opencv提供了很多Mat的操作，其中涉及到两个重要的类：MatOp和MatExpr
C++: MatExpr abs(const Mat& m)
C++: void absdiff(InputArray src1, InputArray src2, OutputArray dst)
C = abs(A-B) is equivalent to absdiff(A, B, C)
C = abs(A) is equivalent to absdiff(A, Scalar::all(0), C)
C++: void add(InputArray src1, InputArray src2, OutputArray dst, InputArray mask=noArray(), int dtype=-1)
C++: void addWeighted(InputArray src1, double alpha, InputArray src2, double beta, double gamma, Out-
putArray dst, int dtype=-1)
C++: void bitwise_and(InputArray src1, InputArray src2, OutputArray dst, InputArray mask=noArray())
C++: void bitwise_not(InputArray src, OutputArray dst, InputArray mask=noArray())
C++: void bitwise_or(InputArray src1, InputArray src2, OutputArray dst, InputArray mask=noArray())
C++: void bitwise_xor(InputArray src1, InputArray src2, OutputArray dst, InputArray mask=noArray())
C++: void calcCovarMatrix(const Mat* samples, int nsamples, Mat& covar, Mat& mean, int flags, int
ctype=CV_64F)
C++: void cartToPolar(InputArray x, InputArray y, OutputArray magnitude, OutputArray angle, bool an-
gleInDegrees=false)
C++: void magnitude(InputArray x, InputArray y, OutputArray magnitude)
C++: bool checkRange(InputArray a, bool quiet=true, Point* pos=0, double minVal=-DBL_MAX, double
maxVal=DBL_MAX )
C++: void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop)
C++: void completeSymm(InputOutputArray mtx, bool lowerToUpper=false)
C++: void convertScaleAbs(InputArray src, OutputArray dst, double alpha=1, double beta=0)
C++: int countNonZero(InputArray src)
C++: Mat cvarrToMat(const CvArr* arr, bool copyData=false, bool allowND=true, int coiMode=0 )
C++: void dct(InputArray src, OutputArray dst, int flags=0)
C++: void idct(InputArray src, OutputArray dst, int flags=0)
C++: void dft(InputArray src, OutputArray dst, int flags=0, int nonzeroRows=0)
C++: void idft(InputArray src, OutputArray dst, int flags=0, int nonzeroRows=0)
C++: void divide(InputArray src1, InputArray src2, OutputArray dst, double scale=1, int dtype=-1)
C++: double determinant(InputArray mtx)
C++: bool eigen(InputArray src, OutputArray eigenvalues, int lowindex=-1, int highindex=-1)
C++: void exp(InputArray src, OutputArray dst)
C++: void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1 )
C++: void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1 )
C++: void flip(InputArray src, OutputArray dst, int flipCode)
C++: void gemm(InputArray src1, InputArray src2, double alpha, InputArray src3, double gamma, OutputArray dst, int flags=0 )
C++: ConvertData getConvertElem(int fromType, int toType)
C++: int getOptimalDFTSize(int vecsize)
C++: void inRange(InputArray src, InputArray lowerb, InputArray upperb, OutputArray dst)
C++: double invert(InputArray src, OutputArray dst, int flags=DECOMP_LU)
C++: void log(InputArray src, OutputArray dst)
C++: void LUT(InputArray src, InputArray lut, OutputArray dst, int interpolation=0 )
C++: double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar)
C++: void max(InputArray src1, InputArray src2, OutputArray dst)
&MatOp负责MatExpr的运算操作
class CV_EXPORTS MatOp
virtual ~MatOp();
virtual bool elementWise(const MatExpr& expr) const;
virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0;
virtual void roi(const MatExpr& expr, const Range& rowRange,
const Range& colRange, MatExpr& res) const;
virtual void diag(const MatExpr& expr, int d, MatExpr& res) const;
virtual void augAssignAdd(const MatExpr& expr, Mat& m) const;
virtual void augAssignSubtract(const MatExpr& expr, Mat& m) const;
virtual void augAssignMultiply(const MatExpr& expr, Mat& m) const;
virtual void augAssignDivide(const MatExpr& expr, Mat& m) const;
virtual void augAssignAnd(const MatExpr& expr, Mat& m) const;
virtual void augAssignOr(const MatExpr& expr, Mat& m) const;
virtual void augAssignXor(const MatExpr& expr, Mat& m) const;
virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const;
virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const;
virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const;
virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const;
virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const;
virtual void divide(double s, const MatExpr& expr, MatExpr& res) const;
virtual void abs(const MatExpr& expr, MatExpr& res) const;
virtual void transpose(const MatExpr& expr, MatExpr& res) const;
virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
virtual void invert(const MatExpr& expr, int method, MatExpr& res) const;
virtual Size size(const MatExpr& expr) const;
virtual int type(const MatExpr& expr) const;
//modules/core/src/matop.cpp
定义了很多不同的操作，每种操作会重载部分函数class MatOp_Identity : public MatOp
MatOp_Identity() {}
virtual ~MatOp_Identity() {}
bool elementWise(const MatExpr& /*expr*/) const { return true; }
void assign(const MatExpr& expr, Mat& m, int type=-1) const;
static void makeExpr(MatExpr& res, const Mat& m);
static MatOp_Identity g_MatOp_I //一种默认的MatOp
static inline bool isIdentity(const MatExpr& e) { return e.op == &g_MatOp_I }
class MatOp_T : public MatOp
MatOp_T() {}
virtual ~MatOp_T() {}
bool elementWise(const MatExpr& /*expr*/) const { return false; }
void assign(const MatExpr& expr, Mat& m, int type=-1) const;
void multiply(const MatExpr& e1, double s, MatExpr& res) const;
void transpose(const MatExpr& expr, MatExpr& res) const;
static void makeExpr(MatExpr& res, const Mat& a, double alpha=1);
static MatOp_T g_MatOp_T;
static inline bool isT(const MatExpr& e) { return e.op == &g_MatOp_T; }//其他函数的实现举例：
void MatOp::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const
if( this == e2.op )
double alpha = 1, beta = 1;
Mat m1, m2;
if( isAddEx(e1) && (!e1.b.data || e1.beta == 0) )
m1 = e1.a;
alpha = e1.
e1.op-&assign(e1, m1);
if( isAddEx(e2) && (!e2.b.data || e2.beta == 0) )
m2 = e2.a;
beta = e2.
s += e2.s;
e2.op-&assign(e2, m2);
MatOp_AddEx::makeExpr(res, m1, m2, alpha, beta, s);
e2.op-&add(e1, e2, res);
void MatOp::add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const
expr1.op-&assign(expr1, m1);
MatOp_AddEx::makeExpr(res, m1, Mat(), 1, 0, s);
void MatOp::subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const
if( this == e2.op )
double alpha = 1, beta = -1;
Mat m1, m2;
if( isAddEx(e1) && (!e1.b.data || e1.beta == 0) )
m1 = e1.a;
alpha = e1.
e1.op-&assign(e1, m1);
if( isAddEx(e2) && (!e2.b.data || e2.beta == 0) )
m2 = e2.a;
beta = -e2.
