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Diffstat (limited to 'src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/ActiveDTWShapeRecognizer.h')
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diff --git a/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/ActiveDTWShapeRecognizer.h b/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/ActiveDTWShapeRecognizer.h deleted file mode 100644 index 9459d5c7..00000000 --- a/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/ActiveDTWShapeRecognizer.h +++ /dev/null @@ -1,1203 +0,0 @@ -/***************************************************************************************** -* Copyright (c) 2006 Hewlett-Packard Development Company, L.P. -* Permission is hereby granted, free of charge, to any person obtaining a copy of -* this software and associated documentation files (the "Software"), to deal in -* the Software without restriction, including without limitation the rights to use, -* copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the -* Software, and to permit persons to whom the Software is furnished to do so, -* subject to the following conditions: -* -* The above copyright notice and this permission notice shall be included in all copies -* or substantial portions of the Software. -* -* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, -* INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A -* PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT -* HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF -* CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE -* OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -*****************************************************************************************/ -/************************************************************************ -* SVN MACROS -* -* $LastChangedDate: 2011-08-23 13:28:15 +0530 (Tue, 23 Aug 2011) $ -* $Revision: 857 $ -* $Author: jitender $ -* -************************************************************************/ -/********************************************************************************************* -* FILE DESCR: Definitions for ActiveDTW Shape Recognition module -* -* CONTENTS: -* -* AUTHOR: S Anand -* -* -* DATE:3-MAR-2009 -* CHANGE HISTORY: -* Author Date Description of change -***********************************************************************************************/ - - -#ifndef __ACTIVEDTWSHAPERECOGNIZER_H -#define __ACTIVEDTWSHAPERECOGNIZER_H - -#include "LTKInc.h" -#include "LTKTypes.h" -#include "LTKMacros.h" -#include "LTKShapeRecognizer.h" -#include "LTKShapeRecoUtil.h" -#include "LTKShapeSample.h" -#include "LTKCheckSumGenerate.h" -#include "LTKDynamicTimeWarping.h" -#include "ActiveDTWShapeModel.h" -#include "ActiveDTWAdapt.h" - -class LTKTraceGroup; -class LTKPreprocessorInterface; -class LTKShapeSample; -class LTKShapeFeatureExtractor; -class LTKShapeFeature; -class LTKAdapt; - -#define SIMILARITY(distance) (1 / (distance + EPS )) -#define SUPPORTED_MIN_VERSION "3.0.0" - -#define CLUSTER 0 -#define SINGLETON 1 - -#define INVALID_SHAPEID -2 -#define NEW_SHAPEID -2 - -//#ifdef _INTERNAL -//#endif - -typedef int (*FN_PTR_LOCAL_DISTANCE)(LTKShapeFeaturePtr, LTKShapeFeaturePtr,float&); -typedef int (*FN_PTR_CREATELTKLIPIPREPROCESSOR)(const LTKControlInfo& , LTKPreprocessorInterface** ); -typedef int (*FN_PTR_DELETELTKLIPIPREPROCESSOR)(LTKPreprocessorInterface* ); - -typedef vector<LTKShapeFeaturePtr> shapeFeature; -typedef vector<shapeFeature> shapeMatrix; - - -/** -* @ingroup ActiveDTWShapeRecognizer.h -* @brief The Header file for the ActiveDTWShapeRecognizer -* @class ActiveDTWShapeRecognizer -*<p> <p> -*/ - -class ActiveDTWShapeRecognizer: public LTKShapeRecognizer -{ - -public: - //#ifdef _INTERNAL - friend class LTKAdapt; - int adapt(int shapeID ); - int adapt(const LTKTraceGroup& sampleTraceGroup, int shapeID ); - - /** - * This function does the recognition function required for training phase (called from trainLVQ) - * The input parameter are the incharacter, which is compared with the existing - * set of prototypes and then the matched code vector and along with its index (and also the shape id) is returned - * @param incharacter is the character which we are trying to recognise. - * @param returnshapeID is the value of the matched character which is returned, codeCharacter is the matched prototype (code vector) vector, and codeVecIndex is the matched prototype (code vector) index - */ - int trainRecognize(LTKShapeSample& inShapeSample, LTKShapeSample& bestShapeSample, int& codeVecIndex); - - int writePrototypeShapesToMDTFile(); - - int addClass(const LTKTraceGroup& sampleTraceGroup, int& shapeID); - - int deleteClass(int shapeID); - - int readInternalClassifierConfig(); - -private: - - int deleteAdaptInstance(); - - - - // #endif - - -private: - - FN_PTR_DELETELTKLIPIPREPROCESSOR m_deleteLTKLipiPreProcessor; - //Function pointer for deleteLTKLipiPreProcessor - - // preproc lib handle - void *m_libHandler; - - // feature extractor lib handle - void *m_libHandlerFE; - - unsigned short m_numShapes; - /**< @brief Number of shapes - * <p> - * It Defines the number of shapes to be recognized - * - * DEFAULT: 0 - * - * Note: If the project is dynamic, then this numShapes was set to 0 - * If the project is not dynamic, then the numShapes was read from project configuration file - * </p> - */ - - - string m_prototypeSelection; - /**< @brief The Prototype Selection - * <p> - * if Prototype Selection = clustering, the training method used was clustering - * - * DEFAULT: LTKPreprocDefaults::NN_DEF_PROTOTYPESELECTION - * Possible values are "clustering" - * </p> - */ - - int m_prototypeReductionFactor; - /**< @brief The prototype Reduction factor - * <p> - * if PrototypeReductionFactor = 0 every training sample is cluster on its own - * = 100 all training samples are represented by one prototype - * = 80 then all samples are represented by 20% of the training samples - * - * DEFAULT: LTKPreprocDefaults::NN_DEF_PROTOTYPEREDUCTIONFACTOR - * RANGE: 0 TO 100 - * </p> - */ - - int m_numClusters; - /**< @brief The number of clusters - * <p> - * if NumClusters = k, then k clusters are found from the training samples - * - * - * - * DEFAULT: There is no default as this and prototype reduction factor are dependent - * RANGE: - * </p> - */ - - - float m_percentEigenEnergy; - /**< @brief The percent of Eigen Energy - * <p> - * if PercentEigenEnergy = 90 then those eigen values contributing to 90% of the Eigen Energy, - * and their corresponding eigen vectors are retained - * - * - * DEFAULT: LTKClassifierDefaults::ACTIVEDTW_DEF_PERCENTEIGENENERGY - * RANGE: 1-100 - * </p> - */ - - int m_eigenSpreadValue; - /**< @brief The eigen values spread range - * <p> - * if EigenSpreadValue = 16 then deformation parameters for computing the optimal deformation, - * can lie in the range [-4*Eigen Value,4*Eigen Value] - * - * - * DEFAULT: LTKClassifierDefaults::ACTIVEDTW_DEF_EIGENSPREADVALUE - * RANGE: greater than 0 - * </p> - */ - - int m_minClusterSize; - /**< @brief The minimum cluster size - * <p> - * It specifies the minimum number of samples required to form a cluster - * - * - * DEFAULT: LTKPreprocDefaults::ADAPT_DEF_MIN_NUMBER_SAMPLES_PER_CLASS - * RANGE: >= 2 - * </p> - */ - - bool m_useSingleton; - /**< @brief Use Singletons - * <p> - * It specifies if singletons should be considered during the recognition process - * If Use Singleton is true, singletons will be considered during the recognition process, - * else they will be ignored - * - * DEFAULT: LTKClassifierDefaults::ACTIVEDTW_DEF_USESINGLETON - * RANGE: True or False - * </p> - */ - - - int m_nearestNeighbors; - /**< @brief Nearest Neighbors - * <p> - * - * DEFAULT: LTKClassifierDefaults::NN_DEF_NEARESTNEIGHBORS - * </p> - */ - - - float m_dtwBanding; - /**< @brief DTW Banding - * <p> - * - * DEFAULT: LTKClassifierDefaults::NN_DEF_BANDING - * </p> - */ - - int m_dtwEuclideanFilter; - /**< @brief DTW Euclidean Filter - * <p> - * - * DEFAULT: LTKClassifierDefaults::ACTIVEDTW_DEF_DTWEUCLIDEANFILTER - * </p> - */ - - string m_featureExtractorName; - /**< @brief The Feature Extractor - * <p> - * - * DEFAULT: - * - * </p> - */ - - bool m_projectTypeDynamic; - /**< @brief Project Dynamic - * <p> - * if projectTypeDynamic = true, then the project is dynamic ie, the numShapes can take any number of value - * = false, then the project is not dynamic ie, the numShape can take value specified in project.cfg file - * - * DEFAULT: false - * </p> - */ - - LTKPreprocessorInterface *m_ptrPreproc; - /**< @brief Pointer to preprocessor instance - * <p> - * Instance which is used to call the preprocessing methods before recognition - * - * DEFAULT: NULL - * </p> - */ - - string m_activedtwCfgFilePath; - /**< @brief Full path of ActiveDTW configuration file - * <p> - * Assigned value in the ActiveDTWShapeRecognizer::initialize function - * </p> - */ - - string m_activedtwMDTFilePath; - /**< @brief Full path of Model data file - * <p> - * Assigned value in the ActiveDTWShapeRecognizer::initialize function - * </p> - */ - - stringStringMap m_headerInfo; - /**< @brief Header Information - * <p> - * </p> - */ - - LTKShapeRecoUtil m_shapeRecUtil; - /**< @brief Pointer to LTKShapeRecoUtil class - * <p> - * Instance which used to call Shape Recognizer Utility functions - * - * DEFAULT: NULL - */ - - string m_lipiRootPath; - /**< @brief Path of the Lipi Root - * <p> - * DEFAULT: LipiEngine::getLipiPath() - * </p> - */ - - string m_lipiLibPath; - /**< @brief Path of the Lipi Libraries - * <p> - * DEFAULT: LipiEngine::getLipiLibPath() - * </p> - */ - - LTKShapeFeatureExtractor *m_ptrFeatureExtractor; - /**< @brief Pointer to LTKShapeFeatureExtractor class - * <p> - * DEFAULT: NULL - * </p> - */ - - string m_preProcSeqn; - /**< @brief Preprocessor Sequence - * <p> - * This string will holds what sequence the preprocessor methods to be executed - * </p> - */ - - LTKCaptureDevice m_captureDevice; - - /** Structure to store information required for recognition and adaptation **/ - struct NeighborInfo - { - int typeId; - int sampleId; - int classId; - double distance; - }; - /**< @brief Structure to store information required for recognition and adaptation - * <p> - * TypeId: Specifies whether it is a singleton (defined by 1) or a cluster (defined by 0) - * SampleId: Specifies which singleton or cluster sample. The range of values depend on - * the number of clusters or singletons (>= 0) - * ClassId: Specifies the class id - * Distance: The distance between the test sample and singleton or - * the distance between the test sample and optimal deformation (cluster case) - * The range of values is >= 0 - * - * DEFAULT: NULL - * Range: Depends on Individual fields - * </p> - */ - - /**Vector to store the distances from each class to test sample**/ - vector <struct NeighborInfo> m_neighborInfoVec; - /**< @brief Neighbor Information Vector - * <p> - * This vector contains the distance information between the test samples and - * prototype data. It is a vector of structures NeighborInfo - * </p> - */ - - /** contains all the prototype data from load Model data **/ - vector<ActiveDTWShapeModel> m_prototypeShapes; - /**< @brief Prototype Shapes - * <p> - * This is a vector of ActiveDTWShapeModels - * This populated via the loadModelData function - * </p> - */ - - /** Sorts dtw distances **/ - static bool sortDist(const NeighborInfo& x, const NeighborInfo& y); - /** - * This method compares distances - * - * Semantics - * - * - Compare distances - * - * @param NeighborInfo(X) - * @param NeighborInfo(Y) - * - * @return TRUE: if X.distance lesser than Y.distance - * FALSE: if X.distance greater than Y.distance - * @exception none - */ - - vector<stringStringPair> m_preprocSequence; - - intIntMap m_shapeIDNumPrototypesMap; - /**< @brief Map of shapeID and Number of Samples per shape - * <p> - * - * </p> - */ - - int m_prototypeSetModifyCount; - /**< @brief - * <p> - * Used to count number of modifications done to m_prototypeShapes. - * Write to MDT after m_prototypeModifyCntCFG such modifications or at Exit. - * </p> - */ - - int m_MDTUpdateFreq; - /**< @brief Update MDT after a specified number of modifications to m_prototypeSet - * <p> - * Specified in ActiveDTW.cfg - * - * </p> - */ - - shapeFeature m_cachedShapeFeature; - /**< @brief Store shapeFeature of the last inTraceGroup to Recognize - * Used during subsequent call to Adapt - * <p> - * - * - * </p> - */ - - float m_rejectThreshold; - /**< @brief Threshold on the confidence to reject a test sample - * <p> - * - * </p> - */ - - bool m_adaptivekNN; - /**< @brief Adaptive kNN method to compute confidence - * <p> - * If m_adaptivekNN = true, the adaptive kNN method is used for confidence computation - * false, NN or kNN method is used, based on the value of m_nearestNeighbors - * </p> - */ - - string m_currentVersion; - - string m_MDTFileOpenMode; - /**< @brief File modes of the mdt file - * <p> - * If m_adaptivekNN = ascii, the mdt file is written in ascii mode - * binary, the mdt file will be written in binary mode - * Default: LTKPreprocDefaults::NN_MDT_OPEN_MODE_ASCII - * </p> - */ - - //dtw obj for computing the dtw distance between features - DynamicTimeWarping<LTKShapeFeaturePtr, float> m_dtwObj; - /**< @brief Dynamic Time Warping Object - * <p> - * This object aids in calculating the dtw distance between two LTKShapeFeaturePtrs - * and the distance is in float - * </p> - */ - - //store the recognitioresults - vector<LTKShapeRecoResult> m_vecRecoResult; - /**< @brief Vector of LTKShapeRecoResult - * <p> - * This vector is used to store the confidence values computed using computeConfidence - * </p> - */ - - //minimum numberOfSamples to form cluster - - - public: - - /** @name Constructors and Destructor */ - - /** - * Constructor - */ - ActiveDTWShapeRecognizer(const LTKControlInfo& controlInfo); - - /** - * Destructor - */ - ~ActiveDTWShapeRecognizer(); - - //@} - - /** - * This method initializes the ActiveDTW shape recognizer - * <p> - * Semantics - * - Set the project name in ActiveDTWShapeRecognizer::headerInfo with the parameter passed.<br> - * m_headerInfo[PROJNAME] = strProjectName; - * - * - Initialize ActiveDTWShapeRecognizer::m_activedtwCfgFilePath <br> - * m_activedtwCfgFilePath = ActiveDTWShapeRecognizer::m_lipiRootPath + LTKMacros::PROJECTS_PATH_STRING + - * strProjectName + LTKMacros::PROFILE_PATH_STRING + strProfileName + - * LTKInc::SEPARATOR + LTKInc::ActiveDTW + LTKInc::CONFIGFILEEXT; - * - * - Initializes ActiveDTWShapeRecognizer::m_activedtwMDTFilePath <br> - * m_activedtwMDTFilePath = ActiveDTWShapeRecognizer::m_lipiRootPath + LTKMacros::PROJECTS_PATH_STRING + - * strProjectName + LTKMacros::PROFILE_PATH_STRING + strProfileName + - * LTKInc::SEPARATOR + LTKInc::ActiveDTW + LTKInc::DATFILEEXT; - * - * - Initializes ActiveDTWShapeRecognizer::m_projectTypeDynamic with