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opencv/apps/traincascade/old_ml_inner_functions.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
//
// 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*/
#include "old_ml_precomp.hpp"
CvStatModel::CvStatModel()
{
default_model_name = "my_stat_model";
}
CvStatModel::~CvStatModel()
{
clear();
}
void CvStatModel::clear()
{
}
void CvStatModel::save( const char* filename, const char* name ) const
{
CvFileStorage* fs = 0;
CV_FUNCNAME( "CvStatModel::save" );
__BEGIN__;
CV_CALL( fs = cvOpenFileStorage( filename, 0, CV_STORAGE_WRITE ));
if( !fs )
CV_ERROR( CV_StsError, "Could not open the file storage. Check the path and permissions" );
write( fs, name ? name : default_model_name );
__END__;
cvReleaseFileStorage( &fs );
}
void CvStatModel::load( const char* filename, const char* name )
{
CvFileStorage* fs = 0;
CV_FUNCNAME( "CvAlgorithm::load" );
__BEGIN__;
CvFileNode* model_node = 0;
CV_CALL( fs = cvOpenFileStorage( filename, 0, CV_STORAGE_READ ));
if( !fs )
EXIT;
if( name )
model_node = cvGetFileNodeByName( fs, 0, name );
else
{
CvFileNode* root = cvGetRootFileNode( fs );
if( root->data.seq->total > 0 )
model_node = (CvFileNode*)cvGetSeqElem( root->data.seq, 0 );
}
read( fs, model_node );
__END__;
cvReleaseFileStorage( &fs );
}
void CvStatModel::write( CvFileStorage*, const char* ) const
{
OPENCV_ERROR( CV_StsNotImplemented, "CvStatModel::write", "" );
}
void CvStatModel::read( CvFileStorage*, CvFileNode* )
{
OPENCV_ERROR( CV_StsNotImplemented, "CvStatModel::read", "" );
}
/* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */
static void cvChol( CvMat* A, CvMat* S )
{
int dim = A->rows;
int i, j, k;
float sum;
for( i = 0; i < dim; i++ )
{
for( j = 0; j < i; j++ )
CV_MAT_ELEM(*S, float, i, j) = 0;
sum = 0;
for( k = 0; k < i; k++ )
sum += CV_MAT_ELEM(*S, float, k, i) * CV_MAT_ELEM(*S, float, k, i);
CV_MAT_ELEM(*S, float, i, i) = (float)sqrt(CV_MAT_ELEM(*A, float, i, i) - sum);
for( j = i + 1; j < dim; j++ )
{
sum = 0;
for( k = 0; k < i; k++ )
sum += CV_MAT_ELEM(*S, float, k, i) * CV_MAT_ELEM(*S, float, k, j);
CV_MAT_ELEM(*S, float, i, j) =
(CV_MAT_ELEM(*A, float, i, j) - sum) / CV_MAT_ELEM(*S, float, i, i);
}
}
}
/* Generates <sample> from multivariate normal distribution, where <mean> - is an
average row vector, <cov> - symmetric covariation matrix */
CV_IMPL void cvRandMVNormal( CvMat* mean, CvMat* cov, CvMat* sample, CvRNG* rng )
{
int dim = sample->cols;
int amount = sample->rows;
CvRNG state = rng ? *rng : cvRNG( cvGetTickCount() );
cvRandArr(&state, sample, CV_RAND_NORMAL, cvScalarAll(0), cvScalarAll(1) );
CvMat* utmat = cvCreateMat(dim, dim, sample->type);
CvMat* vect = cvCreateMatHeader(1, dim, sample->type);
cvChol(cov, utmat);
int i;
for( i = 0; i < amount; i++ )
{
cvGetRow(sample, vect, i);
cvMatMulAdd(vect, utmat, mean, vect);
}
cvReleaseMat(&vect);
cvReleaseMat(&utmat);
}
/* Generates <sample> of <amount> points from a discrete variate xi,
where Pr{xi = k} == probs[k], 0 < k < len - 1. */
static void cvRandSeries( float probs[], int len, int sample[], int amount )
{
CvMat* univals = cvCreateMat(1, amount, CV_32FC1);
float* knots = (float*)cvAlloc( len * sizeof(float) );
int i, j;
CvRNG state = cvRNG(-1);
cvRandArr(&state, univals, CV_RAND_UNI, cvScalarAll(0), cvScalarAll(1) );
knots[0] = probs[0];
for( i = 1; i < len; i++ )
knots[i] = knots[i - 1] + probs[i];
for( i = 0; i < amount; i++ )
for( j = 0; j < len; j++ )
{
if ( CV_MAT_ELEM(*univals, float, 0, i) <= knots[j] )
{
sample[i] = j;
break;
}
}
cvFree(&knots);
}
/* Generates <sample> from gaussian mixture distribution */
CV_IMPL void cvRandGaussMixture( CvMat* means[],
CvMat* covs[],
float weights[],
int clsnum,
CvMat* sample,
CvMat* sampClasses )
{
int dim = sample->cols;
int amount = sample->rows;
int i, clss;
int* sample_clsnum = (int*)cvAlloc( amount * sizeof(int) );
CvMat** utmats = (CvMat**)cvAlloc( clsnum * sizeof(CvMat*) );
CvMat* vect = cvCreateMatHeader(1, dim, CV_32FC1);
CvMat* classes;
if( sampClasses )
classes = sampClasses;
else
classes = cvCreateMat(1, amount, CV_32FC1);
CvRNG state = cvRNG(-1);
cvRandArr(&state, sample, CV_RAND_NORMAL, cvScalarAll(0), cvScalarAll(1));
cvRandSeries(weights, clsnum, sample_clsnum, amount);
for( i = 0; i < clsnum; i++ )
{
utmats[i] = cvCreateMat(dim, dim, CV_32FC1);
cvChol(covs[i], utmats[i]);
}
for( i = 0; i < amount; i++ )
{
CV_MAT_ELEM(*classes, float, 0, i) = (float)sample_clsnum[i];
cvGetRow(sample, vect, i);
clss = sample_clsnum[i];
cvMatMulAdd(vect, utmats[clss], means[clss], vect);
}
if( !sampClasses )
cvReleaseMat(&classes);
for( i = 0; i < clsnum; i++ )
cvReleaseMat(&utmats[i]);
cvFree(&utmats);
cvFree(&sample_clsnum);
cvReleaseMat(&vect);
}
CvMat* icvGenerateRandomClusterCenters ( int seed, const CvMat* data,
int num_of_clusters, CvMat* _centers )
{
CvMat* centers = _centers;
CV_FUNCNAME("icvGenerateRandomClusterCenters");
__BEGIN__;
CvRNG rng;
CvMat data_comp, centers_comp;
CvPoint minLoc, maxLoc; // Not used, just for function "cvMinMaxLoc"
double minVal, maxVal;
int i;
int dim = data ? data->cols : 0;
if( ICV_IS_MAT_OF_TYPE(data, CV_32FC1) )
{
if( _centers && !ICV_IS_MAT_OF_TYPE (_centers, CV_32FC1) )
{
CV_ERROR(CV_StsBadArg,"");
}
else if( !_centers )
CV_CALL(centers = cvCreateMat (num_of_clusters, dim, CV_32FC1));
}
else if( ICV_IS_MAT_OF_TYPE(data, CV_64FC1) )
{
if( _centers && !ICV_IS_MAT_OF_TYPE (_centers, CV_64FC1) )
{
CV_ERROR(CV_StsBadArg,"");
}
else if( !_centers )