s -= e2.s;
e2.op-&assign(e2, m2);
MatOp_AddEx::makeExpr(res, m1, m2, alpha, beta, s);
e2.op-&subtract(e1, e2, res);
void MatOp::subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const
expr.op-&assign(expr, m);
MatOp_AddEx::makeExpr(res, m, Mat(), -1, 0, s);
//core/include/opencv2/core/mat.hppclass CV_EXPORTS MatExpr
MatExpr();
explicit MatExpr(const Mat& m);
MatExpr(const MatOp* _op, int _flags, const Mat& _a = Mat(), const Mat& _b = Mat(),
const Mat& _c = Mat(), double _alpha = 1, double _beta = 1, const Scalar& _s = Scalar());
operator Mat() const;
template&typename _Tp& operator Mat_&_Tp&() const;
Size size() const;
int type() const;
MatExpr row(int y) const;
MatExpr col(int x) const;
MatExpr diag(int d = 0) const;
MatExpr operator()( const Range& rowRange, const Range& colRange ) const;
MatExpr operator()( const Rect& roi ) const;
MatExpr t() const;
MatExpr inv(int method = DECOMP_LU) const;
MatExpr mul(const MatExpr& e, double scale=1) const;
MatExpr mul(const Mat& m, double scale=1) const;
Mat cross(const Mat& m) const;
double dot(const Mat& m) const;
const MatOp*
double alpha,
CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s);
CV_EXPORTS MatExpr operator + (const Scalar& s, const Mat& a);
CV_EXPORTS MatExpr operator + (const MatExpr& e, const Mat& m);
CV_EXPORTS MatExpr operator + (const Mat& m, const MatExpr& e);
CV_EXPORTS MatExpr operator + (const MatExpr& e, const Scalar& s);
CV_EXPORTS MatExpr operator + (const Scalar& s, const MatExpr& e);
CV_EXPORTS MatExpr operator + (const MatExpr& e1, const MatExpr& e2);
CV_EXPORTS MatExpr operator - (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator - (const Mat& a, const Scalar& s);
CV_EXPORTS MatExpr operator - (const Scalar& s, const Mat& a);
CV_EXPORTS MatExpr operator - (const MatExpr& e, const Mat& m);
CV_EXPORTS MatExpr operator - (const Mat& m, const MatExpr& e);
CV_EXPORTS MatExpr operator - (const MatExpr& e, const Scalar& s);
CV_EXPORTS MatExpr operator - (const Scalar& s, const MatExpr& e);
CV_EXPORTS MatExpr operator - (const MatExpr& e1, const MatExpr& e2);
CV_EXPORTS MatExpr operator - (const Mat& m);
CV_EXPORTS MatExpr operator - (const MatExpr& e);
CV_EXPORTS MatExpr operator * (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator * (const Mat& a, double s);
CV_EXPORTS MatExpr operator * (double s, const Mat& a);
CV_EXPORTS MatExpr operator * (const MatExpr& e, const Mat& m);
CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e);
CV_EXPORTS MatExpr operator * (const MatExpr& e, double s);
CV_EXPORTS MatExpr operator * (double s, const MatExpr& e);
CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2);
CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator / (const Mat& a, double s);
CV_EXPORTS MatExpr operator / (double s, const Mat& a);
CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m);
CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e);
CV_EXPORTS MatExpr operator / (const MatExpr& e, double s);
CV_EXPORTS MatExpr operator / (double s, const MatExpr& e);
CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2);
CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator & (const Mat& a, double s);
