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diff --git a/docs/themes/spacelab/nav.html b/docs/themes/spacelab/nav.html
new file mode 100644
index 0000000..1cb451f
--- /dev/null
+++ b/docs/themes/spacelab/nav.html
@@ -0,0 +1,74 @@
+
diff --git a/docs/themes/spacelab/search.html b/docs/themes/spacelab/search.html
new file mode 100644
index 0000000..bd6e108
--- /dev/null
+++ b/docs/themes/spacelab/search.html
@@ -0,0 +1,17 @@
+
+
diff --git a/docs/themes/spacelab/toc.html b/docs/themes/spacelab/toc.html
new file mode 100644
index 0000000..c342f56
--- /dev/null
+++ b/docs/themes/spacelab/toc.html
@@ -0,0 +1,26 @@
+
diff --git a/openbr/core/_cascade.cpp b/openbr/core/_cascade.cpp
deleted file mode 100644
index da41b5f..0000000
--- a/openbr/core/_cascade.cpp
+++ /dev/null
@@ -1,577 +0,0 @@
-#include "_cascade.h"
-#include
-#include
-#include
-
-using namespace std;
-using namespace br;
-using namespace cv;
-
-bool CascadeImageReader::create( const vector &_posImages, const vector &_negImages, Size _winSize )
-{
- posImages = _posImages;
- negImages = _negImages;
- winSize = _winSize;
- return posReader.create(_posImages) && negReader.create(_negImages, _winSize);
-}
-
-CascadeImageReader::NegReader::NegReader()
-{
- src.create( 0, 0 , CV_8UC1 );
- img.create( 0, 0, CV_8UC1 );
- point = offset = Point( 0, 0 );
- scale = 1.0F;
- scaleFactor = 1.4142135623730950488016887242097F;
- stepFactor = 0.5F;
-}
-
-bool CascadeImageReader::NegReader::create( const vector &_negImages, Size _winSize )
-{
- negImages = _negImages;
- winSize = _winSize;
- last = round = 0;
- return true;
-}
-
-bool CascadeImageReader::NegReader::nextImg()
-{
- Point _offset = Point(0,0);
- size_t count = negImages.size();
- for( size_t i = 0; i < count; i++ )
- {
- src = negImages[last++];
- if( src.empty() )
- continue;
- round += last / count;
- round = round % (winSize.width * winSize.height);
- last %= count;
-
- _offset.x = std::min( (int)round % winSize.width, src.cols - winSize.width );
- _offset.y = std::min( (int)round / winSize.width, src.rows - winSize.height );
- if( !src.empty() && src.type() == CV_8UC1
- && _offset.x >= 0 && _offset.y >= 0 )
- break;
- }
-
- if( src.empty() )
- return false; // no appropriate image
- point = offset = _offset;
- scale = max( ((float)winSize.width + point.x) / ((float)src.cols),
- ((float)winSize.height + point.y) / ((float)src.rows) );
-
- Size sz( (int)(scale*src.cols + 0.5F), (int)(scale*src.rows + 0.5F) );
- resize( src, img, sz );
- return true;
-}
-
-bool CascadeImageReader::NegReader::get( Mat& _img )
-{
- CV_Assert( !_img.empty() );
- CV_Assert( _img.type() == CV_8UC1 );
- CV_Assert( _img.cols == winSize.width );
- CV_Assert( _img.rows == winSize.height );
-
- if( img.empty() )
- if ( !nextImg() )
- return false;
-
- Mat mat( winSize.height, winSize.width, CV_8UC1,
- (void*)(img.data + point.y * img.step + point.x * img.elemSize()), img.step );
- mat.copyTo(_img);
-
- if( (int)( point.x + (1.0F + stepFactor ) * winSize.width ) < img.cols )
- point.x += (int)(stepFactor * winSize.width);
- else
- {
- point.x = offset.x;
- if( (int)( point.y + (1.0F + stepFactor ) * winSize.height ) < img.rows )
- point.y += (int)(stepFactor * winSize.height);
- else
- {
- point.y = offset.y;
- scale *= scaleFactor;
- if( scale <= 1.0F )
- resize( src, img, Size( (int)(scale*src.cols), (int)(scale*src.rows) ) );
- else
- {
- if ( !nextImg() )
- return false;
- }
- }
- }
- return true;
-}
-
-CascadeImageReader::PosReader::PosReader()
-{
- file = 0;
- vec = 0;
- last = 0;
-}
-
-bool CascadeImageReader::PosReader::create( const vector &_posImages )
-{
- posImages = _posImages;
- return true;
-}
-
-bool CascadeImageReader::getPos(Mat &_img)
-{
- if (last > (int)posImages.size())
- CV_Error( CV_StsBadArg, "Can not get new positive sample. vec-file is over.\n");
- _img = posImages[posIdx++];
- return true;
-}
-
-//---------------------------- CascadeParams --------------------------------------
-
-static const char* stageTypes[] = { CC_BOOST };
-static const char* featureTypes[] = { CC_LBP, CC_HAAR, CC_HOG, CC_HOGMULTI, CC_NPD };
-
-CascadeParams::CascadeParams() : stageType( defaultStageType ),
- featureType( defaultFeatureType ), winSize( cvSize(24, 24) )
-{
- name = CC_CASCADE_PARAMS;
-}
-CascadeParams::CascadeParams( int _stageType, int _featureType ) : stageType( _stageType ),
- featureType( _featureType ), winSize( cvSize(24, 24) )
-{
- name = CC_CASCADE_PARAMS;
-}
-
-void CascadeParams::write( FileStorage &fs ) const
-{
- string stageTypeStr = stageType == BOOST ? CC_BOOST : string();
- CV_Assert( !stageTypeStr.empty() );
- fs << CC_STAGE_TYPE << stageTypeStr;
- string featureTypeStr = featureType == FeatureParams::LBP ? CC_LBP :
- 0;
- CV_Assert( !stageTypeStr.empty() );
- fs << CC_FEATURE_TYPE << featureTypeStr;
- fs << CC_HEIGHT << winSize.height;
- fs << CC_WIDTH << winSize.width;
-}
-
-bool CascadeParams::read( const FileNode &node )
-{
- if ( node.empty() )
- return false;
- string stageTypeStr, featureTypeStr;
- FileNode rnode = node[CC_STAGE_TYPE];
- if ( !rnode.isString() )
- return false;
- rnode >> stageTypeStr;
- stageType = !stageTypeStr.compare( CC_BOOST ) ? BOOST : -1;
- if (stageType == -1)
- return false;
- rnode = node[CC_FEATURE_TYPE];
- if ( !rnode.isString() )
- return false;
- rnode >> featureTypeStr;
- featureType = !featureTypeStr.compare( CC_LBP ) ? FeatureParams::LBP :
- -1;
- if (featureType == -1)
- return false;
- node[CC_HEIGHT] >> winSize.height;
- node[CC_WIDTH] >> winSize.width;
- return winSize.height > 0 && winSize.width > 0;
-}
-
-void CascadeParams::printDefaults() const
-{
- Params::printDefaults();
- cout << " [-stageType <";
- for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
- {
- cout << (i ? " | " : "") << stageTypes[i];
- if ( i == defaultStageType )
- cout << "(default)";
- }
- cout << ">]" << endl;
-
- cout << " [-featureType <{";
- for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
- {
- cout << (i ? ", " : "") << featureTypes[i];
- if ( i == defaultStageType )
- cout << "(default)";
- }
- cout << "}>]" << endl;
- cout << " [-w ]" << endl;
- cout << " [-h ]" << endl;
-}
-
-void CascadeParams::printAttrs() const
-{
- cout << "stageType: " << stageTypes[stageType] << endl;
- cout << "featureType: " << featureTypes[featureType] << endl;
- cout << "sampleWidth: " << winSize.width << endl;
- cout << "sampleHeight: " << winSize.height << endl;
-}
-
-bool CascadeParams::scanAttr( const string prmName, const string val )
-{
- bool res = true;
- if( !prmName.compare( "-stageType" ) )
- {
- for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
- if( !val.compare( stageTypes[i] ) )
- stageType = i;
- }
- else if( !prmName.compare( "-featureType" ) )
- {
- for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
- if( !val.compare( featureTypes[i] ) )
- featureType = i;
- }
- else if( !prmName.compare( "-w" ) )
- {
- winSize.width = atoi( val.c_str() );
- }
- else if( !prmName.compare( "-h" ) )
- {
- winSize.height = atoi( val.c_str() );
- }
- else
- res = false;
- return res;
-}
-
-//---------------------------- CascadeClassifier --------------------------------------
-
-bool BrCascadeClassifier::train(const string _cascadeDirName,
- const vector &_posImages,
- const vector &_negImages,
- int _numPos, int _numNeg,
- int _precalcValBufSize, int _precalcIdxBufSize,
- int _numStages,
- const CascadeParams& _cascadeParams,
- const FeatureParams& _featureParams,
- const CascadeBoostParams& _stageParams,
- bool baseFormatSave )
-{
- // Start recording clock ticks for training time output
- const clock_t begin_time = clock();
-
- if( _cascadeDirName.empty() )
- CV_Error( CV_StsBadArg, "_cascadeDirName is NULL" );
-
- string dirName;
- if (_cascadeDirName.find_last_of("/\\") == (_cascadeDirName.length() - 1) )
- dirName = _cascadeDirName;
- else
- dirName = _cascadeDirName + '/';
-
- numPos = _numPos;
- numNeg = _numNeg;
- numStages = _numStages;
- imgReader.create( _posImages, _negImages, _cascadeParams.winSize );
-
- if ( !load( dirName ) )
- {
- cascadeParams = _cascadeParams;
- featureParams = FeatureParams::create(cascadeParams.featureType);
- featureParams->init(_featureParams);
- stageParams = new CascadeBoostParams;
- *stageParams = _stageParams;
- featureEvaluator = FeatureEvaluator::create(cascadeParams.featureType);
- featureEvaluator->init( (FeatureParams*)featureParams, numPos + numNeg, cascadeParams.winSize );
- stageClassifiers.reserve( numStages );
- }
- cout << "PARAMETERS:" << endl;
- cout << "cascadeDirName: " << _cascadeDirName << endl;
- cout << "numPos: " << _numPos << endl;
- cout << "numNeg: " << _numNeg << endl;
- cout << "numStages: " << numStages << endl;
- cout << "precalcValBufSize[Mb] : " << _precalcValBufSize << endl;
- cout << "precalcIdxBufSize[Mb] : " << _precalcIdxBufSize << endl;
- cascadeParams.printAttrs();
- stageParams->printAttrs();
- featureParams->printAttrs();
-
- int startNumStages = (int)stageClassifiers.size();
- if ( startNumStages > 1 )
- cout << endl << "Stages 0-" << startNumStages-1 << " are loaded" << endl;
- else if ( startNumStages == 1)
- cout << endl << "Stage 0 is loaded" << endl;
-
- double requiredLeafFARate = pow( (double) stageParams->maxFalseAlarm, (double) numStages ) /
- (double)stageParams->max_depth;
- double tempLeafFARate;
-
- for( int i = startNumStages; i < numStages; i++ )
- {
- cout << endl << "===== TRAINING " << i << "-stage =====" << endl;
- cout << "train( (FeatureEvaluator*)featureEvaluator,
- curNumSamples, _precalcValBufSize, _precalcIdxBufSize,
- *((CascadeBoostParams*)stageParams) );
- cout << "END>" << endl;
-
- if(!isStageTrained)
- break;
-
- stageClassifiers.push_back( tempStage );
-
- // save params
- if( i == 0)
- {
- std::string paramsFilename = dirName + CC_PARAMS_FILENAME;
- FileStorage fs( paramsFilename, FileStorage::WRITE);
- if ( !fs.isOpened() )
- {
- cout << "Parameters can not be written, because file " << paramsFilename
- << " can not be opened." << endl;
- return false;
- }
- fs << FileStorage::getDefaultObjectName(paramsFilename) << "{";
- writeParams( fs );
- fs << "}";
- }
- // save current stage
- char buf[10];
- sprintf(buf, "%s%d", "stage", i );
- string stageFilename = dirName + buf + ".xml";
- FileStorage fs( stageFilename, FileStorage::WRITE );
- if ( !fs.isOpened() )
- {
- cout << "Current stage can not be written, because file " << stageFilename
- << " can not be opened." << endl;
- return false;
- }
- fs << FileStorage::getDefaultObjectName(stageFilename) << "{";
- tempStage->write( fs, Mat() );
- fs << "}";
-
- // Output training time up till now
- float seconds = float( clock () - begin_time ) / CLOCKS_PER_SEC;
- int days = int(seconds) / 60 / 60 / 24;
- int hours = (int(seconds) / 60 / 60) % 24;
- int minutes = (int(seconds) / 60) % 60;
- int seconds_left = int(seconds) % 60;
- cout << "Training until now has taken " << days << " days " << hours << " hours " << minutes << " minutes " << seconds_left <<" seconds." << endl;
- }
-
- if(stageClassifiers.size() == 0)
- {
- cout << "Cascade classifier can't be trained. Check the used training parameters." << endl;
- return false;
- }
-
