2维特征Feature2D—特征点的图像匹配

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
// 新版本写在下面文件中：
#include <opencv2/nonfree/features2d.hpp>
//#include "opencv2/features2d/features2d.hpp"
#include<opencv2/legacy/legacy.hpp>

using namespace std;
using namespace cv;

void readme();

/**
 * @function main
 * @brief Main function
 */
int main( int argc, char** argv )
{
    
    Mat img_1 = imread( "/Users/liupeng/Desktop/my/opencvLearn/opencvLearn/lp1.jpg", CV_LOAD_IMAGE_GRAYSCALE );
    Mat img_2 = imread( "/Users/liupeng/Desktop/my/opencvLearn/opencvLearn/lp1.jpg", CV_LOAD_IMAGE_GRAYSCALE );
    
    if( !img_1.data || !img_2.data )
    { return -1; }
    
    //-- Step 1: Detect the keypoints using SURF Detector
    int minHessian = 400;
    
    SurfFeatureDetector detector( minHessian );
    
    std::vector<KeyPoint> keypoints_1, keypoints_2;
    
    detector.detect( img_1, keypoints_1 );
    detector.detect( img_2, keypoints_2 );
    
    //-- Step 2: Calculate descriptors (feature vectors)
    SurfDescriptorExtractor extractor;
    
    Mat descriptors_1, descriptors_2;
    
    extractor.compute( img_1, keypoints_1, descriptors_1 );
    extractor.compute( img_2, keypoints_2, descriptors_2 );
    
    imshow("descriptors_1", descriptors_1);
    imshow("descriptors_2",descriptors_2);
    
    //-- Step 3: Matching descriptor vectors with a brute force matcher
    BruteForceMatcher< L2<float> > matcher;
    std::vector< DMatch > matches;
    matcher.match( descriptors_1, descriptors_2, matches );
    
    //-- Draw matches
    Mat img_matches;
    drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );
    
    //-- Show detected matches
    imshow("Matches", img_matches );
    
    waitKey(0);
    
    return 0;
}

/**
 * @function readme
 */
void readme()
{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }

//-- Step 3: Matching descriptor vectors using FLANN matcher
    FlannBasedMatcher matcher;
    std::vector< DMatch > matches;
    matcher.match( descriptors_1, descriptors_2, matches );
    
    double max_dist = 0; double min_dist = 100;
    
    //-- Quick calculation of max and min distances between keypoints
    for( int i = 0; i < descriptors_1.rows; i++ )
    { double dist = matches[i].distance;
        if( dist < min_dist ) min_dist = dist;
        if( dist > max_dist ) max_dist = dist;
    }
    
    printf("-- Max dist : %f \n", max_dist );
    printf("-- Min dist : %f \n", min_dist );
    
    //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )
    //-- PS.- radiusMatch can also be used here.
    std::vector< DMatch > good_matches;
    
    if(min_dist!=0)
    {
        for( int i = 0; i < descriptors_1.rows; i++ )
        {
            if( matches[i].distance < 2*min_dist )
            {
                good_matches.push_back( matches[i]);
            }
        }
    }
    else{
        good_matches = matches;
    }
    
    //-- Draw only "good" matches
    Mat img_matches;
    drawMatches( img_1, keypoints_1, img_2, keypoints_2,
                good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
                vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
    
    //-- Show detected matches  
    imshow( "Good Matches", img_matches );

 //-- Localize the object from img_1 in img_2  
 std::vector<Point2f> obj;  
 std::vector<Point2f> scene;  
  
 for( int i = 0; i < good_matches.size(); i++ )  
 {  
  //-- Get the keypoints from the good matches 
   obj.push_back( keypoints_1[ good_matches[i].queryIdx ].pt );  
   scene.push_back( keypoints_2[ good_matches[i].trainIdx ].pt );   
 }  
  
 Mat H = findHomography( obj, scene, CV_RANSAC );  
  
 //-- Get the corners from the image_1 ( the object to be "detected" ) 
 Point2f obj_corners[4] = { cvPoint(0,0), cvPoint( img_1.cols, 0 ), cvPoint( img_1.cols, img_1.rows ), cvPoint( 0, img_1.rows ) };  
 Point scene_corners[4];  
  
 //-- Map these corners in the scene ( image_2) 
 for( int i = 0; i < 4; i++ )  
 {  
  double x = obj_corners[i].x;   
  double y = obj_corners[i].y;  
  
  double Z = 1./( H.at<double>(2,0)*x + H.at<double>(2,1)*y + H.at<double>(2,2) );  
  double X = ( H.at<double>(0,0)*x + H.at<double>(0,1)*y + H.at<double>(0,2) )*Z;  
  double Y = ( H.at<double>(1,0)*x + H.at<double>(1,1)*y + H.at<double>(1,2) )*Z;  
   scene_corners[i] = cvPoint( cvRound(X) + img_1.cols, cvRound(Y) );  
 }    
  
 //-- Draw lines between the corners (the mapped object in the scene - image_2 ) 
 line( img_matches, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 2 );  
 line( img_matches, scene_corners[1], scene_corners[2], Scalar( 0, 255, 0), 2 );  
 line( img_matches, scene_corners[2], scene_corners[3], Scalar( 0, 255, 0), 2 );  
 line( img_matches, scene_corners[3], scene_corners[0], Scalar( 0, 255, 0), 2 );  
  
 //-- Show detected matches 
 imshow( "Good Matches & Object detection", img_matches );