CV_CALL(centers = cvCreateMat (num_of_clusters, dim, CV_64FC1));
}
else
CV_ERROR (CV_StsBadArg,"");
if( num_of_clusters < 1 )
CV_ERROR (CV_StsBadArg,"");
rng = cvRNG(seed);
for (i = 0; i < dim; i++)
{
CV_CALL(cvGetCol (data, &data_comp, i));
CV_CALL(cvMinMaxLoc (&data_comp, &minVal, &maxVal, &minLoc, &maxLoc));
CV_CALL(cvGetCol (centers, &centers_comp, i));
CV_CALL(cvRandArr (&rng, &centers_comp, CV_RAND_UNI, cvScalarAll(minVal), cvScalarAll(maxVal)));
}
__END__;
if( (cvGetErrStatus () < 0) || (centers != _centers) )
cvReleaseMat (&centers);
return _centers ? _centers : centers;
} // end of icvGenerateRandomClusterCenters
// By S. Dilman - begin -
#define ICV_RAND_MAX 4294967296 // == 2^32
// static void cvRandRoundUni (CvMat* center,
// float radius_small,
// float radius_large,
// CvMat* desired_matrix,
// CvRNG* rng_state_ptr)
// {
// float rad, norm, coefficient;
// int dim, size, i, j;
// CvMat *cov, sample;
// CvRNG rng_local;
// CV_FUNCNAME("cvRandRoundUni");
// __BEGIN__
// rng_local = *rng_state_ptr;
// CV_ASSERT ((radius_small >= 0) &&
// (radius_large > 0) &&
// (radius_small <= radius_large));
// CV_ASSERT (center && desired_matrix && rng_state_ptr);
// CV_ASSERT (center->rows == 1);
// CV_ASSERT (center->cols == desired_matrix->cols);
// dim = desired_matrix->cols;
// size = desired_matrix->rows;
// cov = cvCreateMat (dim, dim, CV_32FC1);
// cvSetIdentity (cov);
// cvRandMVNormal (center, cov, desired_matrix, &rng_local);
// for (i = 0; i < size; i++)
// {
// rad = (float)(cvRandReal(&rng_local)*(radius_large - radius_small) + radius_small);
// cvGetRow (desired_matrix, &sample, i);
// norm = (float) cvNorm (&sample, 0, CV_L2);
// coefficient = rad / norm;
// for (j = 0; j < dim; j++)
// CV_MAT_ELEM (sample, float, 0, j) *= coefficient;
// }
// __END__
// }
// By S. Dilman - end -
static int CV_CDECL
icvCmpIntegers( const void* a, const void* b )
{
return *(const int*)a - *(const int*)b;
}
static int CV_CDECL
icvCmpIntegersPtr( const void* _a, const void* _b )
{
int a = **(const int**)_a;
int b = **(const int**)_b;
return (a < b ? -1 : 0)|(a > b);
}
static int icvCmpSparseVecElems( const void* a, const void* b )
{
return ((CvSparseVecElem32f*)a)->idx - ((CvSparseVecElem32f*)b)->idx;
}
CvMat*
cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates )
{
CvMat* idx = 0;
CV_FUNCNAME( "cvPreprocessIndexArray" );
__BEGIN__;
int i, idx_total, idx_selected = 0, step, type, prev = INT_MIN, is_sorted = 1;
uchar* srcb = 0;
int* srci = 0;
int* dsti;
if( !CV_IS_MAT(idx_arr) )
CV_ERROR( CV_StsBadArg, "Invalid index array" );
if( idx_arr->rows != 1 && idx_arr->cols != 1 )
CV_ERROR( CV_StsBadSize, "the index array must be 1-dimensional" );
idx_total = idx_arr->rows + idx_arr->cols - 1;
srcb = idx_arr->data.ptr;
srci = idx_arr->data.i;
type = CV_MAT_TYPE(idx_arr->type);
step = CV_IS_MAT_CONT(idx_arr->type) ? 1 : idx_arr->step/CV_ELEM_SIZE(type);
switch( type )
{
case CV_8UC1:
case CV_8SC1:
// idx_arr is array of 1's and 0's -
// i.e. it is a mask of the selected components
if( idx_total != data_arr_size )
CV_ERROR( CV_StsUnmatchedSizes,
"Component mask should contain as many elements as the total number of input variables" );
for( i = 0; i < idx_total; i++ )
idx_selected += srcb[i*step] != 0;
if( idx_selected == 0 )
CV_ERROR( CV_StsOutOfRange, "No components/input_variables is selected!" );
break;
case CV_32SC1:
// idx_arr is array of integer indices of selected components
if( idx_total > data_arr_size )
CV_ERROR( CV_StsOutOfRange,
"index array may not contain more elements than the total number of input variables" );
idx_selected = idx_total;
// check if sorted already
for( i = 0; i < idx_total; i++ )
{
int val = srci[i*step];
if( val >= prev )
{
is_sorted = 0;
break;
}
prev = val;
}
break;
default:
CV_ERROR( CV_StsUnsupportedFormat, "Unsupported index array data type "
"(it should be 8uC1, 8sC1 or 32sC1)" );
}
CV_CALL( idx = cvCreateMat( 1, idx_selected, CV_32SC1 ));
dsti = idx->data.i;
if( type < CV_32SC1 )
{
for( i = 0; i < idx_total; i++ )
if( srcb[i*step] )
*dsti++ = i;
}
else
{
for( i = 0; i < idx_total; i++ )
dsti[i] = srci[i*step];
if( !is_sorted )
qsort( dsti, idx_total, sizeof(dsti[0]), icvCmpIntegers );
if( dsti[0] < 0 || dsti[idx_total-1] >= data_arr_size )
CV_ERROR( CV_StsOutOfRange, "the index array elements are out of range" );
if( check_for_duplicates )
{
for( i = 1; i < idx_total; i++ )
if( dsti[i] <= dsti[i-1] )
CV_ERROR( CV_StsBadArg, "There are duplicated index array elements" );
}
}
__END__;
if( cvGetErrStatus() < 0 )
cvReleaseMat( &idx );
return idx;
}
CvMat*
cvPreprocessVarType( const CvMat* var_type, const CvMat* var_idx,
int var_count, int* response_type )
{
CvMat* out_var_type = 0;
CV_FUNCNAME( "cvPreprocessVarType" );
if( response_type )
*response_type = -1;
__BEGIN__;
int i, tm_size, tm_step;
//int* map = 0;
const uchar* src;
uchar* dst;
if( !CV_IS_MAT(var_type) )
CV_ERROR( var_type ? CV_StsBadArg : CV_StsNullPtr, "Invalid or absent var_type array" );
if( var_type->rows != 1 && var_type->cols != 1 )
CV_ERROR( CV_StsBadSize, "var_type array must be 1-dimensional" );
if( !CV_IS_MASK_ARR(var_type))
CV_ERROR( CV_StsUnsupportedFormat, "type mask must be 8uC1 or 8sC1 array" );
tm_size = var_type->rows + var_type->cols - 1;
tm_step = var_type->rows == 1 ? 1 : var_type->step/CV_ELEM_SIZE(var_type->type);
if( /*tm_size != var_count &&*/ tm_size != var_count + 1 )
CV_ERROR( CV_StsBadArg,
"type mask must be of <input var count> + 1 size" );
