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opencv/modules/haartraining/cvboost.cpp

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/*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*/
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
#ifdef HAVE_MALLOC_H
#include <malloc.h>
#endif
#ifdef HAVE_MEMORY_H
#include <memory.h>
#endif
#ifdef _OPENMP
#include <omp.h>
#endif /* _OPENMP */
#include <cstdio>
#include <cfloat>
#include <cmath>
#include <ctime>
#include <climits>
#include "_cvcommon.h"
#include "cvclassifier.h"
#ifdef _OPENMP
#include "omp.h"
#endif
#define CV_BOOST_IMPL
typedef struct CvValArray
{
uchar* data;
size_t step;
} CvValArray;
#define CMP_VALUES( idx1, idx2 ) \
( *( (float*) (aux->data + ((int) (idx1)) * aux->step ) ) < \
*( (float*) (aux->data + ((int) (idx2)) * aux->step ) ) )
CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_16s, short, CMP_VALUES, CvValArray* )
CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_32s, int, CMP_VALUES, CvValArray* )
CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_32f, float, CMP_VALUES, CvValArray* )
CV_BOOST_IMPL
void cvGetSortedIndices( CvMat* val, CvMat* idx, int sortcols )
{
int idxtype = 0;
size_t istep = 0;
size_t jstep = 0;
int i = 0;
int j = 0;
CvValArray va;
CV_Assert( idx != NULL );
CV_Assert( val != NULL );
idxtype = CV_MAT_TYPE( idx->type );
CV_Assert( idxtype == CV_16SC1 || idxtype == CV_32SC1 || idxtype == CV_32FC1 );
CV_Assert( CV_MAT_TYPE( val->type ) == CV_32FC1 );
if( sortcols )
{
CV_Assert( idx->rows == val->cols );
CV_Assert( idx->cols == val->rows );
istep = CV_ELEM_SIZE( val->type );
jstep = val->step;
}
else
{
CV_Assert( idx->rows == val->rows );
CV_Assert( idx->cols == val->cols );
istep = val->step;
jstep = CV_ELEM_SIZE( val->type );
}
va.data = val->data.ptr;
va.step = jstep;
switch( idxtype )
{
case CV_16SC1:
for( i = 0; i < idx->rows; i++ )
{
for( j = 0; j < idx->cols; j++ )
{
CV_MAT_ELEM( *idx, short, i, j ) = (short) j;
}
icvSortIndexedValArray_16s( (short*) (idx->data.ptr + (size_t)i * idx->step),
idx->cols, &va );
va.data += istep;
}
break;
case CV_32SC1:
for( i = 0; i < idx->rows; i++ )
{
for( j = 0; j < idx->cols; j++ )
{
CV_MAT_ELEM( *idx, int, i, j ) = j;
}
icvSortIndexedValArray_32s( (int*) (idx->data.ptr + (size_t)i * idx->step),
idx->cols, &va );
va.data += istep;
}
break;
case CV_32FC1:
for( i = 0; i < idx->rows; i++ )
{
for( j = 0; j < idx->cols; j++ )
{
CV_MAT_ELEM( *idx, float, i, j ) = (float) j;
}
icvSortIndexedValArray_32f( (float*) (idx->data.ptr + (size_t)i * idx->step),
idx->cols, &va );
va.data += istep;
}
break;
default:
assert( 0 );
break;
}
}
CV_BOOST_IMPL
void cvReleaseStumpClassifier( CvClassifier** classifier )
{
cvFree( classifier );
*classifier = 0;
}
CV_BOOST_IMPL
float cvEvalStumpClassifier( CvClassifier* classifier, CvMat* sample )
{
assert( classifier != NULL );
assert( sample != NULL );
assert( CV_MAT_TYPE( sample->type ) == CV_32FC1 );
if( (CV_MAT_ELEM( (*sample), float, 0,
((CvStumpClassifier*) classifier)->compidx )) <
((CvStumpClassifier*) classifier)->threshold )
return ((CvStumpClassifier*) classifier)->left;
return ((CvStumpClassifier*) classifier)->right;
}
#define ICV_DEF_FIND_STUMP_THRESHOLD( suffix, type, error ) \
CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( \
uchar* data, size_t datastep, \
uchar* wdata, size_t wstep, \
uchar* ydata, size_t ystep, \
uchar* idxdata, size_t idxstep, int num, \
float* lerror, \
float* rerror, \
float* threshold, float* left, float* right, \
float* sumw, float* sumwy, float* sumwyy ) \
{ \
int found = 0; \
float wyl = 0.0F; \
float wl = 0.0F; \
float wyyl = 0.0F; \
float wyr = 0.0F; \
float wr = 0.0F; \
\
float curleft = 0.0F; \
float curright = 0.0F; \
float* prevval = NULL; \
float* curval = NULL; \
float curlerror = 0.0F; \
float currerror = 0.0F; \
float wposl; \
float wposr; \
\
int i = 0; \
int idx = 0; \
\
wposl = wposr = 0.0F; \
if( *sumw == FLT_MAX ) \
{ \
/* calculate sums */ \
float *y = NULL; \
float *w = NULL; \
float wy = 0.0F; \
\
*sumw = 0.0F; \
*sumwy = 0.0F; \
*sumwyy = 0.0F; \
for( i = 0; i < num; i++ ) \
{ \
idx = (int) ( *((type*) (idxdata + i*idxstep)) ); \
w = (float*) (wdata + idx * wstep); \
*sumw += *w; \
y = (float*) (ydata + idx * ystep); \
wy = (*w) * (*y); \
*sumwy += wy; \
*sumwyy += wy * (*y); \
} \
} \
\
for( i = 0; i < num; i++ ) \
{ \
idx = (int) ( *((type*) (idxdata + i*idxstep)) ); \
curval = (float*) (data + idx * datastep); \
/* for debug purpose */ \
if( i > 0 ) assert( (*prevval) <= (*curval) ); \
\
wyr = *sumwy - wyl; \
wr = *sumw - wl; \
\
if( wl > 0.0 ) curleft = wyl / wl; \
else curleft = 0.0F; \
\
if( wr > 0.0 ) curright = wyr / wr; \
else curright = 0.0F; \
\
error \
\
if( curlerror + currerror < (*lerror) + (*rerror) ) \
{ \
(*lerror) = curlerror; \
(*rerror) = currerror; \
*threshold = *curval; \
if( i > 0 ) { \
*threshold = 0.5F * (*threshold + *prevval); \
} \
*left = curleft; \
*right = curright; \
found = 1; \
} \
\
do \
{ \
wl += *((float*) (wdata + idx * wstep)); \
wyl += (*((float*) (wdata + idx * wstep))) \
* (*((float*) (ydata + idx * ystep))); \
wyyl += *((float*) (wdata + idx * wstep)) \
* (*((float*) (ydata + idx * ystep))) \
* (*((float*) (ydata + idx * ystep))); \
} \
while( (++i) < num && \
( *((float*) (data + (idx = \
(int) ( *((type*) (idxdata + i*idxstep))) ) * datastep)) \
== *curval ) ); \
--i; \
prevval = curval; \
} /* for each value */ \
\
return found; \
}
/* misclassification error
* err = MIN( wpos, wneg );
*/
#define ICV_DEF_FIND_STUMP_THRESHOLD_MISC( suffix, type ) \
ICV_DEF_FIND_STUMP_THRESHOLD( misc_##suffix, type, \
wposl = 0.5F * ( wl + wyl ); \
wposr = 0.5F * ( wr + wyr ); \
curleft = 0.5F * ( 1.0F + curleft ); \
curright = 0.5F * ( 1.0F + curright ); \
curlerror = MIN( wposl, wl - wposl ); \
currerror = MIN( wposr, wr - wposr ); \
)
/* gini error
* err = 2 * wpos * wneg /(wpos + wneg)
*/
#define ICV_DEF_FIND_STUMP_THRESHOLD_GINI( suffix, type ) \
ICV_DEF_FIND_STUMP_THRESHOLD( gini_##suffix, type, \
wposl = 0.5F * ( wl + wyl ); \
wposr = 0.5F * ( wr + wyr ); \
curleft = 0.5F * ( 1.0F + curleft ); \
curright = 0.5F * ( 1.0F + curright ); \
curlerror = 2.0F * wposl * ( 1.0F - curleft ); \
currerror = 2.0F * wposr * ( 1.0F - curright ); \
)
#define CV_ENTROPY_THRESHOLD FLT_MIN
/* entropy error
* err = - wpos * log(wpos / (wpos + wneg)) - wneg * log(wneg / (wpos + wneg))
*/
#define ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( suffix, type ) \
ICV_DEF_FIND_STUMP_THRESHOLD( entropy_##suffix, type, \
wposl = 0.5F * ( wl + wyl ); \
wposr = 0.5F * ( wr + wyr ); \
curleft = 0.5F * ( 1.0F + curleft ); \
curright = 0.5F * ( 1.0F + curright ); \
curlerror = currerror = 0.0F; \
if( curleft > CV_ENTROPY_THRESHOLD ) \
curlerror -= wposl * logf( curleft ); \
if( curleft < 1.0F - CV_ENTROPY_THRESHOLD ) \
curlerror -= (wl - wposl) * logf( 1.0F - curleft ); \
\
if( curright > CV_ENTROPY_THRESHOLD ) \
currerror -= wposr * logf( curright ); \
if( curright < 1.0F - CV_ENTROPY_THRESHOLD ) \
currerror -= (wr - wposr) * logf( 1.0F - curright ); \
)
/* least sum of squares error */
#define ICV_DEF_FIND_STUMP_THRESHOLD_SQ( suffix, type ) \
ICV_DEF_FIND_STUMP_THRESHOLD( sq_##suffix, type, \
/* calculate error (sum of squares) */ \
/* err = sum( w * (y - left(rigt)Val)^2 ) */ \
curlerror = wyyl + curleft * curleft * wl - 2.0F * curleft * wyl; \
currerror = (*sumwyy) - wyyl + curright * curright * wr - 2.0F * curright * wyr; \
)
ICV_DEF_FIND_STUMP_THRESHOLD_MISC( 16s, short )
ICV_DEF_FIND_STUMP_THRESHOLD_MISC( 32s, int )
ICV_DEF_FIND_STUMP_THRESHOLD_MISC( 32f, float )
ICV_DEF_FIND_STUMP_THRESHOLD_GINI( 16s, short )
ICV_DEF_FIND_STUMP_THRESHOLD_GINI( 32s, int )
ICV_DEF_FIND_STUMP_THRESHOLD_GINI( 32f, float )
ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( 16s, short )
ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( 32s, int )
ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( 32f, float )
ICV_DEF_FIND_STUMP_THRESHOLD_SQ( 16s, short )
ICV_DEF_FIND_STUMP_THRESHOLD_SQ( 32s, int )
ICV_DEF_FIND_STUMP_THRESHOLD_SQ( 32f, float )
typedef int (*CvFindThresholdFunc)( uchar* data, size_t datastep,
uchar* wdata, size_t wstep,
uchar* ydata, size_t ystep,
uchar* idxdata, size_t idxstep, int num,
float* lerror,
float* rerror,
float* threshold, float* left, float* right,
float* sumw, float* sumwy, float* sumwyy );
CvFindThresholdFunc findStumpThreshold_16s[4] = {
icvFindStumpThreshold_misc_16s,
icvFindStumpThreshold_gini_16s,
icvFindStumpThreshold_entropy_16s,
icvFindStumpThreshold_sq_16s
};
CvFindThresholdFunc findStumpThreshold_32s[4] = {
icvFindStumpThreshold_misc_32s,
icvFindStumpThreshold_gini_32s,
icvFindStumpThreshold_entropy_32s,
icvFindStumpThreshold_sq_32s
};
CvFindThresholdFunc findStumpThreshold_32f[4] = {
icvFindStumpThreshold_misc_32f,
icvFindStumpThreshold_gini_32f,
icvFindStumpThreshold_entropy_32f,
icvFindStumpThreshold_sq_32f
};
CV_BOOST_IMPL
CvClassifier* cvCreateStumpClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* /*typeMask*/,
CvMat* missedMeasurementsMask,
CvMat* compIdx,
CvMat* sampleIdx,
CvMat* weights,
CvClassifierTrainParams* trainParams
)
{
CvStumpClassifier* stump = NULL;
int m = 0; /* number of samples */
int n = 0; /* number of components */
uchar* data = NULL;
int cstep = 0;
int sstep = 0;
uchar* ydata = NULL;
int ystep = 0;
uchar* idxdata = NULL;
int idxstep = 0;
int l = 0; /* number of indices */
uchar* wdata = NULL;
int wstep = 0;
int* idx = NULL;
int i = 0;
float sumw = FLT_MAX;
float sumwy = FLT_MAX;
float sumwyy = FLT_MAX;
CV_Assert( trainData != NULL );
CV_Assert( CV_MAT_TYPE( trainData->type ) == CV_32FC1 );
CV_Assert( trainClasses != NULL );
CV_Assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
CV_Assert( missedMeasurementsMask == NULL );
CV_Assert( compIdx == NULL );
CV_Assert( weights != NULL );
CV_Assert( CV_MAT_TYPE( weights->type ) == CV_32FC1 );
CV_Assert( trainParams != NULL );
data = trainData->data.ptr;
if( CV_IS_ROW_SAMPLE( flags ) )
{
cstep = CV_ELEM_SIZE( trainData->type );
sstep = trainData->step;
m = trainData->rows;
n = trainData->cols;
}
else
{
sstep = CV_ELEM_SIZE( trainData->type );
cstep = trainData->step;
m = trainData->cols;
n = trainData->rows;
}
ydata = trainClasses->data.ptr;
if( trainClasses->rows == 1 )
{
assert( trainClasses->cols == m );
ystep = CV_ELEM_SIZE( trainClasses->type );
}
else
{
assert( trainClasses->rows == m );
ystep = trainClasses->step;
}
wdata = weights->data.ptr;
if( weights->rows == 1 )
{
assert( weights->cols == m );
wstep = CV_ELEM_SIZE( weights->type );
}
else
{
assert( weights->rows == m );
wstep = weights->step;
}
l = m;
if( sampleIdx != NULL )
