使用opencv进行实时摄像头模板匹配

Question

我在使用 OpenCV website 上提供的代码识别图像（模板）时遇到了一些问题。我一直在使用它来处理在我的 PC（Win10 64 位）上使用相机应用程序捕获的一些图像，它的效果非常好，但是当我尝试从相机获取图像进行比较时，它只显示黑色 window 与轨道栏：

#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;
}

我认为它应该工作的方式是获取捕获的帧 cap>>frame; 并将其复制到变量 img 上 img=frame.clone();。然后，它每次在 MatchingMethod 上发送它进行处理，直到按下任何键。

我和我的项目伙伴会非常感激任何能让我们解决这个问题的东西。

PS:
如果对我使用的 IDE 有任何疑问，我正在使用 Codeblocks。
此外，我将附上一些链接以及我得到的结果：Link to Imgur

Answer 1

感谢用户 api55 我能够进行实时匹配，但在咨询后我发现这不是跟踪机器人的最佳方法，但至少是朝着这个方向迈出的一小步最终的解决方案。我将发布代码更新，以便对需要它的任何人有所帮助。

#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;
}

使用opencv进行实时摄像头模板匹配

Live camera template matching using opencv

c++

opencv

computer-vision