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43 #include "_cvmodelest.h"
47 template<typename T> int icvCompressPoints( T* ptr, const uchar* mask, int mstep, int count )
50 for( i = j = 0; i < count; i++ )
60 class CvHomographyEstimator : public CvModelEstimator2
63 CvHomographyEstimator( int modelPoints );
65 virtual int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model );
66 virtual bool refine( const CvMat* m1, const CvMat* m2,
67 CvMat* model, int maxIters );
69 virtual void computeReprojError( const CvMat* m1, const CvMat* m2,
70 const CvMat* model, CvMat* error );
74 CvHomographyEstimator::CvHomographyEstimator(int _modelPoints)
75 : CvModelEstimator2(_modelPoints, cvSize(3,3), 1)
77 assert( _modelPoints == 4 || _modelPoints == 5 );
78 checkPartialSubsets = false;
81 int CvHomographyEstimator::runKernel( const CvMat* m1, const CvMat* m2, CvMat* H )
83 int i, count = m1->rows*m1->cols;
84 const CvPoint2D64f* M = (const CvPoint2D64f*)m1->data.ptr;
85 const CvPoint2D64f* m = (const CvPoint2D64f*)m2->data.ptr;
87 double LtL[9][9], W[9][9], V[9][9];
88 CvMat _LtL = cvMat( 9, 9, CV_64F, LtL );
89 CvMat _W = cvMat( 9, 9, CV_64F, W );
90 CvMat _V = cvMat( 9, 9, CV_64F, V );
91 CvMat _H0 = cvMat( 3, 3, CV_64F, V[8] );
92 CvMat _Htemp = cvMat( 3, 3, CV_64F, V[7] );
93 CvPoint2D64f cM={0,0}, cm={0,0}, sM={0,0}, sm={0,0};
95 for( i = 0; i < count; i++ )
97 cm.x += m[i].x; cm.y += m[i].y;
98 cM.x += M[i].x; cM.y += M[i].y;
101 cm.x /= count; cm.y /= count;
102 cM.x /= count; cM.y /= count;
104 for( i = 0; i < count; i++ )
106 sm.x += fabs(m[i].x - cm.x);
107 sm.y += fabs(m[i].y - cm.y);
108 sM.x += fabs(M[i].x - cM.x);
109 sM.y += fabs(M[i].y - cM.y);
112 if( fabs(sm.x) < DBL_EPSILON || fabs(sm.y) < DBL_EPSILON ||
113 fabs(sM.x) < DBL_EPSILON || fabs(sM.y) < DBL_EPSILON )
115 sm.x = count/sm.x; sm.y = count/sm.y;
116 sM.x = count/sM.x; sM.y = count/sM.y;
118 double invHnorm[9] = { 1./sm.x, 0, cm.x, 0, 1./sm.y, cm.y, 0, 0, 1 };
119 double Hnorm2[9] = { sM.x, 0, -cM.x*sM.x, 0, sM.y, -cM.y*sM.y, 0, 0, 1 };
120 CvMat _invHnorm = cvMat( 3, 3, CV_64FC1, invHnorm );
121 CvMat _Hnorm2 = cvMat( 3, 3, CV_64FC1, Hnorm2 );
124 for( i = 0; i < count; i++ )
126 double x = (m[i].x - cm.x)*sm.x, y = (m[i].y - cm.y)*sm.y;
127 double X = (M[i].x - cM.x)*sM.x, Y = (M[i].y - cM.y)*sM.y;
128 double Lx[] = { X, Y, 1, 0, 0, 0, -x*X, -x*Y, -x };
129 double Ly[] = { 0, 0, 0, X, Y, 1, -y*X, -y*Y, -y };
131 for( j = 0; j < 9; j++ )
132 for( k = j; k < 9; k++ )
133 LtL[j][k] += Lx[j]*Lx[k] + Ly[j]*Ly[k];
135 cvCompleteSymm( &_LtL );
137 //cvSVD( &_LtL, &_W, 0, &_V, CV_SVD_MODIFY_A + CV_SVD_V_T );
138 cvEigenVV( &_LtL, &_V, &_W );
139 cvMatMul( &_invHnorm, &_H0, &_Htemp );
140 cvMatMul( &_Htemp, &_Hnorm2, &_H0 );
141 cvConvertScale( &_H0, H, 1./_H0.data.db[8] );
147 void CvHomographyEstimator::computeReprojError( const CvMat* m1, const CvMat* m2,
148 const CvMat* model, CvMat* _err )
150 int i, count = m1->rows*m1->cols;
151 const CvPoint2D64f* M = (const CvPoint2D64f*)m1->data.ptr;
152 const CvPoint2D64f* m = (const CvPoint2D64f*)m2->data.ptr;
153 const double* H = model->data.db;
154 float* err = _err->data.fl;
156 for( i = 0; i < count; i++ )