the value returned from LTKShapeRecoUtil::isProjectDynamic - * - * - Initialize the preprocessor using LTKShapeRecoUtil::initializePreprocessor and assign - * default values for - * -# Normalised size - * -# Threshold size - * -# Aspect ratio - * -# Dot threshold - * - * - Initialize the recognizers instance variables with the values given in classifier config file. - * - * </p> - * @param strProjectName : string : Holds the name of the Project - * @param strProfileName : string : Holds the name of the Profile - * - * @return int : LTKInc::SUCCESS if initialization done successfully - * errorValues if initialization has some errors - * - * @exception LTKErrorList::ECONFIG_FILE_OPEN Could not open project.cfg - * @exception LTKErrorList::EINVALID_NUM_OF_SHAPES Negative value for number of shapes - * @exception LTKErrorList::ELOAD_PREPROC_DLL Could not load preprocessor DLL - * @exception LTKErrorList::EDLL_FUNC_ADDRESS_CREATE Could not map createPreprocInst - * @exception LTKErrorList::EDLL_FUNC_ADDRESS_DELETE Could not map destroyPreprocInst - */ - - /** - * This method calls the train method of the ActiveDTW classifier. - * - */ - int train(const string& trainingInputFilePath, - const string& mdtHeaderFilePath, - const string &comment,const string &dataset, - const string &trainFileType=INK_FILE) ; - - /** - * This method loads the Training Data of the ActiveDTW classifier. - * @param - * @return LTKInc::SUCCESS : if the model data was loaded successfully - * @exception - */ - int loadModelData(); - - /** - * This method unloads all the training data - * @param none - * @return LTKInc::SUCCESS : if the model data was unloaded successfully - * @exception none - */ - int unloadModelData(); - - /** - * This method sets the device context for the recognition - * - * @param deviceInfo The parameter to be set - * @return - * @exception - */ - int setDeviceContext(const LTKCaptureDevice& deviceInfo); - - /** - * Populates a vector of LTKShapeRecoResult consisting of top classes with their confidences. - * - * Semantics - * - * - Validate the input arguments - * - Extract the features from traceGroup - * - Compute the optimal deformation vectors for each class - * - If the dtwEuclidean filter is on: - * --Then for Clusters - * -- Calculate the euclidean distances between the test samples and the optimal deformations - * Accordingly populate the distFilter vector - * --Then for singletons - * --Calculate the euclidean distances between the test samples and the singletonVectors - * Accordingly populate the distFilter vector - * - Compute the dtw distances between the test sample and the optimal deformations and singletonVectors - -- If the dtwEuclidean filter is on then calculate the dtw distances only - to those deformations and singletonVectors recommended by the dtw euclidean filter - * - Populate the distIndexPairVector with distances and shapeID - * - Sort the distIndexPairVector based on the distances in ascending order - * - Compute the confidences of the classes appearing in distIndexPairVector, call computeConfidence() - * - Check if the first element of resultVector has confidence less than m_rejectThreshold, if so, - empty the resultVector (reject the sample), log and return. - * - If the confThreshold value was specified by the user (not equal to -1), - delete the entries from resultVector with confidence values less than confThreshold. - * - If the numChoices value was specified by the user (not equal to -1), - update the resultVector with top numChoices entries, delete the other values. - * - * @param traceGroup The co-ordinates of the shape which is to be recognized - * @param screenContext Contains information about the input field like whether it is boxed input - * or continuous writing - * @param subSetOfClasses A subset of the entire class space which is to be used for - * recognizing the input shape. - * @param confThreshold Classes with confidence below this threshold are not returned, - * valid range of confThreshold: (0,1) - * @param numOfChoices Number of top choices to be returned in the result structure - * @param resultVector The result of recognition - * - * @return SUCCESS: resultVector populated successfully - * FAILURE: return ErrorCode - * @exception none - */ - int recognize(const LTKTraceGroup& traceGroup, - const LTKScreenContext& screenContext, - const vector<int>& subSetOfClasses, - float confThreshold, - int numChoices, - vector<LTKShapeRecoResult>& outResultVector); - - /* Overloaded the above function to take vector<LTKShapeFeaturePtr> as - * input - */ - int recognize(const vector<LTKShapeFeaturePtr>& shapeFeatureVec, - const vector<int>& inSubSetOfClasses, - float confThreshold, - int numChoices, - vector<LTKShapeRecoResult>& outResultVector); - - /** - * In case of: - * 1. Singleton Vectors: This method converts them to traceGroups - * 2. Clusters: This method converts the cluster means to traceGroup - * Semantics - * - * - Check if shapeID is