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Commit f16586f8 authored by Vladimir's avatar Vladimir
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Cleaning up debug prints/variables/flags/timers

parent 4537103c
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Showing with 1 addition and 180 deletions
modules/tracking/src/multiTracker.cpp
View file @ f16586f8
......@@ -75,7 +75,6 @@ namespace cv
bool MultiTracker::update(const Mat& image)
{
printf("Naive-Loop MO-TLD Update....\n");
for (int i = 0; i < (int)trackers.size(); i++)
if (!trackers[i]->update(image, boundingBoxes[i]))
return false;
......@@ -87,8 +86,6 @@ namespace cv
/*Optimized update method for TLD Multitracker */
bool MultiTrackerTLD::update_opt(const Mat& image)
{
printf("Optimized MO-TLD Update....\n");
//Get parameters from first object
//TLD Tracker data extraction
Tracker* trackerPtr = trackers[0];
......@@ -186,16 +183,7 @@ namespace cv
#if 1
if (it != candidatesRes[k].end())
{
tld::resample(imageForDetector, candidates[k][it - candidatesRes[k].begin()], standardPatch);
//dfprintf((stderr, "%d %f %f\n", data->frameNum, tldModel->Sc(standardPatch), tldModel->Sr(standardPatch)));
//if( candidatesRes.size() == 2 && it == (candidatesRes.begin() + 1) )
//dfprintf((stderr, "detector WON\n"));
}
else
{
//dfprintf((stderr, "%d x x\n", data->frameNum));
}
#endif
if (*it > tld::CORE_THRESHOLD)
......@@ -226,7 +214,6 @@ namespace cv
detectorResults[k][i].isObject = expertResult;
}
tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults[k]);
//dprintf(("%d relabeled by nExpert\n", negRelabeled));
pExpert.additionalExamples(examplesForModel, examplesForEnsemble);
if (ocl::haveOpenCL())
tldModel->ocl_integrateAdditional(examplesForModel, examplesForEnsemble, true);
......@@ -249,14 +236,7 @@ namespace cv
}
//Debug display candidates after Variance Filter
////////////////////////////////////////////////
Mat tmpImg = image;
for (int i = 0; i < (int)debugStack[0].size(); i++)
//rectangle(tmpImg, debugStack[0][i], Scalar(255, 255, 255), 1, 1, 0);
debugStack[0].clear();
tmpImg.copyTo(image);
////////////////////////////////////////////////
return true;
}
......@@ -290,10 +270,6 @@ namespace cv
std::vector <Point> tmpP;
std::vector <int> tmpI;
//int64 e1, e2;
//double t;
//e1 = getTickCount();
//Detection part
//Generate windows and filter by variance
scaleID = 0;
......@@ -344,10 +320,6 @@ namespace cv
continue;
varBuffer[k].push_back(Point(dx * i, dy * j));
varScaleIDs[k].push_back(scaleID);
//Debug display candidates after Variance Filter
double curScale = pow(tld::SCALE_STEP, scaleID);
debugStack[0].push_back(Rect2d(dx * i* curScale, dy * j*curScale, tldModel->getMinSize().width*curScale, tldModel->getMinSize().height*curScale));
}
}
}
......@@ -361,16 +333,7 @@ namespace cv
blurred_imgs.push_back(tmp);
} while (size.width >= initSize.width && size.height >= initSize.height);
//e2 = getTickCount();
//t = (e2 - e1) / getTickFrequency()*1000.0;
//printf("Variance: %d\t%f\n", varBuffer.size(), t);
//printf("OrigVar 1: %f\n", *tldModel->detector->originalVariancePtr);
//Encsemble classification
//e1 = getTickCount();
for (int k = 0; k < (int)trackers.size(); k++)
{
//TLD Tracker data extraction
......@@ -410,29 +373,9 @@ namespace cv
ensBuffer[k].push_back(varBuffer[k][i]);
ensScaleIDs[k].push_back(varScaleIDs[k][i]);
}
/*
