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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 matW = cvMat( 9, 9, CV_64F, W );
90 CvMat matV = 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, &matW, 0, &matV, CV_SVD_MODIFY_A + CV_SVD_V_T );
138 cvEigenVV( &_LtL, &matV, &matW );
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 matH = 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, &matH, tempMask, confidence, maxIters );
258 else if( method == CV_RANSAC )
259 result = estimator.runRANSAC( M, m, &matH, tempMask, ransacReprojThreshold, confidence, maxIters);
261 result = estimator.runKernel( M, m, &matH ) > 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, &matH, 10 );
272 cvConvert( &matH, __H );
274 if( mask && tempMask )
275 cvCopy( tempMask, mask );
281 /* Evaluation of Fundamental Matrix from point correspondences.
282 The original code has been written by Valery Mosyagin */
284 /* The algorithms (except for RANSAC) and the notation have been taken from
285 Zhengyou Zhang's research report
286 "Determining the Epipolar Geometry and its Uncertainty: A Review"
287 that can be found at http://www-sop.inria.fr/robotvis/personnel/zzhang/zzhang-eng.html */
289 /************************************** 7-point algorithm *******************************/
290 class CvFMEstimator : public CvModelEstimator2
293 CvFMEstimator( int _modelPoints );
295 virtual int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model );
296 virtual int run7Point( const CvMat* m1, const CvMat* m2, CvMat* model );
297 virtual int run8Point( const CvMat* m1, const CvMat* m2, CvMat* model );
299 virtual void computeReprojError( const CvMat* m1, const CvMat* m2,
300 const CvMat* model, CvMat* error );
303 CvFMEstimator::CvFMEstimator( int _modelPoints )
304 : CvModelEstimator2( _modelPoints, cvSize(3,3), _modelPoints == 7 ? 3 : 1 )
306 assert( _modelPoints == 7 || _modelPoints == 8 );
310 int CvFMEstimator::runKernel( const CvMat* m1, const CvMat* m2, CvMat* model )
312 return modelPoints == 7 ? run7Point( m1, m2, model ) : run8Point( m1, m2, model );
315 int CvFMEstimator::run7Point( const CvMat* _m1, const CvMat* _m2, CvMat* _fmatrix )
317 double a[7*9], w[7], v[9*9], c[4], r[3];
320 CvMat A = cvMat( 7, 9, CV_64F, a );
321 CvMat V = cvMat( 9, 9, CV_64F, v );
322 CvMat W = cvMat( 7, 1, CV_64F, w );
323 CvMat coeffs = cvMat( 1, 4, CV_64F, c );
324 CvMat roots = cvMat( 1, 3, CV_64F, r );
325 const CvPoint2D64f* m1 = (const CvPoint2D64f*)_m1->data.ptr;
326 const CvPoint2D64f* m2 = (const CvPoint2D64f*)_m2->data.ptr;
327 double* fmatrix = _fmatrix->data.db;
330 // form a linear system: i-th row of A(=a) represents
331 // the equation: (m2[i], 1)'*F*(m1[i], 1) = 0
332 for( i = 0; i < 7; i++ )
334 double x0 = m1[i].x, y0 = m1[i].y;
335 double x1 = m2[i].x, y1 = m2[i].y;
348 // A*(f11 f12 ... f33)' = 0 is singular (7 equations for 9 variables), so
349 // the solution is linear subspace of dimensionality 2.
350 // => use the last two singular vectors as a basis of the space
351 // (according to SVD properties)
352 cvSVD( &A, &W, 0, &V, CV_SVD_MODIFY_A + CV_SVD_V_T );
356 // f1, f2 is a basis => lambda*f1 + mu*f2 is an arbitrary f. matrix.
357 // as it is determined up to a scale, normalize lambda & mu (lambda + mu = 1),
358 // so f ~ lambda*f1 + (1 - lambda)*f2.
359 // use the additional constraint det(f) = det(lambda*f1 + (1-lambda)*f2) to find lambda.
360 // it will be a cubic equation.
361 // find c - polynomial coefficients.