158 double ww = 1./(H[6]*M[i].x + H[7]*M[i].y + 1.);
159 double dx = (H[0]*M[i].x + H[1]*M[i].y + H[2])*ww - m[i].x;
160 double dy = (H[3]*M[i].x + H[4]*M[i].y + H[5])*ww - m[i].y;
161 err[i] = (float)(dx*dx + dy*dy);
165 bool CvHomographyEstimator::refine( const CvMat* m1, const CvMat* m2, CvMat* model, int maxIters )
167 CvLevMarq solver(8, 0, cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, maxIters, DBL_EPSILON));
168 int i, j, k, count = m1->rows*m1->cols;
169 const CvPoint2D64f* M = (const CvPoint2D64f*)m1->data.ptr;
170 const CvPoint2D64f* m = (const CvPoint2D64f*)m2->data.ptr;
171 CvMat modelPart = cvMat( solver.param->rows, solver.param->cols, model->type, model->data.ptr );
172 cvCopy( &modelPart, solver.param );
176 const CvMat* _param = 0;
177 CvMat *_JtJ = 0, *_JtErr = 0;
178 double* _errNorm = 0;
180 if( !solver.updateAlt( _param, _JtJ, _JtErr, _errNorm ))
183 for( i = 0; i < count; i++ )
185 const double* h = _param->data.db;
186 double Mx = M[i].x, My = M[i].y;
187 double ww = 1./(h[6]*Mx + h[7]*My + 1.);
188 double _xi = (h[0]*Mx + h[1]*My + h[2])*ww;
189 double _yi = (h[3]*Mx + h[4]*My + h[5])*ww;
190 double err[] = { _xi - m[i].x, _yi - m[i].y };
195 { Mx*ww, My*ww, ww, 0, 0, 0, -Mx*ww*_xi, -My*ww*_xi },
196 { 0, 0, 0, Mx*ww, My*ww, ww, -Mx*ww*_yi, -My*ww*_yi }
199 for( j = 0; j < 8; j++ )
201 for( k = j; k < 8; k++ )
202 _JtJ->data.db[j*8+k] += J[0][j]*J[0][k] + J[1][j]*J[1][k];
203 _JtErr->data.db[j] += J[0][j]*err[0] + J[1][j]*err[1];
207 *_errNorm += err[0]*err[0] + err[1]*err[1];
211 cvCopy( solver.param, &modelPart );
217 cvFindHomography( const CvMat* objectPoints, const CvMat* imagePoints,
218 CvMat* __H, int method, double ransacReprojThreshold,
221 const double confidence = 0.995;
222 const int maxIters = 2000;
224 Ptr<CvMat> m, M, tempMask;
227 CvMat _H = cvMat( 3, 3, CV_64FC1, H );
230 CV_Assert( CV_IS_MAT(imagePoints) && CV_IS_MAT(objectPoints) );
232 count = MAX(imagePoints->cols, imagePoints->rows);
233 CV_Assert( count >= 4 );
235 m = cvCreateMat( 1, count, CV_64FC2 );
236 cvConvertPointsHomogeneous( imagePoints, m );
238 M = cvCreateMat( 1, count, CV_64FC2 );
239 cvConvertPointsHomogeneous( objectPoints, M );
243 CV_Assert( CV_IS_MASK_ARR(mask) && CV_IS_MAT_CONT(mask->type) &&
244 (mask->rows == 1 || mask->cols == 1) &&
245 mask->rows*mask->cols == count );
246 tempMask = cvCloneMat(mask);
249 tempMask = cvCreateMat( 1, count, CV_8U );
250 if( !tempMask.empty() )
251 cvSet( tempMask, cvScalarAll(1.) );
253 CvHomographyEstimator estimator( MIN(count, 4) );
256 if( method == CV_LMEDS )
257 result = estimator.runLMeDS( M, m, &_H, tempMask, confidence, maxIters );
258 else if( method == CV_RANSAC )
259 result = estimator.runRANSAC( M, m, &_H, tempMask, ransacReprojThreshold, confidence, maxIters);
261 result = estimator.runKernel( M, m, &_H ) > 0;
263 if( result && count > 4 )
265 icvCompressPoints( (CvPoint2D64f*)M->data.ptr, tempMask->data.ptr, 1, count );
266 count = icvCompressPoints( (CvPoint2D64f*)m->data.ptr, tempMask->data.ptr, 1, count );
267 M->cols = m->cols = count;
268 estimator.refine( M, m, &_H, 10 );
272 cvConvert( &_H, __H );
278 /* Evaluation of Fundamental Matrix from point correspondences.
