引言

• 在OpenCV中有个用于模板匹配的基本函数matchTemplate()，该函数使用某模板在搜索图像中进行搜索时，只能搜索到和模板完全一样的地方，一旦在搜索图像中要搜索的区域相较于模板是旋转了、放大缩小了或者部分遮掩了就无法匹配到结果了。
• 而在halcon中有个基于形状匹配的算子，这个算子非常好用，随便截取一个ROI区域做模板就可以在搜索图像中匹配到相似的区域，并且能输出搜索图像的位置，匹配尺度，匹配角度。也就是说自己截取的一个ROI区域，无论此区域在搜索图像中被放大、缩小还是旋转了、部分遮掩了都可以找到。而OpenCV本身的函数是不能实现此操作的。
• 本文简主要介绍一个类似于halcon算子的使用OpenCV实现的模板匹配算法的使用
  （该算法来源于CSDN，因将其进行本地环境配置的时候无法运行，有些除算法之外的问题，故在此处进行一些简单的地方的修改，最终成功在本地运行，特以此进行记录）
  该匹配算法的来源为:
  一步一步实现多尺度多角度的形状匹配算法(C++版本).

基于形状的匹配算法

算法来源：一步一步实现多尺度多角度的形状匹配算法(C++版本).

具体代码

KcgMatch.h

#pragma once
/*M///
//
// Author	: KayChan
// Explain	: Shape matching
//
//M*/

#ifndef _KCG_MATCH_H_
#define _KCG_MATCH_H_

#include <opencv2/opencv.hpp>
#include <omp.h>

#ifndef ATTR_ALIGN
#  if defined(__GNUC__)
#    define ATTR_ALIGN(n)	__attribute__((aligned(n)))
#  else
#    define ATTR_ALIGN(n)	__declspec(align(n))
#  endif
#endif // #ifndef ATTR_ALIGN

using namespace cv;
using namespace std;

namespace kcg {

	struct MatchRange
	{
		float begin;
		float end;
		float step;

		MatchRange() : begin(0.f), end(0.f), step(0.f) {}
		MatchRange(float b, float e, float s);
	};
	inline MatchRange::MatchRange(float b, float e, float s) : begin(b), end(e), step(s) {}
	typedef struct MatchRange AngleRange;
	typedef struct MatchRange ScaleRange;

	typedef struct ShapeInfo_S
	{
		float angle;
		float scale;
	}ShapeInfo;

	typedef struct Feature_S
	{
		int x;
		int y;
		int lbl;
	}Feature;

	typedef struct Candidate_S
	{
		/// Sort candidates with high score to the front
		bool operator<(const struct Candidate_S &rhs) const
		{
			return score > rhs.score;
		}
		float score;
		Feature feature;
	}Candidate;

	typedef struct Template_S
	{
		int id = 0;
		int pyramid_level = 0;
		int is_valid = 0;
		int x = 0;
		int y = 0;
		int w = 0;
		int h = 0;
		ShapeInfo shape_info;
		vector<Feature> features;
	}Template;

	typedef struct Match_S
	{
		/// Sort matches with high similarity to the front
		bool operator<(const struct Match_S &rhs) const
		{
			// Secondarily sort on template_id for the sake of duplicate removal
			if (similarity != rhs.similarity)
				return similarity > rhs.similarity;
			else
				return template_id < rhs.template_id;
		}

		bool operator==(const struct Match_S &rhs) const
		{
			return x == rhs.x && y == rhs.y && similarity == rhs.similarity;
		}

		int x;
		int y;
		float similarity;
		int template_id;
	}Match;

	typedef enum PyramidLevel_E
	{
		PyramidLevel_0 = 0,
		PyramidLevel_1 = 1,
		PyramidLevel_2 = 2,
		PyramidLevel_3 = 3,
		PyramidLevel_4 = 4,
		PyramidLevel_5 = 5,
		PyramidLevel_6 = 6,
		PyramidLevel_7 = 7,
		PyramidLevel_TabooUse = 16,
	}PyramidLevel;

	typedef enum MatchingStrategy_E
	{
		Strategy_Accurate = 0,
		Strategy_Middling = 1,
		Strategy_Rough = 2,
	}MatchingStrategy;

	class KcgMatch
	{
	public:

		KcgMatch(string model_root, string class_name);
		~KcgMatch();
		/*
		@model: 输入图像
		@angle_range: 角度范围
		@scale_range: 尺度范围
		@num_features: 特征数
		@weak_thresh：弱阈值
		@strong_thresh: 强阈值
		@mask: 掩码
		*/
		void MakingTemplates(Mat model, AngleRange angle_range, ScaleRange scale_range,
			int num_features, float weak_thresh = 30.0f, float strong_thresh = 60.0f,
			Mat mask = Mat());
		/*
		加载模型
		*/
		void LoadModel();
		/*
		@source: 输入图像
		@score_thresh: 匹配分数阈值
		@overlap: 重叠阈值
		@mag_thresh: 最小梯度阈值
		@greediness: 贪婪度，越小匹配越快，但是可能无法匹配到目标
		@pyrd_level: 金字塔层数，越大匹配越快，但是可能无法匹配到目标
		@T: T参数
		@top_k: 最多匹配多少个
		@strategy: 精确匹配(0), 普通匹配(1), 粗略匹配(2)
		@mask: 匹配掩码
		*/
		vector<Match> Matching(Mat source, float score_thresh = 0.9f, float overlap = 0.4f,
			float mag_thresh = 30.f, float greediness = 0.8f, PyramidLevel pyrd_level = PyramidLevel_3,
			int T = 2, int top_k = 0, MatchingStrategy strategy = Strategy_Accurate, const Mat mask = Mat());
		void DrawMatches(Mat &image, vector<Match> matches, Scalar color);

	protected:
		void PaddingModelAndMask(Mat &model, Mat &mask, float max_scale);
		vector<ShapeInfo> ProduceShapeInfos(AngleRange angle_range, ScaleRange scale_range);
		Mat Transform(Mat src, float angle, float scale);
		Mat MdlOf(Mat model, ShapeInfo info);
		Mat MskOf(Mat mask, ShapeInfo info);
		void DrawTemplate(Mat &image, Template templ, Scalar color);
		void QuantifyEdge(Mat image, Mat &angle, Mat &quantized_angle, Mat &mag, float mag_thresh, bool calc_180 = true);
		void Quantify8(Mat angle, Mat &quantized_angle, Mat mag, float mag_thresh);
		void Quantify180(Mat angle, Mat &quantized_angle, Mat mag, float mag_thresh);
		Template ExtractTemplate(Mat angle, Mat quantized_angle, Mat mag, ShapeInfo shape_info,
			PyramidLevel pl, float weak_thresh, float strong_thresh, int num_features, Mat mask);
		Template SelectScatteredFeatures(vector<Candidate> candidates, int num_features, float distance);
		Rect CropTemplate(Template &templ);
		void LoadRegion8Idxes();
		void ClearModel();
		void SaveModel();
		void InitMatchParameter(float score_thresh, float overlap, float mag_thresh, float greediness, int T, int top_k, MatchingStrategy strategy);
		void GetAllPyramidLevelValidSource(Mat &source, PyramidLevel pyrd_level);
		vector<Match> GetTopKMatches(vector<Match> matches);
		vector<Match> DoNmsMatches(vector<Match> matches, PyramidLevel pl, float overlap);
		vector<Match> MatchingPyrd180(Mat src, PyramidLevel pl, vector<int> region_idxes = vector<int>());
		vector<Match> MatchingPyrd8(Mat src, PyramidLevel pl, vector<int> region_idxes = vector<int>());
		void Spread(const Mat quantized_angle, Mat &spread_angle, int T);
		void ComputeResponseMaps(const Mat spread_angle, vector<Mat> &response_maps);
		bool CalcPyUpRoiAndStartPoint(PyramidLevel cur_pl, PyramidLevel obj_pl, Match match,
			Mat &r, Point &p, bool is_padding = false);
		void CalcRegionIndexes(vector<int> &region_idxes, Match match, MatchingStrategy strategy);
		vector<Match> ReconfirmMatches(vector<Match> matches, PyramidLevel pl);
		vector<Match> MatchingFinal(vector<Match> matches, PyramidLevel pl);

