+++ /dev/null
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-// By downloading, copying, installing or using the software you agree to this license.
-// If you do not agree to this license, do not download, install,
-// copy or use the software.
-//
-//
-// Intel License Agreement
-// For Open Source Computer Vision Library
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-// * Redistribution's of source code must retain the above copyright notice,
-// this list of conditions and the following disclaimer.
-//
-// * Redistribution's in binary form must reproduce the above copyright notice,
-// this list of conditions and the following disclaimer in the documentation
-// and/or other materials provided with the distribution.
-//
-// * The name of Intel Corporation may not be used to endorse or promote products
-// derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-/*
- * File cvclassifier.h
- *
- * Classifier types
- */
-
-#ifndef _CVCLASSIFIER_H_
-#define _CVCLASSIFIER_H_
-
-#include <cxcore.h>
-#include <math.h>
-
-#define CV_BOOST_API
-
-/* Convert matrix to vector */
-#define CV_MAT2VEC( mat, vdata, vstep, num ) \
- assert( (mat).rows == 1 || (mat).cols == 1 ); \
- (vdata) = ((mat).data.ptr); \
- if( (mat).rows == 1 ) \
- { \
- (vstep) = CV_ELEM_SIZE( (mat).type ); \
- (num) = (mat).cols; \
- } \
- else \
- { \
- (vstep) = (mat).step; \
- (num) = (mat).rows; \
- }
-
-/* Set up <sample> matrix header to be <num> sample of <trainData> samples matrix */
-#define CV_GET_SAMPLE( trainData, tdflags, num, sample ) \
-if( CV_IS_ROW_SAMPLE( tdflags ) ) \
-{ \
- cvInitMatHeader( &(sample), 1, (trainData).cols, \
- CV_MAT_TYPE( (trainData).type ), \
- ((trainData).data.ptr + (num) * (trainData).step), \
- (trainData).step ); \
-} \
-else \
-{ \
- cvInitMatHeader( &(sample), (trainData).rows, 1, \
- CV_MAT_TYPE( (trainData).type ), \
- ((trainData).data.ptr + (num) * CV_ELEM_SIZE( (trainData).type )), \
- (trainData).step ); \
-}
-
-#define CV_GET_SAMPLE_STEP( trainData, tdflags, sstep ) \
-(sstep) = ( ( CV_IS_ROW_SAMPLE( tdflags ) ) \
- ? (trainData).step : CV_ELEM_SIZE( (trainData).type ) );
-
-
-#define CV_LOGRATIO_THRESHOLD 0.00001F
-
-/* log( val / (1 - val ) ) */
-CV_INLINE float cvLogRatio( float val );
-
-CV_INLINE float cvLogRatio( float val )
-{
- float tval;
-
- tval = MAX(CV_LOGRATIO_THRESHOLD, MIN( 1.0F - CV_LOGRATIO_THRESHOLD, (val) ));
- return logf( tval / (1.0F - tval) );
-}
-
-
-/* flags values for classifier consturctor flags parameter */
-
-/* each trainData matrix column is a sample */
-#define CV_COL_SAMPLE 0
-
-/* each trainData matrix row is a sample */
-#define CV_ROW_SAMPLE 1
-
-#define CV_IS_ROW_SAMPLE( flags ) ( ( flags ) & CV_ROW_SAMPLE )
-
-/* Classifier supports tune function */
-#define CV_TUNABLE (1 << 1)
-
-#define CV_IS_TUNABLE( flags ) ( (flags) & CV_TUNABLE )
-
-
-/* classifier fields common to all classifiers */
-#define CV_CLASSIFIER_FIELDS() \
- int flags; \
- float(*eval)( struct CvClassifier*, CvMat* ); \
- void (*tune)( struct CvClassifier*, CvMat*, int flags, CvMat*, CvMat*, CvMat*, \
- CvMat*, CvMat* ); \
- int (*save)( struct CvClassifier*, const char* file_name ); \
- void (*release)( struct CvClassifier** );
-
-typedef struct CvClassifier
-{
- CV_CLASSIFIER_FIELDS()
-} CvClassifier;
-
-#define CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
-typedef struct CvClassifierTrainParams
-{
- CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
-} CvClassifierTrainParams;
-
-
-/*
- Common classifier constructor:
- CvClassifier* cvCreateMyClassifier( CvMat* trainData,
- int flags,
- CvMat* trainClasses,
- CvMat* typeMask,
- CvMat* missedMeasurementsMask CV_DEFAULT(0),
- CvCompIdx* compIdx CV_DEFAULT(0),
- CvMat* sampleIdx CV_DEFAULT(0),
- CvMat* weights CV_DEFAULT(0),
- CvClassifierTrainParams* trainParams CV_DEFAULT(0)
- )
-
- */
-
-typedef CvClassifier* (*CvClassifierConstructor)( CvMat*, int, CvMat*, CvMat*, CvMat*,
- CvMat*, CvMat*, CvMat*,
- CvClassifierTrainParams* );
-
-typedef enum CvStumpType
-{
- CV_CLASSIFICATION = 0,
- CV_CLASSIFICATION_CLASS = 1,
