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opencv/apps/traincascade/old_ml_data.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"
#include <ctype.h>
#define MISS_VAL FLT_MAX
#define CV_VAR_MISS 0
CvTrainTestSplit::CvTrainTestSplit()
{
train_sample_part_mode = CV_COUNT;
train_sample_part.count = -1;
mix = false;
}
CvTrainTestSplit::CvTrainTestSplit( int _train_sample_count, bool _mix )
{
train_sample_part_mode = CV_COUNT;
train_sample_part.count = _train_sample_count;
mix = _mix;
}
CvTrainTestSplit::CvTrainTestSplit( float _train_sample_portion, bool _mix )
{
train_sample_part_mode = CV_PORTION;
train_sample_part.portion = _train_sample_portion;
mix = _mix;
}
////////////////
CvMLData::CvMLData()
{
values = missing = var_types = var_idx_mask = response_out = var_idx_out = var_types_out = 0;
train_sample_idx = test_sample_idx = 0;
header_lines_number = 0;
sample_idx = 0;
response_idx = -1;
train_sample_count = -1;
delimiter = ',';
miss_ch = '?';
//flt_separator = '.';
rng = &cv::theRNG();
}
CvMLData::~CvMLData()
{
clear();
}
void CvMLData::free_train_test_idx()
{
cvReleaseMat( &train_sample_idx );
cvReleaseMat( &test_sample_idx );
sample_idx = 0;
}
void CvMLData::clear()
{
class_map.clear();
cvReleaseMat( &values );
cvReleaseMat( &missing );
cvReleaseMat( &var_types );
cvReleaseMat( &var_idx_mask );
cvReleaseMat( &response_out );
cvReleaseMat( &var_idx_out );
cvReleaseMat( &var_types_out );
free_train_test_idx();
total_class_count = 0;
response_idx = -1;
train_sample_count = -1;
}
void CvMLData::set_header_lines_number( int idx )
{
header_lines_number = std::max(0, idx);
}
int CvMLData::get_header_lines_number() const
{
return header_lines_number;
}
static char *fgets_chomp(char *str, int n, FILE *stream)
{
char *head = fgets(str, n, stream);
if( head )
{
for(char *tail = head + strlen(head) - 1; tail >= head; --tail)
{
if( *tail != '\r' && *tail != '\n' )
break;
*tail = '\0';
}
}
return head;
}
int CvMLData::read_csv(const char* filename)
{
const int M = 1000000;
const char str_delimiter[3] = { ' ', delimiter, '\0' };
FILE* file = 0;
CvMemStorage* storage;
CvSeq* seq;
char *ptr;
float* el_ptr;
CvSeqReader reader;
int cols_count = 0;
uchar *var_types_ptr = 0;
clear();
file = fopen( filename, "rt" );
if( !file )
return -1;
std::vector<char> _buf(M);
char* buf = &_buf[0];
// skip header lines
for( int i = 0; i < header_lines_number; i++ )
{
if( fgets( buf, M, file ) == 0 )
{
fclose(file);
return -1;
}
}
// read the first data line and determine the number of variables
if( !fgets_chomp( buf, M, file ))
{
fclose(file);
return -1;
}
ptr = buf;
while( *ptr == ' ' )
ptr++;
for( ; *ptr != '\0'; )
{
if(*ptr == delimiter || *ptr == ' ')
{
cols_count++;
ptr++;
while( *ptr == ' ' ) ptr++;
}
else
ptr++;
}
cols_count++;
if ( cols_count == 0)
{
fclose(file);
return -1;
}
// create temporary memory storage to store the whole database
el_ptr = new float[cols_count];
storage = cvCreateMemStorage();
seq = cvCreateSeq( 0, sizeof(*seq), cols_count*sizeof(float), storage );
var_types = cvCreateMat( 1, cols_count, CV_8U );
cvZero( var_types );
var_types_ptr = var_types->data.ptr;
for(;;)
{
char *token = NULL;
int type;
token = strtok(buf, str_delimiter);
if (!token)
break;
for (int i = 0; i < cols_count-1; i++)
{
str_to_flt_elem( token, el_ptr[i], type);
var_types_ptr[i] |= type;
token = strtok(NULL, str_delimiter);
if (!token)
{
fclose(file);
delete [] el_ptr;
return -1;
}
}
str_to_flt_elem( token, el_ptr[cols_count-1], type);
var_types_ptr[cols_count-1] |= type;
cvSeqPush( seq, el_ptr );
if( !fgets_chomp( buf, M, file ) )
break;
}
fclose(file);
values = cvCreateMat( seq->total, cols_count, CV_32FC1 );
missing = cvCreateMat( seq->total, cols_count, CV_8U );
var_idx_mask = cvCreateMat( 1, values->cols, CV_8UC1 );
cvSet( var_idx_mask, cvRealScalar(1) );
train_sample_count = seq->total;
cvStartReadSeq( seq, &reader );