- save( dirName + CC_CASCADE_FILENAME, baseFormatSave );
-
- return true;
-}
-
-int BrCascadeClassifier::predict( int sampleIdx )
-{
- CV_DbgAssert( sampleIdx < numPos + numNeg );
- for (vector< Ptr >::iterator it = stageClassifiers.begin();
- it != stageClassifiers.end(); it++ )
- {
- if ( (*it)->predict( sampleIdx ) == 0.f )
- return 0;
- }
- return 1;
-}
-
-bool BrCascadeClassifier::updateTrainingSet( double& acceptanceRatio)
-{
- int64 posConsumed = 0, negConsumed = 0;
- imgReader.restart();
-
- int posCount = fillPassedSamples( 0, numPos, true, posConsumed );
- if( !posCount )
- return false;
- cout << "POS count : consumed " << posCount << " : " << (int)posConsumed << endl;
-
- int proNumNeg = cvRound( ( ((double)numNeg) * ((double)posCount) ) / numPos ); // apply only a fraction of negative samples. double is required since overflow is possible
- int negCount = fillPassedSamples( posCount, proNumNeg, false, negConsumed );
- if ( !negCount )
- return false;
-
- curNumSamples = posCount + negCount;
- acceptanceRatio = negConsumed == 0 ? 0 : ( (double)negCount/(double)(int64)negConsumed );
- cout << "NEG count : acceptanceRatio " << negCount << " : " << acceptanceRatio << endl;
- return true;
-}
-
-int BrCascadeClassifier::fillPassedSamples( int first, int count, bool isPositive, int64& consumed )
-{
- int getcount = 0;
- Mat img(cascadeParams.winSize, CV_8UC1);
- for( int i = first; i < first + count; i++ )
- {
- for( ; ; )
- {
- bool isGetImg = isPositive ? imgReader.getPos( img ) :
- imgReader.getNeg( img );
- if( !isGetImg )
- return getcount;
- consumed++;
-
- featureEvaluator->setImage( img, isPositive ? 1 : 0, i );
- if( predict( i ) == 1.0F )
- {
- getcount++;
- printf("%s current samples: %d\r", isPositive ? "POS":"NEG", getcount);
- break;
- }
- }
- }
- return getcount;
-}
-
-void BrCascadeClassifier::writeParams( FileStorage &fs ) const
-{
- cascadeParams.write( fs );
- fs << CC_STAGE_PARAMS << "{"; stageParams->write( fs ); fs << "}";
- fs << CC_FEATURE_PARAMS << "{"; featureParams->write( fs ); fs << "}";
-}
-
-void BrCascadeClassifier::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
-{
- ((FeatureEvaluator*)((Ptr)featureEvaluator))->writeFeatures( fs, featureMap );
-}
-
-void BrCascadeClassifier::writeStages( FileStorage &fs, const Mat& featureMap ) const
-{
- char cmnt[30];
- int i = 0;
- fs << CC_STAGES << "[";
- for( vector< Ptr >::const_iterator it = stageClassifiers.begin();
- it != stageClassifiers.end(); it++, i++ )
- {
- sprintf( cmnt, "stage %d", i );
- cvWriteComment( fs.fs, cmnt, 0 );
- fs << "{";
- ((CascadeBoost*)((Ptr)*it))->write( fs, featureMap );
- fs << "}";
- }
- fs << "]";
-}
-
-bool BrCascadeClassifier::readParams( const FileNode &node )
-{
- if ( !node.isMap() || !cascadeParams.read( node ) )
- return false;
- stageParams = new CascadeBoostParams;
- FileNode rnode = node[CC_STAGE_PARAMS];
- if ( !stageParams->read( rnode ) )
- return false;
- featureParams = FeatureParams::create(cascadeParams.featureType);
- rnode = node[CC_FEATURE_PARAMS];
- if ( !featureParams->read( rnode ) )
- return false;
- return true;
-}
-
-bool BrCascadeClassifier::readStages( const FileNode &node)
-{
- FileNode rnode = node[CC_STAGES];
- if (!rnode.empty() || !rnode.isSeq())
- return false;
- stageClassifiers.reserve(numStages);
- FileNodeIterator it = rnode.begin();
- for( int i = 0; i < min( (int)rnode.size(), numStages ); i++, it++ )
- {
- CascadeBoost* tempStage = new CascadeBoost;
- if ( !tempStage->read( *it, (FeatureEvaluator *)featureEvaluator, *((CascadeBoostParams*)stageParams) ) )
- {
- delete tempStage;
- return false;
- }
- stageClassifiers.push_back(tempStage);
- }
- return true;
-}
-
-void BrCascadeClassifier::save( const string filename, bool baseFormat )
-{
- FileStorage fs( filename, FileStorage::WRITE );
-
- if ( !fs.isOpened() )
- return;
-
- fs << FileStorage::getDefaultObjectName(filename) << "{";
- if ( !baseFormat )
- {
- Mat featureMap;
- getUsedFeaturesIdxMap( featureMap );
- writeParams( fs );
- fs << CC_STAGE_NUM << (int)stageClassifiers.size();
- writeStages( fs, featureMap );
- writeFeatures( fs, featureMap );
- }
- else
- {
- qFatal("Old style cascade. Not sure how you got here but it's not supported");
- }
- fs << "}";
-}
-
-bool BrCascadeClassifier::load( const string cascadeDirName )
-{
- FileStorage fs( cascadeDirName + CC_PARAMS_FILENAME, FileStorage::READ );
- if ( !fs.isOpened() )
- return false;
-
- FileNode node = fs.getFirstTopLevelNode();
- if ( !readParams( node ) )
- return false;
-
- featureEvaluator = FeatureEvaluator::create(cascadeParams.featureType);
- featureEvaluator->init( ((FeatureParams*)featureParams), numPos + numNeg, cascadeParams.winSize );
- fs.release();
-
- char buf[10];
- for ( int si = 0; si < numStages; si++ )
- {
- sprintf( buf, "%s%d", "stage", si);
- fs.open( cascadeDirName + buf + ".xml", FileStorage::READ );
- node = fs.getFirstTopLevelNode();
- if ( !fs.isOpened() )
- break;
- CascadeBoost *tempStage = new CascadeBoost;
-
- if ( !tempStage->read( node, (FeatureEvaluator*)featureEvaluator, *((CascadeBoostParams*)stageParams )) )
- {
- delete tempStage;
- fs.release();
- break;
- }
- stageClassifiers.push_back(tempStage);
- }
- return true;
-}
-
-void BrCascadeClassifier::getUsedFeaturesIdxMap( Mat& featureMap )
-{
- int varCount = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
- featureMap.create( 1, varCount, CV_32SC1 );
- featureMap.setTo(Scalar(-1));
-
- for( vector< Ptr >::const_iterator it = stageClassifiers.begin();
- it != stageClassifiers.end(); it++ )
- ((CascadeBoost*)((Ptr)(*it)))->markUsedFeaturesInMap( featureMap );
-
- for( int fi = 0, idx = 0; fi < varCount; fi++ )
- if ( featureMap.at(0, fi) >= 0 )
- featureMap.ptr(0)[fi] = idx++;
-}
-
-
diff --git a/openbr/core/_cascade.h b/openbr/core/_cascade.h
deleted file mode 100644
index 7a3d482..0000000
--- a/openbr/core/_cascade.h
+++ /dev/null
@@ -1,97 +0,0 @@
-#ifndef _CASCADE_H
-#define _CASCADE_H
-
-#include
-#include
-#include "features.h"
-#include "boost.h"
-
-namespace br
-{
-
-class CascadeImageReader
-{
-public:
- bool create( const std::vector &_posImages, const std::vector &_negImages, cv::Size _winSize );
- void restart() { posIdx = 0; }
- bool getNeg(cv::Mat &_img);
- bool getPos(cv::Mat &_img);
-
-private:
- std::vector posImages, negImages;
-
- int posIdx, negIdx;
-
- cv::Mat src, img;
- cv::Point offset, point;
- float scale;
- float scaleFactor;
- float stepFactor;
- size_t round;
- cv::Size winSize;
-};
-
-class CascadeParams : public Params
-{
-public:
- enum { BOOST = 0 };
- static const int defaultStageType = BOOST;
- static const int defaultFeatureType = FeatureParams::LBP;
-
- CascadeParams();
- CascadeParams( int _stageType, int _featureType );
- void write( cv::FileStorage &fs ) const;
- bool read( const cv::FileNode &node );
-
- void printDefaults() const;
- void printAttrs() const;
- bool scanAttr( const std::string prmName, const std::string val );
-
- int stageType;
- int featureType;
- cv::Size winSize;
-};
-
-class BrCascadeClassifier
-{
-public:
- bool train( const std::string _cascadeDirName,
- const std::vector &_posImages,
- const std::vector &_negImages,
- int _numPos, int _numNeg,
- int _precalcValBufSize, int _precalcIdxBufSize,
- int _numStages,
- const CascadeParams& _cascadeParams,
- const FeatureParams& _featureParams,
- const CascadeBoostParams& _stageParams,
- bool baseFormatSave = false );
-private:
- int predict( int sampleIdx );
- void save( const std::string cascadeDirName, bool baseFormat = false );
- bool load( const std::string cascadeDirName );
- bool updateTrainingSet( double& acceptanceRatio );
- int fillPassedSamples( int first, int count, bool isPositive, int64& consumed );
-
- void writeParams( cv::FileStorage &fs ) const;
- void writeStages( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
- void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
- bool readParams( const cv::FileNode &node );
- bool readStages( const cv::FileNode &node );
-
- void getUsedFeaturesIdxMap( cv::Mat& featureMap );
-
- CascadeParams cascadeParams;
- cv::Ptr featureParams;
- cv::Ptr stageParams;
-
- cv::Ptr featureEvaluator;
- std::vector< cv::Ptr > stageClassifiers;
- CascadeImageReader imgReader;
- int numStages, curNumSamples;
- int numPos, numNeg;
-};
-
-} // namespace br
-
-#endif // _CASCADE_H
-
diff --git a/openbr/core/boost.cpp b/openbr/core/boost.cpp
index 0d315e5..c535ea2 100644
--- a/openbr/core/boost.cpp
+++ b/openbr/core/boost.cpp
@@ -171,98 +171,31 @@ void CascadeBoostParams::write( FileStorage &fs ) const
fs << CC_WEAK_COUNT << weak_count;
}
-bool CascadeBoostParams::read( const FileNode &node )
-{
- string boostTypeStr;
- FileNode rnode = node[CC_BOOST_TYPE];
- rnode >> boostTypeStr;
- boost_type = !boostTypeStr.compare( CC_DISCRETE_BOOST ) ? CvBoost::DISCRETE :
- !boostTypeStr.compare( CC_REAL_BOOST ) ? CvBoost::REAL :
- !boostTypeStr.compare( CC_LOGIT_BOOST ) ? CvBoost::LOGIT :
- !boostTypeStr.compare( CC_GENTLE_BOOST ) ? CvBoost::GENTLE : -1;
- if (boost_type == -1)
- CV_Error( CV_StsBadArg, "unsupported Boost type" );
- node[CC_MINHITRATE] >> minHitRate;
- node[CC_MAXFALSEALARM] >> maxFalseAlarm;
- node[CC_TRIM_RATE] >> weight_trim_rate ;
- node[CC_MAX_DEPTH] >> max_depth ;
- node[CC_WEAK_COUNT] >> weak_count ;
- if ( minHitRate <= 0 || minHitRate > 1 ||
- maxFalseAlarm <= 0 || maxFalseAlarm > 1 ||
- weight_trim_rate <= 0 || weight_trim_rate > 1 ||
- max_depth <= 0 || weak_count <= 0 )
- CV_Error( CV_StsBadArg, "bad parameters range");
- return true;
-}
+// ----------------------------- Compatibility ----------------------------------
-void CascadeBoostParams::printDefaults() const
+BoostBRCompatibility::BoostBRCompatibility(Representation *_representation, int _numSamples)
{
- cout << "--boostParams--" << endl;
- cout << " [-bt <{" << CC_DISCRETE_BOOST << ", "
- << CC_REAL_BOOST << ", "
- << CC_LOGIT_BOOST ", "
- << CC_GENTLE_BOOST << "(default)}>]" << endl;
- cout << " [-minHitRate = " << minHitRate << ">]" << endl;
- cout << " [-maxFalseAlarmRate ]" << endl;
- cout << " [-weightTrimRate ]" << endl;
- cout << " [-maxDepth ]" << endl;
- cout << " [-maxWeakCount ]" << endl;
+ representation = _representation;
+ data.create(_numSamples, representation->windowSize().area(), CV_32SC1);
+ cls.create(_numSamples, 1, CV_32FC1);
}
-void CascadeBoostParams::printAttrs() const
+void BoostBRCompatibility::setImage(const Mat &img, uchar clsLabel, int idx)
{
- string boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST :
- boost_type == CvBoost::REAL ? CC_REAL_BOOST :
- boost_type == CvBoost::LOGIT ? CC_LOGIT_BOOST :
- boost_type == CvBoost::GENTLE ? CC_GENTLE_BOOST : string();
- CV_Assert( !boostTypeStr.empty() );
- cout << "boostType: " << boostTypeStr << endl;
- cout << "minHitRate: " << minHitRate << endl;
- cout << "maxFalseAlarmRate: " << maxFalseAlarm << endl;
- cout << "weightTrimRate: " << weight_trim_rate << endl;
- cout << "maxDepth: " << max_depth << endl;
- cout << "maxWeakCount: " << weak_count << endl;
+ cls.ptr(idx)[0] = clsLabel;
+ Mat preprocessed = representation->preprocess(img);
+ Mat copier(representation->windowSize(), CV_32FC1, data.ptr(idx));