{
assert( CV_MAT_TYPE( sampleIdx->type ) == CV_32FC1 );
idxdata = sampleIdx->data.ptr;
if( sampleIdx->rows == 1 )
{
l = sampleIdx->cols;
idxstep = CV_ELEM_SIZE( sampleIdx->type );
}
else
{
l = sampleIdx->rows;
idxstep = sampleIdx->step;
}
assert( l <= m );
}
idx = (int*) cvAlloc( l * sizeof( int ) );
stump = (CvStumpClassifier*) cvAlloc( sizeof( CvStumpClassifier) );
/* START */
memset( (void*) stump, 0, sizeof( CvStumpClassifier ) );
stump->eval = cvEvalStumpClassifier;
stump->tune = NULL;
stump->save = NULL;
stump->release = cvReleaseStumpClassifier;
stump->lerror = FLT_MAX;
stump->rerror = FLT_MAX;
stump->left = 0.0F;
stump->right = 0.0F;
/* copy indices */
if( sampleIdx != NULL )
{
for( i = 0; i < l; i++ )
{
idx[i] = (int) *((float*) (idxdata + i*idxstep));
}
}
else
{
for( i = 0; i < l; i++ )
{
idx[i] = i;
}
}
for( i = 0; i < n; i++ )
{
CvValArray va;
va.data = data + i * ((size_t) cstep);
va.step = sstep;
icvSortIndexedValArray_32s( idx, l, &va );
if( findStumpThreshold_32s[(int) ((CvStumpTrainParams*) trainParams)->error]
( data + i * ((size_t) cstep), sstep,
wdata, wstep, ydata, ystep, (uchar*) idx, sizeof( int ), l,
&(stump->lerror), &(stump->rerror),
&(stump->threshold), &(stump->left), &(stump->right),
&sumw, &sumwy, &sumwyy ) )
{
stump->compidx = i;
}
} /* for each component */
/* END */
cvFree( &idx );
if( ((CvStumpTrainParams*) trainParams)->type == CV_CLASSIFICATION_CLASS )
{
stump->left = 2.0F * (stump->left >= 0.5F) - 1.0F;
stump->right = 2.0F * (stump->right >= 0.5F) - 1.0F;
}
return (CvClassifier*) stump;
}
/*
* cvCreateMTStumpClassifier
*
* Multithreaded stump classifier constructor
* Includes huge train data support through callback function
*/
CV_BOOST_IMPL
CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* /*typeMask*/,
CvMat* missedMeasurementsMask,
CvMat* compIdx,
CvMat* sampleIdx,
CvMat* weights,
CvClassifierTrainParams* trainParams )
{
CvStumpClassifier* stump = NULL;
int m = 0; /* number of samples */
int n = 0; /* number of components */
uchar* data = NULL;
size_t cstep = 0;
size_t sstep = 0;
int datan = 0; /* num components */
uchar* ydata = NULL;
size_t ystep = 0;
uchar* idxdata = NULL;
size_t idxstep = 0;
int l = 0; /* number of indices */
uchar* wdata = NULL;
size_t wstep = 0;
uchar* sorteddata = NULL;
int sortedtype = 0;
size_t sortedcstep = 0; /* component step */
size_t sortedsstep = 0; /* sample step */
int sortedn = 0; /* num components */
int sortedm = 0; /* num samples */
char* filter = NULL;
int i = 0;
int compidx = 0;
int stumperror;
int portion;
/* private variables */
CvMat mat;
CvValArray va;
float lerror;
float rerror;
float left;
float right;
float threshold;
int optcompidx;
float sumw;
float sumwy;
float sumwyy;
int t_compidx;
int t_n;
int ti;
int tj;
int tk;
uchar* t_data;
size_t t_cstep;
size_t t_sstep;
size_t matcstep;
size_t matsstep;
int* t_idx;
/* end private variables */
CV_Assert( trainParams != NULL );
CV_Assert( trainClasses != NULL );
CV_Assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
CV_Assert( missedMeasurementsMask == NULL );
CV_Assert( compIdx == NULL );
stumperror = (int) ((CvMTStumpTrainParams*) trainParams)->error;
ydata = trainClasses->data.ptr;
if( trainClasses->rows == 1 )
{
m = trainClasses->cols;
ystep = CV_ELEM_SIZE( trainClasses->type );
}
else
{
m = trainClasses->rows;
ystep = trainClasses->step;
}
wdata = weights->data.ptr;
if( weights->rows == 1 )
{
CV_Assert( weights->cols == m );
wstep = CV_ELEM_SIZE( weights->type );
}
else
{
CV_Assert( weights->rows == m );
wstep = weights->step;
}
if( ((CvMTStumpTrainParams*) trainParams)->sortedIdx != NULL )
{
sortedtype =
CV_MAT_TYPE( ((CvMTStumpTrainParams*) trainParams)->sortedIdx->type );
assert( sortedtype == CV_16SC1 || sortedtype == CV_32SC1
|| sortedtype == CV_32FC1 );
sorteddata = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->data.ptr;
sortedsstep = CV_ELEM_SIZE( sortedtype );
sortedcstep = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->step;
sortedn = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->rows;
sortedm = ((CvMTStumpTrainParams*) trainParams)->sortedIdx->cols;
}
if( trainData == NULL )
{
assert( ((CvMTStumpTrainParams*) trainParams)->getTrainData != NULL );
n = ((CvMTStumpTrainParams*) trainParams)->numcomp;
assert( n > 0 );
}
else
{
assert( CV_MAT_TYPE( trainData->type ) == CV_32FC1 );
data = trainData->data.ptr;
if( CV_IS_ROW_SAMPLE( flags ) )
{
cstep = CV_ELEM_SIZE( trainData->type );
sstep = trainData->step;
assert( m == trainData->rows );
datan = n = trainData->cols;
}
else
{
sstep = CV_ELEM_SIZE( trainData->type );
cstep = trainData->step;
assert( m == trainData->cols );
datan = n = trainData->rows;
}
if( ((CvMTStumpTrainParams*) trainParams)->getTrainData != NULL )
{
n = ((CvMTStumpTrainParams*) trainParams)->numcomp;
}
}
assert( datan <= n );
if( sampleIdx != NULL )
{
assert( CV_MAT_TYPE( sampleIdx->type ) == CV_32FC1 );
idxdata = sampleIdx->data.ptr;
idxstep = ( sampleIdx->rows == 1 )
? CV_ELEM_SIZE( sampleIdx->type ) : sampleIdx->step;
l = ( sampleIdx->rows == 1 ) ? sampleIdx->cols : sampleIdx->rows;
if( sorteddata != NULL )
{
filter = (char*) cvAlloc( sizeof( char ) * m );
memset( (void*) filter, 0, sizeof( char ) * m );
for( i = 0; i < l; i++ )
{
filter[(int) *((float*) (idxdata + i * idxstep))] = (char) 1;
}
}
}
else
{
l = m;
}
stump = (CvStumpClassifier*) cvAlloc( sizeof( CvStumpClassifier) );
/* START */
memset( (void*) stump, 0, sizeof( CvStumpClassifier ) );
portion = ((CvMTStumpTrainParams*)trainParams)->portion;
if( portion < 1 )
{
/* auto portion */
portion = n;
#ifdef _OPENMP
portion /= omp_get_max_threads();
#endif /* _OPENMP */
}
stump->eval = cvEvalStumpClassifier;
stump->tune = NULL;
stump->save = NULL;
stump->release = cvReleaseStumpClassifier;
stump->lerror = FLT_MAX;
stump->rerror = FLT_MAX;
stump->left = 0.0F;
stump->right = 0.0F;
compidx = 0;
#ifdef _OPENMP
#pragma omp parallel private(mat, va, lerror, rerror, left, right, threshold, \
optcompidx, sumw, sumwy, sumwyy, t_compidx, t_n, \
ti, tj, tk, t_data, t_cstep, t_sstep, matcstep, \
matsstep, t_idx)
#endif /* _OPENMP */
{
lerror = FLT_MAX;
rerror = FLT_MAX;
left = 0.0F;
right = 0.0F;
threshold = 0.0F;
optcompidx = 0;
sumw = FLT_MAX;
sumwy = FLT_MAX;
sumwyy = FLT_MAX;
t_compidx = 0;
t_n = 0;
ti = 0;
tj = 0;
tk = 0;
t_data = NULL;
t_cstep = 0;
t_sstep = 0;
matcstep = 0;
matsstep = 0;
t_idx = NULL;
mat.data.ptr = NULL;
if( datan < n )
{
/* prepare matrix for callback */
if( CV_IS_ROW_SAMPLE( flags ) )
{
mat = cvMat( m, portion, CV_32FC1, 0 );
matcstep = CV_ELEM_SIZE( mat.type );
matsstep = mat.step;
}
else
{
mat = cvMat( portion, m, CV_32FC1, 0 );
matcstep = mat.step;
matsstep = CV_ELEM_SIZE( mat.type );
}
mat.data.ptr = (uchar*) cvAlloc( sizeof( float ) * mat.rows * mat.cols );
}
if( filter != NULL || sortedn < n )
{
t_idx = (int*) cvAlloc( sizeof( int ) * m );
if( sortedn == 0 || filter == NULL )
{
if( idxdata != NULL )
{
for( ti = 0; ti < l; ti++ )
{
t_idx[ti] = (int) *((float*) (idxdata + ti * idxstep));
}
}
else
{
for( ti = 0; ti < l; ti++ )
{
t_idx[ti] = ti;
}
}
}
}
#ifdef _OPENMP
#pragma omp critical(c_compidx)
#endif /* _OPENMP */
{
t_compidx = compidx;
compidx += portion;
}
while( t_compidx < n )
{
t_n = portion;
if( t_compidx < datan )
{
t_n = ( t_n < (datan - t_compidx) ) ? t_n : (datan - t_compidx);
t_data = data;
t_cstep = cstep;
t_sstep = sstep;
}
else
{
t_n = ( t_n < (n - t_compidx) ) ? t_n : (n - t_compidx);
t_cstep = matcstep;
t_sstep = matsstep;
t_data = mat.data.ptr - t_compidx * ((size_t) t_cstep );
/* calculate components */
((CvMTStumpTrainParams*)trainParams)->getTrainData( &mat,
sampleIdx, compIdx, t_compidx, t_n,
((CvMTStumpTrainParams*)trainParams)->userdata );
}
if( sorteddata != NULL )
{
if( filter != NULL )
{
/* have sorted indices and filter */
switch( sortedtype )
{
case CV_16SC1:
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
tk = 0;
for( tj = 0; tj < sortedm; tj++ )
{
int curidx = (int) ( *((short*) (sorteddata
+ ti * sortedcstep + tj * sortedsstep)) );
if( filter[curidx] != 0 )
{
t_idx[tk++] = curidx;
}
}
if( findStumpThreshold_32s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
(uchar*) t_idx, sizeof( int ), tk,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
case CV_32SC1:
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
tk = 0;
for( tj = 0; tj < sortedm; tj++ )
{
int curidx = (int) ( *((int*) (sorteddata
+ ti * sortedcstep + tj * sortedsstep)) );
if( filter[curidx] != 0 )
{
t_idx[tk++] = curidx;
}
}
if( findStumpThreshold_32s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
(uchar*) t_idx, sizeof( int ), tk,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
case CV_32FC1:
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
tk = 0;
for( tj = 0; tj < sortedm; tj++ )
{
int curidx = (int) ( *((float*) (sorteddata
+ ti * sortedcstep + tj * sortedsstep)) );
if( filter[curidx] != 0 )
{
t_idx[tk++] = curidx;
}
}
if( findStumpThreshold_32s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
(uchar*) t_idx, sizeof( int ), tk,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
default:
assert( 0 );
break;
}
}
else
{
/* have sorted indices */
switch( sortedtype )
{
case CV_16SC1:
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
if( findStumpThreshold_16s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
sorteddata + ti * sortedcstep, sortedsstep, sortedm,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
case CV_32SC1:
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
if( findStumpThreshold_32s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
sorteddata + ti * sortedcstep, sortedsstep, sortedm,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
case CV_32FC1:
for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ )
{
if( findStumpThreshold_32f[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
sorteddata + ti * sortedcstep, sortedsstep, sortedm,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
break;
default:
assert( 0 );
break;
}
}
}
ti = MAX( t_compidx, MIN( sortedn, t_compidx + t_n ) );
for( ; ti < t_compidx + t_n; ti++ )
{
va.data = t_data + ti * t_cstep;
va.step = t_sstep;
icvSortIndexedValArray_32s( t_idx, l, &va );
if( findStumpThreshold_32s[stumperror](
t_data + ti * t_cstep, t_sstep,
wdata, wstep, ydata, ystep,
(uchar*)t_idx, sizeof( int ), l,
&lerror, &rerror,
&threshold, &left, &right,
&sumw, &sumwy, &sumwyy ) )
{
optcompidx = ti;
}
}
#ifdef _OPENMP
#pragma omp critical(c_compidx)
#endif /* _OPENMP */
{
t_compidx = compidx;
compidx += portion;
}
} /* while have training data */
/* get the best classifier */
#ifdef _OPENMP
#pragma omp critical(c_beststump)