	private:
		typedef vector<Template> TemplateMatchRange;
		TemplateMatchRange templ_all_[PyramidLevel_TabooUse];
		vector<Mat> sources_;
		ATTR_ALIGN(32) float score_table_[180][180];
		ATTR_ALIGN(8) unsigned char score_table_8map_[8][256];
		string model_root_;
		string class_name_;
		AngleRange angle_range_;
		ScaleRange scale_range_;
		vector<int> region8_idxes_;

		float score_thresh_;
		float overlap_;
		float mag_thresh_;
		float greediness_;
		int T_;
		int top_k_;
		MatchingStrategy strategy_;
	};
}

#endif

KcgMatch.cpp

此处将原文里面的
namespace kcg_matching{}给删除了，注意里面的内容是保存的，只删除了这个外面的命名空间，此时就能在本人的main函数中进行调用了。

#include "KcgMatch.h"
#include <math.h>

using namespace kcg;

#define KCG_EPS 0.00001f
#define KCG_PI	3.1415926535897932384626433832795f
#define KCG_MODEL_SUFFUX string(".yaml")

const float AngleRegionTable[16][2] = {

	0.f		, 22.5f	,
	22.5f	, 45.f	,
	45.f	, 67.5f	,
	67.5f	, 90.f	,
	90.f	, 112.5f,
	112.5f	, 135.f	,
	135.f	, 157.5f,
	157.5f	, 180.f,
	180.f	, 202.5f,
	202.5f	, 225.f,
	225.f	, 247.5f,
	247.5f	, 270.f,
	270.f	, 292.5f,
	292.5f	, 315.f,
	315.f	, 337.5f,
	337.5f	, 360.f
};

namespace cv_dnn_nms {

	template <typename T>
	static inline bool SortScorePairDescend(const std::pair<float, T>& pair1, const std::pair<float, T>& pair2) {

		return pair1.first > pair2.first;
	}

	inline void GetMaxScoreIndex(const std::vector<float>& scores, const float threshold, const int top_k,
		std::vector<std::pair<float, int> >& score_index_vec) {

		for (size_t i = 0; i < scores.size(); ++i)
		{
			if (scores[i] > threshold)
			{
				//score_index_vec.push_back(std::make_pair(scores[i], i));
				std::pair<float, int> psi;
				psi.first = scores[i];
				psi.second = (int)i;
				score_index_vec.push_back(psi);
			}
		}
		std::stable_sort(score_index_vec.begin(), score_index_vec.end(),
			SortScorePairDescend<int>);
		if (top_k > 0 && top_k < (int)score_index_vec.size())
		{
			score_index_vec.resize(top_k);
		}
	}

	template <typename BoxType>
	inline void NMSFast_(const std::vector<BoxType>& bboxes,
		const std::vector<float>& scores, const float score_threshold,
		const float nms_threshold, const float eta, const int top_k,
		std::vector<int>& indices, float(*computeOverlap)(const BoxType&, const BoxType&)) {

		CV_Assert(bboxes.size() == scores.size());
		std::vector<std::pair<float, int> > score_index_vec;
		GetMaxScoreIndex(scores, score_threshold, top_k, score_index_vec);

		float adaptive_threshold = nms_threshold;
		indices.clear();
		for (size_t i = 0; i < score_index_vec.size(); ++i) {
			const int idx = score_index_vec[i].second;
			bool keep = true;
			for (int k = 0; k < (int)indices.size() && keep; ++k) {
				const int kept_idx = indices[k];
				float overlap = computeOverlap(bboxes[idx], bboxes[kept_idx]);
				keep = overlap <= adaptive_threshold;
			}
			if (keep)
				indices.push_back(idx);
			if (keep && eta < 1 && adaptive_threshold > 0.5) {
				adaptive_threshold *= eta;
			}
		}
	}

	template<typename _Tp> static inline
		double jaccardDistance__(const Rect_<_Tp>& a, const Rect_<_Tp>& b) {
		_Tp Aa = a.area();
		_Tp Ab = b.area();

		if ((Aa + Ab) <= std::numeric_limits<_Tp>::epsilon()) {
			// jaccard_index = 1 -> distance = 0
			return 0.0;
		}

		double Aab = (a & b).area();
		// distance = 1 - jaccard_index
		return 1.0 - Aab / (Aa + Ab - Aab);
	}

	template <typename T>
	static inline float rectOverlap(const T& a, const T& b) {

		return 1.f - static_cast<float>(jaccardDistance__(a, b));
	}

	void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
		const float score_threshold, const float nms_threshold,
		std::vector<int>& indices, const float eta = 1, const int top_k = 0) {

		NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rectOverlap);
	}

} // end namespace cv_dnn_nms



	KcgMatch::KcgMatch(string model_root, string class_name) {

		assert(!model_root.empty() && "model_root should not empty.");
		assert(!class_name.empty() && "class_name should not empty.");
		if (model_root[model_root.length() - 1] != '/') {

			model_root.push_back('/');
		}
		model_root_ = model_root;
		class_name_ = class_name;

		/// Create 180*180 table
		for (int i = 0; i < 180; i++) {

			for (int j = 0; j < 180; j++) {

				float rad = (i - j) * KCG_PI / 180.f;
				score_table_[i][j] = fabs(cosf(rad));
			}
		}

		/// Create 8*8 table
		ATTR_ALIGN(8) unsigned char score_table_8d[8][8];
		for (int i = 0; i < 8; i++) {

			for (int j = 0; j < 8; j++) {

				float rad = (i - j) * (180.f / 8.f) * KCG_PI / 180.f;
				score_table_8d[i][j] = (unsigned char)(fabs(cosf(rad))*100.f);
			}
		}