CV_EXPORTS MatExpr operator & (double s, const Mat& a);
CV_EXPORTS MatExpr operator &= (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator &= (const Mat& a, double s);
CV_EXPORTS MatExpr operator &= (double s, const Mat& a);
CV_EXPORTS MatExpr operator == (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator == (const Mat& a, double s);
CV_EXPORTS MatExpr operator == (double s, const Mat& a);
CV_EXPORTS MatExpr operator != (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator != (const Mat& a, double s);
CV_EXPORTS MatExpr operator != (double s, const Mat& a);
CV_EXPORTS MatExpr operator &= (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator &= (const Mat& a, double s);
CV_EXPORTS MatExpr operator &= (double s, const Mat& a);
CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator & (const Mat& a, double s);
CV_EXPORTS MatExpr operator & (double s, const Mat& a);
CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s);
CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a);
CV_EXPORTS MatExpr operator | (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator | (const Mat& a, const Scalar& s);
CV_EXPORTS MatExpr operator | (const Scalar& s, const Mat& a);
CV_EXPORTS MatExpr operator ^ (const Mat& a, const Mat& b);
CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s);
CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a);
CV_EXPORTS MatExpr operator ~(const Mat& m);
CV_EXPORTS MatExpr min(const Mat& a, const Mat& b);
CV_EXPORTS MatExpr min(const Mat& a, double s);
CV_EXPORTS MatExpr min(double s, const Mat& a);
CV_EXPORTS MatExpr max(const Mat& a, const Mat& b);
CV_EXPORTS MatExpr max(const Mat& a, double s);
CV_EXPORTS MatExpr max(double s, const Mat& a);
CV_EXPORTS MatExpr abs(const Mat& m);
CV_EXPORTS MatExpr abs(const MatExpr& e);
MatExpr::MatExpr(const Mat& m) : op(&g_MatOp_Identity), flags(0), a(m), b(Mat()), c(Mat()), alpha(1), beta(0), s(Scalar())
{　　//默认使用的是g_MatOp_Identity
MatExpr MatExpr::diag(int d) const
op-&diag(*this, d, e);
MatExpr MatExpr::t() const
op-&transpose(*this, e);
MatExpr operator + (const Mat& a, const Mat& b)
MatOp_AddEx::makeExpr(e, a, b, 1, 1);
class MatOp_AddEx : public MatOp
MatOp_AddEx() {}
virtual ~MatOp_AddEx() {}
bool elementWise(const MatExpr& /*expr*/) const { return true; }
void assign(const MatExpr& expr, Mat& m, int type=-1) const;
void add(const MatExpr& e1, const Scalar& s, MatExpr& res) const;
void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const;
void multiply(const MatExpr& e1, double s, MatExpr& res) const;
void divide(double s, const MatExpr& e, MatExpr& res) const;
void transpose(const MatExpr& e1, MatExpr& res) const;
void abs(const MatExpr& expr, MatExpr& res) const;
static void makeExpr(MatExpr& res, const Mat& a, const Mat& b, double alpha, double beta, const Scalar& s=Scalar());
static MatOp_AddEx g_MatOp_AddEx;inline void MatOp_AddEx::makeExpr(MatExpr& res, const Mat& a, const Mat& b, double alpha, double beta, const Scalar& s){&&& res = MatExpr(&g_MatOp_AddEx, 0, a, b, Mat(), alpha, beta, s);}
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Mat I,img,I1,I2,dst,A,B;
