#include?
#include?
#include?
#include?
#include?
#include
using?namespace?cv;
using?namespace?ml;

//利用KNN識別字符
int?main（）
{
	////===============================讀取訓練數據===============================////
????//圖片共有10類
????const?int?classSum?=?26;
????//每類共50張圖片
????const?int?imagesSum?=?10;
	//圖片尺寸
	const?int?imageRows?=?33;
????const?int?imageCols?=?26;
????//每一行一個訓練圖片
????float?trainingData[classSum*imagesSum][imageRows*imageCols]?=?{{0}};
????//訓練樣本標簽
????float?labels[classSum*imagesSum]={0};

????for?（int?i?=?0;?i?????{
????????//目標文件夾路徑
????????std::string?inPath?=?“E:\\image\\letters\\“;
		char?label?=?‘a‘;
????????int?k?=?0;
		label?=?label?+?i;
????????inPath?=?inPath?+?label?+?“\\*.jpg“;
????????//用于查找的句柄
????????long?handle;
????????struct?_finddata_t?fileinfo;
????????//第一次查找
????????handle?=?_findfirst（inPath.c_str（）&fileinfo）;
????????if（handle?==?-1）
????????????return?-1;
????????do
????????{
????????????//找到的文件的文件名
????????????std::string?imgname?=?“E:/image/letters/“;
????????????imgname?=?imgname?+?label?+?“/“?+?fileinfo.name;
			//std::cout<????????????Mat?src?=?imread（imgname?0）;
????????????if?（src.empty（））
????????????{
????????????????std::cout<<“can?not?load?image?\n“<????????????????return?-1;
????????????}
			//序列化后放入作為樣本矩陣的一行
????????????for（int?j?=?0;?j????????????{
????????????????trainingData[i*imagesSum?+?k][j]?=?（float）src.data[j];
????????????}
????????????//?設置樣本標簽
			labels[i*imagesSum?+?k]?=?label;
????????????k++;

????????}?while?（!_findnext（handle?&fileinfo））;
????????_findclose（handle）;
????}
????//訓練樣本數據及對應標簽
????Mat?trainingDataMat（classSum*imagesSum?imageRows*imageCols?CV_32FC1?trainingData）;
????Mat?labelsMat（classSum*imagesSum?1?CV_32FC1?labels）;
	//std::cout<	//std::cout<
	////===============================創建KNN模型===============================////
	Ptrmodel?=?KNearest::create（）;
	model->setDefaultK（classSum）;
	model->setIsClassifier（true）;
	PtrtrainData?=?TrainData::create（trainingDataMat?ROW_SAMPLE?labelsMat）;
	model->train（trainData）;

	//model->save（“E:/image/KNearestModel.xml“）;?
	////===============================預測部分===============================////

	Mat?src?dst;
	src?=?imread（“E:/image/image/letters.png“）;
	if?（src.empty（））
	{
		std::cout<<“can?not?load?image?\n“<		return?-1;
	}
	dst?=?src.clone（）;
	//創建感興趣區域選取右側10列作為預測數據
	cvtColor（src?src?COLOR_BGR2GRAY）;
	
	blur（src?src?Size（99））;
	threshold（src?src?210?255?THRESH_BINARY）;
	Canny（src?src?20?80?3?false）;
	std::vector>?contours;
	std::vectorhierarchy;
	findContours（src?contours?hierarchy?RETR_EXTERNAL?CHAIN_APPROX_SIMPLE?Point（00））;

	for?（int?i?=?0;?i?