#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/nonfree/nonfree.hpp"

#include <iostream>

using namespace cv;
using namespace std;

#define ClusterNum 10

void DrawAndMatchKeypoints(const Mat& Img1, const Mat& Img2, const vector<KeyPoint>& Keypoints1,
	const vector<KeyPoint>& Keypoints2, const Mat& Descriptors1, const Mat& Descriptors2)
{
	Mat keyP1, keyP2;
	drawKeypoints(Img1, Keypoints1, keyP1, Scalar::all(-1), 0);
	drawKeypoints(Img2, Keypoints2, keyP2, Scalar::all(-1), 0);
	putText(keyP1, "drawKeyPoints", cvPoint(10, 30), FONT_HERSHEY_SIMPLEX, 1, Scalar::all(-1));
	putText(keyP2, "drawKeyPoints", cvPoint(10, 30), FONT_HERSHEY_SIMPLEX, 1, Scalar::all(-1));
	imshow("img1 keyPoints", keyP1);
	imshow("img2 keyPoints", keyP2);

	Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create("BruteForce");
	vector<DMatch> matches;
	descriptorMatcher->match(Descriptors1, Descriptors2, matches);
	Mat show;
	drawMatches(Img1, Keypoints1, Img2, Keypoints2, matches, show, Scalar::all(-1), CV_RGB(255, 255, 255), Mat(), 4);
	putText(show, "drawMatchKeyPoints", cvPoint(10, 30), FONT_HERSHEY_SIMPLEX, 1, Scalar::all(-1));
	imshow("match", show);
}

//測試OpenCV：class BOWTrainer
void BOWKeams(const Mat& img, const vector<KeyPoint>& Keypoints,
	const Mat& Descriptors, Mat& centers)
{
	//BOW的kmeans演算法聚類;
	BOWKMeansTrainer bowK(ClusterNum,
		cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 0.1), 3, 2);
	centers = bowK.cluster(Descriptors);
	cout << endl << "< cluster num: " << centers.rows << " >" << endl;

	Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create("BruteForce");
	vector<DMatch> matches;
	descriptorMatcher->match(Descriptors, centers, matches);//const Mat& queryDescriptors, const Mat& trainDescriptors第一個引數是待分類節點，第二個引數是聚類中心;
	Mat demoCluster;
	img.copyTo(demoCluster);

	//為每一類keyPoint定義一種顏色
	Scalar color[] = { CV_RGB(255,255,255),
		CV_RGB(255,0,0),CV_RGB(0,255,0),CV_RGB(0,0,255),
		CV_RGB(255,255,0),CV_RGB(255,0,255),CV_RGB(0,255,255),
		CV_RGB(123,123,0),CV_RGB(0,123,123),CV_RGB(123,0,123) };


	for (vector<DMatch>::iterator iter = matches.begin(); iter != matches.end(); iter++)
	{
		cout << "< descriptorsIdx:" << iter->queryIdx << "  centersIdx:" << iter->trainIdx
			<< " distincs:" << iter->distance << " >" << endl;
		Point center = Keypoints[iter->queryIdx].pt;
		circle(demoCluster, center, 2, color[iter->trainIdx], -1);
	}
	putText(demoCluster, "KeyPoints Clustering: 一種顏色代表一種型別",
		cvPoint(10, 30), FONT_HERSHEY_SIMPLEX, 1, Scalar::all(-1));
	imshow("KeyPoints Clusrtering", demoCluster);

}




int main()
{
	cv::initModule_nonfree();//使用SIFT/SURF create之前，必須先initModule_<modulename>(); 

	cout << "< Creating detector, descriptor extractor and descriptor matcher ...";
	Ptr<FeatureDetector> detector = FeatureDetector::create("SIFT");

	Ptr<DescriptorExtractor> descriptorExtractor = DescriptorExtractor::create("SIFT");

	Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create("BruteForce");



	cout << ">" << endl;

	if (detector.empty() || descriptorExtractor.empty())
	{
		cout << "Can not create detector or descriptor exstractor or descriptor matcher of given types" << endl;
		return -1;
	}
	cout << endl << "< Reading images..." << endl;
	Mat img1 = imread("box.png");
	Mat img2 = imread("box_in_scene.png");
	cout << endl << ">" << endl;


	//detect keypoints;
	cout << endl << "< Extracting keypoints from images..." << endl;
	vector<KeyPoint> keypoints1, keypoints2;
	detector->detect(img1, keypoints1);
	detector->detect(img2, keypoints2);
	cout << "img1:" << keypoints1.size() << " points  img2:" << keypoints2.size()
		<< " points" << endl << ">" << endl;

	//compute descriptors for keypoints;
	cout << "< Computing descriptors for keypoints from images..." << endl;
	Mat descriptors1, descriptors2;
	descriptorExtractor->compute(img1, keypoints1, descriptors1);
	descriptorExtractor->compute(img2, keypoints2, descriptors2);

	cout << endl << "< Descriptoers Size: " << descriptors2.size() << " >" << endl;
	cout << endl << "descriptor's col: " << descriptors2.cols << endl
		<< "descriptor's row: " << descriptors2.rows << endl;
	cout << ">" << endl;

	//Draw And Match img1,img2 keypoints
	//匹配的過程是對特徵點的descriptors進行match;
	DrawAndMatchKeypoints(img1, img2, keypoints1, keypoints2, descriptors1, descriptors2);

	Mat center;
	//對img1提取特徵點，並聚類
	//測試OpenCV：class BOWTrainer
	BOWKeams(img1, keypoints1, descriptors1, center);


	waitKey();

}