if( response_type && tm_size > var_count )
*response_type = var_type->data.ptr[var_count*tm_step] != 0;
if( var_idx )
{
if( !CV_IS_MAT(var_idx) || CV_MAT_TYPE(var_idx->type) != CV_32SC1 ||
(var_idx->rows != 1 && var_idx->cols != 1) || !CV_IS_MAT_CONT(var_idx->type) )
CV_ERROR( CV_StsBadArg, "var index array should be continuous 1-dimensional integer vector" );
if( var_idx->rows + var_idx->cols - 1 > var_count )
CV_ERROR( CV_StsBadSize, "var index array is too large" );
//map = var_idx->data.i;
var_count = var_idx->rows + var_idx->cols - 1;
}
CV_CALL( out_var_type = cvCreateMat( 1, var_count, CV_8UC1 ));
src = var_type->data.ptr;
dst = out_var_type->data.ptr;
for( i = 0; i < var_count; i++ )
{
//int idx = map ? map[i] : i;
assert( (unsigned)/*idx*/i < (unsigned)tm_size );
dst[i] = (uchar)(src[/*idx*/i*tm_step] != 0);
}
__END__;
return out_var_type;
}
CvMat*
cvPreprocessOrderedResponses( const CvMat* responses, const CvMat* sample_idx, int sample_all )
{
CvMat* out_responses = 0;
CV_FUNCNAME( "cvPreprocessOrderedResponses" );
__BEGIN__;
int i, r_type, r_step;
const int* map = 0;
float* dst;
int sample_count = sample_all;
if( !CV_IS_MAT(responses) )
CV_ERROR( CV_StsBadArg, "Invalid response array" );
if( responses->rows != 1 && responses->cols != 1 )
CV_ERROR( CV_StsBadSize, "Response array must be 1-dimensional" );
if( responses->rows + responses->cols - 1 != sample_count )
CV_ERROR( CV_StsUnmatchedSizes,
"Response array must contain as many elements as the total number of samples" );
r_type = CV_MAT_TYPE(responses->type);
if( r_type != CV_32FC1 && r_type != CV_32SC1 )
CV_ERROR( CV_StsUnsupportedFormat, "Unsupported response type" );
r_step = responses->step ? responses->step / CV_ELEM_SIZE(responses->type) : 1;
if( r_type == CV_32FC1 && CV_IS_MAT_CONT(responses->type) && !sample_idx )
{
out_responses = cvCloneMat( responses );
EXIT;
}
if( sample_idx )
{
if( !CV_IS_MAT(sample_idx) || CV_MAT_TYPE(sample_idx->type) != CV_32SC1 ||
(sample_idx->rows != 1 && sample_idx->cols != 1) || !CV_IS_MAT_CONT(sample_idx->type) )
CV_ERROR( CV_StsBadArg, "sample index array should be continuous 1-dimensional integer vector" );
if( sample_idx->rows + sample_idx->cols - 1 > sample_count )
CV_ERROR( CV_StsBadSize, "sample index array is too large" );
map = sample_idx->data.i;
sample_count = sample_idx->rows + sample_idx->cols - 1;
}
CV_CALL( out_responses = cvCreateMat( 1, sample_count, CV_32FC1 ));
dst = out_responses->data.fl;
if( r_type == CV_32FC1 )
{
const float* src = responses->data.fl;
for( i = 0; i < sample_count; i++ )
{
int idx = map ? map[i] : i;
assert( (unsigned)idx < (unsigned)sample_all );
dst[i] = src[idx*r_step];
}
}
else
{
const int* src = responses->data.i;
for( i = 0; i < sample_count; i++ )
{
int idx = map ? map[i] : i;
assert( (unsigned)idx < (unsigned)sample_all );
dst[i] = (float)src[idx*r_step];
}
}
__END__;
return out_responses;
}
CvMat*
cvPreprocessCategoricalResponses( const CvMat* responses,
const CvMat* sample_idx, int sample_all,
CvMat** out_response_map, CvMat** class_counts )
{
CvMat* out_responses = 0;
int** response_ptr = 0;
CV_FUNCNAME( "cvPreprocessCategoricalResponses" );
if( out_response_map )
*out_response_map = 0;
if( class_counts )
*class_counts = 0;
__BEGIN__;
int i, r_type, r_step;
int cls_count = 1, prev_cls, prev_i;
const int* map = 0;
const int* srci;
const float* srcfl;
int* dst;
int* cls_map;
int* cls_counts = 0;
int sample_count = sample_all;
if( !CV_IS_MAT(responses) )
CV_ERROR( CV_StsBadArg, "Invalid response array" );
if( responses->rows != 1 && responses->cols != 1 )
CV_ERROR( CV_StsBadSize, "Response array must be 1-dimensional" );
if( responses->rows + responses->cols - 1 != sample_count )
CV_ERROR( CV_StsUnmatchedSizes,
"Response array must contain as many elements as the total number of samples" );
r_type = CV_MAT_TYPE(responses->type);
if( r_type != CV_32FC1 && r_type != CV_32SC1 )
CV_ERROR( CV_StsUnsupportedFormat, "Unsupported response type" );
r_step = responses->rows == 1 ? 1 : responses->step / CV_ELEM_SIZE(responses->type);
if( sample_idx )
{
if( !CV_IS_MAT(sample_idx) || CV_MAT_TYPE(sample_idx->type) != CV_32SC1 ||
(sample_idx->rows != 1 && sample_idx->cols != 1) || !CV_IS_MAT_CONT(sample_idx->type) )
CV_ERROR( CV_StsBadArg, "sample index array should be continuous 1-dimensional integer vector" );
if( sample_idx->rows + sample_idx->cols - 1 > sample_count )
CV_ERROR( CV_StsBadSize, "sample index array is too large" );
map = sample_idx->data.i;
sample_count = sample_idx->rows + sample_idx->cols - 1;
}
CV_CALL( out_responses = cvCreateMat( 1, sample_count, CV_32SC1 ));
if( !out_response_map )
CV_ERROR( CV_StsNullPtr, "out_response_map pointer is NULL" );
CV_CALL( response_ptr = (int**)cvAlloc( sample_count*sizeof(response_ptr[0])));
srci = responses->data.i;
srcfl = responses->data.fl;
dst = out_responses->data.i;
for( i = 0; i < sample_count; i++ )
{
int idx = map ? map[i] : i;
assert( (unsigned)idx < (unsigned)sample_all );
if( r_type == CV_32SC1 )
dst[i] = srci[idx*r_step];
else
{
float rf = srcfl[idx*r_step];
int ri = cvRound(rf);
if( ri != rf )
{
char buf[100];
sprintf( buf, "response #%d is not integral", idx );
CV_ERROR( CV_StsBadArg, buf );
}
dst[i] = ri;
}
response_ptr[i] = dst + i;
}
qsort( response_ptr, sample_count, sizeof(int*), icvCmpIntegersPtr );
// count the classes
for( i = 1; i < sample_count; i++ )
cls_count += *response_ptr[i] != *response_ptr[i-1];
if( cls_count < 2 )
