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Mat
	Point2f 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;
}