C++ cvGEMM函数代码示例

CV_IMPL const CvMat*
cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement )
{
    if( !kalman || !measurement )
        CV_Error( CV_StsNullPtr, "" );

    /* temp2 = H*P'(k) */
    cvMatMulAdd( kalman->measurement_matrix, kalman->error_cov_pre, 0, kalman->temp2 );
    /* temp3 = temp2*Ht + R */
    cvGEMM( kalman->temp2, kalman->measurement_matrix, 1,
            kalman->measurement_noise_cov, 1, kalman->temp3, CV_GEMM_B_T );

    /* temp4 = inv(temp3)*temp2 = Kt(k) */
    cvSolve( kalman->temp3, kalman->temp2, kalman->temp4, CV_SVD );

    /* K(k) */
    cvTranspose( kalman->temp4, kalman->gain );

    /* temp5 = z(k) - H*x'(k) */
    cvGEMM( kalman->measurement_matrix, kalman->state_pre, -1, measurement, 1, kalman->temp5 );

    /* x(k) = x'(k) + K(k)*temp5 */
    cvMatMulAdd( kalman->gain, kalman->temp5, kalman->state_pre, kalman->state_post );

    /* P(k) = P'(k) - K(k)*temp2 */
    cvGEMM( kalman->gain, kalman->temp2, -1, kalman->error_cov_pre, 1,
                     kalman->error_cov_post, 0 );

    return kalman->state_post;
}

// y := alpha * A * X + beta * y
inline void __dgemv(
	char trans, 
	int m, 
	int n, 
	double alpha, 
	double *A, // n * m
	int lda, 
	double *X, // m('T')
	int incx, 
	double beta, 
	double *y, // n('T')
	int incy
) {
	assert(incx==1 && incy==1);
	if(trans=='T') {
		CvMat A_mat= cvMat(n, m, CV_64FC1, A);
		CvMat X_mat= cvMat(m, 1, CV_64FC1, X);
		CvMat y_mat= cvMat(n, 1, CV_64FC1, y);
		cvGEMM(&A_mat, &X_mat, alpha, &y_mat, beta, &y_mat, 0);
	} else if(trans=='N') {
		CvMat A_mat= cvMat(n, m, CV_64FC1, A);
		CvMat X_mat= cvMat(n, 1, CV_64FC1, X);
		CvMat y_mat= cvMat(m, 1, CV_64FC1, y);
		cvGEMM(&A_mat, &X_mat, alpha, &y_mat, beta, &y_mat, CV_GEMM_A_T);
	} else {
		printf("error in function __dgemv");
		exit(-1);
	}
}

CV_IMPL const CvMat*
cvKalmanPredict( CvKalman* kalman, const CvMat* control )
{
    if( !kalman )
        CV_Error( CV_StsNullPtr, "" );

    /* update the state */
    /* x'(k) = A*x(k) */
    cvMatMulAdd( kalman->transition_matrix, kalman->state_post, 0, kalman->state_pre );

    if( control && kalman->CP > 0 )
        /* x'(k) = x'(k) + B*u(k) */
        cvMatMulAdd( kalman->control_matrix, control, kalman->state_pre, kalman->state_pre );

    /* update error covariance matrices */
    /* temp1 = A*P(k) */
    cvMatMulAdd( kalman->transition_matrix, kalman->error_cov_post, 0, kalman->temp1 );

    /* P'(k) = temp1*At + Q */
    cvGEMM( kalman->temp1, kalman->transition_matrix, 1, kalman->process_noise_cov, 1,
                     kalman->error_cov_pre, CV_GEMM_B_T );