279 The original code has been written by Valery Mosyagin */
281 /* The algorithms (except for RANSAC) and the notation have been taken from
282 Zhengyou Zhang's research report
283 "Determining the Epipolar Geometry and its Uncertainty: A Review"
284 that can be found at http://www-sop.inria.fr/robotvis/personnel/zzhang/zzhang-eng.html */
286 /************************************** 7-point algorithm *******************************/
287 class CvFMEstimator : public CvModelEstimator2
290 CvFMEstimator( int _modelPoints );
292 virtual int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model );
293 virtual int run7Point( const CvMat* m1, const CvMat* m2, CvMat* model );
294 virtual int run8Point( const CvMat* m1, const CvMat* m2, CvMat* model );
296 virtual void computeReprojError( const CvMat* m1, const CvMat* m2,
297 const CvMat* model, CvMat* error );
300 CvFMEstimator::CvFMEstimator( int _modelPoints )
301 : CvModelEstimator2( _modelPoints, cvSize(3,3), _modelPoints == 7 ? 3 : 1 )
303 assert( _modelPoints == 7 || _modelPoints == 8 );
307 int CvFMEstimator::runKernel( const CvMat* m1, const CvMat* m2, CvMat* model )
309 return modelPoints == 7 ? run7Point( m1, m2, model ) : run8Point( m1, m2, model );
312 int CvFMEstimator::run7Point( const CvMat* _m1, const CvMat* _m2, CvMat* _fmatrix )
314 double a[7*9], w[7], v[9*9], c[4], r[3];
317 CvMat A = cvMat( 7, 9, CV_64F, a );
318 CvMat V = cvMat( 9, 9, CV_64F, v );
319 CvMat W = cvMat( 7, 1, CV_64F, w );
320 CvMat coeffs = cvMat( 1, 4, CV_64F, c );
321 CvMat roots = cvMat( 1, 3, CV_64F, r );
322 const CvPoint2D64f* m1 = (const CvPoint2D64f*)_m1->data.ptr;
323 const CvPoint2D64f* m2 = (const CvPoint2D64f*)_m2->data.ptr;
324 double* fmatrix = _fmatrix->data.db;
327 // form a linear system: i-th row of A(=a) represents
328 // the equation: (m2[i], 1)'*F*(m1[i], 1) = 0
329 for( i = 0; i < 7; i++ )
331 double x0 = m1[i].x, y0 = m1[i].y;
332 double x1 = m2[i].x, y1 = m2[i].y;
345 // A*(f11 f12 ... f33)' = 0 is singular (7 equations for 9 variables), so
346 // the solution is linear subspace of dimensionality 2.
347 // => use the last two singular vectors as a basis of the space
348 // (according to SVD properties)
349 cvSVD( &A, &W, 0, &V, CV_SVD_MODIFY_A + CV_SVD_V_T );
353 // f1, f2 is a basis => lambda*f1 + mu*f2 is an arbitrary f. matrix.
354 // as it is determined up to a scale, normalize lambda & mu (lambda + mu = 1),
355 // so f ~ lambda*f1 + (1 - lambda)*f2.
356 // use the additional constraint det(f) = det(lambda*f1 + (1-lambda)*f2) to find lambda.
357 // it will be a cubic equation.
358 // find c - polynomial coefficients.
359 for( i = 0; i < 9; i++ )
362 t0 = f2[4]*f2[8] - f2[5]*f2[7];
363 t1 = f2[3]*f2[8] - f2[5]*f2[6];
364 t2 = f2[3]*f2[7] - f2[4]*f2[6];
366 c[3] = f2[0]*t0 - f2[1]*t1 + f2[2]*t2;
368 c[2] = f1[0]*t0 - f1[1]*t1 + f1[2]*t2 -
369 f1[3]*(f2[1]*f2[8] - f2[2]*f2[7]) +
370 f1[4]*(f2[0]*f2[8] - f2[2]*f2[6]) -
371 f1[5]*(f2[0]*f2[7] - f2[1]*f2[6]) +
372 f1[6]*(f2[1]*f2[5] - f2[2]*f2[4]) -
373 f1[7]*(f2[0]*f2[5] - f2[2]*f2[3]) +
374 f1[8]*(f2[0]*f2[4] - f2[1]*f2[3]);
376 t0 = f1[4]*f1[8] - f1[5]*f1[7];
377 t1 = f1[3]*f1[8] - f1[5]*f1[6];
378 t2 = f1[3]*f1[7] - f1[4]*f1[6];
380 c[1] = f2[0]*t0 - f2[1]*t1 + f2[2]*t2 -
381 f2[3]*(f1[1]*f1[8] - f1[2]*f1[7]) +
382 f2[4]*(f1[0]*f1[8] - f1[2]*f1[6]) -
383 f2[5]*(f1[0]*f1[7] - f1[1]*f1[6]) +
384 f2[6]*(f1[1]*f1[5] - f1[2]*f1[4]) -
385 f2[7]*(f1[0]*f1[5] - f1[2]*f1[3]) +
386 f2[8]*(f1[0]*f1[4] - f1[1]*f1[3]);
388 c[0] = f1[0]*t0 - f1[1]*t1 + f1[2]*t2;
390 // solve the cubic equation; there can be 1 to 3 roots ...