362 for( i = 0; i < 9; i++ )
365 t0 = f2[4]*f2[8] - f2[5]*f2[7];
366 t1 = f2[3]*f2[8] - f2[5]*f2[6];
367 t2 = f2[3]*f2[7] - f2[4]*f2[6];
369 c[3] = f2[0]*t0 - f2[1]*t1 + f2[2]*t2;
371 c[2] = f1[0]*t0 - f1[1]*t1 + f1[2]*t2 -
372 f1[3]*(f2[1]*f2[8] - f2[2]*f2[7]) +
373 f1[4]*(f2[0]*f2[8] - f2[2]*f2[6]) -
374 f1[5]*(f2[0]*f2[7] - f2[1]*f2[6]) +
375 f1[6]*(f2[1]*f2[5] - f2[2]*f2[4]) -
376 f1[7]*(f2[0]*f2[5] - f2[2]*f2[3]) +
377 f1[8]*(f2[0]*f2[4] - f2[1]*f2[3]);
379 t0 = f1[4]*f1[8] - f1[5]*f1[7];
380 t1 = f1[3]*f1[8] - f1[5]*f1[6];
381 t2 = f1[3]*f1[7] - f1[4]*f1[6];
383 c[1] = f2[0]*t0 - f2[1]*t1 + f2[2]*t2 -
384 f2[3]*(f1[1]*f1[8] - f1[2]*f1[7]) +
385 f2[4]*(f1[0]*f1[8] - f1[2]*f1[6]) -
386 f2[5]*(f1[0]*f1[7] - f1[1]*f1[6]) +
387 f2[6]*(f1[1]*f1[5] - f1[2]*f1[4]) -
388 f2[7]*(f1[0]*f1[5] - f1[2]*f1[3]) +
389 f2[8]*(f1[0]*f1[4] - f1[1]*f1[3]);
391 c[0] = f1[0]*t0 - f1[1]*t1 + f1[2]*t2;
393 // solve the cubic equation; there can be 1 to 3 roots ...
394 n = cvSolveCubic( &coeffs, &roots );
399 for( k = 0; k < n; k++, fmatrix += 9 )
401 // for each root form the fundamental matrix
402 double lambda = r[k], mu = 1.;
403 double s = f1[8]*r[k] + f2[8];
405 // normalize each matrix, so that F(3,3) (~fmatrix[8]) == 1
406 if( fabs(s) > DBL_EPSILON )
415 for( i = 0; i < 8; i++ )
416 fmatrix[i] = f1[i]*lambda + f2[i]*mu;
423 int CvFMEstimator::run8Point( const CvMat* _m1, const CvMat* _m2, CvMat* _fmatrix )
425 double a[9*9], w[9], v[9*9];
426 CvMat W = cvMat( 1, 9, CV_64F, w );
427 CvMat V = cvMat( 9, 9, CV_64F, v );
428 CvMat A = cvMat( 9, 9, CV_64F, a );
431 CvPoint2D64f m0c = {0,0}, m1c = {0,0};
432 double t, scale0 = 0, scale1 = 0;
434 const CvPoint2D64f* m1 = (const CvPoint2D64f*)_m1->data.ptr;
435 const CvPoint2D64f* m2 = (const CvPoint2D64f*)_m2->data.ptr;
436 double* fmatrix = _fmatrix->data.db;
437 int i, j, k, count = _m1->cols*_m1->rows;
439 // compute centers and average distances for each of the two point sets
440 for( i = 0; i < count; i++ )
442 double x = m1[i].x, y = m1[i].y;
443 m0c.x += x; m0c.y += y;
445 x = m2[i].x, y = m2[i].y;
446 m1c.x += x; m1c.y += y;
449 // calculate the normalizing transformations for each of the point sets:
450 // after the transformation each set will have the mass center at the coordinate origin
451 // and the average distance from the origin will be ~sqrt(2).
