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Added ROC-curve calculating to the cascade detection algorithm

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This commit is contained in:
Alexey Kazakov 2011-04-22 10:03:05 +00:00
parent 06070dfcf6
commit e9aa6fa012
11 changed files with 160 additions and 617 deletions
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36
modules/highgui/src/grfmt_tiff.cpp
View File

@ -301,11 +301,6 @@ TiffEncoder::~TiffEncoder()
{
}
bool TiffEncoder::isFormatSupported( int depth ) const
{
return depth == CV_8U || depth == CV_16U;
}
ImageEncoder TiffEncoder::newEncoder() const
{
return new TiffEncoder;
@ -326,13 +321,7 @@ bool TiffEncoder::write( const Mat& img, const vector<int>& )
{
int channels = img.channels();
int width = img.cols, height = img.rows;
int depth = img.depth();
if (depth != CV_8U && depth != CV_16U)
return false;
int bytesPerChannel = depth == CV_8U ? 1 : 2;
int fileStep = width * channels * bytesPerChannel;
int fileStep = width*channels;
WLByteStream strm;
if( m_buf )
@ -367,7 +356,7 @@ bool TiffEncoder::write( const Mat& img, const vector<int>& )
uchar* buffer = _buffer;
int stripOffsetsOffset = 0;
int stripCountsOffset = 0;
int bitsPerSample = 8 * bytesPerChannel;
int bitsPerSample = 8; // TODO support 16 bit
int y = 0;
strm.putBytes( fmtSignTiffII, 4 );
@ -387,15 +376,9 @@ bool TiffEncoder::write( const Mat& img, const vector<int>& )
for( ; y < limit; y++ )
{
if( channels == 3 )
if (depth == CV_8U)
icvCvt_BGR2RGB_8u_C3R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
else
icvCvt_BGR2RGB_16u_C3R( (const ushort*)(img.data + img.step*y), 0, (ushort*)buffer, 0, cvSize(width,1) );
icvCvt_BGR2RGB_8u_C3R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
else if( channels == 4 )
if (depth == CV_8U)
icvCvt_BGRA2RGBA_8u_C4R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
else
icvCvt_BGRA2RGBA_16u_C4R( (const ushort*)(img.data + img.step*y), 0, (ushort*)buffer, 0, cvSize(width,1) );
icvCvt_BGRA2RGBA_8u_C4R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
strm.putBytes( channels > 1 ? buffer : img.data + img.step*y, fileStep );
}
@ -433,13 +416,12 @@ bool TiffEncoder::write( const Mat& img, const vector<int>& )
if( channels > 1 )
{
int bitsPerSamplePos = strm.getPos();
strm.putWord(bitsPerSample);
strm.putWord(bitsPerSample);
strm.putWord(bitsPerSample);
bitsPerSample = strm.getPos();
strm.putWord(8);
strm.putWord(8);
strm.putWord(8);
if( channels == 4 )
strm.putWord(bitsPerSample);
bitsPerSample = bitsPerSamplePos;
strm.putWord(8);
}
directoryOffset = strm.getPos();

2
modules/highgui/src/grfmt_tiff.hpp
View File

@ -118,8 +118,6 @@ public:
TiffEncoder();
virtual ~TiffEncoder();
bool isFormatSupported( int depth ) const;
bool write( const Mat& img, const vector<int>& params );
ImageEncoder newEncoder() const;

19
modules/highgui/src/utils.cpp
View File

@ -192,25 +192,6 @@ void icvCvt_BGRA2RGBA_8u_C4R( const uchar* bgra, int bgra_step,
}
}
void icvCvt_BGRA2RGBA_16u_C4R( const ushort* bgra, int bgra_step,
ushort* rgba, int rgba_step, CvSize size )
{
int i;
for( ; size.height--; )
{
for( i = 0; i < size.width; i++, bgra += 4, rgba += 4 )
{
ushort t0 = bgra[0], t1 = bgra[1];
ushort t2 = bgra[2], t3 = bgra[3];
rgba[0] = t2; rgba[1] = t1;
rgba[2] = t0; rgba[3] = t3;
}
bgra += bgra_step/sizeof(bgra[0]) - size.width*4;
rgba += rgba_step/sizeof(rgba[0]) - size.width*4;
}
}
void icvCvt_BGR2RGB_8u_C3R( const uchar* bgr, int bgr_step,
uchar* rgb, int rgb_step, CvSize size )

4
modules/highgui/src/utils.hpp
View File

@ -88,10 +88,6 @@ void icvCvt_BGRA2RGBA_8u_C4R( const uchar* bgra, int bgra_step,
uchar* rgba, int rgba_step, CvSize size );
#define icvCvt_RGBA2BGRA_8u_C4R icvCvt_BGRA2RGBA_8u_C4R
void icvCvt_BGRA2RGBA_16u_C4R( const ushort* bgra, int bgra_step,
ushort* rgba, int rgba_step, CvSize size );
#define icvCvt_RGBA2BGRA_16u_C4R icvCvt_BGRA2RGBA_16u_C4R
void icvCvt_BGR5552Gray_8u_C2C1R( const uchar* bgr555, int bgr555_step,
uchar* gray, int gray_step, CvSize size );
void icvCvt_BGR5652Gray_8u_C2C1R( const uchar* bgr565, int bgr565_step,