391 n = cvSolveCubic( &coeffs, &roots );
396 for( k = 0; k < n; k++, fmatrix += 9 )
398 // for each root form the fundamental matrix
399 double lambda = r[k], mu = 1.;
400 double s = f1[8]*r[k] + f2[8];
402 // normalize each matrix, so that F(3,3) (~fmatrix[8]) == 1
403 if( fabs(s) > DBL_EPSILON )
412 for( i = 0; i < 8; i++ )
413 fmatrix[i] = f1[i]*lambda + f2[i]*mu;
420 int CvFMEstimator::run8Point( const CvMat* _m1, const CvMat* _m2, CvMat* _fmatrix )
422 double a[9*9], w[9], v[9*9];
423 CvMat W = cvMat( 1, 9, CV_64F, w );
424 CvMat V = cvMat( 9, 9, CV_64F, v );
425 CvMat A = cvMat( 9, 9, CV_64F, a );
428 CvPoint2D64f m0c = {0,0}, m1c = {0,0};
429 double t, scale0 = 0, scale1 = 0;
431 const CvPoint2D64f* m1 = (const CvPoint2D64f*)_m1->data.ptr;
432 const CvPoint2D64f* m2 = (const CvPoint2D64f*)_m2->data.ptr;
433 double* fmatrix = _fmatrix->data.db;
434 int i, j, k, count = _m1->cols*_m1->rows;
436 // compute centers and average distances for each of the two point sets
437 for( i = 0; i < count; i++ )
439 double x = m1[i].x, y = m1[i].y;
440 m0c.x += x; m0c.y += y;
442 x = m2[i].x, y = m2[i].y;
443 m1c.x += x; m1c.y += y;
446 // calculate the normalizing transformations for each of the point sets:
447 // after the transformation each set will have the mass center at the coordinate origin
448 // and the average distance from the origin will be ~sqrt(2).
450 m0c.x *= t; m0c.y *= t;
451 m1c.x *= t; m1c.y *= t;
453 for( i = 0; i < count; i++ )
455 double x = m1[i].x - m0c.x, y = m1[i].y - m0c.y;
456 scale0 += sqrt(x*x + y*y);
458 x = fabs(m2[i].x - m1c.x), y = fabs(m2[i].y - m1c.y);
459 scale1 += sqrt(x*x + y*y);
465 if( scale0 < FLT_EPSILON || scale1 < FLT_EPSILON )
468 scale0 = sqrt(2.)/scale0;
469 scale1 = sqrt(2.)/scale1;
473 // form a linear system Ax=0: for each selected pair of points m1 & m2,
474 // the row of A(=a) represents the coefficients of equation: (m2, 1)'*F*(m1, 1) = 0
475 // to save computation time, we compute (At*A) instead of A and then solve (At*A)x=0.
476 for( i = 0; i < count; i++ )
478 double x0 = (m1[i].x - m0c.x)*scale0;
479 double y0 = (m1[i].y - m0c.y)*scale0;
480 double x1 = (m2[i].x - m1c.x)*scale1;
481 double y1 = (m2[i].y - m1c.y)*scale1;
482 double r[9] = { x1*x0, x1*y0, x1, y1*x0, y1*y0, y1, x0, y0, 1 };
483 for( j = 0; j < 9; j++ )
484 for( k = 0; k < 9; k++ )
485 a[j*9+k] += r[j]*r[k];
488 cvSVD( &A, &W, 0, &V, CV_SVD_MODIFY_A + CV_SVD_V_T );
490 for( i = 0; i < 8; i++ )
492 if( fabs(w[i]) < DBL_EPSILON )
499 F0 = cvMat( 3, 3, CV_64F, v + 9*8 ); // take the last column of v as a solution of Af = 0
501 // make F0 singular (of rank 2) by decomposing it with SVD,
502 // zeroing the last diagonal element of W and then composing the matrices back.
504 // use v as a temporary storage for different 3x3 matrices
511 cvSVD( &F0, &W, &U, &V, CV_SVD_MODIFY_A + CV_SVD_U_T + CV_SVD_V_T );
514 // F0 <- U*diag([W(1), W(2), 0])*V'
515 cvGEMM( &U, &W, 1., 0, 0., &TF, CV_GEMM_A_T );
516 cvGEMM( &TF, &V, 1., 0, 0., &F0, 0/*CV_GEMM_B_T*/ );
518 // apply the transformation that is inverse
519 // to what we used to normalize the point coordinates
521 double tt0[] = { scale0, 0, -scale0*m0c.x, 0, scale0, -scale0*m0c.y, 0, 0, 1 };
522 double tt1[] = { scale1, 0, -scale1*m1c.x, 0, scale1, -scale1*m1c.y, 0, 0, 1 };
529 cvGEMM( &T1, &F0, 1., 0, 0., &TF, CV_GEMM_A_T );
530 F0.data.db = fmatrix;
531 cvGEMM( &TF, &T0, 1., 0, 0., &F0, 0 );
534 if( fabs(F0.data.db[8]) > FLT_EPSILON )
535 cvScale( &F0, &F0, 1./F0.data.db[8] );
542 void CvFMEstimator::computeReprojError( const CvMat* _m1, const CvMat* _m2,
543 const CvMat* model, CvMat* _err )
545 int i, count = _m1->rows*_m1->cols;
546 const CvPoint2D64f* m1 = (const CvPoint2D64f*)_m1->data.ptr;
547 const CvPoint2D64f* m2 = (const CvPoint2D64f*)_m2->data.ptr;
548 const double* F = model->data.db;