#endif /* _OPENMP */
{
if( lerror + rerror < stump->lerror + stump->rerror )
{
stump->lerror = lerror;
stump->rerror = rerror;
stump->compidx = optcompidx;
stump->threshold = threshold;
stump->left = left;
stump->right = right;
}
}
/* free allocated memory */
if( mat.data.ptr != NULL )
{
cvFree( &(mat.data.ptr) );
}
if( t_idx != NULL )
{
cvFree( &t_idx );
}
} /* end of parallel region */
/* END */
/* free allocated memory */
if( filter != NULL )
{
cvFree( &filter );
}
if( ((CvMTStumpTrainParams*) trainParams)->type == CV_CLASSIFICATION_CLASS )
{
stump->left = 2.0F * (stump->left >= 0.5F) - 1.0F;
stump->right = 2.0F * (stump->right >= 0.5F) - 1.0F;
}
return (CvClassifier*) stump;
}
CV_BOOST_IMPL
float cvEvalCARTClassifier( CvClassifier* classifier, CvMat* sample )
{
CV_FUNCNAME( "cvEvalCARTClassifier" );
int idx = 0;
__BEGIN__;
CV_ASSERT( classifier != NULL );
CV_ASSERT( sample != NULL );
CV_ASSERT( CV_MAT_TYPE( sample->type ) == CV_32FC1 );
CV_ASSERT( sample->rows == 1 || sample->cols == 1 );
if( sample->rows == 1 )
{
do
{
if( (CV_MAT_ELEM( (*sample), float, 0,
((CvCARTClassifier*) classifier)->compidx[idx] )) <
((CvCARTClassifier*) classifier)->threshold[idx] )
{
idx = ((CvCARTClassifier*) classifier)->left[idx];
}
else
{
idx = ((CvCARTClassifier*) classifier)->right[idx];
}
} while( idx > 0 );
}
else
{
do
{
if( (CV_MAT_ELEM( (*sample), float,
((CvCARTClassifier*) classifier)->compidx[idx], 0 )) <
((CvCARTClassifier*) classifier)->threshold[idx] )
{
idx = ((CvCARTClassifier*) classifier)->left[idx];
}
else
{
idx = ((CvCARTClassifier*) classifier)->right[idx];
}
} while( idx > 0 );
}
__END__;
return ((CvCARTClassifier*) classifier)->val[-idx];
}
CV_BOOST_IMPL
float cvEvalCARTClassifierIdx( CvClassifier* classifier, CvMat* sample )
{
CV_FUNCNAME( "cvEvalCARTClassifierIdx" );
int idx = 0;
__BEGIN__;
CV_ASSERT( classifier != NULL );
CV_ASSERT( sample != NULL );
CV_ASSERT( CV_MAT_TYPE( sample->type ) == CV_32FC1 );
CV_ASSERT( sample->rows == 1 || sample->cols == 1 );
if( sample->rows == 1 )
{
do
{
if( (CV_MAT_ELEM( (*sample), float, 0,
((CvCARTClassifier*) classifier)->compidx[idx] )) <
((CvCARTClassifier*) classifier)->threshold[idx] )
{
idx = ((CvCARTClassifier*) classifier)->left[idx];
}
else
{
idx = ((CvCARTClassifier*) classifier)->right[idx];
}
} while( idx > 0 );
}
else
{
do
{
if( (CV_MAT_ELEM( (*sample), float,
((CvCARTClassifier*) classifier)->compidx[idx], 0 )) <
((CvCARTClassifier*) classifier)->threshold[idx] )
{
idx = ((CvCARTClassifier*) classifier)->left[idx];
}
else
{
idx = ((CvCARTClassifier*) classifier)->right[idx];
}
} while( idx > 0 );
}
__END__;
return (float) (-idx);
}
CV_BOOST_IMPL
void cvReleaseCARTClassifier( CvClassifier** classifier )
{
cvFree( classifier );
*classifier = NULL;
}
void CV_CDECL icvDefaultSplitIdx_R( int compidx, float threshold,
CvMat* idx, CvMat** left, CvMat** right,
void* userdata )
{
CvMat* trainData = (CvMat*) userdata;
int i = 0;
*left = cvCreateMat( 1, trainData->rows, CV_32FC1 );
*right = cvCreateMat( 1, trainData->rows, CV_32FC1 );
(*left)->cols = (*right)->cols = 0;
if( idx == NULL )
{
for( i = 0; i < trainData->rows; i++ )
{
if( CV_MAT_ELEM( *trainData, float, i, compidx ) < threshold )
{
(*left)->data.fl[(*left)->cols++] = (float) i;
}
else
{
(*right)->data.fl[(*right)->cols++] = (float) i;
}
}
}
else
{
uchar* idxdata;
int idxnum;
int idxstep;
int index;
idxdata = idx->data.ptr;
idxnum = (idx->rows == 1) ? idx->cols : idx->rows;
idxstep = (idx->rows == 1) ? CV_ELEM_SIZE( idx->type ) : idx->step;
for( i = 0; i < idxnum; i++ )
{
index = (int) *((float*) (idxdata + i * idxstep));
if( CV_MAT_ELEM( *trainData, float, index, compidx ) < threshold )
{
(*left)->data.fl[(*left)->cols++] = (float) index;
}
else
{
(*right)->data.fl[(*right)->cols++] = (float) index;
}
}
}
}
void CV_CDECL icvDefaultSplitIdx_C( int compidx, float threshold,
CvMat* idx, CvMat** left, CvMat** right,
void* userdata )
{
CvMat* trainData = (CvMat*) userdata;
int i = 0;
*left = cvCreateMat( 1, trainData->cols, CV_32FC1 );
*right = cvCreateMat( 1, trainData->cols, CV_32FC1 );
(*left)->cols = (*right)->cols = 0;
if( idx == NULL )
{
for( i = 0; i < trainData->cols; i++ )
{
if( CV_MAT_ELEM( *trainData, float, compidx, i ) < threshold )
{
(*left)->data.fl[(*left)->cols++] = (float) i;
}
else
{
(*right)->data.fl[(*right)->cols++] = (float) i;
}
}
}
else
{
uchar* idxdata;
int idxnum;
int idxstep;
int index;
idxdata = idx->data.ptr;
idxnum = (idx->rows == 1) ? idx->cols : idx->rows;
idxstep = (idx->rows == 1) ? CV_ELEM_SIZE( idx->type ) : idx->step;
for( i = 0; i < idxnum; i++ )
{
index = (int) *((float*) (idxdata + i * idxstep));
if( CV_MAT_ELEM( *trainData, float, compidx, index ) < threshold )
{
(*left)->data.fl[(*left)->cols++] = (float) index;
}
else
{
(*right)->data.fl[(*right)->cols++] = (float) index;
}
}
}
}
/* internal structure used in CART creation */
typedef struct CvCARTNode
{
CvMat* sampleIdx;
CvStumpClassifier* stump;
int parent;
int leftflag;
float errdrop;
} CvCARTNode;
CV_BOOST_IMPL
CvClassifier* cvCreateCARTClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask,
CvMat* compIdx,
CvMat* sampleIdx,
CvMat* weights,
CvClassifierTrainParams* trainParams )
{
CvCARTClassifier* cart = NULL;
size_t datasize = 0;
int count = 0;
int i = 0;
int j = 0;
CvCARTNode* intnode = NULL;
CvCARTNode* list = NULL;
int listcount = 0;
CvMat* lidx = NULL;
CvMat* ridx = NULL;
float maxerrdrop = 0.0F;
int idx = 0;
void (*splitIdxCallback)( int compidx, float threshold,
CvMat* idx, CvMat** left, CvMat** right,
void* userdata );
void* userdata;
count = ((CvCARTTrainParams*) trainParams)->count;
assert( count > 0 );
datasize = sizeof( *cart ) + (sizeof( float ) + 3 * sizeof( int )) * count +
sizeof( float ) * (count + 1);
cart = (CvCARTClassifier*) cvAlloc( datasize );
memset( cart, 0, datasize );
cart->count = count;
cart->eval = cvEvalCARTClassifier;
cart->save = NULL;
cart->release = cvReleaseCARTClassifier;
cart->compidx = (int*) (cart + 1);
cart->threshold = (float*) (cart->compidx + count);
cart->left = (int*) (cart->threshold + count);
cart->right = (int*) (cart->left + count);
cart->val = (float*) (cart->right + count);
datasize = sizeof( CvCARTNode ) * (count + count);
intnode = (CvCARTNode*) cvAlloc( datasize );
memset( intnode, 0, datasize );
list = (CvCARTNode*) (intnode + count);
splitIdxCallback = ((CvCARTTrainParams*) trainParams)->splitIdx;
userdata = ((CvCARTTrainParams*) trainParams)->userdata;
if( splitIdxCallback == NULL )
{
splitIdxCallback = ( CV_IS_ROW_SAMPLE( flags ) )
? icvDefaultSplitIdx_R : icvDefaultSplitIdx_C;
userdata = trainData;
}
/* create root of the tree */
intnode[0].sampleIdx = sampleIdx;
intnode[0].stump = (CvStumpClassifier*)
((CvCARTTrainParams*) trainParams)->stumpConstructor( trainData, flags,
trainClasses, typeMask, missedMeasurementsMask, compIdx, sampleIdx, weights,
((CvCARTTrainParams*) trainParams)->stumpTrainParams );
cart->left[0] = cart->right[0] = 0;
/* build tree */
listcount = 0;
for( i = 1; i < count; i++ )
{
/* split last added node */
splitIdxCallback( intnode[i-1].stump->compidx, intnode[i-1].stump->threshold,
intnode[i-1].sampleIdx, &lidx, &ridx, userdata );
if( intnode[i-1].stump->lerror != 0.0F )
{
list[listcount].sampleIdx = lidx;
list[listcount].stump = (CvStumpClassifier*)
((CvCARTTrainParams*) trainParams)->stumpConstructor( trainData, flags,
trainClasses, typeMask, missedMeasurementsMask, compIdx,
list[listcount].sampleIdx,
weights, ((CvCARTTrainParams*) trainParams)->stumpTrainParams );
list[listcount].errdrop = intnode[i-1].stump->lerror
- (list[listcount].stump->lerror + list[listcount].stump->rerror);
list[listcount].leftflag = 1;
list[listcount].parent = i-1;
listcount++;
}
else
{
cvReleaseMat( &lidx );
}
if( intnode[i-1].stump->rerror != 0.0F )
{
list[listcount].sampleIdx = ridx;
list[listcount].stump = (CvStumpClassifier*)
((CvCARTTrainParams*) trainParams)->stumpConstructor( trainData, flags,
trainClasses, typeMask, missedMeasurementsMask, compIdx,
list[listcount].sampleIdx,
weights, ((CvCARTTrainParams*) trainParams)->stumpTrainParams );
list[listcount].errdrop = intnode[i-1].stump->rerror
- (list[listcount].stump->lerror + list[listcount].stump->rerror);
list[listcount].leftflag = 0;
list[listcount].parent = i-1;
listcount++;
}
else
{
cvReleaseMat( &ridx );
}
if( listcount == 0 ) break;
/* find the best node to be added to the tree */
idx = 0;
maxerrdrop = list[idx].errdrop;
for( j = 1; j < listcount; j++ )
{
if( list[j].errdrop > maxerrdrop )
{
idx = j;
maxerrdrop = list[j].errdrop;
}
}
intnode[i] = list[idx];
if( list[idx].leftflag )
{
cart->left[list[idx].parent] = i;
}
else
{
cart->right[list[idx].parent] = i;
}
if( idx != (listcount - 1) )
{
list[idx] = list[listcount - 1];
}
listcount--;
}
/* fill <cart> fields */
j = 0;
cart->count = 0;
for( i = 0; i < count && (intnode[i].stump != NULL); i++ )
{
cart->count++;
cart->compidx[i] = intnode[i].stump->compidx;
cart->threshold[i] = intnode[i].stump->threshold;
/* leaves */
if( cart->left[i] <= 0 )
{
cart->left[i] = -j;
cart->val[j] = intnode[i].stump->left;
j++;
}
if( cart->right[i] <= 0 )
{
cart->right[i] = -j;
cart->val[j] = intnode[i].stump->right;
j++;
}
}
/* CLEAN UP */
for( i = 0; i < count && (intnode[i].stump != NULL); i++ )
{
intnode[i].stump->release( (CvClassifier**) &(intnode[i].stump) );
if( i != 0 )
{
cvReleaseMat( &(intnode[i].sampleIdx) );
}
}
for( i = 0; i < listcount; i++ )
{
list[i].stump->release( (CvClassifier**) &(list[i].stump) );
cvReleaseMat( &(list[i].sampleIdx) );
}
cvFree( &intnode );
return (CvClassifier*) cart;
}
/****************************************************************************************\
* Boosting *
\****************************************************************************************/
typedef struct CvBoostTrainer
{
CvBoostType type;
int count; /* (idx) ? number_of_indices : number_of_samples */
int* idx;
float* F;
} CvBoostTrainer;
/*
* cvBoostStartTraining, cvBoostNextWeakClassifier, cvBoostEndTraining
*
* These functions perform training of 2-class boosting classifier
* using ANY appropriate weak classifier
*/
CV_BOOST_IMPL
CvBoostTrainer* icvBoostStartTraining( CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* /*weights*/,
CvMat* sampleIdx,
CvBoostType type )
{
uchar* ydata;
int ystep;
int m;
uchar* traindata;
int trainstep;
int trainnum;
int i;
int idx;
size_t datasize;
CvBoostTrainer* ptr;
int idxnum;
int idxstep;
uchar* idxdata;
assert( trainClasses != NULL );
assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
assert( weakTrainVals != NULL );
assert( CV_MAT_TYPE( weakTrainVals->type ) == CV_32FC1 );