for (int i = 0; i < (int)varBuffer[k].size(); i++)
{
tldModel->detector->prepareClassifiers(static_cast<int> (blurred_imgs[varScaleIDs[k][i]].step[0]));
if (tldModel->detector->ensembleClassifierNum(&blurred_imgs[varScaleIDs[k][i]].at<uchar>(varBuffer[k][i].y, varBuffer[k][i].x)) <= tld::ENSEMBLE_THRESHOLD)
continue;
ensBuffer[k].push_back(varBuffer[k][i]);
ensScaleIDs[k].push_back(varScaleIDs[k][i]);
}
*/
}
//e2 = getTickCount();
//t = (e2 - e1) / getTickFrequency()*1000.0;
//printf("Ensemble: %d\t%f\n", ensBuffer.size(), t);
//printf("varBuffer 1: %d\n", varBuffer[0].size());
//printf("ensBuffer 1: %d\n", ensBuffer[0].size());
//printf("varBuffer 2: %d\n", varBuffer[1].size());
//printf("ensBuffer 2: %d\n", ensBuffer[1].size());
//NN classification
//e1 = getTickCount();
for (int k = 0; k < (int)trackers.size(); k++)
{
//TLD Tracker data extraction
......@@ -477,7 +420,6 @@ namespace cv
maxSc = scValue;
maxScRect = labPatch.rect;
}
//printf("%d %f %f\n", k, srValue, scValue);
}
......@@ -487,13 +429,9 @@ namespace cv
else
{
res[k] = maxScRect;
//printf("%f %f %f %f\n", maxScRect.x, maxScRect.y, maxScRect.width, maxScRect.height);
detect_flgs[k] = true;
}
}
//e2 = getTickCount();
//t = (e2 - e1) / getTickFrequency()*1000.0;
//printf("NN: %d\t%f\n", patches.size(), t);
}
void ocl_detect_all(const Mat& img, const Mat& imgBlurred, std::vector<Rect2d>& res, std::vector < std::vector < tld::TLDDetector::LabeledPatch > > &patches, std::vector<bool> &detect_flgs,
......@@ -526,10 +464,6 @@ namespace cv
std::vector <Point> tmpP;
std::vector <int> tmpI;
//int64 e1, e2;
//double t;
//e1 = getTickCount();
//Detection part
//Generate windows and filter by variance
scaleID = 0;
......@@ -580,10 +514,6 @@ namespace cv
continue;
varBuffer[k].push_back(Point(dx * i, dy * j));
varScaleIDs[k].push_back(scaleID);
//Debug display candidates after Variance Filter
double curScale = pow(tld::SCALE_STEP, scaleID);
debugStack[0].push_back(Rect2d(dx * i* curScale, dy * j*curScale, tldModel->getMinSize().width*curScale, tldModel->getMinSize().height*curScale));
}
}
}
......@@ -597,16 +527,7 @@ namespace cv
blurred_imgs.push_back(tmp);
} while (size.width >= initSize.width && size.height >= initSize.height);
//e2 = getTickCount();
//t = (e2 - e1) / getTickFrequency()*1000.0;
//printf("Variance: %d\t%f\n", varBuffer.size(), t);
//printf("OrigVar 1: %f\n", *tldModel->detector->originalVariancePtr);
//Encsemble classification
//e1 = getTickCount();
for (int k = 0; k < (int)trackers.size(); k++)
{
//TLD Tracker data extraction
......@@ -646,28 +567,9 @@ namespace cv
ensBuffer[k].push_back(varBuffer[k][i]);
ensScaleIDs[k].push_back(varScaleIDs[k][i]);
}
/*
for (int i = 0; i < (int)varBuffer[k].size(); i++)
{
tldModel->detector->prepareClassifiers(static_cast<int> (blurred_imgs[varScaleIDs[k][i]].step[0]));
if (tldModel->detector->ensembleClassifierNum(&blurred_imgs[varScaleIDs[k][i]].at<uchar>(varBuffer[k][i].y, varBuffer[k][i].x)) <= tld::ENSEMBLE_THRESHOLD)
continue;
ensBuffer[k].push_back(varBuffer[k][i]);
ensScaleIDs[k].push_back(varScaleIDs[k][i]);
}
*/
}
//e2 = getTickCount();
//t = (e2 - e1) / getTickFrequency()*1000.0;
//printf("varBuffer 1: %d\n", varBuffer[0].size());
//printf("ensBuffer 1: %d\n", ensBuffer[0].size());
//printf("varBuffer 2: %d\n", varBuffer[1].size());
//printf("ensBuffer 2: %d\n", ensBuffer[1].size());
