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diff --git a/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/nn/NNShapeRecognizer.h b/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/nn/NNShapeRecognizer.h deleted file mode 100644 index cce1baf0..00000000 --- a/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/nn/NNShapeRecognizer.h +++ /dev/null @@ -1,1178 +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 13:31:44 +0530 (Tue, 23 Aug 2011) $ - * $Revision: 865 $ - * $Author: jitender $ - * - ************************************************************************/ -/************************************************************************ - * FILE DESCR: Definitions for NN Shape Recognition module - * - * CONTENTS: - * - * AUTHOR: Saravanan R. - * - * DATE: January 23, 2007 - * CHANGE HISTORY: - * Author Date Description of change - ************************************************************************/ - -#ifndef __NNSHAPERECOGNIZER_H -#define __NNSHAPERECOGNIZER_H - -/** Include files */ -#include "LTKInc.h" -#include "LTKTypes.h" -#include "LTKTrace.h" -#include "LTKMacros.h" -#include "LTKShapeRecognizer.h" -#include "LTKShapeRecoUtil.h" -#include "LTKShapeSample.h" -#include "LTKCheckSumGenerate.h" -#include "LTKDynamicTimeWarping.h" - -/** Forward declaration of classes */ -class LTKTraceGroup; -class LTKPreprocessorInterface; -class LTKShapeSample; -class LTKShapeFeatureExtractor; -class LTKShapeFeature; - -#define SIMILARITY(distance) (1 / (distance + EPS )) -#define SUPPORTED_MIN_VERSION "3.0.0" - -class LTKAdapt; - -typedef int (*FN_PTR_LOCAL_DISTANCE)(LTKShapeFeaturePtr, LTKShapeFeaturePtr,float&); -typedef int (*FN_PTR_CREATELTKLIPIPREPROCESSOR)(const LTKControlInfo& , LTKPreprocessorInterface** ); -typedef int (*FN_PTR_DELETELTKLIPIPREPROCESSOR)(LTKPreprocessorInterface* ); - - -/** - * @ingroup NNShapeRecognizer.h - * @brief The Header file for the NNShapeRecognizer - * @class NNShapeRecognizer - *<p> <p> - */ - -class NNShapeRecognizer: public LTKShapeRecognizer -{ - - public: - friend class LTKAdapt; - int adapt(int shapeID ); - int adapt(const LTKTraceGroup& sampleTraceGroup, int shapeID ); - - private: - - int deleteAdaptInstance(); - - - /** @name private data members */ - //@{ - private: - - FN_PTR_DELETELTKLIPIPREPROCESSOR m_deleteLTKLipiPreProcessor; - //Function pointer for deleteLTKLipiPreProcessor - - // preproc lin handle - void *m_libHandler; - - // feature extractor lin 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 - * = lvq, the training method used was LVQ - * - * DEFAULT: LTKPreprocDefaults::NN_DEF_PROTOTYPESELECTION - * Possible values are "clustering" and "lvq" - * </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> - */ - - - string m_prototypeDistance; - /**< @brief The Prototype Distance - * <p> - * if PrototypeDistance = eu, then the distance between the samples can be calculated using the Euclidean distance method - * = dtw, then the distance between the samples can be calculated using the DTW method - * - * DEFAULT: LTKPreprocDefaults::NN_DEF_PROTOTYPEDISTANCE - * Possible values are LTKMacros::DTW_DISTANCE and LTKMacros::EUCLIDEAN_DISTANCE. - * </p> - */ - - int m_nearestNeighbors; - /**< @brief Nearest Neighbors - * <p> - * - * DEFAULT: LTKPreprocDefaults::NN_DEF_NEARESTNEIGHBORS - * </p> - */ - - -// int m_dtwBanding; - float m_dtwBanding; - /**< @brief DTW Banding - * <p> - * - * DEFAULT: LTKPreprocDefaults::NN_DEF_BANDING - * </p> - */ - - int m_dtwEuclideanFilter; - /**< @brief DTW Euclidean Filter - * <p> - * - * DEFAULT: LTKPreprocDefaults::NN_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_nnCfgFilePath; - /**< @brief Full path of NN configuration file - * <p> - * Assigned value in the NNShapeRecognizer::initialize function - * </p> - */ - - string m_nnMDTFilePath; - /**< @brief Full path of Model data file - * <p> - * Assigned value in the NNShapeRecognizer::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> - */ - - vector<LTKShapeSample> m_prototypeSet; - /**< @brief Prototype Set for LVQ - * <p> - * It contains the Vector of Clustered ShapeSamples - * </p> - */ - - LTKCaptureDevice m_captureDevice; - - struct NeighborInfo - { - int classId; - float distance; - int prototypeSetIndex; - }; - - /* - struct MapModFunc - { - string moduleName; - string funcName; - }; - */ - - 