453 m0c.x *= t; m0c.y *= t;
454 m1c.x *= t; m1c.y *= t;
456 for( i = 0; i < count; i++ )
458 double x = m1[i].x - m0c.x, y = m1[i].y - m0c.y;
459 scale0 += sqrt(x*x + y*y);
461 x = fabs(m2[i].x - m1c.x), y = fabs(m2[i].y - m1c.y);
462 scale1 += sqrt(x*x + y*y);
468 if( scale0 < FLT_EPSILON || scale1 < FLT_EPSILON )
471 scale0 = sqrt(2.)/scale0;
472 scale1 = sqrt(2.)/scale1;
476 // form a linear system Ax=0: for each selected pair of points m1 & m2,
477 // the row of A(=a) represents the coefficients of equation: (m2, 1)'*F*(m1, 1) = 0
478 // to save computation time, we compute (At*A) instead of A and then solve (At*A)x=0.
479 for( i = 0; i < count; i++ )
481 double x0 = (m1[i].x - m0c.x)*scale0;
482 double y0 = (m1[i].y - m0c.y)*scale0;
483 double x1 = (m2[i].x - m1c.x)*scale1;
484 double y1 = (m2[i].y - m1c.y)*scale1;
485 double r[9] = { x1*x0, x1*y0, x1, y1*x0, y1*y0, y1, x0, y0, 1 };
486 for( j = 0; j < 9; j++ )
487 for( k = 0; k < 9; k++ )
488 a[j*9+k] += r[j]*r[k];
491 cvSVD( &A, &W, 0, &V, CV_SVD_MODIFY_A + CV_SVD_V_T );
493 for( i = 0; i < 8; i++ )
495 if( fabs(w[i]) < DBL_EPSILON )
502 F0 = cvMat( 3, 3, CV_64F, v + 9*8 ); // take the last column of v as a solution of Af = 0
504 // make F0 singular (of rank 2) by decomposing it with SVD,
505 // zeroing the last diagonal element of W and then composing the matrices back.
507 // use v as a temporary storage for different 3x3 matrices
514 cvSVD( &F0, &W, &U, &V, CV_SVD_MODIFY_A + CV_SVD_U_T + CV_SVD_V_T );
517 // F0 <- U*diag([W(1), W(2), 0])*V'
518 cvGEMM( &U, &W, 1., 0, 0., &TF, CV_GEMM_A_T );
519 cvGEMM( &TF, &V, 1., 0, 0., &F0, 0/*CV_GEMM_B_T*/ );
521 // apply the transformation that is inverse
522 // to what we used to normalize the point coordinates
524 double tt0[] = { scale0, 0, -scale0*m0c.x, 0, scale0, -scale0*m0c.y, 0, 0, 1 };
525 double tt1[] = { scale1, 0, -scale1*m1c.x, 0, scale1, -scale1*m1c.y, 0, 0, 1 };
532 cvGEMM( &T1, &F0, 1., 0, 0., &TF, CV_GEMM_A_T );
533 F0.data.db = fmatrix;
534 cvGEMM( &TF, &T0, 1., 0, 0., &F0, 0 );
537 if( fabs(F0.data.db[8]) > FLT_EPSILON )
538 cvScale( &F0, &F0, 1./F0.data.db[8] );
545 void CvFMEstimator::computeReprojError( const CvMat* _m1, const CvMat* _m2,
546 const CvMat* model, CvMat* _err )
548 int i, count = _m1->rows*_m1->cols;
549 const CvPoint2D64f* m1 = (const CvPoint2D64f*)_m1->data.ptr;
550 const CvPoint2D64f* m2 = (const CvPoint2D64f*)_m2->data.ptr;
551 const double* F = model->data.db;
552 float* err = _err->data.fl;
554 for( i = 0; i < count; i++ )
556 double a, b, c, d1, d2, s1, s2;
558 a = F[0]*m1[i].x + F[1]*m1[i].y + F[2];
559 b = F[3]*m1[i].x + F[4]*m1[i].y + F[5];
560 c = F[6]*m1[i].x + F[7]*m1[i].y + F[8];
563 d2 = m2[i].x*a + m2[i].y*b + c;
565 a = F[0]*m2[i].x + F[3]*m2[i].y + F[6];
566 b = F[1]*m2[i].x + F[4]*m2[i].y + F[7];
567 c = F[2]*m2[i].x + F[5]*m2[i].y + F[8];
570 d1 = m1[i].x*a + m1[i].y*b + c;
572 err[i] = (float)std::max(d1*d1*s1, d2*d2*s2);
