mirror of
https://github.com/opencv/opencv.git
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541 lines
19 KiB
C++
541 lines
19 KiB
C++
#include "opencv2/core/core.hpp"
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#include "opencv2/core/internal.hpp"
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#include "cascadeclassifier.h"
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#include <queue>
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using namespace std;
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static const char* stageTypes[] = { CC_BOOST };
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static const char* featureTypes[] = { CC_HAAR, CC_LBP, CC_HOG };
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CvCascadeParams::CvCascadeParams() : stageType( defaultStageType ),
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featureType( defaultFeatureType ), winSize( cvSize(24, 24) )
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{
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name = CC_CASCADE_PARAMS;
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}
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CvCascadeParams::CvCascadeParams( int _stageType, int _featureType ) : stageType( _stageType ),
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featureType( _featureType ), winSize( cvSize(24, 24) )
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{
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name = CC_CASCADE_PARAMS;
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}
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//---------------------------- CascadeParams --------------------------------------
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void CvCascadeParams::write( FileStorage &fs ) const
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{
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String stageTypeStr = stageType == BOOST ? CC_BOOST : String();
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CV_Assert( !stageTypeStr.empty() );
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fs << CC_STAGE_TYPE << stageTypeStr;
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String featureTypeStr = featureType == CvFeatureParams::HAAR ? CC_HAAR :
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featureType == CvFeatureParams::LBP ? CC_LBP :
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featureType == CvFeatureParams::HOG ? CC_HOG :
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0;
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CV_Assert( !stageTypeStr.empty() );
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fs << CC_FEATURE_TYPE << featureTypeStr;
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fs << CC_HEIGHT << winSize.height;
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fs << CC_WIDTH << winSize.width;
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}
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bool CvCascadeParams::read( const FileNode &node )
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{
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if ( node.empty() )
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return false;
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String stageTypeStr, featureTypeStr;
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FileNode rnode = node[CC_STAGE_TYPE];
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if ( !rnode.isString() )
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return false;
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rnode >> stageTypeStr;
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stageType = !stageTypeStr.compare( CC_BOOST ) ? BOOST : -1;
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if (stageType == -1)
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return false;
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rnode = node[CC_FEATURE_TYPE];
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if ( !rnode.isString() )
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return false;
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rnode >> featureTypeStr;
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featureType = !featureTypeStr.compare( CC_HAAR ) ? CvFeatureParams::HAAR :
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!featureTypeStr.compare( CC_LBP ) ? CvFeatureParams::LBP :
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!featureTypeStr.compare( CC_HOG ) ? CvFeatureParams::HOG :
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-1;
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if (featureType == -1)
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return false;
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node[CC_HEIGHT] >> winSize.height;
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node[CC_WIDTH] >> winSize.width;
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return winSize.height > 0 && winSize.width > 0;
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}
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void CvCascadeParams::printDefaults() const
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{
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CvParams::printDefaults();
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cout << " [-stageType <";
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for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
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{
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cout << (i ? " | " : "") << stageTypes[i];
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if ( i == defaultStageType )
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cout << "(default)";
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}
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cout << ">]" << endl;
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cout << " [-featureType <{";
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for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
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{
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cout << (i ? ", " : "") << featureTypes[i];
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if ( i == defaultStageType )
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cout << "(default)";
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}
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cout << "}>]" << endl;
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cout << " [-w <sampleWidth = " << winSize.width << ">]" << endl;
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cout << " [-h <sampleHeight = " << winSize.height << ">]" << endl;
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}
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void CvCascadeParams::printAttrs() const
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{
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cout << "stageType: " << stageTypes[stageType] << endl;
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cout << "featureType: " << featureTypes[featureType] << endl;
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cout << "sampleWidth: " << winSize.width << endl;
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cout << "sampleHeight: " << winSize.height << endl;
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}
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bool CvCascadeParams::scanAttr( const String prmName, const String val )