CV_MAT2VEC( *trainClasses, ydata, ystep, m );
CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum );
assert( m == trainnum );
idxnum = 0;
idxstep = 0;
idxdata = NULL;
if( sampleIdx )
{
CV_MAT2VEC( *sampleIdx, idxdata, idxstep, idxnum );
}
datasize = sizeof( *ptr ) + sizeof( *ptr->idx ) * idxnum;
ptr = (CvBoostTrainer*) cvAlloc( datasize );
memset( ptr, 0, datasize );
ptr->F = NULL;
ptr->idx = NULL;
ptr->count = m;
ptr->type = type;
if( idxnum > 0 )
{
CvScalar s;
ptr->idx = (int*) (ptr + 1);
ptr->count = idxnum;
for( i = 0; i < ptr->count; i++ )
{
cvRawDataToScalar( idxdata + i*idxstep, CV_MAT_TYPE( sampleIdx->type ), &s );
ptr->idx[i] = (int) s.val[0];
}
}
for( i = 0; i < ptr->count; i++ )
{
idx = (ptr->idx) ? ptr->idx[i] : i;
*((float*) (traindata + idx * trainstep)) =
2.0F * (*((float*) (ydata + idx * ystep))) - 1.0F;
}
return ptr;
}
/*
*
* Discrete AdaBoost functions
*
*/
CV_BOOST_IMPL
float icvBoostNextWeakClassifierDAB( CvMat* weakEvalVals,
CvMat* trainClasses,
CvMat* /*weakTrainVals*/,
CvMat* weights,
CvBoostTrainer* trainer )
{
uchar* evaldata;
int evalstep;
int m;
uchar* ydata;
int ystep;
int ynum;
uchar* wdata;
int wstep;
int wnum;
float sumw;
float err;
int i;
int idx;
CV_Assert( weakEvalVals != NULL );
CV_Assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );
CV_Assert( trainClasses != NULL );
CV_Assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
CV_Assert( weights != NULL );
CV_Assert( CV_MAT_TYPE( weights ->type ) == CV_32FC1 );
CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );
CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );
CV_MAT2VEC( *weights, wdata, wstep, wnum );
assert( m == ynum );
assert( m == wnum );
sumw = 0.0F;
err = 0.0F;
for( i = 0; i < trainer->count; i++ )
{
idx = (trainer->idx) ? trainer->idx[i] : i;
sumw += *((float*) (wdata + idx*wstep));
err += (*((float*) (wdata + idx*wstep))) *
( (*((float*) (evaldata + idx*evalstep))) !=
2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F );
}
err /= sumw;
err = -cvLogRatio( err );
for( i = 0; i < trainer->count; i++ )
{
idx = (trainer->idx) ? trainer->idx[i] : i;
*((float*) (wdata + idx*wstep)) *= expf( err *
((*((float*) (evaldata + idx*evalstep))) !=
2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F) );
sumw += *((float*) (wdata + idx*wstep));
}
for( i = 0; i < trainer->count; i++ )
{
idx = (trainer->idx) ? trainer->idx[i] : i;
*((float*) (wdata + idx * wstep)) /= sumw;
}
return err;
}
/*
*
* Real AdaBoost functions
*
*/
CV_BOOST_IMPL
float icvBoostNextWeakClassifierRAB( CvMat* weakEvalVals,
CvMat* trainClasses,
CvMat* /*weakTrainVals*/,
CvMat* weights,
CvBoostTrainer* trainer )
{
uchar* evaldata;
int evalstep;
int m;
uchar* ydata;
int ystep;
int ynum;
uchar* wdata;
int wstep;
int wnum;
float sumw;
int i, idx;
CV_Assert( weakEvalVals != NULL );
CV_Assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );
CV_Assert( trainClasses != NULL );
CV_Assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
CV_Assert( weights != NULL );
CV_Assert( CV_MAT_TYPE( weights ->type ) == CV_32FC1 );
CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );
CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );
CV_MAT2VEC( *weights, wdata, wstep, wnum );
CV_Assert( m == ynum );
CV_Assert( m == wnum );
sumw = 0.0F;
for( i = 0; i < trainer->count; i++ )
{
idx = (trainer->idx) ? trainer->idx[i] : i;
*((float*) (wdata + idx*wstep)) *= expf( (-(*((float*) (ydata + idx*ystep))) + 0.5F)
* cvLogRatio( *((float*) (evaldata + idx*evalstep)) ) );
sumw += *((float*) (wdata + idx*wstep));
}
for( i = 0; i < trainer->count; i++ )
{
idx = (trainer->idx) ? trainer->idx[i] : i;
*((float*) (wdata + idx*wstep)) /= sumw;
}
return 1.0F;
}
/*
*
* LogitBoost functions
*
*/
#define CV_LB_PROB_THRESH 0.01F
#define CV_LB_WEIGHT_THRESHOLD 0.0001F
CV_BOOST_IMPL
void icvResponsesAndWeightsLB( int num, uchar* wdata, int wstep,
uchar* ydata, int ystep,
uchar* fdata, int fstep,
uchar* traindata, int trainstep,
int* indices )
{
int i, idx;
float p;
for( i = 0; i < num; i++ )
{
idx = (indices) ? indices[i] : i;
p = 1.0F / (1.0F + expf( -(*((float*) (fdata + idx*fstep)))) );
*((float*) (wdata + idx*wstep)) = MAX( p * (1.0F - p), CV_LB_WEIGHT_THRESHOLD );
if( *((float*) (ydata + idx*ystep)) == 1.0F )
{
*((float*) (traindata + idx*trainstep)) =
1.0F / (MAX( p, CV_LB_PROB_THRESH ));
}
else
{
*((float*) (traindata + idx*trainstep)) =
-1.0F / (MAX( 1.0F - p, CV_LB_PROB_THRESH ));
}
}
}
CV_BOOST_IMPL
CvBoostTrainer* icvBoostStartTrainingLB( CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvMat* sampleIdx,
CvBoostType type )
{
size_t datasize;
CvBoostTrainer* ptr;
uchar* ydata;
int ystep;
int m;
uchar* traindata;
int trainstep;
int trainnum;
uchar* wdata;
int wstep;
int wnum;
int i;
int idxnum;
int idxstep;
uchar* idxdata;
assert( trainClasses != NULL );
assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
assert( weakTrainVals != NULL );
assert( CV_MAT_TYPE( weakTrainVals->type ) == CV_32FC1 );
assert( weights != NULL );
assert( CV_MAT_TYPE( weights->type ) == CV_32FC1 );
CV_MAT2VEC( *trainClasses, ydata, ystep, m );
CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum );
CV_MAT2VEC( *weights, wdata, wstep, wnum );
assert( m == trainnum );
assert( m == wnum );
idxnum = 0;
idxstep = 0;
idxdata = NULL;
if( sampleIdx )
{
CV_MAT2VEC( *sampleIdx, idxdata, idxstep, idxnum );
}
datasize = sizeof( *ptr ) + sizeof( *ptr->F ) * m + sizeof( *ptr->idx ) * idxnum;
ptr = (CvBoostTrainer*) cvAlloc( datasize );
memset( ptr, 0, datasize );
ptr->F = (float*) (ptr + 1);
ptr->idx = NULL;
ptr->count = m;
ptr->type = type;
if( idxnum > 0 )
{
CvScalar s;
ptr->idx = (int*) (ptr->F + m);
ptr->count = idxnum;
for( i = 0; i < ptr->count; i++ )
{
cvRawDataToScalar( idxdata + i*idxstep, CV_MAT_TYPE( sampleIdx->type ), &s );
ptr->idx[i] = (int) s.val[0];
}
}
for( i = 0; i < m; i++ )
{
ptr->F[i] = 0.0F;
}
icvResponsesAndWeightsLB( ptr->count, wdata, wstep, ydata, ystep,
(uchar*) ptr->F, sizeof( *ptr->F ),
traindata, trainstep, ptr->idx );
return ptr;
}
CV_BOOST_IMPL
float icvBoostNextWeakClassifierLB( CvMat* weakEvalVals,
CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvBoostTrainer* trainer )
{
uchar* evaldata;
int evalstep;
int m;
uchar* ydata;
int ystep;
int ynum;
uchar* traindata;
int trainstep;
int trainnum;
uchar* wdata;
int wstep;
int wnum;
int i, idx;
assert( weakEvalVals != NULL );
assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );
assert( trainClasses != NULL );
assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
assert( weakTrainVals != NULL );
assert( CV_MAT_TYPE( weakTrainVals->type ) == CV_32FC1 );
assert( weights != NULL );
assert( CV_MAT_TYPE( weights ->type ) == CV_32FC1 );
CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );
CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );
CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum );
CV_MAT2VEC( *weights, wdata, wstep, wnum );
assert( m == ynum );
assert( m == wnum );
assert( m == trainnum );
//assert( m == trainer->count );
for( i = 0; i < trainer->count; i++ )
{
idx = (trainer->idx) ? trainer->idx[i] : i;
trainer->F[idx] += *((float*) (evaldata + idx * evalstep));
}
icvResponsesAndWeightsLB( trainer->count, wdata, wstep, ydata, ystep,
(uchar*) trainer->F, sizeof( *trainer->F ),
traindata, trainstep, trainer->idx );
return 1.0F;
}
/*
*
* Gentle AdaBoost
*
*/
CV_BOOST_IMPL
float icvBoostNextWeakClassifierGAB( CvMat* weakEvalVals,
CvMat* trainClasses,
CvMat* /*weakTrainVals*/,
CvMat* weights,
CvBoostTrainer* trainer )
{
uchar* evaldata;
int evalstep;
int m;
uchar* ydata;
int ystep;
int ynum;
uchar* wdata;
int wstep;
int wnum;
int i, idx;
float sumw;
CV_Assert( weakEvalVals != NULL );
CV_Assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );
CV_Assert( trainClasses != NULL );
CV_Assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
CV_Assert( weights != NULL );
CV_Assert( CV_MAT_TYPE( weights->type ) == CV_32FC1 );
CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );
CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );
CV_MAT2VEC( *weights, wdata, wstep, wnum );
assert( m == ynum );
assert( m == wnum );
sumw = 0.0F;
for( i = 0; i < trainer->count; i++ )
{
idx = (trainer->idx) ? trainer->idx[i] : i;
*((float*) (wdata + idx*wstep)) *=
expf( -(*((float*) (evaldata + idx*evalstep)))
* ( 2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F ) );
sumw += *((float*) (wdata + idx*wstep));
}
for( i = 0; i < trainer->count; i++ )
{
idx = (trainer->idx) ? trainer->idx[i] : i;
*((float*) (wdata + idx*wstep)) /= sumw;
}
return 1.0F;
}
typedef CvBoostTrainer* (*CvBoostStartTraining)( CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvMat* sampleIdx,
CvBoostType type );
typedef float (*CvBoostNextWeakClassifier)( CvMat* weakEvalVals,
CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvBoostTrainer* data );
CvBoostStartTraining startTraining[4] = {
icvBoostStartTraining,
icvBoostStartTraining,
icvBoostStartTrainingLB,
icvBoostStartTraining
};
CvBoostNextWeakClassifier nextWeakClassifier[4] = {
icvBoostNextWeakClassifierDAB,
icvBoostNextWeakClassifierRAB,
icvBoostNextWeakClassifierLB,
icvBoostNextWeakClassifierGAB
};
/*
*
* Dispatchers
*
*/
CV_BOOST_IMPL
CvBoostTrainer* cvBoostStartTraining( CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvMat* sampleIdx,
CvBoostType type )
{
return startTraining[type]( trainClasses, weakTrainVals, weights, sampleIdx, type );
}
CV_BOOST_IMPL
void cvBoostEndTraining( CvBoostTrainer** trainer )
{
cvFree( trainer );
*trainer = NULL;
}
CV_BOOST_IMPL
float cvBoostNextWeakClassifier( CvMat* weakEvalVals,
CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvBoostTrainer* trainer )
{
return nextWeakClassifier[trainer->type]( weakEvalVals, trainClasses,
weakTrainVals, weights, trainer );
}
/****************************************************************************************\
* Boosted tree models *
\****************************************************************************************/
typedef struct CvBtTrainer
{
/* {{ external */
CvMat* trainData;
int flags;
CvMat* trainClasses;
int m;
uchar* ydata;
int ystep;
CvMat* sampleIdx;
int numsamples;
float param[2];
CvBoostType type;
int numclasses;
/* }} external */
CvMTStumpTrainParams stumpParams;
CvCARTTrainParams cartParams;
float* f; /* F_(m-1) */
CvMat* y; /* yhat */
CvMat* weights;
CvBoostTrainer* boosttrainer;
} CvBtTrainer;
/*
* cvBtStart, cvBtNext, cvBtEnd
*
* These functions perform iterative training of
* 2-class (CV_DABCLASS - CV_GABCLASS, CV_L2CLASS), K-class (CV_LKCLASS) classifier
* or fit regression model (CV_LSREG, CV_LADREG, CV_MREG)
* using decision tree as a weak classifier.