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_prototypeSet. - * 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 NN.cfg - * - * </p> - */ - - //Cache Parameters used by Adapt - vector<LTKShapeRecoResult> m_vecRecoResult; - /**< @brief Store Recognize results - * used by subsequent call to Adapt - * <p> - * - * - * </p> - */ - - - vector <struct NeighborInfo> m_neighborInfoVec; - /**< @brief Vector to store the distances of test sample to each of the samples in prototypeSet, - * classIDs and indices within the prototypeset - * Used during subsequent call to Adapt - * <p> - * - * - * </p> - */ - - LTKShapeSample m_cachedShapeSampleFeatures; - /**< @brief Store ShapeSampleFeatures 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; - - DynamicTimeWarping<LTKShapeFeaturePtr, float> m_dtwObj; - - public: - - /** @name Constructors and Destructor */ - //@{ - - NNShapeRecognizer(const LTKControlInfo& controlInfo); - - /** - * Destructor - */ - ~NNShapeRecognizer(); - - //@} - - /** - * This method initializes the NN shape recognizer - * <p> - * Semantics - * - Set the project name in NNShapeRecognizer::headerInfo with the parameter passed.<br> - * m_headerInfo[PROJNAME] = strProjectName; - * - * - Initialize NNShapeRecognizer::m_nnCfgFilePath <br> - * m_nnCfgFilePath = NNShapeRecognizer::m_lipiRootPath + LTKMacros::PROJECTS_PATH_STRING + - * strProjectName + LTKMacros::PROFILE_PATH_STRING + strProfileName + - * LTKInc::SEPARATOR + LTKInc::NN + LTKInc::CONFIGFILEEXT; - * - * - Initializes NNShapeRecognizer::m_nnMDTFilePath <br> - * m_nnMDTFilePath = NNShapeRecognizer::m_lipiRootPath + LTKMacros::PROJECTS_PATH_STRING + - * strProjectName + LTKMacros::PROFILE_PATH_STRING + strProfileName + - * LTKInc::SEPARATOR + LTKInc::NN + LTKInc::DATFILEEXT; - * - * - Initializes NNShapeRecognizer::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 NN 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 NN 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 - * - If the distance method (m_prototypeDistance) is Euclidean (EUCLIDEAN_DISTANCE), - * populate the distIndexPairVector with the distance of the test sample to all the - samples in the prototype set - * - If the distance method is DTW, compute the DTW distance of the test sample to the - samples in the prototype set which passed the Euclidean filter. Populate the - distIndexPairVector - * - 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>& subSetOfClasses, - float confThreshold, - int numChoices, - vector<LTKShapeRecoResult>& resultVector); - - /** - * This method add Class - * - * Semantics - * - * - Check if project is Dynamic, if not return ErrorCode - * - Calculate Features - * - Update PrototypeSet - * - Update MDTFile - * - return shapeID of new class added - * - * @param sampleTraceGroup : LTKTraceGroup : Holds TraceGroup of sample to Add - * @param shapeID : int : Holds shapeID of new Class - * shapeID = m_prototypeSet.at(prototypeSetSize-1).getClassID()+1 - * - * @return SUCCESS:Shape Class added successfully - * FAILURE: return ErrorCode - * @exception none - */ - - int addClass(const LTKTraceGroup& sampleTraceGroup, int& shapeID); - - /** - * This method add Sample Class for adapt - * - * Semantics - * - * - Check if project is Dynamic, if not return ErrorCode - * - Calculate Features - * - Update PrototypeSet - * - Update MDTFile - * - return shapeID of new class added - * - * @param sampleTraceGroup : LTKTraceGroup : Holds TraceGroup of sample to Add - * @param shapeID : int : Holds shapeID of new Class - * shapeID = m_prototypeSet.at(prototypeSetSize-1).getClassID()+1 - * - * @return SUCCESS:Shape Class added successfully - * FAILURE: return ErrorCode - * @exception none - */ - int addSample(const LTKTraceGroup& sampleTraceGroup, int shapeID); - - /** - * This method delete Class - * - * 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 of Shape to be deleted - * - * @return SUCCESS: Shape Class deleted successfully - * FAILURE: return ErrorCode - * @exception none - */ - int deleteClass(int shapeID); - - /** - * This method converts features 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); - - /** - * 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); - - 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 NNShapeRecognizer::initializeFeatureExtractorInstance() method - * - * - Call train method depending on the inFileType - * - NNShapeRecognizer::trainFromListFile() if inFileType = LTKMacros::INK_FILE - * - NNShapeRecognizer::trainFromFeatureFile() if inFileType = LTKMacros ::FEATURE_FILE - * - * - Update the headerInfo with algorithm version and name using NNShapeRecognizer::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 NN.cfg and initializes the instance variable of the classifier with the user defined - * values. - * - * Semantics - * - * - Open the nn.cfg using LTKConfigFileReader - * - * - Incase of file open failure (nn.