在我的程序中,我需要一个3*3矩阵K(相机校准矩阵(和一个3x3矩阵F(基本矩阵(来计算E(基本矩阵,Essential matrix(:E=K.t((FK,但当我使用时
cv::Matx33d K(518.00325.5,0,519.0253.5,0,0,1(;
它产生错误:行中没有匹配"operator*":
E=K.t((*F*K;
但是当我使用时
cv::Mat K(3,3,CV_64FC1); //cannot be 32 (float)
K.at<double>(0,0) = 518.0;
K.at<double>(1,1) = 519.0;
K.at<double>(0,2) = 325.5;
K.at<double>(1,2) = 253.5;
K.at<double>(2,2) = 1;
它有效,但当我使用时
cv::Mat_<double> K = ( cv::Mat_<double>(3, 3) <<
518.0, 0,325.5,
0 ,519.0,253.5,
0 , 0, 1);
它也起作用,但与上面的相比产生了完全不同的结果
有人能告诉我这3个opencv Mat贪得无厌之间有什么区别吗?谢谢
这是的所有代码
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/calib3d/calib3d.hpp>
using namespace std;
using namespace cv;
int main()
{
cout<<"Hello SLAM!"<<endl;
cv::Mat img1,img2,img;
// first method :can run ,but yeild fault result
cv::Mat K(3,3,CV_64FC1); //cannot be 32 (float)
K.at<double>(0,0) = 518.0;
K.at<double>(1,1) = 519.0;
K.at<double>(0,2) = 325.5;
K.at<double>(1,2) = 253.5;
K.at<double>(2,2) = 1;
// second method :cannont build
// cv::Matx33d K (518.0, 0,325.5,
// 0 ,519.0,253.5,
// 0 , 0, 1);//calibration matrix
// cv::Matx33d K=cv::Matx33d(518.0, 0,325.5,
// 0 ,519.0,253.5,
// 0 , 0, 1);//calibration matrix
// third method :can run ,yeild correcy result
cv::Mat_<double> K = ( cv::Mat_<double>(3, 3) <<
518.0, 0,325.5,
0 ,519.0,253.5,
0 , 0, 1);
img1= cv::imread("/home/kylefan/program/cmake_demo/image/rgb1.png",IMREAD_COLOR);
img2= cv::imread("/home/kylefan/program/cmake_demo/image/rgb2.png",IMREAD_COLOR);
vector<cv::Point2f> point1;
vector<cv::Point2f> point2;
cv::ORB orb;
vector<cv::KeyPoint>kp1,kp2;
cv::Mat desp1,desp2;
orb(img1,cv::Mat(),kp1,desp1,false);
orb(img2,cv::Mat(),kp2,desp2,false);
cv::Ptr<cv::DescriptorMatcher> matcher = cv::DescriptorMatcher::create("BruteForce-Hamming");
double knn_match_ratio = 0.3;
vector< vector<cv::DMatch>> matches_knn;
matcher->knnMatch(desp1,desp2,matches_knn,2);
vector<cv::DMatch> goodmatches;
for(int i=0;i<matches_knn.size();i++)
{
if(matches_knn[i][0].distance < knn_match_ratio * matches_knn[i][1].distance)goodmatches.push_back(matches_knn[i][0]);
}
if(goodmatches.size()<20)
{
cout<<"too less goodmatches "<<endl;
}
for(auto m:goodmatches)
{
point1.push_back(kp1[m.queryIdx].pt);
point2.push_back(kp2[m.queryIdx].pt);
}
// for(int j=0;j<goodmatches.size();j++)
// {
// //cout<<j<<":"<<point1[j].x<<","<<point1[j].y<<","<<point2[j].x<<","<<point2[j].y<<","<<endl;
// }
cv::Mat F = cv::findFundamentalMat(point1,point2,FM_RANSAC,3,0.99);//in calib3d
cv::Mat_<double> E = K.t() *F* K;
SVD svd(E);
cv::Matx33d W(0,-1,0,
1,0,0,
0,0,1);
cv::Mat_<double> R = svd.u * Mat(W) * svd.vt;
cv::Mat_<double> t = svd.u.col(2);
cv::Matx34d P1(R(0,0),R(0,1),R(0,2),t(0),
R(1,0),R(1,1),R(1,2),t(1),
R(2,0),R(2,1),R(2,2),t(2));
cout<<goodmatches.size()<<" goodmatches "<<endl;
cout<<"F="<<F<<endl;
cout<<"K="<<K<<endl;
cout<<"E="<<E<<endl;
cout<<"R="<<R<<endl;
cout<<"t="<<t<<endl;
cout<<"P1="<<P1<<endl;
if(fabsf(determinant(R))-1.0 > 1e-07)
{
cerr << "det(R) != +-1.0, this is not a rotation matrix" << endl;
}
else
{
cout<<"rotation matrix is right"<<endl;
}
return 0;
}
在第二个版本中,您没有初始化矩阵元素,也没有将它们全部设置。
cv::Mat K(3,3,CV_64FC1); //cannot be 32 (float)
K.at<double>(0,0) = 518.0;
K.at<double>(1,1) = 519.0;
K.at<double>(0,2) = 325.5;
K.at<double>(1,2) = 253.5;
K.at<double>(2,2) = 1;
这意味着在索引(0,1(中可以存在ANY值;(1,0(;(2,0(和(2,1(。
要在创建时使用0进行初始化,请尝试:cv::Mat::zeros()
cv::Mat K = cv::Mat::zeros(3,3,CV_64FC1); //cannot be 32 (float)
K.at<double>(0,0) = 518.0;
K.at<double>(1,1) = 519.0;
K.at<double>(0,2) = 325.5;
K.at<double>(1,2) = 253.5;
K.at<double>(2,2) = 1;