基于最佳缝合线的拼接：
一个图像如何求取最佳缝合线呢。
//查找接缝 Ptr<SeamFinder> seam_finder; seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR); seam_finder->find(images_warped_f, corners, masks_warped);
这个是opencv的代码 可以看出需要知道conners。目前怎么求conners还没搞清楚
以上是两个图像以及它们分别的最佳缝合线，其实是一个，因为这个两个图像没有拼接。OK
手动把这两个拼接在一起，也就是拼接后的模板。同时把左图和右图也贴上，三个图像大小一致，且都是拼接后的图像
上拉普拉斯融合代码
// SurfTest.cpp : 定义控制台应用程序的入口点。 // #include "stdafx.h" #include <opencv2/opencv.hpp> #include <string.h> #include <atlstr.h> #include <assert.h> #include <math.h> #include <float.h> #include <limits.h> #include <time.h> #include <ctype.h> #include <stdlib.h> #include <stdio.h> #include <vector> #include <cstring> //#include <opencv2/stitching.hpp> #include <iostream> #include <fstream> using namespace cv; using namespace std; //int _tmain(int argc, _TCHAR* argv[]) //{ // // CString imgpath="E:\项目文件\周信达\显微镜样品测试\显微镜样品测试\介质末\"; // CString imgname="ml.jpg"; // CString filepath; // filepath=imgpath+imgname; // IplImage *testimg=cvLoadImage(filepath,-1); // CString savepath="E:\项目文件\周信达\显微镜样品测试\显微镜样品测试\介质末\6\"; // for (int i=0;i<8;i++) // { // for (int j=0;j<7;j++) // { // CString saveimgpath; // CString saveimgname; // saveimgname.Format("0-%d-%d.jpg",i,j); // saveimgpath=savepath+saveimgname; // cvSaveImage(saveimgpath,testimg); // } // } // cvReleaseImage(&testimg); // printf("success"); // system("pause"); //} /************************************************************************/ /* 说明： *金字塔从下到上依次为 [0,1，...，level-1] 层 *blendMask 为图像的掩模 *maskGaussianPyramid为金字塔每一层的掩模 *resultLapPyr 存放每层金字塔中直接用左右两图Laplacian变换拼成的图像 */ /************************************************************************/ class LaplacianBlending { private: Mat_<Vec3f> left; Mat_<Vec3f> right; Mat_<float> blendMask; vector<Mat_<Vec3f> > leftLapPyr,rightLapPyr,resultLapPyr;//Laplacian Pyramids Mat leftHighestLevel, rightHighestLevel, resultHighestLevel; vector<Mat_<Vec3f> > maskGaussianPyramid; //masks are 3-channels for easier multiplication with RGB int levels; void buildPyramids() { buildLaplacianPyramid(left,leftLapPyr,leftHighestLevel); buildLaplacianPyramid(right,rightLapPyr,rightHighestLevel); buildGaussianPyramid(); } void buildGaussianPyramid() {//金字塔内容为每一层的掩模 assert(leftLapPyr.size()>0); maskGaussianPyramid.clear(); Mat currentImg; cvtColor(blendMask, currentImg, CV_GRAY2BGR);//store color img of blend mask into maskGaussianPyramid maskGaussianPyramid.push_back(currentImg); //0-level currentImg = blendMask; for (int l=1; l<levels+1; l++) { Mat _down; if (leftLapPyr.size() > l) pyrDown(currentImg, _down, leftLapPyr[l].size()); else pyrDown(currentImg, _down, leftHighestLevel.size()); //lowest level Mat down; cvtColor(_down, down, CV_GRAY2BGR); maskGaussianPyramid.push_back(down);//add color blend mask into mask Pyramid currentImg = _down; } } void buildLaplacianPyramid(const Mat& img, vector<Mat_<Vec3f> >& lapPyr, Mat& HighestLevel) { lapPyr.clear(); Mat currentImg = img; for (int l=0; l<levels; l++) { Mat down,up; pyrDown(currentImg, down); pyrUp(down, up,currentImg.size()); Mat lap = currentImg - up; lapPyr.push_back(lap); currentImg = down; } currentImg.copyTo(HighestLevel); } Mat_<Vec3f> reconstructImgFromLapPyramid() { //将左右laplacian图像拼成的resultLapPyr金字塔中每一层 //从上到下插值放大并相加，即得blend图像结果 Mat currentImg = resultHighestLevel; for (int l=levels-1; l>=0; l--) { Mat up; pyrUp(currentImg, up, resultLapPyr[l].size()); currentImg = up + resultLapPyr[l]; } return currentImg; } void blendLapPyrs() { //获得每层金字塔中直接用左右两图Laplacian变换拼成的图像resultLapPyr resultHighestLevel = leftHighestLevel.mul(maskGaussianPyramid.back()) + rightHighestLevel.mul(Scalar(1.0,1.0,1.0) - maskGaussianPyramid.back()); for (int l=0; l<levels; l++) { Mat A = leftLapPyr[l].mul(maskGaussianPyramid[l]); Mat antiMask = Scalar(1.0,1.0,1.0) - maskGaussianPyramid[l]; Mat B = rightLapPyr[l].mul(antiMask); Mat_<Vec3f> blendedLevel = A + B; resultLapPyr.push_back(blendedLevel); } } public: LaplacianBlending(const Mat_<Vec3f>& _left, const Mat_<Vec3f>& _right, const Mat_<float>& _blendMask, int _levels)://construct function, used in LaplacianBlending lb(l,r,m,4); left(_left),right(_right),blendMask(_blendMask),levels(_levels) { assert(_left.size() == _right.size()); assert(_left.size() == _blendMask.size()); buildPyramids(); //construct Laplacian Pyramid and Gaussian Pyramid blendLapPyrs(); //blend left & right Pyramids into one Pyramid }; Mat_<Vec3f> blend() { return reconstructImgFromLapPyramid();//reconstruct Image from Laplacian Pyramid } }; Mat_<Vec3f> LaplacianBlend(const Mat_<Vec3f>& l, const Mat_<Vec3f>& r, const Mat_<float>& m) { LaplacianBlending lb(l,r,m,4); return lb.blend(); } int main() { Mat l8u = imread("11.jpg");//左图 Mat r8u = imread("22.jpg");//右图 namedWindow("left",0); imshow("left",l8u); namedWindow("right",0); imshow("right",r8u); Mat_<Vec3f> l; l8u.convertTo(l,CV_32F,1.0/255.0);//Vec3f表示有三个通道，即 l[row][column][depth] Mat_<Vec3f> r; r8u.convertTo(r,CV_32F,1.0/255.0); ////create blend mask matrix m //Mat_<float> m(l.rows,l.cols,0.0); //将m全部赋值为0 //m(Range::all(),Range(0,m.cols/2)) = 1.0; //取m全部行&[0,m.cols/2]列，赋值为1.0 Mat_<float> m(l.rows,l.cols,0.0); Mat C=imread("newmark.jpg"); //模板 for(int i=0;i<l.rows;i++) { for(int j=0;j<l.cols;j++) { if(C.at<Vec3b>(i,j)[0]!=0&&C.at<Vec3b>(i,j)[1]!=0&&C.at<Vec3b>(i,j)[2]!=0) // 因为我要的只是位置 m(i,j)=1.0; } } Mat_<Vec3f> blend = LaplacianBlend(l, r, m); Mat re; blend.convertTo(re,CV_8UC3,255); imwrite("blended.jpg",re); namedWindow("blended",0); imshow("blended",blend); waitKey(0); }
融合后的结果如下：
可以看到图像中间有一段拼接的非常好，其他地方是因为最佳缝合线是我手动生成的，存在误差。也就是说这个方法能走通，首先求解最佳缝合线，然后
上拉普拉斯融合即可。