59
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
View File

@ -125,9 +125,17 @@ CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );
#define CV_HAAR_FIND_BIGGEST_OBJECT 4
#define CV_HAAR_DO_ROUGH_SEARCH 8
CVAPI(CvSeq*) cvHaarDetectObjectsForROC( const CvArr* image,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
std::vector<int>& rejectLevels, std::vector<double>& levelWeightds,
double scale_factor CV_DEFAULT(1.1),
int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)),
bool outputRejectLevels = false );
CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image,
CvHaarClassifierCascade* cascade,
CvMemStorage* storage, double scale_factor CV_DEFAULT(1.1),
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
double scale_factor CV_DEFAULT(1.1),
int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)));
@ -275,7 +283,8 @@ namespace cv
CV_EXPORTS_W void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps=0.2);
CV_EXPORTS_W void groupRectangles(vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<double>& resultWeights, int groupThreshold = 2, double eps=0.2);
CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<int>& rejectLevels,
vector<double>& levelWeights, int groupThreshold, double eps=0.2);
CV_EXPORTS void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights, vector<double>& foundScales,
double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
@ -352,11 +361,12 @@ public:
CV_WRAP virtual void detectMultiScale( const Mat& image,
CV_OUT vector<Rect>& objects,
vector<int>& rejectLevels,
vector<double>& levelWeights,
double scaleFactor=1.1,
int minNeighbors=3, int flags=0,
Size minSize=Size(),
Size maxSize=Size(),
bool outputRejectLevels = false );
bool outputRejectLevels=false );
bool isOldFormatCascade() const;
@ -370,7 +380,7 @@ protected:
virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
int stripSize, int yStep, double factor, vector<Rect>& candidates,
vector<int>& rejectLevels, bool outputRejectLevels = false);
vector<int>& rejectLevels, vector<double>& levelWeights, bool outputRejectLevels=false);
protected:
enum { BOOST = 0 };
@ -380,19 +390,19 @@ protected:
friend struct CascadeClassifierInvoker;
template<class FEval>
friend int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
friend int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
template<class FEval>
friend int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
friend int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
template<class FEval>
friend int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
friend int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
template<class FEval>
friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
bool setImage( Ptr<FeatureEvaluator>&, const Mat& );
virtual int runAt( Ptr<FeatureEvaluator>&, Point );
virtual int runAt( Ptr<FeatureEvaluator>&, Point, double& weight );
class Data
{
@ -436,35 +446,6 @@ protected:
Data data;
Ptr<FeatureEvaluator> featureEvaluator;
Ptr<CvHaarClassifierCascade> oldCascade;
//!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
public:
int getNumStages()
{
int numStages;
if( !isOldFormatCascade() )
{
numStages = data.stages.size();
}
else
{
numStages = this->oldCascade->count;
}
return numStages;
}
void setNumStages(int stageCount)
{
if( !isOldFormatCascade() )
{
if( stageCount )
data.stages.resize(stageCount);
}
else
if( stageCount )
this->oldCascade->count = stageCount;
}
//!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
};
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////

