图像拼接和图像融合技术（基于Opencv）

图像拼接在实际的应用场景很广，比如无人机航拍，遥感图像等等，图像拼接是进一步做图像理解基础步骤，拼接效果的好坏直接影响接下来的工作，所以一个好的图像拼接算法非常重要。

再举一个身边的例子吧，你用你的手机对某一场景拍照，但是你没有办法一次将所有你要拍的景物全部拍下来，所以你对该场景从左往右依次拍了好几张图，来把你要拍的所有景物记录下来。那么我们能不能把这些图像拼接成一个大图呢？我们利用opencv就可以做到图像拼接的效果！

比如我们有对这两张图进行拼接：

从上面两张图可以看出，这两张图有比较多的重叠部分，这也是拼接的基本要求。

那么要实现图像拼接需要那几步呢？简单来说有以下几步：

对每幅图进行特征点提取
对对特征点进行匹配
进行图像配准
把图像拷贝到另一幅图像的特定位置
对重叠边界进行特殊处理
好吧，那就开始正式实现图像配准。

第一步就是特征点提取。现在CV领域有很多特征点的定义，比如sift、surf、harris角点、ORB都是很有名的特征因子，都可以用来做图像拼接的工作，他们各有优势。本文将使用ORB和SURF进行图像拼接，用其他方法进行拼接也是类似的。

基于SURF的图像拼接
用SIFT算法来实现图像拼接是很常用的方法，但是因为SIFT计算量很大，所以在速度要求很高的场合下不再适用。所以，它的改进方法SURF因为在速度方面有了明显的提高（速度是SIFT的3倍），所以在图像拼接领域还是大有作为。虽说SURF精确度和稳定性不及SIFT，但是其综合能力还是优越一些。下面将详细介绍拼接的主要步骤。

1.特征点提取和匹配
特征点提取和匹配的方法我在另一篇博客文章《特征检测和特征匹配方法汇总（基于Opencv）》中做了详细的介绍，在这里直接使用上文所总结的SURF特征提取和特征匹配的方法。

//提取特征点
SurfFeatureDetector Detector(2000);
vector<KeyPoint> keyPoint1, keyPoint2;
Detector.detect(image1, keyPoint1);
Detector.detect(image2, keyPoint2);//特征点描述，为下边的特征点匹配做准备
SurfDescriptorExtractor Descriptor;
Mat imageDesc1, imageDesc2;
Descriptor.compute(image1, keyPoint1, imageDesc1);
Descriptor.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);

2.图像配准
这样子我们就可以得到了两幅待拼接图的匹配点集，接下来我们进行图像的配准，即将两张图像转换为同一坐标下，这里我们需要使用findHomography函数来求得变换矩阵。但是需要注意的是，findHomography函数所要用到的点集是Point2f类型的，所有我们需要对我们刚得到的点集GoodMatchePoints再做一次处理，使其转换为Point2f类型的点集。

vector<Point2f> imagePoints1, imagePoints2;for (int i = 0; i<GoodMatchePoints.size(); i++)
{imagePoints2.push_back(keyPoint2[GoodMatchePoints[i].queryIdx].pt);imagePoints1.push_back(keyPoint1[GoodMatchePoints[i].trainIdx].pt);
}

这样子，我们就可以拿着imagePoints1, imagePoints2去求变换矩阵了，并且实现图像配准。值得注意的是findHomography函数的参数中我们选泽了CV_RANSAC，这表明我们选择RANSAC算法继续筛选可靠地匹配点，这使得匹配点解更为精确。

//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);
也可以使用getPerspectiveTransform方法获得透视变换矩阵，不过要求只能有4个点，效果稍差
//Mat   homo=getPerspectiveTransform(imagePoints1,imagePoints2);
cout << "变换矩阵为：\n" << homo << endl << endl; //输出映射矩阵     //图像配准
Mat imageTransform1, imageTransform2;
warpPerspective(image01, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), image02.rows));
//warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8));
imshow("直接经过透视矩阵变换", imageTransform1);
imwrite("trans1.jpg", imageTransform1);

3. 图像拷贝
拷贝的思路很简单，就是将左图直接拷贝到配准图上就可以了。

//创建拼接后的图,需提前计算图的大小
int dst_width = imageTransform1.cols;  //取最右点的长度为拼接图的长度
int dst_height = image02.rows;Mat dst(dst_height, dst_width, CV_8UC3);
dst.setTo(0);imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));
image02.copyTo(dst(Rect(0, 0, image02.cols, image02.rows)));imshow("b_dst", dst);

