test

cpp

/**
 * @file MatchTemplate_Demo.cpp
 * @brief Sample code to use the function MatchTemplate
 * @author OpenCV team
 */

#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>

using namespace std;
using namespace cv;

//! [declare]
/// Global Variables
bool use_mask;
Mat img; Mat templ; Mat mask; Mat result;
const char* image_window = "Source Image";
const char* result_window = "Result window";

int match_method;
int max_Trackbar = 5;
//! [declare]

/// Function Headers
void MatchingMethod( int, void* );

bool match_one(int argc, char** argv, int& value1)
{
    img = imread("main.jpg", IMREAD_COLOR );
    templ = imread( "chessb.png", IMREAD_COLOR );

    if(argc > 3) {
        use_mask = true;
        mask = imread( argv[3], IMREAD_COLOR );
    }

    if(img.empty() || templ.empty() || (use_mask && mask.empty()))
    {
        cout << "Can't read one of the images" << endl;
        value1 = -1;
        return true;
    }
    namedWindow( image_window, 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";
    createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );

    MatchingMethod( 0, 0 );
    waitKey(0);
    return false;
}

String multipy_name = "multipy_name";

void match_multipy_base(int i, void*)
{
    Mat3b img = imread("main.jpg");
    Mat3b templ = imread("chessb.png");
    // img = imread("main.jpg", IMREAD_COLOR);
    // templ = imread("chessb.png", IMREAD_COLOR);
    Mat1b img_gray;
    Mat1b templ_gray;
    cvtColor(img, img_gray, COLOR_BGR2GRAY);
    cvtColor(templ, templ_gray, COLOR_BGR2GRAY);

    Mat1f result;
    matchTemplate(img, templ, result, match_method);

    double thresh = 0.7;
    threshold(result, result, thresh, 1., THRESH_BINARY);

    Mat1b resb;
    result.convertTo(resb, CV_8U, 255);

    vector<vector<Point>> contours;
    findContours(resb, contours, RETR_LIST, CHAIN_APPROX_SIMPLE);

    for (int i = 0; i < contours.size(); ++i)
    {
        Mat1b mask(result.rows, result.cols, uchar(0));
        drawContours(mask, contours, i, Scalar(255), FILLED);

        Point max_point;
        double max_val;
        minMaxLoc(result, NULL, &max_val, NULL, &max_point, mask);

        rectangle(img, Rect(max_point.x, max_point.y, templ.cols, templ.rows), Scalar(0, 255, 0), 2);

    }
    imshow(multipy_name, img);

};

void match_multipy()
{
    namedWindow(multipy_name, 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";
    createTrackbar(trackbar_label, multipy_name, &match_method, max_Trackbar, match_multipy_base);
    match_multipy_base(0, 0);

    waitKey(0);
};

/**
 * @function main
 */
int main( int argc, char** argv )
{
    // int value1;
    // if (match_one(argc, argv, value1)) return value1;

    match_multipy();




    return 0;
}

/**
 * @function MatchingMethod
 * @brief Trackbar callback
 */
void MatchingMethod( int, void* )
{
  //! [copy_source]
  /// Source image to display
  Mat img_display;
  img.copyTo( img_display );
  //! [copy_source]

  //! [create_result_matrix]
  /// Create the result matrix
  int result_cols =  img.cols - templ.cols + 1;
  int result_rows = img.rows - templ.rows + 1;

  result.create( result_rows, result_cols, CV_32FC1 );
  //! [create_result_matrix]

  //! [match_template]
  /// Do the Matching and Normalize
  bool method_accepts_mask = (TM_SQDIFF == match_method || match_method == TM_CCORR_NORMED);
  if (use_mask && method_accepts_mask)
    { matchTemplate( img, templ, result, match_method, mask); }
  else
    { matchTemplate( img, templ, result, match_method); }
  //! [match_template]

  //! [normalize]
  normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );
  //! [normalize]

  //! [best_match]
  /// Localizing the best match with minMaxLoc
  double minVal; double maxVal; Point minLoc; Point maxLoc;
  Point matchLoc;

  minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
  //! [best_match]

  //! [match_loc]
  /// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
  if( match_method  == TM_SQDIFF || match_method == TM_SQDIFF_NORMED )
    { matchLoc = minLoc; }
  else
    { matchLoc = maxLoc; }
  //! [match_loc]

  //! [imshow]
  /// Show me what you got
  rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
  // rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );

  imshow( image_window, img_display );
  // imshow( result_window, result );
  //! [imshow]

  return;
}