109
modules/objdetect/src/cascadedetect.cpp
View File

@ -63,7 +63,7 @@ public:
};
static void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* foundWeights)
static void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights)
{
if( groupThreshold <= 0 || rectList.empty() )
{
@ -82,7 +82,8 @@ static void groupRectangles(vector<Rect>& rectList, int groupThreshold, double e
vector<Rect> rrects(nclasses);
vector<int> rweights(nclasses, 0);
vector<double> outWeights(nclasses, 0.0);
vector<int> rejectLevels(nclasses, 0);
vector<double> rejectWeights(nclasses, DBL_MIN);
int i, j, nlabels = (int)labels.size();
for( i = 0; i < nlabels; i++ )
{
@ -93,12 +94,18 @@ static void groupRectangles(vector<Rect>& rectList, int groupThreshold, double e
rrects[cls].height += rectList[i].height;
rweights[cls]++;
}
if ( foundWeights && !foundWeights->empty() )
if ( levelWeights && weights && !weights->empty() && !levelWeights->empty() )
{
for( i = 0; i < nlabels; i++ )
{
int cls = labels[i];
outWeights[cls] = outWeights[cls] + (*foundWeights)[i];
if( (*weights)[i] > rejectLevels[cls] )
{
rejectLevels[cls] = (*weights)[i];
rejectWeights[cls] = (*levelWeights)[i];
}
else if( ( (*weights)[i] == rejectLevels[cls] ) && ( (*levelWeights)[i] > rejectWeights[cls] ) )
rejectWeights[cls] = (*levelWeights)[i];
}
}
@ -115,14 +122,14 @@ static void groupRectangles(vector<Rect>& rectList, int groupThreshold, double e
rectList.clear();
if( weights )
weights->clear();
if( foundWeights )
foundWeights->clear();
if( levelWeights )
levelWeights->clear();
for( i = 0; i < nclasses; i++ )
{
Rect r1 = rrects[i];
int n1 = rweights[i];
double w1 = outWeights[i];
int n1 = levelWeights ? rejectLevels[i] : rweights[i];
double w1 = rejectWeights[i];
if( n1 <= groupThreshold )
continue;
// filter out small face rectangles inside large rectangles
@ -151,8 +158,8 @@ static void groupRectangles(vector<Rect>& rectList, int groupThreshold, double e
rectList.push_back(r1);
if( weights )
weights->push_back(n1);
if( foundWeights )
foundWeights->push_back(w1);
if( levelWeights )
levelWeights->push_back(w1);
}
}
}
@ -211,12 +218,12 @@ void groupRectangles(vector<Rect>& rectList, vector<int>& weights, int groupThre
{
groupRectangles(rectList, groupThreshold, eps, &weights, 0);
}
void groupRectangles(vector<Rect>& rectList, vector<double>& foundWeights, int groupThreshold, double eps)
//used for cascade detection algorithm for ROC-curve calculating
void groupRectangles(vector<Rect>& rectList, vector<int>& rejectLevels, vector<double>& levelWeights, int groupThreshold, double eps)
{
groupRectangles(rectList, groupThreshold, eps, 0, &foundWeights);
groupRectangles(rectList, groupThreshold, eps, &rejectLevels, &levelWeights);
}
//can be used for HOG detection algorithm only
void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights,
vector<double>& foundScales, double detectThreshold, Size winDetSize)
{
@ -706,7 +713,7 @@ bool CascadeClassifier::load(const string& filename)
}
template<class FEval>
inline int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator )
inline int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
int nstages = (int)cascade.data.stages.size();
int nodeOfs = 0, leafOfs = 0;
@ -720,7 +727,7 @@ inline int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_f
{
CascadeClassifier::Data::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
double sum = 0;
sum = 0;
for( wi = 0; wi < ntrees; wi++ )
{
@ -745,7 +752,7 @@ inline int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_f
}
template<class FEval>
inline int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator )
inline int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
int nstages = (int)cascade.data.stages.size();
int nodeOfs = 0, leafOfs = 0;
@ -761,7 +768,7 @@ inline int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator>
{
CascadeClassifier::Data::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
double sum = 0;
sum = 0;
for( wi = 0; wi < ntrees; wi++ )
{
@ -786,7 +793,7 @@ inline int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator>
}
template<class FEval>
inline int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator )
inline int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
int nodeOfs = 0, leafOfs = 0;
FEval& featureEvaluator = (FEval&)*_featureEvaluator;
@ -798,7 +805,7 @@ inline int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator
for( int stageIdx = 0; stageIdx < nstages; stageIdx++ )
{
CascadeClassifier::Data::Stage& stage = cascadeStages[stageIdx];
double sum = 0.0;
sum = 0.0;
int ntrees = stage.ntrees;
for( int i = 0; i < ntrees; i++, nodeOfs++, leafOfs+= 2 )
@ -816,7 +823,7 @@ inline int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator
}
template<class FEval>
inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator )
inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
int nstages = (int)cascade.data.stages.size();
int nodeOfs = 0, leafOfs = 0;
@ -831,7 +838,7 @@ inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvalu
{
CascadeClassifier::Data::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
double sum = 0;
sum = 0;
for( wi = 0; wi < ntrees; wi++ )
{
@ -848,7 +855,7 @@ inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvalu
return 1;
}
int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& featureEvaluator, Point pt )
int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& featureEvaluator, Point pt, double& weight )
{
CV_Assert( oldCascade.empty() );
@ -857,11 +864,11 @@ int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& featureEvaluator, Point pt