		/// Create 8*256 table
		for (int i = 0; i < 8; i++) {

			for (int j = 0; j < 256; j++) {

				unsigned char max_score = 0;
				for (int shift_time = 0; shift_time < 8; shift_time++) {

					unsigned char flg = (j >> shift_time) & 0b00000001;
					if (flg) {

						if (score_table_8d[i][shift_time] > max_score) {

							max_score = score_table_8d[i][shift_time];
						}
					}
				}
				score_table_8map_[i][j] = max_score;
			}
		}
	}

	KcgMatch::~KcgMatch() {

	}

	void KcgMatch::MakingTemplates(Mat model, AngleRange angle_range, ScaleRange scale_range,
		int num_features, float weak_thresh, float strong_thresh, Mat mask) {

		ClearModel();
		PaddingModelAndMask(model, mask, scale_range.end);
		angle_range_ = angle_range;
		scale_range_ = scale_range;
		vector<ShapeInfo> shape_infos = ProduceShapeInfos(angle_range, scale_range);
		vector<Mat> l0_mdls; l0_mdls.clear();
		vector<Mat> l0_msks; l0_msks.clear();
		for (int s = 0; s < shape_infos.size(); s++) {

			l0_mdls.push_back(MdlOf(model, shape_infos[s]));
			l0_msks.push_back(MskOf(mask, shape_infos[s]));
		}
		for (int p = 0; p <= PyramidLevel_7; p++) {

			for (int s = 0; s < shape_infos.size(); s++) {

				Mat mdl_pyrd = l0_mdls[s];
				Mat msk_pyrd = l0_msks[s];
				if (p > 0) {

					Size sz = Size(l0_mdls[s].cols >> 1, l0_mdls[s].rows >> 1);
					pyrDown(l0_mdls[s], mdl_pyrd, sz);
					pyrDown(l0_msks[s], msk_pyrd, sz);
				}
				erode(msk_pyrd, msk_pyrd, Mat(), Point(-1, -1), 1, BORDER_REPLICATE);
				l0_mdls[s] = mdl_pyrd;
				l0_msks[s] = msk_pyrd;

				int features_pyrd = (int)((num_features >> p) * shape_infos[s].scale);

				Mat mag8, angle8, quantized_angle8;
				QuantifyEdge(mdl_pyrd, angle8, quantized_angle8, mag8, weak_thresh, false);
				Template templ = ExtractTemplate(angle8, quantized_angle8, mag8,
					shape_infos[s], PyramidLevel(p),
					weak_thresh, strong_thresh,
					features_pyrd, msk_pyrd);
				templ_all_[p].push_back(templ);

				Mat mag180, angle180, quantized_angle180;
				QuantifyEdge(mdl_pyrd, angle180, quantized_angle180, mag180, weak_thresh, true);
				templ = ExtractTemplate(angle180, quantized_angle180, mag180,
					shape_infos[s], PyramidLevel(p),
					weak_thresh, strong_thresh,
					features_pyrd, msk_pyrd);
				templ_all_[p + 8].push_back(templ);

				/// draw
				/*Mat draw_mask;
				msk_pyrd.copyTo(draw_mask);
				DrawTemplate(draw_mask, templ, Scalar(0));
				imshow("draw_mask", draw_mask);
				waitKey(1);*/
			}
			cout << "train pyramid level " << p << " complete." << endl;
		}
		SaveModel();
	}

	vector<Match> KcgMatch::Matching(Mat source, float score_thresh, float overlap,
		float mag_thresh, float greediness, PyramidLevel pyrd_level, int T, int top_k,
		MatchingStrategy strategy, const Mat mask) {

		InitMatchParameter(score_thresh, overlap, mag_thresh, greediness, T, top_k, strategy);
		GetAllPyramidLevelValidSource(source, pyrd_level);

		vector<Match> matches;
		matches = MatchingPyrd8(sources_[pyrd_level], pyrd_level, region8_idxes_);
		matches = GetTopKMatches(matches);

		matches = ReconfirmMatches(matches, pyrd_level);
		matches = GetTopKMatches(matches);

		matches = MatchingFinal(matches, pyrd_level);
		matches = GetTopKMatches(matches);

		return matches;
	}

	void KcgMatch::DrawMatches(Mat &image, vector<Match> matches, Scalar color) {

		//#pragma omp parallel for
		for (int i = 0; i < matches.size(); i++) {

			auto match = matches[i];
			auto templ = templ_all_[8][match.template_id];
			int w = match.x + templ.w;
			int h = match.y + templ.h;
			for (int i = 0; i < (int)templ.features.size(); i++) {

				auto feature = templ.features[i];
				//circle(image, cv::Point(match.x + feature.x, match.y + feature.y), 1, color, 1);
				line(image,
					Point(match.x + feature.x, match.y + feature.y),
					Point(match.x + feature.x, match.y + feature.y),
					color, 1);
			}
			//用来框选出矩形框的
			cv::rectangle(image, { match.x, match.y }, { w, h }, color, 1);
			char info[128];
			sprintf_s(info,
				"%.2f%% [%.2f, %.2f]",
				match.similarity * 100,
				templ.shape_info.angle,
				templ.shape_info.scale);
			cv::putText(image,
				info,
				Point(match.x, match.y), FONT_HERSHEY_PLAIN, 1.f, color, 1);
		}
	}

	void KcgMatch::PaddingModelAndMask(Mat &model, Mat &mask, float max_scale) {

		CV_Assert(!model.empty() && "model is empty.");
		if (mask.empty())
			mask = Mat(model.size(), CV_8UC1, { 255 });
		else
			CV_Assert(model.size() == mask.size());
		int min_side_length = std::min(model.rows, model.cols);
		int diagonal_line_length =
			(int)ceil(std::sqrt(model.rows*model.rows + model.cols*model.cols)*max_scale);
		int padding = ((diagonal_line_length - min_side_length) >> 1) + 16;
		int double_padding = (padding << 1);
		Mat model_padded = Mat(model.rows + double_padding, model.cols + double_padding, model.type(), Scalar::all(0));
		model.copyTo(model_padded(Rect(padding, padding, model.cols, model.rows)));
		Mat mask_padded = Mat(mask.rows + double_padding, mask.cols + double_padding, mask.type(), Scalar::all(0));
		mask.copyTo(mask_padded(Rect(padding, padding, mask.cols, mask.rows)));
		model = model_padded;
		mask = mask_padded;
	}

	vector<ShapeInfo> KcgMatch::ProduceShapeInfos(AngleRange angle_range, ScaleRange scale_range) {

		assert(scale_range.begin > KCG_EPS && scale_range.end > KCG_EPS);
		assert(angle_range.end >= angle_range.begin);
		assert(scale_range.end >= scale_range.begin);
		assert(angle_range.step > KCG_EPS);
		assert(scale_range.step > KCG_EPS);
		vector<ShapeInfo> shape_infos;
		shape_infos.clear();
		for (float scale = scale_range.begin; scale <= scale_range.end + KCG_EPS; scale += scale_range.step) {

			for (float angle = angle_range.begin; angle <= angle_range.end + KCG_EPS; angle += angle_range.step) {

				ShapeInfo info;
				info.angle = angle;
				info.scale = scale;
				shape_infos.push_back(info);
			}
		}
		return shape_infos;
	}

