#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include
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("D:\DSC_0013.jpg" , 1);
templ = imread("D:\DSC_0014.jpg", 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);
}
请问这个源码那里有错