//NN classification
//e1 = getTickCount();
for (int k = 0; k < (int)trackers.size(); k++)
{
//TLD Tracker data extraction
......@@ -675,7 +577,6 @@ namespace cv
tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
//Size InitSize = tldModel->getMinSize();
npos = 0;
nneg = 0;
maxSc = -5.0;
......@@ -730,7 +631,6 @@ namespace cv
maxSc = scValue;
maxScRect = labPatch.rect;
}
//printf("%d %f %f\n", k, srValue, scValue);
}
......@@ -740,13 +640,9 @@ namespace cv
else
{
res[k] = maxScRect;
//printf("%f %f %f %f\n", maxScRect.x, maxScRect.y, maxScRect.width, maxScRect.height);
detect_flgs[k] = true;
}
}
//e2 = getTickCount();
//t = (e2 - e1) / getTickFrequency()*1000.0;
//printf("NN: %d\t%f\n", patches.size(), t);
}
}
\ No newline at end of file
modules/tracking/src/multiTracker.hpp
View file @ f16586f8
......@@ -53,6 +53,5 @@ namespace cv
std::vector<bool>& detect_flgs, std::vector<Ptr<Tracker> >& trackers);
void ocl_detect_all(const Mat& img, const Mat& imgBlurred, std::vector<Rect2d>& res, std::vector < std::vector < tld::TLDDetector::LabeledPatch > > &patches,
std::vector<bool>& detect_flgs, std::vector<Ptr<Tracker> >& trackers);
std::vector <Rect2d> debugStack[10];
}
#endif
\ No newline at end of file
modules/tracking/src/tldDetector.cpp
View file @ f16586f8
This diff is collapsed.
modules/tracking/src/tldDetector.hpp
View file @ f16586f8
......@@ -81,7 +81,6 @@ namespace cv
double ocl_Sc(const Mat_<uchar>& patch);
void ocl_batchSrSc(const Mat_<uchar>& patches, double *resultSr, double *resultSc, int numOfPatches);
std::vector <Rect2d> debugStack[10];
std::vector<TLDEnsembleClassifier> classifiers;
Mat *posExp, *negExp;
int *posNum, *negNum;
......
modules/tracking/src/tldModel.cpp
View file @ f16586f8
......@@ -140,7 +140,6 @@ namespace cv
detector->classifiers[k].integrate(blurredPatch, false);
}
}
//dprintf(("positive patches: %d\nnegative patches: %d\n", (int)positiveExamples.size(), (int)negativeExamples.size()));
}
......@@ -180,16 +179,6 @@ namespace cv
detector->classifiers[i].integrate(blurredPatch, patches[k].isObject);
}
}
/*
if( negativeIntoModel > 0 )
dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
if( positiveIntoModel > 0)
dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
if( negativeIntoEnsemble > 0 )
dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
if( positiveIntoEnsemble > 0 )
dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
dfprintf((stdout, "\n"));*/
}
......@@ -198,9 +187,6 @@ namespace cv
int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
if ((int)eForModel.size() == 0) return;
//int64 e1, e2;
//double t;
//e1 = getTickCount();
for (int k = 0; k < (int)eForModel.size(); k++)
{
double sr = detector->Sr(eForModel[k]);
......@@ -231,19 +217,6 @@ namespace cv
detector->classifiers[i].integrate(eForEnsemble[k], isPositive);
}
}
//e2 = getTickCount();
//t = (e2 - e1) / getTickFrequency() * 1000;
//printf("Integrate Additional: %fms\n", t);
/*
if( negativeIntoModel > 0 )
dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
if( positiveIntoModel > 0 )
dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
if( negativeIntoEnsemble > 0 )
dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
if( positiveIntoEnsemble > 0 )
dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
dfprintf((stdout, "\n"));*/
}
void TrackerTLDModel::ocl_integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive)
