opencv surf特征点匹配拼接源码

http://blog.csdn.net/huixingshao/article/details/42672073

/**
 * @file SURF_Homography
 * @brief SURF detector + descriptor + FLANN Matcher + FindHomography
 * @author A. Huaman
 */

#include <stdio.h>
#include <iostream>
#include <cv.h>
#include "opencv2/core/core.hpp"
#include <opencv2/opencv.hpp>  
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/nonfree/nonfree.hpp>  



using namespace cv;
using namespace std;

#ifdef _DEBUG
#pragma comment (lib, "opencv_calib3d246d.lib")
#pragma comment (lib, "opencv_contrib246d.lib")
#pragma comment (lib,"opencv_imgproc246d.lib")
#pragma comment (lib, "opencv_core246d.lib")
#pragma comment (lib, "opencv_features2d246d.lib")
#pragma comment (lib, "opencv_flann246d.lib")
#pragma comment (lib, "opencv_gpu246d.lib")
#pragma comment (lib, "opencv_highgui246d.lib")
#pragma comment (lib, "opencv_legacy246d.lib")
#pragma comment (lib, "opencv_ml246d.lib")
#pragma comment (lib, "opencv_objdetect246d.lib")
#pragma comment (lib, "opencv_ts246d.lib")
#pragma comment (lib, "opencv_video246d.lib")
#pragma comment (lib, "opencv_nonfree246d.lib")
#else
#pragma comment (lib, "opencv_calib3d246.lib")
#pragma comment (lib, "opencv_contrib246.lib")
#pragma comment (lib, "opencv_imgproc246.lib")
#pragma comment (lib, "opencv_core246.lib")
#pragma comment (lib, "opencv_features2d246.lib")
#pragma comment (lib, "opencv_flann246.lib")
#pragma comment (lib, "opencv_gpu246.lib")
#pragma comment (lib, "opencv_highgui246.lib")
#pragma comment (lib, "opencv_legacy246.lib")
#pragma comment (lib, "opencv_ml246.lib")
#pragma comment (lib, "opencv_objdetect246.lib")
#pragma comment (lib, "opencv_ts246.lib")
#pragma comment (lib, "opencv_video246.lib")
#pragma comment (lib, "opencv_nonfree246.lib")
#endif

int main()
{
	initModule_nonfree();//初始化模块，使用SIFT或SURF时用到 
	Ptr<FeatureDetector> detector = FeatureDetector::create( "SURF" );//创建SIFT特征检测器，可改成SURF/ORB
	Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create( "SURF" );//创建特征向量生成器，可改成SURF/ORB
	Ptr<DescriptorMatcher> descriptor_matcher = DescriptorMatcher::create( "BruteForce" );//创建特征匹配器  
	if( detector.empty() || descriptor_extractor.empty() )  
		cout<<"fail to create detector!";  

	//读入图像  
	Mat img1 = imread("1.jpg");  
	Mat img2 = imread("2.jpg");  

	//特征点检测  
	double t = getTickCount();//当前滴答数  
	vector<KeyPoint> m_LeftKey,m_RightKey;  
	detector->detect( img1, m_LeftKey );//检测img1中的SIFT特征点，存储到m_LeftKey中  
	detector->detect( img2, m_RightKey );  
	cout<<"图像1特征点个数:"<<m_LeftKey.size()<<endl;  
	cout<<"图像2特征点个数:"<<m_RightKey.size()<<endl;  

	//根据特征点计算特征描述子矩阵，即特征向量矩阵  
	Mat descriptors1,descriptors2;  
	descriptor_extractor->compute( img1, m_LeftKey, descriptors1 );  
	descriptor_extractor->compute( img2, m_RightKey, descriptors2 );  
	t = ((double)getTickCount() - t)/getTickFrequency();  
	cout<<"SIFT算法用时："<<t<<"秒"<<endl;  

	cout<<"图像1特征描述矩阵大小："<<descriptors1.size()  
		<<"，特征向量个数："<<descriptors1.rows<<"，维数："<<descriptors1.cols<<endl;  
	cout<<"图像2特征描述矩阵大小："<<descriptors2.size()  
		<<"，特征向量个数："<<descriptors2.rows<<"，维数："<<descriptors2.cols<<endl;  

	//画出特征点  
	Mat img_m_LeftKey,img_m_RightKey;  
	drawKeypoints(img1,m_LeftKey,img_m_LeftKey,Scalar::all(-1),0);  
	drawKeypoints(img2,m_RightKey,img_m_RightKey,Scalar::all(-1),0);  
	//imshow("Src1",img_m_LeftKey);  
	//imshow("Src2",img_m_RightKey);  

	//特征匹配  
	vector<DMatch> matches;//匹配结果  
	descriptor_matcher->match( descriptors1, descriptors2, matches );//匹配两个图像的特征矩阵  
	cout<<"Match个数："<<matches.size()<<endl;  