	Mat KcgMatch::Transform(Mat src, float angle, float scale) {

		Mat dst;
		Point center(src.cols / 2, src.rows / 2);
		Mat rot_mat = cv::getRotationMatrix2D(center, angle, scale);
		warpAffine(src, dst, rot_mat, src.size());
		return dst;
	}

	Mat KcgMatch::MdlOf(Mat model, ShapeInfo info) {

		return Transform(model, info.angle, info.scale);
	}

	Mat KcgMatch::MskOf(Mat mask, ShapeInfo info) {

		return (Transform(mask, info.angle, info.scale) > 0);
	}

	void KcgMatch::DrawTemplate(Mat &image, Template templ, Scalar color) {

		for (int i = 0; i < templ.features.size(); i++) {

			auto feature = templ.features[i];
			line(image,
				Point(templ.x + feature.x, templ.y + feature.y),
				Point(templ.x + feature.x, templ.y + feature.y),
				color, 1);
		}
	}

	void KcgMatch::QuantifyEdge(Mat image, Mat &angle, Mat &quantized_angle, Mat &mag, float mag_thresh, bool calc_180) {

		Mat dx, dy;
		//Sobel(image, dx, CV_32F, 1, 0, 3, 1.0, 0.0, BORDER_REPLICATE);
		//Sobel(image, dy, CV_32F, 0, 1, 3, 1.0, 0.0, BORDER_REPLICATE);
		float mask_x[3][3] = { { -1,0,1 },{ -2,0,2 },{ -1,0,1 } };
		float mask_y[3][3] = { { 1,2,1 },{ 0,0,0 },{ -1,-2,-1 } };
		Mat kernel_x = Mat(3, 3, CV_32F, mask_x);
		Mat kernel_y = Mat(3, 3, CV_32F, mask_y);
		filter2D(image, dx, CV_32F, kernel_x);
		filter2D(image, dy, CV_32F, kernel_y);
		//dx = abs(dx);
		//dy = abs(dy);
		mag = dx.mul(dx) + dy.mul(dy);
		phase(dx, dy, angle, true);

		if (calc_180)
			Quantify180(angle, quantized_angle, mag, mag_thresh);
		else
			Quantify8(angle, quantized_angle, mag, mag_thresh);
	}

	void KcgMatch::Quantify8(Mat angle, Mat &quantized_angle, Mat mag, float mag_thresh) {

		Mat_<unsigned char> quantized_unfiltered;
		angle.convertTo(quantized_unfiltered, CV_8U, 16.0f / 360.0f);
		for (int r = 0; r < angle.rows; ++r)
		{
			unsigned char *quant_ptr = quantized_unfiltered.ptr<unsigned char>(r);
			for (int c = 0; c < angle.cols; ++c)
			{
				quant_ptr[c] &= 7;
			}
		}
		//quantized_unfiltered.copyTo(quantized_angle);
		quantized_angle = Mat::zeros(angle.size(), CV_8U);
		for (int r = 0; r < quantized_angle.rows; ++r) {

			quantized_angle.ptr<unsigned char>(r)[0] = 255;
			quantized_angle.ptr<unsigned char>(r)[quantized_angle.cols - 1] = 255;
		}
		for (int c = 0; c < quantized_angle.cols; ++c) {

			quantized_angle.ptr<unsigned char>(0)[c] = 255;
			quantized_angle.ptr<unsigned char>(quantized_angle.rows - 1)[c] = 255;
		}

		for (int r = 1; r < angle.rows - 1; ++r)
		{
			float *mag_ptr = mag.ptr<float>(r);
			for (int c = 1; c < angle.cols - 1; ++c)
			{
				if (mag_ptr[c] >= (mag_thresh * mag_thresh))
				{
					int histogram[8] = { 0, 0, 0, 0, 0, 0, 0, 0 };

					unsigned char *patch3x3_row = &quantized_unfiltered(r - 1, c - 1);
					histogram[patch3x3_row[0]]++;
					histogram[patch3x3_row[1]]++;
					histogram[patch3x3_row[2]]++;

					patch3x3_row += quantized_unfiltered.step1();
					histogram[patch3x3_row[0]]++;
					histogram[patch3x3_row[1]]++;
					histogram[patch3x3_row[2]]++;

					patch3x3_row += quantized_unfiltered.step1();
					histogram[patch3x3_row[0]]++;
					histogram[patch3x3_row[1]]++;
					histogram[patch3x3_row[2]]++;

					// Find bin with the most votes from the patch
					int max_votes = 0;
					int index = -1;
					for (int i = 0; i < 8; ++i)
					{
						if (max_votes < histogram[i])
						{
							index = i;
							max_votes = histogram[i];
						}
					}

					// Only accept the quantization if majority of pixels in the patch agree
					static const int NEIGHBOR_THRESHOLD = 5;
					if (max_votes >= NEIGHBOR_THRESHOLD)
						quantized_angle.at<unsigned char>(r, c) = index;
					else
						quantized_angle.at<unsigned char>(r, c) = 255;
				}
				else
				{
					quantized_angle.at<unsigned char>(r, c) = 255;
				}
			}
		}
	}

	void KcgMatch::Quantify180(Mat angle, Mat &quantized_angle, Mat mag, float mag_thresh) {

		quantized_angle = Mat::zeros(angle.size(), CV_8U);
#pragma omp parallel for
		for (int r = 0; r < angle.rows; ++r)
		{
			unsigned char *quantized_angle_ptr = quantized_angle.ptr<unsigned char>(r);
			float *angle_ptr = angle.ptr<float>(r);
			float *mag_ptr = mag.ptr<float>(r);
			for (int c = 0; c < angle.cols; ++c)
			{
				if (mag_ptr[c] >= (mag_thresh * mag_thresh))
					quantized_angle_ptr[c] = (int)round(angle_ptr[c]) % 180;
				else
					quantized_angle_ptr[c] = 255;
			}
		}
	}

	Template KcgMatch::ExtractTemplate(Mat angle, Mat quantized_angle, Mat mag, ShapeInfo shape_info,
		PyramidLevel pl, float weak_thresh, float strong_thresh, int num_features, Mat mask) {