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); - - - - - /** - * This function computes the eucildean distance between the two points. - * @param train X and Y coordinate of the first point. - * @param test X and Y coordinate of the second point. - */ - - /*int computeEuclideanDistance(const LTKShapeSample& inFirstShapeSampleFeatures, - const LTKShapeSample& inSecondShapeSampleFeatures, - float& outEuclideanDistance);*/ - - int computeEuclideanDistance(const LTKShapeSample& inFirstShapeSampleFeatures, - const LTKShapeSample& inSecondShapeSampleFeatures, - float& outEuclideanDistance); - - int calculateMedian(const int2DVector& clusteringResult, - const float2DVector& distanceMatrix, vector<int>& outMedianIndexVec); - - - - /** - * This method creates a custom feature extractor instance and stores it's address in - * NNShapeRecognizer::m_ltkFE. The local distance function pointer is also initialized. - * - * Semantics - * - * - * - Intialize the NNShapeRecognizer::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 - * - NNShapeRecognizer::m_numShapes - * - NNShapeRecognizer::m_traceDimension - * - NNShapeRecognizer::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 NNShapeRecognizer::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 NNShapeRecognizer::performClustering - * - * - performClustering results in vector of clustered ShapeSamples. - * - * - Append these clustered vector<ShapeSample> to the mdt file. - * - * - * @param listFilePath : string : Holds the path for train list file - * - * @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 - */ - int trainFromListFile(const string& listFilePath); - - /** - * This method trains the classifier from the feature file whose path is passed as paramater - * - * Semantics - * - * - * @param featureFilePath : string : Holds the path of Feature file - * - * @return none - */ - int trainFromFeatureFile(const string& featureFilePath); - - int PreprocParametersForFeatureFile(stringStringMap& headerSequence); - - /** - * 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 NNShapeRecognizer::m_prototypeReductionFactor is -1 means Automatic clustering could be done - * - * - If the NNShapeRecognizer::m_prototypeReductionFactor is 0 means No clustering was needed - * - * - Otherwise clustering is needed based on the value of NNShapeRecognizer::m_prototypeReductionFactor - * - * - Calculate Median if NNShapeRecognizer::m_prototypeReductionFactor is not equal to zero - * - * - * @param ShapeSamplesVec : ShapeSampleVector : Holds all the ShapeSample for a single class - * @param resultVector : ShapeSampleVector : Holds all the ShapeSample after clustering - * @param sampleCount : int : Holds the number of shapes for a sample - * - * @return none - * @exception none - */ - int performClustering(const vector<LTKShapeSample> & shapeSamplesVec, - vector<LTKShapeSample>& outClusteredShapeSampleVec); - - /** - * 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 NNShapeRecognizer::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 method will writes training results to the mdt file - * - * Semantics - * - * - If the feature representation was float then - * - Iterate through the shape model - * - Write the feature Dimension - * - Write the feature vector size - * - Write all the feature vector - * - Write the class ID - * - * - If the feature representation was custom then - * - Iterate through the shape model - * - Write the feature Size - * - Call the writeFeatureVector() to write all the feature vector - * - Write the class ID - * - * - * @param resultVector : ShapeSampleVector : A vector of ShapeSamples created as a result of training - * mdtFileHandle : ofstream : Specifies the outut stream - * - * @return none - * - * @exception none - */ - - int appendPrototypesToMDTFile(const vector<LTKShapeSample>& prototypeVec, ofstream & mdtFileHandle); - - static