opencv/opencv_contrib
1
0
Fork 0
mirror of https://github.com/opencv/opencv_contrib.git synced 2025-10-24 11:33:26 +08:00
Code Issues Releases Wiki Activity

Added optimization to Multi-target TLD update

Browse Source
This commit is contained in:
Vladimir
2015-07-27 15:41:19 +09:00
parent 7dc95a3a17
commit 8d3470b53d
2 changed files with 108 additions and 270 deletions
Download Patch File Download Diff File Expand all files Collapse all files

181
modules/tracking/src/multiTracker.cpp
View File

@@ -44,7 +44,7 @@
namespace cv
{
//Multitracker
bool MultiTracker::addTarget(const Mat& image, const Rect2d& boundingBox, char* tracker_algorithm_name)
bool MultiTracker::addTarget(const Mat& image, const Rect2d& boundingBox, String tracker_algorithm_name)
{
Ptr<Tracker> tracker = Tracker::create(tracker_algorithm_name);
if (tracker == NULL)
@@ -65,6 +65,8 @@ namespace cv
else
colors.push_back(Scalar(rand() % 256, rand() % 256, rand() % 256));
//Target counter
targetNum++;
@@ -73,8 +75,7 @@ namespace cv
bool MultiTracker::update(const Mat& image)
{
printf("Naive-Loop MO-TLD Update....\n");
for (int i = 0; i < trackers.size(); i++)
for (int i = 0; i < (int)trackers.size(); i++)
if (!trackers[i]->update(image, boundingBoxes[i]))
return false;
@@ -85,16 +86,12 @@ 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
//Set current target(tracker) parameters
Rect2d boundingBox = boundingBoxes[0];
//TLD Tracker data extraction
Tracker* trackerPtr = trackers[0];
tld::TrackerTLDImpl* tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->getModel()));
Ptr<tld::Data> data = tracker->data;
double scale = data->getScale();
@@ -130,11 +127,11 @@ namespace cv
for (int k = 0; k < targetNum; k++)
{
//TLD Tracker data extraction
Tracker* trackerPtr = trackers[k];
tld::TrackerTLDImpl* tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
trackerPtr = trackers[k];
tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
Ptr<tld::Data> data = tracker->data;
tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->getModel()));
data = tracker->data;
data->frameNum++;
@@ -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 < debugStack[0].size(); i++)
//rectangle(tmpImg, debugStack[0][i], Scalar(255, 255, 255), 1, 1, 0);
debugStack[0].clear();
tmpImg.copyTo(image);
////////////////////////////////////////////////
return true;
}
@@ -267,10 +247,10 @@ namespace cv
Tracker* trackerPtr = trackers[0];
cv::tld::TrackerTLDImpl* tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->getModel()));
Size initSize = tldModel->getMinSize();
for (int k = 0; k < trackers.size(); k++)
for (int k = 0; k < (int)trackers.size(); k++)
patches[k].clear();
Mat_<uchar> standardPatch(tld::STANDARD_PATCH_SIZE, tld::STANDARD_PATCH_SIZE);
@@ -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;
@@ -329,13 +305,13 @@ namespace cv
double windowVar = p2 - p * p;
//Loop for on all objects
for (int k=0; k < trackers.size(); k++)
for (int k = 0; k < (int)trackers.size(); k++)
{
//TLD Tracker data extraction
Tracker* trackerPtr = trackers[k];
cv::tld::TrackerTLDImpl* tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
trackerPtr = trackers[k];
tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->getModel()));
//Optimized variance calculation
bool varPass = (windowVar > tld::VARIANCE_THRESHOLD * *tldModel->detector->originalVariancePtr);
@@ -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));
}
}
}
@@ -361,23 +333,14 @@ 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 < trackers.size(); k++)
for (int k = 0; k < (int)trackers.size(); k++)
{
//TLD Tracker data extraction
Tracker* trackerPtr = trackers[k];
cv::tld::TrackerTLDImpl* tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
trackerPtr = trackers[k];
tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->getModel()));
for (int i = 0; i < (int)varBuffer[k].size(); i++)
@@ -410,36 +373,16 @@ 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 < trackers.size(); k++)
for (int k = 0; k < (int)trackers.size(); k++)
{
//TLD Tracker data extraction
Tracker* trackerPtr = trackers[k];
cv::tld::TrackerTLDImpl* tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
trackerPtr = trackers[k];
tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->getModel()));
npos = 0;
nneg = 0;
@@ -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,
@@ -503,10 +441,10 @@ namespace cv
Tracker* trackerPtr = trackers[0];
cv::tld::TrackerTLDImpl* tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->getModel()));
Size initSize = tldModel->getMinSize();
for (int k = 0; k < trackers.size(); k++)
for (int k = 0; k < (int)trackers.size(); k++)
patches[k].clear();
Mat_<uchar> standardPatch(tld::STANDARD_PATCH_SIZE, tld::STANDARD_PATCH_SIZE);
@@ -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;
@@ -565,13 +499,13 @@ namespace cv
double windowVar = p2 - p * p;
//Loop for on all objects
for (int k = 0; k < trackers.size(); k++)
for (int k = 0; k < (int)trackers.size(); k++)
{
//TLD Tracker data extraction
Tracker* trackerPtr = trackers[k];
cv::tld::TrackerTLDImpl* tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
trackerPtr = trackers[k];
tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->getModel()));
//Optimized variance calculation
bool varPass = (windowVar > tld::VARIANCE_THRESHOLD * *tldModel->detector->originalVariancePtr);
@@ -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));
}
}
}
@@ -597,23 +527,14 @@ 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 < trackers.size(); k++)
for (int k = 0; k < (int)trackers.size(); k++)
{
//TLD Tracker data extraction
Tracker* trackerPtr = trackers[k];
cv::tld::TrackerTLDImpl* tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
trackerPtr = trackers[k];
tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->getModel()));
for (int i = 0; i < (int)varBuffer[k].size(); i++)
@@ -646,36 +567,16 @@ 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 < trackers.size(); k++)
for (int k = 0; k < (int)trackers.size(); k++)
{
//TLD Tracker data extraction
Tracker* trackerPtr = trackers[k];
cv::tld::TrackerTLDImpl* tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
trackerPtr = trackers[k];
tracker = static_cast<tld::TrackerTLDImpl*>(trackerPtr);
//TLD Model Extraction
tld::TrackerTLDModel* tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->model));
//Size InitSize = tldModel->getMinSize();
tldModel = ((tld::TrackerTLDModel*)static_cast<TrackerModel*>(tracker->getModel()));
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);
}
@@ -740,12 +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);
}
}