ORB_GMS图像对齐

// OpenCV_Align.cpp : 此文件包含 "main" 函数。程序执行将在此处开始并结束。

#include <iostream>

#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d.hpp>
#include <opencv2/features2d.hpp>
using namespace std;
using namespace cv;
using namespace cv::xfeatures2d;


/**
*
* @param im1 对齐图像
* @param im2 模板图像
* @finalMatches 匹配图像
* @param im1Reg 输出图像
* @param h   
*/
void alignImages(Mat &im1, Mat &im2, Mat &finalMatches,Mat &im1Reg, Mat &h)
{
    // Convert images to grayscale
    Mat im1Gray, im2Gray;
    cvtColor(im1, im1Gray, COLOR_BGR2GRAY);
    cvtColor(im2, im2Gray, COLOR_BGR2GRAY);

    // Variables to store keypoints and descriptors
    vector<KeyPoint> keypoints1, keypoints2;
    Mat descriptors1, descriptors2;
    
  
    Ptr<ORB> orb = ORB::create(1000); //最大数目感觉是对特征点计算Harris得分排序,这个数最好大一点，1000-5000
    orb->setFastThreshold(20);//Fast角点检测中用来确定候选点和角点的比较数目阈值，这个数最好小一点，5-10左右。太大有可能提取不到几个点，再经过细筛就没了

    orb->detectAndCompute(im1Gray, Mat(), keypoints1, descriptors1);
    orb->detectAndCompute(im2Gray, Mat(), keypoints2, descriptors2);

    // Match features. 
    vector<DMatch> matches, matchesGMS;

   //FALNN匹配的计算速度会比暴力匹配快些，但是点相对稳定性差些

    Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
    matcher->match(descriptors1, descriptors2, matches, Mat());

    cout << "matches: " << matches.size() << endl; 

    matchGMS(im1.size(), im2.size(), keypoints1, keypoints2, matches, matchesGMS);
    cout << "matchesGMS: " << matchesGMS.size() << endl;



    drawMatches(im1, keypoints1, im2, keypoints2, matchesGMS, finalMatches);
    

    vector<Point2f> points1, points2;
    for (size_t i = 0; i < matchesGMS.size(); i++)   
    {
        //queryIdx是对齐图像的描述子和特征点的下标。
        points1.push_back(keypoints1[matchesGMS[i].queryIdx].pt);
        //queryIdx是是样本图像的描述子和特征点的下标。
        points2.push_back(keypoints2[matchesGMS[i].trainIdx].pt);
    }


    // Find homography 
    h = findHomography(points1, points2, RANSAC);

    // Use homography to warp image 
    warpPerspective(im1, im1Reg, h, im2.size());
}


int main()
{
    // Read image to be aligned 
    string imFilename("D:/222.jpg");
    cout << "Reading image to align : " << imFilename << endl;
    Mat im = imread(imFilename);

    // Read reference image 
    string refFilename("D:/refer.jpg");
    cout << "Reading reference image : " << refFilename << endl;
    Mat imReference = imread(refFilename);

    // Registered image will be resotred in imReg.
    // The estimated homography will be stored in h.
    Mat im_Aligned, h, final_Match;

    double detecttime = (double)getTickCount();
    // Align images
    cout << "Aligning images ..." << endl;
    alignImages(im, imReference, final_Match,im_Aligned, h);



    //计算检测时间
    detecttime = ((double)getTickCount() - detecttime) / getTickFrequency();
    printf("-----cost time-------:%7.3fs
", detecttime);

    
    // Write aligned image to disk.
    string outFilename("aligned.jpg");
    cout << "Saving aligned image : " << outFilename << endl;
    imwrite(outFilename, im_Aligned);
    imwrite("matches.jpg", final_Match);
    
    // Print estimated homography
    cout << "Estimated homography : 
" << h << endl;
    system("pause");
    return 0;
}