dist[count/2]; if( median < least_median ) { double ep, lp, lep; int new_max_samples; // update the current best fundamental matrix and "goodness" flags if( mask ) memcpy( mask, curr_mask, count ); memcpy( fmatrix, f, 9*sizeof(f[0])); least_median = median; best_good_count = good_count; // try to update (decrease) ep = (double)(count - good_count)/count; lp = log(1. - p); lep = log(1. - pow(ep,7.)); if( lp < lep || lep >= 0 ) break; else { new_max_samples = cvRound(lp/lep); max_samples = MIN( new_max_samples, max_samples ); } } } } if( best_good_count < 7 ) EXIT; result = 1; // optionally, use 8-point algorithm to compute fundamental matrix using only the in-liers if( best_good_count >= 8 && use_8point ) result = icvFMatrix_8Point( m0, m1, mask, count, fmatrix ); __END__; cvFree( &temp_mask ); cvFree( &curr_mask ); cvFree( &dist ); return result; } CV_IMPL int cvFindFundamentalMat( const CvMat* points0, const CvMat* points1, CvMat* fmatrix, int method, double param1, double param2, CvMat* status ) { const unsigned rng_seed = 0xffffffff; int result = 0; int pt_alloc_flag[2] = { 0, 0 }; int i, k; CvPoint2D64f* pt[2] = { 0, 0 }; CvMat* _status = 0; CV_FUNCNAME( "cvFindFundamentalMat" ); __BEGIN__; int count, dims; int depth, cn; uchar* status_data = 0; double fmatrix_data0[9*3]; double* fmatrix_data = 0; if( !CV_IS_MAT(points0) ) CV_ERROR( !points0 ? CV_StsNullPtr : CV_StsBadArg, "points0 is not a valid matrix" ); if( !CV_IS_MAT(points1) ) CV_ERROR( !points1 ? CV_StsNullPtr : CV_StsBadArg, "points1 is not a valid matrix" ); if( !CV_ARE_TYPES_EQ(points0, points1) ) CV_ERROR( CV_StsUnmatchedFormats, "The matrices of points should have the same data type" ); if( !CV_ARE_SIZES_EQ(points0, points1) ) CV_ERROR( CV_StsUnmatchedSizes, "The matrices of points should have the same size" ); depth = CV_MAT_DEPTH(points0->type); cn = CV_MAT_CN(points0->type); if( depth != CV_32S && depth != CV_32F && depth != CV_64F || cn != 1 && cn != 2 && cn != 3 ) CV_ERROR( CV_StsUnsupportedFormat, "The format of point matrices is unsupported" ); if( points0->rows > points0->cols ) { dims = cn*points0->cols; count = points0->rows; } else { if( points0->rows > 1 && cn > 1 || points0->rows == 1 && cn == 1 ) CV_ERROR( CV_StsBadSize, "The point matrices do not have a proper layout (2xn, 3xn, nx2 or nx3)" ); dims = cn * points0->rows; count = points0->cols; } if( dims != 2 && dims != 3 ) CV_ERROR( CV_StsOutOfRange, "The dimensionality of points must be 2 or 3" ); if( method == CV_FM_7POINT && count != 7 || method != CV_FM_7POINT && count < 7 + (method == CV_FM_8POINT) ) CV_ERROR( CV_StsOutOfRange, "The number of points must be 7 for 7-point algorithm, " ">=8 for 8-point algorithm and >=7 for other algorithms" ); if( !CV_IS_MAT(fmatrix) ) CV_ERROR( !fmatrix ? CV_StsNullPtr : CV_StsBadArg, "fmatrix is not a valid matrix" ); if( CV_MAT_TYPE(fmatrix->type) != CV_32FC1 && CV_MAT_TYPE(fmatrix->type) != CV_64FC1 ) CV_ERROR( CV_StsUnsupportedFormat, "fundamental matrix must have 32fC1 or 64fC1 type" ); if( fmatrix->cols != 3 || (fmatrix->rows != 3 && (method != CV_FM_7POINT || fmatrix->rows != 9))) CV_ERROR( CV_StsBadSize, "fundamental matrix must