		Mat local_angle = Mat(angle.size(), angle.type());
		for (int r = 0; r < angle.rows; ++r) {

			float *angle_ptr = angle.ptr<float>(r);
			float *local_angle_ptr = local_angle.ptr<float>(r);
			for (int c = 0; c < angle.cols; ++c) {

				float dir = angle_ptr[c];
				if ((dir > 0. && dir < 22.5) || (dir > 157.5 && dir < 202.5) || (dir > 337.5 && dir < 360.))
					local_angle_ptr[c] = 0.f;
				else if ((dir > 22.5 && dir < 67.5) || (dir > 202.5 && dir < 247.5))
					local_angle_ptr[c] = 45.f;
				else if ((dir > 67.5 && dir < 112.5) || (dir > 247.5 && dir < 292.5))
					local_angle_ptr[c] = 90.f;
				else if ((dir > 112.5 && dir < 157.5) || (dir > 292.5 && dir < 337.5))
					local_angle_ptr[c] = 135.f;
				else
					local_angle_ptr[c] = 0.f;
			}
		}

		vector<Candidate> candidates;
		candidates.clear();
		bool no_mask = mask.empty();
		float weak_sq = weak_thresh * weak_thresh;
		float strong_sq = strong_thresh * strong_thresh;
		float pre_grad, lst_grad;
		for (int r = 1; r < mag.rows - 1; ++r)
		{
			const unsigned char *mask_ptr = no_mask ? NULL : mask.ptr<unsigned char>(r);
			const float* pre_ptr = mag.ptr<float>(r - 1);
			const float* cur_ptr = mag.ptr<float>(r);
			const float* lst_ptr = mag.ptr<float>(r + 1);
			float *local_angle_ptr = local_angle.ptr<float>(r);

			for (int c = 1; c < mag.cols - 1; ++c)
			{
				if (no_mask || mask_ptr[c])
				{
					switch ((int)local_angle_ptr[c]) {

					case 0:
						pre_grad = cur_ptr[c - 1];
						lst_grad = cur_ptr[c + 1];
						break;
					case 45:
						pre_grad = pre_ptr[c + 1];
						lst_grad = lst_ptr[c - 1];
						break;
					case 90:
						pre_grad = pre_ptr[c];
						lst_grad = lst_ptr[c];
						break;
					case 135:
						pre_grad = pre_ptr[c - 1];
						lst_grad = lst_ptr[c + 1];
						break;
					}
					if ((cur_ptr[c] > pre_grad) && (cur_ptr[c] > lst_grad)) {

						float score = cur_ptr[c];
						bool validity = false;
						if (score >= weak_sq) {

							if (score >= strong_sq) {

								validity = true;
							}
							else {

								if (((pre_ptr[c - 1]) >= strong_sq) ||
									((pre_ptr[c]) >= strong_sq) ||
									((pre_ptr[c + 1]) >= strong_sq) ||
									((cur_ptr[c - 1]) >= strong_sq) ||
									((cur_ptr[c + 1]) >= strong_sq) ||
									((lst_ptr[c - 1]) >= strong_sq) ||
									((lst_ptr[c]) >= strong_sq) ||
									((lst_ptr[c + 1]) >= strong_sq))
								{
									validity = true;
								}
							}
						}
						if (validity == true &&
							quantized_angle.at<unsigned char>(r, c) != 255) {

							Candidate cd;
							cd.score = score;
							cd.feature.x = c;
							cd.feature.y = r;
							cd.feature.lbl = quantized_angle.at<unsigned char>(r, c);
							candidates.push_back(cd);
						}
					}

				}
			}
		}

		Template templ;
		templ.shape_info.angle = shape_info.angle;
		templ.shape_info.scale = shape_info.scale;
		templ.pyramid_level = pl;
		templ.is_valid = 0;
		templ.features.clear();

		if (candidates.size() >= num_features && num_features > 0) {

			std::stable_sort(candidates.begin(), candidates.end());
			float distance = static_cast<float>(candidates.size() / num_features + 1);
			templ = SelectScatteredFeatures(candidates, num_features, distance);
		}
		else {

			for (int c = 0; c < candidates.size(); c++) {

				templ.features.push_back(candidates[c].feature);
			}
		}

		if (templ.features.size() > 0) {

			templ.is_valid = 1;
			CropTemplate(templ);
		}

		return templ;
	}

	Template KcgMatch::SelectScatteredFeatures(vector<Candidate> candidates, int num_features, float distance) {

		Template templ;
		templ.features.clear();
		float distance_sq = distance * distance;
		int i = 0;
		while (templ.features.size() < num_features) {

			Candidate c = candidates[i];
			// Add if sufficient distance away from any previously chosen feature
			bool keep = true;
			for (int j = 0; (j < (int)templ.features.size()) && keep; ++j)
			{
				Feature f = templ.features[j];
				keep = ((c.feature.x - f.x) * (c.feature.x - f.x) + (c.feature.y - f.y) * (c.feature.y - f.y) >= distance_sq);
			}
			if (keep)
				templ.features.push_back(c.feature);

			if (++i == (int)candidates.size())
			{
				// Start back at beginning, and relax required distance
				i = 0;
				distance -= 1.0f;
				distance_sq = distance * distance;
				// if (distance < 3)
				// {
				//     // we don't want two features too close
				//     break;
				// }
			}
		}
		return templ;
	}

	Rect KcgMatch::CropTemplate(Template &templ) {

		int min_x = std::numeric_limits<int>::max();
		int min_y = std::numeric_limits<int>::max();
		int max_x = std::numeric_limits<int>::min();
		int max_y = std::numeric_limits<int>::min();

		// First pass: find min/max feature x,y 
		for (int i = 0; i < (int)templ.features.size(); ++i)
		{
			int x = templ.features[i].x;
			int y = templ.features[i].y;
			min_x = std::min(min_x, x);
			min_y = std::min(min_y, y);
			max_x = std::max(max_x, x);
			max_y = std::max(max_y, y);
		}

		/// @todo Why require even min_x, min_y?
		if (min_x % 2 == 1)
			--min_x;
		if (min_y % 2 == 1)
			--min_y;

		// Second pass: set width/height and shift all feature positions
		templ.w = (max_x - min_x);
		templ.h = (max_y - min_y);
		templ.x = min_x;
		templ.y = min_y;

		for (int i = 0; i < (int)templ.features.size(); ++i)
		{
			templ.features[i].x -= templ.x;
			templ.features[i].y -= templ.y;
		}
		return Rect(min_x, min_y, max_x - min_x, max_y - min_y);
	}

	void KcgMatch::LoadRegion8Idxes() {

		int keys[16] = { 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 };
		region8_idxes_.clear();
		int angle_region = (int)((angle_range_.end - angle_range_.begin) / angle_range_.step) + 1;
		int scale_region = (int)((scale_range_.end - scale_range_.begin) / scale_range_.step) + 1;
		for (int ar = 0; ar < angle_region; ar++) {

			float cur_agl = templ_all_[PyramidLevel_0][ar].shape_info.angle;
			if (cur_agl < 0.f) cur_agl += 360.f;
			int idx = 0;
			for (int i = 0; i < 16; i++) {

				if (cur_agl >= AngleRegionTable[i][0] &&
					cur_agl < AngleRegionTable[i][1]) {

					idx = i;
					break;
				}
			}
			if (keys[idx] == 0) {

				for (int sr = 0; sr < scale_region; sr++) {

					region8_idxes_.push_back(ar + sr * angle_region);
				}
			}
			keys[idx] = 1;
		}
	}

