OPENCV 函数cvCreateMat

综述:OpenCV有针对矩阵操作的C语言函数.许多其他方法提供了更加方便的C++接口，其效率与OpenCV一样.OpenCV将向量作为1维矩阵处理.矩阵按行存储，每行有4字节的校整.分配矩阵空间:

CvMat* cvCreateMat(int rows, int cols, int type);

type: 矩阵元素类型. 格式为CV_<bit_depth>(S|U|F)C<number_of_channels>.

例如: CV_8UC1 表示8位无符号单通道矩阵, CV_32SC2表示32位有符号双通道矩阵.

例程:

CvMat* M = cvCreateMat(4,4,CV_32FC1);

释放矩阵空间:CvMat* M = cvCreateMat(4,4,CV_32FC1);cvReleaseMat(&M);复制矩阵:CvMat* M1 = cvCreateMat(4,4,CV_32FC1);CvMat* M2;M2=cvCloneMat(M1);初始化矩阵:double a[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 };CvMat Ma=cvMat(3, 4, CV_64FC1, a);

另一种方法:

CvMat Ma;cvInitMatHeader(&Ma, 3, 4, CV_64FC1, a);

初始化矩阵为单位阵:CvMat* M = cvCreateMat(4,4,CV_32FC1);cvSetIdentity(M); // 这里似乎有问题，不成功

存取矩阵元素

假设需要存取一个2维浮点矩阵的第(i,j)个元素.间接存取矩阵元素:cvmSet(M,i,j,2.0); // Set M(i,j)t = cvmGet(M,i,j); // Get M(i,j)直接存取，假设使用4-字节校正:CvMat* M = cvCreateMat(4,4,CV_32FC1);int n = M->cols;float *data = M->data.fl;data[i*n+j] = 3.0;直接存取，校正字节任意:CvMat* M = cvCreateMat(4,4,CV_32FC1);int step = M->step/sizeof(float);float *data = M->data.fl;(data+i*step)[j] = 3.0;直接存取一个初始化的矩阵元素:double a[16];CvMat Ma = cvMat(3, 4, CV_64FC1, a);a[i*4+j] = 2.0; // Ma(i,j)=2.0;

矩阵/向量操作

矩阵-矩阵操作:CvMat *Ma, *Mb, *Mc;cvAdd(Ma, Mb, Mc); // Ma+Mb -> MccvSub(Ma, Mb, Mc); // Ma-Mb -> MccvMatMul(Ma, Mb, Mc); // Ma*Mb -> Mc按元素的矩阵操作:CvMat *Ma, *Mb, *Mc;cvMul(Ma, Mb, Mc); // Ma.*Mb -> MccvDiv(Ma, Mb, Mc); // Ma./Mb -> MccvAddS(Ma, cvScalar(-10.0), Mc); // Ma.-10 -> Mc向量乘积:double va[] = {1, 2, 3};double vb[] = {0, 0, 1};double vc[3];CvMat Va=cvMat(3, 1, CV_64FC1, va);CvMat Vb=cvMat(3, 1, CV_64FC1, vb);CvMat Vc=cvMat(3, 1, CV_64FC1, vc);double res=cvDotProduct(&Va,&Vb); // 点乘: Va . Vb -> rescvCrossProduct(&Va, &Vb, &Vc); // 向量积: Va x Vb -> Vcend{verbatim}注意 Va, Vb, Vc在向量积中向量元素个数须相同.单矩阵操作:CvMat *Ma, *Mb;cvTranspose(Ma, Mb); // transpose(Ma) -> Mb (不能对自身进行转置)CvScalar t = cvTrace(Ma); // trace(Ma) -> t.val[0]double d = cvDet(Ma); // det(Ma) -> dcvInvert(Ma, Mb); // inv(Ma) -> Mb非齐次线性系统求解:CvMat* A = cvCreateMat(3,3,CV_32FC1);CvMat* x = cvCreateMat(3,1,CV_32FC1);CvMat* b = cvCreateMat(3,1,CV_32FC1);cvSolve(&A, &b, &x); // solve (Ax=b) for x特征值分析(针对对称矩阵):CvMat* A = cvCreateMat(3,3,CV_32FC1);CvMat* E = cvCreateMat(3,3,CV_32FC1);CvMat* l = cvCreateMat(3,1,CV_32FC1);cvEigenVV(&A, &E, &l); // l = A的特征值 (降序排列) ， E = 对应的特征向量 (每行)奇异值分解SVD:CvMat* A = cvCreateMat(3,3,CV_32FC1);CvMat* U = cvCreateMat(3,3,CV_32FC1);CvMat* D = cvCreateMat(3,3,CV_32FC1);CvMat* V = cvCreateMat(3,3,CV_32FC1);cvSVD(A, D, U, V, CV_SVD_U_T|CV_SVD_V_T); // A = U D V^T