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{
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bool res = true;
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if( !prmName.compare( "-stageType" ) )
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{
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for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
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if( !val.compare( stageTypes[i] ) )
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stageType = i;
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}
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else if( !prmName.compare( "-featureType" ) )
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{
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for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
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if( !val.compare( featureTypes[i] ) )
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featureType = i;
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}
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else if( !prmName.compare( "-w" ) )
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{
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winSize.width = atoi( val.c_str() );
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}
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else if( !prmName.compare( "-h" ) )
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{
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winSize.height = atoi( val.c_str() );
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}
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else
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res = false;
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return res;
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}
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//---------------------------- CascadeClassifier --------------------------------------
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bool CvCascadeClassifier::train( const String _cascadeDirName,
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const String _posFilename,
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const String _negFilename,
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int _numPos, int _numNeg,
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int _precalcValBufSize, int _precalcIdxBufSize,
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int _numStages,
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const CvCascadeParams& _cascadeParams,
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const CvFeatureParams& _featureParams,
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const CvCascadeBoostParams& _stageParams,
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bool baseFormatSave )
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{
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if( _cascadeDirName.empty() || _posFilename.empty() || _negFilename.empty() )
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CV_Error( CV_StsBadArg, "_cascadeDirName or _bgfileName or _vecFileName is NULL" );
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string dirName;
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if (_cascadeDirName.find_last_of("/\\") == (_cascadeDirName.length() - 1) )
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dirName = _cascadeDirName;
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else
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dirName = _cascadeDirName + '/';
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numPos = _numPos;
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numNeg = _numNeg;
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numStages = _numStages;
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if ( !imgReader.create( _posFilename, _negFilename, _cascadeParams.winSize ) )
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{
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cout << "Image reader can not be created from -vec " << _posFilename
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<< " and -bg " << _negFilename << "." << endl;
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return false;
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}
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if ( !load( dirName ) )
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{
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cascadeParams = _cascadeParams;
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featureParams = CvFeatureParams::create(cascadeParams.featureType);
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featureParams->init(_featureParams);
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stageParams = new CvCascadeBoostParams;
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*stageParams = _stageParams;
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featureEvaluator = CvFeatureEvaluator::create(cascadeParams.featureType);
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featureEvaluator->init( (CvFeatureParams*)featureParams, numPos + numNeg, cascadeParams.winSize );
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stageClassifiers.reserve( numStages );
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}
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cout << "PARAMETERS:" << endl;
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cout << "cascadeDirName: " << _cascadeDirName << endl;
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cout << "vecFileName: " << _posFilename << endl;
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cout << "bgFileName: " << _negFilename << endl;
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cout << "numPos: " << _numPos << endl;
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cout << "numNeg: " << _numNeg << endl;
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cout << "numStages: " << numStages << endl;
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cout << "precalcValBufSize[Mb] : " << _precalcValBufSize << endl;
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cout << "precalcIdxBufSize[Mb] : " << _precalcIdxBufSize << endl;
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cascadeParams.printAttrs();
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stageParams->printAttrs();
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featureParams->printAttrs();
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int startNumStages = (int)stageClassifiers.size();
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if ( startNumStages > 1 )
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cout << endl << "Stages 0-" << startNumStages-1 << " are loaded" << endl;
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else if ( startNumStages == 1)
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cout << endl << "Stage 0 is loaded" << endl;
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double requiredLeafFARate = pow( (double) stageParams->maxFalseAlarm, (double) numStages ) /
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(double)stageParams->max_depth;
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double tempLeafFARate;
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for( int i = startNumStages; i < numStages; i++ )
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{
