opencv 模板匹配-优快云博客

本文链接：https://blog.youkuaiyun.com/greless/article/details/115675207

该博客介绍了一个使用OpenCV进行图像匹配的C++程序。程序读取两张图像，一张作为原始图，另一张作为模板，通过调用`matchTemplate`函数实现模板匹配，并在原始图上显示匹配结果。匹配方法可以通过滑动条选择，包括平方差匹配等。当找到最佳匹配位置后，会在图像上用矩形框标出。

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https://www.cnblogs.com/skyfsm/p/6884253.html

// opencv_match.cpp : 此文件包含 "main" 函数。程序执行将在此处开始并结束。
//

#include "pch.h"
#include <iostream>
#include "../../install/include/opencv2/core/core.hpp"
#include "../../install/include/opencv2/imgproc/imgproc.hpp"
#include "../../install/include/opencv2/highgui/highgui.hpp"
#include <iostream>
#include <stdio.h>

#pragma comment(lib,"../../install/x86/vc15/lib/opencv_core2413d.lib")
#pragma comment(lib,"../../install/x86/vc15/lib/opencv_imgproc2413d.lib")
#pragma comment(lib,"../../install/x86/vc15/lib/opencv_highgui2413d.lib")

using namespace std;
using namespace cv;

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

void on_matching(int, void*)
{
	Mat srcImage;
	g_srcImage.copyTo(srcImage);
	int resultImage_cols = g_srcImage.cols - g_tempalteImage.cols + 1;
	int resultImage_rows = g_srcImage.rows - g_tempalteImage.rows + 1;
	g_resultImage.create(resultImage_cols, resultImage_rows, CV_32FC1);

	matchTemplate(g_srcImage, g_tempalteImage, g_resultImage, g_nMatchMethod);
	//normalize(g_resultImage, g_resultImage, 0, 2, NORM_MINMAX, -1, Mat());
	double minValue, maxValue;
	Point minLocation, maxLocation, matchLocation;
	minMaxLoc(g_resultImage, &minValue, &maxValue, &minLocation, &maxLocation);

	if (g_nMatchMethod == TM_SQDIFF || g_nMatchMethod == CV_TM_SQDIFF_NORMED)
	{
		matchLocation = minLocation;
	}
	else
	{
		matchLocation = maxLocation;
	}
	 
	printf("minValue=%f,maxValue=%f\n", minValue, maxValue);
	rectangle(srcImage, matchLocation, Point(matchLocation.x + g_tempalteImage.cols, matchLocation.y + g_tempalteImage.rows), Scalar(0, 0, 255), 2, 8, 0);
	rectangle(g_resultImage, matchLocation, Point(matchLocation.x + g_tempalteImage.cols, matchLocation.y + g_tempalteImage.rows), Scalar(0, 0, 255), 2, 8, 0);

	imshow("原始图", srcImage);
	imshow("效果图", g_resultImage);

}

int main()
{
	/*
	Mat img, templ, result;
	img = imread("C:/normal.png");
	templ = imread("C:/4.png");

	int result_cols = img.cols - templ.cols + 1;
	int result_rows = img.rows - templ.rows + 1;
	result.create(result_cols, result_rows, CV_32FC1);

	matchTemplate(img, templ, result, CV_TM_SQDIFF_NORMED);//这里我们使用的匹配算法是标准平方差匹配 method=CV_TM_SQDIFF_NORMED，数值越小匹配度越好
	normalize(result, result, 0, 1, NORM_MINMAX, -1, Mat());

	double minVal = -1;
	double maxVal;
	Point minLoc;
	Point maxLoc;
	Point matchLoc;
	cout << "匹配度：" << minVal << endl;
	minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, Mat());


	cout << "匹配度：" << minVal << endl;

	matchLoc = minLoc;

	rectangle(img, matchLoc, Point(matchLoc.x + templ.cols, matchLoc.y + templ.rows), Scalar(0, 255, 0), 2, 8, 0);

	imshow("img", img);
	waitKey(0);
	*/
	 
	g_srcImage = imread("C:\\1.png");
	if (!g_srcImage.data)
	{
		cout << "原始图读取失败" << endl;
		return -1;
	}
	g_tempalteImage = imread("C:\\back.png");
	if (!g_tempalteImage.data)
	{
		cout << "模板图读取失败" << endl;
		return -1;
	}

	namedWindow("原始图", CV_WINDOW_AUTOSIZE);
	namedWindow("效果图", CV_WINDOW_AUTOSIZE);
	createTrackbar("方法", "原始图", &g_nMatchMethod, g_nMaxTrackbarNum, on_matching);

	on_matching(0, NULL);


	waitKey(0);
	 
	return 0;
}