Opencv实现图像无缝拼接，Sift查找特征点，Flann进行匹配

Posted 2022-11-30 晴堂

Sift和Surf算法实现两幅图像拼接的过程是一样的，主要分为4大部分：
1. 特征点提取和描述
2. 特征点配对，找到两幅图像中匹配点的位置
3. 通过配对点，生成变换矩阵，并对图像1应用变换矩阵生成对图像2的映射图像
4. 图像2拼接到映射图像上，完成拼接

具体请转到http://m.blog.csdn.net/article/details?id=52629856

代码如下：

#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,image02;
	if (argc < 2)
	
		image01 = imread("left.jpg");
		image02 = imread("right.jpg");
	
	else
	
		image01 = imread(argv[1]);
		image02 = imread(argv[2]);
	
	if (image01.empty() || image02.empty())
	
		return 0;//图像没有全部读取成功
	
	imshow("拼接图像1", image01);
	imshow("拼接图像2", image02);
	double time = getTickCount();
	//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  
	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());
	if (matchePoints.size() < 10)
	
		return 0;
	
	sort(matchePoints.begin(), matchePoints.end()); //特征点排序，opencv按照匹配点准确度排序    
	//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);//向后偏移image01.cols矩阵
	Mat adjustHomo = adjustMat*homo;//矩阵相乘，先偏移

	//获取最强配对点（就是第一个配对点）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 imageTransform;
	//将图片1进行映射到图像2，本来映射后x值为负值，但是把映射矩阵向后偏移image01.cols矩阵
	//我们很难判断出拼接后图像的大小尺寸，为了尽可能保留原来的像素，我们尽可能的大一些，对于拼接后的图片可以进一步剪切无效或者不规则的边缘
	warpPerspective(image01, imageTransform, adjustMat*homo, Size(image02.cols + image01.cols+10, image02.rows));

	//在最强匹配点的位置处衔接，最强匹配点左侧是图1，右侧是图2，这样直接替换图像衔接不好，光线有突变  
	//Mat ROIMat = image02(Rect(Point(basedImagePoint.x, 0), Point(image02.cols, image02.rows)));
	//ROIMat.copyTo(Mat(imageTransform1, Rect(targetLinkPoint.x, 0, image02.cols - basedImagePoint.x + 1, 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 = imageTransform(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(imageTransform, Rect(targetLinkPoint.x, 0, ROIMat.cols, image02.rows))); //不重合的部分直接衔接上去  

	time = getTickCount() - time;
	time /= getTickFrequency();
	printf("match time=%f\\n",time);
	namedWindow("拼接结果", 0);
	imshow("拼接结果", imageTransform);
	imwrite("matchResult.jpg",imageTransform);
	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);

测试结果：

                                               left左边图片

                                               right右边图片

                                             result拼接结果

从测试结果能发现，合并后的图片两边会有黑色区域，如果相机位置不是同高的化，上下两边也会有黑色区域，需要对拼接后的图片进行二次剪切。算法思路是：分别扫描四个边缘，分别找到最大黑色区域长度，然后删掉就行了。