+ preprocessed.copyTo(copier);
}
-bool CascadeBoostParams::scanAttr( const string prmName, const string val)
+float BoostBRCompatibility::operator ()(int featureIdx, int sampleIdx) const
{
- bool res = true;
-
- if( !prmName.compare( "-bt" ) )
- {
- boost_type = !val.compare( CC_DISCRETE_BOOST ) ? CvBoost::DISCRETE :
- !val.compare( CC_REAL_BOOST ) ? CvBoost::REAL :
- !val.compare( CC_LOGIT_BOOST ) ? CvBoost::LOGIT :
- !val.compare( CC_GENTLE_BOOST ) ? CvBoost::GENTLE : -1;
- if (boost_type == -1)
- res = false;
- }
- else if( !prmName.compare( "-minHitRate" ) )
- {
- minHitRate = (float) atof( val.c_str() );
- }
- else if( !prmName.compare( "-maxFalseAlarmRate" ) )
- {
- maxFalseAlarm = (float) atof( val.c_str() );
- }
- else if( !prmName.compare( "-weightTrimRate" ) )
- {
- weight_trim_rate = (float) atof( val.c_str() );
- }
- else if( !prmName.compare( "-maxDepth" ) )
- {
- max_depth = atoi( val.c_str() );
- }
- else if( !prmName.compare( "-maxWeakCount" ) )
- {
- weak_count = atoi( val.c_str() );
- }
- else
- res = false;
-
- return res;
+ return representation->evaluate(data.row(sampleIdx).reshape(0, representation->windowSize().height),
+ QList() << featureIdx).ptr()[0];
}
+//---------------------------- CascadeBoostTrainData -----------------------------
+
CvDTreeNode* CascadeBoostTrainData::subsample_data( const CvMat* _subsample_idx )
{
CvDTreeNode* root = 0;
@@ -428,20 +361,18 @@ CvDTreeNode* CascadeBoostTrainData::subsample_data( const CvMat* _subsample_idx
return root;
}
-//---------------------------- CascadeBoostTrainData -----------------------------
-
-CascadeBoostTrainData::CascadeBoostTrainData( const FeatureEvaluator* _featureEvaluator,
+CascadeBoostTrainData::CascadeBoostTrainData( const BoostBRCompatibility* _compat,
const CvDTreeParams& _params )
{
is_classifier = true;
- var_all = var_count = (int)_featureEvaluator->getNumFeatures();
+ var_all = var_count = _compat->numFeatures();
- featureEvaluator = _featureEvaluator;
+ compat = _compat;
shared = true;
set_params( _params );
- max_c_count = MAX( 2, featureEvaluator->getMaxCatCount() );
+ max_c_count = MAX( 2, compat->maxCatCount() );
var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
- if ( featureEvaluator->getMaxCatCount() > 0 )
+ if ( compat->maxCatCount() > 0 )
{
numPrecalcIdx = 0;
cat_var_count = var_count;
@@ -471,15 +402,15 @@ CascadeBoostTrainData::CascadeBoostTrainData( const FeatureEvaluator* _featureEv
split_heap = cvCreateSet( 0, sizeof(split_heap[0]), maxSplitSize, tree_storage );
}
-CascadeBoostTrainData::CascadeBoostTrainData( const FeatureEvaluator* _featureEvaluator,
+CascadeBoostTrainData::CascadeBoostTrainData( const BoostBRCompatibility* _compat,
int _numSamples,
int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params )
{
- setData( _featureEvaluator, _numSamples, _precalcValBufSize, _precalcIdxBufSize, _params );
+ setData( _compat, _numSamples, _precalcValBufSize, _precalcIdxBufSize, _params );
}
-void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
+void CascadeBoostTrainData::setData( const BoostBRCompatibility* _compat,
int _numSamples,
int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params )
@@ -501,18 +432,17 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
set_params( _params );
- CV_Assert( _featureEvaluator );
- featureEvaluator = _featureEvaluator;
+ compat = _compat;
- max_c_count = MAX( 2, featureEvaluator->getMaxCatCount() );
- _resp = featureEvaluator->getCls();
+ max_c_count = MAX( 2, compat->maxCatCount() );
+ _resp = compat->getCls();
responses = &_resp;
// TODO: check responses: elements must be 0 or 1
if( _precalcValBufSize < 0 || _precalcIdxBufSize < 0)
CV_Error( CV_StsOutOfRange, "_numPrecalcVal and _numPrecalcIdx must be positive or 0" );
- var_count = var_all = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
+ var_count = var_all = compat->numFeatures();
sample_count = _numSamples;
is_buf_16u = false;
@@ -527,7 +457,7 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
valCache.create( numPrecalcVal, sample_count, CV_32FC1 );
var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
- if ( featureEvaluator->getMaxCatCount() > 0 )
+ if ( compat->maxCatCount() > 0 )
{
numPrecalcIdx = 0;
cat_var_count = var_count;
@@ -693,7 +623,7 @@ void CascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord
{
int idx = (*sortedIndices)[i];
idx = sampleIndices[idx];
- ordValuesBuf[i] = (*featureEvaluator)( vi, idx);
+ ordValuesBuf[i] = (*compat)( vi, idx);
}
}
}
@@ -715,7 +645,7 @@ void CascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord
for( int i = 0; i < nodeSampleCount; i++ )
{
sortedIndicesBuf[i] = i;
- sampleValues[i] = (*featureEvaluator)( vi, sampleIndices[i]);
+ sampleValues[i] = (*compat)( vi, sampleIndices[i]);
}
}
icvSortIntAux( sortedIndicesBuf, nodeSampleCount, &sampleValues[0] );
@@ -743,7 +673,7 @@ const int* CascadeBoostTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int*
if( vi >= numPrecalcVal && vi < var_count )
{
for( int i = 0; i < nodeSampleCount; i++ )
- catValuesBuf[i] = (int)(*featureEvaluator)( vi, sampleIndices[i] );
+ catValuesBuf[i] = (int)(*compat)( vi, sampleIndices[i] );
}
else
{
@@ -758,15 +688,15 @@ float CascadeBoostTrainData::getVarValue( int vi, int si )
{
if ( vi < numPrecalcVal && !valCache.empty() )
return valCache.at( vi, si );
- return (*featureEvaluator)( vi, si );
+ return (*compat)( vi, si );
}
struct FeatureIdxOnlyPrecalc : ParallelLoopBody
{
- FeatureIdxOnlyPrecalc( const FeatureEvaluator* _featureEvaluator, CvMat* _buf, int _sample_count, bool _is_buf_16u )
+ FeatureIdxOnlyPrecalc( const BoostBRCompatibility* _compat, CvMat* _buf, int _sample_count, bool _is_buf_16u )
{
- featureEvaluator = _featureEvaluator;
+ compat = _compat;
sample_count = _sample_count;
udst = (unsigned short*)_buf->data.s;
idst = _buf->data.i;
@@ -780,7 +710,7 @@ struct FeatureIdxOnlyPrecalc : ParallelLoopBody
{
for( int si = 0; si < sample_count; si++ )
{
- valCachePtr[si] = (*featureEvaluator)( fi, si );
+ valCachePtr[si] = (*compat)( fi, si );
if ( is_buf_16u )
*(udst + fi*sample_count + si) = (unsigned short)si;
else
@@ -792,7 +722,7 @@ struct FeatureIdxOnlyPrecalc : ParallelLoopBody
icvSortIntAux( idst + fi*sample_count, sample_count, valCachePtr );
}
}
- const FeatureEvaluator* featureEvaluator;
+ const BoostBRCompatibility* compat;
int sample_count;
int* idst;
unsigned short* udst;
@@ -801,9 +731,9 @@ struct FeatureIdxOnlyPrecalc : ParallelLoopBody
struct FeatureValAndIdxPrecalc : ParallelLoopBody
{
- FeatureValAndIdxPrecalc( const FeatureEvaluator* _featureEvaluator, CvMat* _buf, Mat* _valCache, int _sample_count, bool _is_buf_16u )
+ FeatureValAndIdxPrecalc( const BoostBRCompatibility* _compat, CvMat* _buf, Mat* _valCache, int _sample_count, bool _is_buf_16u )
{
- featureEvaluator = _featureEvaluator;
+ compat = _compat;
valCache = _valCache;
sample_count = _sample_count;
udst = (unsigned short*)_buf->data.s;
@@ -816,7 +746,7 @@ struct FeatureValAndIdxPrecalc : ParallelLoopBody
{
for( int si = 0; si < sample_count; si++ )
{
- valCache->at(fi,si) = (*featureEvaluator)( fi, si );
+ valCache->at(fi,si) = (*compat)( fi, si );
if ( is_buf_16u )
*(udst + fi*sample_count + si) = (unsigned short)si;
else
@@ -828,7 +758,7 @@ struct FeatureValAndIdxPrecalc : ParallelLoopBody
icvSortIntAux( idst + fi*sample_count, sample_count, valCache->ptr(fi) );
}
}
- const FeatureEvaluator* featureEvaluator;
+ const BoostBRCompatibility* compat;
Mat* valCache;
int sample_count;
int* idst;
@@ -838,9 +768,9 @@ struct FeatureValAndIdxPrecalc : ParallelLoopBody
struct FeatureValOnlyPrecalc : ParallelLoopBody
{
- FeatureValOnlyPrecalc( const FeatureEvaluator* _featureEvaluator, Mat* _valCache, int _sample_count )
+ FeatureValOnlyPrecalc( const BoostBRCompatibility* _compat, Mat* _valCache, int _sample_count )
{
- featureEvaluator = _featureEvaluator;
+ compat = _compat;
valCache = _valCache;
sample_count = _sample_count;
}
@@ -848,9 +778,9 @@ struct FeatureValOnlyPrecalc : ParallelLoopBody
{
for ( int fi = range.start; fi < range.end; fi++)
for( int si = 0; si < sample_count; si++ )
- valCache->at(fi,si) = (*featureEvaluator)( fi, si );
+ valCache->at(fi,si) = (*compat)( fi, si );
}
- const FeatureEvaluator* featureEvaluator;
+ const BoostBRCompatibility* compat;
Mat* valCache;
int sample_count;
};
@@ -861,11 +791,11 @@ void CascadeBoostTrainData::precalculate()
double proctime = -TIME( 0 );
parallel_for_( Range(numPrecalcVal, numPrecalcIdx),
- FeatureIdxOnlyPrecalc(featureEvaluator, buf, sample_count, is_buf_16u!=0) );
+ FeatureIdxOnlyPrecalc(compat, buf, sample_count, is_buf_16u!=0) );
parallel_for_( Range(0, minNum),
- FeatureValAndIdxPrecalc(featureEvaluator, buf, &valCache, sample_count, is_buf_16u!=0) );
+ FeatureValAndIdxPrecalc(compat, buf, &valCache, sample_count, is_buf_16u!=0) );
parallel_for_( Range(minNum, numPrecalcVal),
- FeatureValOnlyPrecalc(featureEvaluator, &valCache, sample_count) );
+ FeatureValOnlyPrecalc(compat, &valCache, sample_count) );
cout << "Precalculation time: " << (proctime + TIME( 0 )) << endl;
}
@@ -877,7 +807,7 @@ CvDTreeNode* CascadeBoostTree::predict( int sampleIdx ) const
if( !node )
CV_Error( CV_StsError, "The tree has not been trained yet" );
- if ( ((CascadeBoostTrainData*)data)->featureEvaluator->getMaxCatCount() == 0 ) // ordered
+ if ( ((CascadeBoostTrainData*)data)->compat->maxCatCount() == 0 ) // ordered
{
while( node->left )
{
@@ -900,7 +830,7 @@ CvDTreeNode* CascadeBoostTree::predict( int sampleIdx ) const
void CascadeBoostTree::write( FileStorage &fs, const Mat& featureMap )
{
- int maxCatCount = ((CascadeBoostTrainData*)data)->featureEvaluator->getMaxCatCount();
+ int maxCatCount = ((CascadeBoostTrainData*)data)->compat->maxCatCount();
int subsetN = (maxCatCount + 31)/32;
queue internalNodesQueue;
int size = (int)pow( 2.f, (float)ensemble->get_params().max_depth);
@@ -958,81 +888,6 @@ void CascadeBoostTree::write( FileStorage &fs, const Mat& featureMap )
fs << "}";
}
-void CascadeBoostTree::read( const FileNode &node, CvBoost* _ensemble,
- CvDTreeTrainData* _data )
-{
- int maxCatCount = ((CascadeBoostTrainData*)_data)->featureEvaluator->getMaxCatCount();
- int subsetN = (maxCatCount + 31)/32;
- int step = 3 + ( maxCatCount>0 ? subsetN : 1 );
-
- queue internalNodesQueue;
- FileNodeIterator internalNodesIt, leafValsuesIt;
- CvDTreeNode* prntNode, *cldNode;
-
- clear();
- data = _data;
- ensemble = _ensemble;
- pruned_tree_idx = 0;
-
- // read tree nodes
- FileNode rnode = node[CC_INTERNAL_NODES];
- internalNodesIt = rnode.end();
- leafValsuesIt = node[CC_LEAF_VALUES].end();
- internalNodesIt--; leafValsuesIt--;
- for( size_t i = 0; i < rnode.size()/step; i++ )
- {
- prntNode = data->new_node( 0, 0, 0, 0 );
- if ( maxCatCount > 0 )
- {
- prntNode->split = data->new_split_cat( 0, 0 );
- for( int j = subsetN-1; j>=0; j--)
- {
- *internalNodesIt >> prntNode->split->subset[j]; internalNodesIt--;
- }
- }
- else
- {
- float split_value;
- *internalNodesIt >> split_value; internalNodesIt--;
- prntNode->split = data->new_split_ord( 0, split_value, 0, 0, 0);
- }
- *internalNodesIt >> prntNode->split->var_idx; internalNodesIt--;