return !featureEvaluator->setWindow(pt) ? -1 :
data.isStumpBased ? ( data.featureType == FeatureEvaluator::HAAR ?
predictOrderedStump<HaarEvaluator>( *this, featureEvaluator ) :
predictCategoricalStump<LBPEvaluator>( *this, featureEvaluator ) ) :
predictOrderedStump<HaarEvaluator>( *this, featureEvaluator, weight ) :
predictCategoricalStump<LBPEvaluator>( *this, featureEvaluator, weight ) ) :
( data.featureType == FeatureEvaluator::HAAR ?
predictOrdered<HaarEvaluator>( *this, featureEvaluator ) :
predictCategorical<LBPEvaluator>( *this, featureEvaluator ) );
predictOrdered<HaarEvaluator>( *this, featureEvaluator, weight ) :
predictCategorical<LBPEvaluator>( *this, featureEvaluator, weight ) );
}
bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>& featureEvaluator, const Mat& image )
@ -872,7 +879,7 @@ bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>& featureEvaluator, const
struct CascadeClassifierInvoker
{
CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor,
ConcurrentRectVector& _vec, vector<int>& _levels, bool outputLevels = false )
ConcurrentRectVector& _vec, vector<int>& _levels, vector<double>& _weights, bool outputLevels = false )
{
classifier = &_cc;
processingRectSize = _sz1;
@ -881,6 +888,7 @@ struct CascadeClassifierInvoker
scalingFactor = _factor;
rectangles = &_vec;
rejectLevels = outputLevels ? &_levels : 0;
levelWeights = outputLevels ? &_weights : 0;
}
void operator()(const BlockedRange& range) const
@ -894,15 +902,17 @@ struct CascadeClassifierInvoker
{
for( int x = 0; x < processingRectSize.width; x += yStep )
{
int result = classifier->runAt(evaluator, Point(x, y));
double gypWeight;
int result = classifier->runAt(evaluator, Point(x, y), gypWeight);
if( rejectLevels )
{
if( result == 1 )
result = -1*classifier->data.stages.size();
if( classifier->data.stages.size() + result < 6 )
if( classifier->data.stages.size() + result < 4 )
{
rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor), winSize.width, winSize.height));
rejectLevels->push_back(-result);
levelWeights->push_back(gypWeight);
}
}
else if( result > 0 )
@ -920,48 +930,44 @@ struct CascadeClassifierInvoker
int stripSize, yStep;
double scalingFactor;
vector<int> *rejectLevels;
vector<double> *levelWeights;
};
struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } };
bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
int stripSize, int yStep, double factor, vector<Rect>& candidates,
vector<int>& levels, bool outputRejectLevels )
vector<int>& levels, vector<double>& weights, bool outputRejectLevels )
{
if( !featureEvaluator->setImage( image, data.origWinSize ) )
return false;
ConcurrentRectVector concurrentCandidates;
vector<int> rejectLevels;
vector<double> levelWeights;
if( outputRejectLevels )
{
parallel_for(BlockedRange(0, stripCount), CascadeClassifierInvoker( *this, processingRectSize, stripSize, yStep, factor,
concurrentCandidates, rejectLevels, true));
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,
concurrentCandidates, rejectLevels, false));
concurrentCandidates, rejectLevels, levelWeights, false));
}
candidates.insert( candidates.end(), concurrentCandidates.begin(), concurrentCandidates.end() );
return true;
}
//bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
// int stripSize, int yStep, double factor, vector<Rect>& candidates )
//{
// vector<int> fakeLevels;
// return detectSingleScale( image, stripCount, processingRectSize,
// stripSize, yStep, factor, candidates, fakeLevels, false );
//}
bool CascadeClassifier::isOldFormatCascade() const
{
return !oldCascade.empty();
}
int CascadeClassifier::getFeatureType() const
{
return featureEvaluator->getFeatureType();
@ -979,6 +985,7 @@ bool CascadeClassifier::setImage(const Mat& image)
void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects,
vector<int>& rejectLevels,
vector<double>& levelWeights,
double scaleFactor, int minNeighbors,
int flags, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels )
@ -994,8 +1001,8 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
{
MemStorage storage(cvCreateMemStorage(0));
CvMat _image = image;
CvSeq* _objects = cvHaarDetectObjects( &_image, oldCascade, storage, scaleFactor,
minNeighbors, flags, minObjectSize );
CvSeq* _objects = cvHaarDetectObjectsForROC( &_image, oldCascade, storage, rejectLevels, levelWeights, scaleFactor,
minNeighbors, flags, minObjectSize, maxObjectSize, outputRejectLevels );
vector<CvAvgComp> vecAvgComp;
Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
objects.resize(vecAvgComp.size());
@ -1051,15 +1058,22 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
#endif
if( !detectSingleScale( scaledImage, stripCount, processingRectSize, stripSize, yStep, factor, candidates,
rejectLevels, outputRejectLevels ) )
rejectLevels, levelWeights, outputRejectLevels ) )
break;
}
objects.resize(candidates.size());
std::copy(candidates.begin(), candidates.end(), objects.begin());
groupRectangles( objects, rejectLevels, minNeighbors, GROUP_EPS );
if( outputRejectLevels )
{
groupRectangles( objects, rejectLevels, levelWeights, minNeighbors, GROUP_EPS );
}
else
{
groupRectangles( objects, minNeighbors, GROUP_EPS );
}
}
void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects,
@ -1067,7 +1081,8 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
int flags, Size minObjectSize, Size maxObjectSize)
{
vector<int> fakeLevels;
detectMultiScale( image, objects, fakeLevels, scaleFactor,
vector<double> fakeWeights;
detectMultiScale( image, objects, fakeLevels, fakeWeights, scaleFactor,
minNeighbors, flags, minObjectSize, maxObjectSize, false );
}