- CV_REGRESSION = 2
-} CvStumpType;
-
-typedef enum CvStumpError
-{
- CV_MISCLASSIFICATION = 0,
- CV_GINI = 1,
- CV_ENTROPY = 2,
- CV_SQUARE = 3
-} CvStumpError;
-
-
-typedef struct CvStumpTrainParams
-{
- CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
- CvStumpType type;
- CvStumpError error;
-} CvStumpTrainParams;
-
-typedef struct CvMTStumpTrainParams
-{
- CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
- CvStumpType type;
- CvStumpError error;
- int portion; /* number of components calculated in each thread */
- int numcomp; /* total number of components */
-
- /* callback which fills <mat> with components [first, first+num[ */
- void (*getTrainData)( CvMat* mat, CvMat* sampleIdx, CvMat* compIdx,
- int first, int num, void* userdata );
- CvMat* sortedIdx; /* presorted samples indices */
- void* userdata; /* passed to callback */
-} CvMTStumpTrainParams;
-
-typedef struct CvStumpClassifier
-{
- CV_CLASSIFIER_FIELDS()
- int compidx;
-
- float lerror; /* impurity of the right node */
- float rerror; /* impurity of the left node */
-
- float threshold;
- float left;
- float right;
-} CvStumpClassifier;
-
-typedef struct CvCARTTrainParams
-{
- CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
- /* desired number of internal nodes */
- int count;
- CvClassifierTrainParams* stumpTrainParams;
- CvClassifierConstructor stumpConstructor;
-
- /*
- * Split sample indices <idx>
- * on the "left" indices <left> and "right" indices <right>
- * according to samples components <compidx> values and <threshold>.
- *
- * NOTE: Matrices <left> and <right> must be allocated using cvCreateMat function
- * since they are freed using cvReleaseMat function
- *
- * If it is NULL then the default implementation which evaluates training
- * samples from <trainData> passed to classifier constructor is used
- */
- void (*splitIdx)( int compidx, float threshold,
- CvMat* idx, CvMat** left, CvMat** right,
- void* userdata );
- void* userdata;
-} CvCARTTrainParams;
-
-typedef struct CvCARTClassifier
-{
- CV_CLASSIFIER_FIELDS()
- /* number of internal nodes */
- int count;
-
- /* internal nodes (each array of <count> elements) */
- int* compidx;
- float* threshold;
- int* left;
- int* right;
-
- /* leaves (array of <count>+1 elements) */
- float* val;
-} CvCARTClassifier;
-
-CV_BOOST_API
-void cvGetSortedIndices( CvMat* val, CvMat* idx, int sortcols CV_DEFAULT( 0 ) );
-
-CV_BOOST_API
-void cvReleaseStumpClassifier( CvClassifier** classifier );
-
-CV_BOOST_API
-float cvEvalStumpClassifier( CvClassifier* classifier, CvMat* sample );
-
-CV_BOOST_API
-CvClassifier* cvCreateStumpClassifier( CvMat* trainData,
- int flags,
- CvMat* trainClasses,
- CvMat* typeMask,
- CvMat* missedMeasurementsMask CV_DEFAULT(0),
- CvMat* compIdx CV_DEFAULT(0),
- CvMat* sampleIdx CV_DEFAULT(0),
- CvMat* weights CV_DEFAULT(0),
- CvClassifierTrainParams* trainParams CV_DEFAULT(0) );
-
-/*
- * cvCreateMTStumpClassifier
- *
- * Multithreaded stump classifier constructor
- * Includes huge train data support through callback function
- */
-CV_BOOST_API
-CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData,
- int flags,
- CvMat* trainClasses,
- CvMat* typeMask,
- CvMat* missedMeasurementsMask,
- CvMat* compIdx,
- CvMat* sampleIdx,
- CvMat* weights,
- CvClassifierTrainParams* trainParams );
-
-/*
- * cvCreateCARTClassifier
- *
- * CART classifier constructor
- */
-CV_BOOST_API
-CvClassifier* cvCreateCARTClassifier( CvMat* trainData,
- int flags,
- CvMat* trainClasses,
- CvMat* typeMask,
- CvMat* missedMeasurementsMask,
- CvMat* compIdx,
- CvMat* sampleIdx,
- CvMat* weights,
- CvClassifierTrainParams* trainParams );
-
-CV_BOOST_API
-void cvReleaseCARTClassifier( CvClassifier** classifier );
-
-CV_BOOST_API
-float cvEvalCARTClassifier( CvClassifier* classifier, CvMat* sample );
-
-/****************************************************************************************\
-* Boosting *
-\****************************************************************************************/
-
-/*
- * CvBoostType
- *
- * The CvBoostType enumeration specifies the boosting type.