CV_ERROR( CV_StsBadArg, "There is only a single class" );
CV_CALL( *out_response_map = cvCreateMat( 1, cls_count, CV_32SC1 ));
if( class_counts )
{
CV_CALL( *class_counts = cvCreateMat( 1, cls_count, CV_32SC1 ));
cls_counts = (*class_counts)->data.i;
}
// compact the class indices and build the map
prev_cls = ~*response_ptr[0];
cls_count = -1;
cls_map = (*out_response_map)->data.i;
for( i = 0, prev_i = -1; i < sample_count; i++ )
{
int cur_cls = *response_ptr[i];
if( cur_cls != prev_cls )
{
if( cls_counts && cls_count >= 0 )
cls_counts[cls_count] = i - prev_i;
cls_map[++cls_count] = prev_cls = cur_cls;
prev_i = i;
}
*response_ptr[i] = cls_count;
}
if( cls_counts )
cls_counts[cls_count] = i - prev_i;
__END__;
cvFree( &response_ptr );
return out_responses;
}
const float**
cvGetTrainSamples( const CvMat* train_data, int tflag,
const CvMat* var_idx, const CvMat* sample_idx,
int* _var_count, int* _sample_count,
bool always_copy_data )
{
float** samples = 0;
CV_FUNCNAME( "cvGetTrainSamples" );
__BEGIN__;
int i, j, var_count, sample_count, s_step, v_step;
bool copy_data;
const float* data;
const int *s_idx, *v_idx;
if( !CV_IS_MAT(train_data) )
CV_ERROR( CV_StsBadArg, "Invalid or NULL training data matrix" );
var_count = var_idx ? var_idx->cols + var_idx->rows - 1 :
tflag == CV_ROW_SAMPLE ? train_data->cols : train_data->rows;
sample_count = sample_idx ? sample_idx->cols + sample_idx->rows - 1 :
tflag == CV_ROW_SAMPLE ? train_data->rows : train_data->cols;
if( _var_count )
*_var_count = var_count;
if( _sample_count )
*_sample_count = sample_count;
copy_data = tflag != CV_ROW_SAMPLE || var_idx || always_copy_data;
CV_CALL( samples = (float**)cvAlloc(sample_count*sizeof(samples[0]) +
(copy_data ? 1 : 0)*var_count*sample_count*sizeof(samples[0][0])) );
data = train_data->data.fl;
s_step = train_data->step / sizeof(samples[0][0]);
v_step = 1;
s_idx = sample_idx ? sample_idx->data.i : 0;
v_idx = var_idx ? var_idx->data.i : 0;
if( !copy_data )
{
for( i = 0; i < sample_count; i++ )
samples[i] = (float*)(data + (s_idx ? s_idx[i] : i)*s_step);
}
else
{
samples[0] = (float*)(samples + sample_count);
if( tflag != CV_ROW_SAMPLE )
CV_SWAP( s_step, v_step, i );
for( i = 0; i < sample_count; i++ )
{
float* dst = samples[i] = samples[0] + i*var_count;
const float* src = data + (s_idx ? s_idx[i] : i)*s_step;
if( !v_idx )
for( j = 0; j < var_count; j++ )
dst[j] = src[j*v_step];
else
for( j = 0; j < var_count; j++ )
dst[j] = src[v_idx[j]*v_step];
}
}
__END__;
return (const float**)samples;
}
void
cvCheckTrainData( const CvMat* train_data, int tflag,
const CvMat* missing_mask,
int* var_all, int* sample_all )
{
CV_FUNCNAME( "cvCheckTrainData" );
if( var_all )
*var_all = 0;
if( sample_all )
*sample_all = 0;
__BEGIN__;
// check parameter types and sizes
if( !CV_IS_MAT(train_data) || CV_MAT_TYPE(train_data->type) != CV_32FC1 )
CV_ERROR( CV_StsBadArg, "train data must be floating-point matrix" );
if( missing_mask )
{
if( !CV_IS_MAT(missing_mask) || !CV_IS_MASK_ARR(missing_mask) ||
!CV_ARE_SIZES_EQ(train_data, missing_mask) )
CV_ERROR( CV_StsBadArg,
"missing value mask must be 8-bit matrix of the same size as training data" );
}
if( tflag != CV_ROW_SAMPLE && tflag != CV_COL_SAMPLE )
CV_ERROR( CV_StsBadArg,
"Unknown training data layout (must be CV_ROW_SAMPLE or CV_COL_SAMPLE)" );
if( var_all )
*var_all = tflag == CV_ROW_SAMPLE ? train_data->cols : train_data->rows;
if( sample_all )
*sample_all = tflag == CV_ROW_SAMPLE ? train_data->rows : train_data->cols;
__END__;
}
int
cvPrepareTrainData( const char* /*funcname*/,
const CvMat* train_data, int tflag,
const CvMat* responses, int response_type,
const CvMat* var_idx,
const CvMat* sample_idx,
bool always_copy_data,
const float*** out_train_samples,
int* _sample_count,
int* _var_count,
int* _var_all,
CvMat** out_responses,
CvMat** out_response_map,
CvMat** out_var_idx,
CvMat** out_sample_idx )
{
int ok = 0;
CvMat* _var_idx = 0;
CvMat* _sample_idx = 0;
CvMat* _responses = 0;
int sample_all = 0, sample_count = 0, var_all = 0, var_count = 0;
CV_FUNCNAME( "cvPrepareTrainData" );
// step 0. clear all the output pointers to ensure we do not try
// to call free() with uninitialized pointers
if( out_responses )
*out_responses = 0;
if( out_response_map )
*out_response_map = 0;
if( out_var_idx )
*out_var_idx = 0;
if( out_sample_idx )
*out_sample_idx = 0;
if( out_train_samples )
*out_train_samples = 0;
if( _sample_count )
*_sample_count = 0;
if( _var_count )
*_var_count = 0;
if( _var_all )
*_var_all = 0;
__BEGIN__;
if( !out_train_samples )
CV_ERROR( CV_StsBadArg, "output pointer to train samples is NULL" );
CV_CALL( cvCheckTrainData( train_data, tflag, 0, &var_all, &sample_all ));
if( sample_idx )
CV_CALL( _sample_idx = cvPreprocessIndexArray( sample_idx, sample_all ));
if( var_idx )
CV_CALL( _var_idx = cvPreprocessIndexArray( var_idx, var_all ));
if( responses )
{
if( !out_responses )
CV_ERROR( CV_StsNullPtr, "output response pointer is NULL" );
if( response_type == CV_VAR_NUMERICAL )
{
CV_CALL( _responses = cvPreprocessOrderedResponses( responses,
_sample_idx, sample_all ));
}
else
{
CV_CALL( _responses = cvPreprocessCategoricalResponses( responses,
_sample_idx, sample_all, out_response_map, 0 ));
}
}
CV_CALL( *out_train_samples =
cvGetTrainSamples( train_data, tflag, _var_idx, _sample_idx,
&var_count, &sample_count, always_copy_data ));
ok = 1;
__END__;
if( ok )
{
if( out_responses )
*out_responses = _responses, _responses = 0;
if( out_var_idx )
