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/**
* @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;
/// 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;
/// Function Headers
void MatchingMethod( int, void* );
/**
* @function main
*/
int main( int argc, char** argv )
{
if (argc < 3)
{
cout << "Not enough parameters" << endl;
cout << "Usage:\n./MatchTemplate_Demo <image_name> <template_name> [<mask_name>]" << endl;
return -1;
}
/// Load image and template
img = imread( argv[1], IMREAD_COLOR );
templ = imread( argv[2], 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;
return -1;
}
/// Create windows
namedWindow( image_window, WINDOW_AUTOSIZE );
namedWindow( result_window, WINDOW_AUTOSIZE );
/// Create Trackbar
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 0;
}
/**
* @function MatchingMethod
* @brief Trackbar callback
*/
void MatchingMethod( int, void* )
{
/// Source image to display
Mat img_display;
img.copyTo( img_display );
/// 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 );
/// Do the Matching and Normalize
bool method_accepts_mask = (CV_TM_SQDIFF == match_method || match_method == CV_TM_CCORR_NORMED);
if (use_mask && method_accepts_mask)
{ matchTemplate( img, templ, result, match_method, mask); }
else
{ matchTemplate( img, templ, result, match_method); }
normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );
/// Localizing the best match with minMaxLoc
double minVal; double maxVal; Point minLoc; Point maxLoc;
Point matchLoc;
minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
/// 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; }
/// 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 );
return;
}