//Scalar 是一个结构体，常用来存储像素，比如Scalar
s=cvGet2D(pImg,x,y);
s.val[0],s.val[1],s.val[2]就是对应的图像BGR的值
1.加法I=I1+I2;//等同add(I1,I2,I);
add(I1,I2,dst,mask,dtype);
scaleAdd(I1,scale,I2,dst);//dst=scale*I1+I2;
2.减法absdiff(I1,I2,I);//I=|I1-I2|;
A-B;A-s;s-A;-A;
subtract(I1,I2,dst);
3.乘法I=I.mul(I);//点乘,I.mul(I,3);--&I=3*I.^2
C=A.mul(5/B);//==divide(A,B,C,5);
A*B;矩阵相乘
I=alpha*I;
Mat::cross(Mat);//三维向量(或矩阵)的叉乘,A.cross(B)
double Mat::dot(Mat);//2个向量(或矩阵)的点乘的结果,A.dot(B)
mul-------multiply
pow(src,double
p,dst);//如果p是整数dst(I)=src(I)^p;其他|src(I)|^p
log(src,dst)//自然对数运算
4.除法divide(I1,I2,dst,scale,int
dtype=-1);//dst=saturate_cast(I1*scale/I2);
A/B;alpha/A;都是点除
5.转换I.convertTo(I1,CV_32F);//类型转换
A.t();//转置
flip(I,dst,int
flipCode);//flipCode=0是上下翻转，&0时左右翻转,&0时一起来
sqrt(I,dst);
cvtColor(I,dst,int code,int dstCn=0);
resize:对图像进行形变
--------------------------------------------------------------------------
6.其他Scalar
s=sum(I);各通道求和
norm,countNonZero,trace,determinant,repeat都是返回Mat或者Scalar
countNonZero:用来统计非零的向量个数.(rows*cols个)
Scalar m=mean(I);//各通道求平均
Mat RowClone=C.row(1).clone();//复制第2行
addWeight(I1,alpha,I2,beta,gamma,dst,int
dtype=-1);//dst=saturate(alpha*I1+beta*I2+gamma);dtype是dst的深度
----------------------------------------------------------------------------
log(I,dst);//如果Iij!=0;则dstij=log(|Iij|)
exp(I,dst);//dst=exp(I);计算每个数组元素的指数
randu(I,Scalar::all(0),Scalar::all(255));
Mat::t()转置
Mat::inv(int
method=DECOMP_LU)求逆。method=DECOMP_CHOLESKY(专门用于对称，速度是LU的2倍),DECOMP_SVD//A.inv();A.inv()*B;
invert(I1,dst,int method=DECOMP_LU);//用法同上
MatExpr abs(Mat)//求绝对值
A cmpop B;Aalpha cmpop
A;这里cmpop表示&,&=,==,!=,&=,&等，结果是CV_8UC1的mask的0或255
9.按位运算：
A logicop B;As logicop
A;~A;这里logicop代表&,|,^
bitwise_not(I,dst,mask);//inverts所有的队列
还有bitwise_and,bitwise_or,bitwise_xor,
min(A,B);min(A,alpha);max(A,B);max(A,alpha);都返回MatExpr,返回的dst和A的类型一样
11.行列式运算
determinant(Mat);//行列式
bool eigen(I1,dst,int lowindex=-1,int
highindex=-1);//
bool eigen(I1,dst,I,int...);//得到特征值向量dst和对应特征值的特征向量
minMaxLoc(I1,&minVal,&maxVal,Point *minLoc=0,Point*
MaxLoc=0,mask);
//minLoc是2D时距原点最小的点(未考证)
------------------------------------------------------------------------------
二.初始化Mat
I(img,Rect(10,10,100,100));//用一块地方初始化。
Mat I=img(Range:all(),Range(1,3));//所有行，1~3列
Mat I=img.clone();//完全复制
img.copyTo(I);//传递矩阵头
I(2,2,CV_8UC3,Scalar(0,0,255));//I=[0,0,255,0,0,255;0,0,255,0,0,255];
Mat E=Mat::eye(4,4,CV_64F);//对角矩阵
Mat O=Mat::ones(2,2,CV_32F);//全一矩阵
Mat Z=Mat::zeros(3,3,CV_8UC1);//全零矩阵
Mat C=(Mat_(2,2)&&0,-1,2,3);//如果是简单矩阵的初始化
Mat::row(i);Mat::row(j);Mat::rowRange(start,end);Mat::colRange(start,end);都只是创建个头
Mat::diag(int d);d=0是是主对角线，d=1是比主低的对角线,d=-1....
static Mat Mat::diag(const Mat& matD)
Mat::setTo(Scalar &s);以s初始化矩阵
Mat::push_back(Mat);在原来的Mat的最后一行后再加几行
Mat::pop_back(size_t nelems=1);//移出最下面几行
-------------------------------------------------------------------------------
三.矩阵读取和修改(1)1个通道：
for(int i=0;i
for(int j=0;j
I.at(i,j)=k;
(2)3个通道：
Mat_ _I=I;//他没有4个通道寸，只有3个通道！
for(int i=0;i
for(int j=0;j
_I(i,j)[0]=b;
_I(i,j)[1]=g;
_I(i,j)[2]=r;
------------------------------------------------------------
或者直接用I.at(i,j)[0]....