*out_var_idx = _var_idx, _var_idx = 0;
if( out_sample_idx )
*out_sample_idx = _sample_idx, _sample_idx = 0;
if( _sample_count )
*_sample_count = sample_count;
if( _var_count )
*_var_count = var_count;
if( _var_all )
*_var_all = var_all;
}
else
{
if( out_response_map )
cvReleaseMat( out_response_map );
cvFree( out_train_samples );
}
if( _responses != responses )
cvReleaseMat( &_responses );
cvReleaseMat( &_var_idx );
cvReleaseMat( &_sample_idx );
return ok;
}
typedef struct CvSampleResponsePair
{
const float* sample;
const uchar* mask;
int response;
int index;
}
CvSampleResponsePair;
static int
CV_CDECL icvCmpSampleResponsePairs( const void* a, const void* b )
{
int ra = ((const CvSampleResponsePair*)a)->response;
int rb = ((const CvSampleResponsePair*)b)->response;
int ia = ((const CvSampleResponsePair*)a)->index;
int ib = ((const CvSampleResponsePair*)b)->index;
return ra < rb ? -1 : ra > rb ? 1 : ia - ib;
//return (ra > rb ? -1 : 0)|(ra < rb);
}
void
cvSortSamplesByClasses( const float** samples, const CvMat* classes,
int* class_ranges, const uchar** mask )
{
CvSampleResponsePair* pairs = 0;
CV_FUNCNAME( "cvSortSamplesByClasses" );
__BEGIN__;
int i, k = 0, sample_count;
if( !samples || !classes || !class_ranges )
CV_ERROR( CV_StsNullPtr, "INTERNAL ERROR: some of the args are NULL pointers" );
if( classes->rows != 1 || CV_MAT_TYPE(classes->type) != CV_32SC1 )
CV_ERROR( CV_StsBadArg, "classes array must be a single row of integers" );
sample_count = classes->cols;
CV_CALL( pairs = (CvSampleResponsePair*)cvAlloc( (sample_count+1)*sizeof(pairs[0])));
for( i = 0; i < sample_count; i++ )
{
pairs[i].sample = samples[i];
pairs[i].mask = (mask) ? (mask[i]) : 0;
pairs[i].response = classes->data.i[i];
pairs[i].index = i;
assert( classes->data.i[i] >= 0 );
}
qsort( pairs, sample_count, sizeof(pairs[0]), icvCmpSampleResponsePairs );
pairs[sample_count].response = -1;
class_ranges[0] = 0;
for( i = 0; i < sample_count; i++ )
{
samples[i] = pairs[i].sample;
if (mask)
mask[i] = pairs[i].mask;
classes->data.i[i] = pairs[i].response;
if( pairs[i].response != pairs[i+1].response )
class_ranges[++k] = i+1;
}
__END__;
cvFree( &pairs );
}
void
cvPreparePredictData( const CvArr* _sample, int dims_all,
const CvMat* comp_idx, int class_count,
const CvMat* prob, float** _row_sample,
int as_sparse )
{
float* row_sample = 0;
int* inverse_comp_idx = 0;
CV_FUNCNAME( "cvPreparePredictData" );
__BEGIN__;
const CvMat* sample = (const CvMat*)_sample;
float* sample_data;
int sample_step;
int is_sparse = CV_IS_SPARSE_MAT(sample);
int d, sizes[CV_MAX_DIM];
int i, dims_selected;
int vec_size;
if( !is_sparse && !CV_IS_MAT(sample) )
CV_ERROR( !sample ? CV_StsNullPtr : CV_StsBadArg, "The sample is not a valid vector" );
if( cvGetElemType( sample ) != CV_32FC1 )
CV_ERROR( CV_StsUnsupportedFormat, "Input sample must have 32fC1 type" );
CV_CALL( d = cvGetDims( sample, sizes ));
if( !((is_sparse && d == 1) || (!is_sparse && d == 2 && (sample->rows == 1 || sample->cols == 1))) )
CV_ERROR( CV_StsBadSize, "Input sample must be 1-dimensional vector" );
if( d == 1 )
sizes[1] = 1;
if( sizes[0] + sizes[1] - 1 != dims_all )
CV_ERROR( CV_StsUnmatchedSizes,
"The sample size is different from what has been used for training" );
if( !_row_sample )
CV_ERROR( CV_StsNullPtr, "INTERNAL ERROR: The row_sample pointer is NULL" );
if( comp_idx && (!CV_IS_MAT(comp_idx) || comp_idx->rows != 1 ||
CV_MAT_TYPE(comp_idx->type) != CV_32SC1) )
CV_ERROR( CV_StsBadArg, "INTERNAL ERROR: invalid comp_idx" );
dims_selected = comp_idx ? comp_idx->cols : dims_all;
if( prob )
{
if( !CV_IS_MAT(prob) )
CV_ERROR( CV_StsBadArg, "The output matrix of probabilities is invalid" );
if( (prob->rows != 1 && prob->cols != 1) ||
(CV_MAT_TYPE(prob->type) != CV_32FC1 &&
CV_MAT_TYPE(prob->type) != CV_64FC1) )
CV_ERROR( CV_StsBadSize,
"The matrix of probabilities must be 1-dimensional vector of 32fC1 type" );
if( prob->rows + prob->cols - 1 != class_count )
CV_ERROR( CV_StsUnmatchedSizes,
"The vector of probabilities must contain as many elements as "
"the number of classes in the training set" );
}
vec_size = !as_sparse ? dims_selected*sizeof(row_sample[0]) :
(dims_selected + 1)*sizeof(CvSparseVecElem32f);
if( CV_IS_MAT(sample) )
{
sample_data = sample->data.fl;
sample_step = CV_IS_MAT_CONT(sample->type) ? 1 : sample->step/sizeof(row_sample[0]);
if( !comp_idx && CV_IS_MAT_CONT(sample->type) && !as_sparse )
*_row_sample = sample_data;
else
{
CV_CALL( row_sample = (float*)cvAlloc( vec_size ));
if( !comp_idx )
for( i = 0; i < dims_selected; i++ )
row_sample[i] = sample_data[sample_step*i];
else
{
int* comp = comp_idx->data.i;
for( i = 0; i < dims_selected; i++ )
row_sample[i] = sample_data[sample_step*comp[i]];
}
*_row_sample = row_sample;
}
if( as_sparse )
{
const float* src = (const float*)row_sample;
CvSparseVecElem32f* dst = (CvSparseVecElem32f*)row_sample;
dst[dims_selected].idx = -1;
for( i = dims_selected - 1; i >= 0; i-- )
{
dst[i].idx = i;
dst[i].val = src[i];
}
}
}
else
{
CvSparseNode* node;
CvSparseMatIterator mat_iterator;
const CvSparseMat* sparse = (const CvSparseMat*)sample;
assert( is_sparse );
node = cvInitSparseMatIterator( sparse, &mat_iterator );
CV_CALL( row_sample = (float*)cvAlloc( vec_size ));
if( comp_idx )
{
CV_CALL( inverse_comp_idx = (int*)cvAlloc( dims_all*sizeof(int) ));
memset( inverse_comp_idx, -1, dims_all*sizeof(int) );
for( i = 0; i < dims_selected; i++ )
inverse_comp_idx[comp_idx->data.i[i]] = i;
}
if( !as_sparse )
{