434
modules/objdetect/src/datamatrix.cpp
View File

@ -1,434 +0,0 @@
#if CV_SSE2
#include <xmmintrin.h>
#endif
#include "precomp.hpp"
#include <deque>
using namespace std;
#undef NDEBUG
#include <assert.h>
class Sampler {
public:
CvMat *im;
CvPoint o;
CvPoint c, cc;
CvMat *perim;
CvPoint fcoord(float fx, float fy);
CvPoint coord(int ix, int iy);
Sampler() {}
Sampler(CvMat *_im, CvPoint _o, CvPoint _c, CvPoint _cc);
uint8 getpixel(int ix, int iy);
int isinside(int x, int y);
int overlap(Sampler &other);
int hasbars();
void timing();
CvMat *extract();
};
class code { // used in this file only
public:
char msg[4];
CvMat *original;
Sampler sa;
};
#include "followblk.h"
#define dethresh 0.92f
#define eincO (2 * dethresh) // e increment orthogonal
#define eincD (1.414f * dethresh) // e increment diagonal
static const float eincs[] = {
eincO, eincD,
eincO, eincD,
eincO, eincD,
eincO, eincD,
999 };
#define Ki(x) _mm_set_epi32((x),(x),(x),(x))
#define Kf(x) _mm_set_ps((x),(x),(x),(x))
static const int CV_DECL_ALIGNED(16) absmask[] = {0x7fffffff, 0x7fffffff, 0x7fffffff, 0x7fffffff};
#define _mm_abs_ps(x) _mm_and_ps((x), *(const __m128*)absmask)
static void writexy(CvMat *m, int r, CvPoint p)
{
int *pdst = (int*)cvPtr2D(m, r, 0);
pdst[0] = p.x;
pdst[1] = p.y;
}
Sampler::Sampler(CvMat *_im, CvPoint _o, CvPoint _c, CvPoint _cc)
{
im = _im;
o = _o;
c = _c;
cc = _cc;
perim = cvCreateMat(4, 1, CV_32SC2);
writexy(perim, 0, fcoord(-.2f,-.2f));
writexy(perim, 1, fcoord(-.2f,1.2f));
writexy(perim, 2, fcoord(1.2f,1.2f));
writexy(perim, 3, fcoord(1.2f,-.2f));
// printf("Sampler %d,%d %d,%d %d,%d\n", o.x, o.y, c.x, c.y, cc.x, cc.y);
}
CvPoint Sampler::fcoord(float fx, float fy)
{
CvPoint r;
r.x = (int)(o.x + fx * (cc.x - o.x) + fy * (c.x - o.x));
r.y = (int)(o.y + fx * (cc.y - o.y) + fy * (c.y - o.y));
return r;
}
CvPoint Sampler::coord(int ix, int iy)
{
return fcoord(0.05f + 0.1f * ix, 0.05f + 0.1f * iy);
}
uint8 Sampler::getpixel(int ix, int iy)
{
CvPoint pt = coord(ix, iy);
// printf("%d,%d\n", pt.x, pt.y);
return *cvPtr2D(im, pt.y, pt.x);
}
int Sampler::isinside(int x, int y)
{
CvPoint2D32f fp;
fp.x = (float)x;
fp.y = (float)y;
return cvPointPolygonTest(perim, fp, 0) < 0;
}
int Sampler::overlap(Sampler &other)
{
for (int i = 0; i < 4; i++) {
CvScalar p;
p = cvGet2D(other.perim, i, 0);
if (isinside((int)p.val[0], (int)p.val[1]))
return 1;
p = cvGet2D(perim, i, 0);
if (other.isinside((int)p.val[0], (int)p.val[1]))
return 1;
}
return 0;
}
int Sampler::hasbars()
{
return getpixel(9, 1) > getpixel(9, 0);
}
void Sampler::timing()
{
uint8 dark = getpixel(9, 0);
for (int i = 1; i < 3; i += 2) {
uint8 light = getpixel(9, i);
// if (light <= dark)
// goto endo;
dark = getpixel(9, i + 1);
// if (up <= down)
// goto endo;
}
}
CvMat *Sampler::extract()
{
// return a 10x10 CvMat for the current contents, 0 is black, 255 is white
// Sampler has (0,0) at bottom left, so invert Y
CvMat *r = cvCreateMat(10, 10, CV_8UC1);
for (int x = 0; x < 10; x++)
for (int y = 0; y < 10; y++)
*cvPtr2D(r, 9 - y, x) = (getpixel(x, y) < 128) ? 0 : 255;
return r;
}
static void apron(CvMat *v)
{
int r = v->rows;
int c = v->cols;
memset(cvPtr2D(v, 0, 0), 0x22, c);
memset(cvPtr2D(v, 1, 0), 0x22, c);
memset(cvPtr2D(v, r - 2, 0), 0x22, c);
memset(cvPtr2D(v, r - 1, 0), 0x22, c);
int y;
for (y = 2; y < r - 2; y++) {
uchar *lp = cvPtr2D(v, y, 0);
lp[0] = 0x22;
lp[1] = 0x22;
lp[c-2] = 0x22;
lp[c-1] = 0x22;
}
}
static void cfollow(CvMat *src, CvMat *dst)
{
int sx, sy;
uint8 *vpd = cvPtr2D(src, 0, 0);
for (sy = 0; sy < src->rows; sy++) {
short *wr = (short*)cvPtr2D(dst, sy, 0);
for (sx = 0; sx < src->cols; sx++) {
int x = sx;
int y = sy;
float e = 0;
int ontrack = true;
int dir;
while (ontrack) {
dir = vpd[y * src->step + x];
int xd = ((dir & 0xf) - 2);
int yd = ((dir >> 4) - 2);
e += (dir == 0x22) ? 999 : ((dir & 1) ? eincD : eincO);
x += xd;
y += yd;
if (e > 10.) {
float d = (float)(((x - sx) * (x - sx)) + ((y - sy) * (y - sy)));
ontrack = d > (e * e);
}
}
if ((24 <= e) && (e < 999)) {
// printf("sx=%d, sy=%d, x=%d, y=%d\n", sx, sy, x, y);
*wr++ = (short)(x - sx);
*wr++ = (short)(y - sy);
} else {
*wr++ = 0;
*wr++ = 0;
}
}
}
}
static uint8 gf256mul(uint8 a, uint8 b)
{
return Alog[(Log[a] + Log[b]) % 255];
}
static int decode(Sampler &sa, code &cc)
{
uint8 binary[8] = {0,0,0,0,0,0,0,0};
uint8 b = 0;
for (int i = 0; i < 64; i++) {
b = (b << 1) + (sa.getpixel(pickup[i].x, pickup[i].y) <= 128);
if ((i & 7) == 7) {
binary[i >> 3] = b;
b = 0;
}
}
// Compute the 5 RS codewords for the 3 datawords
uint8 c[5] = {0,0,0,0,0};
{
int i, j;
uint8 a[5] = {228, 48, 15, 111, 62};
int k = 5;
for (i = 0; i < 3; i++) {
uint8 t = binary[i] ^ c[4];
for (j = k - 1; j != -1; j--) {
if (t == 0)
c[j] = 0;
else
c[j] = gf256mul(t, a[j]);
if (j > 0)
c[j] = c[j - 1] ^ c[j];
}
}
}
if ((c[4] == binary[3]) &&
(c[3] == binary[4]) &&