577 CV_IMPL int cvFindFundamentalMat( const CvMat* points1, const CvMat* points2,
578 CvMat* fmatrix, int method,
579 double param1, double param2, CvMat* mask )
582 Ptr<CvMat> m1, m2, tempMask;
585 CvMat _F3x3 = cvMat( 3, 3, CV_64FC1, F ), _F9x3 = cvMat( 9, 3, CV_64FC1, F );
588 CV_Assert( CV_IS_MAT(points1) && CV_IS_MAT(points2) && CV_ARE_SIZES_EQ(points1, points2) );
589 CV_Assert( CV_IS_MAT(fmatrix) && fmatrix->cols == 3 &&
590 (fmatrix->rows == 3 || (fmatrix->rows == 9 && method == CV_FM_7POINT)) );
592 count = MAX(points1->cols, points1->rows);
596 m1 = cvCreateMat( 1, count, CV_64FC2 );
597 cvConvertPointsHomogeneous( points1, m1 );
599 m2 = cvCreateMat( 1, count, CV_64FC2 );
600 cvConvertPointsHomogeneous( points2, m2 );
604 CV_Assert( CV_IS_MASK_ARR(mask) && CV_IS_MAT_CONT(mask->type) &&
605 (mask->rows == 1 || mask->cols == 1) &&
606 mask->rows*mask->cols == count );
607 tempMask = cvCloneMat(mask);
610 tempMask = cvCreateMat( 1, count, CV_8U );
611 if( !tempMask.empty() )
612 cvSet( tempMask, cvScalarAll(1.) );
614 CvFMEstimator estimator( MIN(count, (method & 3) == CV_FM_7POINT ? 7 : 8) );
616 result = estimator.run7Point(m1, m2, &_F9x3);
617 else if( count == 8 || method == CV_FM_8POINT )
618 result = estimator.run8Point(m1, m2, &_F3x3);
623 if( param2 < DBL_EPSILON || param2 > 1 - DBL_EPSILON )
626 if( (method & ~3) == CV_RANSAC )
627 result = estimator.runRANSAC(m1, m2, &_F3x3, tempMask, param1, param2 );
629 result = estimator.runLMeDS(m1, m2, &_F3x3, tempMask, param2 );
632 /*icvCompressPoints( (CvPoint2D64f*)m1->data.ptr, tempMask->data.ptr, 1, count );
633 count = icvCompressPoints( (CvPoint2D64f*)m2->data.ptr, tempMask->data.ptr, 1, count );
634 assert( count >= 8 );
635 m1->cols = m2->cols = count;
636 estimator.run8Point(m1, m2, &_F3x3);*/
640 cvConvert( fmatrix->rows == 3 ? &_F3x3 : &_F9x3, fmatrix );
642 if( mask && tempMask )
643 cvCopy( tempMask, mask );
649 CV_IMPL void cvComputeCorrespondEpilines( const CvMat* points, int pointImageID,
650 const CvMat* fmatrix, CvMat* lines )
652 int abc_stride, abc_plane_stride, abc_elem_size;
653 int plane_stride, stride, elem_size;
654 int i, dims, count, depth, cn, abc_dims, abc_count, abc_depth, abc_cn;
656 const uchar *xp, *yp, *zp;
658 CvMat F = cvMat( 3, 3, CV_64F, f );
660 if( !CV_IS_MAT(points) )
661 CV_Error( !points ? CV_StsNullPtr : CV_StsBadArg, "points parameter is not a valid matrix" );
663 depth = CV_MAT_DEPTH(points->type);
664 cn = CV_MAT_CN(points->type);
665 if( (depth != CV_32F && depth != CV_64F) || (cn != 1 && cn != 2 && cn != 3) )
666 CV_Error( CV_StsUnsupportedFormat, "The format of point matrix is unsupported" );
668 if( points->rows > points->cols )
670 dims = cn*points->cols;
671 count = points->rows;
675 if( (points->rows > 1 && cn > 1) || (points->rows == 1 && cn == 1) )
676 CV_Error( CV_StsBadSize, "The point matrix does not have a proper layout (2xn, 3xn, nx2 or nx3)" );
677 dims = cn * points->rows;
678 count = points->cols;
