(五)基于matchTemplate的图像区域匹配

见OpenCV tutorial_template_matching

1.基础理论

模板匹配是找到图像中与模板图像相匹配的区域。通常是矩形，有时候要匹配的对象不是矩形时，可借助掩码 mask实现。

• 将template逐像素的在source image上滑动，分别计算每个位置(x,y)的匹配度R,R表示的值可通过多种基准来计算。
• 常用基准

模板的宽高为$\omega$, $h$,对于 TM_SQDIFF基准，模板匹配时找最小值，对于 TM_CCORR和 TM_CCOEFF是找其最大值。

目前只有 TM_SQDIFF和 TM_CCORR_NORMED支持使用 mask掩码。

2.matchTemplate API

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
using namespace std;

bool use_mask;
cv::Mat img;
cv::Mat templ;
cv::Mat mask;
cv::Mat res;

const char *img_window = "Source";
const char *res_window = "Result";

int match_method;
int max_trackbar = 5;

void MatchingMethod(int, void *);

int main(int argc, char **argv)
{
    if (argc < 3)
    {
        cout << "Not enough parameters" << endl;
        cout << "Usage:\n" << argv[0] << " <image_name> <template_name> [<mask_name>]" << endl;
        return -1;
    }
    img = cv::imread( argv[1], cv::IMREAD_COLOR );
    templ = cv::imread( argv[2], cv::IMREAD_COLOR );
    if(argc > 3) {
        use_mask = true;
        mask = cv::imread( argv[3], cv::IMREAD_COLOR );
    }
    if(img.empty() || templ.empty() || (use_mask && mask.empty()))
    {
        cout << "Can't read one of the images" << endl;
        return EXIT_FAILURE;
    }
    cv::namedWindow(img_window, cv::WINDOW_AUTOSIZE );
    cv::namedWindow(res_window, cv::WINDOW_AUTOSIZE );
    const 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";
    cv::createTrackbar( trackbar_label, img_window, &match_method, max_trackbar, MatchingMethod );
    MatchingMethod( 0, 0 );
    cv::waitKey(0);
    return EXIT_SUCCESS;
}
// void做形参指函数没有形参
// 暂时无法确定参数的类型时，可用 void*做形参再转为其他类型
void MatchingMethod(int, void *)
{
    cv::Mat img_display;
    img_display = img.clone();
    int res_width = img.cols - templ.cols + 1;
    int res_height = img.rows - templ.rows + 1;
    // 需要的话为新的数组分配数据
    res.create(res_height, res_width, CV_32FC1);
    bool method_accepts_mask = (cv::TM_SQDIFF == match_method || match_method == cv::TM_CCORR_NORMED);
    if(use_mask && method_accepts_mask)
    {
        cv::matchTemplate(img, templ, res, match_method, mask);
    }
    else
    {
        cv::matchTemplate(img, templ, res, match_method);
    }
    // 归一化
    cv::normalize(res, res, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
    double min_val; double max_val; cv::Point min_loc; cv::Point max_loc;
    cv::Point match_loc;
    cv::minMaxLoc(res, &min_val, &max_val, &min_loc, &max_loc, cv::Mat());
    if(match_method == cv::TM_SQDIFF || match_method == cv::TM_SQDIFF_NORMED)
    {
        match_loc = min_loc;
    } else {
        match_loc = max_loc;
    }
    cv::rectangle(img_display, match_loc, cv::Point(match_loc.x + templ.cols, match_loc.y + templ.rows),
        cv::Scalar::all(0), 2, 9, 0);
    cv::rectangle(res, match_loc, cv::Point(match_loc.x + templ.cols, match_loc.y + templ.rows),
        cv::Scalar::all(0), 2, 9, 0);
    std::cout << "===>>> template match res size: " << res.size << std::endl;
    cv::imwrite("display_img.png", img_display);
    cv::imwrite("res_match.png", res);
    return;
}

输出结果：

原图宽高 WxH, 模板宽高$\omega$ x $h$, 因为模板是在原图上逐像素计算，类似图像的空间卷积操作，则得到的结果为宽$\frac{W-\omega }{1}+1$高为$\frac{H-h}{1}+1$的矩阵。