[OpenCV] sift demo

运行环境：vs2012+opencv320

sift 需要的头文件为 <opencv2/xfeatures2d.hpp>

#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d.hpp>

using namespace cv;
using namespace std;

bool refineMatchesWithHomography(
		const std::vector<cv::KeyPoint>& queryKeypoints,
		const std::vector<cv::KeyPoint>& trainKeypoints,
		float reprojectionThreshold, 
		std::vector<cv::DMatch>& matches,
		cv::Mat& homography) 
{
	const int minNumberMatchesAllowed = 8;
 
	if (matches.size() < minNumberMatchesAllowed)
		return false;
 
	// Prepare data for cv::findHomography
	std::vector<cv::Point2f> srcPoints(matches.size());
	std::vector<cv::Point2f> dstPoints(matches.size());
 
	for (size_t i = 0; i < matches.size(); i++) {
		srcPoints[i] = trainKeypoints[matches[i].trainIdx].pt;
		dstPoints[i] = queryKeypoints[matches[i].queryIdx].pt;
	}
 
	// Find homography matrix and get inliers mask
	std::vector<unsigned char> inliersMask(srcPoints.size());
	homography = cv::findHomography(srcPoints, dstPoints, CV_FM_RANSAC,reprojectionThreshold, inliersMask);
 
	std::vector<cv::DMatch> inliers;
	for (size_t i = 0; i < inliersMask.size(); i++) {
		if (inliersMask[i])
			inliers.push_back(matches[i]);
	}
 
	matches.swap(inliers);
	return matches.size() > minNumberMatchesAllowed;
}


bool comp(vector<DMatch>& a,vector<DMatch>& b)
{
	return a[0].distance/a[1].distance < b[0].distance/b[1].distance;
}

void main()
{
	Ptr<xfeatures2d::SIFT>feature=xfeatures2d::SIFT::create();

	Mat input1 = imread("sift_img\16.png",1);
	Mat input2 = imread("sift_img\11.png",1);

	vector<KeyPoint>kp1,kp2;
	Mat des1,des2;
	Mat output1,output2;

	feature->detectAndCompute(input1,cv::noArray(),kp1,des1);
	drawKeypoints(input1,kp1,output1);

	feature->detectAndCompute(input2,cv::noArray(),kp2,des2);
	drawKeypoints(input2,kp2,output2);

	vector<DMatch>matches;
	vector<vector<DMatch> >Dmatches;
    Ptr<cv::DescriptorMatcher> matcher_knn = new BFMatcher();
	Ptr<cv::DescriptorMatcher> matcher = new BFMatcher(NORM_L2,true);
    matcher->match(des1,des2,matches);

	matcher_knn->knnMatch(des1,des2,Dmatches,2);
	sort(Dmatches.begin(),Dmatches.end(),comp);

	vector<DMatch> good;
	for(int i=0;i<Dmatches.size();i++){
		if(Dmatches[i][0].distance < 0.75*Dmatches[i][1].distance)
			good.push_back(Dmatches[i][0]);
	}

	Mat imResultOri;
	drawMatches(output1, kp1, output2, kp2, matches, imResultOri,CV_RGB(0,255,0), CV_RGB(0,255,0));

	Mat matHomo;
	refineMatchesWithHomography(kp1, kp2, 3, matches, matHomo);
	cout << "[Info] Homography T : " << endl << matHomo << endl;

	Mat imResult;
	drawMatches(output1, kp1, output2, kp2, matches, imResult,CV_RGB(0,255,0), CV_RGB(0,255,0));

	Mat Mgood;
	drawMatches(output1, kp1, output2, kp2, good, Mgood,CV_RGB(0,255,0), CV_RGB(0,255,0));

	imshow("ransc",imResult);
	imshow("knn_match",Mgood);
	waitKey(0);
	
	return;
}