使用 opencv 进行实时摄像机模板匹配
Live camera template matching using opencv
我在使用 OpenCV 网站上提供的代码识别图像(模板)时遇到了一些问题。我一直在用我的 PC 上的相机应用程序(Win10 64 位)拍摄的一些图像使用它,它的效果非常好,但是当我尝试从相机中获取图像进行比较时,它只向我显示一个带有跟踪栏的黑色窗口:
#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拍摄帧 cap>>frame;
并将其复制到变量 img 上 img=frame.clone();
.然后,它每次在MatchingMethod
上发送它以执行处理,直到按下任何键。
我和我的项目伙伴们会非常感谢任何能让我们解决这个问题的东西。
附注:
如果对我正在使用的 IDE 有任何疑问,我正在使用代码块。
另外,我将在我得到的结果中附加一些链接:链接到Imgur
多亏了
用户api55,我才能进行实时匹配,但是在咨询后,我发现这不是跟踪机器人的最佳方法,但至少是迈向最终解决方案的一小步。我将发布代码更新,以便对任何需要它的人有所帮助。
#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;
}
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