void* Run(cv::VideoCapture& capture)
{
int size = ivWidth*ivHeight;
int count = 1;
DebugInfo dbgInfo;
cv::CascadeClassifier cascade;
std::vector<cv::Rect> detectedFaces;
std::vector<ObjectBox> trackBoxes;
ObjectBox detectBox;
// Initialize MultiObjectTLD
#if LOADCLASSIFIERATSTART
MultiObjectTLD p = MultiObjectTLD::loadClassifier((char*)CLASSIFIERFILENAME);
#else
MOTLDSettings settings(COLOR_MODE_RGB);
settings.useColor = false;
MultiObjectTLD p(ivWidth, ivHeight, settings);
#endif
if(cascadePath != "")
cascade.load( cascadePath );
Matrix maRed;
Matrix maGreen;
Matrix maBlue;
unsigned char img[size*3];
while(!ivQuit)
{
/*
if(reset){
p = *(new MultiObjectTLD(ivWidth, ivHeight, COLOR_MODE_RGB));
reset = false;
}
if(load){
p = MultiObjectTLD::loadClassifier(CLASSIFIERFILENAME);
load = false;
}
*/
#if TIMING
c_end = std::clock();
std::cout << "Total: " << (c_end-c_start) << std::endl;
c_start = std::clock();
#endif
// Grab an image
if(!capture.grab()){
std::cout << "error grabbing frame" << std::endl;
break;
}
cv::Mat frame;
capture.retrieve(frame);
frame.copyTo(curImage);
//BGR to RGB
// for(int j = 0; j<size; j++){
// img[j] = frame.at<cv::Vec3b>(j).val[2];
// img[j+size] = frame.at<cv::Vec3b>(j).val[1];
// img[j+2*size] = frame.at<cv::Vec3b>(j).val[0];
// }
#if TIMING
c_start1 = std::clock();
#endif
for(int i = 0; i < ivHeight; ++i){
for(int j = 0; j < ivWidth; ++j){
img[i*ivWidth+j] = curImage.at<cv::Vec3b>(i,j).val[2];
img[i*ivWidth+j+size] = curImage.at<cv::Vec3b>(i,j).val[1];
img[i*ivWidth+j+2*size] = curImage.at<cv::Vec3b>(i,j).val[0];
}
}
#if TIMING
c_end1 = std::clock();
std::cout << "time1: " << (c_end1-c_start1) << std::endl;
#endif
// for(int i = 0; i < ivHeight; ++i){
// for(int j = 0; j < ivWidth; ++j){
// curImage.at<cv::Vec3b>(i,j).val[2] = 0;
// curImage.at<cv::Vec3b>(i,j).val[1] = 0;
// curImage.at<cv::Vec3b>(i,j).val[0] = 0;
// }
// }
// cv::imshow("MOCTLD", curImage);
#if TIMING
c_start2 = std::clock();
#endif
// Process it with motld
p.processFrame(img);
#if TIMING
c_end2 = std::clock();
std::cout << "time2: " << (c_end2-c_start2) << std::endl;
#endif
// Add new box
if(mouseMode == MOUSE_MODE_ADD_BOX){
p.addObject(mouseBox);
mouseMode = MOUSE_MODE_IDLE;
}
if(mouseMode == MOUSE_MODE_ADD_GATE){
p.addGate(gate);
mouseMode = MOUSE_MODE_IDLE;
}
if(((count%20)==0) && cascadeDetect)
{
cascade.detectMultiScale( frame, detectedFaces,
1.1, 2, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
|cv::CASCADE_SCALE_IMAGE,
cv::Size(30, 30) );
Ndetections = detectedFaces.size();
for( std::vector<cv::Rect>::const_iterator r = detectedFaces.begin(); r != detectedFaces.end(); r++ )
{
detectBox.x = r->x;
detectBox.y = r->y;
detectBox.width = r->width;
detectBox.height = r->height;
if(p.isNewObject(detectBox))
p.addObject(detectBox);
}
//printf("size detectedFaces: %i\n", detectedFaces.size());
}
count++;
// Display result
HandleInput();
p.getDebugImage(img, maRed, maGreen, maBlue, drawMode);
#if TIMING
c_start3 = std::clock();
#endif
BGR2RGB(maRed, maGreen, maBlue);
#if TIMING
c_end3 = std::clock();
std::cout << "time3: " << (c_end3-c_start3) << std::endl;
#endif
drawGate();
drawMouseBox();
dbgInfo.NObjects = p.getObjectTotal();
dbgInfo.side0Cnt = p.getSide0Cnt();
dbgInfo.side1Cnt = p.getSide1Cnt();
writeDebug(dbgInfo);
cv::imshow("MOCTLD", curImage);
p.enableLearning(learningEnabled);
if(save){
p.saveClassifier((char*)CLASSIFIERFILENAME);
save = false;
}
}
//delete[] img;
capture.release();
return 0;
}