	//计算匹配结果中距离的最大和最小值  
	//距离是指两个特征向量间的欧式距离，表明两个特征的差异，值越小表明两个特征点越接近  
	double max_dist = 0;  
	double min_dist = 100;  
	for(int i=0; i<matches.size(); i++)  
	{  
		double dist = matches[i].distance;  
		if(dist < min_dist) min_dist = dist;  
		if(dist > max_dist) max_dist = dist;  
	}  
	cout<<"最大距离："<<max_dist<<endl;  
	cout<<"最小距离："<<min_dist<<endl;  

	//筛选出较好的匹配点  
	vector<DMatch> goodMatches;  
	for(int i=0; i<matches.size(); i++)  
	{  
		if(matches[i].distance < 0.2 * max_dist)  
		{  
			goodMatches.push_back(matches[i]);  
		}  
	}  
	cout<<"goodMatch个数："<<goodMatches.size()<<endl;  

	//画出匹配结果  
	Mat img_matches;  
	//红色连接的是匹配的特征点对，绿色是未匹配的特征点  
	drawMatches(img1,m_LeftKey,img2,m_RightKey,goodMatches,img_matches,  
		Scalar::all(-1)/*CV_RGB(255,0,0)*/,CV_RGB(0,255,0),Mat(),2);  

	imshow("MatchSIFT",img_matches);  
	IplImage result=img_matches;

	waitKey(0);


	//RANSAC匹配过程
	vector<DMatch> m_Matches=goodMatches;
	// 分配空间
	int ptCount = (int)m_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 = m_LeftKey[m_Matches[i].queryIdx].pt;
		p1.at<float>(i, 0) = pt.x;
		p1.at<float>(i, 1) = pt.y;

		pt = m_RightKey[m_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;
	vector<uchar> m_RANSACStatus;       // 这个变量用于存储RANSAC后每个点的状态
	findFundamentalMat(p1, p2, m_RANSACStatus, FM_RANSAC);

	// 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++;
		}
	}
	int InlinerCount = ptCount - OutlinerCount;   // 计算内点
	cout<<"内点数为："<<InlinerCount<<endl;


	// 这三个变量用于保存内点和匹配关系
	vector<Point2f> m_LeftInlier;
	vector<Point2f> m_RightInlier;
	vector<DMatch> m_InlierMatches;

	m_InlierMatches.resize(InlinerCount);
	m_LeftInlier.resize(InlinerCount);
	m_RightInlier.resize(InlinerCount);
	InlinerCount=0;
	float inlier_minRx=img1.cols;        //用于存储内点中右图最小横坐标，以便后续融合

	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;

			if(m_RightInlier[InlinerCount].x<inlier_minRx) inlier_minRx=m_RightInlier[InlinerCount].x;   //存储内点中右图最小横坐标

			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);

	// 显示计算F过后的内点匹配
	Mat OutImage;
	drawMatches(img1, key1, img2, key2, m_InlierMatches, OutImage);
	cvNamedWindow( "Match features", 1);
	cvShowImage("Match features", &IplImage(OutImage));
	waitKey(0);

	cvDestroyAllWindows();

	//矩阵H用以存储RANSAC得到的单应矩阵
	Mat H = findHomography( m_LeftInlier, m_RightInlier, RANSAC );

	//存储左图四角，及其变换到右图位置
	std::vector<Point2f> obj_corners(4);
	obj_corners[0] = Point(0,0); obj_corners[1] = Point( img1.cols, 0 );
	obj_corners[2] = Point( img1.cols, img1.rows ); obj_corners[3] = Point( 0, img1.rows );
	std::vector<Point2f> scene_corners(4);
	perspectiveTransform( obj_corners, scene_corners, H);

	//画出变换后图像位置
	Point2f offset( (float)img1.cols, 0);  
	line( OutImage, scene_corners[0]+offset, scene_corners[1]+offset, Scalar( 0, 255, 0), 4 );
	line( OutImage, scene_corners[1]+offset, scene_corners[2]+offset, Scalar( 0, 255, 0), 4 );
	line( OutImage, scene_corners[2]+offset, scene_corners[3]+offset, Scalar( 0, 255, 0), 4 );
	line( OutImage, scene_corners[3]+offset, scene_corners[0]+offset, Scalar( 0, 255, 0), 4 );
	imshow( "Good Matches & Object detection", OutImage );

	waitKey(0);
	imwrite("warp_position.jpg",OutImage);

 
 	int drift = scene_corners[1].x;                                                        //储存偏移量
 