- int ridx, lidx;
- *internalNodesIt >> ridx; internalNodesIt--;
- *internalNodesIt >> lidx;internalNodesIt--;
- if ( ridx <= 0)
- {
- prntNode->right = cldNode = data->new_node( 0, 0, 0, 0 );
- *leafValsuesIt >> cldNode->value; leafValsuesIt--;
- cldNode->parent = prntNode;
- }
- else
- {
- prntNode->right = internalNodesQueue.front();
- prntNode->right->parent = prntNode;
- internalNodesQueue.pop();
- }
-
- if ( lidx <= 0)
- {
- prntNode->left = cldNode = data->new_node( 0, 0, 0, 0 );
- *leafValsuesIt >> cldNode->value; leafValsuesIt--;
- cldNode->parent = prntNode;
- }
- else
- {
- prntNode->left = internalNodesQueue.front();
- prntNode->left->parent = prntNode;
- internalNodesQueue.pop();
- }
-
- internalNodesQueue.push( prntNode );
- }
-
- root = internalNodesQueue.front();
- internalNodesQueue.pop();
-}
-
void CascadeBoostTree::split_node_data( CvDTreeNode* node )
{
int n = node->sample_count, nl, nr, scount = data->sample_count;
@@ -1268,7 +1123,7 @@ void CascadeBoostTree::markFeaturesInMap( Mat& featureMap )
//----------------------------------- CascadeBoost --------------------------------------
-bool CascadeBoost::train( const FeatureEvaluator* _featureEvaluator,
+bool CascadeBoost::train( const BoostBRCompatibility* _compat,
int _numSamples,
int _precalcValBufSize, int _precalcIdxBufSize,
const CascadeBoostParams& _params )
@@ -1277,7 +1132,7 @@ bool CascadeBoost::train( const FeatureEvaluator* _featureEvaluator,
CV_Assert( !data );
clear();
- data = new CascadeBoostTrainData( _featureEvaluator, _numSamples,
+ data = new CascadeBoostTrainData( _compat, _numSamples,
_precalcValBufSize, _precalcIdxBufSize, _params );
CvMemStorage *storage = cvCreateMemStorage();
@@ -1299,6 +1154,7 @@ bool CascadeBoost::train( const FeatureEvaluator* _featureEvaluator,
CascadeBoostTree* tree = new CascadeBoostTree;
if( !tree->train( data, subsample_mask, this ) )
{
+ qDebug("Couldn't train tree");
delete tree;
break;
}
@@ -1598,7 +1454,7 @@ bool CascadeBoost::isErrDesired()
vector eval(sCount);
for( int i = 0; i < sCount; i++ )
- if( ((CascadeBoostTrainData*)data)->featureEvaluator->getCls( i ) == 1.0F )
+ if( ((CascadeBoostTrainData*)data)->compat->getCls( i ) == 1.0F )
eval[numPos++] = predict( i, true );
icvSortFlt( &eval[0], numPos, 0 );
int thresholdIdx = (int)((1.0F - minHitRate) * numPos);
@@ -1611,7 +1467,7 @@ bool CascadeBoost::isErrDesired()
for( int i = 0; i < sCount; i++ )
{
- if( ((CascadeBoostTrainData*)data)->featureEvaluator->getCls( i ) == 0.0F )
+ if( ((CascadeBoostTrainData*)data)->compat->getCls( i ) == 0.0F )
{
numNeg++;
if( predict( i ) )
@@ -1646,29 +1502,6 @@ void CascadeBoost::write( FileStorage &fs, const Mat& featureMap ) const
fs << "]";
}
-bool CascadeBoost::read( const FileNode &node,
- const FeatureEvaluator* _featureEvaluator,
- const CascadeBoostParams& _params )
-{
- CvMemStorage* storage;
- clear();
- data = new CascadeBoostTrainData( _featureEvaluator, _params );
- set_params( _params );
-
- node[CC_STAGE_THRESHOLD] >> threshold;
- FileNode rnode = node[CC_WEAK_CLASSIFIERS];
-
- storage = cvCreateMemStorage();
- weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage );
- for( FileNodeIterator it = rnode.begin(); it != rnode.end(); it++ )
- {
- CascadeBoostTree* tree = new CascadeBoostTree();
- tree->read( *it, this, data );
- cvSeqPush( weak, &tree );
- }
- return true;
-}
-
void CascadeBoost::markUsedFeaturesInMap( Mat& featureMap )
{
for( int wi = 0; wi < weak->total; wi++ )
diff --git a/openbr/core/boost.h b/openbr/core/boost.h
index e39c12a..2bb5d1f 100644
--- a/openbr/core/boost.h
+++ b/openbr/core/boost.h
@@ -17,20 +17,31 @@ struct CascadeBoostParams : CvBoostParams
double _weightTrimRate, int _maxDepth, int _maxWeakCount );
virtual ~CascadeBoostParams() {}
void write( cv::FileStorage &fs ) const;
- bool read( const cv::FileNode &node );
- virtual void printDefaults() const;
- virtual void printAttrs() const;
- virtual bool scanAttr( const std::string prmName, const std::string val);
+};
+
+struct BoostBRCompatibility
+{
+ BoostBRCompatibility(Representation *_representation, int _numSamples);
+ void setImage(const cv::Mat &img, uchar clsLabel, int idx);
+ float operator()(int featureIdx, int sampleIdx) const;
+ const cv::Mat &getCls() const { return cls; }
+ float getCls(int idx) const { return cls.at(idx, 0); }
+
+ int numFeatures() const { return representation->numFeatures(); }
+ int maxCatCount() const { return 256; }
+
+ Representation *representation;
+ cv::Mat data, cls;
};
struct CascadeBoostTrainData : CvDTreeTrainData
{
- CascadeBoostTrainData( const FeatureEvaluator* _featureEvaluator,
+ CascadeBoostTrainData( const BoostBRCompatibility* _compat,
const CvDTreeParams& _params );
- CascadeBoostTrainData( const FeatureEvaluator* _featureEvaluator,
+ CascadeBoostTrainData( const BoostBRCompatibility* _compat,
int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params = CvDTreeParams() );
- virtual void setData( const FeatureEvaluator* _featureEvaluator,
+ virtual void setData( const BoostBRCompatibility* _compat,
int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params=CvDTreeParams() );
void precalculate();
@@ -47,7 +58,7 @@ struct CascadeBoostTrainData : CvDTreeTrainData
virtual float getVarValue( int vi, int si );
virtual void free_train_data();
- const FeatureEvaluator* featureEvaluator;
+ const BoostBRCompatibility* compat;
cv::Mat valCache; // precalculated feature values (CV_32FC1)
CvMat _resp; // for casting
int numPrecalcVal, numPrecalcIdx;
@@ -58,7 +69,6 @@ class CascadeBoostTree : public CvBoostTree
public:
virtual CvDTreeNode* predict( int sampleIdx ) const;
void write( cv::FileStorage &fs, const cv::Mat& featureMap );
- void read( const cv::FileNode &node, CvBoost* _ensemble, CvDTreeTrainData* _data );
void markFeaturesInMap( cv::Mat& featureMap );
protected:
virtual void split_node_data( CvDTreeNode* n );
@@ -67,15 +77,13 @@ protected:
class CascadeBoost : public CvBoost
{
public:
- virtual bool train( const FeatureEvaluator* _featureEvaluator,
+ virtual bool train( const BoostBRCompatibility* _compat,
int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
const CascadeBoostParams& _params=CascadeBoostParams() );
virtual float predict( int sampleIdx, bool returnSum = false ) const;
float getThreshold() const { return threshold; }
void write( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
- bool read( const cv::FileNode &node, const FeatureEvaluator* _featureEvaluator,
- const CascadeBoostParams& _params );
void markUsedFeaturesInMap( cv::Mat& featureMap );
protected:
virtual bool set_params( const CvBoostParams& _params );
diff --git a/openbr/core/cascade.cpp b/openbr/core/cascade.cpp
index 0e24593..c766102 100644
--- a/openbr/core/cascade.cpp
+++ b/openbr/core/cascade.cpp
@@ -3,77 +3,40 @@
#include
#include
-using namespace std;
using namespace br;
using namespace cv;
-/*
-bool CascadeImageReader::create( const string _posFilename, const string _negFilename, Size _winSize )
-{
- return posReader.create(_posFilename) && negReader.create(_negFilename, _winSize);
-}
-CascadeImageReader::NegReader::NegReader()
+bool CascadeImageReader::create( const QList &_posImages, const QList &_negImages, Size _winSize )
{
+ posImages = _posImages;
+ negImages = _negImages;
+ winSize = _winSize;
+
+ posIdx = 0; negIdx = 0;
+ round = 0;
+
src.create( 0, 0 , CV_8UC1 );
img.create( 0, 0, CV_8UC1 );
point = offset = Point( 0, 0 );
scale = 1.0F;
scaleFactor = 1.4142135623730950488016887242097F;
stepFactor = 0.5F;
-}
-
-bool CascadeImageReader::NegReader::create( const string _filename, Size _winSize )
-{
- string dirname, str;
- std::ifstream file(_filename.c_str());
- if ( !file.is_open() )
- return false;
- size_t pos = _filename.rfind('\\');
- char dlmrt = '\\';
- if (pos == string::npos)
- {
- pos = _filename.rfind('/');
- dlmrt = '/';
- }
- dirname = pos == string::npos ? "" : _filename.substr(0, pos) + dlmrt;
- while( !file.eof() )
- {
- std::getline(file, str);
- if (str.empty()) break;
- if (str.at(0) == '#' ) continue;
- imgFilenames.push_back(dirname + str);
- }
- file.close();
-
- winSize = _winSize;
- last = round = 0;
return true;
}
-bool CascadeImageReader::NegReader::nextImg()
+bool CascadeImageReader::nextNeg()
{
- Point _offset = Point(0,0);
- size_t count = imgFilenames.size();
- for( size_t i = 0; i < count; i++ )
- {
- src = imread( imgFilenames[last++], 0 );
- if( src.empty() )
- continue;
- round += last / count;
- round = round % (winSize.width * winSize.height);
- last %= count;
-
- _offset.x = std::min( (int)round % winSize.width, src.cols - winSize.width );
- _offset.y = std::min( (int)round / winSize.width, src.rows - winSize.height );
- if( !src.empty() && src.type() == CV_8UC1
- && _offset.x >= 0 && _offset.y >= 0 )
- break;
- }
+ src = negImages[negIdx++];
- if( src.empty() )
- return false; // no appropriate image
- point = offset = _offset;
+ round += negIdx / negImages.size();
+ round = round % (winSize.width * winSize.height);
+ negIdx %= negImages.size();
+
+ offset.x = std::min( (int)round % winSize.width, src.cols - winSize.width );
+ offset.y = std::min( (int)round / winSize.width, src.rows - winSize.height );
+
+ point = offset;
scale = max( ((float)winSize.width + point.x) / ((float)src.cols),
((float)winSize.height + point.y) / ((float)src.rows) );
@@ -82,7 +45,7 @@ bool CascadeImageReader::NegReader::nextImg()
return true;
}
-bool CascadeImageReader::NegReader::get( Mat& _img )
+bool CascadeImageReader::getNeg( Mat& _img )
{
CV_Assert( !_img.empty() );
CV_Assert( _img.type() == CV_8UC1 );
@@ -90,11 +53,10 @@ bool CascadeImageReader::NegReader::get( Mat& _img )
CV_Assert( _img.rows == winSize.height );
if( img.empty() )
- if ( !nextImg() )
+ if ( !nextNeg() )
return false;
- Mat mat( winSize.height, winSize.width, CV_8UC1,
- (void*)(img.data + point.y * img.step + point.x * img.elemSize()), img.step );
+ Mat mat( winSize.height, winSize.width, CV_8UC1, (void*)(img.data + point.y * img.step + point.x * img.elemSize()), img.step );
mat.copyTo(_img);
if( (int)( point.x + (1.0F + stepFactor ) * winSize.width ) < img.cols )
@@ -112,7 +74,7 @@ bool CascadeImageReader::NegReader::get( Mat& _img )
resize( src, img, Size( (int)(scale*src.cols), (int)(scale*src.rows) ) );
else
{
- if ( !nextImg() )
+ if ( !nextNeg() )
return false;
}
}
@@ -120,204 +82,29 @@ bool CascadeImageReader::NegReader::get( Mat& _img )
return true;
}
-CascadeImageReader::PosReader::PosReader()
-{
- file = 0;
- vec = 0;
-}
-
-bool CascadeImageReader::PosReader::create( const string _filename )
-{
- if ( file )
- fclose( file );
- file = fopen( _filename.c_str(), "rb" );
-
- if( !file )
- return false;
- short tmp = 0;
- if( fread( &count, sizeof( count ), 1, file ) != 1 ||
- fread( &vecSize, sizeof( vecSize ), 1, file ) != 1 ||
- fread( &tmp, sizeof( tmp ), 1, file ) != 1 ||
- fread( &tmp, sizeof( tmp ), 1, file ) != 1 )
- CV_Error_( CV_StsParseError, ("wrong file format for %s\n", _filename.c_str()) );
- base = sizeof( count ) + sizeof( vecSize ) + 2*sizeof( tmp );
- if( feof( file ) )
- return false;
- last = 0;
- vec = (short*) cvAlloc( sizeof( *vec ) * vecSize );
- CV_Assert( vec );