    /* handle the case when there will be measurement before the next predict */
    cvCopy(kalman->state_pre, kalman->state_post);

    return kalman->state_pre;
}

//! Performs one step of extremum interpolation. 
void interpolateStep(int r, int c, ResponseLayer *t, ResponseLayer *m, ResponseLayer *b, double* xi, double* xr, double* xc )
//void interpolateStep()
{
	CvMat* dD, * H, * H_inv, X;
	double x[3] = { 0 };

	dD = deriv3D( r, c, t, m, b );
	H = hessian3D( r, c, t, m, b );
	H_inv = CreateMat( 3, 3, CV_64FC1 );
	cvInvert( H, H_inv, CV_SVD ); // incomplete check after invert() => CreateSVD()
	//cvInitMatHeader( &X, 3, 1, CV_64FC1, x, CV_AUTOSTEP );
	InitMatHeader( &X, 3, 1, CV_64FC1, x, CV_AUTOSTEP );  //check
	cvGEMM( H_inv, dD, -1, NULL, 0, &X, 0 );  //incomplete

	free(&dD);
	free(&H);
	free(&H_inv);

	//cvReleaseMat( &dD );
	//cvReleaseMat( &H );
	//cvReleaseMat( &H_inv );

	*xi = x[2];
	*xr = x[1];
	*xc = x[0];
}

//============================================================================
void AAM_Basic::CalcGradientMatrix(const CvMat* CParams, 
								   const CvMat* vCDisps,
								   const CvMat* vPoseDisps,
								   const std::vector<AAM_Shape>& AllShapes, 
								   const std::vector<IplImage*>& AllImages)
{
	int npixels = __cam.__texture.nPixels();
	int np = __cam.nModes();

	// do model parameter experiments
    {
		printf("Calculating parameter gradient matrix...\n");
		CvMat* GParam = cvCreateMat(np, npixels, CV_64FC1);cvZero(GParam);
        CvMat* GtG = cvCreateMat(np, np, CV_64FC1);
        CvMat* GtGInv = cvCreateMat(np, np, CV_64FC1);
        
		// estimate Rc
		EstCParamGradientMatrix(GParam, CParams, AllShapes, AllImages, vCDisps);
        __Rc = cvCreateMat(np, npixels, CV_64FC1);
        cvGEMM(GParam, GParam, 1, NULL, 0, GtG, CV_GEMM_B_T);
        cvInvert(GtG, GtGInv, CV_SVD );
		cvMatMul(GtGInv, GParam, __Rc);
        
		cvReleaseMat(&GtG);
		cvReleaseMat(&GtGInv);
		cvReleaseMat(&GParam);
    }

	// do pose experiments, this is for global shape normalization
    {
		printf("Calculating pose gradient matrix...\n");
		CvMat* GtG = cvCreateMat(4, 4, CV_64FC1);
        CvMat* GtGInv = cvCreateMat(4, 4, CV_64FC1);
        CvMat* GPose = cvCreateMat(4, npixels, CV_64FC1);   cvZero(GPose);

        // estimate Rt
        EstPoseGradientMatrix(GPose, CParams, AllShapes, AllImages, vPoseDisps);
        __Rq = cvCreateMat(4, npixels, CV_64FC1);
        cvGEMM(GPose, GPose, 1, NULL, 0, GtG, CV_GEMM_B_T);
        cvInvert(GtG, GtGInv, CV_SVD);
		cvMatMul(GtGInv, GPose, __Rq);
        
		cvReleaseMat(&GtG);
		cvReleaseMat(&GtGInv);
		cvReleaseMat(&GPose);
    }
}

CvSize get_Stitched_Size(CvSize im1_size, CvSize im2_size,  CvMat* Homo_Mat, double XDATA[], double YDATA[]  )/*{{{*/
{
	int Width  = 0;
	int Height = 0;
	double p1[3] = { 0, 0, 1 };
	double p2[3] = { im2_size.width-1 , 0, 1 };
	double p3[3] = { 0, im2_size.height-1, 1 };
	double p4[3] = { im2_size.width-1, im2_size.height-1, 1 };

	
	CvMat pp1 = cvMat( 3, 1,  CV_64FC1, p1 );
	CvMat pp2 = cvMat( 3, 1,  CV_64FC1, p2 );
	CvMat pp3 = cvMat( 3, 1,  CV_64FC1, p3 );
	CvMat pp4 = cvMat( 3, 1,  CV_64FC1, p4 );
	
	cvGEMM( Homo_Mat, &pp1, 1, NULL, 0, &pp1, 0 );
	cvGEMM( Homo_Mat, &pp2, 1, NULL, 0, &pp2, 0 );
	cvGEMM( Homo_Mat, &pp3, 1, NULL, 0, &pp3, 0 );
	cvGEMM( Homo_Mat, &pp4, 1, NULL, 0, &pp4, 0 );