-------------------------------------------------
{s=proImg.ptr(i);
{a1=s[3*j+1]-m1;
a2=s[3*j+2]-m2;}}
-------------------------------------------------------------------------
(3)其他机制
I.rows(0).setTo(Scalar(0));//把第一行清零
saturate_cast(...);//可以确保内容为0~255的整数
Mat::total();返回一共的元素数量
Mat::elemSize();返回元素的大小:CV_16SC3--&3*sizeof(short)--&6
Mat::elemSize1();返回元素一个通道的大小CV_16SC3--&sizeof(short)--&2
int Mat::type()返回他的类型CV_16SC3之类
int Mat::depth()返回深度:CV_16SC3--&CV_16S
int Mat::channels()返回通道数
size_t Mat:step1()返回一个被elemSize1()除以过的step
Size Mat::size()返回Size(cols,rows);如果大于2维，则返回(-1,-1)，都是先宽再高的
bool Mat::empty()如果没有元素返回1,即Mat::total()==0或者Mat::data==NULL
uchar *Mat::ptr(int i=0)指向第i行
Mat::at(int i)(int i,int j)(Point pt)(int i,int j,int k)
RNG随机类:next,float RNG::uniform(float a,float b);..
double RNG::gaussian(double sigma);
RNG::fill(I,int distType,Mat low,Mat up);//用随机数填充
randu(I,low,high);
randn(I,Mat mean,Mat stddev);
reduce(I,dst,int dim,int reduceOp,int
dtype=-1);//可以统计每行或每列的最大、最小、平均值、和
setIdentity(dst,Scalar &value=Scalar(1));//把对角线替换为value
//效果等同：Mat A=Mat::eye(4,3,CV_32F)*5;
--------------------------------------------------------------
四.较复杂运算
gemm(I1,I2,alpha,I3,beta,dst,int
flags=0);//I1至少是浮点型,I2同I1,flags用来转置
//gemm(I1,I2,alpha,I3,beta,dst,GEMM_1_T,GEMM_3_T);--&dst=alpha*I1.t()*I2+beta*I3.t();可用此完全代替此函数
mulTransposed(I,dst,bool aTa,Mat delta=noArray(),double scale=1,int
rtype=-1);
//I是1通道的,和gemm不同,他可用于任何类型。
//如果aTa=flase时,dst=scale*(I-delta).t()*(I-delta);
//如果是true,dst=scale*(I-delta)(I-delta).t();
calcCovarMatrix(Mat,int,Mat,Mat,int,int=);calcCovarMatrix(Mat I,Mat
covar,Mat mean,int flags,int=);
cartToPolar//转到极坐标
compare(I1,I2,dst,cmpop);cmpop=CMP_EQ,CMP_GT,CMP_GE,CMP_LT,CMP_LE,COM_NE
completeSymm(M,bool
lowerToUpper=false);当lowerToUpper=true时Mij=Mji(ij)
变成可显示图像:convertScaleAbs(I,dst,alpha,beta);dst=saturate_cast(|alpha*I+beta|);
dct(I,dst,int
flags=0);//DCT变换，1维、2维的矩阵;flags=DCT_INVERSE,DCT_ROWS
idct,dft,idft
inRange(I1,I_low,I_up,dst);//dst是CV_8UC1,在2者之间就是255
Mahalanobis(vec1,vec2,covar);
merge(vector,Mat);//把多个Mat组合成一个和split相反
double norm(...)：当src2木有时,norm可以计算出最长向量、向量距离和、向量距离和的算术平方根
solveCubic解3次方程，solvePoly解n次方程
排列：sort,sortIdx
mixChannels();对某个通道进行各种传递
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未懂的函数
getConvertElem,extractImageCOI,LUT
magnitude(x,y,dst);//I1,I2都是1维向量,dst=sqrt(x(I)^2+y(I)^2);
meanStdDev,
MulSpectrums(I1,I2,dst,flags);傅里叶
normalize(I,dst,alpha,beta,int normType=NORM_L2,int
rtype=-1,mask);//归一化
PCA,SVD,solve,transform,transpose
五、其他数据结构Point2f P(5,1);
Point3f P3f(2,6,7);
v;v.push_back((float)CV_PI);v.push_back(2);v.push_back(3.01f);//不断入
vector vPoints(20);//一次定义20个
常用方法：
mask=src&0;这样很快建立一个mask了
函数表见http://tmjfzy.blog.163.com/blog/static//
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