opencv学习之路（21）、模板匹配及应用

一、模板匹配概念

二、单模板匹配

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

void main()
{
    Mat temp=imread("E://mu.jpg");
    Mat src=imread("E://lena.jpg");
    Mat dst=src.clone();
    imshow("temp",temp);

    int width=src.cols-temp.cols+1;//result宽度
    int height=src.rows-temp.rows+1;//result高度

    Mat result(height,width,CV_32FC1);//创建结果映射图像
    //matchTemplate(srcImg, templateImg, resultImg, CV_TM_SQDIFF); //平方差匹配法(最好匹配0)
    //matchTemplate(srcImg, templateImg, resultImg, CV_TM_SQDIFF_NORMED); //归一化平方差匹配法(最好匹配0)
    //matchTemplate(srcImg, templateImg, resultImg, CV_TM_CCORR); //相关匹配法(最坏匹配0)
    //matchTemplate(srcImg, templateImg, resultImg, CV_TM_CCORR_NORMED); //归一化相关匹配法(最坏匹配0)
    //matchTemplate(srcImg, templateImg, resultImg, CV_TM_CCOEFF); //系数匹配法(最好匹配1)
    matchTemplate(src,temp,result,CV_TM_CCOEFF_NORMED);//化相关系数匹配,最佳值1
    imshow("result",result);
    normalize(result,result,0,1,NORM_MINMAX,-1);//归一化到0-1范围

    double minValue,maxValue;
    Point minLoc,maxLoc;
    minMaxLoc(result,&minValue,&maxValue,&minLoc,&maxLoc);
    cout<<"minValue="<<minValue<<endl;
    cout<<"maxValue="<<maxValue<<endl;

    rectangle(dst,maxLoc,Point(maxLoc.x+temp.cols,maxLoc.y+temp.rows),Scalar(0,255,0),2,8);
    imshow("dst",dst);

    waitKey(0);
}

注意：result的长宽正好是原图-模板图的长宽，result图中白亮程度表示匹配程度

 三、视频模板匹配

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

void main()
{
    Mat frame,resultImg;
    Mat templateImg = imread("E://green.jpg");
    VideoCapture cap("E://1.mp4");
    if(!cap.isOpened())
        return;
    int resultImg_cols,resultImg_rows;

    while(1)
    {
        cap>>frame;
        if(frame.empty())    break;
        Mat showImg = frame.clone();
        resultImg_cols = frame.cols -  templateImg.cols + 1;
        resultImg_rows = frame.rows -  templateImg.rows + 1;
        resultImg.create(resultImg_cols, resultImg_rows, CV_32FC1);
        matchTemplate(frame, templateImg, resultImg, CV_TM_CCOEFF_NORMED); //化相关系数匹配法(最好匹配1)
        normalize(resultImg, resultImg, 0, 1, NORM_MINMAX);

        double minValue, maxValue;
        Point minLoc, maxLoc;
        Point matchLoc;

        minMaxLoc(resultImg, &minValue, &maxValue, &minLoc, &maxLoc);
        cout<<"max_value= "<<maxValue<<endl;
        //cout<<"min_value= "<<minValue<<endl;
        if(maxValue>=0.7)
            rectangle(showImg, maxLoc, Point(maxLoc.x + templateImg.cols, maxLoc.y + templateImg.rows), Scalar(0, 255, 0), 2);
        imshow("frame", frame);
        imshow("result", showImg);
        if(27 == waitKey(10))
            break;
    }
    destroyAllWindows();

    waitKey(0);
}

四、多模板匹配（没懂/(ㄒoㄒ)/~~）

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

void main()
{
    Mat srcImg = imread("E://src.png");
    Mat templateImg = imread("E://temp.png");
    Mat resultImg;
    Mat showImg = srcImg.clone();

    int resultImg_cols = srcImg.cols -  templateImg.cols + 1;
    int resultImg_rows = srcImg.rows -  templateImg.rows + 1;
    
    resultImg.create(resultImg_cols, resultImg_rows, CV_32FC1);
    matchTemplate(srcImg, templateImg, resultImg, CV_TM_CCOEFF_NORMED); //化相关系数匹配法(最好匹配1)
    normalize(resultImg, resultImg, 0, 1, NORM_MINMAX);
    Mat midImg = resultImg.clone();

    //多目标模板匹配---方法一
    /*double matchValue;
    int count0=0;
    int tempW=0, tempH=0;
    char matchRate[10];

    for(int i=0; i<resultImg_rows; i++)
    {
        for(int j=0; j<resultImg_cols; j++)
        {
            matchValue = resultImg.at<float>(i, j);
            sprintf(matchRate, "%0.2f", matchValue);
            if(matchValue>=0.85 && (abs(j - tempW)>5) && (abs(i - tempH)>5) )
            {
                count0++;
                putText(showImg, matchRate, Point(j-5, i-5), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0, 0, 255), 1);
                rectangle(showImg, Point(j, i), Point(j + templateImg.cols, i + templateImg.rows), Scalar(0, 255, 0), 2);
                tempW = j;
                tempH = i;
            }
        }
    }
    cout<<"count="<<count0<<endl;
    imshow("resultImg", resultImg);
    imshow("dst", showImg);*/ 

    //多目标模板匹配---方法二
    double minValue, maxValue;
    Point minLoc, maxLoc;
    Point matchLoc;
    char matchRate[10];
    
    for(int i=0; i<100; i++)
    {
        int startX = maxLoc.x - 4;
        int startY = maxLoc.y - 4;
        int endX = maxLoc.x + 4;
        int endY = maxLoc.y + 4;
        if(startX<0 || startY<0)
        {
            startX = 0;
            startY = 0;
        }
         if(endX > resultImg.cols - 1 || endY > resultImg.rows - 1)  
        {  
            endX = resultImg.cols - 1;  
            endY = resultImg.rows- 1;  
        }  
         Mat temp = Mat::zeros(endX - startX, endY - startY, CV_32FC1);
         //Mat ROI = resultImg(Rect(Point(startX, startY), temp.cols, temp.rows));
         temp.copyTo(resultImg(Rect(startX, startY, temp.cols, temp.rows)));
         minMaxLoc(resultImg, &minValue, &maxValue, &minLoc, &maxLoc);
         if(maxValue<0.8)    break;

         cout<<"max_value= "<<maxValue<<endl;
         sprintf(matchRate, "%0.2f", maxValue);
         putText(showImg, matchRate, Point(maxLoc.x - 5, maxLoc.y - 5), CV_FONT_HERSHEY_COMPLEX, 1, Scalar(0, 0, 255), 1);
         rectangle(showImg, maxLoc, Point(maxLoc.x + templateImg.cols, maxLoc.y + templateImg.rows), Scalar(0, 255, 0), 2);

    }
    imshow("midImg", midImg);
    imshow("resultImg", resultImg);
    imshow("dst", showImg);

    waitKey(0);
}