	/* Normalization pp --->(x y 1) */
	
	
	double L1 = cvmGet( &pp1, 2, 0 );  
	double L2 = cvmGet( &pp2, 2, 0 );  
	double L3 = cvmGet( &pp3, 2, 0 );  
	double L4 = cvmGet( &pp4, 2, 0 );  

	XDATA[0] = min( min( min( cvmGet(&pp1,0,0)/L1, cvmGet(&pp2,0,0)/L2 ), cvmGet(&pp3,0,0)/L3 ),cvmGet(&pp4,0,0 )/L4 ); 
	YDATA[0] = min( min( min( cvmGet(&pp1,1,0)/L1, cvmGet(&pp2,1,0)/L2 ), cvmGet(&pp3,1,0)/L3 ),cvmGet(&pp4,1,0 )/L4 ); 

	XDATA[1] = max( max( max( cvmGet(&pp1,0,0)/L1, cvmGet(&pp2,0,0)/L2 ), cvmGet(&pp3,0,0)/L3 ),cvmGet(&pp4,0,0 )/L4 ); 
	YDATA[1] = max( max( max( cvmGet(&pp1,1,0)/L1, cvmGet(&pp2,1,0)/L2 ), cvmGet(&pp3,1,0)/L3 ),cvmGet(&pp4,1,0 )/L4 ); 
	
	Width  = max( max( max( im1_size.width,  XDATA[1] ), im1_size.width-XDATA[0]   ), XDATA[1]-XDATA[0] ) ;
	Height = max( max( max( im1_size.height, YDATA[1] ), im1_size.height-YDATA[0] ), YDATA[1]-YDATA[0] ) ;

	Width  = cvRound( Width );
	printf("New image's Width is %d\n", Width );
	Height = cvRound( Height );
	printf("New image's Height is %d\n", Height );

	return cvSize( Width, Height );

}/*}}}*/

	void BazARTracker::show_result(CamAugmentation &augment, IplImage *video, IplImage **dst)
	{
		if (getDebugMode()){
			if (*dst==0) *dst=cvCloneImage(video);
			else cvCopy(video, *dst);
		}

		CvMat *m  = augment.GetProjectionMatrix(0);
		// Flip...(This occured from OpenGL origin / camera origin)
		CvMat *coordinateTrans = cvCreateMat(3, 3, CV_64F);
		cvmSetIdentity(coordinateTrans);
		cvmSet(coordinateTrans, 1, 1, -1);
		cvmSet(coordinateTrans, 1, 2, m_cparam->cparam.ysize);
		cvMatMul(coordinateTrans, m, m);
	
		// extract intrinsic camera parameters from bazar's projection matrix..
		GetARToolKitRTfromBAZARProjMat(g_matIntrinsic, m, matCameraRT4_4);
			
		cvTranspose(matCameraRT4_4, matCameraRT4_4);
		cvReleaseMat(&coordinateTrans);

		// Debug
		if (getDebugMode()) {
			// draw the coordinate system axes
			double w =video->width/2.0;
			double h =video->height/2.0;

			// 3D coordinates of an object
			double pts[4][4] = {
				{w,h,0, 1}, // 0,0,0,1
				{w*2,h,0, 1}, // w, 0
				{w,h*2,0, 1}, // 0, h
				{w,h,-w-h, 1} // 0, 0, -
			};

			CvMat ptsMat, projectedMat;
			cvInitMatHeader(&ptsMat, 4, 4, CV_64FC1, pts);
			cvInitMatHeader(&projectedMat, 3, 4, CV_64FC1, projected);
		
			cvGEMM(m, &ptsMat, 1, 0, 0, &projectedMat, CV_GEMM_B_T );

			for (int i=0; i<4; i++) 
			{
				projected[0][i] /= projected[2][i];
				projected[1][i] /= projected[2][i];
			}