bool sortDist(const NeighborInfo& x, const NeighborInfo& y); - - static void getDistance(const LTKShapeFeaturePtr& f1,const LTKShapeFeaturePtr& f2, float& distance); - - int getShapeSampleFromString(const string& inString, LTKShapeSample& outShapeSample); - - 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 create MDTFile - * - * Semantics - * - * - * @param None - * - * @return None - * - * @exception none - */ - int writePrototypeSetToMDTFile(); - - /** - * This method adds Sample To Prototype - * - * Semantics - * - * - Add data in ascending order to ShapeID - * - - * - * @param shapeSampleFeatures : LTKShapeSample : Holds features of sample to be added to PrototypeSet - * - * @return SUCCESS: if shapeSampleFeatures is populated successfully - * FAILURE: return ErrorCode - * @exception none - */ - int insertSampleToPrototypeSet(const LTKShapeSample &shapeSampleFeatures); - - /** - * 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); - - /** - * The comparison function object of STL's max_element() method, overloaded for the map<int, int>, used to retrieve the maximum of the value field (second element) of map - * - * Semantics - * - * - Check if the first object's second value is greater than the second object's second value - * - Return true or false - * - - * - * @param lhs : map<int, int>::value_type : First object for comparison - * @param rhs : map<int, int>::value_type : Second object for comparison - * - * @return true: If lhs.second > rhs.second - * false: If lhs.second <= rhs.second - * @exception none - */ - - static bool compareMap( const map<int, int>::value_type& lhs, const map<int, int>::value_type& rhs ); - - /** 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(); - - /** - * This function is the train method using LVQ - * - * Semantics - * - * - Note the start time for time calculations. - * - * - Create an instance of the feature extractor using NNShapeRecognizer::initializeFeatureExtractorInstance() method - * - * - Call train method depending on the inFileType - * - NNShapeRecognizer::trainFromListFile if inFileType() = LTKMacros::INK_FILE - * - tNNShapeRecognizer::rainFromFeatureFile if inFileType() = LTKMacros::FEATURE_FILE - * - * NOTE : - * The NNShapeRecognizer::trainFromListFile populates the following data structures - * - * - NNShapeRecognizer::m_prototypeSet : Vector of clustered ShapeSample and - * - * - Process the prototype set using NNShapeRecognizer::processPrototypeSetForLVQ() - * - * - Update the headerInfo with algorithm version and name using NNShapeRecognizer::updateHeaderWithAlgoInfo() - * - * - 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 trainLVQ(const string& inputFilePath, - const string& mdtHeaderFilePath, - const string& inFileType); - - /** - * This function is used to compute the learning parameter that is used in Learning Vector Quantization (called from trainLVQ) - * The input parameters are the iteration number, number of iterations, and the start value of the learning parameter (alpha) - * the function returns the value of the learning parameter (linearly decreasing) - */ - float linearAlpha(long iter, long length, double& initialAlpha, - double lastAlpha,int correctDecision); - - /** - * This function does the reshaping of prototype vector (called from trainLVQ) - * The input parameters are the code vector, data vector (learning example in the context of LVQ), and alpha (learning parameter) - * @param bestcodeVec is the character which we are trying to morph - * the function modifies the character bestcodeVec - */ - int morphVector(const LTKShapeSample& dataShapeSample, - double talpha, LTKShapeSample& bestShapeSample); - - int processPrototypeSetForLVQ(); - - int validatePreprocParameters(stringStringMap& headerSequence); - - - /**< @brief LVQ Iteration Scale - * <p> - * - * DEFAULT: LTKPreprocDefaults::NN_DEF_LVQITERATIONSCALE - * </p> - */ - int m_LVQIterationScale; - - /**< @brief LVQ Initial Alpha - * <p> - * - * DEFAULT: LTKPreprocDefaults::NN_DEF_LVQINITIALALPHA - * </p> - */ - double m_LVQInitialAlpha; - - /**< @brief LVQ Distance Measure - * <p> - * - * DEFAULT: LTKPreprocDefaults::NN_DEF_LVQDISTANCEMEASURE - * </p> - */ - string m_LVQDistanceMeasure; - - /**< @brief Pointer to LTKOSUtil interface - * <p> - * - * </p> - */ - LTKOSUtil* m_OSUtilPtr; - - vector<LTKShapeSample> m_trainSet; - -}; - - -#endif |