图像处理之图像拼接三

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。