for(int i = 0; i < seq->total; i++ )
{
const float* sdata = (float*)reader.ptr;
float* ddata = values->data.fl + cols_count*i;
uchar* dm = missing->data.ptr + cols_count*i;
for( int j = 0; j < cols_count; j++ )
{
ddata[j] = sdata[j];
dm[j] = ( fabs( MISS_VAL - sdata[j] ) <= FLT_EPSILON );
}
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
}
if ( cvNorm( missing, 0, CV_L1 ) <= FLT_EPSILON )
cvReleaseMat( &missing );
cvReleaseMemStorage( &storage );
delete []el_ptr;
return 0;
}
const CvMat* CvMLData::get_values() const
{
return values;
}
const CvMat* CvMLData::get_missing() const
{
CV_FUNCNAME( "CvMLData::get_missing" );
__BEGIN__;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
__END__;
return missing;
}
const std::map<cv::String, int>& CvMLData::get_class_labels_map() const
{
return class_map;
}
void CvMLData::str_to_flt_elem( const char* token, float& flt_elem, int& type)
{
char* stopstring = NULL;
flt_elem = (float)strtod( token, &stopstring );
assert( stopstring );
type = CV_VAR_ORDERED;
if ( *stopstring == miss_ch && strlen(stopstring) == 1 ) // missed value
{
flt_elem = MISS_VAL;
type = CV_VAR_MISS;
}
else
{
if ( (*stopstring != 0) && (*stopstring != '\n') && (strcmp(stopstring, "\r\n") != 0) ) // class label
{
int idx = class_map[token];
if ( idx == 0)
{
total_class_count++;
idx = total_class_count;
class_map[token] = idx;
}
flt_elem = (float)idx;
type = CV_VAR_CATEGORICAL;
}
}
}
void CvMLData::set_delimiter(char ch)
{
CV_FUNCNAME( "CvMLData::set_delimited" );
__BEGIN__;
if (ch == miss_ch /*|| ch == flt_separator*/)
CV_ERROR(cv::Error::StsBadArg, "delimited, miss_character and flt_separator must be different");
delimiter = ch;
__END__;
}
char CvMLData::get_delimiter() const
{
return delimiter;
}
void CvMLData::set_miss_ch(char ch)
{
CV_FUNCNAME( "CvMLData::set_miss_ch" );
__BEGIN__;
if (ch == delimiter/* || ch == flt_separator*/)
CV_ERROR(cv::Error::StsBadArg, "delimited, miss_character and flt_separator must be different");
miss_ch = ch;
__END__;
}
char CvMLData::get_miss_ch() const
{
return miss_ch;
}
void CvMLData::set_response_idx( int idx )
{
CV_FUNCNAME( "CvMLData::set_response_idx" );
__BEGIN__;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
if ( idx >= values->cols)
CV_ERROR( cv::Error::StsBadArg, "idx value is not correct" );
if ( response_idx >= 0 )
chahge_var_idx( response_idx, true );
if ( idx >= 0 )
chahge_var_idx( idx, false );
response_idx = idx;
__END__;
}
int CvMLData::get_response_idx() const
{
CV_FUNCNAME( "CvMLData::get_response_idx" );
__BEGIN__;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
__END__;
return response_idx;
}
void CvMLData::change_var_type( int var_idx, int type )
{
CV_FUNCNAME( "CvMLData::change_var_type" );
__BEGIN__;
int var_count = 0;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
var_count = values->cols;
if ( var_idx < 0 || var_idx >= var_count)
CV_ERROR( cv::Error::StsBadArg, "var_idx is not correct" );
if ( type != CV_VAR_ORDERED && type != CV_VAR_CATEGORICAL)
CV_ERROR( cv::Error::StsBadArg, "type is not correct" );
assert( var_types );
if ( var_types->data.ptr[var_idx] == CV_VAR_CATEGORICAL && type == CV_VAR_ORDERED)
CV_ERROR( cv::Error::StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
var_types->data.ptr[var_idx] = (uchar)type;
__END__;
return;
}
void CvMLData::set_var_types( const char* str )
{
CV_FUNCNAME( "CvMLData::set_var_types" );
__BEGIN__;
const char* ord = 0, *cat = 0;
int var_count = 0, set_var_type_count = 0;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
var_count = values->cols;
assert( var_types );
ord = strstr( str, "ord" );
cat = strstr( str, "cat" );
if ( !ord && !cat )
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
if ( !ord && strlen(cat) == 3 ) // str == "cat"
{
cvSet( var_types, cvScalarAll(CV_VAR_CATEGORICAL) );
return;
}
if ( !cat && strlen(ord) == 3 ) // str == "ord"
