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Open Source Computer Vision Library
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127 lines
3.7 KiB
127 lines
3.7 KiB
15 years ago
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#ifndef _OPENCV_CASCADECLASSIFIER_H_
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#define _OPENCV_CASCADECLASSIFIER_H_
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#include <ctime>
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#include "traincascade_features.h"
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#include "haarfeatures.h"
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#include "lbpfeatures.h"
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#include "HOGfeatures.h" //new
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#include "boost.h"
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#include "cv.h"
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#include "cxcore.h"
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#define CC_CASCADE_FILENAME "cascade.xml"
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#define CC_PARAMS_FILENAME "params.xml"
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#define CC_CASCADE_PARAMS "cascadeParams"
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#define CC_STAGE_TYPE "stageType"
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#define CC_FEATURE_TYPE "featureType"
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#define CC_HEIGHT "height"
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#define CC_WIDTH "width"
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#define CC_STAGE_NUM "stageNum"
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#define CC_STAGES "stages"
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#define CC_STAGE_PARAMS "stageParams"
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#define CC_BOOST "BOOST"
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#define CC_BOOST_TYPE "boostType"
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#define CC_DISCRETE_BOOST "DAB"
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#define CC_REAL_BOOST "RAB"
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#define CC_LOGIT_BOOST "LB"
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#define CC_GENTLE_BOOST "GAB"
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#define CC_MINHITRATE "minHitRate"
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#define CC_MAXFALSEALARM "maxFalseAlarm"
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#define CC_TRIM_RATE "weightTrimRate"
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#define CC_MAX_DEPTH "maxDepth"
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#define CC_WEAK_COUNT "maxWeakCount"
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#define CC_STAGE_THRESHOLD "stageThreshold"
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#define CC_WEAK_CLASSIFIERS "weakClassifiers"
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#define CC_INTERNAL_NODES "internalNodes"
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#define CC_LEAF_VALUES "leafValues"
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#define CC_FEATURES FEATURES
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#define CC_FEATURE_PARAMS "featureParams"
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#define CC_MAX_CAT_COUNT "maxCatCount"
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#define CC_FEATURE_SIZE "featSize"
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#define CC_HAAR "HAAR"
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#define CC_MODE "mode"
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#define CC_MODE_BASIC "BASIC"
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#define CC_MODE_CORE "CORE"
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#define CC_MODE_ALL "ALL"
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#define CC_RECTS "rects"
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#define CC_TILTED "tilted"
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#define CC_LBP "LBP"
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#define CC_RECT "rect"
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#define CC_HOG "HOG"
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#ifdef _WIN32
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#define TIME( arg ) (((double) clock()) / CLOCKS_PER_SEC)
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#else
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#define TIME( arg ) (time( arg ))
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#endif
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class CvCascadeParams : public CvParams
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{
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public:
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enum { BOOST = 0 };
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static const int defaultStageType = BOOST;
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static const int defaultFeatureType = CvFeatureParams::HAAR;
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CvCascadeParams();
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CvCascadeParams( int _stageType, int _featureType );
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void write( FileStorage &fs ) const;
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bool read( const FileNode &node );
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void printDefaults() const;
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void printAttrs() const;
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bool scanAttr( const String prmName, const String val );
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int stageType;
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int featureType;
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Size winSize;
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};
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class CvCascadeClassifier
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{
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public:
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bool 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 = false );
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private:
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int predict( int sampleIdx );
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void save( const String cascadeDirName, bool baseFormat = false );
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bool load( const String cascadeDirName );
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bool updateTrainingSet( double& acceptanceRatio );
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int fillPassedSamples( int first, int count, bool isPositive, int64& consumed );
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15 years ago
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void writeParams( FileStorage &fs ) const;
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void writeStages( FileStorage &fs, const Mat& featureMap ) const;
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void writeFeatures( FileStorage &fs, const Mat& featureMap ) const;
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bool readParams( const FileNode &node );
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bool readStages( const FileNode &node );
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void getUsedFeaturesIdxMap( Mat& featureMap );
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CvCascadeParams cascadeParams;
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Ptr<CvFeatureParams> featureParams;
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Ptr<CvCascadeBoostParams> stageParams;
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Ptr<CvFeatureEvaluator> featureEvaluator;
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vector< Ptr<CvCascadeBoost> > stageClassifiers;
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CvCascadeImageReader imgReader;
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int numStages, curNumSamples;
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int numPos, numNeg;
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};
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#endif
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