*/
typedef void (*CvZeroApproxFunc)( float* approx, CvBtTrainer* trainer );
/* Mean zero approximation */
void icvZeroApproxMean( float* approx, CvBtTrainer* trainer )
{
int i;
int idx;
approx[0] = 0.0F;
for( i = 0; i < trainer->numsamples; i++ )
{
idx = icvGetIdxAt( trainer->sampleIdx, i );
approx[0] += *((float*) (trainer->ydata + idx * trainer->ystep));
}
approx[0] /= (float) trainer->numsamples;
}
/*
* Median zero approximation
*/
void icvZeroApproxMed( float* approx, CvBtTrainer* trainer )
{
int i;
int idx;
for( i = 0; i < trainer->numsamples; i++ )
{
idx = icvGetIdxAt( trainer->sampleIdx, i );
trainer->f[i] = *((float*) (trainer->ydata + idx * trainer->ystep));
}
icvSort_32f( trainer->f, trainer->numsamples, 0 );
approx[0] = trainer->f[trainer->numsamples / 2];
}
/*
* 0.5 * log( mean(y) / (1 - mean(y)) ) where y in {0, 1}
*/
void icvZeroApproxLog( float* approx, CvBtTrainer* trainer )
{
float y_mean;
icvZeroApproxMean( &y_mean, trainer );
approx[0] = 0.5F * cvLogRatio( y_mean );
}
/*
* 0 zero approximation
*/
void icvZeroApprox0( float* approx, CvBtTrainer* trainer )
{
int i;
for( i = 0; i < trainer->numclasses; i++ )
{
approx[i] = 0.0F;
}
}
static CvZeroApproxFunc icvZeroApproxFunc[] =
{
icvZeroApprox0, /* CV_DABCLASS */
icvZeroApprox0, /* CV_RABCLASS */
icvZeroApprox0, /* CV_LBCLASS */
icvZeroApprox0, /* CV_GABCLASS */
icvZeroApproxLog, /* CV_L2CLASS */
icvZeroApprox0, /* CV_LKCLASS */
icvZeroApproxMean, /* CV_LSREG */
icvZeroApproxMed, /* CV_LADREG */
icvZeroApproxMed, /* CV_MREG */
};
CV_BOOST_IMPL
void cvBtNext( CvCARTClassifier** trees, CvBtTrainer* trainer );
CV_BOOST_IMPL
CvBtTrainer* cvBtStart( CvCARTClassifier** trees,
CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* sampleIdx,
int numsplits,
CvBoostType type,
int numclasses,
float* param )
{
CvBtTrainer* ptr = 0;
CV_FUNCNAME( "cvBtStart" );
__BEGIN__;
size_t data_size;
float* zero_approx;
int m;
int i, j;
if( trees == NULL )
{
CV_ERROR( CV_StsNullPtr, "Invalid trees parameter" );
}
if( type < CV_DABCLASS || type > CV_MREG )
{
CV_ERROR( CV_StsUnsupportedFormat, "Unsupported type parameter" );
}
if( type == CV_LKCLASS )
{
CV_ASSERT( numclasses >= 2 );
}
else
{
numclasses = 1;
}
m = MAX( trainClasses->rows, trainClasses->cols );
ptr = NULL;
data_size = sizeof( *ptr );
if( type > CV_GABCLASS )
{
data_size += m * numclasses * sizeof( *(ptr->f) );
}
CV_CALL( ptr = (CvBtTrainer*) cvAlloc( data_size ) );
memset( ptr, 0, data_size );
ptr->f = (float*) (ptr + 1);
ptr->trainData = trainData;
ptr->flags = flags;
ptr->trainClasses = trainClasses;
CV_MAT2VEC( *trainClasses, ptr->ydata, ptr->ystep, ptr->m );
memset( &(ptr->cartParams), 0, sizeof( ptr->cartParams ) );
memset( &(ptr->stumpParams), 0, sizeof( ptr->stumpParams ) );
switch( type )
{
case CV_DABCLASS:
ptr->stumpParams.error = CV_MISCLASSIFICATION;
ptr->stumpParams.type = CV_CLASSIFICATION_CLASS;
break;
case CV_RABCLASS:
ptr->stumpParams.error = CV_GINI;
ptr->stumpParams.type = CV_CLASSIFICATION;
break;
default:
ptr->stumpParams.error = CV_SQUARE;
ptr->stumpParams.type = CV_REGRESSION;
}
ptr->cartParams.count = numsplits;
ptr->cartParams.stumpTrainParams = (CvClassifierTrainParams*) &(ptr->stumpParams);
ptr->cartParams.stumpConstructor = cvCreateMTStumpClassifier;
ptr->param[0] = param[0];
ptr->param[1] = param[1];
ptr->type = type;
ptr->numclasses = numclasses;
CV_CALL( ptr->y = cvCreateMat( 1, m, CV_32FC1 ) );
ptr->sampleIdx = sampleIdx;
ptr->numsamples = ( sampleIdx == NULL ) ? ptr->m
: MAX( sampleIdx->rows, sampleIdx->cols );
ptr->weights = cvCreateMat( 1, m, CV_32FC1 );
cvSet( ptr->weights, cvScalar( 1.0 ) );
if( type <= CV_GABCLASS )
{
ptr->boosttrainer = cvBoostStartTraining( ptr->trainClasses, ptr->y,
ptr->weights, NULL, type );
CV_CALL( cvBtNext( trees, ptr ) );
}
else
{
data_size = sizeof( *zero_approx ) * numclasses;
CV_CALL( zero_approx = (float*) cvAlloc( data_size ) );
icvZeroApproxFunc[type]( zero_approx, ptr );
for( i = 0; i < m; i++ )
{
for( j = 0; j < numclasses; j++ )
{
ptr->f[i * numclasses + j] = zero_approx[j];
}
}
CV_CALL( cvBtNext( trees, ptr ) );
for( i = 0; i < numclasses; i++ )
{
for( j = 0; j <= trees[i]->count; j++ )
{
trees[i]->val[j] += zero_approx[i];
}
}
CV_CALL( cvFree( &zero_approx ) );
}
__END__;
return ptr;
}
void icvBtNext_LSREG( CvCARTClassifier** trees, CvBtTrainer* trainer )
{
int i;
/* yhat_i = y_i - F_(m-1)(x_i) */
for( i = 0; i < trainer->m; i++ )
{
trainer->y->data.fl[i] =
*((float*) (trainer->ydata + i * trainer->ystep)) - trainer->f[i];
}
trees[0] = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData,
trainer->flags,
trainer->y, NULL, NULL, NULL, trainer->sampleIdx, trainer->weights,
(CvClassifierTrainParams*) &trainer->cartParams );
}
void icvBtNext_LADREG( CvCARTClassifier** trees, CvBtTrainer* trainer )
{
CvCARTClassifier* ptr;
int i, j;
CvMat sample;
int sample_step;
uchar* sample_data;
int index;
int data_size;
int* idx;
float* resp;
int respnum;
float val;
data_size = trainer->m * sizeof( *idx );
idx = (int*) cvAlloc( data_size );
data_size = trainer->m * sizeof( *resp );
resp = (float*) cvAlloc( data_size );
/* yhat_i = sign(y_i - F_(m-1)(x_i)) */
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
trainer->y->data.fl[index] = (float)
CV_SIGN( *((float*) (trainer->ydata + index * trainer->ystep))
- trainer->f[index] );
}
ptr = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData, trainer->flags,
trainer->y, NULL, NULL, NULL, trainer->sampleIdx, trainer->weights,
(CvClassifierTrainParams*) &trainer->cartParams );
CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample );
CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step );
sample_data = sample.data.ptr;
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
sample.data.ptr = sample_data + index * sample_step;
idx[index] = (int) cvEvalCARTClassifierIdx( (CvClassifier*) ptr, &sample );
}
for( j = 0; j <= ptr->count; j++ )
{
respnum = 0;
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
if( idx[index] == j )
{
resp[respnum++] = *((float*) (trainer->ydata + index * trainer->ystep))
- trainer->f[index];
}
}
if( respnum > 0 )
{
icvSort_32f( resp, respnum, 0 );
val = resp[respnum / 2];
}
else
{
val = 0.0F;
}
ptr->val[j] = val;
}
cvFree( &idx );
cvFree( &resp );
trees[0] = ptr;
}
void icvBtNext_MREG( CvCARTClassifier** trees, CvBtTrainer* trainer )
{
CvCARTClassifier* ptr;
int i, j;
CvMat sample;
int sample_step;
uchar* sample_data;
int data_size;
int* idx;
float* resid;
float* resp;
int respnum;
float rhat;
float val;
float delta;
int index;
data_size = trainer->m * sizeof( *idx );
idx = (int*) cvAlloc( data_size );
data_size = trainer->m * sizeof( *resp );
resp = (float*) cvAlloc( data_size );
data_size = trainer->m * sizeof( *resid );
resid = (float*) cvAlloc( data_size );
/* resid_i = (y_i - F_(m-1)(x_i)) */
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
resid[index] = *((float*) (trainer->ydata + index * trainer->ystep))
- trainer->f[index];
/* for delta */
resp[i] = (float) fabs( resid[index] );
}
/* delta = quantile_alpha{abs(resid_i)} */
icvSort_32f( resp, trainer->numsamples, 0 );
delta = resp[(int)(trainer->param[1] * (trainer->numsamples - 1))];
/* yhat_i */
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
trainer->y->data.fl[index] = MIN( delta, ((float) fabs( resid[index] )) ) *
CV_SIGN( resid[index] );
}
ptr = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData, trainer->flags,
trainer->y, NULL, NULL, NULL, trainer->sampleIdx, trainer->weights,
(CvClassifierTrainParams*) &trainer->cartParams );
CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample );
CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step );
sample_data = sample.data.ptr;
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
sample.data.ptr = sample_data + index * sample_step;
idx[index] = (int) cvEvalCARTClassifierIdx( (CvClassifier*) ptr, &sample );
}
for( j = 0; j <= ptr->count; j++ )
{
respnum = 0;
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
if( idx[index] == j )
{
resp[respnum++] = *((float*) (trainer->ydata + index * trainer->ystep))
- trainer->f[index];
}
}
if( respnum > 0 )
{
/* rhat = median(y_i - F_(m-1)(x_i)) */
icvSort_32f( resp, respnum, 0 );
rhat = resp[respnum / 2];
/* val = sum{sign(r_i - rhat_i) * min(delta, abs(r_i - rhat_i)}
* r_i = y_i - F_(m-1)(x_i)
*/
val = 0.0F;
for( i = 0; i < respnum; i++ )
{
val += CV_SIGN( resp[i] - rhat )
* MIN( delta, (float) fabs( resp[i] - rhat ) );
}
val = rhat + val / (float) respnum;
}
else
{
val = 0.0F;
}
ptr->val[j] = val;
}
cvFree( &resid );
cvFree( &resp );
cvFree( &idx );
trees[0] = ptr;
}
//#define CV_VAL_MAX 1e304
//#define CV_LOG_VAL_MAX 700.0
#define CV_VAL_MAX 1e+8
#define CV_LOG_VAL_MAX 18.0
void icvBtNext_L2CLASS( CvCARTClassifier** trees, CvBtTrainer* trainer )
{
CvCARTClassifier* ptr;
int i, j;
CvMat sample;
int sample_step;
uchar* sample_data;
int data_size;
int* idx;
int respnum;
float val;
double val_f;
float sum_weights;
float* weights;
float* sorted_weights;
CvMat* trimmed_idx;
CvMat* sample_idx;
int index;
int trimmed_num;
data_size = trainer->m * sizeof( *idx );
idx = (int*) cvAlloc( data_size );
data_size = trainer->m * sizeof( *weights );
weights = (float*) cvAlloc( data_size );
data_size = trainer->m * sizeof( *sorted_weights );
sorted_weights = (float*) cvAlloc( data_size );
/* yhat_i = (4 * y_i - 2) / ( 1 + exp( (4 * y_i - 2) * F_(m-1)(x_i) ) ).
