基于ORB的图像匹配与拼接算法程序源码（C/Opencv）

//ORB #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("Image4.jpg", 1); //右图 Mat image02 = imread("Image3.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); imwrite("first_match.jpg ", 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); imwrite("b_dst.jpg", 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); } } }