{
cvSet( var_types, cvScalarAll(CV_VAR_ORDERED) );
return;
}
if ( ord ) // parse ord str
{
char* stopstring = NULL;
if ( ord[3] != '[')
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
ord += 4; // pass "ord["
do
{
int b1 = (int)strtod( ord, &stopstring );
if ( *stopstring == 0 || (*stopstring != ',' && *stopstring != ']' && *stopstring != '-') )
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
ord = stopstring + 1;
if ( (stopstring[0] == ',') || (stopstring[0] == ']'))
{
if ( var_types->data.ptr[b1] == CV_VAR_CATEGORICAL)
CV_ERROR( cv::Error::StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
var_types->data.ptr[b1] = CV_VAR_ORDERED;
set_var_type_count++;
}
else
{
if ( stopstring[0] == '-')
{
int b2 = (int)strtod( ord, &stopstring);
if ( (*stopstring == 0) || (*stopstring != ',' && *stopstring != ']') )
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
ord = stopstring + 1;
for (int i = b1; i <= b2; i++)
{
if ( var_types->data.ptr[i] == CV_VAR_CATEGORICAL)
CV_ERROR( cv::Error::StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
var_types->data.ptr[i] = CV_VAR_ORDERED;
}
set_var_type_count += b2 - b1 + 1;
}
else
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
}
}
while (*stopstring != ']');
if ( stopstring[1] != '\0' && stopstring[1] != ',')
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
}
if ( cat ) // parse cat str
{
char* stopstring = NULL;
if ( cat[3] != '[')
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
cat += 4; // pass "cat["
do
{
int b1 = (int)strtod( cat, &stopstring );
if ( *stopstring == 0 || (*stopstring != ',' && *stopstring != ']' && *stopstring != '-') )
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
cat = stopstring + 1;
if ( (stopstring[0] == ',') || (stopstring[0] == ']'))
{
var_types->data.ptr[b1] = CV_VAR_CATEGORICAL;
set_var_type_count++;
}
else
{
if ( stopstring[0] == '-')
{
int b2 = (int)strtod( cat, &stopstring);
if ( (*stopstring == 0) || (*stopstring != ',' && *stopstring != ']') )
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
cat = stopstring + 1;
for (int i = b1; i <= b2; i++)
var_types->data.ptr[i] = CV_VAR_CATEGORICAL;
set_var_type_count += b2 - b1 + 1;
}
else
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
}
}
while (*stopstring != ']');
if ( stopstring[1] != '\0' && stopstring[1] != ',')
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
}
if (set_var_type_count != var_count)
CV_ERROR( cv::Error::StsBadArg, "types string is not correct" );
__END__;
}
const CvMat* CvMLData::get_var_types()
{
CV_FUNCNAME( "CvMLData::get_var_types" );
__BEGIN__;
uchar *var_types_out_ptr = 0;
int avcount, vt_size;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
assert( var_idx_mask );
avcount = cvFloor( cvNorm( var_idx_mask, 0, CV_L1 ) );
vt_size = avcount + (response_idx >= 0);
if ( avcount == values->cols || (avcount == values->cols-1 && response_idx == values->cols-1) )
return var_types;
if ( !var_types_out || ( var_types_out && var_types_out->cols != vt_size ) )
{
cvReleaseMat( &var_types_out );
var_types_out = cvCreateMat( 1, vt_size, CV_8UC1 );
}
var_types_out_ptr = var_types_out->data.ptr;
for( int i = 0; i < var_types->cols; i++)
{
if (i == response_idx || !var_idx_mask->data.ptr[i]) continue;
*var_types_out_ptr = var_types->data.ptr[i];
var_types_out_ptr++;
}
if ( response_idx >= 0 )
*var_types_out_ptr = var_types->data.ptr[response_idx];
__END__;
return var_types_out;
}
int CvMLData::get_var_type( int var_idx ) const
{
return var_types->data.ptr[var_idx];
}
const CvMat* CvMLData::get_responses()
{
CV_FUNCNAME( "CvMLData::get_responses_ptr" );
__BEGIN__;
int var_count = 0;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
var_count = values->cols;
if ( response_idx < 0 || response_idx >= var_count )
return 0;
if ( !response_out )
response_out = cvCreateMatHeader( values->rows, 1, CV_32FC1 );
else
