使用opencv進行實時攝像機模板匹配

Question

我在使用OpenCV網站上提供的代碼識別圖像（模板）時遇到一些麻煩。 我一直在使用PC（Win10 64位）上的“相機”應用程序捕獲的一些圖像來使用它，它的確非常好，但是當我嘗試從相機中獲取圖像進行比較時，它只顯示了一個帶有軌跡欄：

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

using namespace std;
using namespace cv;

/// Global Variables
Mat img;
Mat templ;
Mat result;
char* image_window = "Source Image";
char* result_window = "Result window";

int match_method;
int i=0;
int max_Trackbar = 5;

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

/** @function main enter code heren */
int main( int argc, char** argv )
{
VideoCapture cap(0); // open the default camera
if(!cap.isOpened()) // check if we succeeded
    return -1;
for(;;)
{
    Mat frame;
    cap >> frame;
    /// Load image and template
    img=frame.clone();
    templ = imread( "Template.jpg", 1 );

    /// Create windows
    namedWindow( image_window, CV_WINDOW_AUTOSIZE );
    namedWindow( result_window, CV_WINDOW_AUTOSIZE );

    /// Create Trackbar
    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
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  == CV_TM_SQDIFF || match_method == CV_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;
}

我認為它應該工作的方式是捕獲在cap>>frame;上捕獲的cap>>frame; 並將其復制到變量img img=frame.clone(); 。 然后，它每次在MatchingMethod上發送它以進行處理，直到按下任何鍵為止。

我和我的項目伙伴將非常感謝使我們解決此問題的任何事情。

PS：
如果對我使用的IDE有任何疑問，請使用Codeblock。
另外，我將在獲得的結果中附加一些鏈接： 鏈接到Imgur

Answer 1

多虧了api55用戶，我才能夠進行實時匹配，但是在咨詢后我發現這不是跟蹤機器人的最佳方法，但至少是邁向最終解決方案的一小步。 我將在代碼上發布更新，以便對需要它的任何人有所幫助。

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

using namespace std;
using namespace cv;

/// Global Variables
Mat img;
Mat templ;
Mat result;
char* image_window = "Source Image";
char* result_window = "Result window";

int match_method;
int i=0;
int max_Trackbar = 5;

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

/** @function main */
int main( int argc, char** argv )
{
    VideoCapture cap(0); // open the default camera
if(!cap.isOpened())  // check if we succeeded
{
    return -1;
}
templ = imread( "template3.jpg", 1 );
for(;;)
{
    Mat frame;
    cap >> frame; // get a new frame from camera
    if(waitKey(30) >= 0) break;
    while(frame.empty())
    {
        std::cout<<"Frame Vacio"<<std::endl;
    }
    // do any processing
    /// Load image and template
    img=frame.clone();
    //frame.clone();

    /// Create windows
    namedWindow( image_window, CV_WINDOW_AUTOSIZE );
    namedWindow( result_window, CV_WINDOW_AUTOSIZE );

    /// Create Trackbar
    //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(30);

}
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
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  == CV_TM_SQDIFF || match_method == CV_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;
}