	void KcgMatch::SaveModel() {

		int total_templ = 0;
		for (int i = 0; i < PyramidLevel_TabooUse; i++) {

			total_templ += (int)templ_all_[i].size();
		}
		assert((total_templ / PyramidLevel_TabooUse) == templ_all_[0].size());
		int match_range_size = (int)templ_all_[0].size();
		string model_name = model_root_ + class_name_ + KCG_MODEL_SUFFUX;
		FileStorage fs(model_name, FileStorage::WRITE);
		fs << "class_name" << class_name_;
		fs << "total_pyramid_levels" << PyramidLevel_7;
		fs << "angle_range_bgin" << angle_range_.begin;
		fs << "angle_range_end" << angle_range_.end;
		fs << "angle_range_step" << angle_range_.step;
		fs << "scale_range_bgin" << scale_range_.begin;
		fs << "scale_range_end" << scale_range_.end;
		fs << "scale_range_step" << scale_range_.step;
		fs << "templates"
			<< "[";
		{
			for (int i = 0; i < match_range_size; i++) {

				fs << "{";
				fs << "template_id" << int(i);
				fs << "template_pyrds"
					<< "[";
				{
					for (int j = 0; j < PyramidLevel_TabooUse; j++) {

						auto templ = templ_all_[j][i];
						fs << "{";
						fs << "id" << int(i);
						fs << "pyramid_level" << templ.pyramid_level;
						fs << "is_valid" << templ.is_valid;
						fs << "x" << templ.x;
						fs << "y" << templ.y;
						fs << "w" << templ.w;
						fs << "h" << templ.h;
						fs << "shape_scale" << templ.shape_info.scale;
						fs << "shape_angle" << templ.shape_info.angle;
						fs << "feature_size" << (int)templ.features.size();
						fs << "features"
							<< "[";
						{
							for (int k = 0; k < (int)templ.features.size(); k++) {

								auto feat = templ.features[k];
								fs << "[:" << feat.x << feat.y << feat.lbl << "]";
							}
						}
						fs << "]";
						fs << "}";
					}
				}
				fs << "]";
				fs << "}";
			}
		}
		fs << "]";
	}

	void KcgMatch::LoadModel() {

		ClearModel();
		string model_name = model_root_ + class_name_ + KCG_MODEL_SUFFUX;
		FileStorage fs(model_name, FileStorage::READ);
		assert(fs.isOpened() && "load model failed.");
		FileNode fn = fs.root();
		angle_range_.begin = fn["angle_range_bgin"];
		angle_range_.end = fn["angle_range_end"];
		angle_range_.step = fn["angle_range_step"];
		scale_range_.begin = fn["scale_range_bgin"];
		scale_range_.end = fn["scale_range_end"];
		scale_range_.step = fn["scale_range_step"];
		FileNode tps_fn = fn["templates"];
		FileNodeIterator tps_it = tps_fn.begin(), tps_it_end = tps_fn.end();
		for (; tps_it != tps_it_end; ++tps_it)
		{
			int template_id = (*tps_it)["template_id"];
			FileNode pyrds_fn = (*tps_it)["template_pyrds"];
			FileNodeIterator pyrd_it = pyrds_fn.begin(), pyrd_it_end = pyrds_fn.end();
			int pl = 0;
			for (; pyrd_it != pyrd_it_end; ++pyrd_it)
			{
				FileNode pyrd_fn = (*pyrd_it);
				Template templ;
				templ.id = pyrd_fn["id"];
				templ.pyramid_level = pyrd_fn["pyramid_level"];
				templ.is_valid = pyrd_fn["is_valid"];
				templ.x = pyrd_fn["x"];
				templ.y = pyrd_fn["y"];
				templ.w = pyrd_fn["w"];
				templ.h = pyrd_fn["h"];
				templ.shape_info.scale = pyrd_fn["shape_scale"];
				templ.shape_info.angle = pyrd_fn["shape_angle"];
				FileNode features_fn = pyrd_fn["features"];
				FileNodeIterator feature_it = features_fn.begin(), feature_it_end = features_fn.end();
				for (; feature_it != feature_it_end; ++feature_it)
				{
					FileNode feature_fn = (*feature_it);
					FileNodeIterator feature_info = feature_fn.begin();
					Feature feat;
					feature_info >> feat.x >> feat.y >> feat.lbl;
					templ.features.push_back(feat);
				}
				templ_all_[pl].push_back(templ);
				pl++;
			}
		}

		LoadRegion8Idxes();
	}

	void KcgMatch::ClearModel() {

		for (int i = 0; i < PyramidLevel_TabooUse; i++) {

			templ_all_[i].clear();
		}
	}

	void KcgMatch::InitMatchParameter(float score_thresh, float overlap, float mag_thresh, float greediness, int T, int top_k, MatchingStrategy strategy) {

		score_thresh_ = score_thresh;
		overlap_ = overlap;
		mag_thresh_ = mag_thresh;
		greediness_ = greediness;
		T_ = T;
		top_k_ = top_k;
		strategy_ = strategy;
	}

	void KcgMatch::GetAllPyramidLevelValidSource(cv::Mat &source, PyramidLevel pyrd_level) {

		sources_.clear();
		for (int pl = 0; pl <= pyrd_level; pl++) {

			Mat source_pyrd;
			if (pl == 0) source_pyrd = source;
			else pyrDown(source, source_pyrd, Size(source.cols >> 1, source.rows >> 1));
			source = source_pyrd;
			sources_.push_back(source_pyrd);
		}
	}

	vector<Match> KcgMatch::GetTopKMatches(vector<Match> matches) {

		vector<Match> top_k_matches;
		top_k_matches.clear();
		if (top_k_ > 0 && (top_k_ < matches.size()) && (matches.size() > 0)) {

			int k = 0;
			top_k_matches.push_back(matches[0]);
			for (int m = 1; m < matches.size(); m++) {

				if (matches[m].similarity < matches[m - 1].similarity) {

					++k;
					if (k >= top_k_) break;
				}
				top_k_matches.push_back(matches[m]);
			}
		}
		else
		{
			top_k_matches = matches;
		}
		return top_k_matches;
	}

	vector<Match> KcgMatch::DoNmsMatches(vector<Match> matches, PyramidLevel pl, float overlap) {

		vector<Rect> boxes; boxes.clear();
		vector<float> scores; scores.clear();
		vector<int> indices; indices.clear();
		for (int m = 0; m < matches.size(); m++) {

			auto templ = templ_all_[pl][matches[m].template_id];
			Rect box = Rect(matches[m].x, matches[m].y, templ.w, templ.h);
			boxes.insert(boxes.end(), box);
			scores.insert(scores.end(), matches[m].similarity);
		}
		cv_dnn_nms::NMSBoxes(boxes, scores, overlap, overlap, indices);
		vector<Match> final_matches; final_matches.clear();
		for (auto index : indices) {

			final_matches.push_back(matches[index]);
		}
		return final_matches;
	}

	vector<Match> KcgMatch::MatchingPyrd180(Mat src, PyramidLevel pl, vector<int> region_idxes) {

		pl = PyramidLevel(pl + 8);
		vector<Match> matches; matches.clear();
		Mat angle, quantized_angle, mag;
		QuantifyEdge(src, angle, quantized_angle, mag, mag_thresh_, true);
#pragma omp parallel 
		{
			int tlsz = region_idxes.empty() ? ((int)templ_all_[pl].size()) : ((int)region_idxes.size());
#pragma omp for nowait
			for (int t = 0; t < tlsz; t++) {