549 float* err = _err->data.fl;
551 for( i = 0; i < count; i++ )
553 double a, b, c, d1, d2, s1, s2;
555 a = F[0]*m1[i].x + F[1]*m1[i].y + F[2];
556 b = F[3]*m1[i].x + F[4]*m1[i].y + F[5];
557 c = F[6]*m1[i].x + F[7]*m1[i].y + F[8];
560 d2 = m2[i].x*a + m2[i].y*b + c;
562 a = F[0]*m2[i].x + F[3]*m2[i].y + F[6];
563 b = F[1]*m2[i].x + F[4]*m2[i].y + F[7];
564 c = F[2]*m2[i].x + F[5]*m2[i].y + F[8];
567 d1 = m1[i].x*a + m1[i].y*b + c;
569 err[i] = (float)std::max(d1*d1*s1, d2*d2*s2);
574 CV_IMPL int cvFindFundamentalMat( const CvMat* points1, const CvMat* points2,
575 CvMat* fmatrix, int method,
576 double param1, double param2, CvMat* mask )
579 Ptr<CvMat> m1, m2, tempMask;
582 CvMat _F3x3 = cvMat( 3, 3, CV_64FC1, F ), _F9x3 = cvMat( 9, 3, CV_64FC1, F );
585 CV_Assert( CV_IS_MAT(points1) && CV_IS_MAT(points2) && CV_ARE_SIZES_EQ(points1, points2) );
586 CV_Assert( CV_IS_MAT(fmatrix) && fmatrix->cols == 3 &&
587 (fmatrix->rows == 3 || (fmatrix->rows == 9 && method == CV_FM_7POINT)) );
589 count = MAX(points1->cols, points1->rows);
593 m1 = cvCreateMat( 1, count, CV_64FC2 );
594 cvConvertPointsHomogeneous( points1, m1 );
596 m2 = cvCreateMat( 1, count, CV_64FC2 );
597 cvConvertPointsHomogeneous( points2, m2 );
601 CV_Assert( CV_IS_MASK_ARR(mask) && CV_IS_MAT_CONT(mask->type) &&
602 (mask->rows == 1 || mask->cols == 1) &&
603 mask->rows*mask->cols == count );
604 tempMask = cvCreateMatHeader(1, count, CV_8U);
605 cvSetData(tempMask, mask->data.ptr, 0);
608 tempMask = cvCreateMat( 1, count, CV_8U );
609 if( !tempMask.empty() )
610 cvSet( tempMask, cvScalarAll(1.) );
612 CvFMEstimator estimator( MIN(count, (method & 3) == CV_FM_7POINT ? 7 : 8) );
614 result = estimator.run7Point(m1, m2, &_F9x3);
615 else if( count == 8 || method == CV_FM_8POINT )
616 result = estimator.run8Point(m1, m2, &_F3x3);
621 if( param2 < DBL_EPSILON || param2 > 1 - DBL_EPSILON )
624 if( (method & ~3) == CV_RANSAC )
625 result = estimator.runRANSAC(m1, m2, &_F3x3, tempMask, param1, param2 );
627 result = estimator.runLMeDS(m1, m2, &_F3x3, tempMask, param2 );
630 /*icvCompressPoints( (CvPoint2D64f*)m1->data.ptr, tempMask->data.ptr, 1, count );
631 count = icvCompressPoints( (CvPoint2D64f*)m2->data.ptr, tempMask->data.ptr, 1, count );
632 assert( count >= 8 );
633 m1->cols = m2->cols = count;
634 estimator.run8Point(m1, m2, &_F3x3);*/
638 cvConvert( fmatrix->rows == 3 ? &_F3x3 : &_F9x3, fmatrix );
644 CV_IMPL void cvComputeCorrespondEpilines( const CvMat* points, int pointImageID,
645 const CvMat* fmatrix, CvMat* lines )
647 int abc_stride, abc_plane_stride, abc_elem_size;
648 int plane_stride, stride, elem_size;
649 int i, dims, count, depth, cn, abc_dims, abc_count, abc_depth, abc_cn;
651 const uchar *xp, *yp, *zp;
653 CvMat F = cvMat( 3, 3, CV_64F, f );
655 if( !CV_IS_MAT(points) )
656 CV_Error( !points ? CV_StsNullPtr : CV_StsBadArg, "points parameter is not a valid matrix" );
658 depth = CV_MAT_DEPTH(points->type);
659 cn = CV_MAT_CN(points->type);
660 if( (depth != CV_32F && depth != CV_64F) || (cn != 1 && cn != 2 && cn != 3) )
661 CV_Error( CV_StsUnsupportedFormat, "The format of point matrix is unsupported" );
663 if( points->rows > points->cols )
665 dims = cn*points->cols;
666 count = points->rows;
670 if( (points->rows > 1 && cn > 1) || (points->rows == 1 && cn == 1) )
671 CV_Error( CV_StsBadSize, "The point matrix does not have a proper layout (2xn, 3xn, nx2 or nx3)" );
672 dims = cn * points->rows;
673 count = points->cols;
676 if( dims != 2 && dims != 3 )
677 CV_Error( CV_StsOutOfRange, "The dimensionality of points must be 2 or 3" );
679 if( !CV_IS_MAT(fmatrix) )
680 CV_Error( !fmatrix ? CV_StsNullPtr : CV_StsBadArg, "fmatrix is not a valid matrix" );
682 if( CV_MAT_TYPE(fmatrix->type) != CV_32FC1 && CV_MAT_TYPE(fmatrix->type) != CV_64FC1 )
