圖像處理之圖像拼接三

基於最佳縫合線的拼接：

一個圖像如何求取最佳縫合線呢。

    //查找接縫
    Ptr<SeamFinder> seam_finder;
    seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR);
    seam_finder->find(images_warped_f, corners, masks_warped);

這個是opencv的代碼 可以看出需要知道conners。目前怎么求conners還沒搞清楚

以上是兩個圖像以及它們分別的最佳縫合線，其實是一個，因為這個兩個圖像沒有拼接。OK

手動把這兩個拼接在一起，也就是拼接后的模板。同時把左圖和右圖也貼上，三個圖像大小一致，且都是拼接后的圖像

 

上拉普拉斯融合代碼

// SurfTest.cpp : 定義控制台應用程序的入口點。
//

#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:\\項目文件\\周信達\\顯微鏡樣品測試\\顯微鏡樣品測試\\介質末\\";
//    CString imgname="ml.jpg";
//    CString filepath;
//    filepath=imgpath+imgname;
//    IplImage *testimg=cvLoadImage(filepath,-1);
//    CString savepath="E:\\項目文件\\周信達\\顯微鏡樣品測試\\顯微鏡樣品測試\\介質末\\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");
//}

/************************************************************************/
/* 說明：
*金字塔從下到上依次為 [0,1，...，level-1] 層
*blendMask 為圖像的掩模
*maskGaussianPyramid為金字塔每一層的掩模
*resultLapPyr 存放每層金字塔中直接用左右兩圖Laplacian變換拼成的圖像
*/
/************************************************************************/


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() 
    {//金字塔內容為每一層的掩模
        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() 
    {
        //將左右laplacian圖像拼成的resultLapPyr金字塔中每一層
        //從上到下插值放大並相加，即得blend圖像結果
        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() 
    {
        //獲得每層金字塔中直接用左右兩圖Laplacian變換拼成的圖像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");//左圖
    Mat r8u = imread("22.jpg");//右圖

    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表示有三個通道，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);                    //將m全部賦值為0
    //m(Range::all(),Range(0,m.cols/2)) = 1.0;    //取m全部行&[0,m.cols/2]列，賦值為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)  // 因為我要的只是位置
                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);
}

融合后的結果如下：

 

可以看到圖像中間有一段拼接的非常好，其他地方是因為最佳縫合線是我手動生成的，存在誤差。也就是說這個方法能走通，首先求解最佳縫合線，然后

上拉普拉斯融合即可。