* y_i in {0, 1}
*/
sum_weights = 0.0F;
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
val = 4.0F * (*((float*) (trainer->ydata + index * trainer->ystep))) - 2.0F;
val_f = val * trainer->f[index];
val_f = ( val_f < CV_LOG_VAL_MAX ) ? exp( val_f ) : CV_LOG_VAL_MAX;
val = (float) ( (double) val / ( 1.0 + val_f ) );
trainer->y->data.fl[index] = val;
val = (float) fabs( val );
weights[index] = val * (2.0F - val);
sorted_weights[i] = weights[index];
sum_weights += sorted_weights[i];
}
trimmed_idx = NULL;
sample_idx = trainer->sampleIdx;
trimmed_num = trainer->numsamples;
if( trainer->param[1] < 1.0F )
{
/* perform weight trimming */
float threshold;
int count;
icvSort_32f( sorted_weights, trainer->numsamples, 0 );
sum_weights *= (1.0F - trainer->param[1]);
i = -1;
do { sum_weights -= sorted_weights[++i]; }
while( sum_weights > 0.0F && i < (trainer->numsamples - 1) );
threshold = sorted_weights[i];
while( i > 0 && sorted_weights[i-1] == threshold ) i--;
if( i > 0 )
{
trimmed_num = trainer->numsamples - i;
trimmed_idx = cvCreateMat( 1, trimmed_num, CV_32FC1 );
count = 0;
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
if( weights[index] >= threshold )
{
CV_MAT_ELEM( *trimmed_idx, float, 0, count ) = (float) index;
count++;
}
}
assert( count == trimmed_num );
sample_idx = trimmed_idx;
printf( "Used samples %%: %g\n",
(float) trimmed_num / (float) trainer->numsamples * 100.0F );
}
}
ptr = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData, trainer->flags,
trainer->y, NULL, NULL, NULL, sample_idx, trainer->weights,
(CvClassifierTrainParams*) &trainer->cartParams );
CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample );
CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step );
sample_data = sample.data.ptr;
for( i = 0; i < trimmed_num; i++ )
{
index = icvGetIdxAt( sample_idx, i );
sample.data.ptr = sample_data + index * sample_step;
idx[index] = (int) cvEvalCARTClassifierIdx( (CvClassifier*) ptr, &sample );
}
for( j = 0; j <= ptr->count; j++ )
{
respnum = 0;
val = 0.0F;
sum_weights = 0.0F;
for( i = 0; i < trimmed_num; i++ )
{
index = icvGetIdxAt( sample_idx, i );
if( idx[index] == j )
{
val += trainer->y->data.fl[index];
sum_weights += weights[index];
respnum++;
}
}
if( sum_weights > 0.0F )
{
val /= sum_weights;
}
else
{
val = 0.0F;
}
ptr->val[j] = val;
}
if( trimmed_idx != NULL ) cvReleaseMat( &trimmed_idx );
cvFree( &sorted_weights );
cvFree( &weights );
cvFree( &idx );
trees[0] = ptr;
}
void icvBtNext_LKCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer )
{
int i, j, k, kk, num;
CvMat sample;
int sample_step;
uchar* sample_data;
int data_size;
int* idx;
int respnum;
float val;
float sum_weights;
float* weights;
float* sorted_weights;
CvMat* trimmed_idx;
CvMat* sample_idx;
int index;
int trimmed_num;
double sum_exp_f;
double exp_f;
double f_k;
data_size = trainer->m * sizeof( *idx );
idx = (int*) cvAlloc( data_size );
data_size = trainer->m * sizeof( *weights );
weights = (float*) cvAlloc( data_size );
data_size = trainer->m * sizeof( *sorted_weights );
sorted_weights = (float*) cvAlloc( data_size );
trimmed_idx = cvCreateMat( 1, trainer->numsamples, CV_32FC1 );
for( k = 0; k < trainer->numclasses; k++ )
{
/* yhat_i = y_i - p_k(x_i), y_i in {0, 1} */
/* p_k(x_i) = exp(f_k(x_i)) / (sum_exp_f(x_i)) */
sum_weights = 0.0F;
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
/* p_k(x_i) = 1 / (1 + sum(exp(f_kk(x_i) - f_k(x_i)))), kk != k */
num = index * trainer->numclasses;
f_k = (double) trainer->f[num + k];
sum_exp_f = 1.0;
for( kk = 0; kk < trainer->numclasses; kk++ )
{
if( kk == k ) continue;
exp_f = (double) trainer->f[num + kk] - f_k;
exp_f = (exp_f < CV_LOG_VAL_MAX) ? exp( exp_f ) : CV_VAL_MAX;
if( exp_f == CV_VAL_MAX || exp_f >= (CV_VAL_MAX - sum_exp_f) )
{
sum_exp_f = CV_VAL_MAX;
break;
}
sum_exp_f += exp_f;
}
val = (float) ( (*((float*) (trainer->ydata + index * trainer->ystep)))
== (float) k );
val -= (float) ( (sum_exp_f == CV_VAL_MAX) ? 0.0 : ( 1.0 / sum_exp_f ) );
assert( val >= -1.0F );
assert( val <= 1.0F );
trainer->y->data.fl[index] = val;
val = (float) fabs( val );
weights[index] = val * (1.0F - val);
sorted_weights[i] = weights[index];
sum_weights += sorted_weights[i];
}
sample_idx = trainer->sampleIdx;
trimmed_num = trainer->numsamples;
if( trainer->param[1] < 1.0F )
{
/* perform weight trimming */
float threshold;
int count;
icvSort_32f( sorted_weights, trainer->numsamples, 0 );
sum_weights *= (1.0F - trainer->param[1]);
i = -1;
do { sum_weights -= sorted_weights[++i]; }
while( sum_weights > 0.0F && i < (trainer->numsamples - 1) );
threshold = sorted_weights[i];
while( i > 0 && sorted_weights[i-1] == threshold ) i--;
if( i > 0 )
{
trimmed_num = trainer->numsamples - i;
trimmed_idx->cols = trimmed_num;
count = 0;
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
if( weights[index] >= threshold )
{
CV_MAT_ELEM( *trimmed_idx, float, 0, count ) = (float) index;
count++;
}
}
assert( count == trimmed_num );
sample_idx = trimmed_idx;
printf( "k: %d Used samples %%: %g\n", k,
(float) trimmed_num / (float) trainer->numsamples * 100.0F );
}
} /* weight trimming */
trees[k] = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData,
trainer->flags, trainer->y, NULL, NULL, NULL, sample_idx, trainer->weights,
(CvClassifierTrainParams*) &trainer->cartParams );
CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample );
CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step );
sample_data = sample.data.ptr;
for( i = 0; i < trimmed_num; i++ )
{
index = icvGetIdxAt( sample_idx, i );
sample.data.ptr = sample_data + index * sample_step;
idx[index] = (int) cvEvalCARTClassifierIdx( (CvClassifier*) trees[k],
&sample );
}
for( j = 0; j <= trees[k]->count; j++ )
{
respnum = 0;
val = 0.0F;
sum_weights = 0.0F;
for( i = 0; i < trimmed_num; i++ )
{
index = icvGetIdxAt( sample_idx, i );
if( idx[index] == j )
{
val += trainer->y->data.fl[index];
sum_weights += weights[index];
respnum++;
}
}
if( sum_weights > 0.0F )
{
val = ((float) (trainer->numclasses - 1)) * val /
((float) (trainer->numclasses)) / sum_weights;
}
else
{
val = 0.0F;
}
trees[k]->val[j] = val;
}
} /* for each class */
cvReleaseMat( &trimmed_idx );
cvFree( &sorted_weights );
cvFree( &weights );
cvFree( &idx );
}
void icvBtNext_XXBCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer )
{
float alpha;
int i;
CvMat* weak_eval_vals;
CvMat* sample_idx;
int num_samples;
CvMat sample;
uchar* sample_data;
int sample_step;
weak_eval_vals = cvCreateMat( 1, trainer->m, CV_32FC1 );
sample_idx = cvTrimWeights( trainer->weights, trainer->sampleIdx,
trainer->param[1] );
num_samples = ( sample_idx == NULL )
? trainer->m : MAX( sample_idx->rows, sample_idx->cols );
printf( "Used samples %%: %g\n",
(float) num_samples / (float) trainer->numsamples * 100.0F );
trees[0] = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData,
trainer->flags, trainer->y, NULL, NULL, NULL,
sample_idx, trainer->weights,
(CvClassifierTrainParams*) &trainer->cartParams );
/* evaluate samples */
CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample );
CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step );
sample_data = sample.data.ptr;
for( i = 0; i < trainer->m; i++ )
{
sample.data.ptr = sample_data + i * sample_step;
weak_eval_vals->data.fl[i] = trees[0]->eval( (CvClassifier*) trees[0], &sample );
}
alpha = cvBoostNextWeakClassifier( weak_eval_vals, trainer->trainClasses,
trainer->y, trainer->weights, trainer->boosttrainer );
/* multiply tree by alpha */
for( i = 0; i <= trees[0]->count; i++ )
{
trees[0]->val[i] *= alpha;
}
if( trainer->type == CV_RABCLASS )
{
for( i = 0; i <= trees[0]->count; i++ )
{
trees[0]->val[i] = cvLogRatio( trees[0]->val[i] );
}
}
if( sample_idx != NULL && sample_idx != trainer->sampleIdx )
{
cvReleaseMat( &sample_idx );
}
cvReleaseMat( &weak_eval_vals );
}
typedef void (*CvBtNextFunc)( CvCARTClassifier** trees, CvBtTrainer* trainer );
static CvBtNextFunc icvBtNextFunc[] =
{
icvBtNext_XXBCLASS,
icvBtNext_XXBCLASS,
icvBtNext_XXBCLASS,
icvBtNext_XXBCLASS,
icvBtNext_L2CLASS,
icvBtNext_LKCLASS,
icvBtNext_LSREG,
icvBtNext_LADREG,
icvBtNext_MREG
};
CV_BOOST_IMPL
void cvBtNext( CvCARTClassifier** trees, CvBtTrainer* trainer )
{
int i, j;
int index;
CvMat sample;
int sample_step;
uchar* sample_data;
icvBtNextFunc[trainer->type]( trees, trainer );
/* shrinkage */
if( trainer->param[0] != 1.0F )
{
for( j = 0; j < trainer->numclasses; j++ )
{
for( i = 0; i <= trees[j]->count; i++ )
{
trees[j]->val[i] *= trainer->param[0];
}
}
}
if( trainer->type > CV_GABCLASS )
{
/* update F_(m-1) */
CV_GET_SAMPLE( *(trainer->trainData), trainer->flags, 0, sample );
CV_GET_SAMPLE_STEP( *(trainer->trainData), trainer->flags, sample_step );
sample_data = sample.data.ptr;
for( i = 0; i < trainer->numsamples; i++ )
{
index = icvGetIdxAt( trainer->sampleIdx, i );
sample.data.ptr = sample_data + index * sample_step;
for( j = 0; j < trainer->numclasses; j++ )