683 CV_Error( CV_StsUnsupportedFormat, "fundamental matrix must have 32fC1 or 64fC1 type" );
685 if( fmatrix->cols != 3 || fmatrix->rows != 3 )
686 CV_Error( CV_StsBadSize, "fundamental matrix must be 3x3" );
688 if( !CV_IS_MAT(lines) )
689 CV_Error( !lines ? CV_StsNullPtr : CV_StsBadArg, "lines parameter is not a valid matrix" );
691 abc_depth = CV_MAT_DEPTH(lines->type);
692 abc_cn = CV_MAT_CN(lines->type);
693 if( (abc_depth != CV_32F && abc_depth != CV_64F) || (abc_cn != 1 && abc_cn != 3) )
694 CV_Error( CV_StsUnsupportedFormat, "The format of the matrix of lines is unsupported" );
696 if( lines->rows > lines->cols )
698 abc_dims = abc_cn*lines->cols;
699 abc_count = lines->rows;
703 if( (lines->rows > 1 && abc_cn > 1) || (lines->rows == 1 && abc_cn == 1) )
704 CV_Error( CV_StsBadSize, "The lines matrix does not have a proper layout (3xn or nx3)" );
705 abc_dims = abc_cn * lines->rows;
706 abc_count = lines->cols;
710 CV_Error( CV_StsOutOfRange, "The lines matrix does not have a proper layout (3xn or nx3)" );
712 if( abc_count != count )
713 CV_Error( CV_StsUnmatchedSizes, "The numbers of points and lines are different" );
715 elem_size = CV_ELEM_SIZE(depth);
716 abc_elem_size = CV_ELEM_SIZE(abc_depth);
718 if( points->rows == dims )
720 plane_stride = points->step;
725 plane_stride = elem_size;
726 stride = points->rows == 1 ? dims*elem_size : points->step;
729 if( lines->rows == 3 )
731 abc_plane_stride = lines->step;
732 abc_stride = abc_elem_size;
736 abc_plane_stride = abc_elem_size;
737 abc_stride = lines->rows == 1 ? 3*abc_elem_size : lines->step;
740 cvConvert( fmatrix, &F );
741 if( pointImageID == 2 )
742 cvTranspose( &F, &F );
744 xp = points->data.ptr;
745 yp = xp + plane_stride;
746 zp = dims == 3 ? yp + plane_stride : 0;
748 ap = lines->data.ptr;
749 bp = ap + abc_plane_stride;
750 cp = bp + abc_plane_stride;
752 for( i = 0; i < count; i++ )
757 if( depth == CV_32F )
759 x = *(float*)xp; y = *(float*)yp;
761 z = *(float*)zp, zp += stride;
765 x = *(double*)xp; y = *(double*)yp;
767 z = *(double*)zp, zp += stride;
770 xp += stride; yp += stride;
772 a = f[0]*x + f[1]*y + f[2]*z;
773 b = f[3]*x + f[4]*y + f[5]*z;
774 c = f[6]*x + f[7]*y + f[8]*z;
776 nu = nu ? 1./sqrt(nu) : 1.;
777 a *= nu; b *= nu; c *= nu;
779 if( abc_depth == CV_32F )
781 *(float*)ap = (float)a;
782 *(float*)bp = (float)b;
783 *(float*)cp = (float)c;
799 CV_IMPL void cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst )
801 Ptr<CvMat> temp, denom;
803 int i, s_count, s_dims, d_count, d_dims;
804 CvMat _src, _dst, _ones;
807 if( !CV_IS_MAT(src) )
808 CV_Error( !src ? CV_StsNullPtr : CV_StsBadArg,
809 "The input parameter is not a valid matrix" );
811 if( !CV_IS_MAT(dst) )
812 CV_Error( !dst ? CV_StsNullPtr : CV_StsBadArg,
813 "The output parameter is not a valid matrix" );
815 if( src == dst || src->data.ptr == dst->data.ptr )
817 if( src != dst && (!CV_ARE_TYPES_EQ(src, dst) || !CV_ARE_SIZES_EQ(src,dst)) )
818 CV_Error( CV_StsBadArg, "Invalid inplace operation" );
822 if( src->rows > src->cols )
824 if( !((src->cols > 1) ^ (CV_MAT_CN(src->type) > 1)) )
825 CV_Error( CV_StsBadSize, "Either the number of channels or columns or rows must be =1" );
827 s_dims = CV_MAT_CN(src->type)*src->cols;
832 if( !((src->rows > 1) ^ (CV_MAT_CN(src->type) > 1)) )
833 CV_Error( CV_StsBadSize, "Either the number of channels or columns or rows must be =1" );
835 s_dims = CV_MAT_CN(src->type)*src->rows;
839 if( src->rows == 1 || src->cols == 1 )
840 src = cvReshape( src, &_src, 1, s_count );
842 if( dst->rows > dst->cols )
844 if( !((dst->cols > 1) ^ (CV_MAT_CN(dst->type) > 1)) )
845 CV_Error( CV_StsBadSize,
846 "Either the number of channels or columns or rows in the input matrix must be =1" );
848 d_dims = CV_MAT_CN(dst->type)*dst->cols;
853 if( !((dst->rows > 1) ^ (CV_MAT_CN(dst->type) > 1)) )
854 CV_Error( CV_StsBadSize,