memset( row_sample, 0, vec_size );
for( ; node != 0; node = cvGetNextSparseNode(&mat_iterator) )
{
int idx = *CV_NODE_IDX( sparse, node );
if( inverse_comp_idx )
{
idx = inverse_comp_idx[idx];
if( idx < 0 )
continue;
}
row_sample[idx] = *(float*)CV_NODE_VAL( sparse, node );
}
}
else
{
CvSparseVecElem32f* ptr = (CvSparseVecElem32f*)row_sample;
for( ; node != 0; node = cvGetNextSparseNode(&mat_iterator) )
{
int idx = *CV_NODE_IDX( sparse, node );
if( inverse_comp_idx )
{
idx = inverse_comp_idx[idx];
if( idx < 0 )
continue;
}
ptr->idx = idx;
ptr->val = *(float*)CV_NODE_VAL( sparse, node );
ptr++;
}
qsort( row_sample, ptr - (CvSparseVecElem32f*)row_sample,
sizeof(ptr[0]), icvCmpSparseVecElems );
ptr->idx = -1;
}
*_row_sample = row_sample;
}
__END__;
if( inverse_comp_idx )
cvFree( &inverse_comp_idx );
if( cvGetErrStatus() < 0 && _row_sample )
{
cvFree( &row_sample );
*_row_sample = 0;
}
}
static void
icvConvertDataToSparse( const uchar* src, int src_step, int src_type,
uchar* dst, int dst_step, int dst_type,
CvSize size, int* idx )
{
CV_FUNCNAME( "icvConvertDataToSparse" );
__BEGIN__;
int i, j;
src_type = CV_MAT_TYPE(src_type);
dst_type = CV_MAT_TYPE(dst_type);
if( CV_MAT_CN(src_type) != 1 || CV_MAT_CN(dst_type) != 1 )
CV_ERROR( CV_StsUnsupportedFormat, "The function supports only single-channel arrays" );
if( src_step == 0 )
src_step = CV_ELEM_SIZE(src_type);
if( dst_step == 0 )
dst_step = CV_ELEM_SIZE(dst_type);
// if there is no "idx" and if both arrays are continuous,
// do the whole processing (copying or conversion) in a single loop
if( !idx && CV_ELEM_SIZE(src_type)*size.width == src_step &&
CV_ELEM_SIZE(dst_type)*size.width == dst_step )
{
size.width *= size.height;
size.height = 1;
}
if( src_type == dst_type )
{
int full_width = CV_ELEM_SIZE(dst_type)*size.width;
if( full_width == sizeof(int) ) // another common case: copy int's or float's
for( i = 0; i < size.height; i++, src += src_step )
*(int*)(dst + dst_step*(idx ? idx[i] : i)) = *(int*)src;
else
for( i = 0; i < size.height; i++, src += src_step )
memcpy( dst + dst_step*(idx ? idx[i] : i), src, full_width );
}
else if( src_type == CV_32SC1 && (dst_type == CV_32FC1 || dst_type == CV_64FC1) )
for( i = 0; i < size.height; i++, src += src_step )
{
uchar* _dst = dst + dst_step*(idx ? idx[i] : i);
if( dst_type == CV_32FC1 )
for( j = 0; j < size.width; j++ )
((float*)_dst)[j] = (float)((int*)src)[j];
else
for( j = 0; j < size.width; j++ )
((double*)_dst)[j] = ((int*)src)[j];
}
else if( (src_type == CV_32FC1 || src_type == CV_64FC1) && dst_type == CV_32SC1 )
for( i = 0; i < size.height; i++, src += src_step )
{
uchar* _dst = dst + dst_step*(idx ? idx[i] : i);
if( src_type == CV_32FC1 )
for( j = 0; j < size.width; j++ )
((int*)_dst)[j] = cvRound(((float*)src)[j]);
else
for( j = 0; j < size.width; j++ )
((int*)_dst)[j] = cvRound(((double*)src)[j]);
}
else if( (src_type == CV_32FC1 && dst_type == CV_64FC1) ||
(src_type == CV_64FC1 && dst_type == CV_32FC1) )
for( i = 0; i < size.height; i++, src += src_step )
{
uchar* _dst = dst + dst_step*(idx ? idx[i] : i);
if( src_type == CV_32FC1 )
for( j = 0; j < size.width; j++ )
((double*)_dst)[j] = ((float*)src)[j];
else
for( j = 0; j < size.width; j++ )
((float*)_dst)[j] = (float)((double*)src)[j];
}
else
CV_ERROR( CV_StsUnsupportedFormat, "Unsupported combination of input and output vectors" );
__END__;
}
void
cvWritebackLabels( const CvMat* labels, CvMat* dst_labels,
const CvMat* centers, CvMat* dst_centers,
const CvMat* probs, CvMat* dst_probs,
const CvMat* sample_idx, int samples_all,
const CvMat* comp_idx, int dims_all )
{
CV_FUNCNAME( "cvWritebackLabels" );
__BEGIN__;
int samples_selected = samples_all, dims_selected = dims_all;
if( dst_labels && !CV_IS_MAT(dst_labels) )
CV_ERROR( CV_StsBadArg, "Array of output labels is not a valid matrix" );
if( dst_centers )
if( !ICV_IS_MAT_OF_TYPE(dst_centers, CV_32FC1) &&
!ICV_IS_MAT_OF_TYPE(dst_centers, CV_64FC1) )
CV_ERROR( CV_StsBadArg, "Array of cluster centers is not a valid matrix" );
if( dst_probs && !CV_IS_MAT(dst_probs) )
CV_ERROR( CV_StsBadArg, "Probability matrix is not valid" );
if( sample_idx )
{
CV_ASSERT( sample_idx->rows == 1 && CV_MAT_TYPE(sample_idx->type) == CV_32SC1 );
samples_selected = sample_idx->cols;
}
if( comp_idx )
{
CV_ASSERT( comp_idx->rows == 1 && CV_MAT_TYPE(comp_idx->type) == CV_32SC1 );
dims_selected = comp_idx->cols;
}
if( dst_labels && (!labels || labels->data.ptr != dst_labels->data.ptr) )
{
if( !labels )
CV_ERROR( CV_StsNullPtr, "NULL labels" );
CV_ASSERT( labels->rows == 1 );
if( dst_labels->rows != 1 && dst_labels->cols != 1 )
CV_ERROR( CV_StsBadSize, "Array of output labels should be 1d vector" );
if( dst_labels->rows + dst_labels->cols - 1 != samples_all )
CV_ERROR( CV_StsUnmatchedSizes,
"Size of vector of output labels is not equal to the total number of input samples" );
CV_ASSERT( labels->cols == samples_selected );
CV_CALL( icvConvertDataToSparse( labels->data.ptr, labels->step, labels->type,
dst_labels->data.ptr, dst_labels->step, dst_labels->type,
cvSize( 1, samples_selected ), sample_idx ? sample_idx->data.i : 0 ));
}
if( dst_centers && (!centers || centers->data.ptr != dst_centers->data.ptr) )
{
int i;
if( !centers )
CV_ERROR( CV_StsNullPtr, "NULL centers" );
if( centers->rows != dst_centers->rows )
CV_ERROR( CV_StsUnmatchedSizes, "Invalid number of rows in matrix of output centers" );
if( dst_centers->cols != dims_all )
CV_ERROR( CV_StsUnmatchedSizes,
"Number of columns in matrix of output centers is "
