#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace std;
using namespace cv;
/// 全局变量
Mat img; Mat templ; Mat result;
char* image_window = "Source Image";
char* result_window = "Result window";
int match_method;
int max_Trackbar = 5;
/// 函数声明
void MatchingMethod( int, void* );
int main( int argc, char** argv )
{
// 读图片
img = imread( argv[1], 1 );
templ = imread( argv[2], 1 );
// 创建图像显示窗口
namedWindow( image_window, CV_WINDOW_AUTOSIZE );
namedWindow( result_window, CV_WINDOW_AUTOSIZE );
// 创建混动条
char* trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );
MatchingMethod( 0, 0 );
waitKey(0);
return 0;
}
// 模板匹配
void MatchingMethod( int, void* )
{
// 用于显示结果
Mat img_display;
img.copyTo( img_display );
// 用于存储匹配结果的矩阵
int result_cols = img.cols - templ.cols + 1;
int result_rows = img.rows - templ.rows + 1;
result.create( result_cols, result_rows, CV_32FC1 );
// 进行模板匹配
matchTemplate( img, templ, result, match_method );
// 归一化结果（方便显示结果）
normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );
// 找到最佳匹配位置
double minVal;
double maxVal;
Point minLoc;
Point maxLoc;
Point matchLoc;
minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() ); // 寻找result中的最大和最小值，以及它们所处的像素位置
// 使用SQDIFF和SQDIFF_NORMED方法时：值越小代表越相似
// 使用其他方法时：值越大代表越相似
if( match_method == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
{
matchLoc = minLoc;
}
else
{
matchLoc = maxLoc;
}
// 显示匹配结果
rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
imshow( image_window, img_display );
imshow( result_window, result );
}