- return true;
-}
-
-bool CascadeImageReader::PosReader::get( Mat &_img )
+bool CascadeImageReader::getPos(Mat &_img)
{
- CV_Assert( _img.rows * _img.cols == vecSize );
- uchar tmp = 0;
- size_t elements_read = fread( &tmp, sizeof( tmp ), 1, file );
- if( elements_read != 1 )
- CV_Error( CV_StsBadArg, "Can not get new positive sample. The most possible reason is "
- "insufficient count of samples in given vec-file.\n");
- elements_read = fread( vec, sizeof( vec[0] ), vecSize, file );
- if( elements_read != (size_t)(vecSize) )
- CV_Error( CV_StsBadArg, "Can not get new positive sample. Seems that vec-file has incorrect structure.\n");
-
- if( feof( file ) || last++ >= count )
- CV_Error( CV_StsBadArg, "Can not get new positive sample. vec-file is over.\n");
-
- for( int r = 0; r < _img.rows; r++ )
- {
- for( int c = 0; c < _img.cols; c++ )
- _img.ptr(r)[c] = (uchar)vec[r * _img.cols + c];
- }
+ if (posIdx > (int)posImages.size())
+ CV_Error( CV_StsBadArg, "Can not get new positive sample. Not enough positive samples.\n");
+ _img = posImages[posIdx++];
return true;
}
-void CascadeImageReader::PosReader::restart()
-{
- CV_Assert( file );
- last = 0;
- fseek( file, base, SEEK_SET );
-}
-
-CascadeImageReader::PosReader::~PosReader()
-{
- if (file)
- fclose( file );
- cvFree( &vec );
-}
-
-//---------------------------- CascadeParams --------------------------------------
-
-static const char* stageTypes[] = { CC_BOOST };
-static const char* featureTypes[] = { CC_LBP, CC_HAAR, CC_HOG, CC_HOGMULTI, CC_NPD };
-
-CascadeParams::CascadeParams() : stageType( defaultStageType ),
- featureType( defaultFeatureType ), winSize( cvSize(24, 24) )
-{
- name = CC_CASCADE_PARAMS;
-}
-CascadeParams::CascadeParams( int _stageType, int _featureType ) : stageType( _stageType ),
- featureType( _featureType ), winSize( cvSize(24, 24) )
-{
- name = CC_CASCADE_PARAMS;
-}
-
-void CascadeParams::write( FileStorage &fs ) const
-{
- string stageTypeStr = stageType == BOOST ? CC_BOOST : string();
- CV_Assert( !stageTypeStr.empty() );
- fs << CC_STAGE_TYPE << stageTypeStr;
- string featureTypeStr = featureType == FeatureParams::LBP ? CC_LBP :
- 0;
- CV_Assert( !stageTypeStr.empty() );
- fs << CC_FEATURE_TYPE << featureTypeStr;
- fs << CC_HEIGHT << winSize.height;
- fs << CC_WIDTH << winSize.width;
-}
-
-bool CascadeParams::read( const FileNode &node )
-{
- if ( node.empty() )
- return false;
- string stageTypeStr, featureTypeStr;
- FileNode rnode = node[CC_STAGE_TYPE];
- if ( !rnode.isString() )
- return false;
- rnode >> stageTypeStr;
- stageType = !stageTypeStr.compare( CC_BOOST ) ? BOOST : -1;
- if (stageType == -1)
- return false;
- rnode = node[CC_FEATURE_TYPE];
- if ( !rnode.isString() )
- return false;
- rnode >> featureTypeStr;
- featureType = !featureTypeStr.compare( CC_LBP ) ? FeatureParams::LBP :
- -1;
- if (featureType == -1)
- return false;
- node[CC_HEIGHT] >> winSize.height;
- node[CC_WIDTH] >> winSize.width;
- return winSize.height > 0 && winSize.width > 0;
-}
-
-void CascadeParams::printDefaults() const
-{
- Params::printDefaults();
- cout << " [-stageType <";
- for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
- {
- cout << (i ? " | " : "") << stageTypes[i];
- if ( i == defaultStageType )
- cout << "(default)";
- }
- cout << ">]" << endl;
-
- cout << " [-featureType <{";
- for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
- {
- cout << (i ? ", " : "") << featureTypes[i];
- if ( i == defaultStageType )
- cout << "(default)";
- }
- cout << "}>]" << endl;
- cout << " [-w ]" << endl;
- cout << " [-h ]" << endl;
-}
-
-void CascadeParams::printAttrs() const
-{
- cout << "stageType: " << stageTypes[stageType] << endl;
- cout << "featureType: " << featureTypes[featureType] << endl;
- cout << "sampleWidth: " << winSize.width << endl;
- cout << "sampleHeight: " << winSize.height << endl;
-}
-
-bool CascadeParams::scanAttr( const string prmName, const string val )
-{
- bool res = true;
- if( !prmName.compare( "-stageType" ) )
- {
- for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
- if( !val.compare( stageTypes[i] ) )
- stageType = i;
- }
- else if( !prmName.compare( "-featureType" ) )
- {
- for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
- if( !val.compare( featureTypes[i] ) )
- featureType = i;
- }
- else if( !prmName.compare( "-w" ) )
- {
- winSize.width = atoi( val.c_str() );
- }
- else if( !prmName.compare( "-h" ) )
- {
- winSize.height = atoi( val.c_str() );
- }
- else
- res = false;
- return res;
-}
-
//---------------------------- CascadeClassifier --------------------------------------
bool BrCascadeClassifier::train(const string _cascadeDirName,
- const string _posFilename,
- const string _negFilename,
- int _numPos, int _numNeg,
+ const QList &_posImages,
+ const QList &_negImages,
int _precalcValBufSize, int _precalcIdxBufSize,
- int _numStages,
- const CascadeParams& _cascadeParams,
- const FeatureParams& _featureParams,
- const CascadeBoostParams& _stageParams,
- bool baseFormatSave )
+ int _numPos, int _numNeg, int _numStages,
+ Representation *_representation,
+ const CascadeBoostParams& _stageParams)
{
// Start recording clock ticks for training time output
const clock_t begin_time = clock();
- if( _cascadeDirName.empty() || _posFilename.empty() || _negFilename.empty() )
- CV_Error( CV_StsBadArg, "_cascadeDirName or _bgfileName or _vecFileName is NULL" );
+ if( _cascadeDirName.empty() )
+ CV_Error( CV_StsBadArg, "_cascadeDirName is NULL" );
string dirName;
if (_cascadeDirName.find_last_of("/\\") == (_cascadeDirName.length() - 1) )
@@ -325,123 +112,63 @@ bool BrCascadeClassifier::train(const string _cascadeDirName,
else
dirName = _cascadeDirName + '/';
+ compat = new BoostBRCompatibility(_representation, _numPos + _numNeg);
numPos = _numPos;
numNeg = _numNeg;
numStages = _numStages;
- if ( !imgReader.create( _posFilename, _negFilename, _cascadeParams.winSize ) )
- {
- cout << "Image reader can not be created from -vec " << _posFilename
- << " and -bg " << _negFilename << "." << endl;
- return false;
- }
- if ( !load( dirName ) )
- {
- cascadeParams = _cascadeParams;
- featureParams = FeatureParams::create(cascadeParams.featureType);
- featureParams->init(_featureParams);
- stageParams = new CascadeBoostParams;
- *stageParams = _stageParams;
- featureEvaluator = FeatureEvaluator::create(cascadeParams.featureType);
- featureEvaluator->init( (FeatureParams*)featureParams, numPos + numNeg, cascadeParams.winSize );
- stageClassifiers.reserve( numStages );
- }
- cout << "PARAMETERS:" << endl;
- cout << "cascadeDirName: " << _cascadeDirName << endl;
- cout << "vecFileName: " << _posFilename << endl;
- cout << "bgFileName: " << _negFilename << endl;
- cout << "numPos: " << _numPos << endl;
- cout << "numNeg: " << _numNeg << endl;
- cout << "numStages: " << numStages << endl;
- cout << "precalcValBufSize[Mb] : " << _precalcValBufSize << endl;
- cout << "precalcIdxBufSize[Mb] : " << _precalcIdxBufSize << endl;
- cascadeParams.printAttrs();
- stageParams->printAttrs();
- featureParams->printAttrs();
-
- int startNumStages = (int)stageClassifiers.size();
- if ( startNumStages > 1 )
- cout << endl << "Stages 0-" << startNumStages-1 << " are loaded" << endl;
- else if ( startNumStages == 1)
- cout << endl << "Stage 0 is loaded" << endl;
+ imgReader.create(_posImages, _negImages, _representation->windowSize());
+
+ stageParams = new CascadeBoostParams;
+ *stageParams = _stageParams;
+ stageClassifiers.reserve( numStages );
double requiredLeafFARate = pow( (double) stageParams->maxFalseAlarm, (double) numStages ) /
(double)stageParams->max_depth;
double tempLeafFARate;
- for( int i = startNumStages; i < numStages; i++ )
- {
- cout << endl << "===== TRAINING " << i << "-stage =====" << endl;
- cout << "train( (FeatureEvaluator*)featureEvaluator,
+ bool isStageTrained = tempStage->train( (BoostBRCompatibility*)compat,
curNumSamples, _precalcValBufSize, _precalcIdxBufSize,
*((CascadeBoostParams*)stageParams) );
- cout << "END>" << endl;
+ qDebug() << "END>";
if(!isStageTrained)
break;
stageClassifiers.push_back( tempStage );
- // save params
- if( i == 0)
- {
- std::string paramsFilename = dirName + CC_PARAMS_FILENAME;
- FileStorage fs( paramsFilename, FileStorage::WRITE);
- if ( !fs.isOpened() )
- {
- cout << "Parameters can not be written, because file " << paramsFilename
- << " can not be opened." << endl;
- return false;
- }
- fs << FileStorage::getDefaultObjectName(paramsFilename) << "{";
- writeParams( fs );
- fs << "}";
- }
- // save current stage
- char buf[10];
- sprintf(buf, "%s%d", "stage", i );
- string stageFilename = dirName + buf + ".xml";
- FileStorage fs( stageFilename, FileStorage::WRITE );
- if ( !fs.isOpened() )
- {
- cout << "Current stage can not be written, because file " << stageFilename
- << " can not be opened." << endl;
- return false;
- }
- fs << FileStorage::getDefaultObjectName(stageFilename) << "{";
- tempStage->write( fs, Mat() );
- fs << "}";
-
// Output training time up till now
float seconds = float( clock () - begin_time ) / CLOCKS_PER_SEC;
int days = int(seconds) / 60 / 60 / 24;
int hours = (int(seconds) / 60 / 60) % 24;
int minutes = (int(seconds) / 60) % 60;
int seconds_left = int(seconds) % 60;
- cout << "Training until now has taken " << days << " days " << hours << " hours " << minutes << " minutes " << seconds_left <<" seconds." << endl;
+ qDebug() << "Training until now has taken " << days << " days " << hours << " hours " << minutes << " minutes " << seconds_left <<" seconds.";
}
if(stageClassifiers.size() == 0)
{
- cout << "Cascade classifier can't be trained. Check the used training parameters." << endl;
+ qDebug() << "Cascade classifier can't be trained. Check the used training parameters.";
return false;
}
- save( dirName + CC_CASCADE_FILENAME, baseFormatSave );
+ save(dirName + CC_CASCADE_FILENAME);
return true;
}
@@ -466,7 +193,7 @@ bool BrCascadeClassifier::updateTrainingSet( double& acceptanceRatio)
int posCount = fillPassedSamples( 0, numPos, true, posConsumed );
if( !posCount )
return false;
- cout << "POS count : consumed " << posCount << " : " << (int)posConsumed << endl;
+ qDebug() << "POS count : consumed " << posCount << " : " << (int)posConsumed;
int proNumNeg = cvRound( ( ((double)numNeg) * ((double)posCount) ) / numPos ); // apply only a fraction of negative samples. double is required since overflow is possible
int negCount = fillPassedSamples( posCount, proNumNeg, false, negConsumed );
@@ -475,25 +202,25 @@ bool BrCascadeClassifier::updateTrainingSet( double& acceptanceRatio)
curNumSamples = posCount + negCount;
acceptanceRatio = negConsumed == 0 ? 0 : ( (double)negCount/(double)(int64)negConsumed );
- cout << "NEG count : acceptanceRatio " << negCount << " : " << acceptanceRatio << endl;
+ qDebug() << "NEG count : acceptanceRatio " << negCount << " : " << acceptanceRatio;
return true;
}
int BrCascadeClassifier::fillPassedSamples( int first, int count, bool isPositive, int64& consumed )
{
int getcount = 0;
- Mat img(cascadeParams.winSize, CV_8UC1);
+ Mat img(compat->representation->windowSize(), CV_8UC1);
for( int i = first; i < first + count; i++ )
{
for( ; ; )
{
bool isGetImg = isPositive ? imgReader.getPos( img ) :
