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TLD accelerated with parallel_for_ helper

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Using opencv/core/utility.hpp parallel primitives,
Intel TBB as backend is desirable.
pull/758/head
Eugene Mamin 9 years ago
parent 3468ae572c
commit 8f04976f48
4 changed files with 105 additions and 17 deletions
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  1. 74
      modules/tracking/src/tldDetector.cpp
  2. 12
      modules/tracking/src/tldDetector.hpp
  3. 33
      modules/tracking/src/tldModel.cpp
  4. 3
      modules/tracking/src/tldModel.hpp

74
modules/tracking/src/tldDetector.cpp
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@ -41,6 +41,8 @@
#include "tldDetector.hpp"
#include <opencv2/core/utility.hpp>
namespace cv
{
namespace tld
@ -63,7 +65,7 @@ namespace cv
}
// Calculate Relative similarity of the patch (NN-Model)
double TLDDetector::Sr(const Mat_<uchar>& patch)
double TLDDetector::Sr(const Mat_<uchar>& patch) const
{
double splus = 0.0, sminus = 0.0;
Mat_<uchar> modelSample(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
@ -189,7 +191,7 @@ namespace cv
#endif
// Calculate Conservative similarity of the patch (NN-Model)
double TLDDetector::Sc(const Mat_<uchar>& patch)
double TLDDetector::Sc(const Mat_<uchar>& patch) const
{
double splus = 0.0, sminus = 0.0;
Mat_<uchar> modelSample(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
@ -298,10 +300,38 @@ namespace cv
//Detection - returns most probable new target location (Max Sc)
class CalcScSrParallelLoopBody: public cv::ParallelLoopBody
{
public:
explicit CalcScSrParallelLoopBody (TLDDetector * detector, Size initSize):
detectorF (detector),
initSizeF (initSize)
{
}
virtual void operator () (const cv::Range & r) const
{
for (int ind = r.start; ind < r.end; ++ind)
{
resample(detectorF->resized_imgs[detectorF->ensScaleIDs[ind]],
Rect2d(detectorF->ensBuffer[ind], initSizeF),
detectorF->standardPatches[ind]);
detectorF->scValues[ind] = detectorF->Sc (detectorF->standardPatches[ind]);
detectorF->srValues[ind] = detectorF->Sr (detectorF->standardPatches[ind]);
}
}
TLDDetector * detectorF;
const Size initSizeF;
private:
CalcScSrParallelLoopBody (const CalcScSrParallelLoopBody&);
CalcScSrParallelLoopBody& operator= (const CalcScSrParallelLoopBody&);
};
bool TLDDetector::detect(const Mat& img, const Mat& imgBlurred, Rect2d& res, std::vector<LabeledPatch>& patches, Size initSize)
{
patches.clear();
Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
Mat tmp;
int dx = initSize.width / 10, dy = initSize.height / 10;
Size2d size = img.size();
@ -310,9 +340,13 @@ namespace cv
double maxSc = -5.0;
Rect2d maxScRect;
int scaleID;
std::vector <Mat> resized_imgs, blurred_imgs;
std::vector <Point> varBuffer, ensBuffer;
std::vector <int> varScaleIDs, ensScaleIDs;
resized_imgs.clear ();
blurred_imgs.clear ();
varBuffer.clear ();
ensBuffer.clear ();
varScaleIDs.clear ();
ensScaleIDs.clear ();
//Detection part
//Generate windows and filter by variance
@ -353,16 +387,34 @@ namespace cv
ensScaleIDs.push_back(varScaleIDs[i]);
}
//Batch preparation
srValues.resize (ensBuffer.size());
scValues.resize (ensBuffer.size());
//Carefully resize standard patches with reference-counted Mat members
const int oldPatchesSize = (int)standardPatches.size();
standardPatches.resize (ensBuffer.size());
if ((int)ensBuffer.size() > oldPatchesSize)
{
Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
for (int i = oldPatchesSize; i < (int)ensBuffer.size(); ++i)
{
standardPatches[i] = standardPatch.clone();
}
}
//Batch calculation
cv::parallel_for_ (cv::Range (0, (int)ensBuffer.size ()), CalcScSrParallelLoopBody (this, initSize));
//NN classification
for (int i = 0; i < (int)ensBuffer.size(); i++)
{
LabeledPatch labPatch;
double curScale = pow(SCALE_STEP, ensScaleIDs[i]);
labPatch.rect = Rect2d(ensBuffer[i].x*curScale, ensBuffer[i].y*curScale, initSize.width * curScale, initSize.height * curScale);
resample(resized_imgs[ensScaleIDs[i]], Rect2d(ensBuffer[i], initSize), standardPatch);
double srValue, scValue;
srValue = Sr(standardPatch);
const double srValue = srValues[i];
const double scValue = scValues[i];
////To fix: Check the paper, probably this cause wrong learning
//
@ -380,7 +432,7 @@ namespace cv
{
npos++;
}
scValue = Sc(standardPatch);
if (scValue > maxSc)
{
maxSc = scValue;
@ -540,4 +592,4 @@ namespace cv
}
}
}
}

