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#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; }

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