【CS】尺度不变特征变换匹配算法SIFT（3）

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

using namespace std;
using namespace cv;

int main()
{
	Mat firstImage=imread("ww.jpg");
	Mat secondImage = imread("ee.jpg");
	if(firstImage.empty()||secondImage.empty())
	{
		cout<<"error"<<endl;
		return 0;
	}

	//resize(firstImage,firstImage,Size(800,1000),0,0,1);
	//resize(secondImage,secondImage,Size(800,1000),0,0,1);

	////////////////////////////////////////////////////////////////////////////////
	//第一步:获取SIFT特征
	//@Author:code陈
	////////////////////////////////////////////////////////////////////////////////
	//difine a sift detector
	SiftFeatureDetector siftDetector;
	//store key points
	vector<KeyPoint> firstKeypoint,secondKeypoint;
	//detect image with SIFT,get key points
	siftDetector.detect(firstImage,firstKeypoint);
	siftDetector.detect(secondImage,secondKeypoint);
	Mat firstOutImage,secondOutImage;
	//draw key points at the out image and show to the user
	drawKeypoints(firstImage,firstKeypoint,firstOutImage,Scalar(255,0,0));
	drawKeypoints(secondImage,secondKeypoint,secondOutImage,Scalar(0,255,0));
	imshow("first",firstOutImage);
	imshow("second",secondOutImage);
	// difine a sift descriptor extractor
	SiftDescriptorExtractor extractor;
	//store the descriptor of each image
	Mat firstDescriptor,secondDescriptor;
	BruteForceMatcher<L2<float>> matcher;

	vector<DMatch> matches;
	Mat matcheImage;
	//compute the descriptor of each image
	extractor.compute(firstImage,firstKeypoint,firstDescriptor);
	extractor.compute(secondImage,secondKeypoint,secondDescriptor);
	//match	

	matcher.match(firstDescriptor,secondDescriptor,matches);
	
	////////////////////////////////////////////////////////////////////////////////
	//第二步:RANSAC方法剔除outliner
	//@Author:code陈
	////////////////////////////////////////////////////////////////////////////////

	//将vector转化成Mat
	Mat firstKeypointMat(matches.size(),2,CV_32F),secondKeypointMat(matches.size(),2,CV_32F);
	for(int i = 0;i<matches.size();i++)
	{
		firstKeypointMat.at<float>(i,0) = firstKeypoint[matches[i].queryIdx].pt.x;
		firstKeypointMat.at<float>(i,1) = firstKeypoint[matches[i].queryIdx].pt.y;
		secondKeypointMat.at<float>(i,0) = secondKeypoint[matches[i].trainIdx].pt.x;
		secondKeypointMat.at<float>(i,1) = secondKeypoint[matches[i].trainIdx].pt.y;
	}
	//Calculate the fundamental Mat;
	vector<uchar> ransacStatus;	
	Mat fundamentalMat = findFundamentalMat(firstKeypointMat,secondKeypointMat,ransacStatus,FM_RANSAC);
	cout<<fundamentalMat<<endl;
	//Calculate the number of outliner points;
	int outlinerCount = 0;
	for(int i=0;i<matches.size();i++)
	{
		if(ransacStatus[i]==0)
		{
			outlinerCount++;
		}
	}
	//Calculate inliner points;
	vector<Point2f> firstInliner;
	vector<Point2f> secondInliner;
	vector<DMatch> inlinerMatches;
	int inlinerCount = matches.size()-outlinerCount;
	firstInliner.resize(inlinerCount);
	secondInliner.resize(inlinerCount);
	inlinerMatches.resize(inlinerCount);
	int index = 0;
	for(int i=0;i<matches.size();i++)
	{
		if(ransacStatus[i]!=0)
		{
			firstInliner[index].x = firstKeypointMat.at<float>(i,0);
			firstInliner[index].y = firstKeypointMat.at<float>(i,1);
			secondInliner[index].x = secondKeypointMat.at<float>(i,0);
			secondInliner[index].y = secondKeypointMat.at<float>(i,1);
			inlinerMatches[index].queryIdx = index;
			inlinerMatches[index].trainIdx = index;
			index ++;
		}
	}
	vector<KeyPoint> inlinerFirstKeypoint(inlinerCount);
	vector<KeyPoint> inlinerSecondKeypoint(inlinerCount);
	KeyPoint::convert(firstInliner,inlinerFirstKeypoint);
	KeyPoint::convert(secondInliner,inlinerSecondKeypoint);
	//cout<<fundamentalMat<<endl;
	//select 50 keypoints
	//matches.erase(matches.begin()+50,matches.end());
	//inlinerMatches.erase(inlinerMatches.begin()+50,inlinerMatches.end());
	drawMatches(firstImage,inlinerFirstKeypoint,secondImage,inlinerSecondKeypoint,inlinerMatches,matcheImage);
	imshow("ransacMatches",matcheImage);
	drawMatches(firstImage,firstKeypoint,secondImage,secondKeypoint,matches,matcheImage);
	imshow("matches",matcheImage);

	//imshow();
	waitKey(0);
	return 0;
}

版权声明：本文为博主原创文章，未经博主允许不得转载。