(c[2] == binary[5]) &&
(c[1] == binary[6]) &&
(c[0] == binary[7])) {
uint8 x = 0xff & (binary[0] - 1);
uint8 y = 0xff & (binary[1] - 1);
uint8 z = 0xff & (binary[2] - 1);
cc.msg[0] = x;
cc.msg[1] = y;
cc.msg[2] = z;
cc.msg[3] = 0;
cc.sa = sa;
cc.original = sa.extract();
return 1;
} else {
return 0;
}
}
static deque<CvPoint> trailto(CvMat *v, int x, int y, CvMat *terminal)
{
CvPoint np;
/* Return the last 10th of the trail of points following v from (x,y)
* to terminal
*/
int ex = x + ((short*)cvPtr2D(terminal, y, x))[0];
int ey = y + ((short*)cvPtr2D(terminal, y, x))[1];
deque<CvPoint> r;
while ((x != ex) || (y != ey)) {
np.x = x;
np.y = y;
r.push_back(np);
int dir = *cvPtr2D(v, y, x);
int xd = ((dir & 0xf) - 2);
int yd = ((dir >> 4) - 2);
x += xd;
y += yd;
}
int l = r.size() * 9 / 10;
while (l--)
r.pop_front();
return r;
}
deque <DataMatrixCode> cvFindDataMatrix(CvMat *im)
{
#if CV_SSE2
int r = im->rows;
int c = im->cols;
#define SAMESIZE(nm, ty) CvMat *nm = cvCreateMat(r, c, ty);
SAMESIZE(thresh, CV_8UC1)
SAMESIZE(vecpic, CV_8UC1)
SAMESIZE(vc, CV_8UC1)
SAMESIZE(vcc, CV_8UC1)
SAMESIZE(cxy, CV_16SC2)
SAMESIZE(ccxy, CV_16SC2)
cvAdaptiveThreshold(im, thresh, 255.0, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, 13);
{
int x, y;
int sstride = thresh->step;
int sw = thresh->cols; // source width
for (y = 2; y < thresh->rows - 2; y++) {
uint8 *ps = cvPtr2D(thresh, y, 0);
uint8 *pd = cvPtr2D(vecpic, y, 0);
uint8 *pvc = cvPtr2D(vc, y, 0);
uint8 *pvcc = cvPtr2D(vcc, y, 0);
for (x = 0; x < sw; x++) {
uint8 v =
(0x01 & ps[-2 * sstride]) |
(0x02 & ps[-sstride + 1]) |
(0x04 & ps[2]) |
(0x08 & ps[sstride + 1]) |
(0x10 & ps[2 * sstride]) |
(0x20 & ps[sstride - 1]) |
(0x40 & ps[-2]) |
(0x80 & ps[-sstride -1]);
*pd++ = v;
*pvc++ = cblk[v];
*pvcc++ = ccblk[v];
ps++;
}
}
apron(vc);
apron(vcc);
}
cfollow(vc, cxy);
cfollow(vcc, ccxy);
deque <CvPoint> candidates;
{
int x, y;
int r = cxy->rows;
int c = cxy->cols;
for (y = 0; y < r; y++) {
const short *cd = (const short*)cvPtr2D(cxy, y, 0);
const short *ccd = (const short*)cvPtr2D(ccxy, y, 0);
for (x = 0; x < c; x += 4, cd += 8, ccd += 8) {
__m128i v = _mm_loadu_si128((const __m128i*)cd);
__m128 cyxyxA = _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(v, v), 16));
__m128 cyxyxB = _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpackhi_epi16(v, v), 16));
__m128 cx = _mm_shuffle_ps(cyxyxA, cyxyxB, _MM_SHUFFLE(0, 2, 0, 2));
__m128 cy = _mm_shuffle_ps(cyxyxA, cyxyxB, _MM_SHUFFLE(1, 3, 1, 3));
__m128 cmag = _mm_sqrt_ps(_mm_add_ps(_mm_mul_ps(cx, cx), _mm_mul_ps(cy, cy)));
__m128 crmag = _mm_rcp_ps(cmag);
__m128 ncx = _mm_mul_ps(cx, crmag);
__m128 ncy = _mm_mul_ps(cy, crmag);
v = _mm_loadu_si128((const __m128i*)ccd);
__m128 ccyxyxA = _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(v, v), 16));
__m128 ccyxyxB = _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpackhi_epi16(v, v), 16));
__m128 ccx = _mm_shuffle_ps(ccyxyxA, ccyxyxB, _MM_SHUFFLE(0, 2, 0, 2));
__m128 ccy = _mm_shuffle_ps(ccyxyxA, ccyxyxB, _MM_SHUFFLE(1, 3, 1, 3));
__m128 ccmag = _mm_sqrt_ps(_mm_add_ps(_mm_mul_ps(ccx, ccx), _mm_mul_ps(ccy, ccy)));
__m128 ccrmag = _mm_rcp_ps(ccmag);
__m128 nccx = _mm_mul_ps(ccx, ccrmag);
__m128 nccy = _mm_mul_ps(ccy, ccrmag);
__m128 dot = _mm_mul_ps(_mm_mul_ps(ncx, nccx), _mm_mul_ps(ncy, nccy));
// iscand = (cmag > 30) & (ccmag > 30) & (numpy.minimum(cmag, ccmag) * 1.1 > numpy.maximum(cmag, ccmag)) & (abs(dot) < 0.25)
__m128 iscand = _mm_and_ps(_mm_cmpgt_ps(cmag, Kf(30)), _mm_cmpgt_ps(ccmag, Kf(30)));
iscand = _mm_and_ps(iscand, _mm_cmpgt_ps(_mm_mul_ps(_mm_min_ps(cmag, ccmag), Kf(1.1f)), _mm_max_ps(cmag, ccmag)));
iscand = _mm_and_ps(iscand, _mm_cmplt_ps(_mm_abs_ps(dot), Kf(0.25f)));
unsigned int CV_DECL_ALIGNED(16) result[4];
_mm_store_ps((float*)result, iscand);
int ix;
CvPoint np;
for (ix = 0; ix < 4; ix++) {
if (result[ix]) {
np.x = x + ix;
np.y = y;
candidates.push_back(np);
}
}
}
}
}
deque <code> codes;
size_t i, j, k;
while (!candidates.empty()) {
CvPoint o = candidates.front();
candidates.pop_front();
deque<CvPoint> ptc = trailto(vc, o.x, o.y, cxy);
deque<CvPoint> ptcc = trailto(vcc, o.x, o.y, ccxy);
for (j = 0; j < ptc.size(); j++) {
for (k = 0; k < ptcc.size(); k++) {
code cc;
Sampler sa(im, o, ptc[j], ptcc[k]);
for (i = 0; i < codes.size(); i++) {
if (sa.overlap(codes[i].sa))
goto endo;
}
if (codes.size() > 0) {
printf("searching for more\n");
}
if (decode(sa, cc)) {
codes.push_back(cc);
goto endo;
}
}
}
endo: ; // end search for this o
}
cvFree(&thresh);
cvFree(&vecpic);
cvFree(&vc);
cvFree(&vcc);
cvFree(&cxy);
cvFree(&ccxy);
deque <DataMatrixCode> rc;
for (i = 0; i < codes.size(); i++) {
DataMatrixCode cc;
strcpy(cc.msg, codes[i].msg);
cc.original = codes[i].original;
cc.corners = codes[i].sa.perim;
rc.push_back(cc);
}
return rc;
#else
deque <DataMatrixCode> rc;
return rc;
#endif
}