valid, if not return error code - * - Check if project is Dynamic, if not return ErrorCode - * - Update PrototypeSet - * - Update MDTFile - * - * @param shapeID : int : Holds shapeID - * @param numberOfTraceGroups : int : Maximum number of Trace Groups to populate - * @param outTraceGroups : vector<LTKTraceGroup> : TraceGroup - * - * @return SUCCESS: TraceGroup is populated successfully - * FAILURE: return ErrorCode - * @exception none - */ - int getTraceGroups(int shapeID, int numberOfTraceGroups, vector<LTKTraceGroup> &outTraceGroups); - - - - private: - /** - * This function is the train method using Clustering prototype selection technique. - * - * - * Semantics - * - * - Note the start time for time calculations. - * - * - Create an instance of the feature extractor using ActiveDTWShapeRecognizer::initializeFeatureExtractorInstance() method - * - * - Call train method depending on the inFileType - * - ActiveDTWShapeRecognizer::trainFromListFile() if inFileType = LTKMacros::INK_FILE - * - ActiveDTWShapeRecognizer::trainFromFeatureFile() if inFileType = LTKMacros ::FEATURE_FILE - * - * - Update the headerInfo with algorithm version and name using ActiveDTWShapeRecognizer::updateHeaderWithAlgoInfo() method - * - * - Calculate the checksum. - * - * - Note the finish time for time calculations. - * - * - * @param inputFilePath :string : Path of trainListFile / featureFile - * @param strModelDataHeaderInfoFile : string : Holds the Header information of Model Data File - * @param inFileType : string : Possible values ink / featureFile - * - * @return LTKInc::SUCCESS : if the training done successfully - * @return errorCode : if it contains some errors - */ - int trainClustering(const string& trainingInputFilePath, - const string& mdtHeaderFilePath, - const string& trainFileType); - - - /** - * This method do the map between the module name and function names from the cfg file - * - * Semantics - * - * - Read the Preprocess Sequence from the nn.cfg - * - * - Split the sequence into tokens with delimiter LTKMacros::DELEMITER_SEQUENCE using LTKStringUtil::tokenizeString - * - * - Split each token with delimiter LTKMacrosDELEMITER_FUNC using LTKStringUtil::tokenizeString - * to get the Module name and Function name - * - * - Store the Module name and the Function name into a structure - * - * - * @param none - * @return LTKInc::SUCCESS : if functions are successfully mapped, - * @return errorCodes : if contains any errors - * @exception none - */ - int mapPreprocFunctions(); - - /** - * This method will assign default values to the members - * - * Semantics - * - * - Assign Default values to all the data members - * - * - * @param none - * - * @return none - */ - void assignDefaultValues(); - - /** Reads the ActiveDTW.cfg and initializes the instance variable of the classifier with the user defined - * values. - * - * Semantics - * - * - Open the activedtw.cfg using LTKConfigFileReader - * - * - Incase of file open failure (activedtw.cfg), default values of the classifier parameters are used. - * - * - The valid values of the classifier parameters are cached in to the class data members. - * LTKConfigFileReader::getConfigValue is used to get the value fora key defined in the config file - * - * - Exception is thrown if the user has specified an invalid valid for a parameter - * - * - * @param none - * @return SUCCESS : If the Config file read successfully - * @return errorCode : If it contains some errors - * @exception LTKErrorList::ECONFIG_FILE_RANGE The config file variable is not within the correct range - */ - int readClassifierConfig(); - - /** - * This function serves as wrapper function to the Dtw distance computation function - * (for use by clustering prototype selection) - * @param train This is an input parameter and corresponds to the training character. - * @param test This is an input parameter and corresponds to the testing character. - */ - int computeDTWDistance(const LTKShapeSample& inFirstShapeSampleFeatures, - const LTKShapeSample& inSecondShapeSampleFeatures, - float& outDTWDistance); - - /** - * computes the dtw distance between two shape feature vectors - **/ - int computeDTWDistance(const vector<LTKShapeFeaturePtr>& inFirstFeatureVector, - const vector<LTKShapeFeaturePtr>& inSecondFeatureVector, - float& outDTWDistance); - /** - * This function serves as wrapper function to the Dtw distance computation function - * (for use by recognize function) - * @param train This is an input parameter and corresponds to the training character. - * @param test This is an input parameter and corresponds to the testing character. - */ - - - /** - * Computes the euclidean distance between two shape Features - **/ - int computeEuclideanDistance(const shapeFeature& inFirstFeature, - const shapeFeature& inSecondFeature, - float& outEuclideanDistance); - /** - * This function is used to compute the Euclidean distance between two shape Features - * (for use by recognize when