- imgReader.getNeg( img );
+ imgReader.getNeg( img );
if( !isGetImg )
return getcount;
consumed++;
- featureEvaluator->setImage( img, isPositive ? 1 : 0, i );
+ compat->setImage( img, isPositive ? 1 : 0, i );
if( predict( i ) == 1.0F )
{
getcount++;
@@ -507,14 +234,17 @@ int BrCascadeClassifier::fillPassedSamples( int first, int count, bool isPositiv
void BrCascadeClassifier::writeParams( FileStorage &fs ) const
{
- cascadeParams.write( fs );
+ fs << CC_STAGE_TYPE << CC_BOOST;
+ fs << CC_FEATURE_TYPE << CC_LBP;
+ fs << CC_HEIGHT << winSize.height;
+ fs << CC_WIDTH << winSize.width;
fs << CC_STAGE_PARAMS << "{"; stageParams->write( fs ); fs << "}";
- fs << CC_FEATURE_PARAMS << "{"; featureParams->write( fs ); fs << "}";
+ fs << CC_FEATURE_PARAMS << "{"; fs << CC_MAX_CAT_COUNT << 256; fs << CC_FEATURE_SIZE << 1; fs << "}";
}
void BrCascadeClassifier::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
{
- ((FeatureEvaluator*)((Ptr)featureEvaluator))->writeFeatures( fs, featureMap );
+ compat->representation->write(fs, featureMap);
}
void BrCascadeClassifier::writeStages( FileStorage &fs, const Mat& featureMap ) const
@@ -534,42 +264,7 @@ void BrCascadeClassifier::writeStages( FileStorage &fs, const Mat& featureMap )
fs << "]";
}
-bool BrCascadeClassifier::readParams( const FileNode &node )
-{
- if ( !node.isMap() || !cascadeParams.read( node ) )
- return false;
- stageParams = new CascadeBoostParams;
- FileNode rnode = node[CC_STAGE_PARAMS];
- if ( !stageParams->read( rnode ) )
- return false;
- featureParams = FeatureParams::create(cascadeParams.featureType);
- rnode = node[CC_FEATURE_PARAMS];
- if ( !featureParams->read( rnode ) )
- return false;
- return true;
-}
-
-bool BrCascadeClassifier::readStages( const FileNode &node)
-{
- FileNode rnode = node[CC_STAGES];
- if (!rnode.empty() || !rnode.isSeq())
- return false;
- stageClassifiers.reserve(numStages);
- FileNodeIterator it = rnode.begin();
- for( int i = 0; i < min( (int)rnode.size(), numStages ); i++, it++ )
- {
- CascadeBoost* tempStage = new CascadeBoost;
- if ( !tempStage->read( *it, (FeatureEvaluator *)featureEvaluator, *((CascadeBoostParams*)stageParams) ) )
- {
- delete tempStage;
- return false;
- }
- stageClassifiers.push_back(tempStage);
- }
- return true;
-}
-
-void BrCascadeClassifier::save( const string filename, bool baseFormat )
+void BrCascadeClassifier::save(const string filename)
{
FileStorage fs( filename, FileStorage::WRITE );
@@ -577,60 +272,20 @@ void BrCascadeClassifier::save( const string filename, bool baseFormat )
return;
fs << FileStorage::getDefaultObjectName(filename) << "{";
- if ( !baseFormat )
- {
- Mat featureMap;
- getUsedFeaturesIdxMap( featureMap );
- writeParams( fs );
- fs << CC_STAGE_NUM << (int)stageClassifiers.size();
- writeStages( fs, featureMap );
- writeFeatures( fs, featureMap );
- }
- else
- {
- qFatal("Old style cascade. Not sure how you got here but it's not supported");
- }
- fs << "}";
-}
-bool BrCascadeClassifier::load( const string cascadeDirName )
-{
- FileStorage fs( cascadeDirName + CC_PARAMS_FILENAME, FileStorage::READ );
- if ( !fs.isOpened() )
- return false;
-
- FileNode node = fs.getFirstTopLevelNode();
- if ( !readParams( node ) )
- return false;
-
- featureEvaluator = FeatureEvaluator::create(cascadeParams.featureType);
- featureEvaluator->init( ((FeatureParams*)featureParams), numPos + numNeg, cascadeParams.winSize );
- fs.release();
-
- char buf[10];
- for ( int si = 0; si < numStages; si++ )
- {
- sprintf( buf, "%s%d", "stage", si);
- fs.open( cascadeDirName + buf + ".xml", FileStorage::READ );
- node = fs.getFirstTopLevelNode();
- if ( !fs.isOpened() )
- break;
- CascadeBoost *tempStage = new CascadeBoost;
+ Mat featureMap;
+ getUsedFeaturesIdxMap( featureMap );
+ writeParams( fs );
+ fs << CC_STAGE_NUM << (int)stageClassifiers.size();
+ writeStages( fs, featureMap );
+ writeFeatures( fs, featureMap );
- if ( !tempStage->read( node, (FeatureEvaluator*)featureEvaluator, *((CascadeBoostParams*)stageParams )) )
- {
- delete tempStage;
- fs.release();
- break;
- }
- stageClassifiers.push_back(tempStage);
- }
- return true;
+ fs << "}";
}
void BrCascadeClassifier::getUsedFeaturesIdxMap( Mat& featureMap )
{
- int varCount = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
+ int varCount = compat->numFeatures();
featureMap.create( 1, varCount, CV_32SC1 );
featureMap.setTo(Scalar(-1));
@@ -641,5 +296,6 @@ void BrCascadeClassifier::getUsedFeaturesIdxMap( Mat& featureMap )
for( int fi = 0, idx = 0; fi < varCount; fi++ )
if ( featureMap.at(0, fi) >= 0 )
featureMap.ptr(0)[fi] = idx++;
-}*/
+}
+
diff --git a/openbr/core/cascade.h b/openbr/core/cascade.h
index b71d919..be5da8b 100644
--- a/openbr/core/cascade.h
+++ b/openbr/core/cascade.h
@@ -1,5 +1,5 @@
-#ifndef CASCADE_H
-#define CASCADE_H
+#ifndef _CASCADE_H
+#define _CASCADE_H
#include
#include
@@ -8,112 +8,65 @@
namespace br
{
-/*
+
class CascadeImageReader
{
public:
- bool create( const std::string _posFilename, const std::string _negFilename, cv::Size _winSize );
- void restart() { posReader.restart(); }
- bool getNeg(cv::Mat &_img) { return negReader.get( _img ); }
- bool getPos(cv::Mat &_img) { return posReader.get( _img ); }
+ bool create( const QList &_posImages, const QList &_negImages, cv::Size _winSize );
+ void restart() { posIdx = 0; }
+ bool getNeg(cv::Mat &_img);
+ bool getPos(cv::Mat &_img);
private:
- class PosReader
- {
- public:
- PosReader();
- virtual ~PosReader();
- bool create( const std::string _filename );
- bool get( cv::Mat &_img );
- void restart();
-
- short* vec;
- FILE* file;
- int count;
- int vecSize;
- int last;
- int base;
- } posReader;
-
- class NegReader
- {
- public:
- NegReader();
- bool create( const std::string _filename, cv::Size _winSize );
- bool get( cv::Mat& _img );
- bool nextImg();
-
- cv::Mat src, img;
- std::vector imgFilenames;
- cv::Point offset, point;
- float scale;
- float scaleFactor;
- float stepFactor;
- size_t last, round;
- cv::Size winSize;
- } negReader;
-};
-
-class CascadeParams : public Params
-{
-public:
- enum { BOOST = 0 };
- static const int defaultStageType = BOOST;
- static const int defaultFeatureType = FeatureParams::LBP;
+ bool nextNeg();
- CascadeParams();
- CascadeParams( int _stageType, int _featureType );
- void write( cv::FileStorage &fs ) const;
- bool read( const cv::FileNode &node );
+ QList posImages, negImages;
- void printDefaults() const;
- void printAttrs() const;
- bool scanAttr( const std::string prmName, const std::string val );
+ int posIdx, negIdx;
- int stageType;
- int featureType;
- cv::Size winSize;
+ cv::Mat src, img;
+ cv::Point offset, point;
+ float scale;
+ float scaleFactor;
+ float stepFactor;
+ size_t round;
+ cv::Size winSize;
};
class BrCascadeClassifier
{
public:
- bool train( const std::string _cascadeDirName,
- const std::string _posFilename,
- const std::string _negFilename,
- int _numPos, int _numNeg,
+ bool train(const std::string _cascadeDirName,
+ const QList &_posImages,
+ const QList &_negImages,
int _precalcValBufSize, int _precalcIdxBufSize,
- int _numStages,
- const CascadeParams& _cascadeParams,
- const FeatureParams& _featureParams,
- const CascadeBoostParams& _stageParams,
- bool baseFormatSave = false );
+ int _numPos, int _numNeg, int _numStages,
+ Representation *_representation,
+ const CascadeBoostParams& _stageParams);
private:
int predict( int sampleIdx );
- void save( const std::string cascadeDirName, bool baseFormat = false );
- bool load( const std::string cascadeDirName );
+ void save(const std::string cascadeDirName);
bool updateTrainingSet( double& acceptanceRatio );
int fillPassedSamples( int first, int count, bool isPositive, int64& consumed );
void writeParams( cv::FileStorage &fs ) const;
void writeStages( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
- bool readParams( const cv::FileNode &node );
- bool readStages( const cv::FileNode &node );
void getUsedFeaturesIdxMap( cv::Mat& featureMap );
- CascadeParams cascadeParams;
- cv::Ptr featureParams;
cv::Ptr stageParams;
- cv::Ptr featureEvaluator;
+ cv::Ptr featureEvaluator;
+ cv::Ptr compat;
std::vector< cv::Ptr > stageClassifiers;
CascadeImageReader imgReader;
+ cv::Size winSize;
int numStages, curNumSamples;
int numPos, numNeg;
};
-*/
+
} // namespace br
-#endif // CASCADE_H
+#endif // _CASCADE_H
+
diff --git a/openbr/core/features.cpp b/openbr/core/features.cpp
index e727678..e07fba4 100644
--- a/openbr/core/features.cpp
+++ b/openbr/core/features.cpp
@@ -1,120 +1,37 @@
#include "features.h"
+#include "opencvutils.h"
using namespace cv;
using namespace br;
-//------------------------- Params -----------------------------------------------
+// ------------------------------------ LBP Training -----------------------------------------------
-float calcNormFactor( const Mat& sum, const Mat& sqSum )
+void LBPTrainingEvaluator::init(int _maxSampleCount, Size _winSize)
{
- CV_DbgAssert( sum.cols > 3 && sqSum.rows > 3 );
- Rect normrect( 1, 1, sum.cols - 3, sum.rows - 3 );
- size_t p0, p1, p2, p3;
- CV_SUM_OFFSETS( p0, p1, p2, p3, normrect, sum.step1() )
- double area = normrect.width * normrect.height;
- const int *sp = (const int*)sum.data;
- int valSum = sp[p0] - sp[p1] - sp[p2] + sp[p3];
- const double *sqp = (const double *)sqSum.data;
- double valSqSum = sqp[p0] - sqp[p1] - sqp[p2] + sqp[p3];
- return (float) sqrt( (double) (area * valSqSum - (double)valSum * valSum) );
-}
-
-Params::Params() : name( "params" ) {}
-void Params::printDefaults() const { std::cout << "--" << name << "--" << endl; }
-void Params::printAttrs() const {}
-bool Params::scanAttr( const string, const string ) { return false; }
-
-
-//---------------------------- FeatureParams --------------------------------------
-
-FeatureParams::FeatureParams() : maxCatCount( 0 ), featSize( 1 )
-{
- name = CC_FEATURE_PARAMS;
-}
-
-void FeatureParams::init( const FeatureParams& fp )
-{
- maxCatCount = fp.maxCatCount;
- featSize = fp.featSize;
-}
-
-void FeatureParams::write( FileStorage &fs ) const
-{
- fs << CC_MAX_CAT_COUNT << maxCatCount;
- fs << CC_FEATURE_SIZE << featSize;
-}
-
-bool FeatureParams::read( const FileNode &node )
-{
- if ( node.empty() )
- return false;
- maxCatCount = node[CC_MAX_CAT_COUNT];
- featSize = node[CC_FEATURE_SIZE];
- return ( maxCatCount >= 0 && featSize >= 1 );
-}
-
-Ptr FeatureParams::create( int featureType )
-{
- return featureType == LBP ? Ptr(new LBPFeatureParams) :
- Ptr();
-}
-
-//------------------------------------- FeatureEvaluator ---------------------------------------
+ CV_Assert( _maxSampleCount > 0);
+ sum.create((int)_maxSampleCount, (_winSize.width + 1) * (_winSize.height + 1), CV_32SC1);
-void FeatureEvaluator::init(const FeatureParams *_featureParams,
- int _maxSampleCount, Size _winSize )
-{
- CV_Assert(_maxSampleCount > 0);
- featureParams = (FeatureParams *)_featureParams;
winSize = _winSize;
numFeatures = 0;
+ maxCatCount = 256;
cls.create( (int)_maxSampleCount, 1, CV_32FC1 );
generateFeatures();
}
-void FeatureEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
-{
- CV_Assert(img.cols == winSize.width);
- CV_Assert(img.rows == winSize.height);