681 if( dims != 2 && dims != 3 )
682 CV_Error( CV_StsOutOfRange, "The dimensionality of points must be 2 or 3" );
684 if( !CV_IS_MAT(fmatrix) )
685 CV_Error( !fmatrix ? CV_StsNullPtr : CV_StsBadArg, "fmatrix is not a valid matrix" );
687 if( CV_MAT_TYPE(fmatrix->type) != CV_32FC1 && CV_MAT_TYPE(fmatrix->type) != CV_64FC1 )
688 CV_Error( CV_StsUnsupportedFormat, "fundamental matrix must have 32fC1 or 64fC1 type" );
690 if( fmatrix->cols != 3 || fmatrix->rows != 3 )
691 CV_Error( CV_StsBadSize, "fundamental matrix must be 3x3" );
693 if( !CV_IS_MAT(lines) )
694 CV_Error( !lines ? CV_StsNullPtr : CV_StsBadArg, "lines parameter is not a valid matrix" );
696 abc_depth = CV_MAT_DEPTH(lines->type);
697 abc_cn = CV_MAT_CN(lines->type);
698 if( (abc_depth != CV_32F && abc_depth != CV_64F) || (abc_cn != 1 && abc_cn != 3) )
699 CV_Error( CV_StsUnsupportedFormat, "The format of the matrix of lines is unsupported" );
701 if( lines->rows > lines->cols )
703 abc_dims = abc_cn*lines->cols;
704 abc_count = lines->rows;
708 if( (lines->rows > 1 && abc_cn > 1) || (lines->rows == 1 && abc_cn == 1) )
709 CV_Error( CV_StsBadSize, "The lines matrix does not have a proper layout (3xn or nx3)" );
710 abc_dims = abc_cn * lines->rows;
711 abc_count = lines->cols;
715 CV_Error( CV_StsOutOfRange, "The lines matrix does not have a proper layout (3xn or nx3)" );
717 if( abc_count != count )
718 CV_Error( CV_StsUnmatchedSizes, "The numbers of points and lines are different" );
720 elem_size = CV_ELEM_SIZE(depth);
721 abc_elem_size = CV_ELEM_SIZE(abc_depth);
723 if( points->rows == dims )
725 plane_stride = points->step;
730 plane_stride = elem_size;
731 stride = points->rows == 1 ? dims*elem_size : points->step;
734 if( lines->rows == 3 )
736 abc_plane_stride = lines->step;
737 abc_stride = abc_elem_size;
741 abc_plane_stride = abc_elem_size;
742 abc_stride = lines->rows == 1 ? 3*abc_elem_size : lines->step;
745 cvConvert( fmatrix, &F );
746 if( pointImageID == 2 )
747 cvTranspose( &F, &F );
749 xp = points->data.ptr;
750 yp = xp + plane_stride;
751 zp = dims == 3 ? yp + plane_stride : 0;
753 ap = lines->data.ptr;
754 bp = ap + abc_plane_stride;
755 cp = bp + abc_plane_stride;
757 for( i = 0; i < count; i++ )
762 if( depth == CV_32F )
764 x = *(float*)xp; y = *(float*)yp;
766 z = *(float*)zp, zp += stride;
770 x = *(double*)xp; y = *(double*)yp;
772 z = *(double*)zp, zp += stride;
775 xp += stride; yp += stride;
777 a = f[0]*x + f[1]*y + f[2]*z;
778 b = f[3]*x + f[4]*y + f[5]*z;
779 c = f[6]*x + f[7]*y + f[8]*z;
781 nu = nu ? 1./sqrt(nu) : 1.;
782 a *= nu; b *= nu; c *= nu;
784 if( abc_depth == CV_32F )
786 *(float*)ap = (float)a;
787 *(float*)bp = (float)b;
788 *(float*)cp = (float)c;
804 CV_IMPL void cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst )
806 Ptr<CvMat> temp, denom;
808 int i, s_count, s_dims, d_count, d_dims;
809 CvMat _src, _dst, _ones;
812 if( !CV_IS_MAT(src) )
813 CV_Error( !src ? CV_StsNullPtr : CV_StsBadArg,