855 "Either the number of channels or columns or rows in the output matrix must be =1" );
857 d_dims = CV_MAT_CN(dst->type)*dst->rows;
861 if( dst->rows == 1 || dst->cols == 1 )
862 dst = cvReshape( dst, &_dst, 1, d_count );
864 if( s_count != d_count )
865 CV_Error( CV_StsUnmatchedSizes, "Both matrices must have the same number of points" );
867 if( CV_MAT_DEPTH(src->type) < CV_32F || CV_MAT_DEPTH(dst->type) < CV_32F )
868 CV_Error( CV_StsUnsupportedFormat,
869 "Both matrices must be floating-point (single or double precision)" );
871 if( s_dims < 2 || s_dims > 4 || d_dims < 2 || d_dims > 4 )
872 CV_Error( CV_StsOutOfRange,
873 "Both input and output point dimensionality must be 2, 3 or 4" );
875 if( s_dims < d_dims - 1 || s_dims > d_dims + 1 )
876 CV_Error( CV_StsUnmatchedSizes,
877 "The dimensionalities of input and output point sets differ too much" );
879 if( s_dims == d_dims - 1 )
881 if( d_count == dst->rows )
883 ones = cvGetSubRect( dst, &_ones, cvRect( s_dims, 0, 1, d_count ));
884 dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, s_dims, d_count ));
888 ones = cvGetSubRect( dst, &_ones, cvRect( 0, s_dims, d_count, 1 ));
889 dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, d_count, s_dims ));
893 if( s_dims <= d_dims )
895 if( src->rows == dst->rows && src->cols == dst->cols )
897 if( CV_ARE_TYPES_EQ( src, dst ) )
900 cvConvert( src, dst );
904 if( !CV_ARE_TYPES_EQ( src, dst ))
906 temp = cvCreateMat( src->rows, src->cols, dst->type );
907 cvConvert( src, temp );
910 cvTranspose( src, dst );
914 cvSet( ones, cvRealScalar(1.) );
918 int s_plane_stride, s_stride, d_plane_stride, d_stride, elem_size;
920 if( !CV_ARE_TYPES_EQ( src, dst ))
922 temp = cvCreateMat( src->rows, src->cols, dst->type );
923 cvConvert( src, temp );
927 elem_size = CV_ELEM_SIZE(src->type);
929 if( s_count == src->cols )
930 s_plane_stride = src->step / elem_size, s_stride = 1;
932 s_stride = src->step / elem_size, s_plane_stride = 1;
934 if( d_count == dst->cols )
935 d_plane_stride = dst->step / elem_size, d_stride = 1;
937 d_stride = dst->step / elem_size, d_plane_stride = 1;
939 denom = cvCreateMat( 1, d_count, dst->type );
941 if( CV_MAT_DEPTH(dst->type) == CV_32F )
943 const float* xs = src->data.fl;
944 const float* ys = xs + s_plane_stride;
946 const float* ws = xs + (s_dims - 1)*s_plane_stride;
948 float* iw = denom->data.fl;
950 float* xd = dst->data.fl;
951 float* yd = xd + d_plane_stride;
956 zs = ys + s_plane_stride;
957 zd = yd + d_plane_stride;
960 for( i = 0; i < d_count; i++, ws += s_stride )
963 iw[i] = fabs((double)t) > FLT_EPSILON ? t : 1.f;
966 cvDiv( 0, denom, denom );
969 for( i = 0; i < d_count; i++ )
972 float x = *xs * w, y = *ys * w, z = *zs * w;
973 xs += s_stride; ys += s_stride; zs += s_stride;
974 *xd = x; *yd = y; *zd = z;
975 xd += d_stride; yd += d_stride; zd += d_stride;
978 for( i = 0; i < d_count; i++ )
981 float x = *xs * w, y = *ys * w;
982 xs += s_stride; ys += s_stride;
984 xd += d_stride; yd += d_stride;
989 const double* xs = src->data.db;
990 const double* ys = xs + s_plane_stride;
991 const double* zs = 0;
992 const double* ws = xs + (s_dims - 1)*s_plane_stride;
994 double* iw = denom->data.db;
996 double* xd = dst->data.db;
997 double* yd = xd + d_plane_stride;
1002 zs = ys + s_plane_stride;
1003 zd = yd + d_plane_stride;
1006 for( i = 0; i < d_count; i++, ws += s_stride )
1009 iw[i] = fabs(t) > DBL_EPSILON ? t : 1.;
1012 cvDiv( 0, denom, denom );
1015 for( i = 0; i < d_count; i++ )
1018 double x = *xs * w, y = *ys * w, z = *zs * w;
1019 xs += s_stride; ys += s_stride; zs += s_stride;
1020 *xd = x; *yd = y; *zd = z;
1021 xd += d_stride; yd += d_stride; zd += d_stride;
1024 for( i = 0; i < d_count; i++ )
1027 double x = *xs * w, y = *ys * w;
1028 xs += s_stride; ys += s_stride;
1030 xd += d_stride; yd += d_stride;