dtwEuclidean filter is on) - * @param train This is an input parameter and corresponds to the training character. - * @param test This is an input parameter and corresponds to the testing character. - */ - - - - - - /** - * This method creates a custom feature extractor instance and stores it's address in - * ActiveDTWShapeRecognizer::m_ltkFE. The local distance function pointer is also initialized. - * - * Semantics - * - * - * - Intialize the ActiveDTWShapeRecognizer::m_ptrFeatureExtractor with address of the feature extractor instance created - * using LTKShapeFeatureExtractorFactory::createFeatureExtractor - * - * - Cache the address of LTKShapeFeatureExtractor::getLocalDistance() in an instance variable - * - * @param none - * - * @return 0 on LTKInc::SUCCESS and 1 on LTKInc::FAILURE - * - * @exception none - */ - int initializeFeatureExtractorInstance(const LTKControlInfo& controlInfo); - - /** - * This method trains the classifier from the train list file whose path is passed as paramater. - * - * Semantics - * - * - Open the trainListFile for reading. - * - * - Open the mdt file for writing. - * - * - Write header information to the mdt file - * - ActiveDTWShapeRecognizer::m_numShapes - * - ActiveDTWShapeRecognizer::m_traceDimension - * - ActiveDTWShapeRecognizer::m_flexibilityIndex - * - * - Get a valid line from the train list file - * - Skip commented lines - * - Skip lines where number_of_tokens != 2 - * - Throw error LTKErrorList::EINITSHAPE_NONZERO, if the first shape in the list file is not zero - * - Throw error LTKErrorList::EINVALID_ORDER_LISTFILE if the shapes are not in sequential order - * - * - For every valid line get the ShapeSample from the ink file using ActiveDTWShapeRecognizer::getShapeSampleFromInkFile - * - Read ink from UNIPEN ink file - * - Skip if the trace group is empty - * - Pre process the trace group read from the ink file - * - Extract features - * - * - Push all the ShapeSamples corresponding to a shape into a vector of ShapeSample ShapeSamplesVec. - * - * - When all the ShapeSamples corresponding to a Shape have been collected, cluster them using ActiveDTWShapeRecognizer::performClustering - * - * - performClustering results in vector of clustered ShapeSamples. - * - * - computeCovarianceMatrix of clusters - * - * - computeEigenVectorsForLargeDimension for the covariance matrices of the clusters - * - * - construct shape models using cluster models and singletons - * - * - Append these clustered vector<ActiveDTWShapeModel> to the mdt file. - * - * - * @param listFilePath : string : Holds the path for train list file - * @param trainSet : ShapeSampleVector: Holds the ShapeSample for all shapes, used for LVQ only - * - * @return none - * - * @exception LTKErrorList::EFILE_OPEN_ERROR : Error in Opening a file (may be mdt file or list file) - * @exception LTKErrorList::EINVALID_NUM_OF_SHAPES : Invalid value for number of shapes - * @exception LTKErrorList::EINVALID_ORDER_LISTFILE: Invalid order of shapeId in List file - * @exception LTKErrorList::EINITSHAPE_NONZERO : Initial shapeId must not be zero - * @exception LTKErrorList::EEMPTY_EIGENVECTORS : Number of eigen vectors must be a positive number - * @exception LTKErrorList::EINVALID_NUM_OF_EIGENVECTORS : Number of eigen vector must be a positive number - */ - - int trainFromListFile(const string& listFilePath); - - /** - * This method will get the ShapeSample by giving the ink file path as input - * - * Semantics - * - * - Call the LTKShapeRecoUtil::readInkFromFile() method (Utility Method) to read the ink file - * By reading this file, an inTraceGroup was generated - * - * - Preprocess the inTraceGroup and get the preprocessed trace group - * LTKTraceGroup preprocessedTraceGroup - * - * - Extract features from the preprocessed trace group to get the ShapeSamples. - * - * - * @param path : string : The path for Ink file - * @param ShapeSample : ShapeSample : The ShapeSample generated after feature extraction - * - * @return SUCCESS : If the ShapeSample was got successfully - * @return FAILURE : Empty traces group detected for current shape - * - * @exception LTKErrorList::EINKFILE_EMPTY : Ink file is empty - * @exception LTKErrorList::EINK_FILE_OPEN : Unable to open unipen ink file - * @exception LTKErrorList::EINKFILE_CORRUPTED : Incorrect or corrupted unipen ink file. - * @exception LTKErrorList::EEMPTY_TRACE : Number of points in the trace is zero - * @exception LTKErrorList::EEMPTY_TRACE_GROUP : Number of traces in the trace group is zero - */ - int getShapeFeatureFromInkFile(const string& inkFilePath, - vector<LTKShapeFeaturePtr>& shapeFeatureVec); - - - /** - * This method will do Custering for the given ShapeSamples - * - * Semantics - * - * - If the ActiveDTWShapeRecognizer::m_prototypeReductionFactor is -1 means Automatic clustering could be done - * - * - If the ActiveDTWShapeRecognizer::m_prototypeReductionFactor is 0 means No clustering was needed - * - * - Otherwise clustering is