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Changed parallel_for to parallel_for_ in hog.cpp and cascadedetect.cpp

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This commit is contained in:
Evgeny Talanin
2012-09-25 12:18:33 +04:00
parent b8c185de9f
commit 6308be2c3e
2 changed files with 69 additions and 41 deletions
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40
modules/objdetect/src/cascadedetect.cpp
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@@ -943,10 +943,11 @@ void CascadeClassifier::setFaceDetectionMaskGenerator()
#endif
}
struct CascadeClassifierInvoker
class CascadeClassifierInvoker : public ParallelLoopBody
{
public:
CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor,
ConcurrentRectVector& _vec, vector<int>& _levels, vector<double>& _weights, bool outputLevels, const Mat& _mask)
vector<Rect>& _vec, vector<int>& _levels, vector<double>& _weights, bool outputLevels, const Mat& _mask, Mutex* _mtx)
{
classifier = &_cc;
processingRectSize = _sz1;
@@ -954,19 +955,20 @@ struct CascadeClassifierInvoker
yStep = _yStep;
scalingFactor = _factor;
rectangles = &_vec;
rejectLevels = outputLevels ? &_levels : 0;
levelWeights = outputLevels ? &_weights : 0;
mask=_mask;
rejectLevels = outputLevels ? &_levels : 0;
levelWeights = outputLevels ? &_weights : 0;
mask = _mask;
mtx = _mtx;
}
void operator()(const BlockedRange& range) const
void operator()(const Range& range) const
{
Ptr<FeatureEvaluator> evaluator = classifier->featureEvaluator->clone();
Size winSize(cvRound(classifier->data.origWinSize.width * scalingFactor), cvRound(classifier->data.origWinSize.height * scalingFactor));
int y1 = range.begin() * stripSize;
int y2 = min(range.end() * stripSize, processingRectSize.height);
int y1 = range.start * stripSize;
int y2 = min(range.end * stripSize, processingRectSize.height);
for( int y = y1; y < y2; y += yStep )
{
for( int x = 0; x < processingRectSize.width; x += yStep )
@@ -988,14 +990,20 @@ struct CascadeClassifierInvoker
result = -(int)classifier->data.stages.size();
if( classifier->data.stages.size() + result < 4 )
{
mtx->lock();
rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor), winSize.width, winSize.height));
mtx->unlock();
rejectLevels->push_back(-result);
levelWeights->push_back(gypWeight);
}
}
else if( result > 0 )
{
mtx->lock();
rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor),
winSize.width, winSize.height));
mtx->unlock();
}
if( result == 0 )
x += yStep;
}
@@ -1003,13 +1011,14 @@ struct CascadeClassifierInvoker
}
CascadeClassifier* classifier;
ConcurrentRectVector* rectangles;
vector<Rect>* rectangles;
Size processingRectSize;
int stripSize, yStep;
double scalingFactor;
vector<int> *rejectLevels;
vector<double> *levelWeights;
Mat mask;
Mutex* mtx;
};
struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } };
@@ -1031,22 +1040,23 @@ bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Siz
currentMask=maskGenerator->generateMask(image);
}
ConcurrentRectVector concurrentCandidates;
vector<Rect> candidatesVector;
vector<int> rejectLevels;
vector<double> levelWeights;
Mutex mtx;
if( outputRejectLevels )
{
parallel_for(BlockedRange(0, stripCount), CascadeClassifierInvoker( *this, processingRectSize, stripSize, yStep, factor,
concurrentCandidates, rejectLevels, levelWeights, true, currentMask));
parallel_for_(Range(0, stripCount), CascadeClassifierInvoker( *this, processingRectSize, stripSize, yStep, factor,
candidatesVector, rejectLevels, levelWeights, true, currentMask, &mtx));
levels.insert( levels.end(), rejectLevels.begin(), rejectLevels.end() );
weights.insert( weights.end(), levelWeights.begin(), levelWeights.end() );
}
else
{
parallel_for(BlockedRange(0, stripCount), CascadeClassifierInvoker( *this, processingRectSize, stripSize, yStep, factor,
concurrentCandidates, rejectLevels, levelWeights, false, currentMask));
parallel_for_(Range(0, stripCount), CascadeClassifierInvoker( *this, processingRectSize, stripSize, yStep, factor,
candidatesVector, rejectLevels, levelWeights, false, currentMask, &mtx));
}
candidates.insert( candidates.end(), concurrentCandidates.begin(), concurrentCandidates.end() );
candidates.insert( candidates.end(), candidatesVector.begin(), candidatesVector.end() );
#if defined (LOG_CASCADE_STATISTIC)
logger.write();