1039 static Mat _findHomography( const Mat& points1, const Mat& points2,
1040 int method, double ransacReprojThreshold,
1041 vector<uchar>* mask )
1043 CV_Assert(points1.isContinuous() && points2.isContinuous() &&
1044 points1.type() == points2.type() &&
1045 ((points1.rows == 1 && points1.channels() == 2) ||
1046 points1.cols*points1.channels() == 2) &&
1047 ((points2.rows == 1 && points2.channels() == 2) ||
1048 points2.cols*points2.channels() == 2));
1050 Mat H(3, 3, CV_64F);
1051 CvMat _pt1 = Mat(points1), _pt2 = Mat(points2);
1052 CvMat _H = H, _mask, *pmask = 0;
1055 mask->resize(points1.cols*points1.rows*points1.channels()/2);
1056 pmask = &(_mask = cvMat(1, (int)mask->size(), CV_8U, (void*)&(*mask)[0]));
1058 bool ok = cvFindHomography( &_pt1, &_pt2, &_H, method, ransacReprojThreshold, pmask ) > 0;
1064 static Mat _findFundamentalMat( const Mat& points1, const Mat& points2,
1065 int method, double param1, double param2,
1066 vector<uchar>* mask )
1068 CV_Assert(points1.isContinuous() && points2.isContinuous() &&
1069 points1.type() == points2.type() &&
1070 ((points1.rows == 1 && points1.channels() == 2) ||
1071 points1.cols*points1.channels() == 2) &&
1072 ((points2.rows == 1 && points2.channels() == 2) ||
1073 points2.cols*points2.channels() == 2));
1075 Mat F(3, 3, CV_64F);
1076 CvMat _pt1 = Mat(points1), _pt2 = Mat(points2);
1077 CvMat _F = F, _mask, *pmask = 0;
1080 mask->resize(points1.cols*points1.rows*points1.channels()/2);
1081 pmask = &(_mask = cvMat(1, (int)mask->size(), CV_8U, (void*)&(*mask)[0]));
1083 int n = cvFindFundamentalMat( &_pt1, &_pt2, &_F, method, param1, param2, pmask );
1092 cv::Mat cv::findHomography( const Mat& srcPoints, const Mat& dstPoints,
1093 vector<uchar>& mask, int method,
1094 double ransacReprojThreshold )
1096 return _findHomography(srcPoints, dstPoints, method, ransacReprojThreshold, &mask);
1099 cv::Mat cv::findHomography( const Mat& srcPoints, const Mat& dstPoints,
1100 int method, double ransacReprojThreshold )
1102 return _findHomography(srcPoints, dstPoints, method, ransacReprojThreshold, 0);
1106 cv::Mat cv::findFundamentalMat( const Mat& points1, const Mat& points2,
1107 vector<uchar>& mask, int method, double param1, double param2 )
1109 return _findFundamentalMat( points1, points2, method, param1, param2, &mask );
1112 cv::Mat cv::findFundamentalMat( const Mat& points1, const Mat& points2,
1113 int method, double param1, double param2 )
1115 return _findFundamentalMat( points1, points2, method, param1, param2, 0 );
1118 void cv::computeCorrespondEpilines( const Mat& points, int whichImage,
1119 const Mat& F, vector<Vec3f>& lines )
1121 CV_Assert(points.isContinuous() &&
1122 (points.depth() == CV_32S || points.depth() == CV_32F) &&
1123 ((points.rows == 1 && points.channels() == 2) ||
1124 points.cols*points.channels() == 2));
1126 lines.resize(points.cols*points.rows*points.channels()/2);
1127 CvMat _points = points, _lines = Mat(lines), _F = F;
1128 cvComputeCorrespondEpilines(&_points, whichImage, &_F, &_lines);
1131 void cv::convertPointsHomogeneous( const Mat& src, vector<Point3f>& dst )
1133 CV_Assert(src.isContinuous() &&
1134 (src.depth() == CV_32S || src.depth() == CV_32F) &&
1135 ((src.rows == 1 && src.channels() == 2) ||
1136 src.cols*src.channels() == 2));
1138 dst.resize(src.cols*src.rows*src.channels()/2);
1139 CvMat _src = src, _dst = Mat(dst);
1140 cvConvertPointsHomogeneous(&_src, &_dst);
1143 void cv::convertPointsHomogeneous( const Mat& src, vector<Point2f>& dst )
1145 CV_Assert(src.isContinuous() &&
1146 (src.depth() == CV_32S || src.depth() == CV_32F) &&
1147 ((src.rows == 1 && src.channels() == 3) ||
1148 src.cols*src.channels() == 3));
1150 dst.resize(src.cols*src.rows*src.channels()/3);
1151 CvMat _src = Mat(src), _dst = Mat(dst);
1152 cvConvertPointsHomogeneous(&_src, &_dst);