模板匹配

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;


//-----------------------------------【宏定义部分】-------------------------------------------- 
//  描述：定义一些辅助宏 
//------------------------------------------------------------------------------------------------ 
#define WINDOW_NAME1 "【原始图片】"        //为窗口标题定义的宏 
#define WINDOW_NAME2 "【匹配窗口】"        //为窗口标题定义的宏 

//-----------------------------------【全局变量声明部分】------------------------------------
//          描述：全局变量的声明
//-----------------------------------------------------------------------------------------------
Mat g_srcImage; Mat g_templateImage; Mat g_resultImage;
int g_nMatchMethod;
int g_nMaxTrackbarNum = 5;

//-----------------------------------【全局函数声明部分】--------------------------------------
//          描述：全局函数的声明
//-----------------------------------------------------------------------------------------------
void on_Matching(int, void*);
static void ShowHelpText();


//-----------------------------------【main( )函数】--------------------------------------------
//          描述：控制台应用程序的入口函数，我们的程序从这里开始执行
//-----------------------------------------------------------------------------------------------
int main()
{
    
    //【0】显示帮助文字
    ShowHelpText();

    //【1】载入原图像和模板块
    g_srcImage = imread("8.jpg", 1);
    g_templateImage = imread("9.jpg", 1);

    //【2】创建窗口
    namedWindow(WINDOW_NAME1, WINDOW_AUTOSIZE);
    namedWindow(WINDOW_NAME2, WINDOW_AUTOSIZE);

    //【3】创建滑动条并进行一次初始化
    createTrackbar("方法", WINDOW_NAME1, &g_nMatchMethod, g_nMaxTrackbarNum, on_Matching);
    on_Matching(0, 0);

    waitKey(0);
    return 0;

}

//-----------------------------------【on_Matching( )函数】--------------------------------
//          描述：回调函数
//-------------------------------------------------------------------------------------------
void on_Matching(int, void*)
{
    //【1】给局部变量初始化
    Mat srcImage;
    g_srcImage.copyTo(srcImage);

    //【2】初始化用于结果输出的矩阵
    int resultImage_cols = g_srcImage.cols - g_templateImage.cols + 1;
    int resultImage_rows = g_srcImage.rows - g_templateImage.rows + 1;
    g_resultImage.create(resultImage_cols, resultImage_rows, CV_32FC1);

    //【3】进行匹配和标准化
    matchTemplate(g_srcImage, g_templateImage, g_resultImage, g_nMatchMethod);
    normalize(g_resultImage, g_resultImage, 0, 1, NORM_MINMAX, -1, Mat());

    //【4】通过函数 minMaxLoc 定位最匹配的位置
    double minValue; double maxValue; Point minLocation; Point maxLocation;
    Point matchLocation;
    minMaxLoc(g_resultImage, &minValue, &maxValue, &minLocation, &maxLocation, Mat());

    //【5】对于方法 SQDIFF 和 SQDIFF_NORMED, 越小的数值有着更高的匹配结果. 而其余的方法, 数值越大匹配效果越好
    //此句代码的OpenCV2版为：
    //if( g_nMatchMethod  == CV_TM_SQDIFF || g_nMatchMethod == CV_TM_SQDIFF_NORMED )
    //此句代码的OpenCV3版为：
    if (g_nMatchMethod == TM_SQDIFF || g_nMatchMethod == TM_SQDIFF_NORMED)
    {
        matchLocation = minLocation;
    }
    else
    {
        matchLocation = maxLocation;
    }

    //【6】绘制出矩形，并显示最终结果
    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);

}



//-----------------------------------【ShowHelpText( )函数】----------------------------------
//          描述：输出一些帮助信息
//----------------------------------------------------------------------------------------------
static void ShowHelpText()
{
    //输出欢迎信息和OpenCV版本
    printf("\n\n\t\t\t非常感谢购买《OpenCV3编程入门》一书！\n");
    printf("\n\n\t\t\t此为本书OpenCV3版的第84个配套示例程序\n");
    printf("\n\n\t\t\t   当前使用的OpenCV版本为：" CV_VERSION);
    printf("\n\n  ----------------------------------------------------------------------------\n");
    //输出一些帮助信息
    printf("\t欢迎来到【模板匹配】示例程序~\n");
    printf("\n\n\t请调整滑动条观察图像效果\n\n");
    printf("\n\t滑动条对应的方法数值说明: \n\n"
        "\t\t方法【0】- 平方差匹配法(SQDIFF)\n"
        "\t\t方法【1】- 归一化平方差匹配法(SQDIFF NORMED)\n"
        "\t\t方法【2】- 相关匹配法(TM CCORR)\n"
        "\t\t方法【3】- 归一化相关匹配法(TM CCORR NORMED)\n"
        "\t\t方法【4】- 相关系数匹配法(TM COEFF)\n"
        "\t\t方法【5】- 归一化相关系数匹配法(TM COEFF NORMED)\n");
}