- CV_Assert(idx < cls.rows);
- cls.ptr(idx)[0] = clsLabel;
-}
-
-Ptr FeatureEvaluator::create(int type)
-{
- return type == FeatureParams::LBP ? Ptr(new LBPEvaluator) :
- Ptr();
-}
-
-// ------------------------------------ LBP -----------------------------------------------
-
-LBPFeatureParams::LBPFeatureParams()
-{
- maxCatCount = 256;
- name = LBPF_NAME;
-}
-
-void LBPEvaluator::init(const FeatureParams *_featureParams, int _maxSampleCount, Size _winSize)
-{
- CV_Assert( _maxSampleCount > 0);
- sum.create((int)_maxSampleCount, (_winSize.width + 1) * (_winSize.height + 1), CV_32SC1);
- FeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
-}
-
-void LBPEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
+void LBPTrainingEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
{
CV_DbgAssert( !sum.empty() );
- FeatureEvaluator::setImage( img, clsLabel, idx );
+ cls.ptr(idx)[0] = clsLabel;
Mat innSum(winSize.height + 1, winSize.width + 1, sum.type(), sum.ptr((int)idx));
integral( img, innSum );
}
-void LBPEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
+void LBPTrainingEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
{
_writeFeatures( features, fs, featureMap );
}
-void LBPEvaluator::generateFeatures()
+void LBPTrainingEvaluator::generateFeatures()
{
int offset = winSize.width + 1;
for( int x = 0; x < winSize.width; x++ )
@@ -126,12 +43,12 @@ void LBPEvaluator::generateFeatures()
numFeatures = (int)features.size();
}
-LBPEvaluator::Feature::Feature()
+LBPTrainingEvaluator::Feature::Feature()
{
rect = cvRect(0, 0, 0, 0);
}
-LBPEvaluator::Feature::Feature( int offset, int x, int y, int _blockWidth, int _blockHeight )
+LBPTrainingEvaluator::Feature::Feature( int offset, int x, int y, int _blockWidth, int _blockHeight )
{
Rect tr = rect = cvRect(x, y, _blockWidth, _blockHeight);
CV_SUM_OFFSETS( p[0], p[1], p[4], p[5], tr, offset )
@@ -143,7 +60,7 @@ LBPEvaluator::Feature::Feature( int offset, int x, int y, int _blockWidth, int _
CV_SUM_OFFSETS( p[8], p[9], p[12], p[13], tr, offset )
}
-void LBPEvaluator::Feature::write(FileStorage &fs) const
+void LBPTrainingEvaluator::Feature::write(FileStorage &fs) const
{
fs << CC_RECT << "[:" << rect.x << rect.y << rect.width << rect.height << "]";
}
diff --git a/openbr/core/features.h b/openbr/core/features.h
index 94fb020..a8de284 100644
--- a/openbr/core/features.h
+++ b/openbr/core/features.h
@@ -84,11 +84,35 @@
(p3) = (rect).x + (rect).width - (rect).height \
+ (step) * ((rect).y + (rect).width + (rect).height);
+#define CV_SUM_PTRS( p0, p1, p2, p3, sum, rect, step ) \
+ /* (x, y) */ \
+ (p0) = sum + (rect).x + (step) * (rect).y, \
+ /* (x + w, y) */ \
+ (p1) = sum + (rect).x + (rect).width + (step) * (rect).y, \
+ /* (x, y + h) */ \
+ (p2) = sum + (rect).x + (step) * ((rect).y + (rect).height), \
+ /* (x + w, y + h) */ \
+ (p3) = sum + (rect).x + (rect).width + (step) * ((rect).y + (rect).height)
+
+#define CV_TILTED_PTRS( p0, p1, p2, p3, tilted, rect, step ) \
+ /* (x, y) */ \
+ (p0) = tilted + (rect).x + (step) * (rect).y, \
+ /* (x - h, y + h) */ \
+ (p1) = tilted + (rect).x - (rect).height + (step) * ((rect).y + (rect).height), \
+ /* (x + w, y + w) */ \
+ (p2) = tilted + (rect).x + (rect).width + (step) * ((rect).y + (rect).width), \
+ /* (x + w - h, y + w + h) */ \
+ (p3) = tilted + (rect).x + (rect).width - (rect).height \
+ + (step) * ((rect).y + (rect).width + (rect).height)
+
+#define CALC_SUM_(p0, p1, p2, p3, offset) \
+ ((p0)[offset] - (p1)[offset] - (p2)[offset] + (p3)[offset])
+
+#define CALC_SUM(rect,offset) CALC_SUM_((rect)[0], (rect)[1], (rect)[2], (rect)[3], offset)
+
namespace br
{
-float calcNormFactor( const cv::Mat& sum, const cv::Mat& sqSum );
-
template
void _writeFeatures( const std::vector features, cv::FileStorage &fs, const cv::Mat& featureMap )
{
@@ -104,81 +128,23 @@ void _writeFeatures( const std::vector features, cv::FileStorage &fs, c
fs << "]";
}
-class Params
-{
-public:
- Params();
- virtual ~Params() {}
- // from|to file
- virtual void write( cv::FileStorage &fs ) const = 0;
- virtual bool read( const cv::FileNode &node ) = 0;
- // from|to screen
- virtual void printDefaults() const;
- virtual void printAttrs() const;
- virtual bool scanAttr( const std::string prmName, const std::string val );
- std::string name;
-};
-
-class FeatureParams : public Params
-{
-public:
- enum { LBP = 0 };
- FeatureParams();
- virtual void init( const FeatureParams& fp );
- virtual void write( cv::FileStorage &fs ) const;
- virtual bool read( const cv::FileNode &node );
- static cv::Ptr create( int featureType );
- int maxCatCount; // 0 in case of numerical features
- int featSize; // 1 in case of simple features (HAAR, LBP) and N_BINS(9)*N_CELLS(4) in case of Dalal's HOG features
-};
+//------------------------- LBP Feature ---------------------------------
-class FeatureEvaluator
+class LBPTrainingEvaluator
{
public:
- virtual ~FeatureEvaluator() {}
- virtual void init(const FeatureParams *_featureParams,
- int _maxSampleCount, cv::Size _winSize );
+ virtual ~LBPTrainingEvaluator() {}
+ virtual void init(int _maxSampleCount, cv::Size _winSize );
virtual void setImage(const cv::Mat& img, uchar clsLabel, int idx);
- virtual void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const = 0;
- virtual float operator()(int featureIdx, int sampleIdx) const = 0;
- static cv::Ptr create(int type);
+ virtual float operator()(int featureIdx, int sampleIdx) const { return (float)features[featureIdx].calc( sum, sampleIdx); }
+ virtual void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
int getNumFeatures() const { return numFeatures; }
- int getMaxCatCount() const { return featureParams->maxCatCount; }
- int getFeatureSize() const { return featureParams->featSize; }
+ int getMaxCatCount() const { return maxCatCount; }
+ int getFeatureSize() const { return 1; }
const cv::Mat& getCls() const { return cls; }
float getCls(int si) const { return cls.at(si, 0); }
-protected:
- virtual void generateFeatures() = 0;
-
- int npos, nneg;
- int numFeatures;
- cv::Size winSize;
- FeatureParams *featureParams;
- cv::Mat cls;
-};
-
-
-//------------------------- LBP Feature ---------------------------------
-
-#define LBPF_NAME "lbpFeatureParams"
-
-struct LBPFeatureParams : FeatureParams
-{
- LBPFeatureParams();
-};
-
-class LBPEvaluator : public FeatureEvaluator
-{
-public:
- virtual ~LBPEvaluator() {}
- virtual void init(const FeatureParams *_featureParams,
- int _maxSampleCount, cv::Size _winSize );
- virtual void setImage(const cv::Mat& img, uchar clsLabel, int idx);
- virtual float operator()(int featureIdx, int sampleIdx) const
- { return (float)features[featureIdx].calc( sum, sampleIdx); }
- virtual void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
protected:
virtual void generateFeatures();
@@ -195,10 +161,14 @@ protected:
};
std::vector features;
- cv::Mat sum;
+ int npos, nneg;
+ int maxCatCount;
+ int numFeatures;
+ cv::Size winSize;
+ cv::Mat cls, sum;
};
-inline uchar LBPEvaluator::Feature::calc(const cv::Mat &_sum, size_t y) const
+inline uchar LBPTrainingEvaluator::Feature::calc(const cv::Mat &_sum, size_t y) const
{
const int* psum = _sum.ptr((int)y);
int cval = psum[p[5]] - psum[p[6]] - psum[p[9]] + psum[p[10]];
diff --git a/openbr/openbr_plugin.h b/openbr/openbr_plugin.h
index 493495f..bce5790 100644
--- a/openbr/openbr_plugin.h
+++ b/openbr/openbr_plugin.h
@@ -1404,7 +1404,12 @@ public:
// By convention, an empty indices list will result in all feature responses being calculated
// and returned.
virtual cv::Mat evaluate(const cv::Mat &image, const QList &indices = QList()) const = 0;
+
virtual int numFeatures() const = 0;
+ virtual cv::Size windowSize() const = 0;
+
+ // Temporary for OpenCV compatibility
+ virtual void write( cv::FileStorage &fs, const cv::Mat &featureMap ) { (void)fs; (void)featureMap; }
};
class BR_EXPORT Classifier : public Object
diff --git a/openbr/plugins/classification/boost.cpp b/openbr/plugins/classification/boostedforest.cpp
index b61ff5d..9a98992 100644
--- a/openbr/plugins/classification/boost.cpp
+++ b/openbr/plugins/classification/boostedforest.cpp
@@ -7,7 +7,7 @@ using namespace cv;
namespace br
{
-class BoostClassifier : public Classifier
+class BoostedForestClassifier : public Classifier
{
Q_OBJECT
@@ -27,17 +27,7 @@ class BoostClassifier : public Classifier
void train(const QList &images, const QList &labels)
{
- FeatureEvaluator *featureEvaluator = FeatureEvaluator::create(0);
- featureEvaluator->init(new FeatureParams, images.size(), Size(24, 24));
- for (int i = 0; i < images.size(); i++)
- featureEvaluator->setImage(images[i], (uchar)labels[i], i);
-
- CascadeBoostParams stageParams(CvBoost::GENTLE, minTAR, maxFAR, trimRate, maxDepth, maxWeakCount);
-
- if (boost) delete boost;
-
- boost = new CascadeBoost;
- boost->train(featureEvaluator, images.size(), 1024, 1024, stageParams);
+ (void)images; (void)labels;
}
float classify(const Mat &image) const
@@ -47,8 +37,8 @@ class BoostClassifier : public Classifier
}
};
-BR_REGISTER(Classifier, BoostClassifier)
+BR_REGISTER(Classifier, BoostedForestClassifier)
} // namespace br
-#include "classification/boost.moc"
+#include "classification/boostedforest.moc"
diff --git a/openbr/plugins/classification/cascade.cpp b/openbr/plugins/classification/cascade.cpp
index 03f2066..4006403 100644
--- a/openbr/plugins/classification/cascade.cpp
+++ b/openbr/plugins/classification/cascade.cpp
@@ -6,7 +6,7 @@ using namespace cv;
namespace br
{
-class CascadeClassifier : public Classifier
+class _CascadeClassifier : public Classifier
{
Q_OBJECT
@@ -151,7 +151,7 @@ private:
};
};
-BR_REGISTER(Classifier, CascadeClassifier)
+BR_REGISTER(Classifier, _CascadeClassifier)
} // namespace br
diff --git a/openbr/plugins/imgproc/slidingwindow.cpp b/openbr/plugins/imgproc/slidingwindow.cpp
index 214dea1..24ce263 100644
--- a/openbr/plugins/imgproc/slidingwindow.cpp
+++ b/openbr/plugins/imgproc/slidingwindow.cpp
@@ -14,50 +14,287 @@
* limitations under the License. *
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+#include
+
#include
#include
+#include
+
+#include
using namespace cv;
namespace br
{
+/*
+class CascadeResourceMaker : public ResourceMaker
+{
+ QString file;
+public:
+ CascadeResourceMaker(const QString &model)
+ {
+ file = Globals->sdkPath + "/share/openbr/models/";
+ if (model == "Ear") file += "haarcascades/haarcascade_ear.xml";
+ else if (model == "Eye") file += "haarcascades/haarcascade_eye_tree_eyeglasses.xml";
+ else if (model == "FrontalFace") file += "haarcascades/haarcascade_frontalface_alt2.xml";
+ else if (model == "ProfileFace") file += "haarcascades/haarcascade_profileface.xml";
+ else {
+ // Create a directory for trainable cascades
+ file += "openbrcascades/"+model+"/cascade.xml";
+ QFile touchFile(file);
+ QtUtils::touchDir(touchFile);
+ }
+ }
+
+private:
+ CascadeClassifier *make() const
+ {
+ CascadeClassifier *cascade = new CascadeClassifier();
+ if (!cascade->load(file.toStdString()))
+ qFatal("Failed to load: %s", qPrintable(file));
+ return cascade;
+ }
+};
+*/
/*!
* \ingroup transforms
* \brief Applies a classifier to a sliding window.
* Discards negative detections.