814 "The input parameter is not a valid matrix" );
816 if( !CV_IS_MAT(dst) )
817 CV_Error( !dst ? CV_StsNullPtr : CV_StsBadArg,
818 "The output parameter is not a valid matrix" );
820 if( src == dst || src->data.ptr == dst->data.ptr )
822 if( src != dst && (!CV_ARE_TYPES_EQ(src, dst) || !CV_ARE_SIZES_EQ(src,dst)) )
823 CV_Error( CV_StsBadArg, "Invalid inplace operation" );
827 if( src->rows > src->cols )
829 if( !((src->cols > 1) ^ (CV_MAT_CN(src->type) > 1)) )
830 CV_Error( CV_StsBadSize, "Either the number of channels or columns or rows must be =1" );
832 s_dims = CV_MAT_CN(src->type)*src->cols;
837 if( !((src->rows > 1) ^ (CV_MAT_CN(src->type) > 1)) )
838 CV_Error( CV_StsBadSize, "Either the number of channels or columns or rows must be =1" );
840 s_dims = CV_MAT_CN(src->type)*src->rows;
844 if( src->rows == 1 || src->cols == 1 )
845 src = cvReshape( src, &_src, 1, s_count );
847 if( dst->rows > dst->cols )
849 if( !((dst->cols > 1) ^ (CV_MAT_CN(dst->type) > 1)) )
850 CV_Error( CV_StsBadSize,
851 "Either the number of channels or columns or rows in the input matrix must be =1" );
853 d_dims = CV_MAT_CN(dst->type)*dst->cols;
858 if( !((dst->rows > 1) ^ (CV_MAT_CN(dst->type) > 1)) )
859 CV_Error( CV_StsBadSize,
860 "Either the number of channels or columns or rows in the output matrix must be =1" );
862 d_dims = CV_MAT_CN(dst->type)*dst->rows;
866 if( dst->rows == 1 || dst->cols == 1 )
867 dst = cvReshape( dst, &_dst, 1, d_count );
869 if( s_count != d_count )
870 CV_Error( CV_StsUnmatchedSizes, "Both matrices must have the same number of points" );
872 if( CV_MAT_DEPTH(src->type) < CV_32F || CV_MAT_DEPTH(dst->type) < CV_32F )
873 CV_Error( CV_StsUnsupportedFormat,
874 "Both matrices must be floating-point (single or double precision)" );
876 if( s_dims < 2 || s_dims > 4 || d_dims < 2 || d_dims > 4 )
877 CV_Error( CV_StsOutOfRange,
878 "Both input and output point dimensionality must be 2, 3 or 4" );
880 if( s_dims < d_dims - 1 || s_dims > d_dims + 1 )
881 CV_Error( CV_StsUnmatchedSizes,
882 "The dimensionalities of input and output point sets differ too much" );
884 if( s_dims == d_dims - 1 )
886 if( d_count == dst->rows )
888 ones = cvGetSubRect( dst, &_ones, cvRect( s_dims, 0, 1, d_count ));
889 dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, s_dims, d_count ));
893 ones = cvGetSubRect( dst, &_ones, cvRect( 0, s_dims, d_count, 1 ));
894 dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, d_count, s_dims ));
898 if( s_dims <= d_dims )
900 if( src->rows == dst->rows && src->cols == dst->cols )
902 if( CV_ARE_TYPES_EQ( src, dst ) )
905 cvConvert( src, dst );
909 if( !CV_ARE_TYPES_EQ( src, dst ))
911 temp = cvCreateMat( src->rows, src->cols, dst->type );
912 cvConvert( src, temp );
915 cvTranspose( src, dst );
919 cvSet( ones, cvRealScalar(1.) );
923 int s_plane_stride, s_stride, d_plane_stride, d_stride, elem_size;
925 if( !CV_ARE_TYPES_EQ( src, dst ))
927 temp = cvCreateMat( src->rows, src->cols, dst->type );
