引入极线约束的surf特征匹配

#include <stdio.h>#include "opencv2/core/core.hpp"#include "opencv2/features2d/features2d.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/nonfree/nonfree.hpp"#include <opencv2/calib3d/calib3d.hpp>#include <iostream>using namespace std;using namespace cv;static void help(){    printf("\nThis program demonstrates using features2d detector, descriptor extractor and simple matcher\n"            "Using the SURF desriptor:\n"            "\n"            "Usage:\n matcher_simple <image1> <image2>\n");}int main(int argc, char** argv){    //if(argc != 3)    //{    //    help();    //    return -1;    //}    Mat img1 = imread("D:\\faceData\\stereopairs\\cones\\imL.jpg", CV_LOAD_IMAGE_GRAYSCALE);    Mat img2 = imread("D:\\faceData\\stereopairs\\cones\\imR.jpg", CV_LOAD_IMAGE_GRAYSCALE);    if(img1.empty() || img2.empty())    {        printf("Can't read one of the images\n");        return -1;    }    // detecting keypoints    SurfFeatureDetector detector(400);    vector<KeyPoint> keypoints1, keypoints2;    detector.detect(img1, keypoints1);    detector.detect(img2, keypoints2);    // computing descriptors    SurfDescriptorExtractor extractor;    Mat descriptors1, descriptors2;    extractor.compute(img1, keypoints1, descriptors1);    extractor.compute(img2, keypoints2, descriptors2);    // matching descriptors    BFMatcher matcher(NORM_L2);    vector<DMatch> matches;    matcher.match(descriptors1, descriptors2, matches);    // drawing the results    namedWindow("matches", 1);    Mat img_matches0,img_matches1;drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches0);//for(int i=0;i<matches.size();i++)//{////if(abs((keypoints1[matches[i].queryIdx].pt.y-keypoints2[matches[i].trainIdx].pt.y)>5)////{////}////cout<<keypoints1[matches[i].queryIdx].pt.x<<endl;//Point2f pt1,pt2;//pt1=keypoints1[matches[i].queryIdx].pt;//pt2=keypoints2[matches[i].trainIdx].pt;//if(abs(pt1.y-pt2.y)>5)//{//matches[i].distance=100;//}//} //   drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches1);// 分配空间int ptCount = (int)matches.size();Mat p1(ptCount, 2, CV_32F);Mat p2(ptCount, 2, CV_32F);// 把Keypoint转换为MatPoint2f pt;for (int i=0; i<ptCount; i++){pt = keypoints1[matches[i].queryIdx].pt;p1.at<float>(i, 0) = pt.x;p1.at<float>(i, 1) = pt.y;pt = keypoints2[matches[i].trainIdx].pt;p2.at<float>(i, 0) = pt.x;p2.at<float>(i, 1) = pt.y;}// 用RANSAC方法计算F// Mat m_Fundamental;// 上面这个变量是基本矩阵 vector<uchar> m_RANSACStatus;// 上面这个变量已经定义过，用于存储RANSAC后每个点的状态//m_Fundamental = findFundamentalMat(p1, p2, m_RANSACStatus, FM_RANSAC); Mat m_Fundamental=findFundamentalMat(p1, p2, m_RANSACStatus, FM_RANSAC); cout<<m_Fundamental<<endl;// 计算野点个数int OutlinerCount = 0;for (int i=0; i<ptCount; i++){if (m_RANSACStatus[i] == 0) // 状态为0表示野点{OutlinerCount++;}}// 计算内点vector<Point2f> m_LeftInlier;vector<Point2f> m_RightInlier;vector<DMatch> m_InlierMatches;// 上面三个变量用于保存内点和匹配关系int InlinerCount = ptCount - OutlinerCount;m_InlierMatches.resize(InlinerCount);m_LeftInlier.resize(InlinerCount);m_RightInlier.resize(InlinerCount);InlinerCount = 0;for (int i=0; i<ptCount; i++){if (m_RANSACStatus[i] != 0){m_LeftInlier[InlinerCount].x = p1.at<float>(i, 0);m_LeftInlier[InlinerCount].y = p1.at<float>(i, 1);m_RightInlier[InlinerCount].x = p2.at<float>(i, 0);m_RightInlier[InlinerCount].y = p2.at<float>(i, 1);m_InlierMatches[InlinerCount].queryIdx = InlinerCount;m_InlierMatches[InlinerCount].trainIdx = InlinerCount;InlinerCount++;}}// 把内点转换为drawMatches可以使用的格式vector<KeyPoint> key1(InlinerCount);vector<KeyPoint> key2(InlinerCount);KeyPoint::convert(m_LeftInlier, key1);KeyPoint::convert(m_RightInlier, key2);for(int i=0;i<10;i++)    cout<<key1[i].pt<<" "<<key2[i].pt<<" "<<key1[i].pt.x-key2[i].pt.x<<endl;drawMatches(img1, key1, img2, key2, m_InlierMatches, img_matches1);    imshow("matches0", img_matches0); imshow("matches1", img_matches1); Mat test(img1.rows,img1.cols,CV_32FC1); float a=0.0105591872,b=0.1024243227,c=10.106085341; for(int i=0;i<test.rows;i++) { float* pt=test.ptr<float>(i); for(int j=0;j<test.cols;j++) { float val=j*a+i*b+c; pt[j]=val; } } //cout<<test<<endl; cv::normalize(test,test,0,1,CV_MINMAX); cv::convertScaleAbs(test,test,255); test.convertTo(test,CV_8UC1); imshow("test",test);    waitKey(0);    return 0;}