#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/nonfree/nonfree.hpp>

using namespace cv;

void readme();

/** @function main */
int main( int argc, char** argv )
{
    //if( argc != 3 )
    //{ readme(); return -1; }
    
    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 );
    
    if( !img_1.data || !img_2.data )                                                    //如果數據為空
    { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
    
    
    
    //-- Step 1: Detect the keypoints using SURF Detector     //第一步，用SIFT算子檢測關鍵點
    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 );
    
    //-- Draw keypoints  //在圖像中畫出特徵點
    Mat img_keypoints_1; Mat img_keypoints_2;
    
    drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
    drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
    
    //-- Show detected (drawn) keypoints
    imshow("Keypoints 1", img_keypoints_1 );
    imshow("Keypoints 2", img_keypoints_2 );
    
    //計算特徵
    SurfDescriptorExtractor extractor;//定義對象
    
    Mat descriptors_1,descriptors_2;//存放特徵向量的舉陣
    
    extractor.compute(img_1,keypoints_1,descriptors_1);//計算特徵向量
    extractor.compute(img_2,keypoints_2,descriptors_2);
    
    
    //-- Step 3: Matching descriptor vectors with a brute force matcher
    BFMatcher matcher(NORM_L2);
    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 );
    
    //-- Step 3: Matching descriptor vectors using FLANN matcher
    cv::FlannBasedMatcher matcher2;
    std::vector< DMatch > matches2;
    matcher2.match( descriptors_1, descriptors_2, matches2 );
    
    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;
    
    for( int i = 0; i < descriptors_1.rows; i++ )
    { if( matches[i].distance < 2*min_dist )
    { good_matches.push_back( matches[i]); }
    }
    
    //-- Draw only "good" matches
    Mat img_matches2;
    drawMatches( img_1, keypoints_1, img_2, keypoints_2,
                good_matches, img_matches2, Scalar::all(-1), Scalar::all(-1),
                vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
    
    //-- Show detected matches  
    imshow( "Good Matches", img_matches2 );
    
    
    waitKey(0);
    
    return 0;
}

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