			// draw the projected lines
			cvLine(*dst, cvPoint((int)projected[0][0], (int)projected[1][0]),
				cvPoint((int)projected[0][1], (int)projected[1][1]), CV_RGB(255,0,0), 2);
			cvLine(*dst, cvPoint((int)projected[0][0], (int)projected[1][0]),
				cvPoint((int)projected[0][2], (int)projected[1][2]), CV_RGB(0,255,0), 2);
			cvLine(*dst, cvPoint((int)projected[0][0], (int)projected[1][0]),
				cvPoint((int)projected[0][3], (int)projected[1][3]), CV_RGB(0,0,255), 2);
		}
	}

//计算图2的四个角经矩阵H变换后的坐标
void SiftMatch::CalcFourCorner()
{
    //计算图2的四个角经矩阵H变换后的坐标
    double v2[]={0,0,1};//左上角
    double v1[3];//变换后的坐标值
    CvMat V2 = cvMat(3,1,CV_64FC1,v2);
    CvMat V1 = cvMat(3,1,CV_64FC1,v1);
    cvGEMM(H,&V2,1,0,1,&V1);//矩阵乘法
    leftTop.x = cvRound(v1[0]/v1[2]);
    leftTop.y = cvRound(v1[1]/v1[2]);
    //cvCircle(xformed,leftTop,7,CV_RGB(255,0,0),2);

    //将v2中数据设为左下角坐标
    v2[0] = 0;
    v2[1] = img2->height;
    V2 = cvMat(3,1,CV_64FC1,v2);
    V1 = cvMat(3,1,CV_64FC1,v1);
    cvGEMM(H,&V2,1,0,1,&V1);
    leftBottom.x = cvRound(v1[0]/v1[2]);
    leftBottom.y = cvRound(v1[1]/v1[2]);
    //cvCircle(xformed,leftBottom,7,CV_RGB(255,0,0),2);

    //将v2中数据设为右上角坐标
    v2[0] = img2->width;
    v2[1] = 0;
    V2 = cvMat(3,1,CV_64FC1,v2);
    V1 = cvMat(3,1,CV_64FC1,v1);
    cvGEMM(H,&V2,1,0,1,&V1);
    rightTop.x = cvRound(v1[0]/v1[2]);
    rightTop.y = cvRound(v1[1]/v1[2]);
    //cvCircle(xformed,rightTop,7,CV_RGB(255,0,0),2);

    //将v2中数据设为右下角坐标
    v2[0] = img2->width;
    v2[1] = img2->height;
    V2 = cvMat(3,1,CV_64FC1,v2);
    V1 = cvMat(3,1,CV_64FC1,v1);
    cvGEMM(H,&V2,1,0,1,&V1);
    rightBottom.x = cvRound(v1[0]/v1[2]);
    rightBottom.y = cvRound(v1[1]/v1[2]);
    //cvCircle(xformed,rightBottom,7,CV_RGB(255,0,0),2);

}

void EyeTracker::calculateScenePoint()
{
	centerMatrix->data.db[0] = aver_center.x;
	centerMatrix->data.db[1] = aver_center.y;
	centerMatrix->data.db[2] = 1;

	cvGEMM(homography, centerMatrix, 1, 0 , 0, hedefp);

	scenePoint = cvPoint(cvRound(hedefp->data.db[0] / hedefp->data.db[2]),
						 cvRound(hedefp->data.db[1] / hedefp->data.db[2]));
}

void MT_CVQuadraticMul(const CvMat* X,
                       const CvMat* W,
                       CvMat* dst,
                       bool transpose_X,
                       CvMat* tmp_prod)
{
    bool own_prod = (tmp_prod == NULL);

    if(own_prod)
    {
        tmp_prod = cvCreateMat(W->rows, X->cols, cvGetElemType(X));
    }

    cvGEMM(W, X, 1.0, NULL, 1.0, tmp_prod, transpose_X ? CV_GEMM_B_T : 0);
    cvGEMM(X, tmp_prod, 1.0, NULL, 1.0, dst, transpose_X ? 0 : CV_GEMM_A_T);