cvInitMatHeader( response_out, values->rows, 1, CV_32FC1);
cvGetCol( values, response_out, response_idx );
__END__;
return response_out;
}
void CvMLData::set_train_test_split( const CvTrainTestSplit * spl)
{
CV_FUNCNAME( "CvMLData::set_division" );
__BEGIN__;
int sample_count = 0;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
sample_count = values->rows;
float train_sample_portion;
if (spl->train_sample_part_mode == CV_COUNT)
{
train_sample_count = spl->train_sample_part.count;
if (train_sample_count > sample_count)
CV_ERROR( cv::Error::StsBadArg, "train samples count is not correct" );
train_sample_count = train_sample_count<=0 ? sample_count : train_sample_count;
}
else // dtype.train_sample_part_mode == CV_PORTION
{
train_sample_portion = spl->train_sample_part.portion;
if ( train_sample_portion > 1)
CV_ERROR( cv::Error::StsBadArg, "train samples count is not correct" );
train_sample_portion = train_sample_portion <= FLT_EPSILON ||
1 - train_sample_portion <= FLT_EPSILON ? 1 : train_sample_portion;
train_sample_count = std::max(1, cvFloor( train_sample_portion * sample_count ));
}
if ( train_sample_count == sample_count )
{
free_train_test_idx();
return;
}
if ( train_sample_idx && train_sample_idx->cols != train_sample_count )
free_train_test_idx();
if ( !sample_idx)
{
int test_sample_count = sample_count- train_sample_count;
sample_idx = (int*)cvAlloc( sample_count * sizeof(sample_idx[0]) );
for (int i = 0; i < sample_count; i++ )
sample_idx[i] = i;
train_sample_idx = cvCreateMatHeader( 1, train_sample_count, CV_32SC1 );
*train_sample_idx = cvMat( 1, train_sample_count, CV_32SC1, &sample_idx[0] );
CV_Assert(test_sample_count > 0);
test_sample_idx = cvCreateMatHeader( 1, test_sample_count, CV_32SC1 );
*test_sample_idx = cvMat( 1, test_sample_count, CV_32SC1, &sample_idx[train_sample_count] );
}
mix = spl->mix;
if ( mix )
mix_train_and_test_idx();
__END__;
}
const CvMat* CvMLData::get_train_sample_idx() const
{
CV_FUNCNAME( "CvMLData::get_train_sample_idx" );
__BEGIN__;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
__END__;
return train_sample_idx;
}
const CvMat* CvMLData::get_test_sample_idx() const
{
CV_FUNCNAME( "CvMLData::get_test_sample_idx" );
__BEGIN__;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
__END__;
return test_sample_idx;
}
void CvMLData::mix_train_and_test_idx()
{
CV_FUNCNAME( "CvMLData::mix_train_and_test_idx" );
__BEGIN__;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
__END__;
if ( !sample_idx)
return;
if ( train_sample_count > 0 && train_sample_count < values->rows )
{
int n = values->rows;
for (int i = 0; i < n; i++)
{
int a = (*rng)(n);
int b = (*rng)(n);
int t;
CV_SWAP( sample_idx[a], sample_idx[b], t );
}
}
}
const CvMat* CvMLData::get_var_idx()
{
CV_FUNCNAME( "CvMLData::get_var_idx" );
__BEGIN__;
int avcount = 0;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
assert( var_idx_mask );
avcount = cvFloor( cvNorm( var_idx_mask, 0, CV_L1 ) );
int* vidx;
if ( avcount == values->cols )
return 0;
if ( !var_idx_out || ( var_idx_out && var_idx_out->cols != avcount ) )
{
cvReleaseMat( &var_idx_out );
var_idx_out = cvCreateMat( 1, avcount, CV_32SC1);
if ( response_idx >=0 )
var_idx_mask->data.ptr[response_idx] = 0;
}
vidx = var_idx_out->data.i;
for(int i = 0; i < var_idx_mask->cols; i++)
if ( var_idx_mask->data.ptr[i] )
{
*vidx = i;
vidx++;
}
__END__;
return var_idx_out;
}
void CvMLData::chahge_var_idx( int vi, bool state )
{
change_var_idx( vi, state );
}
void CvMLData::change_var_idx( int vi, bool state )
{
CV_FUNCNAME( "CvMLData::change_var_idx" );
__BEGIN__;
int var_count = 0;
if ( !values )
CV_ERROR( cv::Error::StsInternal, "data is empty" );
var_count = values->cols;
if ( vi < 0 || vi >= var_count)
CV_ERROR( cv::Error::StsBadArg, "variable index is not correct" );
assert( var_idx_mask );
var_idx_mask->data.ptr[vi] = state;
__END__;
}
/* End of file. */