12
modules/tracking/src/tldDetector.hpp
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@ -75,8 +75,8 @@ namespace cv
~TLDDetector(){}
double ensembleClassifierNum(const uchar* data);
void prepareClassifiers(int rowstep);
double Sr(const Mat_<uchar>& patch);
double Sc(const Mat_<uchar>& patch);
double Sr(const Mat_<uchar>& patch) const;
double Sc(const Mat_<uchar>& patch) const;
#ifdef HAVE_OPENCL
double ocl_Sr(const Mat_<uchar>& patch);
double ocl_Sc(const Mat_<uchar>& patch);
@ -89,6 +89,12 @@ namespace cv
std::vector<Mat_<uchar> > *positiveExamples, *negativeExamples;
std::vector<int> *timeStampsPositive, *timeStampsNegative;
double *originalVariancePtr;
std::vector<double> scValues, srValues;
std::vector<Mat_<uchar> > standardPatches;
std::vector <Mat> resized_imgs, blurred_imgs;
std::vector <Point> varBuffer, ensBuffer;
std::vector <int> varScaleIDs, ensScaleIDs;
static void generateScanGrid(int rows, int cols, Size initBox, std::vector<Rect2d>& res, bool withScaling = false);
struct LabeledPatch
@ -108,4 +114,4 @@ namespace cv
}
}
#endif
#endif

33
modules/tracking/src/tldModel.cpp
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@ -41,6 +41,8 @@
#include "tldModel.hpp"
#include <opencv2/core/utility.hpp>
namespace cv
{
namespace tld
@ -182,14 +184,41 @@ namespace cv
}
class CalcSrParallelLoopBody: public cv::ParallelLoopBody
{
public:
explicit CalcSrParallelLoopBody (TrackerTLDModel * model, const std::vector<Mat_<uchar> >& eForModel):
modelF (model),
eForModelF (eForModel)
{
}
virtual void operator () (const cv::Range & r) const
{
for (int ind = r.start; ind < r.end; ++ind)
{
modelF->srValues[ind] = modelF->detector->Sr (eForModelF[ind]);
}
}
TrackerTLDModel * modelF;
const std::vector<Mat_<uchar> >& eForModelF;
private:
CalcSrParallelLoopBody (const CalcSrParallelLoopBody&);
CalcSrParallelLoopBody& operator= (const CalcSrParallelLoopBody&);
};
void TrackerTLDModel::integrateAdditional(const std::vector<Mat_<uchar> >& eForModel, const std::vector<Mat_<uchar> >& eForEnsemble, bool isPositive)
{
int positiveIntoModel = 0, negativeIntoModel = 0, positiveIntoEnsemble = 0, negativeIntoEnsemble = 0;
if ((int)eForModel.size() == 0) return;
srValues.resize (eForModel.size ());
cv::parallel_for_ (cv::Range (0, (int)eForModel.size ()), CalcSrParallelLoopBody (this, eForModel));
for (int k = 0; k < (int)eForModel.size(); k++)
{
double sr = detector->Sr(eForModel[k]);
const double sr = srValues[k];
if ((sr > THETA_NN) != isPositive)
{
if (isPositive)
@ -331,4 +360,4 @@ namespace cv
dfprintf((port, "\tnegativeExamples.size() = %d\n", (int)negativeExamples.size()));
}
}
}
}

3
modules/tracking/src/tldModel.hpp
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@ -71,6 +71,7 @@ namespace cv
std::vector<int> timeStampsPositive, timeStampsNegative;
int timeStampPositiveNext, timeStampNegativeNext;
double originalVariance_;
std::vector<double> srValues;
double getOriginalVariance(){ return originalVariance_; }
@ -87,4 +88,4 @@ namespace cv
}
}
#endif
#endif

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