4.图像融合（去裂缝处理）
从上图可以看出，两图的拼接并不自然，原因就在于拼接图的交界处，两图因为光照色泽的原因使得两图交界处的过渡很糟糕，所以需要特定的处理解决这种不自然。这里的处理思路是加权融合，在重叠部分由前一幅图像慢慢过渡到第二幅图像，即将图像的重叠区域的像素值按一定的权值相加合成新的图像。

//优化两图的连接处，使得拼接自然
void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
{int start = MIN(corners.left_top.x, corners.left_bottom.x);//开始位置，即重叠区域的左边界  double processWidth = img1.cols - start;//重叠区域的宽度  int rows = dst.rows;int cols = img1.cols; //注意，是列数*通道数double alpha = 1;//img1中像素的权重  for (int i = 0; i < rows; i++){uchar* p = img1.ptr<uchar>(i);  //获取第i行的首地址uchar* t = trans.ptr<uchar>(i);uchar* d = dst.ptr<uchar>(i);for (int j = start; j < cols; j++){//如果遇到图像trans中无像素的黑点，则完全拷贝img1中的数据if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0){alpha = 1;}else{//img1中像素的权重，与当前处理点距重叠区域左边界的距离成正比，实验证明，这种方法确实好  alpha = (processWidth - (j - start)) / processWidth;}d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);}}}

多尝试几张，验证拼接效果：
测试一：

测试二：

测试三：

最后给出完整的SURF算法实现的拼接代码：

#include "highgui/highgui.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/legacy/legacy.hpp"
#include <iostream>  using namespace cv;
using namespace std;void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst);typedef struct
{Point2f left_top;Point2f left_bottom;Point2f right_top;Point2f right_bottom;
}four_corners_t;four_corners_t corners;void CalcCorners(const Mat& H, const Mat& src)
{double v2[] = { 0, 0, 1 };//左上角double v1[3];//变换后的坐标值Mat V2 = Mat(3, 1, CV_64FC1, v2);  //列向量Mat V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;//左上角(0,0,1)cout << "V2: " << V2 << endl;cout << "V1: " << V1 << endl;corners.left_top.x = v1[0] / v1[2];corners.left_top.y = v1[1] / v1[2];//左下角(0,src.rows,1)v2[0] = 0;v2[1] = src.rows;v2[2] = 1;V2 = Mat(3, 1, CV_64FC1, v2);  //列向量V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;corners.left_bottom.x = v1[0] / v1[2];corners.left_bottom.y = v1[1] / v1[2];//右上角(src.cols,0,1)v2[0] = src.cols;v2[1] = 0;v2[2] = 1;V2 = Mat(3, 1, CV_64FC1, v2);  //列向量V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;corners.right_top.x = v1[0] / v1[2];corners.right_top.y = v1[1] / v1[2];//右下角(src.cols,src.rows,1)v2[0] = src.cols;v2[1] = src.rows;v2[2] = 1;V2 = Mat(3, 1, CV_64FC1, v2);  //列向量V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;corners.right_bottom.x = v1[0] / v1[2];corners.right_bottom.y = v1[1] / v1[2];}int main(int argc, char *argv[])
{Mat image01 = imread("g5.jpg", 1);    //右图Mat image02 = imread("g4.jpg", 1);    //左图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    SurfFeatureDetector Detector(2000);  vector<KeyPoint> keyPoint1, keyPoint2;Detector.detect(image1, keyPoint1);Detector.detect(image2, keyPoint2);//特征点描述，为下边的特征点匹配做准备    SurfDescriptorExtractor Descriptor;Mat imageDesc1, imageDesc2;Descriptor.compute(image1, keyPoint1, imageDesc1);Descriptor.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);vector<Point2f> imagePoints1, imagePoints2;for (int i = 0; i<GoodMatchePoints.size(); i++){imagePoints2.push_back(keyPoint2[GoodMatchePoints[i].queryIdx].pt);imagePoints1.push_back(keyPoint1[GoodMatchePoints[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);也可以使用getPerspectiveTransform方法获得透视变换矩阵，不过要求只能有4个点，效果稍差  //Mat   homo=getPerspectiveTransform(imagePoints1,imagePoints2);  cout << "变换矩阵为：\n" << homo << endl << endl; //输出映射矩阵      //计算配准图的四个顶点坐标CalcCorners(homo, image01);cout << "left_top:" << corners.left_top << endl;cout << "left_bottom:" << corners.left_bottom << endl;cout << "right_top:" << corners.right_top << endl;cout << "right_bottom:" << corners.right_bottom << endl;//图像配准  Mat imageTransform1, imageTransform2;warpPerspective(image01, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), image02.rows));//warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8));imshow("直接经过透视矩阵变换", imageTransform1);imwrite("trans1.jpg", imageTransform1);//创建拼接后的图,需提前计算图的大小int dst_width = imageTransform1.cols;  //取最右点的长度为拼接图的长度int dst_height = image02.rows;Mat dst(dst_height, dst_width, CV_8UC3);dst.setTo(0);imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));image02.copyTo(dst(Rect(0, 0, image02.cols, image02.rows)));imshow("b_dst", dst);OptimizeSeam(image02, imageTransform1, dst);imshow("dst", dst);imwrite("dst.jpg", dst);waitKey();return 0;
}//优化两图的连接处，使得拼接自然
void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
{int start = MIN(corners.left_top.x, corners.left_bottom.x);//开始位置，即重叠区域的左边界  double processWidth = img1.cols - start;//重叠区域的宽度  int rows = dst.rows;int cols = img1.cols; //注意，是列数*通道数double alpha = 1;//img1中像素的权重  for (int i = 0; i < rows; i++){uchar* p = img1.ptr<uchar>(i);  //获取第i行的首地址uchar* t = trans.ptr<uchar>(i);uchar* d = dst.ptr<uchar>(i);for (int j = start; j < cols; j++){//如果遇到图像trans中无像素的黑点，则完全拷贝img1中的数据if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0){alpha = 1;}else{//img1中像素的权重，与当前处理点距重叠区域左边界的距离成正比，实验证明，这种方法确实好  alpha = (processWidth - (j - start)) / processWidth;}d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);}}}