be 3x3 or 3x9 (for 7-point method only)" ); fmatrix_data = fmatrix->data.db; if( !CV_IS_MAT_CONT(fmatrix->type) || CV_MAT_TYPE(fmatrix->type) != CV_64FC1 || method == CV_FM_7POINT && fmatrix->rows != 9 ) fmatrix_data = fmatrix_data0; if( status ) { if( !CV_IS_MAT(status) ) CV_ERROR( CV_StsBadArg, "The output status is not a valid matrix" ); if( status->cols != 1 && status->rows != 1 || status->cols + status->rows - 1 != count ) CV_ERROR( CV_StsUnmatchedSizes, "The status matrix must have the same size as the point matrices" ); if( method == CV_FM_7POINT || method == CV_FM_8POINT ) cvSet( status, cvScalarAll(1.) ); else { status_data = status->data.ptr; if( !CV_IS_MAT_CONT(status->type) || !CV_IS_MASK_ARR(status) ) { CV_CALL( _status = cvCreateMat( status->rows, status->cols, CV_8UC1 )); status_data = _status->data.ptr; } } } for( k = 0; k < 2; k++ ) { const CvMat* spt = k == 0 ? points0 : points1; CvPoint2D64f* dpt = pt[k] = (CvPoint2D64f*)spt->data.db; int plane_stride, stride, elem_size; if( CV_IS_MAT_CONT(spt->type) && CV_MAT_DEPTH(spt->type) == CV_64F && dims == 2 && (spt->rows == 1 || spt->rows == count) ) continue; elem_size = CV_ELEM_SIZE(depth); if( spt->rows == dims ) { plane_stride = spt->step / elem_size; stride = 1; } else { plane_stride = 1; stride = spt->rows == 1 ? dims : spt->step / elem_size; } CV_CALL( dpt = pt[k] = (CvPoint2D64f*)cvAlloc( count*sizeof(dpt[0]) )); pt_alloc_flag[k] = 1; if( depth == CV_32F ) { const float* xp = spt->data.fl; const float* yp = xp + plane_stride; const float* zp = dims == 3 ? yp + plane_stride : 0; for( i = 0; i < count; i++ ) { double x = *xp, y = *yp; xp += stride; yp += stride; if( dims == 3 ) { double z = *zp; zp += stride; z = z ? 1./z : 1.; x *= z; y *= z; } dpt[i].x = x; dpt[i].y = y; } } else { const double* xp = spt->data.db; const double* yp = xp + plane_stride; const double* zp = dims == 3 ? yp + plane_stride : 0; for( i = 0; i < count; i++ ) { double x = *xp, y = *yp; xp += stride; yp += stride; if( dims == 3 ) { double z = *zp; zp += stride; z = z ? 1./z : 1.; x *= z; y *= z; } dpt[i].x = x; dpt[i].y = y; } } } if( method == CV_FM_7POINT ) result = icvFMatrix_7Point( pt[0], pt[1], fmatrix_data ); else if( method == CV_FM_8POINT ) result = icvFMatrix_8Point( pt[0], pt[1], 0, count, fmatrix_data ); else { if( param1 < 0 ) CV_ERROR( CV_StsOutOfRange, "param1 (threshold) must be > 0" ); if( param2 < 0 || param2 > 1 ) CV_ERROR( CV_StsOutOfRange, "param2 (confidence level) must be between 0 and 1" ); if( param2 < DBL_EPSILON || param2 > 1 - DBL_EPSILON ) param2 = 0.99; if( method < CV_FM_RANSAC_ONLY ) result = icvFMatrix_LMedS( pt[0], pt[1], status_data, count, fmatrix_data, param1, param2, rng_seed, method & CV_FM_8POINT ); else result = icvFMatrix_RANSAC( pt[0], pt[1], status_data, count, fmatrix_data, param1, param2, rng_seed, method & CV_FM_8POINT ); } if( result && fmatrix->data.db != fmatrix_data ) { CvMat hdr; cvZero( fmatrix ); hdr = cvMat( MIN(fmatrix->rows, result*3), fmatrix->cols, CV_64F, fmatrix_data ); cvConvert( &hdr, fmatrix ); } if( status && status_data && status->data.ptr != status_data ) cvConvert( _status, status ); __END__; cvReleaseMat( &_status ); for( k = 0; k < 2; k++ ) if( pt_alloc_flag[k] ) cvFree( &pt[k] ); return result; } CV_IMPL void cvComputeCorrespondEpilines( const CvMat* points, int pointImageID, const CvMat* fmatrix, CvMat* lines ) { CV_FUNCNAME( "cvComputeCorrespondEpilines" ); __BEGIN__; int abc_stride, abc_plane_stride, abc_elem_size; int plane_stride, stride, elem_size; int i, dims, count, depth, cn, abc_dims, abc_count, abc_depth, abc_cn; uchar *ap, *bp, *cp; const uchar *xp, *yp, *zp; double f[9]; CvMat F = cvMat( 3, 3, CV_64F, f ); if( !CV_IS_MAT(points) ) CV_ERROR( !points ? CV_StsNullPtr : CV_StsBadArg, "points parameter is not a valid matrix" ); depth = CV_MAT_DEPTH(points->type); cn = CV_MAT_CN(points->type); if( depth != CV_32F && depth != CV_64F || cn != 1 && cn != 2 && cn != 3 ) CV_ERROR( CV_StsUnsupportedFormat, "The format of point matrix is unsupported" ); if( points->rows > points->cols ) { dims = cn*points->cols; count = points->rows; } else { if( points->rows > 1 && cn > 1 || points->rows == 1 && cn == 1 ) CV_ERROR( CV_StsBadSize, "The point matrix does not have a proper layout (2xn, 3xn, nx2 or nx3)" ); dims = cn * points->rows; count = points->cols; } if( dims != 2 && dims != 3 ) CV_ERROR( CV_StsOutOfRange, "The dimensionality of points must be 2 or 3" ); if( !CV_IS_MAT(fmatrix) ) CV_ERROR( !fmatrix ? CV_StsNullPtr : CV_StsBadArg, "fmatrix is not a valid matrix" ); if( CV_MAT_TYPE(fmatrix->type) != CV_32FC1 && CV_MAT_TYPE(fmatrix->type) != CV_64FC1 ) CV_ERROR( CV_StsUnsupportedFormat, "fundamental matrix must have 32fC1 or 64fC1 type" ); if( fmatrix->cols != 3 || fmatrix->rows != 3 ) CV_ERROR( CV_StsBadSize, "fundamental matrix must be 3x3" ); if( !CV_IS_MAT(lines) ) CV_ERROR( !lines ? CV_StsNullPtr : CV_StsBadArg, "lines parameter is not a valid matrix" ); abc_depth = CV_MAT_DEPTH(lines->type); abc_cn = CV_MAT_CN(lines->type); if( abc_depth != CV_32F && abc_depth != CV_64F || abc_cn != 1 && abc_cn != 3 ) CV_ERROR( CV_StsUnsupportedFormat, "The format of the matrix of lines is unsupported" ); if( lines->rows > lines->cols ) { abc_dims = abc_cn*lines->cols; abc_count = lines->rows; } else { if( lines->rows > 1 && abc_cn > 1 || lines->rows == 1 && abc_cn == 1 ) CV_ERROR( CV_StsBadSize, "The lines matrix does not have a proper layout (3xn or nx3)" ); abc_dims = abc_cn * lines->rows; abc_count = lines->cols; } if( abc_dims != 3 ) CV_ERROR( CV_StsOutOfRange, "The lines matrix does not have a proper layout (3xn or nx3)" ); if( abc_count != count ) CV_ERROR( CV_StsUnmatchedSizes, "The numbers of points and lines are different" ); elem_size = CV_ELEM_SIZE(depth); abc_elem_size = CV_ELEM_SIZE(abc_depth); if( points->rows == dims ) { plane_stride = points->step; stride = elem_size; } else { plane_stride = elem_size; stride = points->rows == 1 ? dims*elem_size : points->step; } if( lines->rows == 3 ) { abc_plane_stride = lines->step; abc_stride = abc_elem_size; } else { abc_plane_stride = abc_elem_size; abc_stride = lines->rows == 1 ? 