5
modules/objdetect/src/followblk.h
View File

@ -1,5 +0,0 @@
unsigned char cblk[256] = { 34,19,36,36,51,19,51,51,66,19,36,36,66,19,66,66,49,19,36,36,51,19,51,51,49,19,36,36,49,19,49,49,32,19,36,36,51,19,51,51,66,19,36,36,66,19,66,66,32,19,36,36,51,19,51,51,32,19,36,36,32,19,32,32,17,19,36,36,51,19,51,51,66,19,36,36,66,19,66,66,49,19,36,36,51,19,51,51,49,19,36,36,49,19,49,49,17,19,36,36,51,19,51,51,66,19,36,36,66,19,66,66,17,19,36,36,51,19,51,51,17,19,36,36,17,19,17,17,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,66,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,49,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,66,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,32,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,66,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,49,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,66,2,19,2,36,2,19,2,51,2,19,2,36,2,19,2,34 };
unsigned char ccblk[256] = { 34,17,2,17,19,19,2,17,36,36,2,36,19,19,2,17,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,17,66,66,2,66,19,19,2,66,36,36,2,36,19,19,2,66,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,17,49,49,2,49,19,19,2,49,36,36,2,36,19,19,2,49,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,49,66,66,2,66,19,19,2,66,36,36,2,36,19,19,2,66,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,17,32,32,2,32,19,19,2,32,36,36,2,36,19,19,2,32,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,32,66,66,2,66,19,19,2,66,36,36,2,36,19,19,2,66,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,32,49,49,2,49,19,19,2,49,36,36,2,36,19,19,2,49,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,49,66,66,2,66,19,19,2,66,36,36,2,36,19,19,2,66,51,51,2,51,19,19,2,51,36,36,2,36,19,19,2,34 };
static const CvPoint pickup[64] = { {7,6},{8,6},{7,5},{8,5},{1,5},{7,4},{8,4},{1,4},{1,8},{2,8},{1,7},{2,7},{3,7},{1,6},{2,6},{3,6},{3,2},{4,2},{3,1},{4,1},{5,1},{3,8},{4,8},{5,8},{6,1},{7,1},{6,8},{7,8},{8,8},{6,7},{7,7},{8,7},{4,7},{5,7},{4,6},{5,6},{6,6},{4,5},{5,5},{6,5},{2,5},{3,5},{2,4},{3,4},{4,4},{2,3},{3,3},{4,3},{8,3},{1,3},{8,2},{1,2},{2,2},{8,1},{1,1},{2,1},{5,4},{6,4},{5,3},{6,3},{7,3},{5,2},{6,2},{7,2} };
static const uint8 Alog[256] = { 1,2,4,8,16,32,64,128,45,90,180,69,138,57,114,228,229,231,227,235,251,219,155,27,54,108,216,157,23,46,92,184,93,186,89,178,73,146,9,18,36,72,144,13,26,52,104,208,141,55,110,220,149,7,14,28,56,112,224,237,247,195,171,123,246,193,175,115,230,225,239,243,203,187,91,182,65,130,41,82,164,101,202,185,95,190,81,162,105,210,137,63,126,252,213,135,35,70,140,53,106,212,133,39,78,156,21,42,84,168,125,250,217,159,19,38,76,152,29,58,116,232,253,215,131,43,86,172,117,234,249,223,147,11,22,44,88,176,77,154,25,50,100,200,189,87,174,113,226,233,255,211,139,59,118,236,245,199,163,107,214,129,47,94,188,85,170,121,242,201,191,83,166,97,194,169,127,254,209,143,51,102,204,181,71,142,49,98,196,165,103,206,177,79,158,17,34,68,136,61,122,244,197,167,99,198,161,111,222,145,15,30,60,120,240,205,183,67,134,33,66,132,37,74,148,5,10,20,40,80,160,109,218,153,31,62,124,248,221,151,3,6,12,24,48,96,192,173,119,238,241,207,179,75,150,1 };
static const uint8 Log[256] = { -255,255,1,240,2,225,241,53,3,38,226,133,242,43,54,210,4,195,39,114,227,106,134,28,243,140,44,23,55,118,211,234,5,219,196,96,40,222,115,103,228,78,107,125,135,8,29,162,244,186,141,180,45,99,24,49,56,13,119,153,212,199,235,91,6,76,220,217,197,11,97,184,41,36,223,253,116,138,104,193,229,86,79,171,108,165,126,145,136,34,9,74,30,32,163,84,245,173,187,204,142,81,181,190,46,88,100,159,25,231,50,207,57,147,14,67,120,128,154,248,213,167,200,63,236,110,92,176,7,161,77,124,221,102,218,95,198,90,12,152,98,48,185,179,42,209,37,132,224,52,254,239,117,233,139,22,105,27,194,113,230,206,87,158,80,189,172,203,109,175,166,62,127,247,146,66,137,192,35,252,10,183,75,216,31,83,33,73,164,144,85,170,246,65,174,61,188,202,205,157,143,169,82,72,182,215,191,251,47,178,89,151,101,94,160,123,26,112,232,21,51,238,208,131,58,69,148,18,15,16,68,17,121,149,129,19,155,59,249,70,214,250,168,71,201,156,64,60,237,130,111,20,93,122,177,150 };

