Template Matching(Image Searching) - 부분 이미지 검색

Template matching is a technique for finding areas of an image that match (are similar) to a template image (patch).

Python Pillow library로 구현해 봤던 Image searching 기술을 OpenCV matchTemplate 함수로 간단히 만들 수 있다.

2018/11/30 - [Software/Python] - Pillow 이미지 서치(Image Search) 1

2018/12/02 - [Software/Python] - Pillow 이미지 서치(Image Search) 2

2019/07/10 - [Software/OpenCV] - Template Matching(Image Searching) for multiple objects - 반복되는 이미지 모두 찾기

2019/07/12 - [Software/OpenCV] - Template Matching(Image Searching) with a mask for multiple objects - 마스크를 이용해 (배경이 다른) 반복되는 이미지 모두 찾기

<Target>

<Source>

Type of the template matching operation: TM_SQDIFF

#include <opencv2/opencv.hpp>
#include <time.h>
 
using namespace cv;
using namespace std;
 
int main()
{
    clock_t start, end;
    double minVal;
    Point minLoc;
 
    Mat FinalImage = imread("source.jpg", IMREAD_COLOR);
    if (FinalImage.empty())
        return -1;
 
    // Grayscale source and target for faster calculation.
    Mat SourceImage;
    cvtColor(FinalImage, SourceImage, CV_BGR2GRAY);
 
    Mat TargetImage = imread("target.jpg", IMREAD_GRAYSCALE);
    if (TargetImage.empty())
        return -1;
 
    Mat Result;
 
    start = clock();
    matchTemplate(SourceImage, TargetImage, Result, TM_SQDIFF); // Type of the template matching operation: TM_SQDIFF
    normalize(Result, Result, 0, 1, NORM_MINMAX, -1, Mat());
    minMaxLoc(Result, &minVal, NULL, &minLoc, NULL);
    end = clock();
 
    cout << "Searching time: " << difftime(end, start) / CLOCKS_PER_SEC << endl;
    cout << "Minimum Value: " << minVal << endl << "Location: " << minLoc << endl;
    rectangle(FinalImage, minLoc, Point(minLoc.x + TargetImage.cols, minLoc.y + TargetImage.rows), Scalar(0, 0, 255), 1);
 
    imshow("TargetImage", TargetImage);
    imshow("Result", Result);
    imshow("FinalImage", FinalImage);
 
    waitKey(0);
 
    return 0;
}

<Result>

Found the target at the husky's front paw in 0.014 secs.