928 cvConvert( src, temp );
932 elem_size = CV_ELEM_SIZE(src->type);
934 if( s_count == src->cols )
935 s_plane_stride = src->step / elem_size, s_stride = 1;
937 s_stride = src->step / elem_size, s_plane_stride = 1;
939 if( d_count == dst->cols )
940 d_plane_stride = dst->step / elem_size, d_stride = 1;
942 d_stride = dst->step / elem_size, d_plane_stride = 1;
944 denom = cvCreateMat( 1, d_count, dst->type );
946 if( CV_MAT_DEPTH(dst->type) == CV_32F )
948 const float* xs = src->data.fl;
949 const float* ys = xs + s_plane_stride;
951 const float* ws = xs + (s_dims - 1)*s_plane_stride;
953 float* iw = denom->data.fl;
955 float* xd = dst->data.fl;
956 float* yd = xd + d_plane_stride;
961 zs = ys + s_plane_stride;
962 zd = yd + d_plane_stride;
965 for( i = 0; i < d_count; i++, ws += s_stride )
968 iw[i] = fabs((double)t) > FLT_EPSILON ? t : 1.f;
971 cvDiv( 0, denom, denom );
974 for( i = 0; i < d_count; i++ )
977 float x = *xs * w, y = *ys * w, z = *zs * w;
978 xs += s_stride; ys += s_stride; zs += s_stride;
979 *xd = x; *yd = y; *zd = z;
980 xd += d_stride; yd += d_stride; zd += d_stride;
983 for( i = 0; i < d_count; i++ )
986 float x = *xs * w, y = *ys * w;
987 xs += s_stride; ys += s_stride;
989 xd += d_stride; yd += d_stride;
994 const double* xs = src->data.db;
995 const double* ys = xs + s_plane_stride;
996 const double* zs = 0;
997 const double* ws = xs + (s_dims - 1)*s_plane_stride;
999 double* iw = denom->data.db;
1001 double* xd = dst->data.db;
1002 double* yd = xd + d_plane_stride;
1007 zs = ys + s_plane_stride;
1008 zd = yd + d_plane_stride;
1011 for( i = 0; i < d_count; i++, ws += s_stride )
1014 iw[i] = fabs(t) > DBL_EPSILON ? t : 1.;
1017 cvDiv( 0, denom, denom );
1020 for( i = 0; i < d_count; i++ )
1023 double x = *xs * w, y = *ys * w, z = *zs * w;
1024 xs += s_stride; ys += s_stride; zs += s_stride;
1025 *xd = x; *yd = y; *zd = z;
1026 xd += d_stride; yd += d_stride; zd += d_stride;
1029 for( i = 0; i < d_count; i++ )
1032 double x = *xs * w, y = *ys * w;
1033 xs += s_stride; ys += s_stride;
1035 xd += d_stride; yd += d_stride;
1044 static Mat _findHomography( const Mat& points1, const Mat& points2,
1045 int method, double ransacReprojThreshold,
1046 vector<uchar>* mask )
1048 CV_Assert(points1.isContinuous() && points2.isContinuous() &&
1049 points1.type() == points2.type() &&
1050 ((points1.rows == 1 && points1.channels() == 2) ||
1051 points1.cols*points1.channels() == 2) &&
1052 ((points2.rows == 1 && points2.channels() == 2) ||
1053 points2.cols*points2.channels() == 2));
1055 Mat H(3, 3, CV_64F);
1056 CvMat _pt1 = Mat(points1), _pt2 = Mat(points2);
1057 CvMat matH = H, _mask, *pmask = 0;
1060 mask->resize(points1.cols*points1.rows*points1.channels()/2);
1061 pmask = &(_mask = cvMat(1, (int)mask->size(), CV_8U, (void*)&(*mask)[0]));
1063 bool ok = cvFindHomography( &_pt1, &_pt2, &matH, method, ransacReprojThreshold, pmask ) > 0;
1069 static Mat _findFundamentalMat( const Mat& points1, const Mat& points2,
