opencv3.1.0 特征点检测与图像匹配

图片资源

主要代码

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

using namespace std;
using namespace cv;
using namespace cv::xfeatures2d;

#define POINTSIZE	5000			// 海塞矩阵阈值，在这里调整精度，值越大点越少，越精准 

int main()
{
	Mat image01 = imread("1.jpg", 1);
	Mat image02 = imread("2.jpg", 1);
	imshow("p2", image01);
	imshow("p1", image02);

	//灰度图转换  
	Mat image1, image2;
	cvtColor(image01, image1, CV_RGB2GRAY);
	cvtColor(image02, image2, CV_RGB2GRAY);


	//提取特征点    
	Ptr<SURF> surfDetector = SURF::create(POINTSIZE);
	vector<KeyPoint> keyPoint1, keyPoint2;
	surfDetector->detect(image1, keyPoint1);
	surfDetector->detect(image2, keyPoint2);

	//特征点描述，为下边的特征点匹配做准备    
	Ptr<SURF> SurfDescriptor = SURF::create(POINTSIZE);
	Mat imageDesc1, imageDesc2;
	SurfDescriptor->compute(image1, keyPoint1, imageDesc1);
	SurfDescriptor->compute(image2, keyPoint2, imageDesc2);

	FlannBasedMatcher matcher;
	vector<vector<DMatch> > matchePoints;
	vector<DMatch> GoodMatchePoints;

	vector<Mat> train_desc(1, imageDesc1);
	matcher.add(train_desc);
	matcher.train();

	matcher.knnMatch(imageDesc2, matchePoints, 2);
	cout << "total match points: " << matchePoints.size() << endl;

	// Lowe's algorithm,获取优秀匹配点
	for (int i = 0; i < matchePoints.size(); i++)
	{
		if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
		{
			GoodMatchePoints.push_back(matchePoints[i][0]);
		}
	}

	Mat first_match;
	drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);
	imshow("first_match ", first_match);
	waitKey();
	return 0;
}

匹配结果