needed based on the value of ActiveDTWShapeRecognizer::m_prototypeReductionFactor - * - * - * - * @param ShapeSamplesVec : ShapeSampleVector : Holds all the ShapeSample for a single class - * @param resultVector : int2DVector : Vector of indices of samples belonging to a cluster - * - * @return SUCCESS ; On successfully performing clustering - * @return ErrorCode ; On some error - * @exception none - */ - int performClustering(const vector<LTKShapeSample>& shapeSamplesVec, - int2DVector& outputVector); - - /** - * This method computes the covariance matrix and the mean for a given feature matrix - * - * Semantics - * - * - Computes the mean of the feature matrix - * - * - Computes the mean corrected data - * - * - Computes the covariance matrix - * - * - * - * @param featureMatrix : double2DVector : Holds all the features of a cluster - * @param covarianceMatrix : double2DVector : covariance matrix of the cluster - * @param meanFeature : doubleVector : mean feature of the cluster - * - * @return SUCCESS ; On successfully computing the covariance matrix - * @return ErrorCode ; On some error - * @exception LTKErrorList:: EEMPTY_FEATUREMATRIX, Feature matrix is empty - */ - int computeCovarianceMatrix(double2DVector& featureMatrix, - double2DVector& covarianceMatrix,doubleVector& meanFeature); - - /** - * compute the eigen vectors - * for a covariance Matrix - * @inparam covarianceMatrix,rank,nrot --> no of rotations - * @outparam eigenValueVec, eigenVectorMatrix - **/ - int computeEigenVectors(double2DVector &covarianceMatrix,const int rank, - doubleVector &eigenValueVec, double2DVector &eigenVectorMatrix, int& nrot); - - - /** - * This method will Update the Header information for the MDT file - * - * Semantics - * - * - Copy the version number to a string - * - * - Update the version info and algoName to ActiveDTWShapeRecognizer::m_headerInfo, which specifies the - * header information for MDT file - * - * - * @param none - * - * @return none - - * @exception none - */ - void updateHeaderWithAlgoInfo(); - - int preprocess (const LTKTraceGroup& inTraceGroup, LTKTraceGroup& outPreprocessedTraceGroup); - - - /** - * This append the shape model data to the mdt file - * - * Semantics - * - * - Append cluster models - * - * - Append singleton vectors - * - * - * - * @param shapeModel : ActiveDTWShapeModel : Holds clusters and singletons of a shape - * @param mdtFileHandle : ofstream : file handle for the mdt file - * - * @return SUCCESS ; On successfully appending the data to mdt file - * @return ErrorCode: - * @exception LTKErrorList:: EINVALID_FILE_HANDLE, unable to open mdt file - */ - int appendShapeModelToMDTFile(const ActiveDTWShapeModel& shapeModel,ofstream& mdtFileHandle); - - /** - * find optimal deformation parameters using bound constrained optimization - * here any third part library can be called - * we will solve the equation - * Min f(x) - * Subject to: lb <= x <= ub - * where lb -- lower bound ub --- upper bound - **/ - - /** - * This solves the optimization problem and finds the deformation parameters - * It constructs the optimal deformation, the sample in the cluster closest to the test sample - * Semantics - * - * - Solve the optimization problem - * Min f(x) - * Subject to: lb <= x <= ub - * where lb -- lower bound ub --- upper bound - * - * - * - * @param eigenValues : doubleVector : eigen values of a cluster - * @param eigenVector : double2DVector : eigen vectorsof a cluster - * @param clusterMean : doubleVector : mean of the cluster - * @param testSample : doubleVector : test sample - * @param deformationParameters : doubleVector : parameters required to construct the optimal deformation - * - * @return SUCCESS ; On successfully appending the data to mdt file - * @return ErrorCode: - * @exception LTKErrorList:: EEMPTY_EIGENVALUES, eigen values are empty - * @exception LTKErrorList:: EEMPTY_EIGENVECTORS, eigen vectors are empty - * @exception LTKErrorList:: EEMPTY_CLUSTERMEAN, cluster mean is empty - * @exception LTKErrorList:: ENUM_EIGVALUES_NOTEQUALTO_NUM_EIGVECTORS, number of eigen value is not equal to the number of eigen vectors - * @exception LTKErrorList:: EEMPTY_EIGENVECTORS, eigen vectors are empty - */ - int findOptimalDeformation(doubleVector& deformationParameters,doubleVector& eigenValues, double2DVector& eigenVector, - doubleVector& clusterMean, doubleVector& testSample); - - static void getDistance(const LTKShapeFeaturePtr& f1,const LTKShapeFeaturePtr& f2, float& distance); - - - /** - * This method computes the confidences of test sample belonging to various classes - * - * Semantics - * - * - Compute the confidence based on the values of m_nearestNeighbors and m_adaptiveKNN - * - Populate the resultVector - * - Sort the resultVector - * - - * - * @param distIndexPairVector : vector<struct NeighborInfo>: Holds the samples, classIDs and distances to the test sample - * @param resultVector : vector<LTKShapeRecoResult> : Holds the classIDs and the