运行期间终端中会显示一些警告：
OpenCV错误：setSize中的断言失败（s＆gt; = 0），文件/home/me/opencv2.4/modules/core/src/matrix.cpp，第116行
编译程序没有错误并执行，问题是当用户离网络摄像头更近/更远时眼睛ROI大小改变，由于大小的改变，警告出现。我设法通过将眼睛ROI大小设置为等于我的眼睛模板大小来解决这些警告。但是，由于获得的minVal为0，最终程序无法对用户的眼睛打开/关闭进行分类。使用的方法是OpenCV Template Matching。或者，我修复我与网络摄像头的距离并修复眼睛模板大小可以避免警告。每次出现警告时，程序都无法对打开/关闭眼睛进行分类。该程序不能有效地工作，因为有时它会错误地将睁开的眼睛分类为闭合，反之亦然。
问题：
除了模板匹配之外，还有其他方法可以识别开眼和闭眼吗？
如何改进闪烁分类程序？
您在opencv C / C ++ API中所知道的任何工作示例都可以对开眼和闭眼进行分类并准确计算闪烁次数？
static CvMemStorage* storage = 0;
// Create a new Haar classifier
static CvHaarClassifierCascade* cascade = 0;
// Function prototype for detecting and drawing an object from an image
bool detect_and_draw( IplImage* image ,CvHaarClassifierCascade* cascade);
const char *cascade_name[1]={"eyes.xml"};
cv::Mat roiImg;
int threshold_value = 200;
int threshold_type = 3;;
int const max_value = 255;
int const max_type = 4;
int const max_BINARY_value = 255;
int hough_thr = 35;
cv::Mat src_gray, dst;
using namespace cv;
Mat img1; Mat img2; Mat templ; Mat result;
const char* image_window = "Source Image";
const char* result_window = "Result window";
int match_method=0;
int max_Trackbar = 5;
int eye_open=0;
int eye_close=0;
//Matching with 2 images ,eye closed or open
void MatchingMethod(cv::Mat templ,int id )
{
/// Source image to display
cv::Mat img_display;
roiImg.copyTo( img_display );
/// Create the result matrix
int result_cols = roiImg.cols - templ.cols + 1;
int result_rows = roiImg.rows - templ.rows + 1;
result.create( result_cols, result_rows, CV_32FC1 );
/// Do the Matching and Normalize
cv::matchTemplate( roiImg, templ, result, match_method );
cv::normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );
/// Localizing the best match with minMaxLoc
double minVal; double maxVal; Point minLoc; Point maxLoc;
cv::Point matchLoc;
cv::minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
///Justing checkin the match template value reaching the threashold
if(id == 0 && (minVal < 0))
{
eye_open=eye_open+1;
if(eye_open == 1)
{
std::cout<<"Eye Open"<<std::endl;
eye_open=0;
eye_close=0;
}
}
else if(id == 1 && (minVal < 0))
eye_close=eye_close+1;
if(eye_close == 1)
{
std::cout<<"Eye Closed"<<std::endl;
eye_close=0;
system("python send_arduino.py");
}
/// 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
cv::rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
cv::rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
cv::imshow( image_window, img_display );
cv::imshow( result_window, result );
return;
}
void detect_blink(cv::Mat roi)
{
try
{
MatchingMethod(img1,0);
MatchingMethod(img2,1);
}
catch( cv::Exception& e )
{
std::cout<<"An exception occued"<<std::endl;
}
}
// Main function, defines the entry point for the program.
int main( int argc, char** argv )
{
if(argc <= 1)
{
std::cout<<"\n Help "<<std::endl;
std::cout<<"\n ------------------------------------\n"<<std::endl;
std::cout<<"./blink_detect open_eye.jpg close_eye.jpg\n"<<std::endl;
std::cout<<"Eg :: ./blink_detect 2.jpg 3.jpg\n"<<std::endl;
std::cout<<"\n ------------------------------------\n"<<std::endl;
exit(0);
}
// Structure for getting video from camera or avi
CvCapture* capture = 0;
// Images to capture the frame from video or camera or from file
IplImage *frame, *frame_copy = 0;
// Used for calculations
int optlen = strlen("--cascade=");
// Input file name for avi or image file.
