在FLANN特徵上，還可以進一步利用Homography映射找出已知物體。
就是利用findHomography函數，利用匹配的關鍵點找出相應的變換，再利用perspectiveTransform函數映射點群。

#include <stdio.h>
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/nonfree/nonfree.hpp>


using namespace cv;

void readme();

/** @function main */
int main( int argc, char** argv )
{
   
    
    Mat img_1 = imread( "/Users/powenko/Desktop/doughnut.png", CV_LOAD_IMAGE_GRAYSCALE );
    Mat img_2 = imread( "/Users/powenko/Desktop/doughnuts.png", CV_LOAD_IMAGE_GRAYSCALE );
    
    
    Mat img_object =img_1; // imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
    Mat img_scene = img_2; //imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
    
    if( !img_object.data || !img_scene.data )
    { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
    
    //-- Step 1: Detect the keypoints using SURF Detector
    int minHessian = 400;
    
    SurfFeatureDetector detector( minHessian );
    
    std::vector<KeyPoint> keypoints_object, keypoints_scene;
    
    detector.detect( img_object, keypoints_object );
    detector.detect( img_scene, keypoints_scene );
    
    //-- Step 2: Calculate descriptors (feature vectors)
    SurfDescriptorExtractor extractor;
    
    Mat descriptors_object, descriptors_scene;
    
    extractor.compute( img_object, keypoints_object, descriptors_object );
    extractor.compute( img_scene, keypoints_scene, descriptors_scene );
    
    //-- Step 3: Matching descriptor vectors using FLANN matcher
    FlannBasedMatcher matcher;
    std::vector< DMatch > matches;
    matcher.match( descriptors_object, descriptors_scene, matches );
    
    double max_dist = 0; double min_dist = 100;
    
    //-- Quick calculation of max and min distances between keypoints
    for( int i = 0; i < descriptors_object.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 3*min_dist )
    std::vector< DMatch > good_matches;
    
    for( int i = 0; i < descriptors_object.rows; i++ )
    { if( matches[i].distance < 3*min_dist )
    { good_matches.push_back( matches[i]); }
    }
    
    Mat img_matches;
    drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
                good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
                vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
    
    //-- Localize the object
    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_object[ good_matches[i].queryIdx ].pt );
        scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
    }
    
    Mat H = findHomography( obj, scene, CV_RANSAC );
    
    //-- Get the corners from the image_1 ( the object to be "detected" )
    std::vector<Point2f> obj_corners(4);
    obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
    obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
    std::vector<Point2f> scene_corners(4);
    
    perspectiveTransform( obj_corners, scene_corners, H);
    
    //-- Draw lines between the corners (the mapped object in the scene - image_2 )
    line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
    line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
    line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
    line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
    
    //-- Show detected matches
    imshow( "Good Matches & Object detection", img_matches );
    
    waitKey(0);
    return 0;
}

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