基于ORB的图像拼接

利用ORB进行图像拼接的思路跟上面的思路基本一样，只是特征提取和特征点匹配的方式略有差异罢了。这里就不再详细介绍思路了，直接贴代码看效果：

#include "highgui/highgui.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/legacy/legacy.hpp"
#include <iostream>  using namespace cv;
using namespace std;void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst);typedef struct
{Point2f left_top;Point2f left_bottom;Point2f right_top;Point2f right_bottom;
}four_corners_t;four_corners_t corners;void CalcCorners(const Mat& H, const Mat& src)
{double v2[] = { 0, 0, 1 };//左上角double v1[3];//变换后的坐标值Mat V2 = Mat(3, 1, CV_64FC1, v2);  //列向量Mat V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;//左上角(0,0,1)cout << "V2: " << V2 << endl;cout << "V1: " << V1 << endl;corners.left_top.x = v1[0] / v1[2];corners.left_top.y = v1[1] / v1[2];//左下角(0,src.rows,1)v2[0] = 0;v2[1] = src.rows;v2[2] = 1;V2 = Mat(3, 1, CV_64FC1, v2);  //列向量V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;corners.left_bottom.x = v1[0] / v1[2];corners.left_bottom.y = v1[1] / v1[2];//右上角(src.cols,0,1)v2[0] = src.cols;v2[1] = 0;v2[2] = 1;V2 = Mat(3, 1, CV_64FC1, v2);  //列向量V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;corners.right_top.x = v1[0] / v1[2];corners.right_top.y = v1[1] / v1[2];//右下角(src.cols,src.rows,1)v2[0] = src.cols;v2[1] = src.rows;v2[2] = 1;V2 = Mat(3, 1, CV_64FC1, v2);  //列向量V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;corners.right_bottom.x = v1[0] / v1[2];corners.right_bottom.y = v1[1] / v1[2];}int main(int argc, char *argv[])
{Mat image01 = imread("t1.jpg", 1);    //右图Mat image02 = imread("t2.jpg", 1);    //左图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    OrbFeatureDetector  surfDetector(3000);  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    OrbDescriptorExtractor  SurfDescriptor;Mat imageDesc1, imageDesc2;SurfDescriptor.compute(image1, keyPoint1, imageDesc1);SurfDescriptor.compute(image2, keyPoint2, imageDesc2);flann::Index flannIndex(imageDesc1, flann::LshIndexParams(12, 20, 2), cvflann::FLANN_DIST_HAMMING);vector<DMatch> GoodMatchePoints;Mat macthIndex(imageDesc2.rows, 2, CV_32SC1), matchDistance(imageDesc2.rows, 2, CV_32FC1);flannIndex.knnSearch(imageDesc2, macthIndex, matchDistance, 2, flann::SearchParams());// Lowe's algorithm,获取优秀匹配点for (int i = 0; i < matchDistance.rows; i++){if (matchDistance.at<float>(i, 0) < 0.4 * matchDistance.at<float>(i, 1)){DMatch dmatches(i, macthIndex.at<int>(i, 0), matchDistance.at<float>(i, 0));GoodMatchePoints.push_back(dmatches);}}Mat first_match;drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);imshow("first_match ", first_match);vector<Point2f> imagePoints1, imagePoints2;for (int i = 