				Template templ = region_idxes.empty() ? (templ_all_[pl][t]) : (templ_all_[pl][region_idxes[t]]);
				for (int r = 0; r < quantized_angle.rows - templ.h; r++) {

					for (int c = 0; c < quantized_angle.cols - templ.w; c++) {

						int fsz = (int)templ.features.size();
						float partial_sum = 0.f;
						bool valid = true;
						for (int f = 0; f < fsz; f++) {

							Feature feat = templ.features[f];
							int sidx = quantized_angle.ptr<unsigned char>(r + feat.y)[c + feat.x];
							int tidx = feat.lbl;
							if (sidx != 255) {

								partial_sum += score_table_[sidx][tidx];
							}
							if (partial_sum + (fsz - f) * greediness_ < score_thresh_ * fsz) {

								valid = false;
								break;
							}
						}
						if (valid) {

							float score = partial_sum / fsz;
							if (score >= score_thresh_) {

								Match match;
								match.x = c;
								match.y = r;
								match.similarity = score;
								match.template_id = templ.id;
#pragma omp critical
								matches.insert(matches.end(), match);
							}
						}

					}
				}
			}
		}
		matches = DoNmsMatches(matches, pl, overlap_);
		return matches;
	}

	vector<Match> KcgMatch::MatchingPyrd8(Mat src, PyramidLevel pl, vector<int> region_idxes) {

		vector<Match> matches; matches.clear();
		Mat angle, quantized_angle, mag;
		QuantifyEdge(src, angle, quantized_angle, mag, mag_thresh_, false);
		Mat spread_angle;
		Spread(quantized_angle, spread_angle, T_);
		vector<Mat> response_maps;
		ComputeResponseMaps(spread_angle, response_maps);
#pragma omp parallel 
		{
			int tlsz = region_idxes.empty() ? ((int)templ_all_[pl].size()) : ((int)region_idxes.size());
#pragma omp for nowait
			for (int t = 0; t < tlsz; t++) {

				Template templ = region_idxes.empty() ? (templ_all_[pl][t]) : (templ_all_[pl][region_idxes[t]]);
				for (int r = 0; r < quantized_angle.rows - templ.h; r += T_) {

					for (int c = 0; c < quantized_angle.cols - templ.w; c += T_) {

						int fsz = (int)templ.features.size();
						int partial_sum = 0;
						bool valid = true;
						for (int f = 0; f < fsz; f++) {

							Feature feat = templ.features[f];
							int label = feat.lbl;
							partial_sum +=
								response_maps[label].ptr<unsigned char>(r + feat.y)[c + feat.x];
							if (partial_sum + (fsz - f) * greediness_ < score_thresh_ * fsz) {

								valid = false;
								break;
							}
						}
						if (valid) {

							float score = partial_sum / (100.f * fsz);
							if (score >= score_thresh_) {

								Match match;
								match.x = c;
								match.y = r;
								match.similarity = score;
								match.template_id = templ.id;
#pragma omp critical
								matches.insert(matches.end(), match);
							}
						}
					}
				}
			}
		}
		matches = DoNmsMatches(matches, pl, overlap_);
		return matches;
	}

	void KcgMatch::Spread(const Mat quantized_angle, Mat &spread_angle, int T) {

		spread_angle = Mat::zeros(quantized_angle.size(), CV_8U);
		int cols = quantized_angle.cols;
		int rows = quantized_angle.rows;
		int half_T = 0;
		if (T != 1) half_T = T / 2;
#pragma omp parallel for
		for (int r = half_T; r < rows - half_T; r++) {

			for (int c = half_T; c < cols - half_T; c++) {

				for (int i = -half_T; i <= half_T; i++) {

					for (int j = -half_T; j <= half_T; j++) {

						unsigned char shift_bits =
							quantized_angle.ptr<unsigned char>(r + i)[c + j];
						if (shift_bits < 8) {

							spread_angle.ptr<unsigned char>(r)[c] |=
								(unsigned char)(1 << shift_bits);
						}
					}
				}
			}
		}
	}

	void KcgMatch::ComputeResponseMaps(const Mat spread_angle, vector<Mat> &response_maps) {

		response_maps.clear();
		for (int i = 0; i < 8; i++) {

			Mat rm;
			rm.create(spread_angle.size(), CV_8U);
			response_maps.push_back(rm);
		}
		int cols = spread_angle.cols;
		int rows = spread_angle.rows;
#pragma omp parallel for
		for (int i = 0; i < 8; i++) {

			for (int r = 0; r < rows; r++) {

				for (int c = 0; c < cols; c++) {

					response_maps[i].ptr<unsigned char>(r)[c] =
						score_table_8map_[i][spread_angle.ptr<unsigned char>(r)[c]];
				}
			}
		}
	}

	bool KcgMatch::CalcPyUpRoiAndStartPoint(PyramidLevel cur_pl, PyramidLevel obj_pl, Match match,
		Mat &r, Point &p, bool is_padding) {

		auto templ = templ_all_[cur_pl][match.template_id];
		int padding = 0;
		if (is_padding) {

			int min_side = std::min(templ.w, templ.h);
			int diagonal_line_length = (int)ceil(sqrt(templ.w*templ.w + templ.h*templ.h));
			padding = diagonal_line_length - min_side;
		}
		int err_pl = cur_pl - obj_pl;
		int T = 2 * T_;
		int extend_pixel = 1;
		cv::Point bp, ep;
		int multiple = (1 << err_pl);
		match.x -= (T + padding) / 2;
		match.y -= (T + padding) / 2;
		templ.w += (T + padding);
		templ.h += (T + padding);
		bp.x = (match.x - extend_pixel) * multiple;
		bp.y = (match.y - extend_pixel) * multiple;
		ep.x = (match.x + templ.w + extend_pixel) * multiple;
		ep.y = (match.y + templ.h + extend_pixel) * multiple;
		if (bp.x < 0) bp.x = 0;
		if (bp.y < 0) bp.y = 0;
		if (ep.x < 0) ep.x = 0;
		if (ep.y < 0) ep.y = 0;
		if (bp.x >= sources_[obj_pl].cols) bp.x = sources_[obj_pl].cols - 1;
		if (bp.y >= sources_[obj_pl].rows) bp.y = sources_[obj_pl].rows - 1;
		if (ep.x >= sources_[obj_pl].cols) ep.x = sources_[obj_pl].cols - 1;
		if (ep.y >= sources_[obj_pl].rows) ep.y = sources_[obj_pl].rows - 1;
		if (bp.x != ep.x || bp.y != ep.y) {