1070 int method, double param1, double param2,
1071 vector<uchar>* mask )
1073 CV_Assert(points1.isContinuous() && points2.isContinuous() &&
1074 points1.type() == points2.type() &&
1075 ((points1.rows == 1 && points1.channels() == 2) ||
1076 points1.cols*points1.channels() == 2) &&
1077 ((points2.rows == 1 && points2.channels() == 2) ||
1078 points2.cols*points2.channels() == 2));
1080 Mat F(3, 3, CV_64F);
1081 CvMat _pt1 = Mat(points1), _pt2 = Mat(points2);
1082 CvMat matF = F, _mask, *pmask = 0;
1085 mask->resize(points1.cols*points1.rows*points1.channels()/2);
1086 pmask = &(_mask = cvMat(1, (int)mask->size(), CV_8U, (void*)&(*mask)[0]));
1088 int n = cvFindFundamentalMat( &_pt1, &_pt2, &matF, method, param1, param2, pmask );
1097 cv::Mat cv::findHomography( const Mat& srcPoints, const Mat& dstPoints,
1098 vector<uchar>& mask, int method,
1099 double ransacReprojThreshold )
1101 return _findHomography(srcPoints, dstPoints, method, ransacReprojThreshold, &mask);
1104 cv::Mat cv::findHomography( const Mat& srcPoints, const Mat& dstPoints,
1105 int method, double ransacReprojThreshold )
1107 return _findHomography(srcPoints, dstPoints, method, ransacReprojThreshold, 0);
1111 cv::Mat cv::findFundamentalMat( const Mat& points1, const Mat& points2,
1112 vector<uchar>& mask, int method, double param1, double param2 )
1114 return _findFundamentalMat( points1, points2, method, param1, param2, &mask );
1117 cv::Mat cv::findFundamentalMat( const Mat& points1, const Mat& points2,
1118 int method, double param1, double param2 )
1120 return _findFundamentalMat( points1, points2, method, param1, param2, 0 );
1123 void cv::computeCorrespondEpilines( const Mat& points, int whichImage,
1124 const Mat& F, vector<Vec3f>& lines )
1126 CV_Assert(points.isContinuous() &&
1127 (points.depth() == CV_32S || points.depth() == CV_32F) &&
1128 ((points.rows == 1 && points.channels() == 2) ||
1129 points.cols*points.channels() == 2));
1131 lines.resize(points.cols*points.rows*points.channels()/2);
1132 CvMat _points = points, _lines = Mat(lines), matF = F;
1133 cvComputeCorrespondEpilines(&_points, whichImage, &matF, &_lines);
1136 void cv::convertPointsHomogeneous( const Mat& src, vector<Point3f>& dst )
1138 CV_Assert(src.isContinuous() &&
1139 (src.depth() == CV_32S || src.depth() == CV_32F) &&
1140 ((src.rows == 1 && src.channels() == 2) ||
1141 src.cols*src.channels() == 2));
1143 dst.resize(src.cols*src.rows*src.channels()/2);
1144 CvMat _src = src, _dst = Mat(dst);
1145 cvConvertPointsHomogeneous(&_src, &_dst);
1148 void cv::convertPointsHomogeneous( const Mat& src, vector<Point2f>& dst )
1150 CV_Assert(src.isContinuous() &&
1151 (src.depth() == CV_32S || src.depth() == CV_32F) &&
1152 ((src.rows == 1 && src.channels() == 3) ||
1153 src.cols*src.channels() == 3));
1155 dst.resize(src.cols*src.rows*src.channels()/3);
1156 CvMat _src = Mat(src), _dst = Mat(dst);
1157 cvConvertPointsHomogeneous(&_src, &_dst);