0; i<GoodMatchePoints.size(); i++){imagePoints2.push_back(keyPoint2[GoodMatchePoints[i].queryIdx].pt);imagePoints1.push_back(keyPoint1[GoodMatchePoints[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);也可以使用getPerspectiveTransform方法获得透视变换矩阵，不过要求只能有4个点，效果稍差  //Mat   homo=getPerspectiveTransform(imagePoints1,imagePoints2);  cout << "变换矩阵为：\n" << homo << endl << endl; //输出映射矩阵      //计算配准图的四个顶点坐标CalcCorners(homo, image01);cout << "left_top:" << corners.left_top << endl;cout << "left_bottom:" << corners.left_bottom << endl;cout << "right_top:" << corners.right_top << endl;cout << "right_bottom:" << corners.right_bottom << endl;//图像配准  Mat imageTransform1, imageTransform2;warpPerspective(image01, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), image02.rows));//warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8));imshow("直接经过透视矩阵变换", imageTransform1);imwrite("trans1.jpg", imageTransform1);//创建拼接后的图,需提前计算图的大小int dst_width = imageTransform1.cols;  //取最右点的长度为拼接图的长度int dst_height = image02.rows;Mat dst(dst_height, dst_width, CV_8UC3);dst.setTo(0);imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));image02.copyTo(dst(Rect(0, 0, image02.cols, image02.rows)));imshow("b_dst", dst);OptimizeSeam(image02, imageTransform1, dst);imshow("dst", dst);imwrite("dst.jpg", dst);waitKey();return 0;
}//优化两图的连接处，使得拼接自然
void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
{int start = MIN(corners.left_top.x, corners.left_bottom.x);//开始位置，即重叠区域的左边界  double processWidth = img1.cols - start;//重叠区域的宽度  int rows = dst.rows;int cols = img1.cols; //注意，是列数*通道数double alpha = 1;//img1中像素的权重  for (int i = 0; i < rows; i++){uchar* p = img1.ptr<uchar>(i);  //获取第i行的首地址uchar* t = trans.ptr<uchar>(i);uchar* d = dst.ptr<uchar>(i);for (int j = start; j < cols; j++){//如果遇到图像trans中无像素的黑点，则完全拷贝img1中的数据if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0){alpha = 1;}else{//img1中像素的权重，与当前处理点距重叠区域左边界的距离成正比，实验证明，这种方法确实好  alpha = (processWidth - (j - start)) / processWidth;}d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);}}}

看一看拼接效果，我觉得还是不错的：

看一下这一组图片，这组图片产生了鬼影，为什么？因为两幅图中的人物走动了啊！所以要做图像拼接，尽量保证使用的是静态图片，不要加入一些动态因素干扰拼接：

1. opencv自带的拼接算法stitch

opencv其实自己就有实现图像拼接的算法，当然效果也是相当好的，但是因为其实现很复杂，而且代码量很庞大，其实在一些小应用下的拼接有点杀鸡用牛刀的感觉。最近在阅读sticth源码时，发现其中有几个很有意思的地方。

1.opencv stitch选择的特征检测方式
一直很好奇opencv stitch算法到底选用了哪个算法作为其特征检测方式，是ORB，SIFT还是SURF？读源码终于看到答案。

#ifdef HAVE_OPENCV_NONFREEstitcher.setFeaturesFinder(new detail::SurfFeaturesFinder());
#elsestitcher.setFeaturesFinder(new detail::OrbFeaturesFinder());
#endif

在源码createDefault函数中（默认设置），第一选择是SURF,第二选择才是ORB（没有NONFREE模块才选），所以既然大牛们这么选择，必然是经过综合考虑的，所以应该SURF算法在图像拼接有着更优秀的效果。