"not equal to the total number of components in the input samples" );
CV_ASSERT( centers->cols == dims_selected );
for( i = 0; i < centers->rows; i++ )
CV_CALL( icvConvertDataToSparse( centers->data.ptr + i*centers->step, 0, centers->type,
dst_centers->data.ptr + i*dst_centers->step, 0, dst_centers->type,
cvSize( 1, dims_selected ), comp_idx ? comp_idx->data.i : 0 ));
}
if( dst_probs && (!probs || probs->data.ptr != dst_probs->data.ptr) )
{
if( !probs )
CV_ERROR( CV_StsNullPtr, "NULL probs" );
if( probs->cols != dst_probs->cols )
CV_ERROR( CV_StsUnmatchedSizes, "Invalid number of columns in output probability matrix" );
if( dst_probs->rows != samples_all )
CV_ERROR( CV_StsUnmatchedSizes,
"Number of rows in output probability matrix is "
"not equal to the total number of input samples" );
CV_ASSERT( probs->rows == samples_selected );
CV_CALL( icvConvertDataToSparse( probs->data.ptr, probs->step, probs->type,
dst_probs->data.ptr, dst_probs->step, dst_probs->type,
cvSize( probs->cols, samples_selected ),
sample_idx ? sample_idx->data.i : 0 ));
}
__END__;
}
#if 0
CV_IMPL void
cvStatModelMultiPredict( const CvStatModel* stat_model,
const CvArr* predict_input,
int flags, CvMat* predict_output,
CvMat* probs, const CvMat* sample_idx )
{
CvMemStorage* storage = 0;
CvMat* sample_idx_buffer = 0;
CvSparseMat** sparse_rows = 0;
int samples_selected = 0;
CV_FUNCNAME( "cvStatModelMultiPredict" );
__BEGIN__;
int i;
int predict_output_step = 1, sample_idx_step = 1;
int type;
int d, sizes[CV_MAX_DIM];
int tflag = flags == CV_COL_SAMPLE;
int samples_all, dims_all;
int is_sparse = CV_IS_SPARSE_MAT(predict_input);
CvMat predict_input_part;
CvArr* sample = &predict_input_part;
CvMat probs_part;
CvMat* probs1 = probs ? &probs_part : 0;
if( !CV_IS_STAT_MODEL(stat_model) )
CV_ERROR( !stat_model ? CV_StsNullPtr : CV_StsBadArg, "Invalid statistical model" );
if( !stat_model->predict )
CV_ERROR( CV_StsNotImplemented, "There is no \"predict\" method" );
if( !predict_input || !predict_output )
CV_ERROR( CV_StsNullPtr, "NULL input or output matrices" );
if( !is_sparse && !CV_IS_MAT(predict_input) )
CV_ERROR( CV_StsBadArg, "predict_input should be a matrix or a sparse matrix" );
if( !CV_IS_MAT(predict_output) )
CV_ERROR( CV_StsBadArg, "predict_output should be a matrix" );
type = cvGetElemType( predict_input );
if( type != CV_32FC1 ||
(CV_MAT_TYPE(predict_output->type) != CV_32FC1 &&
CV_MAT_TYPE(predict_output->type) != CV_32SC1 ))
CV_ERROR( CV_StsUnsupportedFormat, "The input or output matrix has unsupported format" );
CV_CALL( d = cvGetDims( predict_input, sizes ));
if( d > 2 )
CV_ERROR( CV_StsBadSize, "The input matrix should be 1- or 2-dimensional" );
if( !tflag )
{
samples_all = samples_selected = sizes[0];
dims_all = sizes[1];
}
else
{
samples_all = samples_selected = sizes[1];
dims_all = sizes[0];
}
if( sample_idx )
{
if( !CV_IS_MAT(sample_idx) )
CV_ERROR( CV_StsBadArg, "Invalid sample_idx matrix" );
if( sample_idx->cols != 1 && sample_idx->rows != 1 )
CV_ERROR( CV_StsBadSize, "sample_idx must be 1-dimensional matrix" );
samples_selected = sample_idx->rows + sample_idx->cols - 1;
if( CV_MAT_TYPE(sample_idx->type) == CV_32SC1 )
{
if( samples_selected > samples_all )
CV_ERROR( CV_StsBadSize, "sample_idx is too large vector" );
}
else if( samples_selected != samples_all )
CV_ERROR( CV_StsUnmatchedSizes, "sample_idx has incorrect size" );
sample_idx_step = sample_idx->step ?
sample_idx->step / CV_ELEM_SIZE(sample_idx->type) : 1;
}
if( predict_output->rows != 1 && predict_output->cols != 1 )
CV_ERROR( CV_StsBadSize, "predict_output should be a 1-dimensional matrix" );
if( predict_output->rows + predict_output->cols - 1 != samples_all )
CV_ERROR( CV_StsUnmatchedSizes, "predict_output and predict_input have uncoordinated sizes" );
predict_output_step = predict_output->step ?
predict_output->step / CV_ELEM_SIZE(predict_output->type) : 1;
if( probs )
{
if( !CV_IS_MAT(probs) )
CV_ERROR( CV_StsBadArg, "Invalid matrix of probabilities" );
if( probs->rows != samples_all )
CV_ERROR( CV_StsUnmatchedSizes,
"matrix of probabilities must have as many rows as the total number of samples" );
if( CV_MAT_TYPE(probs->type) != CV_32FC1 )
CV_ERROR( CV_StsUnsupportedFormat, "matrix of probabilities must have 32fC1 type" );
}
if( is_sparse )
{
CvSparseNode* node;
CvSparseMatIterator mat_iterator;
CvSparseMat* sparse = (CvSparseMat*)predict_input;
if( sample_idx && CV_MAT_TYPE(sample_idx->type) == CV_32SC1 )
{
CV_CALL( sample_idx_buffer = cvCreateMat( 1, samples_all, CV_8UC1 ));
cvZero( sample_idx_buffer );
for( i = 0; i < samples_selected; i++ )
sample_idx_buffer->data.ptr[sample_idx->data.i[i*sample_idx_step]] = 1;
samples_selected = samples_all;
sample_idx = sample_idx_buffer;
sample_idx_step = 1;
}
CV_CALL( sparse_rows = (CvSparseMat**)cvAlloc( samples_selected*sizeof(sparse_rows[0])));
for( i = 0; i < samples_selected; i++ )
{
if( sample_idx && sample_idx->data.ptr[i*sample_idx_step] == 0 )
continue;
CV_CALL( sparse_rows[i] = cvCreateSparseMat( 1, &dims_all, type ));
if( !storage )
storage = sparse_rows[i]->heap->storage;
else
{
// hack: to decrease memory footprint, make all the sparse matrices
// reside in the same storage
int elem_size = sparse_rows[i]->heap->elem_size;
cvReleaseMemStorage( &sparse_rows[i]->heap->storage );
sparse_rows[i]->heap = cvCreateSet( 0, sizeof(CvSet), elem_size, storage );
}
}
// put each row (or column) of predict_input into separate sparse matrix.
