模板匹配是一項在一幅影象中尋找與另一幅模板影象最相似部分的技術。 

#include <opencv2/opencv.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <iostream>
using namespace std;
using namespace cv;

#define WINDOW_NAME1 "原始圖"
#define WINDOW_NAME2 "效果視窗"

Mat g_srcImage, g_templateImage, g_resultImage;
int g_nMatchMethod;
int g_nMaxTrackbarNum = 5;

void on_Matching(int, void*);

int main() {
	//載入原影象和模板塊
	g_srcImage = imread("1.jpg", 1);
	g_templateImage = imread("2.jpg", 1);

	imshow("template", g_templateImage);

	//建立視窗
	namedWindow(WINDOW_NAME1, CV_WINDOW_AUTOSIZE);
	namedWindow(WINDOW_NAME2, CV_WINDOW_AUTOSIZE);

	//建立滑動條並進行一次初始化
	createTrackbar("方法", WINDOW_NAME1, &g_nMatchMethod, g_nMaxTrackbarNum, on_Matching);
	on_Matching(0, 0);
	waitKey(0);
	return 0;
}

void on_Matching(int, void*) {
	//給區域性變數初始化
	Mat srcImage;
	g_srcImage.copyTo(srcImage);

	//初始化用於結果輸出的矩陣
	int resultImage_rows = g_srcImage.rows - g_templateImage.rows + 1;
	int resultImage_cols = g_srcImage.cols - g_templateImage.cols + 1;
	g_resultImage.create(resultImage_rows, resultImage_cols, CV_32FC1);

	//進行匹配和標準化
	matchTemplate(g_srcImage, g_templateImage, g_resultImage, g_nMatchMethod);
	normalize(g_resultImage, g_resultImage, 0, 1, NORM_MINMAX, -1, Mat());

	//通過函式minMaxLoc定位最匹配的位置
	double minValue, maxValue;
	Point minLocation, maxLocation;
	Point matchLocation;
	minMaxLoc(g_resultImage, &minValue, &maxValue, &minLocation, &maxLocation, Mat());

	//對於方法SQDIFF和SQDIFF_NORMED越小的數值有著更高的匹配效果，而其餘的方法，數值越大匹配效果越好
	if (g_nMatchMethod == TM_SQDIFF || g_nMatchMethod == TM_SQDIFF_NORMED)
		matchLocation = minLocation;
	else
		matchLocation = maxLocation;

	//繪製出矩形，並顯示最終結果
	rectangle(srcImage, matchLocation, Point(matchLocation.x + g_templateImage.cols, matchLocation.y + g_templateImage.rows), Scalar(0, 0, 255), 2, 8, 0);
	rectangle(g_resultImage, matchLocation, Point(matchLocation.x + g_templateImage.cols, matchLocation.y + g_templateImage.rows), Scalar(0, 0, 255), 2, 8, 0);

	imshow(WINDOW_NAME1, srcImage);
	imshow(WINDOW_NAME2, g_resultImage);

}