			Rect rect = Rect(bp, ep);
			Mat roi(sources_[obj_pl], rect);
			r = roi;
			p = bp;
			return true;
		}
		else
		{
			return false;
		}
	}

	void KcgMatch::CalcRegionIndexes(vector<int> &region_idxes, Match match, MatchingStrategy strategy) {

		region_idxes.clear();
		Template templ = templ_all_[PyramidLevel_0][match.template_id];
		float match_agl = templ.shape_info.angle;
		float match_sal = templ.shape_info.scale;
		int angle_region = (int)((angle_range_.end - angle_range_.begin) / angle_range_.step) + 1;
		int scale_region = (int)((scale_range_.end - scale_range_.begin) / scale_range_.step) + 1;
		if (strategy <= Strategy_Middling) {

			if (match_agl < 0.f) match_agl += 360.f;
			int key = (int)floor(match_agl / 22.5f);
			float left_agl = match_agl - key * 22.5f;
			for (int ar = 0; ar < angle_region; ar++) {

				float cur_agl = templ_all_[PyramidLevel_0][ar].shape_info.angle;
				if (cur_agl < 0.f) cur_agl += 360.f;
				int k = key;
				if (cur_agl >= AngleRegionTable[k][0] && cur_agl < AngleRegionTable[k][1]) {

					for (int sr = 0; sr < scale_region; sr++) {

						region_idxes.push_back(ar + sr * angle_region);
					}
				}
				if (strategy == Strategy_Accurate) {

					if (left_agl < 11.25f) {

						k = key - 1;
						if (k < 0) k = 15;
						if (cur_agl >= AngleRegionTable[k][0] && cur_agl < AngleRegionTable[k][1]) {

							for (int sr = 0; sr < scale_region; sr++) {

								region_idxes.push_back(ar + sr * angle_region);
							}
						}
					}
					else
					{
						k = key + 1;
						if (k > 15) k = 0;
						if (cur_agl >= AngleRegionTable[k][0] && cur_agl < AngleRegionTable[k][1]) {

							for (int sr = 0; sr < scale_region; sr++) {

								region_idxes.push_back(ar + sr * angle_region);
							}
						}
					}
				}
			}
		}
		else if (strategy == Strategy_Rough) {

			float err_range = 3.f;
			for (int ar = 0; ar < angle_region; ar++) {

				float cur_agl = templ_all_[PyramidLevel_0][ar].shape_info.angle;
				if (cur_agl >= (match_agl - angle_range_.step * err_range) &&
					cur_agl <= (match_agl + angle_range_.step * err_range)) {

					for (int sr = 0; sr < scale_region; sr++) {

						float cur_sal = templ_all_[PyramidLevel_0][ar + sr * angle_region].shape_info.scale;
						if (cur_sal >= (match_sal - scale_range_.step * err_range) &&
							cur_sal <= (match_sal + scale_range_.step * err_range)) {

							region_idxes.push_back(ar + sr * angle_region);
						}
					}
				}
			}
		}
	}

	vector<Match> KcgMatch::ReconfirmMatches(vector<Match> matches, PyramidLevel pl) {

		vector<Match> rf_matches;
		rf_matches.clear();
		for (int i = 0; i < matches.size(); i++) {

			Mat roi;
			Point sp;
			CalcPyUpRoiAndStartPoint(pl, pl, matches[i], roi, sp, true);
			vector<int> region_idxes;
			CalcRegionIndexes(region_idxes, matches[i], Strategy_Accurate);
			auto tmp_matches = MatchingPyrd8(roi, pl, region_idxes);
			if (tmp_matches.size() > 0) {

				tmp_matches[0].x += sp.x;
				tmp_matches[0].y += sp.y;
				rf_matches.push_back(tmp_matches[0]);
			}
		}
		rf_matches = DoNmsMatches(rf_matches, pl, overlap_);
		return rf_matches;
	}

	vector<Match> KcgMatch::MatchingFinal(vector<Match> matches, PyramidLevel pl) {

		vector<Match> final_matches;
		final_matches.clear();
		for (int i = 0; i < matches.size(); i++) {

			Mat roi;
			Point sp;
			CalcPyUpRoiAndStartPoint(pl, PyramidLevel_0, matches[i], roi, sp, false);
			vector<int> region_idxes;
			CalcRegionIndexes(region_idxes, matches[i], strategy_);
			auto tmp_matches = MatchingPyrd180(roi, PyramidLevel_0, region_idxes);
			if (tmp_matches.size() > 0) {

				tmp_matches[0].x += sp.x;
				tmp_matches[0].y += sp.y;
				final_matches.push_back(tmp_matches[0]);
			}
		}
		final_matches = DoNmsMatches(final_matches, pl, overlap_);
		return final_matches;
	}
 // end namespace kcg_matching

main.cpp

#include "KcgMatch.h"

using namespace kcg;

int main(int argc, char **argv) {

	// 实例化KcgMatch 
	// "demo/k"为存储模板的根目录 
	// "k"为模板的名字
	KcgMatch kcg("G:/模板/template3", "template3");

	// 读取模板图像
	Mat model = imread("G:模板/template3/template.png");

	// 转灰度
	if (model.channels() == 3) {
		cvtColor(model, model, COLOR_BGR2GRAY);
	}
	
	// 指定要制作的模板角度，尺度范围
	AngleRange ar(-180.f, 180.f, 10.f);
	ScaleRange sr(0.70f, 1.3f, 0.05f);
	// 开始制作模板（会在G:/模板/template3的路径下生成一个yaml文件，里面保存着生成的模板特征信息）
	kcg.MakingTemplates(model, ar, sr, 0, 30.f, 60.f);

	// 加载模板
	cout << "Loading model ......" << endl;
	kcg.LoadModel();
	cout << "Load succeed." << endl;

	// 读取搜索图像
	Mat source = imread("G:/模板/template3/search.png");

	Mat draw_source;
	source.copyTo(draw_source);
	if (source.channels() == 3) {
		cvtColor(source, source, COLOR_BGR2GRAY);		
	}

	//计算匹配时间（不需要windows.h的计算毫秒时间的方法）
	double dur;
	clock_t start, end;
	start = clock();
	//进行模板匹配
	auto matches = kcg.Matching(source, 0.80f, 0.1f, 30.f, 0.9f,PyramidLevel_2, 2, 12, Strategy_Accurate);
	end = clock();
	dur = (double)(end - start);
	printf("Use Time:%f\n", (dur / CLOCKS_PER_SEC));
	cout << "Final match size: " << matches.size() << endl << endl;

	// 画出匹配结果
	kcg.DrawMatches(draw_source, matches, Scalar(0, 0, 255));

	// 画出匹配时间
	rectangle(draw_source, Rect(Point(0, 0), Point(136, 20)), Scalar(255, 255, 255), -1);
	cv::putText(draw_source,
		"time: " + to_string(dur / CLOCKS_PER_SEC) + "s",
		Point(0, 16), FONT_HERSHEY_PLAIN, 1.f, Scalar(0, 0, 0), 1);

	// 显示结果图像
	namedWindow("draw_source", 0);
	imshow("draw_source", draw_source);
	imwrite("draw_source.jpg", draw_source);
	waitKey(0);
	system("pause");
}

匹配的结果

1. 用于制作模板的图片，使用的是原作者的图片，此处将其命名为template.png,并将此图片放在了template3的文件夹中。图片为下：
   
2. 搜索图像，即用来寻找的图像如下，将其命名为search.png,也放在了template3的文件夹下。图片如下：
   
3. 最终匹配的结果如下