const char* input_name;
img1 = imread( argv[1], 1 );
img2 = imread( argv[2], 1 );
// Load the HaarClassifierCascade
/// Create windows
cv::namedWindow( image_window, CV_WINDOW_AUTOSIZE );
cv::namedWindow( result_window, CV_WINDOW_AUTOSIZE );
// Allocate the memory storage
storage = cvCreateMemStorage(0);
capture = cvCaptureFromCAM( 0);
// Create a new named window with title: result
cvNamedWindow( "original_frame", 1 );
// If loaded succesfully, then:
if( capture )
{
// Capture from the camera.
for(;;)
{
// Capture the frame and load it in IplImage
if( !cvGrabFrame( capture ))
break;
frame = cvRetrieveFrame( capture );
// If the frame does not exist, quit the loop
if( !frame )
break;
// Allocate framecopy as the same size of the frame
if( !frame_copy )
frame_copy = cvCreateImage( cvSize(frame->width,frame->height),
IPL_DEPTH_8U, frame->nChannels );
// Check the origin of image. If top left, copy the image frame to frame_copy.
if( frame->origin == IPL_ORIGIN_TL )
cvCopy( frame, frame_copy, 0 );
// Else flip and copy the image
for(int i=0;i<1;i++)
{
cascade = (CvHaarClassifierCascade*)cvLoad( cascade_name[i], 0, 0, 0 );
// Check whether the cascade has loaded successfully. Else report and error and quit
if( !cascade )
{
fprintf( stderr, "ERROR: Could not load classifier cascade\n" );
return -1;
}
// Call the function to detect and draw the face
if(detect_and_draw(frame_copy,cascade))
{
std::cout<<"Detected"<<std::endl;
}
}
// Wait for a while before proceeding to the next frame
if( cvWaitKey( 1 ) >= 0 )
break;
}
// Release the images, and capture memory
cvReleaseHaarClassifierCascade(&cascade);
cvReleaseImage( &frame_copy );
cvReleaseCapture( &capture );
cvReleaseMemStorage(&storage);
}
return 0;
}
// Function to detect and draw any faces that is present in an image
bool detect_and_draw( IplImage* img,CvHaarClassifierCascade* cascade )
{
int scale = 1;
// Create a new image based on the input image
IplImage* temp = cvCreateImage( cvSize(img->width/scale,img->height/scale), 8, 3 );
// Create two points to represent the face locations
CvPoint pt1, pt2;
int i;
// Clear the memory storage which was used before
cvClearMemStorage( storage );
// Find whether the cascade is loaded, to find the faces. If yes, then:
if( cascade )
{
// There can be more than one face in an image. So create a growable sequence of faces.
// Detect the objects and store them in the sequence
CvSeq* faces = cvHaarDetectObjects( img, cascade, storage,
1.1, 8, CV_HAAR_DO_CANNY_PRUNING,
cvSize(40, 40) );
// Loop the number of faces found.
for( i = 0; i < (faces ? faces->total : 0); i++ )
{
// Create a new rectangle for drawing the face
CvRect* r = (CvRect*)cvGetSeqElem( faces, i );
// Find the dimensions of the face,and scale it if necessary
pt1.x = r->x*scale;
pt2.x = (r->x+r->width)*scale;
pt1.y = r->y*scale;
pt2.y = (r->y+r->height)*scale;
// Draw the rectangle in the input image
cvRectangle( img, pt1, pt2, CV_RGB(255,0,0), 3, 8, 0 );
cv::Mat image(img);
cv::Rect rect;
rect = cv::Rect(pt1.x,pt1.y,(pt2.x-pt1.x),(pt2.y-pt1.y));
roiImg = image(rect);
cv::imshow("roi",roiImg);
///Send to arduino
detect_blink(roiImg);
}
}
cvShowImage( "original_frame", img );
if(i > 0)
return 1;
else
return 0;
cvReleaseImage( &temp );
}
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