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cout << endl << "===== TRAINING " << i << "-stage =====" << endl;
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cout << "<BEGIN" << endl;
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if ( !updateTrainingSet( tempLeafFARate ) )
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{
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cout << "Train dataset for temp stage can not be filled. "
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"Branch training terminated." << endl;
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break;
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}
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if( tempLeafFARate <= requiredLeafFARate )
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{
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cout << "Required leaf false alarm rate achieved. "
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"Branch training terminated." << endl;
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break;
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}
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CvCascadeBoost* tempStage = new CvCascadeBoost;
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bool isStageTrained = tempStage->train( (CvFeatureEvaluator*)featureEvaluator,
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curNumSamples, _precalcValBufSize, _precalcIdxBufSize,
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*((CvCascadeBoostParams*)stageParams) );
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cout << "END>" << endl;
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if(!isStageTrained)
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break;
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stageClassifiers.push_back( tempStage );
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// save params
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if( i == 0)
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{
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std::string paramsFilename = dirName + CC_PARAMS_FILENAME;
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FileStorage fs( paramsFilename, FileStorage::WRITE);
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if ( !fs.isOpened() )
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{
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cout << "Parameters can not be written, because file " << paramsFilename
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<< " can not be opened." << endl;
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return false;
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}
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fs << FileStorage::getDefaultObjectName(paramsFilename) << "{";
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writeParams( fs );
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fs << "}";
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}
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// save current stage
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char buf[10];
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sprintf(buf, "%s%d", "stage", i );
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string stageFilename = dirName + buf + ".xml";
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FileStorage fs( stageFilename, FileStorage::WRITE );
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if ( !fs.isOpened() )
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{
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cout << "Current stage can not be written, because file " << stageFilename
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<< " can not be opened." << endl;
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return false;
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}
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fs << FileStorage::getDefaultObjectName(stageFilename) << "{";
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tempStage->write( fs, Mat() );
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fs << "}";
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}
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if(stageClassifiers.size() == 0)
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{
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cout << "Cascade classifier can't be trained. Check the used training parameters." << endl;
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return false;
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}
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save( dirName + CC_CASCADE_FILENAME, baseFormatSave );
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return true;
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}
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int CvCascadeClassifier::predict( int sampleIdx )
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{
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CV_DbgAssert( sampleIdx < numPos + numNeg );
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for (vector< Ptr<CvCascadeBoost> >::iterator it = stageClassifiers.begin();
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it != stageClassifiers.end(); it++ )
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{
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if ( (*it)->predict( sampleIdx ) == 0.f )
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return 0;
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}
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return 1;
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}
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bool CvCascadeClassifier::updateTrainingSet( double& acceptanceRatio)
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{
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int64 posConsumed = 0, negConsumed = 0;
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imgReader.restart();
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int posCount = fillPassedSamples( 0, numPos, true, posConsumed );
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if( !posCount )
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return false;
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cout << "POS count : consumed " << posCount << " : " << (int)posConsumed << endl;
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int proNumNeg = cvRound( ( ((double)numNeg) * ((double)posCount) ) / numPos ); // apply only a fraction of negative samples. double is required since overflow is possible
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int negCount = fillPassedSamples( posCount, proNumNeg, false, negConsumed );
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if ( !negCount )
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return false;
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curNumSamples = posCount + negCount;
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acceptanceRatio = negConsumed == 0 ? 0 : ( (double)negCount/(double)(int64)negConsumed );
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cout << "NEG count : acceptanceRatio " << negCount << " : " << acceptanceRatio << endl;
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return true;
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}
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int CvCascadeClassifier::fillPassedSamples( int first, int count, bool isPositive, int64& consumed )