2. opencv stitch获取匹配点的方式

以下代码是opencv stitch源码中的特征点提取部分，作者使用了两次特征点提取的思路：先对图一进行特征点提取和筛选匹配（1->2），再对图二进行特征点的提取和匹配（2->1），这跟我们平时的一次提取的思路不同，这种二次提取的思路可以保证更多的匹配点被选中，匹配点越多，findHomography求出的变换越准确。这个思路值得借鉴。

matches_info.matches.clear();Ptr<flann::IndexParams> indexParams = new flann::KDTreeIndexParams();
Ptr<flann::SearchParams> searchParams = new flann::SearchParams();if (features2.descriptors.depth() == CV_8U)
{indexParams->setAlgorithm(cvflann::FLANN_INDEX_LSH);searchParams->setAlgorithm(cvflann::FLANN_INDEX_LSH);
}FlannBasedMatcher matcher(indexParams, searchParams);
vector< vector<DMatch> > pair_matches;
MatchesSet matches;// Find 1->2 matches
matcher.knnMatch(features1.descriptors, features2.descriptors, pair_matches, 2);
for (size_t i = 0; i < pair_matches.size(); ++i)
{if (pair_matches[i].size() < 2)continue;const DMatch& m0 = pair_matches[i][0];const DMatch& m1 = pair_matches[i][1];if (m0.distance < (1.f - match_conf_) * m1.distance){matches_info.matches.push_back(m0);matches.insert(make_pair(m0.queryIdx, m0.trainIdx));}
}
LOG("\n1->2 matches: " << matches_info.matches.size() << endl);// Find 2->1 matches
pair_matches.clear();
matcher.knnMatch(features2.descriptors, features1.descriptors, pair_matches, 2);
for (size_t i = 0; i < pair_matches.size(); ++i)
{if (pair_matches[i].size() < 2)continue;const DMatch& m0 = pair_matches[i][0];const DMatch& m1 = pair_matches[i][1];if (m0.distance < (1.f - match_conf_) * m1.distance)if (matches.find(make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
}
LOG("1->2 & 2->1 matches: " << matches_info.matches.size() << endl);

这里仿照opencv源码二次提取特征点的思路对我原有拼接代码进行改写，实验证明获取的匹配点确实较一次提取要多。

//提取特征点
SiftFeatureDetector Detector(1000);  // 海塞矩阵阈值，在这里调整精度，值越大点越少，越精准
vector<KeyPoint> keyPoint1, keyPoint2;
Detector.detect(image1, keyPoint1);
Detector.detect(image2, keyPoint2);//特征点描述，为下边的特征点匹配做准备
SiftDescriptorExtractor Descriptor;
Mat imageDesc1, imageDesc2;
Descriptor.compute(image1, keyPoint1, imageDesc1);
Descriptor.compute(image2, keyPoint2, imageDesc2);FlannBasedMatcher matcher;
vector<vector<DMatch> > matchePoints;
vector<DMatch> GoodMatchePoints;MatchesSet matches;vector<Mat> train_desc(1, imageDesc1);
matcher.add(train_desc);
matcher.train();matcher.knnMatch(imageDesc2, matchePoints, 2);// 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]);matches.insert(make_pair(matchePoints[i][0].queryIdx, matchePoints[i][0].trainIdx));}
}
cout<<"\n1->2 matches: " << GoodMatchePoints.size() << endl;#if 1FlannBasedMatcher matcher2;
matchePoints.clear();
vector<Mat> train_desc2(1, imageDesc2);
matcher2.add(train_desc2);
matcher2.train();matcher2.knnMatch(imageDesc1, matchePoints, 2);
// 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){if (matches.find(make_pair(matchePoints[i][0].trainIdx, matchePoints[i][0].queryIdx)) == matches.end()){GoodMatchePoints.push_back(DMatch(matchePoints[i][0].trainIdx, matchePoints[i][0].queryIdx, matchePoints[i][0].distance));}}
}
cout<<"1->2 & 2->1 matches: " << GoodMatchePoints.size() << endl;
#endif

最后再看一下opencv stitch的拼接效果吧~速度虽然比较慢，但是效果还是很好的。

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/stitching/stitcher.hpp>
using namespace std;
using namespace cv;
bool try_use_gpu = false;
vector<Mat> imgs;
string result_name = "dst1.jpg";
int main(int argc, char * argv[])
{Mat img1 = imread("34.jpg");Mat img2 = imread("35.jpg");imshow("p1", img1);imshow("p2", img2);if (img1.empty() || img2.empty()){cout << "Can't read image" << endl;return -1;}imgs.push_back(img1);imgs.push_back(img2);Stitcher stitcher = Stitcher::createDefault(try_use_gpu);// 使用stitch函数进行拼接Mat pano;Stitcher::Status status = stitcher.stitch(imgs, pano);if (status != Stitcher::OK){cout << "Can't stitch images, error code = " << int(status) << endl;return -1;}imwrite(result_name, pano);Mat pano2 = pano.clone();// 显示源图像，和结果图像imshow("全景图像", pano);if (waitKey() == 27)return 0;
}