OpenCV基于SIFT特征和SURF特征的微旋转图像拼接与融合生成全景图像

#include <opencv2/opencv.hpp> #include "highgui/highgui.hpp" #include <opencv2/xfeatures2d.hpp> using namespace cv; using namespace std; //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("left2.jpg"); Mat image02 = imread("right2.jpg"); if (image01.data == NULL || image02.data == NULL) return 0; imshow("待拼接图像左图", image01); imshow("待拼接图像右图", image02); //灰度图转换 Mat image1, image2; cvtColor(image01, image1, CV_RGB2GRAY); cvtColor(image02, image2, CV_RGB2GRAY); //提取特征点 Ptr<Feature2D> f2d = xfeatures2d::SIFT::create(); //修改SIFT参数即可修改算法（比如SURF） vector<KeyPoint> keyPoint1, keyPoint2; f2d->detect(image1, keyPoint1); f2d->detect(image2, keyPoint2); //特征点描述，为下边的特征点匹配做准备 Mat imageDesc1, imageDesc2; f2d->compute(image1, keyPoint1, imageDesc1); f2d->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-SIFT", 0); imshow("拼接结果-SIFT", imageTransform1); imwrite("拼接结果-SIFT.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); }