- *
- * Remarks
- * Four different boosting variants for 2 class classification problems are supported:
- * Discrete AdaBoost, Real AdaBoost, LogitBoost and Gentle AdaBoost.
- * The L2 (2 class classification problems) and LK (K class classification problems)
- * algorithms are close to LogitBoost but more numerically stable than last one.
- * For regression three different loss functions are supported:
- * Least square, least absolute deviation and huber loss.
- */
-typedef enum CvBoostType
-{
- CV_DABCLASS = 0, /* 2 class Discrete AdaBoost */
- CV_RABCLASS = 1, /* 2 class Real AdaBoost */
- CV_LBCLASS = 2, /* 2 class LogitBoost */
- CV_GABCLASS = 3, /* 2 class Gentle AdaBoost */
- CV_L2CLASS = 4, /* classification (2 class problem) */
- CV_LKCLASS = 5, /* classification (K class problem) */
- CV_LSREG = 6, /* least squares regression */
- CV_LADREG = 7, /* least absolute deviation regression */
- CV_MREG = 8, /* M-regression (Huber loss) */
-} CvBoostType;
-
-/****************************************************************************************\
-* Iterative training functions *
-\****************************************************************************************/
-
-/*
- * CvBoostTrainer
- *
- * The CvBoostTrainer structure represents internal boosting trainer.
- */
-typedef struct CvBoostTrainer CvBoostTrainer;
-
-/*
- * cvBoostStartTraining
- *
- * The cvBoostStartTraining function starts training process and calculates
- * response values and weights for the first weak classifier training.
- *
- * Parameters
- * trainClasses
- * Vector of classes of training samples classes. Each element must be 0 or 1 and
- * of type CV_32FC1.
- * weakTrainVals
- * Vector of response values for the first trained weak classifier.
- * Must be of type CV_32FC1.
- * weights
- * Weight vector of training samples for the first trained weak classifier.
- * Must be of type CV_32FC1.
- * type
- * Boosting type. CV_DABCLASS, CV_RABCLASS, CV_LBCLASS, CV_GABCLASS
- * types are supported.
- *
- * Return Values
- * The return value is a pointer to internal trainer structure which is used
- * to perform next training iterations.
- *
- * Remarks
- * weakTrainVals and weights must be allocated before calling the function
- * and of the same size as trainingClasses. Usually weights should be initialized
- * with 1.0 value.
- * The function calculates response values and weights for the first weak
- * classifier training and stores them into weakTrainVals and weights
- * respectively.
- * Note, the training of the weak classifier using weakTrainVals, weight,
- * trainingData is outside of this function.
- */
-CV_BOOST_API
-CvBoostTrainer* cvBoostStartTraining( CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvMat* sampleIdx,
- CvBoostType type );
-/*
- * cvBoostNextWeakClassifier
- *
- * The cvBoostNextWeakClassifier function performs next training
- * iteration and caluclates response values and weights for the next weak
- * classifier training.
- *
- * Parameters
- * weakEvalVals
- * Vector of values obtained by evaluation of each sample with
- * the last trained weak classifier (iteration i). Must be of CV_32FC1 type.
- * trainClasses
- * Vector of classes of training samples. Each element must be 0 or 1,
- * and of type CV_32FC1.
- * weakTrainVals
- * Vector of response values for the next weak classifier training
- * (iteration i+1). Must be of type CV_32FC1.
- * weights
- * Weight vector of training samples for the next weak classifier training
- * (iteration i+1). Must be of type CV_32FC1.
- * trainer
- * A pointer to internal trainer returned by the cvBoostStartTraining
- * function call.
- *
- * Return Values
- * The return value is the coefficient for the last trained weak classifier.
- *
- * Remarks
- * weakTrainVals and weights must be exactly the same vectors as used in
- * the cvBoostStartTraining function call and should not be modified.
