C++图像辅助工具包Eigen入门代码学习（很好的配合Opencv）

首先要说这个工具包非常的方便，解压出来就可以用了。在工程里面加入这个文件夹的路径就可以了。

下面是一些代码的练习：

#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
using namespace std;
int main()
{  

MatrixXd m = MatrixXd::Random(3,3);
MatrixXd n;
 n=MatrixXd::Constant(3,3,1.2); //这是一个3*3的矩阵，里面的值全部是1。2
 cout << "n =" << endl << n << endl;
m = (m + MatrixXd::Constant(3,3,1.2)) * 50;
cout << "m =" << endl << m << endl;
VectorXd v(3);
v << 1, 2, 3;  // 以下的结果说明是列向量3*1的。
cout << "m * v =" << endl << m * v << endl;
return 0;

}  

下面会用到转置：

#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
int main()
{  

Matrix2d mat;
mat << 1, 2,
3, 4;
Vector2d u(-1,1), v(2,0);
std::cout << "Here is mat*mat:\n" << mat*mat << std::endl;
std::cout << "Here is mat*u:\n" << mat*u << std::endl;
std::cout << "Here is u^T*mat:\n" << u.transpose()*mat << std::endl;  //可以看出来.transpose()用来求转置
std::cout << "Here is u^T*v:\n" << u.transpose()*v << std::endl;
std::cout << "Here is u*v^T:\n" << u*v.transpose() << std::endl;
std::cout << "Let's multiply mat by itself" << std::endl;
mat = mat*mat;
std::cout << "Now mat is mat:\n" << mat << std::endl;  

}  

下面的例子是从一个矩阵当中取出一个子矩阵（注意两种方式是一样的。转载的那篇论文里面这里说错了。）

#include <Eigen/Dense>
#include <iostream>
using namespace std;
int main()
{
Eigen::MatrixXf m(4,4);
m << 1, 2, 3, 4,
          5, 6, 7, 8,
          9,10,11,12,
          13,14,15,16;
cout << "Block in the middle" << endl;  // 两种不同的方式
cout << m.block<2,3>(1,1) << endl << endl;
cout << "other kind Block in the middle" << endl;
cout << m.block(1,1,2,3) << endl << endl;
for (int i = 1; i <= 3; ++i)
{
cout << "Block of size " << i << "x" << i << endl;
cout << m.block(0,0,i,i) << endl << endl;
}
}  

下面是向量的一些操作：

#include <Eigen/Dense>
#include <iostream>
using namespace std;
int main()
{
Eigen::ArrayXf v(6);
v << 1, 2, 3, 4, 5, 6;
cout << "v.head(3) =" << endl << v.head(3) << endl << endl;
cout << "v.tail<3>() = " << endl << v.tail<3>() << endl << endl;
v.segment(1,4) *= 2;
cout << "after 'v.segment(1,4) *= 2', v =" << endl << v << endl; //这里选出第2和第5个数来分别的*2 

Eigen::VectorXf v1(6);  //可以看出来是一样的
v1 << 1 ,2 ,2 , 4, 5, 6;
cout << "v1.head(3) =" << endl << v1.head(3) << endl << endl;
cout << "v.tail<3>() = " << endl << v1.tail<3>() << endl << endl;
v1.segment(1,4) *= 2;
cout << "after 'v.segment(1,4) *= 2', v =" << endl << v1 << endl;
} 

下来看一下基本的赋值情况：

#include <Eigen/Core>
#include <iostream>
#define SIZE 2

using namespace std;
using namespace Eigen;
void  main()
{
	MatrixXi m(SIZE,SIZE+1); // a (size)x(size+1)-matrix of int's
    cout<<m.cols()<<endl;
    m<<1,2,3,4,5,6; // 这样的赋值 m(1,2,3,4,5,6)是不正确的Vector定义时才是这样的
    cout << m <<endl;
	cout<<m.col(1)<<endl;

	VectorXf v(4); // a vector of 4 float's

v[0] = 1;
v[1] = 2;
v(2) = 3;
v(3) = 4;
cout << "v:" << v << std::endl;
Vector4f v2(0,9,8,2);//这个地方写成VectorXf v2(0,9,8,2)就会报错。
cout << "v2:" << v2 << std::endl;
}

下面的这张赋值方式：注意之间用的是逗号“ ，”

#include <Eigen/Core>
#include <iostream>
#define SIZE 2

using namespace std;
using namespace Eigen;
void  main()
{

int rows=5, cols=5;
MatrixXf m(rows,cols);
m << (Matrix3f() << 1, 2, 3, 4, 5, 6, 7, 8, 9).finished(),//不一样的赋值方式e .finished() is used to get the actual matrix object once the comma initialization of our temporary submatrix is done.
MatrixXf::Zero(3,cols-3),//用0来填补
MatrixXf::Zero(rows-3,3),
MatrixXf::Identity(rows-3,cols-3);//用单位矩阵来填补最下面的矩阵
cout << m<<endl;

}