* \author Jordan Cheney \cite JordanCheney
*/
+ /*
class SlidingWindowTransform : public Transform
{
Q_OBJECT
+
Q_PROPERTY(br::Classifier *classifier READ get_classifier WRITE set_classifier RESET reset_classifier STORED false)
+ Q_PROPERTY(QString vecFile READ get_vecFile WRITE set_vecFile RESET reset_vecFile STORED false)
+ Q_PROPERTY(QString negFile READ get_negFile WRITE set_negFile RESET reset_negFile STORED false)
Q_PROPERTY(int minSize READ get_minSize WRITE set_minSize RESET reset_minSize STORED false)
Q_PROPERTY(int maxSize READ get_maxSize WRITE set_maxSize RESET reset_maxSize STORED false)
Q_PROPERTY(float scaleFactor READ get_scaleFactor WRITE set_scaleFactor RESET reset_scaleFactor STORED false)
BR_PROPERTY(br::Classifier *, classifier, NULL)
+ BR_PROPERTY(QString, vecFile, "vec.vec")
+ BR_PROPERTY(QString, negFile, "neg.txt")
BR_PROPERTY(int, minSize, 20)
BR_PROPERTY(int, maxSize, -1)
BR_PROPERTY(float, scaleFactor, 1.2)
+ Resource cascadeResource;
+
+ QList getPos()
+ {
+ FILE *file = fopen(vecFile.toStdString().c_str(), "rb");
+ if ( !file )
+ qFatal("Couldn't open the file");
+
+ short* vec = 0;
+ int count, vecSize, last, base;
+
+ short tmp = 0;
+ if( fread( &count, sizeof( count ), 1, file ) != 1 ||
+ fread( &vecSize, sizeof( vecSize ), 1, file ) != 1 ||
+ fread( &tmp, sizeof( tmp ), 1, file ) != 1 ||
+ fread( &tmp, sizeof( tmp ), 1, file ) != 1 )
+ CV_Error_( CV_StsParseError, ("wrong file format for %s\n", qPrintable(vecFile)) );
+ base = sizeof( count ) + sizeof( vecSize ) + 2*sizeof( tmp );
+
+ last = 0;
+ vec = (short*) cvAlloc( sizeof( *vec ) * vecSize );
+ CV_Assert( vec );
+
+ QList posImages;
+ for (int i = 0; i < 35770; i++) {
+ Mat pos(24, 24, CV_8UC1);
+
+ CV_Assert( pos.rows * pos.cols == vecSize );
+ uchar tmp = 0;
+ size_t elements_read = fread( &tmp, sizeof( tmp ), 1, file );
+ if( elements_read != 1 )
+ CV_Error( CV_StsBadArg, "Can not get new positive sample. The most possible reason is "
+ "insufficient count of samples in given vec-file.\n");
+ elements_read = fread( vec, sizeof( vec[0] ), vecSize, file );
+ if( elements_read != (size_t)(vecSize) )
+ CV_Error( CV_StsBadArg, "Can not get new positive sample. Seems that vec-file has incorrect structure.\n");
+
+ if( feof( file ) || last++ >= count )
+ CV_Error( CV_StsBadArg, "Can not get new positive sample. vec-file is over.\n");
+
+ for( int r = 0; r < pos.rows; r++ )
+ for( int c = 0; c < pos.cols; c++ )
+ pos.ptr(r)[c] = (uchar)vec[r * pos.cols + c];
+ posImages.append(pos);
+ }
+ return posImages;
+ }
+
+ QList getNeg()
+ {
+ QList negs;
+
+ std::string dirname, str;
+ std::ifstream file(negFile.toStdString().c_str());
+
+ size_t pos = negFile.toStdString().rfind('\\');
+ char dlmrt = '\\';
+ if (pos == string::npos)
+ {
+ pos = negFile.toStdString().rfind('/');
+ dlmrt = '/';
+ }
+ dirname = pos == string::npos ? "" : negFile.toStdString().substr(0, pos) + dlmrt;
+ while( !file.eof() )
+ {
+ std::getline(file, str);
+ if (str.empty()) break;
+ if (str.at(0) == '#' ) continue;
+ negs.append(imread(dirname + str, CV_LOAD_IMAGE_GRAYSCALE));
+ }
+ file.close();
+
+ return negs;
+ }
+
+ void init()
+ {
+ cascadeResource.setResourceMaker(new CascadeResourceMaker(model));
+ if (model == "Ear" || model == "Eye" || model == "FrontalFace" || model == "ProfileFace")
+ this->trainable = false;
+ }
+
void train(const TemplateList &_data)
{
- QList data = _data.data();
- QList labels = File::get(_data, "Label");
+ (void)_data;
+
+ QList posImages = getPos();
+ QList negImages = getNeg();
- classifier->train(data, labels);
+ QList images; QList labels;
+ foreach (const Mat &pos, posImages) {
+ images.append(pos);
+ labels.append(1.);
+ }
+
+ foreach (const Mat &neg, negImages) {
+ images.append(neg);
+ labels.append(0.);
+ }
+
+ classifier->train(images, labels);
}
- void project(const Template &src, Template &dst) const
+ void project(const Template &src, Template &dst) const
{
- (void) src;
- (void) dst;
+ TemplateList temp;
+ project(TemplateList() << src, temp);
+ if (!temp.isEmpty()) dst = temp.first();
+ }
+
+ void project(const TemplateList &src, TemplateList &dst) const
+ {
+ CascadeClassifier *cascade = cascadeResource.acquire();
+ foreach (const Template &t, src) {
+ const bool enrollAll = t.file.getBool("enrollAll");
+
+ // Mirror the behavior of ExpandTransform in the special case
+ // of an empty template.
+ if (t.empty() && !enrollAll) {
+ dst.append(t);
+ continue;
+ }
+
+ for (int i=0; i rects;
+ QList levels;
+ QList weights;
+
+ for (double factor = 1; ; factor *= 1.2) {
+ Size originalWindowSize = Size(winWidth, winHeight);
+
+ Size windowSize( cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) );
+ Size scaledImageSize( cvRound( m.cols/factor ), cvRound( m.rows/factor ) );
+ Size processingRectSize( scaledImageSize.width - originalWindowSize.width, scaledImageSize.height - originalWindowSize.height );
+
+ if( processingRectSize.width <= 0 || processingRectSize.height <= 0 )
+ break;
+ if( windowSize.width > maxSize.width || windowSize.height > maxSize.height )
+ break;
+ if( windowSize.width < minSize || windowSize.height < minSize )
+ continue;
+
+ Mat scaledImage( scaledImageSize, CV_8U, imageBuffer.data );
+ resize( m, scaledImage, scaledImageSize, 0, 0, CV_INTER_LINEAR );
+
+ int yStep = factor > 2. ? 1 : 2;
+ int stripCount, stripSize;
+
+ const int PTS_PER_THREAD = 1000;
+ stripCount = ((processingRectSize.width/yStep)*(processingRectSize.height + yStep-1)/yStep + PTS_PER_THREAD/2)/PTS_PER_THREAD;
+ stripCount = std::min(std::max(stripCount, 1), 100);
+ stripSize = (((processingRectSize.height + stripCount - 1)/stripCount + yStep-1)/yStep)*yStep;
+
+ if( !cascade->setImage( scaledImage ) )
+ qFatal("Can't set an image. Don't know why");
+
+ for( int y = 0; y < min(stripCount * stripSize, processingRectSize.height); y += yStep ) {
+ for( int x = 0; x < processingRectSize.width; x += yStep ) {
+ double gypWeight;
+ int result = cascade->runAt(cascade->featureEvaluator, Point(x, y), gypWeight);
+
+ if (ROCMode) {
+ if (result == 1)
+ result = -(int)cascade->data.stages.size();
+ if (cascade->data.stages.size() + result < 4 ) {
+ rects.append(Rect(cvRound(x*factor), cvRound(y*factor), windowSize.width, windowSize.height));
+ levels.append(-result);
+ weights.append(gypWeight);
+ }
+ }
+ else if( result > 0 ) {
+ rects.append(Rect(cvRound(x*factor), cvRound(y*factor), windowSize.width, windowSize.height));
+ }
+ if( result == 0 )
+ x += yStep;
+ }
+ }
+ }
+
+ if (ROCMode)
+ groupRectangles(rects, levels, weights, minNeighbors, 0.2);
+ else
+ groupRectangles(rects, minNeighbors, 0.2);
+
+ if (!enrollAll && rects.empty())
+ rects.append(Rect(0, 0, m.cols, m.rows));
+
+ for (int j = 0; j < rects.size(); j++) {
+ Template u(t.file, m);
+ if (levels.size() > j)
+ u.file.set("Confidence", levels[j]*weights[j]);
+ else
+ u.file.set("Confidence", 1);
+ const QRectF rect = OpenCVUtils::fromRect(rects[j]);
+ u.file.appendRect(rect);
+ u.file.set(model, rect);
+ dst.append(u);
+ }
+ }
+ }
+ cascadeResource.release(cascade);
}
};
BR_REGISTER(Transform, SlidingWindowTransform)
-
+*/
} // namespace br
diff --git a/openbr/plugins/metadata/cascade.cpp b/openbr/plugins/metadata/cascade.cpp
index 1576ce5..74bea66 100644
--- a/openbr/plugins/metadata/cascade.cpp
+++ b/openbr/plugins/metadata/cascade.cpp
@@ -22,7 +22,7 @@
#include
#include
#include
-#include
+#include
using namespace cv;
@@ -187,18 +187,15 @@ class CascadeTransform : public MetaTransform
BrCascadeClassifier classifier;
- CascadeParams cascadeParams(CascadeParams::BOOST, FeatureParams::LBP);
- cascadeParams.winSize = Size(winWidth, winHeight);
-
CascadeBoostParams stageParams(CvBoost::GENTLE, 0.999, 0.5, 0.95, 1, 200);
- LBPFeatureParams featureParams;
+
+ Representation *representation = Representation::make("MBLBP(24,24)", NULL);
QString cascadeDir = Globals->sdkPath + "/share/openbr/models/openbrcascades/" + model;
classifier.train(cascadeDir.toStdString(),
- posImages.toVector().toStdVector(),
- negImages.toVector().toStdVector(),
- numPos, numNeg, 1024, 1024, numStages,
- cascadeParams, featureParams, stageParams);
+ posImages, negImages,
+ 1024, 1024, numPos, numNeg, numStages,
+ representation, stageParams);
}
void project(const Template &src, Template &dst) const
diff --git a/openbr/plugins/representation/mblbp.cpp b/openbr/plugins/representation/mblbp.cpp
new file mode 100644
index 0000000..53aad12
--- /dev/null
+++ b/openbr/plugins/representation/mblbp.cpp
@@ -0,0 +1,117 @@
+#include
+
+#include
+
+using namespace cv;
+
+namespace br
+{
+
+#define CV_SUM_OFFSETS( p0, p1, p2, p3, rect, step ) \
+ /* (x, y) */ \
+ (p0) = (rect).x + (step) * (rect).y; \
+ /* (x + w, y) */ \
+ (p1) = (rect).x + (rect).width + (step) * (rect).y; \
+ /* (x + w, y) */ \
+ (p2) = (rect).x + (step) * ((rect).y + (rect).height); \
+ /* (x + w, y + h) */ \
+ (p3) = (rect).x + (rect).width + (step) * ((rect).y + (rect).height);
+
+class MBLBPRepresentation : public Representation
+{
+ Q_OBJECT
+
+ Q_PROPERTY(int winWidth READ get_winWidth WRITE set_winWidth RESET reset_winWidth STORED false)
+ Q_PROPERTY(int winHeight READ get_winHeight WRITE set_winHeight RESET reset_winHeight STORED false)
+ BR_PROPERTY(int, winWidth, 24)
+ BR_PROPERTY(int, winHeight, 24)
+
+ void init()
+ {
+ int offset = winWidth + 1;
+ for( int x = 0; x < winWidth; x++ )
+ for( int y = 0; y < winHeight; y++ )
+ for( int w = 1; w <= winWidth / 3; w++ )
+ for( int h = 1; h <= winHeight / 3; h++ )
+ if ( (x+3*w <= winWidth) && (y+3*h <= winHeight) )
+ features.push_back( Feature(offset, x, y, w, h ) );
+ }
+
+ Mat preprocess(const Mat &image) const
+ {
+ Mat integralImage;
+ integral(image, integralImage);
+ return integralImage;
+ }
+
+ Mat evaluate(const Mat &image, const QList &indices) const
+ {
+ Mat result(1, indices.size(), CV_32FC1);
+ for (int i = 0; i < indices.size(); i++)
+ result.at(i) = (float)features[indices[i]].calc(image);
+ return result;
+ }
+
+ void write(FileStorage &fs, const Mat &featureMap);
+ int numFeatures() const { return features.size(); }
+ Size windowSize() const { return Size(winWidth + 1, winHeight + 1); }
+
+ struct Feature
+ {
+ Feature() { rect = Rect(0, 0, 0, 0); }
+ Feature( int offset, int x, int y, int _block_w, int _block_h );
+ uchar calc(const Mat &img) const;
+ void write( FileStorage &fs ) const { fs << "rect" << "[:" << rect.x << rect.y << rect.width << rect.height << "]"; }
+
+ Rect rect;
+ int p[16];
+ };
+ QList features;
+};
+
+BR_REGISTER(Representation, MBLBPRepresentation)
+
+void MBLBPRepresentation::write(FileStorage &fs, const Mat &featureMap)
+{
+ fs << "features" << "[";
+ const Mat_& featureMap_ = (const Mat_&)featureMap;
+ for ( int fi = 0; fi < featureMap.cols; fi++ )
+ if ( featureMap_(0, fi) >= 0 )
+ {
+ fs << "{";
+ features[fi].write( fs );
+ fs << "}";
+ }
+ fs << "]";
+}
+
+MBLBPRepresentation::Feature::Feature( int offset, int x, int y, int _blockWidth, int _blockHeight )
+{
+ Rect tr = rect = cvRect(x, y, _blockWidth, _blockHeight);
+ CV_SUM_OFFSETS( p[0], p[1], p[4], p[5], tr, offset )
+ tr.x += 2*rect.width;
+ CV_SUM_OFFSETS( p[2], p[3], p[6], p[7], tr, offset )
+ tr.y +=2*rect.height;
+ CV_SUM_OFFSETS( p[10], p[11], p[14], p[15], tr, offset )
+ tr.x -= 2*rect.width;
+ CV_SUM_OFFSETS( p[8], p[9], p[12], p[13], tr, offset )
+}
+
+inline uchar MBLBPRepresentation::Feature::calc(const Mat &img) const
+{
+ const int* psum = img.ptr();
+ int cval = psum[p[5]] - psum[p[6]] - psum[p[9]] + psum[p[10]];
+
+ return (uchar)((psum[p[0]] - psum[p[1]] - psum[p[4]] + psum[p[5]] >= cval ? 128 : 0) | // 0
+ (psum[p[1]] - psum[p[2]] - psum[p[5]] + psum[p[6]] >= cval ? 64 : 0) | // 1
+ (psum[p[2]] - psum[p[3]] - psum[p[6]] + psum[p[7]] >= cval ? 32 : 0) | // 2
+ (psum[p[6]] - psum[p[7]] - psum[p[10]] + psum[p[11]] >= cval ? 16 : 0) | // 5
+ (psum[p[10]] - psum[p[11]] - psum[p[14]] + psum[p[15]] >= cval ? 8 : 0) | // 8
+ (psum[p[9]] - psum[p[10]] - psum[p[13]] + psum[p[14]] >= cval ? 4 : 0) | // 7
+ (psum[p[8]] - psum[p[9]] - psum[p[12]] + psum[p[13]] >= cval ? 2 : 0) | // 6
+ (psum[p[4]] - psum[p[5]] - psum[p[8]] + psum[p[9]] >= cval ? 1 : 0)); // 3
+}
+
+} // namespace br
+
+#include "representation/mblbp.moc"