3*abc_elem_size : lines->step; } CV_CALL( cvConvert( fmatrix, &F )); if( pointImageID == 2 ) cvTranspose( &F, &F ); xp = points->data.ptr; yp = xp + plane_stride; zp = dims == 3 ? yp + plane_stride : 0; ap = lines->data.ptr; bp = ap + abc_plane_stride; cp = bp + abc_plane_stride; for( i = 0; i < count; i++ ) { double x, y, z = 1.; double a, b, c, nu; if( depth == CV_32F ) { x = *(float*)xp; y = *(float*)yp; if( zp ) z = *(float*)zp, zp += stride; } else { x = *(double*)xp; y = *(double*)yp; if( zp ) z = *(double*)zp, zp += stride; } xp += stride; yp += stride; a = f[0]*x + f[1]*y + f[2]*z; b = f[3]*x + f[4]*y + f[5]*z; c = f[6]*x + f[7]*y + f[8]*z; nu = a*a + b*b; nu = nu ? 1./sqrt(nu) : 1.; a *= nu; b *= nu; c *= nu; if( abc_depth == CV_32F ) { *(float*)ap = (float)a; *(float*)bp = (float)b; *(float*)cp = (float)c; } else { *(double*)ap = a; *(double*)bp = b; *(double*)cp = c; } ap += abc_stride; bp += abc_stride; cp += abc_stride; } __END__; } CV_IMPL void cvConvertPointsHomogenious( const CvMat* src, CvMat* dst ) { CvMat* temp = 0; CvMat* denom = 0; CV_FUNCNAME( "cvConvertPointsHomogenious" ); __BEGIN__; int i, s_count, s_dims, d_count, d_dims; CvMat _src, _dst, _ones; CvMat* ones = 0; if( !CV_IS_MAT(src) ) CV_ERROR( !src ? CV_StsNullPtr : CV_StsBadArg, "The input parameter is not a valid matrix" ); if( !CV_IS_MAT(dst) ) CV_ERROR( !dst ? CV_StsNullPtr : CV_StsBadArg, "The output parameter is not a valid matrix" ); if( src == dst || src->data.ptr == dst->data.ptr ) { if( src != dst && (!CV_ARE_TYPES_EQ(src, dst) || !CV_ARE_SIZES_EQ(src,dst)) ) CV_ERROR( CV_StsBadArg, "Invalid inplace operation" ); EXIT; } if( src->rows > src->cols ) { if( !((src->cols > 1) ^ (CV_MAT_CN(src->type) > 1)) ) CV_ERROR( CV_StsBadSize, "Either the number of channels or columns or rows must be =1" ); s_dims = CV_MAT_CN(src->type)*src->cols; s_count = src->rows; } else { if( !((src->rows > 1) ^ (CV_MAT_CN(src->type) > 1)) ) CV_ERROR( CV_StsBadSize, "Either the number of channels or columns or rows must be =1" ); s_dims = CV_MAT_CN(src->type)*src->rows; s_count = src->cols; } if( src->rows == 1 || src->cols == 1 ) src = cvReshape( src, &_src, 1, s_count ); if( dst->rows > dst->cols ) { if( !((dst->cols > 1) ^ (CV_MAT_CN(dst->type) > 1)) ) CV_ERROR( CV_StsBadSize, "Either the number of channels or columns or rows in the input matrix must be =1" ); d_dims = CV_MAT_CN(dst->type)*dst->cols; d_count = dst->rows; } else { if( !((dst->rows > 1) ^ (CV_MAT_CN(dst->type) > 1)) ) CV_ERROR( CV_StsBadSize, "Either the number of channels or columns or rows in the output matrix must be =1" ); d_dims = CV_MAT_CN(dst->type)*dst->rows; d_count = dst->cols; } if( dst->rows == 1 || dst->cols == 1 ) dst = cvReshape( dst, &_dst, 1, d_count ); if( s_count != d_count ) CV_ERROR( CV_StsUnmatchedSizes, "Both matrices must have the same number of points" ); if( CV_MAT_DEPTH(src->type) < CV_32F || CV_MAT_DEPTH(dst->type) < CV_32F ) CV_ERROR( CV_StsUnsupportedFormat, "Both matrices must be floating-point (single or double precision)" ); if( s_dims < 2 || s_dims > 4 || d_dims < 2 || d_dims > 4 ) CV_ERROR( CV_StsOutOfRange, "Both input and output point dimensionality must be 2, 3 or 4" ); if( s_dims < d_dims - 1 || s_dims > d_dims + 1 ) CV_ERROR( CV_StsUnmatchedSizes, "The dimensionalities of input and output point sets differ too