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Implemented the first variant of working with masks in CascadeClassifier. Probably, will be rewritten soon.

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This commit is contained in:
Leonid Beynenson
2011-09-28 21:14:20 +00:00
parent 4d3b1a4a02
commit 87a21016d8
3 changed files with 35 additions and 5 deletions
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18
modules/objdetect/src/cascadedetect.cpp
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@@ -861,9 +861,10 @@ bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>& featureEvaluator, const
struct CascadeClassifierInvoker
{
CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor,
CascadeClassifierInvoker( const Mat& _image, CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor,
ConcurrentRectVector& _vec, vector<int>& _levels, vector<double>& _weights, bool outputLevels = false )
{
image=_image;
classifier = &_cc;
processingRectSize = _sz1;
stripSize = _stripSize;
@@ -877,6 +878,10 @@ struct CascadeClassifierInvoker
void operator()(const BlockedRange& range) const
{
Ptr<FeatureEvaluator> evaluator = classifier->featureEvaluator->clone();
#ifdef HAVE_TEGRA_OPTIMIZATION
Mat currentMask=tegra::getCascadeClassifierMask(image, classifier->data.origWinSize);
#endif
Size winSize(cvRound(classifier->data.origWinSize.width * scalingFactor), cvRound(classifier->data.origWinSize.height * scalingFactor));
int y1 = range.begin() * stripSize;
@@ -885,6 +890,12 @@ struct CascadeClassifierInvoker
{
for( int x = 0; x < processingRectSize.width; x += yStep )
{
#ifdef HAVE_TEGRA_OPTIMIZATION
if ( (!currentMask.empty()) && (currentMask.at<uchar>(Point(x,y))==0)) {
continue;
}
#endif
double gypWeight;
int result = classifier->runAt(evaluator, Point(x, y), gypWeight);
if( rejectLevels )
@@ -907,6 +918,7 @@ struct CascadeClassifierInvoker
}
}
Mat image;
CascadeClassifier* classifier;
ConcurrentRectVector* rectangles;
Size processingRectSize;
@@ -930,14 +942,14 @@ bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Siz
vector<double> levelWeights;
if( outputRejectLevels )
{
parallel_for(BlockedRange(0, stripCount), CascadeClassifierInvoker( *this, processingRectSize, stripSize, yStep, factor,
parallel_for(BlockedRange(0, stripCount), CascadeClassifierInvoker( image, *this, processingRectSize, stripSize, yStep, factor,
concurrentCandidates, rejectLevels, levelWeights, true));
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,
parallel_for(BlockedRange(0, stripCount), CascadeClassifierInvoker( image, *this, processingRectSize, stripSize, yStep, factor,
concurrentCandidates, rejectLevels, levelWeights, false));
}
candidates.insert( candidates.end(), concurrentCandidates.begin(), concurrentCandidates.end() );