    if(own_prod)
    {
        cvReleaseMat(&tmp_prod);
    }
}

int cvL1QCSolve( CvMat* A, CvMat* B, CvMat* X, double epsilon, double mu, CvTermCriteria lb_term_crit, CvTermCriteria cg_term_crit )
{
	CvMat* AAt = cvCreateMat( A->rows, A->rows, CV_MAT_TYPE(A->type) );
	cvGEMM( A, A, 1, NULL, 0, AAt, CV_GEMM_B_T );
	CvMat* W = cvCreateMat( A->rows, 1, CV_MAT_TYPE(X->type) );
	if ( cvCGSolve( AAt, B, W, cg_term_crit ) > .5 )
	{
		cvReleaseMat( &W );
		cvReleaseMat( &AAt );
		return -1;
	}
	cvGEMM( A, W, 1, NULL, 0, X, CV_GEMM_A_T );
	cvReleaseMat( &W );
	cvReleaseMat( &AAt );

	CvMat* U = cvCreateMat( X->rows, X->cols, CV_MAT_TYPE(X->type) );
	cvAbsDiffS( X, U, cvScalar(0) );
	CvScalar sumAbsX = cvSum( U );
	double minAbsX, maxAbsX;
	cvMinMaxLoc( U, &minAbsX, &maxAbsX );
	cvConvertScale( U, U, .95, maxAbsX * .1 );
	
	double tau = MAX( (2 * X->rows + 1) / sumAbsX.val[0], 1 );

	if ( !(lb_term_crit.type & CV_TERMCRIT_ITER) )
		lb_term_crit.max_iter = ceil( (log(2 * X->rows + 1) - log(lb_term_crit.epsilon) - log(tau)) / log(mu) );

	CvTermCriteria nt_term_crit = cvTermCriteria( CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 50, lb_term_crit.epsilon );
	
	for ( int i = 0; i < lb_term_crit.max_iter; ++i )
	{
		icvL1QCNewton( A, B, X, U, epsilon, tau, nt_term_crit, cg_term_crit );
		tau *= mu;
	}

	cvReleaseMat( &U );

	return 0;
}

/*
Calculates interpolated pixel contrast.  Based on Eqn. (3) in Lowe's paper.

@param dog_pyr difference of Gaussians scale space pyramid
@param octv octave of scale space
@param intvl within-octave interval
@param r pixel row
@param c pixel column
@param xi interpolated subpixel increment to interval
@param xr interpolated subpixel increment to row
@param xc interpolated subpixel increment to col

@param Returns interpolated contrast.
*/
double interp_contr( IplImage*** dog_pyr, int octv, int intvl, int r,
					int c, double xi, double xr, double xc )
{
	CvMat* dD, X, T;
	double t[1], x[3] = { xc, xr, xi };

	cvInitMatHeader( &X, 3, 1, CV_64FC1, x, CV_AUTOSTEP );
	cvInitMatHeader( &T, 1, 1, CV_64FC1, t, CV_AUTOSTEP );
	dD = deriv_3D( dog_pyr, octv, intvl, r, c );
	cvGEMM( dD, &X, 1, NULL, 0, &T,  CV_GEMM_A_T );
	cvReleaseMat( &dD );

	return pixval32f( dog_pyr[octv][intvl], r, c ) + t[0] * 0.5;
}

void ConformalResizing::Constrian(const ConstrainUnits& unit, CvMat*& M)
{
	// Preprocess unit to make Matrix M less singular
	double meanX(0), meanY(0);
	for (int i = 0; i < unit.n; i++)
	{
		meanX += unit.pnts[i].x;
		meanY += unit.pnts[i].y;
	}
	meanX /= unit.n;
	meanY /= unit.n;

	int n = unit.n * 2;
	M = cvCreateMat(n, n, CV_64F);
	CvMat* A = cvCreateMat(n, 4, CV_64F);
	CvMat* Q = cvCreateMat(n, 4, CV_64F);
	CvMat* P = cvCreateMat(4, 4, CV_64F);