much" ); if( s_dims == d_dims - 1 ) { if( d_count == dst->rows ) { ones = cvGetSubRect( dst, &_ones, cvRect( s_dims, 0, 1, d_count )); dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, s_dims, d_count )); } else { ones = cvGetSubRect( dst, &_ones, cvRect( 0, s_dims, d_count, 1 )); dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, d_count, s_dims )); } } if( s_dims <= d_dims ) { if( src->rows == dst->rows && src->cols == dst->cols ) { if( CV_ARE_TYPES_EQ( src, dst ) ) cvCopy( src, dst ); else cvConvert( src, dst ); } else { if( !CV_ARE_TYPES_EQ( src, dst )) { CV_CALL( temp = cvCreateMat( src->rows, src->cols, dst->type )); cvConvert( src, temp ); src = temp; } cvTranspose( src, dst ); } if( ones ) cvSet( ones, cvRealScalar(1.) ); } else { int s_plane_stride, s_stride, d_plane_stride, d_stride, elem_size; if( !CV_ARE_TYPES_EQ( src, dst )) { CV_CALL( temp = cvCreateMat( src->rows, src->cols, dst->type )); cvConvert( src, temp ); src = temp; } elem_size = CV_ELEM_SIZE(src->type); if( s_count == src->cols ) s_plane_stride = src->step / elem_size, s_stride = 1; else s_stride = src->step / elem_size, s_plane_stride = 1; if( d_count == dst->cols ) d_plane_stride = dst->step / elem_size, d_stride = 1; else d_stride = dst->step / elem_size, d_plane_stride = 1; CV_CALL( denom = cvCreateMat( 1, d_count, dst->type )); if( CV_MAT_DEPTH(dst->type) == CV_32F ) { const float* xs = src->data.fl; const float* ys = xs + s_plane_stride; const float* zs = 0; const float* ws = xs + (s_dims - 1)*s_plane_stride; float* iw = denom->data.fl; float* xd = dst->data.fl; float* yd = xd + d_plane_stride; float* zd = 0; if( d_dims == 3 ) { zs = ys + s_plane_stride; zd = yd + d_plane_stride; } for( i = 0; i < d_count; i++, ws += s_stride ) { float t = *ws; iw[i] = t ? t : 1.f; } cvDiv( 0, denom, denom ); if( d_dims == 3 ) for( i = 0; i < d_count; i++ ) { float w = iw[i]; float x = *xs * w, y = *ys * w, z = *zs * w; xs += s_stride; ys += s_stride; zs += s_stride; *xd = x; *yd = y; *zd = z; xd += d_stride; yd += d_stride; zd += d_stride; } else for( i = 0; i < d_count; i++ ) { float w = iw[i]; float x = *xs * w, y = *ys * w; xs += s_stride; ys += s_stride; *xd = x; *yd = y; xd += d_stride; yd += d_stride; } } else { const double* xs = src->data.db; const double* ys = xs + s_plane_stride; const double* zs = 0; const double* ws = xs + (s_dims - 1)*s_plane_stride; double* iw = denom->data.db; double* xd = dst->data.db; double* yd = xd + d_plane_stride; double* zd = 0; if( d_dims == 3 ) { zs = ys + s_plane_stride; zd = yd + d_plane_stride; } for( i = 0; i < d_count; i++, ws += s_stride ) { double t = *ws; iw[i] = t ? t : 1.; } cvDiv( 0, denom, denom ); if( d_dims == 3 ) for( i = 0; i < d_count; i++ ) { double w = iw[i]; double x = *xs * w, y = *ys * w, z = *zs * w; xs += s_stride; ys += s_stride; zs += s_stride; *xd = x; *yd = y; *zd = z; xd += d_stride; yd += d_stride; zd += d_stride; } else for( i = 0; i < d_count; i++ ) { double w = iw[i]; double x = *xs * w, y = *ys * w; xs += s_stride; ys += s_stride; *xd = x; *yd = y; xd += d_stride; yd += d_stride; } } } __END__; cvReleaseMat( &denom ); cvReleaseMat( &temp ); } /* End of file. */