respective confidences - * - * @return SUCCESS: resultVector populated - * FAILURE: return ErrorCode - * @exception none - */ - int computeConfidence(); - - /** - * The comparison function object of STL's sort() method, overloaded for class LTKShapeRecoResult, used to sort the vector of LTKShapeRecoResult based on the member variable confidence - * - * Semantics - * - * - Check if the first object's confidence value is greater than the second object's confidence value - * - Return true or false - * - - * - * @param x : LTKShapeRecoResult : First object for comparison - * @param y : LTKShapeRecoResult : Second object for comparison - * - * @return true: If x.confidence > y.confidence - * false: If x.confidence <= y.confidence - * @exception none - */ - static bool sortResultByConfidence(const LTKShapeRecoResult& x, const LTKShapeRecoResult& y); - - static bool compareMap( const map<int, int>::value_type& lhs, const map<int, int>::value_type& rhs ); - - int mapFeatureExtractor(); - - int deleteFeatureExtractorInstance(); - /** - * This method extracts shape features from given TraceGroup - * - * Semantics - * - * - PreProcess tracegroup - * - Extract Features - * - * @param inTraceGroup : LTKTraceGroup : Holds TraceGroup of sample - * - * @return SUCCESS: if shapeFeatures is populated successfully - * FAILURE: return ErrorCode - * @exception none - */ - - int extractFeatVecFromTraceGroup(const LTKTraceGroup& traceGroup, - vector<LTKShapeFeaturePtr>& featureVec); - - - /** This method is used to initialize the PreProcessor - * - * Semantics - * - * - Load the preprocessor DLL using LTKLoadDLL(). - * - * - Get the proc address for creating and deleting the preprocessor instance. - * - * - Create preprocessor instance. - * - * - Start the logging for the preprocessor module. - * - * @param preprocDLLPath : string : Holds the Path of the Preprocessor DLL, - * @param errorStatus : int : Holds SUCCESS or Error Values, if occurs - * @return preprocessor instance - * - * @exception ELOAD_PREPROC_DLL Could not load preprocessor DLL - * @exception EDLL_FUNC_ADDRESS_CREATE Could not map createPreprocInst - * @exception EDLL_FUNC_ADDRESS_DELETE Could not map destroyPreprocInst - */ - int initializePreprocessor(const LTKControlInfo& controlInfo, - LTKPreprocessorInterface** preprocInstance); - - /** This method is used to deletes the PreProcessor instance - * - * Semantics - * - * - Call deleteLTKPreprocInst from the preproc.dll. - * - * - Unload the preprocessor DLL. - * - * @param ptrPreprocInstance : Holds the pointer to the LTKPreprocessorInterface - * @return none - * @exception none - */ - - int deletePreprocessor(); - - /** This method is used to Unloads the preprocessor DLL. - * - * Semantics - * - * - If m_libHandler != NULL, unload the DLL - * LTKUnloadDLL(m_libHandler); - * m_libHandler = NULL; - * - * @param none - * @return none - * @exception none - */ - int unloadPreprocessorDLL(); - - /**< @brief Pointer to LTKOSUtil interface - * <p> - * - * </p> - */ - LTKOSUtil* m_OSUtilPtr; - - int validatePreprocParameters(stringStringMap& headerSequence); - - /** - * Computes the eigen values and eigen vectors of the larger covariance matrix using the - * a smaller covariance matrix - * - * Semantics - * - * - Compute the smaller covariance matrix, using meanCorrectedData(Transpose)*meanCorrectedData(Transpose) - * - * - Compute the eigen vectors of the smaller covariance matrix - * - * - Determine the number of eigen vectors, depending on the eigen energy to be retained - * - * - Compute the eigen vectors of the larger covariance matrix - * - * - Normalizing the eigen vectors - * - * - * - * @param meanCorrectedData : double2DVector : mean corrected data - * @param covarianceMatrix : double2DVector : covariance matrix of the corresponding mean corrected data - * @param eigenValues : doubleVector : output selected eigen values - * @param eigenVector : double2DVectorr : output selected eigen vectors - * - * @return SUCCESS ; On successfully computing eigen values and eigen vectors - * @return ErrorCode: - * @exception LTKErrorList:: EEMPTY_MEANCORRECTEDDATA, empty mean corrected data - * @exception LTKErrorList:: EEMPTY_COVARIANCEMATRIX, empty covariance matrix - */ - int computeEigenVectorsForLargeDimension(double2DVector& meanCorrectedData,double2DVector& covarianceMatrix, - double2DVector& eigenVector,doubleVector& eigenValues); - - /** - * This converts the double vector to a feature vector - * It constructs the optimal deformation, the sample in the cluster closest to the test sample - * - * - * @param featureVec : doubleVector : input double feature vector - * @param shapeFeatureVec : vector<LTkShapeFeaturePtr> : output feature vector - * - * @return SUCCESS ; On successfully conversion - * @return ErrorCode: - * @exception LTKErrorList:: EINVALID_INPUT_FORMAT - */ - int convertDoubleToFeatureVector(vector<LTKShapeFeaturePtr>& shapeFeatureVec,doubleVector& featureVec); - - -}; - -#endif |