- * The function calculates response values and weights for the next weak
- * classifier training and stores them into weakTrainVals and weights
- * respectively.
- * Note, the training of the weak classifier of iteration i+1 using
- * weakTrainVals, weight, trainingData is outside of this function.
- */
-CV_BOOST_API
-float cvBoostNextWeakClassifier( CvMat* weakEvalVals,
- CvMat* trainClasses,
- CvMat* weakTrainVals,
- CvMat* weights,
- CvBoostTrainer* trainer );
-
-/*
- * cvBoostEndTraining
- *
- * The cvBoostEndTraining function finishes training process and releases
- * internally allocated memory.
- *
- * Parameters
- * trainer
- * A pointer to a pointer to internal trainer returned by the cvBoostStartTraining
- * function call.
- */
-CV_BOOST_API
-void cvBoostEndTraining( CvBoostTrainer** trainer );
-
-/****************************************************************************************\
-* Boosted tree models *
-\****************************************************************************************/
-
-/*
- * CvBtClassifier
- *
- * The CvBtClassifier structure represents boosted tree model.
- *
- * Members
- * flags
- * Flags. If CV_IS_TUNABLE( flags ) != 0 then the model supports tuning.
- * eval
- * Evaluation function. Returns sample predicted class (0, 1, etc.)
- * for classification or predicted value for regression.
- * tune
- * Tune function. If the model supports tuning then tune call performs
- * one more boosting iteration if passed to the function flags parameter
- * is CV_TUNABLE otherwise releases internally allocated for tuning memory
- * and makes the model untunable.
- * NOTE: Since tuning uses the pointers to parameters,
- * passed to the cvCreateBtClassifier function, they should not be modified
- * or released between tune calls.
- * save
- * This function stores the model into given file.
- * release
- * This function releases the model.
- * type
- * Boosted tree model type.
- * numclasses
- * Number of classes for CV_LKCLASS type or 1 for all other types.
- * numiter
- * Number of iterations. Number of weak classifiers is equal to number
- * of iterations for all types except CV_LKCLASS. For CV_LKCLASS type
- * number of weak classifiers is (numiter * numclasses).
- * numfeatures
- * Number of features in sample.
- * trees
- * Stores weak classifiers when the model does not support tuning.
- * seq
- * Stores weak classifiers when the model supports tuning.
- * trainer
- * Pointer to internal tuning parameters if the model supports tuning.
- */
-typedef struct CvBtClassifier
-{
- CV_CLASSIFIER_FIELDS()
-
- CvBoostType type;
- int numclasses;
- int numiter;
- int numfeatures;
- union
- {
- CvCARTClassifier** trees;
- CvSeq* seq;
- };
- void* trainer;
-} CvBtClassifier;
-
-/*
- * CvBtClassifierTrainParams
- *
- * The CvBtClassifierTrainParams structure stores training parameters for
- * boosted tree model.
- *
- * Members
- * type
- * Boosted tree model type.
- * numiter
- * Desired number of iterations.
- * param
- * Parameter Model Type Parameter Meaning
- * param[0] Any Shrinkage factor
- * param[1] CV_MREG alpha. (1-alpha) determines "break-down" point of
- * the training procedure, i.e. the fraction of samples
- * that can be arbitrary modified without serious
- * degrading the quality of the result.
- * CV_DABCLASS, Weight trimming factor.
- * CV_RABCLASS,
- * CV_LBCLASS,
- * CV_GABCLASS,
- * CV_L2CLASS,
- * CV_LKCLASS
- * numsplits
- * Desired number of splits in each tree.
- */
-typedef struct CvBtClassifierTrainParams
-{
- CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
-
- CvBoostType type;
- int numiter;
- float param[2];
- int numsplits;
-} CvBtClassifierTrainParams;
-
-/*
- * cvCreateBtClassifier
- *
- * The cvCreateBtClassifier function creates boosted tree model.
- *
- * Parameters
- * trainData
- * Matrix of feature values. Must have CV_32FC1 type.
- * flags
- * Determines how samples are stored in trainData.
- * One of CV_ROW_SAMPLE or CV_COL_SAMPLE.
- * Optionally may be combined with CV_TUNABLE to make tunable model.
- * trainClasses
- * Vector of responses for regression or classes (0, 1, 2, etc.) for classification.
- * typeMask,
- * missedMeasurementsMask,
- * compIdx
- * Not supported. Must be NULL.
- * sampleIdx
- * Indices of samples used in training. If NULL then all samples are used.
- * For CV_DABCLASS, CV_RABCLASS, CV_LBCLASS and CV_GABCLASS must be NULL.