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{
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int getcount = 0;
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Mat img(cascadeParams.winSize, CV_8UC1);
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for( int i = first; i < first + count; i++ )
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{
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for( ; ; )
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{
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bool isGetImg = isPositive ? imgReader.getPos( img ) :
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imgReader.getNeg( img );
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if( !isGetImg )
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return getcount;
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consumed++;
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featureEvaluator->setImage( img, isPositive ? 1 : 0, i );
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if( predict( i ) == 1.0F )
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{
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getcount++;
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break;
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}
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}
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}
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return getcount;
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}
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void CvCascadeClassifier::writeParams( FileStorage &fs ) const
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{
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cascadeParams.write( fs );
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fs << CC_STAGE_PARAMS << "{"; stageParams->write( fs ); fs << "}";
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fs << CC_FEATURE_PARAMS << "{"; featureParams->write( fs ); fs << "}";
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}
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void CvCascadeClassifier::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
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{
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((CvFeatureEvaluator*)((Ptr<CvFeatureEvaluator>)featureEvaluator))->writeFeatures( fs, featureMap );
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}
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void CvCascadeClassifier::writeStages( FileStorage &fs, const Mat& featureMap ) const
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{
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char cmnt[30];
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int i = 0;
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fs << CC_STAGES << "[";
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for( vector< Ptr<CvCascadeBoost> >::const_iterator it = stageClassifiers.begin();
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it != stageClassifiers.end(); it++, i++ )
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{
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sprintf( cmnt, "stage %d", i );
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cvWriteComment( fs.fs, cmnt, 0 );
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fs << "{";
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((CvCascadeBoost*)((Ptr<CvCascadeBoost>)*it))->write( fs, featureMap );
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fs << "}";
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}
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fs << "]";
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}
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bool CvCascadeClassifier::readParams( const FileNode &node )
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{
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if ( !node.isMap() || !cascadeParams.read( node ) )
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return false;
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stageParams = new CvCascadeBoostParams;
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FileNode rnode = node[CC_STAGE_PARAMS];
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if ( !stageParams->read( rnode ) )
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return false;
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featureParams = CvFeatureParams::create(cascadeParams.featureType);
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rnode = node[CC_FEATURE_PARAMS];
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if ( !featureParams->read( rnode ) )
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return false;
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return true;
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}
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bool CvCascadeClassifier::readStages( const FileNode &node)
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{
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FileNode rnode = node[CC_STAGES];
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if (!rnode.empty() || !rnode.isSeq())
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return false;
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stageClassifiers.reserve(numStages);
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FileNodeIterator it = rnode.begin();
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for( int i = 0; i < min( (int)rnode.size(), numStages ); i++, it++ )
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{
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CvCascadeBoost* tempStage = new CvCascadeBoost;
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if ( !tempStage->read( *it, (CvFeatureEvaluator *)featureEvaluator, *((CvCascadeBoostParams*)stageParams) ) )
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{
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delete tempStage;
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return false;
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}
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stageClassifiers.push_back(tempStage);
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}
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return true;
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}
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// For old Haar Classifier file saving
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#define ICV_HAAR_SIZE_NAME "size"
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#define ICV_HAAR_STAGES_NAME "stages"
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#define ICV_HAAR_TREES_NAME "trees"
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#define ICV_HAAR_FEATURE_NAME "feature"
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#define ICV_HAAR_RECTS_NAME "rects"
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#define ICV_HAAR_TILTED_NAME "tilted"
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#define ICV_HAAR_THRESHOLD_NAME "threshold"
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#define ICV_HAAR_LEFT_NODE_NAME "left_node"
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#define ICV_HAAR_LEFT_VAL_NAME "left_val"
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#define ICV_HAAR_RIGHT_NODE_NAME "right_node"
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#define ICV_HAAR_RIGHT_VAL_NAME "right_val"