86
modules/objdetect/src/haar.cpp
View File

@ -654,8 +654,8 @@ double icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier,
CV_IMPL int
cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
CvPoint pt, int start_stage )
cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
CvPoint pt, double& stage_sum, int start_stage )
{
int result = -1;
@ -698,7 +698,7 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
while( ptr )
{
double stage_sum = 0;
stage_sum = 0.0;
for( j = 0; j < ptr->count; j++ )
{
@ -724,7 +724,7 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
for( i = start_stage; i < cascade->count; i++ )
{
#ifndef CV_HAAR_USE_SSE
double stage_sum = 0;
stage_sum = 0.0;
#else
__m128d stage_sum = _mm_setzero_pd();
#endif
@ -796,7 +796,7 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
{
for( i = start_stage; i < cascade->count; i++ )
{
double stage_sum = 0;
stage_sum = 0.0;
for( j = 0; j < cascade->stage_classifier[i].count; j++ )
{
@ -809,10 +809,16 @@ cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
return -i;
}
}
return 1;
}
CV_IMPL int
cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
CvPoint pt, int start_stage )
{
double stage_sum;
return cvRunHaarClassifierCascadeSum(_cascade, pt, stage_sum, start_stage);
}
namespace cv
{
@ -822,7 +828,9 @@ struct HaarDetectObjects_ScaleImage_Invoker
HaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade* _cascade,
int _stripSize, double _factor,
const Mat& _sum1, const Mat& _sqsum1, Mat* _norm1,
Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec )
Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec,
std::vector<int>& _levels, std::vector<double>& _weights,
bool _outputLevels )
{
cascade = _cascade;
stripSize = _stripSize;
@ -833,6 +841,8 @@ struct HaarDetectObjects_ScaleImage_Invoker
mask1 = _mask1;
equRect = _equRect;
vec = &_vec;
rejectLevels = _outputLevels ? &_levels : 0;
levelWeights = _outputLevels ? &_weights : 0;
}
void operator()( const BlockedRange& range ) const
@ -902,9 +912,26 @@ struct HaarDetectObjects_ScaleImage_Invoker
for( y = y1; y < y2; y += ystep )
for( x = 0; x < ssz.width; x += ystep )
{
if( cvRunHaarClassifierCascade( cascade, cvPoint(x,y), 0 ) > 0 )
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
double gypWeight;
int result = cvRunHaarClassifierCascadeSum( cascade, cvPoint(x,y), gypWeight, 0 );
if( rejectLevels )
{
if( result == 1 )
result = -1*cascade->count;
if( cascade->count + result < 4 )
{
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
rejectLevels->push_back(-result);
levelWeights->push_back(gypWeight);
}
}
else
{
if( result > 0 )
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
}
}
}
@ -914,6 +941,8 @@ struct HaarDetectObjects_ScaleImage_Invoker
Mat sum1, sqsum1, *norm1, *mask1;
Rect equRect;
ConcurrentRectVector* vec;
std::vector<int>* rejectLevels;
std::vector<double>* levelWeights;
};
@ -983,10 +1012,11 @@ struct HaarDetectObjects_ScaleCascade_Invoker
CV_IMPL CvSeq*
cvHaarDetectObjects( const CvArr* _img,
CvHaarClassifierCascade* cascade,
CvMemStorage* storage, double scaleFactor,
int minNeighbors, int flags, CvSize minSize, CvSize maxSize )
cvHaarDetectObjectsForROC( const CvArr* _img,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
double scaleFactor, int minNeighbors, int flags,
CvSize minSize, CvSize maxSize, bool outputRejectLevels )
{
const double GROUP_EPS = 0.2;
CvMat stub, *img = (CvMat*)_img;
@ -1119,7 +1149,7 @@ cvHaarDetectObjects( const CvArr* _img,
cv::HaarDetectObjects_ScaleImage_Invoker(cascade,
(((sz1.height + stripCount - 1)/stripCount + ystep-1)/ystep)*ystep,
factor, cv::Mat(&sum1), cv::Mat(&sqsum1), &_norm1, &_mask1,
cv::Rect(equRect), allCandidates));
cv::Rect(equRect), allCandidates, rejectLevels, levelWeights, outputRejectLevels));
}
}
else
@ -1250,7 +1280,16 @@ cvHaarDetectObjects( const CvArr* _img,
std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
if( minNeighbors != 0 || findBiggestObject )
groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
{
if( outputRejectLevels )
{
groupRectangles(rectList, rejectLevels, levelWeights, minNeighbors, GROUP_EPS );
}
else
{
groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
}
}
else
rweights.resize(rectList.size(),0);
@ -1275,7 +1314,7 @@ cvHaarDetectObjects( const CvArr* _img,
{
CvAvgComp c;
c.rect = rectList[i];
c.neighbors = rweights[i];
c.neighbors = !rweights.empty() ? rweights[i] : 0;
cvSeqPush( result_seq, &c );
}
}
@ -1283,6 +1322,19 @@ cvHaarDetectObjects( const CvArr* _img,
return result_seq;
}
CV_IMPL CvSeq*
cvHaarDetectObjects( const CvArr* _img,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
double scaleFactor,
int minNeighbors, int flags, CvSize minSize, CvSize maxSize )
{
std::vector<int> fakeLevels;
std::vector<double> fakeWeights;
return cvHaarDetectObjectsForROC( _img, cascade, storage, fakeLevels, fakeWeights,
scaleFactor, minNeighbors, flags, minSize, maxSize, false );
}
static CvHaarClassifierCascade*
icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )

23
modules/python/src/cv.cpp
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@ -3677,28 +3677,6 @@ static PyObject *pycvClipLine(PyObject *self, PyObject *args)
}
}
static PyObject *pyfinddatamatrix(PyObject *self, PyObject *args)
{
PyObject *pyim;
if (!PyArg_ParseTuple(args, "O", &pyim))
return NULL;
CvMat *image;
if (!convert_to_CvMat(pyim, &image, "image")) return NULL;
std::deque <DataMatrixCode> codes;
ERRWRAP(codes = cvFindDataMatrix(image));
PyObject *pycodes = PyList_New(codes.size());
int i;
for (i = 0; i < codes.size(); i++) {
DataMatrixCode *pc = &codes[i];
PyList_SetItem(pycodes, i, Py_BuildValue("(sOO)", pc->msg, FROM_CvMat(pc->corners), FROM_CvMat(pc->original)));
}
return pycodes;
}
static PyObject *temp_test(PyObject *self, PyObject *args)
{
#if 0
@ -3993,7 +3971,6 @@ static PyMethodDef methods[] = {
//{"_HOGDetect", (PyCFunction)pycvHOGDetect, METH_KEYWORDS, "_HOGDetect(image, svm_classifier, win_stride=block_stride, locations=None, padding=(0,0), win_size=(64,128), block_size=(16,16), block_stride=(8,8), cell_size=(8,8), nbins=9, gammaCorrection=true) -> list_of_points"},
//{"_HOGDetectMultiScale", (PyCFunction)pycvHOGDetectMultiScale, METH_KEYWORDS, "_HOGDetectMultiScale(image, svm_classifier, win_stride=block_stride, scale=1.05, group_threshold=2, padding=(0,0), win_size=(64,128), block_size=(16,16), block_stride=(8,8), cell_size=(8,8), nbins=9, gammaCorrection=true) -> list_of_points"},
{"FindDataMatrix", pyfinddatamatrix, METH_VARARGS},
{"temp_test", temp_test, METH_VARARGS},
#include "generated1.i"

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samples/c/JCB.png
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