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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/perf/perf_cascadeclassifier.cpp
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@ -1,4 +1,5 @@
#include "perf_precomp.hpp"
#include <opencv2/imgproc/imgproc.hpp>
using namespace std;
using namespace cv;
@ -8,7 +9,18 @@ typedef std::tr1::tuple<std::string, int> ImageName_MinSize_t;
typedef perf::TestBaseWithParam<ImageName_MinSize_t> ImageName_MinSize;
PERF_TEST_P( ImageName_MinSize, CascadeClassifierLBPFrontalFace,
testing::Combine(testing::Values( std::string("cv/shared/lena.jpg")), testing::Values(24, 30, 40, 50, 60, 70, 80, 90) ) )
testing::Combine(testing::Values( std::string("cv/shared/lena.jpg"),
std::string("cv/shared/1_itseez-0000247.jpg"),
std::string("cv/shared/1_itseez-0000289.jpg"),
std::string("cv/shared/1_itseez-0000492.jpg"),
std::string("cv/shared/1_itseez-0000573.jpg"),
std::string("cv/shared/1_itseez-0000803.jpg"),
std::string("cv/shared/1_itseez-0000892.jpg"),
std::string("cv/shared/1_itseez-0000984.jpg"),
std::string("cv/shared/1_itseez-0001238.jpg"),
std::string("cv/shared/1_itseez-0001438.jpg"),
std::string("cv/shared/1_itseez-0002524.jpg")),
testing::Values(24, 30, 40, 50, 60, 70, 80, 90) ) )
{
const string filename = std::tr1::get<0>(GetParam());
int min_size = std::tr1::get<1>(GetParam());
@ -18,12 +30,13 @@ PERF_TEST_P( ImageName_MinSize, CascadeClassifierLBPFrontalFace,
if (cc.empty())
FAIL() << "Can't load cascade file";
Mat img=imread(getDataPath(filename));
Mat img=imread(getDataPath(filename), 0);
if (img.empty())
FAIL() << "Can't load source image";
vector<Rect> res;
declare.in(img);//.out(res)
while(next())
@ -31,6 +44,7 @@ PERF_TEST_P( ImageName_MinSize, CascadeClassifierLBPFrontalFace,
res.clear();
startTimer();
equalizeHist(img, img);
cc.detectMultiScale(img, res, 1.1, 3, 0, minSize);
stopTimer();
}

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() );

4
modules/objdetect/src/precomp.hpp
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@ -60,4 +60,8 @@
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#ifdef HAVE_TEGRA_OPTIMIZATION
#include "opencv2/objdetect/objdetect_tegra.hpp"
#endif
#endif