 	//新建一个矩阵存储配准后四角的位置
 	int width = int(max(abs(scene_corners[1].x), abs(scene_corners[2].x)));
 	int height= img1.rows;                                                                  //或者：int height = int(max(abs(scene_corners[2].y), abs(scene_corners[3].y)));
 	float origin_x=0,origin_y=0;
 	if(scene_corners[0].x<0) {
 		if (scene_corners[3].x<0) origin_x+=min(scene_corners[0].x,scene_corners[3].x);
 		else origin_x+=scene_corners[0].x;}
 	width-=int(origin_x);
 	if(scene_corners[0].y<0) {
 		if (scene_corners[1].y) origin_y+=min(scene_corners[0].y,scene_corners[1].y);
 		else origin_y+=scene_corners[0].y;}
 	//可选：height-=int(origin_y);
 	Mat imageturn=Mat::zeros(width,height,img1.type());
 
 	//获取新的变换矩阵，使图像完整显示
 	for (int i=0;i<4;i++) {scene_corners[i].x -= origin_x; } 	//可选：scene_corners[i].y -= (float)origin_y; }
 	Mat H1=getPerspectiveTransform(obj_corners, scene_corners);
 
 	//进行图像变换，显示效果
	warpPerspective(img1,imageturn,H1,Size(width,height));	
 	imshow("image_Perspective", imageturn);
 	waitKey(0);
 
 
 	//图像融合
 	int width_ol=width-int(inlier_minRx-origin_x);
 	int start_x=int(inlier_minRx-origin_x);
 	cout<<"width: "<<width<<endl;
 	cout<<"img1.width: "<<img1.cols<<endl;
 	cout<<"start_x: "<<start_x<<endl;
 	cout<<"width_ol: "<<width_ol<<endl;
 
 	uchar* ptr=imageturn.data;
 	double alpha=0, beta=1;
 	for (int row=0;row<height;row++) {
 		ptr=imageturn.data+row*imageturn.step+(start_x)*imageturn.elemSize();
 		for(int col=0;col<width_ol;col++)
 		{
 			uchar* ptr_c1=ptr+imageturn.elemSize1();  uchar*  ptr_c2=ptr_c1+imageturn.elemSize1();
 			uchar* ptr2=img2.data+row*img2.step+(col+int(inlier_minRx))*img2.elemSize();
 			uchar* ptr2_c1=ptr2+img2.elemSize1();  uchar* ptr2_c2=ptr2_c1+img2.elemSize1();
 
 
 			alpha=double(col)/double(width_ol); beta=1-alpha;
 
 			if (*ptr==0&&*ptr_c1==0&&*ptr_c2==0) {
 				*ptr=(*ptr2);
 				*ptr_c1=(*ptr2_c1);
 				*ptr_c2=(*ptr2_c2);
 			}
 
 			*ptr=(*ptr)*beta+(*ptr2)*alpha;
 			*ptr_c1=(*ptr_c1)*beta+(*ptr2_c1)*alpha;
 			*ptr_c2=(*ptr_c2)*beta+(*ptr2_c2)*alpha;
 
 			ptr+=imageturn.elemSize();
 		}	}
 
 	//imshow("image_overlap", imageturn);
 	//waitKey(0);
 
 	Mat img_result=Mat::zeros(height,width+img2.cols-drift,img1.type());
 	uchar* ptr_r=imageturn.data;
 
 	for (int row=0;row<height;row++) {
 		ptr_r=img_result.data+row*img_result.step;
 
 		for(int col=0;col<imageturn.cols;col++)
 		{
 			uchar* ptr_rc1=ptr_r+imageturn.elemSize1();  uchar*  ptr_rc2=ptr_rc1+imageturn.elemSize1();
 
 			uchar* ptr=imageturn.data+row*imageturn.step+col*imageturn.elemSize();
 			uchar* ptr_c1=ptr+imageturn.elemSize1();  uchar*  ptr_c2=ptr_c1+imageturn.elemSize1();
 
 			*ptr_r=*ptr;
 			*ptr_rc1=*ptr_c1;
 			*ptr_rc2=*ptr_c2;
 
 			ptr_r+=img_result.elemSize();
 		}	
 
 		ptr_r=img_result.data+row*img_result.step+imageturn.cols*img_result.elemSize();
 		for(int col=imageturn.cols;col<img_result.cols;col++)
 		{
 			uchar* ptr_rc1=ptr_r+imageturn.elemSize1();  uchar*  ptr_rc2=ptr_rc1+imageturn.elemSize1();
 
 			uchar* ptr2=img2.data+row*img2.step+(col-imageturn.cols+drift)*img2.elemSize();
 			uchar* ptr2_c1=ptr2+img2.elemSize1();  uchar* ptr2_c2=ptr2_c1+img2.elemSize1();
 
 			*ptr_r=*ptr2;
 			*ptr_rc1=*ptr2_c1;
 			*ptr_rc2=*ptr2_c2;
 
 			ptr_r+=img_result.elemSize();
 		}	
 	}
 
 	imshow("image_result", img_result);
	//imwrite("final_result.jpg",img_result);
	return 0;
}