double SimpleTracker::track_frame(Matrixu &frame, Matrixu &framedisp)
{
static SampleSet posx, negx, detectx;
static vectorf prob;
static vectori order;
static Matrixu *img;
double resp;
// copy a color version into framedisp (this is where we will draw a colored box around the object for output)
frame.conv2RGB(framedisp);
img = &frame;
frame.initII();
// run current clf on search window
detectx.sampleImage(img,(uint)_curState[0],(uint)_curState[1],(uint)_curState[2],(uint)_curState[3], (float)_trparams._srchwinsz);
prob = _clf->classify(detectx,_trparams._useLogR);
/////// DEBUG /////// display actual probability map
if( _trparams._debugv ){
Matrixf probimg(frame.rows(),frame.cols());
for( uint k=0; k<(uint)detectx.size(); k++ )
probimg(detectx[k]._row, detectx[k]._col) = prob[k];
probimg.convert2img().display(2,2);
cvWaitKey(1);
}
// find best location
int bestind = max_idx(prob);
resp=prob[bestind];
_curState[1] = (float)detectx[bestind]._row;
_curState[0] = (float)detectx[bestind]._col;
// train location clf (negx are randomly selected from image, posx is just the current tracker location)
if( _trparams._negsamplestrat == 0 )
negx.sampleImage(img, _trparams._negnumtrain, (int)_curState[2], (int)_curState[3]);
else
negx.sampleImage(img, (int)_curState[0], (int)_curState[1], (int)_curState[2], (int)_curState[3],
(1.5f*_trparams._srchwinsz), _trparams._posradtrain+5, _trparams._negnumtrain);
if( _trparams._posradtrain == 1 )
posx.push_back(img, (int)_curState[0], (int)_curState[1], (int)_curState[2], (int)_curState[3]);
else
posx.sampleImage(img, (int)_curState[0], (int)_curState[1], (int)_curState[2], (int)_curState[3], _trparams._posradtrain, 0, _trparams._posmaxtrain);
_clf->update(posx,negx);
/////// DEBUG /////// display sampled negative points
if( _trparams._debugv ){
for( int j=0; j<negx.size(); j++ )
framedisp.drawEllipse(1,1,(float)negx[j]._col,(float)negx[j]._row,1,255,0,255);
}
// clean up
img->FreeII();
posx.clear(); negx.clear(); detectx.clear();
// draw a colored box around object
framedisp.drawRect(_curState[2], _curState[3], _curState[0], _curState[1], 1, 0,
_trparams._lineWidth, _trparams._boxcolor[0], _trparams._boxcolor[1], _trparams._boxcolor[2] );
_cnt++;
return resp;
}
bool Tracker::initFace(TrackerParams* params, Matrixu &frame)
{
const char* cascade_name = "haarcascade_frontalface_alt_tree.xml";
const int minsz = 20;
if( Tracker::facecascade == NULL )
Tracker::facecascade = (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 );
frame.createIpl();
IplImage *img = frame.getIpl();
IplImage* gray = cvCreateImage( cvSize(img->width, img->height), IPL_DEPTH_8U, 1 );
cvCvtColor(img, gray, CV_BGR2GRAY );
frame.freeIpl();
cvEqualizeHist(gray, gray);
CvMemStorage* storage = cvCreateMemStorage(0);
cvClearMemStorage(storage);
CvSeq* faces = cvHaarDetectObjects(gray, Tracker::facecascade, storage, 1.05, 3, CV_HAAR_DO_CANNY_PRUNING ,cvSize(minsz, minsz));
int index = faces->total-1;
CvRect* r = (CvRect*)cvGetSeqElem( faces, index );
while(r && (r->width<minsz || r->height<minsz || (r->y+r->height+10)>frame.rows() || (r->x+r->width)>frame.cols() ||
r->y<0 || r->x<0)){
r = (CvRect*)cvGetSeqElem( faces, --index);
}
//if( r == NULL ){
// cout << "ERROR: no face" << endl;
// return false;
//}
//else
// cout << "Face Found: " << r->x << " " << r->y << " " << r->width << " " << r->height << endl;
if( r==NULL )
return false;
//fprintf(stderr,"x=%f y=%f xmax=%f ymax=%f imgw=%f imgh=%f\n",(float)r->x,(float)r->y,(float)r->x+r->width,(float)r->y+r->height,(float)frame.cols(),(float)frame.rows());
params->_initstate.resize(4);
params->_initstate[0] = (float)r->x;// - r->width;
params->_initstate[1] = (float)r->y;// - r->height;
params->_initstate[2] = (float)r->width;
params->_initstate[3] = (float)r->height+10;
return true;
}
/****************************************************************
Tracker::InitializeWithFace
Initialize the tracker with opencv's face detector.
Exceptions:
None
****************************************************************/
bool Tracker::InitializeWithFace( TrackerParameters* params, Matrixu& frame )
{
const int minsz = 20;
//Get the name of the haar-cascade .xml file
const char* cascade_name = HAAR_CASCADE_FILE_NAME;
ASSERT_TRUE( cascade_name != NULL );
//Load the cascade
if ( Tracker::s_faceCascade == NULL )
{
Tracker::s_faceCascade = (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 );
}
frame.createIpl();
IplImage* img = frame.getIpl();
ASSERT_TRUE( img != NULL );
//convert to grayscale
IplImage* gray = cvCreateImage( cvSize(img->width, img->height), IPL_DEPTH_8U, 1 );
ASSERT_TRUE( gray != NULL );
cvCvtColor(img, gray, CV_BGR2GRAY );
frame.freeIpl();
//histogram equalization
cvEqualizeHist( gray, gray );
//memory storage
CvMemStorage* storage = cvCreateMemStorage(0);
cvClearMemStorage(storage);
//call opencv's haar feature based detector
CvSeq* faces = cvHaarDetectObjects( gray,
Tracker::s_faceCascade,
storage,
1.05,
3,
CV_HAAR_DO_CANNY_PRUNING,
cvSize( minsz, minsz ) );
int index = faces->total-1;
CvRect* r = (CvRect*)cvGetSeqElem( faces, index );
if ( r == NULL )
{
return false;
}
while ( r && (r->width<minsz || r->height < minsz || (r->y+r->height+10)>frame.rows() || (r->x+r->width)>frame.cols() ||
r->y<0 || r->x<0) )
{
r = (CvRect*)cvGetSeqElem( faces, --index );
}
//set the params
params->m_initState.resize(4);
params->m_initState[0] = (float)r->x;
params->m_initState[1] = (float)r->y;
params->m_initState[2] = (float)r->width;
params->m_initState[3] = (float)r->height+10;
return true;
}