#include?“stdafx.h“
#include?“BowTrain.h“
#include?“stdafx.h“
#include
#include?
#include

#include?
#include?
#include?“opencv2/core/core.hpp“
#include?“opencv2/features2d/features2d.hpp“
#include?“opencv2/calib3d/calib3d.hpp“


BowTrain::BowTrain（）
{
}


BowTrain::~BowTrain（）
{
}

void?BowTrain::SiftExtract（string?path）
{
	//Mat?features;
	Directory?dir;


	string?path2?=?“.\\DATA\\“;
	string?exten2?=?“*“;//“Image*“;//“*“??
	bool?addPath2?=?true;//false??


	vector?foldernames?=?dir.GetListFolders（path2?exten2?addPath2）;

	int?i?=?0;
	int?j?=?0;
	int?cnt?=?0;
	//Mat?features;
	//vector?vectimageName;
	int?clusterNum?=50;
	cout?<	BOWKMeansTrainer?trainer（clusterNum）;
	for?each?（String?var?in?foldernames）
	{
		String?dirtmp?=?foldernames.at（i）;
		vector?foldernamesTmp?=?dir.GetListFiles（dirtmp?exten2?addPath2）;
		cout?<		j?=?0;
		for?each?（String?vari?in?foldernamesTmp）
		{
			Mat?curImg;
			curImg?=?imread（foldernamesTmp.at（j）.c_str（）?-1）;
			vector?keyPoints;
			Mat?descriptor;
			//sift關鍵點檢測
			SiftFeatureDetector?detector;
			detector.detect（curImg?keyPoints）;
			//sift關鍵點描述，角度，強度等
			SiftDescriptorExtractor?extractor;
			extractor.compute（curImg?keyPoints?descriptor）;
			//descriptor是每幅圖像的sift關鍵點描述
			trainer.add（descriptor）;
			j++;
		}
		cout?<		i++;
	}

	Mat?dictionary?=?trainer.cluster（）;

	//將提取的特征存放到xml文件方便使用
	//cout?<	/*FileStorage?fs（“.\\Lab.xml“?FileStorage::WRITE）;
	fs?<	fs.release（）;*/

	FileStorage?fs（“.\\dictionary50.xml“?FileStorage::WRITE）;
	fs?<