node = cvInitSparseMatIterator( sparse, &mat_iterator );
for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator ))
{
int* idx = CV_NODE_IDX( sparse, node );
int idx0 = idx[tflag ^ 1];
int idx1 = idx[tflag];
if( sample_idx && sample_idx->data.ptr[idx0*sample_idx_step] == 0 )
continue;
assert( sparse_rows[idx0] != 0 );
*(float*)cvPtrND( sparse, &idx1, 0, 1, 0 ) = *(float*)CV_NODE_VAL( sparse, node );
}
}
for( i = 0; i < samples_selected; i++ )
{
int idx = i;
float response;
if( sample_idx )
{
if( CV_MAT_TYPE(sample_idx->type) == CV_32SC1 )
{
idx = sample_idx->data.i[i*sample_idx_step];
if( (unsigned)idx >= (unsigned)samples_all )
CV_ERROR( CV_StsOutOfRange, "Some of sample_idx elements are out of range" );
}
else if( CV_MAT_TYPE(sample_idx->type) == CV_8UC1 &&
sample_idx->data.ptr[i*sample_idx_step] == 0 )
continue;
}
if( !is_sparse )
{
if( !tflag )
cvGetRow( predict_input, &predict_input_part, idx );
else
{
cvGetCol( predict_input, &predict_input_part, idx );
}
}
else
sample = sparse_rows[idx];
if( probs )
cvGetRow( probs, probs1, idx );
CV_CALL( response = stat_model->predict( stat_model, (CvMat*)sample, probs1 ));
if( CV_MAT_TYPE(predict_output->type) == CV_32FC1 )
predict_output->data.fl[idx*predict_output_step] = response;
else
{
CV_ASSERT( cvRound(response) == response );
predict_output->data.i[idx*predict_output_step] = cvRound(response);
}
}
__END__;
if( sparse_rows )
{
int i;
for( i = 0; i < samples_selected; i++ )
if( sparse_rows[i] )
{
sparse_rows[i]->heap->storage = 0;
cvReleaseSparseMat( &sparse_rows[i] );
}
cvFree( &sparse_rows );
}
cvReleaseMat( &sample_idx_buffer );
cvReleaseMemStorage( &storage );
}
#endif
// By P. Yarykin - begin -
void cvCombineResponseMaps (CvMat* _responses,
const CvMat* old_response_map,
CvMat* new_response_map,
CvMat** out_response_map)
{
int** old_data = NULL;
int** new_data = NULL;
CV_FUNCNAME ("cvCombineResponseMaps");
__BEGIN__
int i,j;
int old_n, new_n, out_n;
int samples, free_response;
int* first;
int* responses;
int* out_data;
if( out_response_map )
*out_response_map = 0;
// Check input data.
if ((!ICV_IS_MAT_OF_TYPE (_responses, CV_32SC1)) ||
(!ICV_IS_MAT_OF_TYPE (old_response_map, CV_32SC1)) ||
(!ICV_IS_MAT_OF_TYPE (new_response_map, CV_32SC1)))
{
CV_ERROR (CV_StsBadArg, "Some of input arguments is not the CvMat")
}
// Prepare sorted responses.
first = new_response_map->data.i;
new_n = new_response_map->cols;
CV_CALL (new_data = (int**)cvAlloc (new_n * sizeof (new_data[0])));
for (i = 0; i < new_n; i++)
new_data[i] = first + i;
qsort (new_data, new_n, sizeof(int*), icvCmpIntegersPtr);
first = old_response_map->data.i;
old_n = old_response_map->cols;
CV_CALL (old_data = (int**)cvAlloc (old_n * sizeof (old_data[0])));
for (i = 0; i < old_n; i++)
old_data[i] = first + i;
qsort (old_data, old_n, sizeof(int*), icvCmpIntegersPtr);
// Count the number of different responses.
for (i = 0, j = 0, out_n = 0; i < old_n && j < new_n; out_n++)
{
if (*old_data[i] == *new_data[j])
{
i++;
j++;
}
else if (*old_data[i] < *new_data[j])
i++;
else
j++;
}
out_n += old_n - i + new_n - j;
// Create and fill the result response maps.
CV_CALL (*out_response_map = cvCreateMat (1, out_n, CV_32SC1));
out_data = (*out_response_map)->data.i;
memcpy (out_data, first, old_n * sizeof (int));
free_response = old_n;
for (i = 0, j = 0; i < old_n && j < new_n; )
{
if (*old_data[i] == *new_data[j])
{
*new_data[j] = (int)(old_data[i] - first);
i++;
j++;
}
else if (*old_data[i] < *new_data[j])
i++;
else
{
out_data[free_response] = *new_data[j];
*new_data[j] = free_response++;
j++;
}
}
for (; j < new_n; j++)
{
out_data[free_response] = *new_data[j];
*new_data[j] = free_response++;
}
CV_ASSERT (free_response == out_n);
// Change <responses> according to out response map.
samples = _responses->cols + _responses->rows - 1;
responses = _responses->data.i;
first = new_response_map->data.i;
for (i = 0; i < samples; i++)
{
responses[i] = first[responses[i]];
}
__END__
cvFree(&old_data);
cvFree(&new_data);
}
static int icvGetNumberOfCluster( double* prob_vector, int num_of_clusters, float r,
float outlier_thresh, int normalize_probs )
{
int max_prob_loc = 0;
CV_FUNCNAME("icvGetNumberOfCluster");
__BEGIN__;
double prob, maxprob, sum;
int i;
CV_ASSERT(prob_vector);
CV_ASSERT(num_of_clusters >= 0);
maxprob = prob_vector[0];
max_prob_loc = 0;
sum = maxprob;
for( i = 1; i < num_of_clusters; i++ )
{
prob = prob_vector[i];
sum += prob;
if( prob > maxprob )
{
max_prob_loc = i;
maxprob = prob;
}
}
if( normalize_probs && fabs(sum - 1.) > FLT_EPSILON )
{
for( i = 0; i < num_of_clusters; i++ )
prob_vector[i] /= sum;
}
if( fabs(r - 1.) > FLT_EPSILON && fabs(sum - 1.) < outlier_thresh )
max_prob_loc = -1;
__END__;
return max_prob_loc;
} // End of icvGetNumberOfCluster
void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r,
const CvMat* labels )
{
CvMat* counts = 0;
CV_FUNCNAME("icvFindClusterLabels");
__BEGIN__;
int nclusters, nsamples;
int i, j;
double* probs_data;
CV_ASSERT( ICV_IS_MAT_OF_TYPE(probs, CV_64FC1) );
CV_ASSERT( ICV_IS_MAT_OF_TYPE(labels, CV_32SC1) );
nclusters = probs->cols;
nsamples = probs->rows;
CV_ASSERT( nsamples == labels->cols );
CV_CALL( counts = cvCreateMat( 1, nclusters + 1, CV_32SC1 ) );
CV_CALL( cvSetZero( counts ));
for( i = 0; i < nsamples; i++ )
{
labels->data.i[i] = icvGetNumberOfCluster( probs->data.db + i*probs->cols,
nclusters, r, outlier_thresh, 1 );
counts->data.i[labels->data.i[i] + 1]++;
}
CV_ASSERT((int)cvSum(counts).val[0] == nsamples);
// Filling empty clusters with the vector, that has the maximal probability
for( j = 0; j < nclusters; j++ ) // outliers are ignored
{
int maxprob_loc = -1;
double maxprob = 0;
if( counts->data.i[j+1] ) // j-th class is not empty
continue;
// look for the presentative, which is not lonely in it's cluster
// and that has a maximal probability among all these vectors
probs_data = probs->data.db;
for( i = 0; i < nsamples; i++, probs_data++ )
{
int label = labels->data.i[i];
double prob;
if( counts->data.i[label+1] == 0 ||
(counts->data.i[label+1] <= 1 && label != -1) )
continue;
prob = *probs_data;
if( prob >= maxprob )
{
maxprob = prob;
maxprob_loc = i;
}
}
// maxprob_loc == 0 <=> number of vectors less then number of clusters
CV_ASSERT( maxprob_loc >= 0 );
counts->data.i[labels->data.i[maxprob_loc] + 1]--;
labels->data.i[maxprob_loc] = j;
counts->data.i[j + 1]++;
}
__END__;
cvReleaseMat( &counts );
} // End of icvFindClusterLabels
/* End of file */