	// Initial A
	cvZero(A);
	for (int i = 0; i < unit.n; i++)
	{
		double x = unit.pnts[i].x - meanX;
		double y = unit.pnts[i].y - meanY;
		CV_MAT_ELEM(*A, double, 2*i, 0) = x;
		CV_MAT_ELEM(*A, double, 2*i, 1) = -y;
		CV_MAT_ELEM(*A, double, 2*i, 2) = 1;

		CV_MAT_ELEM(*A, double, 2*i+1, 0) = y;
		CV_MAT_ELEM(*A, double, 2*i+1, 1) = x;
		CV_MAT_ELEM(*A, double, 2*i+1, 3) = 1;
	}
	cvMulTransposed(A, P, 1); // P = (A^T * A)
	cvInvert(P, P, CV_SVD_SYM); // P = (A^T * A)^(-1)
	cvMatMul(A, P, Q); 
	cvGEMM(Q, A, 1, NULL, 0, M, CV_GEMM_B_T);

	// M = M - I
	double* d = M->data.db;
	for (int i = 0; i < n; i++, d += n+1)
	{
		*d -= 1;
	}

	cvReleaseMat(&A);
	cvReleaseMat(&Q);
	cvReleaseMat(&P);
}

//============================================================================
void AAM_IC::Draw(IplImage* image, const AAM_Shape& Shape, int type)
{
	if(type == 0) AAM_Common::DrawPoints(image, Shape);
	else if(type == 1) AAM_Common::DrawTriangles(image, Shape, __paw.__tri);
	else if(type == 2) 
	{
		cvGEMM(__error_t, __texture.GetBases(), 1, NULL, 1, __lamda, CV_GEMM_B_T);
		__texture.CalcTexture(__lamda, __warp_t);
		AAM_PAW paw;
		double minV, maxV;
		cvMinMaxLoc(__warp_t, &minV, &maxV);
		cvConvertScale(__warp_t, __warp_t, 255/(maxV-minV), -minV*255/(maxV-minV));
		paw.Train(Shape, __Points, __Storage, __paw.GetTri(), false);
		AAM_Common::DrawAppearance(image, Shape, __warp_t, paw, __paw);
	}
	else fprintf(stderr, "ERROR(%s, %d): Unsupported drawing type\n",
		__FILE__, __LINE__);
}

//============================================================================
void AAM_IC::CalcModifiedSD(CvMat* SD, const CvMat* dTx, const CvMat* dTy, 
							const CvMat* Jx, const CvMat* Jy)
{
	int i, j;
	
	//create steepest descent images
	double* _x = dTx->data.db;
	double* _y = dTy->data.db;
	double temp;
	for(i = 0; i < __shape.nModes()+4; i++)
	{
		for(j = 0; j < __paw.nPix(); j++)
		{
			temp = _x[3*j  ]*cvmGet(Jx,j,i) +_y[3*j  ]*cvmGet(Jy,j,i);
			cvmSet(SD,i,3*j,temp); 

			temp = _x[3*j+1]*cvmGet(Jx,j,i) +_y[3*j+1]*cvmGet(Jy,j,i);
			cvmSet(SD,i,3*j+1,temp); 

			temp = _x[3*j+2]*cvmGet(Jx,j,i) +_y[3*j+2]*cvmGet(Jy,j,i);
			cvmSet(SD,i,3*j+2,temp); 
		}
	}

	//project out appearance variation (and linear lighting parameters)
	const CvMat* B = __texture.GetBases();
	CvMat* V = cvCreateMat(4+__shape.nModes(), __texture.nModes(), CV_64FC1);
	CvMat SDMat, BMat;
	
	cvGEMM(SD, B, 1., NULL, 1., V, CV_GEMM_B_T);
	// Equation (63),(64)
	for(i = 0; i < __shape.nModes()+4; i++)
	{
		for(j = 0; j < __texture.nModes(); j++)
		{
			cvGetRow(SD, &SDMat, i);
			cvGetRow(B, &BMat, j);
			cvScaleAdd(&BMat, cvScalar(-cvmGet(V,i,j)), &SDMat, &SDMat);
		}
	}

	cvReleaseMat(&V);
}