{
trainer->f[index * trainer->numclasses + j] +=
trees[j]->eval( (CvClassifier*) (trees[j]), &sample );
}
}
}
}
CV_BOOST_IMPL
void cvBtEnd( CvBtTrainer** trainer )
{
CV_FUNCNAME( "cvBtEnd" );
__BEGIN__;
if( trainer == NULL || (*trainer) == NULL )
{
CV_ERROR( CV_StsNullPtr, "Invalid trainer parameter" );
}
if( (*trainer)->y != NULL )
{
CV_CALL( cvReleaseMat( &((*trainer)->y) ) );
}
if( (*trainer)->weights != NULL )
{
CV_CALL( cvReleaseMat( &((*trainer)->weights) ) );
}
if( (*trainer)->boosttrainer != NULL )
{
CV_CALL( cvBoostEndTraining( &((*trainer)->boosttrainer) ) );
}
CV_CALL( cvFree( trainer ) );
__END__;
}
/****************************************************************************************\
* Boosted tree model as a classifier *
\****************************************************************************************/
CV_BOOST_IMPL
float cvEvalBtClassifier( CvClassifier* classifier, CvMat* sample )
{
float val;
CV_FUNCNAME( "cvEvalBtClassifier" );
__BEGIN__;
int i;
val = 0.0F;
if( CV_IS_TUNABLE( classifier->flags ) )
{
CvSeqReader reader;
CvCARTClassifier* tree;
CV_CALL( cvStartReadSeq( ((CvBtClassifier*) classifier)->seq, &reader ) );
for( i = 0; i < ((CvBtClassifier*) classifier)->numiter; i++ )
{
CV_READ_SEQ_ELEM( tree, reader );
val += tree->eval( (CvClassifier*) tree, sample );
}
}
else
{
CvCARTClassifier** ptree;
ptree = ((CvBtClassifier*) classifier)->trees;
for( i = 0; i < ((CvBtClassifier*) classifier)->numiter; i++ )
{
val += (*ptree)->eval( (CvClassifier*) (*ptree), sample );
ptree++;
}
}
__END__;
return val;
}
CV_BOOST_IMPL
float cvEvalBtClassifier2( CvClassifier* classifier, CvMat* sample )
{
float val;
CV_FUNCNAME( "cvEvalBtClassifier2" );
__BEGIN__;
CV_CALL( val = cvEvalBtClassifier( classifier, sample ) );
__END__;
return (float) (val >= 0.0F);
}
CV_BOOST_IMPL
float cvEvalBtClassifierK( CvClassifier* classifier, CvMat* sample )
{
int cls = 0;
CV_FUNCNAME( "cvEvalBtClassifierK" );
__BEGIN__;
int i, k;
float max_val;
int numclasses;
float* vals;
size_t data_size;
numclasses = ((CvBtClassifier*) classifier)->numclasses;
data_size = sizeof( *vals ) * numclasses;
CV_CALL( vals = (float*) cvAlloc( data_size ) );
memset( vals, 0, data_size );
if( CV_IS_TUNABLE( classifier->flags ) )
{
CvSeqReader reader;
CvCARTClassifier* tree;
CV_CALL( cvStartReadSeq( ((CvBtClassifier*) classifier)->seq, &reader ) );
for( i = 0; i < ((CvBtClassifier*) classifier)->numiter; i++ )
{
for( k = 0; k < numclasses; k++ )
{
CV_READ_SEQ_ELEM( tree, reader );
vals[k] += tree->eval( (CvClassifier*) tree, sample );
}
}
}
else
{
CvCARTClassifier** ptree;
ptree = ((CvBtClassifier*) classifier)->trees;
for( i = 0; i < ((CvBtClassifier*) classifier)->numiter; i++ )
{
for( k = 0; k < numclasses; k++ )
{
vals[k] += (*ptree)->eval( (CvClassifier*) (*ptree), sample );
ptree++;
}
}
}
max_val = vals[cls];
for( k = 1; k < numclasses; k++ )
{
if( vals[k] > max_val )
{
max_val = vals[k];
cls = k;
}
}
CV_CALL( cvFree( &vals ) );
__END__;
return (float) cls;
}
typedef float (*CvEvalBtClassifier)( CvClassifier* classifier, CvMat* sample );
static CvEvalBtClassifier icvEvalBtClassifier[] =
{
cvEvalBtClassifier2,
cvEvalBtClassifier2,
cvEvalBtClassifier2,
cvEvalBtClassifier2,
cvEvalBtClassifier2,
cvEvalBtClassifierK,
cvEvalBtClassifier,
cvEvalBtClassifier,
cvEvalBtClassifier
};
CV_BOOST_IMPL
int cvSaveBtClassifier( CvClassifier* classifier, const char* filename )
{
CV_FUNCNAME( "cvSaveBtClassifier" );
__BEGIN__;
FILE* file;
int i, j;
CvSeqReader reader;
memset(&reader, 0, sizeof(reader));
CvCARTClassifier* tree;
CV_ASSERT( classifier );
CV_ASSERT( filename );
if( !icvMkDir( filename ) || (file = fopen( filename, "w" )) == 0 )
{
CV_ERROR( CV_StsError, "Unable to create file" );
}
if( CV_IS_TUNABLE( classifier->flags ) )
{
CV_CALL( cvStartReadSeq( ((CvBtClassifier*) classifier)->seq, &reader ) );
}
fprintf( file, "%d %d\n%d\n%d\n", (int) ((CvBtClassifier*) classifier)->type,
((CvBtClassifier*) classifier)->numclasses,
((CvBtClassifier*) classifier)->numfeatures,
((CvBtClassifier*) classifier)->numiter );
for( i = 0; i < ((CvBtClassifier*) classifier)->numclasses *
((CvBtClassifier*) classifier)->numiter; i++ )
{
if( CV_IS_TUNABLE( classifier->flags ) )
{
CV_READ_SEQ_ELEM( tree, reader );
}
else
{
tree = ((CvBtClassifier*) classifier)->trees[i];
}
fprintf( file, "%d\n", tree->count );
for( j = 0; j < tree->count; j++ )
{
fprintf( file, "%d %g %d %d\n", tree->compidx[j],
tree->threshold[j],
tree->left[j],
tree->right[j] );
}
for( j = 0; j <= tree->count; j++ )
{
fprintf( file, "%g ", tree->val[j] );
}
fprintf( file, "\n" );
}
fclose( file );
__END__;
return 1;
}
CV_BOOST_IMPL
void cvReleaseBtClassifier( CvClassifier** ptr )
{
CV_FUNCNAME( "cvReleaseBtClassifier" );
__BEGIN__;
int i;
if( ptr == NULL || *ptr == NULL )
{
CV_ERROR( CV_StsNullPtr, "" );
}
if( CV_IS_TUNABLE( (*ptr)->flags ) )
{
CvSeqReader reader;
CvCARTClassifier* tree;
CV_CALL( cvStartReadSeq( ((CvBtClassifier*) *ptr)->seq, &reader ) );
for( i = 0; i < ((CvBtClassifier*) *ptr)->numclasses *
((CvBtClassifier*) *ptr)->numiter; i++ )
{
CV_READ_SEQ_ELEM( tree, reader );
tree->release( (CvClassifier**) (&tree) );
}
CV_CALL( cvReleaseMemStorage( &(((CvBtClassifier*) *ptr)->seq->storage) ) );
}
else
{
CvCARTClassifier** ptree;
ptree = ((CvBtClassifier*) *ptr)->trees;
for( i = 0; i < ((CvBtClassifier*) *ptr)->numclasses *
((CvBtClassifier*) *ptr)->numiter; i++ )
{
(*ptree)->release( (CvClassifier**) ptree );
ptree++;
}
}
CV_CALL( cvFree( ptr ) );
*ptr = NULL;
__END__;
}
void cvTuneBtClassifier( CvClassifier* classifier, CvMat*, int flags,
CvMat*, CvMat* , CvMat*, CvMat*, CvMat* )
{
CV_FUNCNAME( "cvTuneBtClassifier" );
__BEGIN__;
size_t data_size;
if( CV_IS_TUNABLE( flags ) )
{
if( !CV_IS_TUNABLE( classifier->flags ) )
{
CV_ERROR( CV_StsUnsupportedFormat,
"Classifier does not support tune function" );
}
else
{
/* tune classifier */
CvCARTClassifier** trees;
printf( "Iteration %d\n", ((CvBtClassifier*) classifier)->numiter + 1 );
data_size = sizeof( *trees ) * ((CvBtClassifier*) classifier)->numclasses;
CV_CALL( trees = (CvCARTClassifier**) cvAlloc( data_size ) );
CV_CALL( cvBtNext( trees,
(CvBtTrainer*) ((CvBtClassifier*) classifier)->trainer ) );
CV_CALL( cvSeqPushMulti( ((CvBtClassifier*) classifier)->seq,
trees, ((CvBtClassifier*) classifier)->numclasses ) );
CV_CALL( cvFree( &trees ) );
((CvBtClassifier*) classifier)->numiter++;
}
}
else
{
if( CV_IS_TUNABLE( classifier->flags ) )
{
/* convert */
void* ptr;
assert( ((CvBtClassifier*) classifier)->seq->total ==
((CvBtClassifier*) classifier)->numiter *
((CvBtClassifier*) classifier)->numclasses );
data_size = sizeof( ((CvBtClassifier*) classifier)->trees[0] ) *
((CvBtClassifier*) classifier)->seq->total;
CV_CALL( ptr = cvAlloc( data_size ) );
CV_CALL( cvCvtSeqToArray( ((CvBtClassifier*) classifier)->seq, ptr ) );
CV_CALL( cvReleaseMemStorage(
&(((CvBtClassifier*) classifier)->seq->storage) ) );
((CvBtClassifier*) classifier)->trees = (CvCARTClassifier**) ptr;
classifier->flags &= ~CV_TUNABLE;
CV_CALL( cvBtEnd( (CvBtTrainer**)
&(((CvBtClassifier*) classifier)->trainer )) );
((CvBtClassifier*) classifier)->trainer = NULL;
}
}
__END__;
}
CvBtClassifier* icvAllocBtClassifier( CvBoostType type, int flags, int numclasses,
int numiter )
{
CvBtClassifier* ptr;
size_t data_size;
assert( numclasses >= 1 );
assert( numiter >= 0 );
assert( ( numclasses == 1 ) || (type == CV_LKCLASS) );
data_size = sizeof( *ptr );
ptr = (CvBtClassifier*) cvAlloc( data_size );
memset( ptr, 0, data_size );
if( CV_IS_TUNABLE( flags ) )
{
ptr->seq = cvCreateSeq( 0, sizeof( *(ptr->seq) ), sizeof( *(ptr->trees) ),
cvCreateMemStorage() );
ptr->numiter = 0;
}
else
{
data_size = numclasses * numiter * sizeof( *(ptr->trees) );
ptr->trees = (CvCARTClassifier**) cvAlloc( data_size );
memset( ptr->trees, 0, data_size );
ptr->numiter = numiter;
}
ptr->flags = flags;
ptr->numclasses = numclasses;
ptr->type = type;
ptr->eval = icvEvalBtClassifier[(int) type];
ptr->tune = cvTuneBtClassifier;
ptr->save = cvSaveBtClassifier;
ptr->release = cvReleaseBtClassifier;
return ptr;
}
CV_BOOST_IMPL
CvClassifier* cvCreateBtClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask,
CvMat* compIdx,
CvMat* sampleIdx,
CvMat* weights,
CvClassifierTrainParams* trainParams )
{
CvBtClassifier* ptr = 0;
CV_FUNCNAME( "cvCreateBtClassifier" );
__BEGIN__;
CvBoostType type;
int num_classes;
int num_iter;
int i;
CvCARTClassifier** trees;
size_t data_size;
CV_ASSERT( trainData != NULL );
CV_ASSERT( trainClasses != NULL );
CV_ASSERT( typeMask == NULL );
CV_ASSERT( missedMeasurementsMask == NULL );
CV_ASSERT( compIdx == NULL );
CV_ASSERT( weights == NULL );
CV_ASSERT( trainParams != NULL );
type = ((CvBtClassifierTrainParams*) trainParams)->type;
if( type >= CV_DABCLASS && type <= CV_GABCLASS && sampleIdx )
{
CV_ERROR( CV_StsBadArg, "Sample indices are not supported for this type" );
}
if( type == CV_LKCLASS )