......@@ -251,10 +224,6 @@ namespace cv
int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
if ((int)eForModel.size() == 0) return;
//int64 e1, e2;
//double t;
//e1 = getTickCount();
//Prepare batch of patches
int numOfPatches = (int)eForModel.size();
Mat_<uchar> stdPatches(numOfPatches, 225);
......@@ -301,19 +270,6 @@ namespace cv
detector->classifiers[i].integrate(eForEnsemble[k], isPositive);
}
}
//e2 = getTickCount();
//t = (e2 - e1) / getTickFrequency() * 1000;
//printf("Integrate Additional OCL: %fms\n", t);
/*
if( negativeIntoModel > 0 )
dfprintf((stdout, "negativeIntoModel = %d ", negativeIntoModel));
if( positiveIntoModel > 0 )
dfprintf((stdout, "positiveIntoModel = %d ", positiveIntoModel));
if( negativeIntoEnsemble > 0 )
dfprintf((stdout, "negativeIntoEnsemble = %d ", negativeIntoEnsemble));
if( positiveIntoEnsemble > 0 )
dfprintf((stdout, "positiveIntoEnsemble = %d ", positiveIntoEnsemble));
dfprintf((stdout, "\n"));*/
}
//Push the patch to the model
......
modules/tracking/src/tldTracker.cpp
View file @ f16586f8
......@@ -151,12 +151,6 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
}
std::vector<double>::iterator it = std::max_element(candidatesRes.begin(), candidatesRes.end());
//dfprintf((stdout, "scale = %f\n", log(1.0 * boundingBox.width / (data->getMinSize()).width) / log(SCALE_STEP)));
//for( int i = 0; i < (int)candidatesRes.size(); i++ )
//dprintf(("\tcandidatesRes[%d] = %f\n", i, candidatesRes[i]));
//data->printme();
//tldModel->printme(stdout);
if( it == candidatesRes.end() )
{
data->confident = false;
......@@ -173,16 +167,7 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
#if 1
if( it != candidatesRes.end() )
{
resample(imageForDetector, candidates[it - candidatesRes.begin()], standardPatch);
//dfprintf((stderr, "%d %f %f\n", data->frameNum, tldModel->Sc(standardPatch), tldModel->Sr(standardPatch)));
//if( candidatesRes.size() == 2 && it == (candidatesRes.begin() + 1) )
//dfprintf((stderr, "detector WON\n"));
}
else
{
//dfprintf((stderr, "%d x x\n", data->frameNum));
}
#endif
if( *it > CORE_THRESHOLD )
......@@ -213,7 +198,6 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
detectorResults[i].isObject = expertResult;
}
tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults);
//dprintf(("%d relabeled by nExpert\n", negRelabeled));
pExpert.additionalExamples(examplesForModel, examplesForEnsemble);
if (ocl::haveOpenCL())
tldModel->ocl_integrateAdditional(examplesForModel, examplesForEnsemble, true);
......@@ -234,16 +218,6 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
#endif
}
//Debug display candidates after Variance Filter
////////////////////////////////////////////////
Mat tmpImg = image;
for (int i = 0; i < (int)tldModel->detector->debugStack[0].size(); i++)
//rectangle(tmpImg, tldModel->detector->debugStack[0][i], Scalar(255, 255, 255), 1, 1, 0);
tldModel->detector->debugStack[0].clear();
tmpImg.copyTo(image);
////////////////////////////////////////////////
return true;
}
......@@ -310,7 +284,6 @@ Data::Data(Rect2d initBox)
minSize.width = (int)(initBox.width * 20.0 / minDim);
minSize.height = (int)(initBox.height * 20.0 / minDim);
frameNum = 0;
//dprintf(("minSize = %dx%d\n", minSize.width, minSize.height));
}
void Data::printme(FILE* port)
......
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