目录基于SURF的图像拼接1.特征点提取和匹配2.图像配准3. 图像拷贝4.图像融合（去裂缝处理）基于ORB的图像拼接opencv自带的拼接算法stitch1.opencv stitch选择的特征检测方式2.opencv stitch获取匹配点的方式
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上一篇文章《OpenCV特征检测和特征匹配方法汇总》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。
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_NONFREE stitcher.setFeaturesFinder(new detail::SurfFeaturesFinder());#else stitcher.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 matchesmatcher.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 matchespair_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);
这里我仿照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;}
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