{
double min_val;
double max_val;
cvMinMaxLoc( trainClasses, &min_val, &max_val );
num_classes = (int) (max_val + 1.0);
CV_ASSERT( num_classes >= 2 );
}
else
{
num_classes = 1;
}
num_iter = ((CvBtClassifierTrainParams*) trainParams)->numiter;
CV_ASSERT( num_iter > 0 );
ptr = icvAllocBtClassifier( type, CV_TUNABLE | flags, num_classes, num_iter );
ptr->numfeatures = (CV_IS_ROW_SAMPLE( flags )) ? trainData->cols : trainData->rows;
i = 0;
printf( "Iteration %d\n", 1 );
data_size = sizeof( *trees ) * ptr->numclasses;
CV_CALL( trees = (CvCARTClassifier**) cvAlloc( data_size ) );
CV_CALL( ptr->trainer = cvBtStart( trees, trainData, flags, trainClasses, sampleIdx,
((CvBtClassifierTrainParams*) trainParams)->numsplits, type, num_classes,
&(((CvBtClassifierTrainParams*) trainParams)->param[0]) ) );
CV_CALL( cvSeqPushMulti( ptr->seq, trees, ptr->numclasses ) );
CV_CALL( cvFree( &trees ) );
ptr->numiter++;
for( i = 1; i < num_iter; i++ )
{
ptr->tune( (CvClassifier*) ptr, NULL, CV_TUNABLE, NULL, NULL, NULL, NULL, NULL );
}
if( !CV_IS_TUNABLE( flags ) )
{
/* convert */
ptr->tune( (CvClassifier*) ptr, NULL, 0, NULL, NULL, NULL, NULL, NULL );
}
__END__;
return (CvClassifier*) ptr;
}
CV_BOOST_IMPL
CvClassifier* cvCreateBtClassifierFromFile( const char* filename )
{
CvBtClassifier* ptr = 0;
CV_FUNCNAME( "cvCreateBtClassifierFromFile" );
__BEGIN__;
FILE* file;
int i, j;
int data_size;
int num_classifiers;
int num_features;
int num_classes;
int type;
CV_ASSERT( filename != NULL );
ptr = NULL;
file = fopen( filename, "r" );
if( !file )
{
CV_ERROR( CV_StsError, "Unable to open file" );
}
fscanf( file, "%d %d %d %d", &type, &num_classes, &num_features, &num_classifiers );
CV_ASSERT( type >= (int) CV_DABCLASS && type <= (int) CV_MREG );
CV_ASSERT( num_features > 0 );
CV_ASSERT( num_classifiers > 0 );
if( (CvBoostType) type != CV_LKCLASS )
{
num_classes = 1;
}
ptr = icvAllocBtClassifier( (CvBoostType) type, 0, num_classes, num_classifiers );
ptr->numfeatures = num_features;
for( i = 0; i < num_classes * num_classifiers; i++ )
{
int count;
CvCARTClassifier* tree;
fscanf( file, "%d", &count );
data_size = sizeof( *tree )
+ count * ( sizeof( *(tree->compidx) ) + sizeof( *(tree->threshold) ) +
sizeof( *(tree->right) ) + sizeof( *(tree->left) ) )
+ (count + 1) * ( sizeof( *(tree->val) ) );
CV_CALL( tree = (CvCARTClassifier*) cvAlloc( data_size ) );
memset( tree, 0, data_size );
tree->eval = cvEvalCARTClassifier;
tree->tune = NULL;
tree->save = NULL;
tree->release = cvReleaseCARTClassifier;
tree->compidx = (int*) ( tree + 1 );
tree->threshold = (float*) ( tree->compidx + count );
tree->left = (int*) ( tree->threshold + count );
tree->right = (int*) ( tree->left + count );
tree->val = (float*) ( tree->right + count );
tree->count = count;
for( j = 0; j < tree->count; j++ )
{
fscanf( file, "%d %g %d %d", &(tree->compidx[j]),
&(tree->threshold[j]),
&(tree->left[j]),
&(tree->right[j]) );
}
for( j = 0; j <= tree->count; j++ )
{
fscanf( file, "%g", &(tree->val[j]) );
}
ptr->trees[i] = tree;
}
fclose( file );
__END__;
return (CvClassifier*) ptr;
}
/****************************************************************************************\
* Utility functions *
\****************************************************************************************/
CV_BOOST_IMPL
CvMat* cvTrimWeights( CvMat* weights, CvMat* idx, float factor )
{
CvMat* ptr = 0;
CV_FUNCNAME( "cvTrimWeights" );
__BEGIN__;
int i, index, num;
float sum_weights;
uchar* wdata;
size_t wstep;
int wnum;
float threshold;
int count;
float* sorted_weights;
CV_ASSERT( CV_MAT_TYPE( weights->type ) == CV_32FC1 );
ptr = idx;
sorted_weights = NULL;
if( factor > 0.0F && factor < 1.0F )
{
size_t data_size;
CV_MAT2VEC( *weights, wdata, wstep, wnum );
num = ( idx == NULL ) ? wnum : MAX( idx->rows, idx->cols );
data_size = num * sizeof( *sorted_weights );
sorted_weights = (float*) cvAlloc( data_size );
memset( sorted_weights, 0, data_size );
sum_weights = 0.0F;
for( i = 0; i < num; i++ )
{
index = icvGetIdxAt( idx, i );
sorted_weights[i] = *((float*) (wdata + index * wstep));
sum_weights += sorted_weights[i];
}
icvSort_32f( sorted_weights, num, 0 );
sum_weights *= (1.0F - factor);
i = -1;
do { sum_weights -= sorted_weights[++i]; }
while( sum_weights > 0.0F && i < (num - 1) );
threshold = sorted_weights[i];
while( i > 0 && sorted_weights[i-1] == threshold ) i--;
if( i > 0 || ( idx != NULL && CV_MAT_TYPE( idx->type ) != CV_32FC1 ) )
{
CV_CALL( ptr = cvCreateMat( 1, num - i, CV_32FC1 ) );
count = 0;
for( i = 0; i < num; i++ )
{
index = icvGetIdxAt( idx, i );
if( *((float*) (wdata + index * wstep)) >= threshold )
{
CV_MAT_ELEM( *ptr, float, 0, count ) = (float) index;
count++;
}
}
assert( count == ptr->cols );
}
cvFree( &sorted_weights );
}
__END__;
return ptr;
}
CV_BOOST_IMPL
void cvReadTrainData( const char* filename, int flags,
CvMat** trainData,
CvMat** trainClasses )
{
CV_FUNCNAME( "cvReadTrainData" );
__BEGIN__;
FILE* file;
int m, n;
int i, j;
float val;
if( filename == NULL )
{
CV_ERROR( CV_StsNullPtr, "filename must be specified" );
}
if( trainData == NULL )
{
CV_ERROR( CV_StsNullPtr, "trainData must be not NULL" );
}
if( trainClasses == NULL )
{
CV_ERROR( CV_StsNullPtr, "trainClasses must be not NULL" );
}
*trainData = NULL;
*trainClasses = NULL;
file = fopen( filename, "r" );
if( !file )
{
CV_ERROR( CV_StsError, "Unable to open file" );
}
fscanf( file, "%d %d", &m, &n );
if( CV_IS_ROW_SAMPLE( flags ) )
{
CV_CALL( *trainData = cvCreateMat( m, n, CV_32FC1 ) );
}
else
{
CV_CALL( *trainData = cvCreateMat( n, m, CV_32FC1 ) );
}
CV_CALL( *trainClasses = cvCreateMat( 1, m, CV_32FC1 ) );
for( i = 0; i < m; i++ )
{
for( j = 0; j < n; j++ )
{
fscanf( file, "%f", &val );
if( CV_IS_ROW_SAMPLE( flags ) )
{
CV_MAT_ELEM( **trainData, float, i, j ) = val;
}
else
{
CV_MAT_ELEM( **trainData, float, j, i ) = val;
}
}
fscanf( file, "%f", &val );
CV_MAT_ELEM( **trainClasses, float, 0, i ) = val;
}
fclose( file );
__END__;
}
CV_BOOST_IMPL
void cvWriteTrainData( const char* filename, int flags,
CvMat* trainData, CvMat* trainClasses, CvMat* sampleIdx )
{
CV_FUNCNAME( "cvWriteTrainData" );
__BEGIN__;
FILE* file;
int m, n;
int i, j;
int clsrow;
int count;
int idx;
CvScalar sc;
if( filename == NULL )
{
CV_ERROR( CV_StsNullPtr, "filename must be specified" );
}
if( trainData == NULL || CV_MAT_TYPE( trainData->type ) != CV_32FC1 )
{
CV_ERROR( CV_StsUnsupportedFormat, "Invalid trainData" );
}
if( CV_IS_ROW_SAMPLE( flags ) )
{
m = trainData->rows;
n = trainData->cols;
}
else
{
n = trainData->rows;
m = trainData->cols;
}
if( trainClasses == NULL || CV_MAT_TYPE( trainClasses->type ) != CV_32FC1 ||
MIN( trainClasses->rows, trainClasses->cols ) != 1 )
{
CV_ERROR( CV_StsUnsupportedFormat, "Invalid trainClasses" );
}
clsrow = (trainClasses->rows == 1);
if( m != ( (clsrow) ? trainClasses->cols : trainClasses->rows ) )
{
CV_ERROR( CV_StsUnmatchedSizes, "Incorrect trainData and trainClasses sizes" );
}
if( sampleIdx != NULL )
{
count = (sampleIdx->rows == 1) ? sampleIdx->cols : sampleIdx->rows;
}
else
{
count = m;
}
file = fopen( filename, "w" );
if( !file )
{
CV_ERROR( CV_StsError, "Unable to create file" );
}
fprintf( file, "%d %d\n", count, n );
for( i = 0; i < count; i++ )
{
if( sampleIdx )
{
if( sampleIdx->rows == 1 )
{
sc = cvGet2D( sampleIdx, 0, i );
}
else
{
sc = cvGet2D( sampleIdx, i, 0 );
}
idx = (int) sc.val[0];
}
else
{
idx = i;
}
for( j = 0; j < n; j++ )
{
fprintf( file, "%g ", ( (CV_IS_ROW_SAMPLE( flags ))
? CV_MAT_ELEM( *trainData, float, idx, j )
: CV_MAT_ELEM( *trainData, float, j, idx ) ) );
}
fprintf( file, "%g\n", ( (clsrow)
? CV_MAT_ELEM( *trainClasses, float, 0, idx )
: CV_MAT_ELEM( *trainClasses, float, idx, 0 ) ) );
}
fclose( file );
__END__;
}
#define ICV_RAND_SHUFFLE( suffix, type ) \
void icvRandShuffle_##suffix( uchar* data, size_t step, int num ) \
{ \
time_t seed; \
type tmp; \
int i; \
float rn; \
\
time( &seed ); \
CvRNG state = cvRNG((int)seed); \
\
for( i = 0; i < (num-1); i++ ) \
{ \
rn = ((float) cvRandInt( &state )) / (1.0F + UINT_MAX); \
CV_SWAP( *((type*)(data + i * step)), \
*((type*)(data + ( i + (int)( rn * (num - i ) ) )* step)), \
tmp ); \
} \
}
ICV_RAND_SHUFFLE( 8U, uchar )
ICV_RAND_SHUFFLE( 16S, short )
ICV_RAND_SHUFFLE( 32S, int )
ICV_RAND_SHUFFLE( 32F, float )
CV_BOOST_IMPL
void cvRandShuffleVec( CvMat* mat )
{
CV_FUNCNAME( "cvRandShuffle" );
__BEGIN__;
uchar* data;
size_t step;
int num;
if( (mat == NULL) || !CV_IS_MAT( mat ) || MIN( mat->rows, mat->cols ) != 1 )
{
CV_ERROR( CV_StsUnsupportedFormat, "" );
}
CV_MAT2VEC( *mat, data, step, num );
switch( CV_MAT_TYPE( mat->type ) )
{
case CV_8UC1:
icvRandShuffle_8U( data, step, num);
break;
case CV_16SC1:
icvRandShuffle_16S( data, step, num);
break;
case CV_32SC1:
icvRandShuffle_32S( data, step, num);
break;
case CV_32FC1:
icvRandShuffle_32F( data, step, num);
break;
default:
CV_ERROR( CV_StsUnsupportedFormat, "" );
}
__END__;
}
/* End of file. */