#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
// #include "extra.h" // use this if in OpenCV2
using namespace std;
using namespace cv;
/****************************************************
* 本程序演示了如何使用2D-2D的特征匹配估计相机运动
* **************************************************/
void find_feature_matches (
const Mat& img_1, const Mat& img_2,
std::vector<KeyPoint>& keypoints_1,
std::vector<KeyPoint>& keypoints_2,
std::vector< DMatch >& matches );
void pose_estimation_2d2d (
std::vector<KeyPoint> keypoints_1,
std::vector<KeyPoint> keypoints_2,
std::vector< DMatch > matches,
Mat& R, Mat& t );
// 像素坐标转相机归一化坐标
Point2d pixel2cam ( const Point2d& p, const Mat& K );
int main ( int argc, char** argv )
{
if ( argc != 3 )
{
cout<<"usage: pose_estimation_2d2d img1 img2"<<endl;
return 1;
}
//-- 读取图像
Mat img_1 = imread ( argv[1], CV_LOAD_IMAGE_COLOR );
Mat img_2 = imread ( argv[2], CV_LOAD_IMAGE_COLOR );
vector<KeyPoint> keypoints_1, keypoints_2;
vector<DMatch> matches;
find_feature_matches ( img_1, img_2, keypoints_1, keypoints_2, matches );
cout<<"一共找到了"<<matches.size() <<"组匹配点"<<endl;
//-- 估计两张图像间运动
Mat R,t;
pose_estimation_2d2d ( keypoints_1, keypoints_2, matches, R, t );
//-- 验证E=t^R*scale t_x就是t^
Mat t_x = ( Mat_<double> ( 3,3 ) <<
0, -t.at<double> ( 2,0 ), t.at<double> ( 1,0 ),
t.at<double> ( 2,0 ), 0, -t.at<double> ( 0,0 ),
-t.at<double> ( 1.0 ), t.at<double> ( 0,0 ), 0 );
cout<<"t^R="<<endl<<t_x*R<<endl;
//-- 验证对极约束
Mat K = ( Mat_<double> ( 3,3 ) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1 );
for ( DMatch m: matches )
{
Point2d pt1 = pixel2cam ( keypoints_1[ m.queryIdx ].pt, K );
Mat y1 = ( Mat_<double> ( 3,1 ) << pt1.x, pt1.y, 1 );//归一化平面坐标
Point2d pt2 = pixel2cam ( keypoints_2[ m.trainIdx ].pt, K );
Mat y2 = ( Mat_<double> ( 3,1 ) << pt2.x, pt2.y, 1 );
Mat d = y2.t() * t_x * R * y1;
cout << "epipolar constraint = " << d << endl;
}
return 0;
}
void find_feature_matches ( const Mat& img_1, const Mat& img_2,
std::vector<KeyPoint>& keypoints_1,
std::vector<KeyPoint>& keypoints_2,
std::vector< DMatch >& matches )
{
//-- 初始化
Mat descriptors_1, descriptors_2;
// used in OpenCV3
Ptr<FeatureDetector> detector = ORB::create();
Ptr<DescriptorExtractor> descriptor = ORB::create();
// use this if you are in OpenCV2
// Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
// Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create ( "BruteForce-Hamming" );
//-- 第一步:检测 Oriented FAST 角点位置
detector->detect ( img_1,keypoints_1 );
detector->detect ( img_2,keypoints_2 );
//-- 第二步:根据角点位置计算 BRIEF 描述子
descriptor->compute ( img_1, keypoints_1, descriptors_1 );
descriptor->compute ( img_2, keypoints_2, descriptors_2 );
//-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
vector<DMatch> match;
//BFMatcher matcher ( NORM_HAMMING );
matcher->match ( descriptors_1, descriptors_2, match );
//-- 第四步:匹配点对筛选
double min_dist=10000, max_dist=0;
//找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
for ( int i = 0; i < descriptors_1.rows; i++ )
{
double dist = match[i].distance;
if ( dist < min_dist ) min_dist = dist;
if ( dist > max_dist ) max_dist = dist;
}
printf ( "-- Max dist : %f \n", max_dist );
printf ( "-- Min dist : %f \n", min_dist );
//当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
for ( int i = 0; i < descriptors_1.rows; i++ )
{
if ( match[i].distance <= max ( 2*min_dist, 30.0 ) )
{
matches.push_back ( match[i] );
}
}
}
Point2d pixel2cam ( const Point2d& p, const Mat& K )
{
return Point2d//为什么cam坐标系是二维？归一化平面?(u-cx)/fx
(
( p.x - k.at<double> ( 0,2 ) ) / K.at<double> ( 0,0 ),
( p.y - K.at<double> ( 1,2 ) ) / K.at<double> ( 1,1 )
);
}
void pose_estimation_2d2d ( std::vector<KeyPoint> keypoints_1,
std::vector<KeyPoint> keypoints_2,
std::vector< DMatch > matches,
Mat& R, Mat& t )
{
// 相机内参,TUM Freiburg2
Mat K = ( Mat_<double> ( 3,3 ) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1 );
//-- 把匹配点转换为vector<Point2f>的形式
vector<Point2f> points1;
vector<Point2f> points2;
for ( int i = 0; i < ( int ) matches.size(); i++ )
{
points1.push_back ( keypoints_1[matches[i].queryIdx].pt );
points2.push_back ( keypoints_2[matches[i].trainIdx].pt );
}
//-- 计算基础矩阵
Mat fundamental_matrix;
fundamental_matrix = findFundamentalMat ( points1, points2, CV_FM_8POINT );
//CV_EXPORTS Mat findFundamentalMat( const Mat& points1, const Mat& points2,
//CV_OUT vector<uchar>& mask, int method=FM_RANSAC,
// double param1=3., double param2=0.99 );
// FM_7POINT = CV_FM_7POINT, //!< 7-point algorithm
//FM_8POINT = CV_FM_8POINT, //!< 8-point algorithm
//FM_LMEDS = CV_FM_LMEDS, //!< least-median algorithm
//FM_RANSAC = CV_FM_RANSAC //!< RANSAC algorithm
cout<<"fundamental_matrix is "<<endl<< fundamental_matrix<<endl;
//-- 计算本质矩阵
Point2d principal_point ( 325.1, 249.7 );//相机光心, TUM dataset标定值
double focal_length = 521;//相机焦距, TUM dataset标定值
Mat essential_matrix;
essential_matrix = findEssentialMat ( points1, points2, focal_length, principal_point );
cout<<"essential_matrix is "<<endl<< essential_matrix<<endl;
//-- 计算单应矩阵
Mat homography_matrix;
homography_matrix = findHomography ( points1, points2, RANSAC, 3 );
//第三个参数 Method used to computed a homography matrix. The following methods are possible:
//#0 - a regular method using all the points
// #CV_RANSAC - RANSAC-based robust method
// #CV_LMEDS - Least-Median robust method
//# 第四个参数取值范围在 1 到 10 , 绝一个点对的阈值。原图像的点经过变换后点与目标图像上对应点的误差
// # 超过误差就认为是 outlier
// # 返回值中 H 为变换矩阵。mask是掩模，online的点
// H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
cout<<"homography_matrix is "<<endl<<homography_matrix<<endl;
//-- 从本质矩阵中恢复旋转和平移信息.
recoverPose ( essential_matrix, points1, points2, R, t, focal_length, principal_point );
cout<<"R is "<<endl<<R<<endl;
cout<<"t is "<<endl<<t<<endl;
}