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#define ICV_HAAR_STAGE_THRESHOLD_NAME "stage_threshold"
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#define ICV_HAAR_PARENT_NAME "parent"
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#define ICV_HAAR_NEXT_NAME "next"
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void CvCascadeClassifier::save( const String filename, bool baseFormat )
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{
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FileStorage fs( filename, FileStorage::WRITE );
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if ( !fs.isOpened() )
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return;
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fs << FileStorage::getDefaultObjectName(filename) << "{";
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if ( !baseFormat )
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{
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Mat featureMap;
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getUsedFeaturesIdxMap( featureMap );
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writeParams( fs );
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fs << CC_STAGE_NUM << (int)stageClassifiers.size();
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writeStages( fs, featureMap );
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writeFeatures( fs, featureMap );
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}
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else
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{
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//char buf[256];
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CvSeq* weak;
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if ( cascadeParams.featureType != CvFeatureParams::HAAR )
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CV_Error( CV_StsBadFunc, "old file format is used for Haar-like features only");
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fs << ICV_HAAR_SIZE_NAME << "[:" << cascadeParams.winSize.width <<
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cascadeParams.winSize.height << "]";
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fs << ICV_HAAR_STAGES_NAME << "[";
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for( size_t si = 0; si < stageClassifiers.size(); si++ )
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{
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fs << "{"; //stage
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/*sprintf( buf, "stage %d", si );
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CV_CALL( cvWriteComment( fs, buf, 1 ) );*/
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weak = stageClassifiers[si]->get_weak_predictors();
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fs << ICV_HAAR_TREES_NAME << "[";
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for( int wi = 0; wi < weak->total; wi++ )
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{
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int inner_node_idx = -1, total_inner_node_idx = -1;
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queue<const CvDTreeNode*> inner_nodes_queue;
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CvCascadeBoostTree* tree = *((CvCascadeBoostTree**) cvGetSeqElem( weak, wi ));
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fs << "[";
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/*sprintf( buf, "tree %d", wi );
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CV_CALL( cvWriteComment( fs, buf, 1 ) );*/
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const CvDTreeNode* tempNode;
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inner_nodes_queue.push( tree->get_root() );
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total_inner_node_idx++;
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while (!inner_nodes_queue.empty())
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{
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tempNode = inner_nodes_queue.front();
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inner_node_idx++;
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fs << "{";
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fs << ICV_HAAR_FEATURE_NAME << "{";
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((CvHaarEvaluator*)((CvFeatureEvaluator*)featureEvaluator))->writeFeature( fs, tempNode->split->var_idx );
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fs << "}";
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fs << ICV_HAAR_THRESHOLD_NAME << tempNode->split->ord.c;
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if( tempNode->left->left || tempNode->left->right )
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{
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inner_nodes_queue.push( tempNode->left );
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total_inner_node_idx++;
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fs << ICV_HAAR_LEFT_NODE_NAME << total_inner_node_idx;
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}
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else
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fs << ICV_HAAR_LEFT_VAL_NAME << tempNode->left->value;
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if( tempNode->right->left || tempNode->right->right )
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{
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inner_nodes_queue.push( tempNode->right );
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total_inner_node_idx++;
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fs << ICV_HAAR_RIGHT_NODE_NAME << total_inner_node_idx;
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}
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else
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fs << ICV_HAAR_RIGHT_VAL_NAME << tempNode->right->value;
|
|
fs << "}"; // ICV_HAAR_FEATURE_NAME
|
|
inner_nodes_queue.pop();
|
|
}
|
|
fs << "]";
|
|
}
|
|
fs << "]"; //ICV_HAAR_TREES_NAME
|
|
fs << ICV_HAAR_STAGE_THRESHOLD_NAME << stageClassifiers[si]->getThreshold();
|
|
fs << ICV_HAAR_PARENT_NAME << (int)si-1 << ICV_HAAR_NEXT_NAME << -1;
|
|
fs << "}"; //stage
|
|
} /* for each stage */
|
|
fs << "]"; //ICV_HAAR_STAGES_NAME
|
|
}
|
|
fs << "}";
|
|
}
|
|
|
|
bool CvCascadeClassifier::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 = CvFeatureEvaluator::create(cascadeParams.featureType);
|
|
featureEvaluator->init( ((CvFeatureParams*)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;
|
|
CvCascadeBoost *tempStage = new CvCascadeBoost;
|
|
|
|
if ( !tempStage->read( node, (CvFeatureEvaluator*)featureEvaluator, *((CvCascadeBoostParams*)stageParams )) )
|
|
{
|
|
delete tempStage;
|
|
fs.release();
|
|
break;
|
|
}
|
|
stageClassifiers.push_back(tempStage);
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void CvCascadeClassifier::getUsedFeaturesIdxMap( Mat& featureMap )
|
|
{
|
|
int varCount = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
|
|
featureMap.create( 1, varCount, CV_32SC1 );
|
|
featureMap.setTo(Scalar(-1));
|
|
|
|
for( vector< Ptr<CvCascadeBoost> >::const_iterator it = stageClassifiers.begin();
|
|
it != stageClassifiers.end(); it++ )
|
|
((CvCascadeBoost*)((Ptr<CvCascadeBoost>)(*it)))->markUsedFeaturesInMap( featureMap );
|
|
|
|
for( int fi = 0, idx = 0; fi < varCount; fi++ )
|
|
if ( featureMap.at<int>(0, fi) >= 0 )
|
|
featureMap.ptr<int>(0)[fi] = idx++;
|
|
}
|