FlannBasedMatcher 立体匹配

#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <opencv2/nonfree/nonfree.hpp>
#include<opencv2/legacy/legacy.hpp>
#include <iostream>
using namespace cv;
using namespace std;


//-----------------------------------【全局函数声明部分】--------------------------------------    
//      描述：全局函数声明    
//----------------------------------------------------------------------------------------------- 
static void ShowHelpText( );


//-----------------------------------【main( )函数】--------------------------------------------
//   描述：控制台应用程序的入口函数，我们的程序从这里开始执行
//-----------------------------------------------------------------------------------------------
int main( int argc, char** argv )
{
    //【0】改变console字体颜色    
    system("color 4F");    

    //【1】载入源图片
    Mat img_1 = imread("1.jpg", 1 );
    Mat img_2 = imread( "2.jpg", 1 );
    if( !img_1.data || !img_2.data ) { printf("读取图片image0错误~！ \n"); return false; }  

    //【2】利用SURF检测器检测的关键点
    int minHessian = 300;
    SURF detector( minHessian );
    std::vector<KeyPoint> keypoints_1, keypoints_2;
    detector.detect( img_1, keypoints_1 );
    detector.detect( img_2, keypoints_2 );

    //【3】计算描述符（特征向量）
    SURF extractor;
    Mat descriptors_1, descriptors_2;
    extractor.compute( img_1, keypoints_1, descriptors_1 );
    extractor.compute( img_2, keypoints_2, descriptors_2 );

    //【4】采用FLANN算法匹配描述符向量
    FlannBasedMatcher matcher;
    std::vector< DMatch > matches;
    matcher.match( descriptors_1, descriptors_2, matches );
    double max_dist = 0; double min_dist = 100;

    //【5】快速计算关键点之间的最大和最小距离
    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;
    }
    //输出距离信息
    printf("> 最大距离（Max dist） : %f \n", max_dist );
    printf("> 最小距离（Min dist） : %f \n", min_dist );

    //【6】存下符合条件的匹配结果（即其距离小于2* min_dist的），使用radiusMatch同样可行
    std::vector< DMatch > good_matches;
    for( int i = 0; i < descriptors_1.rows; i++ )
    { 
        if( matches[i].distance < 2*min_dist )
        { good_matches.push_back( matches[i]); }
    }

    //【7】绘制出符合条件的匹配点
    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 );

    //【8】输出相关匹配点信息
    for( int i = 0; i < good_matches.size(); i++ )
    { printf( ">符合条件的匹配点 [%d] 特征点1: %d  -- 特征点2: %d  \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }

    //【9】显示效果图
    imshow( "匹配效果图", img_matches );

    //按任意键退出程序
    waitKey(0);
    return 0;
}