Opencv Surf特征实现图像无缝拼接生成全景图像（二）

https://blog.youkuaiyun.com/b695886658/article/details/80856080?utm_medium=distribute.pc_relevant.none-task-blog-2_defaultbaidujs_baidulandingword~default-0.essearch_pc_relevant&spm=1001.2101.3001.4242.1

1. 找到图像1和图像2中最强的匹配点所在的位置
2. 通过映射矩阵变换，得到图像1的最强匹配点经过映射后投影到新图像上的位置坐标
3. 在新图像上的最强匹配点的映射坐标处，衔接两幅图像，该点左侧图像完全是图像1，右侧完全是图像2

#include "highgui/highgui.hpp"  
#include "opencv2/nonfree/nonfree.hpp"  
#include "opencv2/legacy/legacy.hpp" 

using namespace cv;

//计算原始图像点位在经过矩阵变换后在目标图像上对应位置
Point2f getTransformPoint(const Point2f originalPoint,const Mat &transformMaxtri);

int main(int argc,char *argv[])  
{  
	Mat image01=imread(argv[1]);  
	Mat image02=imread(argv[2]);
	imshow("拼接图像1",image01);
	imshow("拼接图像2",image02);

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

	//提取特征点  
	SiftFeatureDetector siftDetector(800);  // 海塞矩阵阈值    //编译报错，不能用，申请了专利，不提供此api了
	vector<KeyPoint> keyPoint1,keyPoint2;  
	siftDetector.detect(image1,keyPoint1);  
	siftDetector.detect(image2,keyPoint2);	

	//特征点描述，为下边的特征点匹配做准备  
	SiftDescriptorExtractor siftDescriptor;  
	Mat imageDesc1,imageDesc2;  
	siftDescriptor.compute(image1,keyPoint1,imageDesc1);  
	siftDescriptor.compute(image2,keyPoint2,imageDesc2);	

	//获得匹配特征点，并提取最优配对  	
	FlannBasedMatcher matcher;
	vector<DMatch> matchePoints;  
	matcher.match(imageDesc1,imageDesc2,matchePoints,Mat());
	sort(matchePoints.begin(),matchePoints.end()); //特征点排序	
	//获取排在前N个的最优匹配特征点
	vector<Point2f> imagePoints1,imagePoints2;
	for(int i=0;i<10;i++)
	{		
		imagePoints1.push_back(keyPoint1[matchePoints[i].queryIdx].pt);		
		imagePoints2.push_back(keyPoint2[matchePoints[i].trainIdx].pt);		
	}

	//获取图像1到图像2的投影映射矩阵，尺寸为3*3
	Mat homo=findHomography(imagePoints1,imagePoints2,CV_RANSAC);		
	Mat adjustMat=(Mat_<double>(3,3)<<1.0,0,image01.cols,0,1.0,0,0,0,1.0);
	Mat adjustHomo=adjustMat*homo;

	//获取最强配对点在原始图像和矩阵变换后图像上的对应位置，用于图像拼接点的定位
	Point2f originalLinkPoint,targetLinkPoint,basedImagePoint;
	originalLinkPoint=keyPoint1[matchePoints[0].queryIdx].pt;
	targetLinkPoint=getTransformPoint(originalLinkPoint,adjustHomo);
	basedImagePoint=keyPoint2[matchePoints[0].trainIdx].pt;

	//图像配准
	Mat imageTransform1;
	warpPerspective(image01,imageTransform1,adjustMat*homo,Size(image02.cols+image01.cols+110,image02.rows));

	//在最强匹配点左侧的重叠区域进行累加，是衔接稳定过渡，消除突变
	Mat image1Overlap,image2Overlap; //图1和图2的重叠部分	
	image1Overlap=imageTransform1(Rect(Point(targetLinkPoint.x-basedImagePoint.x,0),Point(targetLinkPoint.x,image02.rows)));
	image2Overlap=image02(Rect(0,0,image1Overlap.cols,image1Overlap.rows));
	Mat image1ROICopy=image1Overlap.clone();  //复制一份图1的重叠部分
	for(int i=0;i<image1Overlap.rows;i++)
	{
		for(int j=0;j<image1Overlap.cols;j++)
		{
			double weight;
			weight=(double)j/image1Overlap.cols;  //随距离改变而改变的叠加系数
			image1Overlap.at<Vec3b>(i,j)[0]=(1-weight)*image1ROICopy.at<Vec3b>(i,j)[0]+weight*image2Overlap.at<Vec3b>(i,j)[0];
			image1Overlap.at<Vec3b>(i,j)[1]=(1-weight)*image1ROICopy.at<Vec3b>(i,j)[1]+weight*image2Overlap.at<Vec3b>(i,j)[1];
			image1Overlap.at<Vec3b>(i,j)[2]=(1-weight)*image1ROICopy.at<Vec3b>(i,j)[2]+weight*image2Overlap.at<Vec3b>(i,j)[2];
		}
	}
	Mat ROIMat=image02(Rect(Point(image1Overlap.cols,0),Point(image02.cols,image02.rows)));	 //图2中不重合的部分
	ROIMat.copyTo(Mat(imageTransform1,Rect(targetLinkPoint.x,0, ROIMat.cols,image02.rows))); //不重合的部分直接衔接上去
	namedWindow("拼接结果",0);
	imshow("拼接结果",imageTransform1);	
	imwrite("D:\\拼接结果.jpg",imageTransform1);
	waitKey();  
	return 0;  
}

//计算原始图像点位在经过矩阵变换后在目标图像上对应位置
Point2f getTransformPoint(const Point2f originalPoint,const Mat &transformMaxtri)
{
	Mat originelP,targetP;
	originelP=(Mat_<double>(3,1)<<originalPoint.x,originalPoint.y,1.0);
	targetP=transformMaxtri*originelP;
	float x=targetP.at<double>(0,0)/targetP.at<double>(2,0);
	float y=targetP.at<double>(1,0)/targetP.at<double>(2,0);
	return Point2f(x,y);
}

测试用例三原图1：

测试用例三原图2：

处理后结果