OPENCV 模板匹配
/**
* @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*);/**
* @function main
*/
int main(int argc, char** argv)
{//! [load_image]
/// Load image and template
img = imread("F://opencv//VS//demo//data//lena//lena.jpg", IMREAD_COLOR);
templ = imread("F://opencv//VS//demo//data//lena//lenaTemp_1.jpg", IMREAD_COLOR);
//! [load_image]//! [create_windows]
/// Create windows
namedWindow(image_window, WINDOW_AUTOSIZE);
namedWindow(result_window, WINDOW_AUTOSIZE);
//! [create_windows]//! [create_trackbar]
/// 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);
//! [create_trackbar]MatchingMethod(0, 0);
//! [wait_key]
waitKey(0);
return 0;
//! [wait_key]
}/**
* @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("Source Image Part", templ);
//! [imshow]return;
}
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