- * weights
- * Not supported. Must be NULL.
- * trainParams
- * A pointer to CvBtClassifierTrainParams structure. Training parameters.
- * See CvBtClassifierTrainParams description for details.
- *
- * Return Values
- * The return value is a pointer to created boosted tree model of type CvBtClassifier.
- *
- * Remarks
- * The function performs trainParams->numiter training iterations.
- * If CV_TUNABLE flag is specified then created model supports tuning.
- * In this case additional training iterations may be performed by
- * tune function call.
- */
-CV_BOOST_API
-CvClassifier* cvCreateBtClassifier( CvMat* trainData,
- int flags,
- CvMat* trainClasses,
- CvMat* typeMask,
- CvMat* missedMeasurementsMask,
- CvMat* compIdx,
- CvMat* sampleIdx,
- CvMat* weights,
- CvClassifierTrainParams* trainParams );
-
-/*
- * cvCreateBtClassifierFromFile
- *
- * The cvCreateBtClassifierFromFile function restores previously saved
- * boosted tree model from file.
- *
- * Parameters
- * filename
- * The name of the file with boosted tree model.
- *
- * Remarks
- * The restored model does not support tuning.
- */
-CV_BOOST_API
-CvClassifier* cvCreateBtClassifierFromFile( const char* filename );
-
-/****************************************************************************************\
-* Utility functions *
-\****************************************************************************************/
-
-/*
- * cvTrimWeights
- *
- * The cvTrimWeights function performs weight trimming.
- *
- * Parameters
- * weights
- * Weights vector.
- * idx
- * Indices vector of weights that should be considered.
- * If it is NULL then all weights are used.
- * factor
- * Weight trimming factor. Must be in [0, 1] range.
- *
- * Return Values
- * The return value is a vector of indices. If all samples should be used then
- * it is equal to idx. In other case the cvReleaseMat function should be called
- * to release it.
- *
- * Remarks
- */
-CV_BOOST_API
-CvMat* cvTrimWeights( CvMat* weights, CvMat* idx, float factor );
-
-/*
- * cvReadTrainData
- *
- * The cvReadTrainData function reads feature values and responses from file.
- *
- * Parameters
- * filename
- * The name of the file to be read.
- * flags
- * One of CV_ROW_SAMPLE or CV_COL_SAMPLE. Determines how feature values
- * will be stored.
- * trainData
- * A pointer to a pointer to created matrix with feature values.
- * cvReleaseMat function should be used to destroy created matrix.
- * trainClasses
- * A pointer to a pointer to created matrix with response values.
- * cvReleaseMat function should be used to destroy created matrix.
- *
- * Remarks
- * File format:
- * ============================================
- * m n
- * value_1_1 value_1_2 ... value_1_n response_1
- * value_2_1 value_2_2 ... value_2_n response_2
- * ...
- * value_m_1 value_m_2 ... value_m_n response_m
- * ============================================
- * m
- * Number of samples
- * n
- * Number of features in each sample
- * value_i_j
- * Value of j-th feature of i-th sample
- * response_i
- * Response value of i-th sample
- * For classification problems responses represent classes (0, 1, etc.)
- * All values and classes are integer or real numbers.
- */
-CV_BOOST_API
-void cvReadTrainData( const char* filename,
- int flags,
- CvMat** trainData,
- CvMat** trainClasses );
-
-
-/*
- * cvWriteTrainData
- *
- * The cvWriteTrainData function stores feature values and responses into file.
- *
- * Parameters
- * filename
- * The name of the file.
- * flags
- * One of CV_ROW_SAMPLE or CV_COL_SAMPLE. Determines how feature values
- * are stored.
- * trainData
- * Feature values matrix.
- * trainClasses
- * Response values vector.
- * sampleIdx
- * Vector of idicies of the samples that should be stored. If it is NULL
- * then all samples will be stored.
- *
- * Remarks
- * See the cvReadTrainData function for file format description.
- */
-CV_BOOST_API
-void cvWriteTrainData( const char* filename,
- int flags,
- CvMat* trainData,
- CvMat* trainClasses,
- CvMat* sampleIdx );
-
-/*
- * cvRandShuffle
- *
- * The cvRandShuffle function perfroms random shuffling of given vector.
- *
- * Parameters
- * vector
- * Vector that should be shuffled.
- * Must have CV_8UC1, CV_16SC1, CV_32SC1 or CV_32FC1 type.
- */
-CV_BOOST_API
-void cvRandShuffleVec( CvMat* vector );
-
-#endif /* _CVCLASSIFIER_H_ */
projects / opencv / blobdiff