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Commit 921c68de authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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removed duplicated letter_recog sample

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samples/c/letter_recog.cpp deleted 100644 → 0
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#include "opencv2/core/core_c.h"
#include "opencv2/ml/ml.hpp"
#include <stdio.h>
/*
The sample demonstrates how to train Random Trees classifier
(or Boosting classifier, or MLP - see main()) using the provided dataset.
We use the sample database letter-recognition.data
from UCI Repository, here is the link:
Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).
UCI Repository of machine learning databases
[http://www.ics.uci.edu/~mlearn/MLRepository.html].
Irvine, CA: University of California, Department of Information and Computer Science.
The dataset consists of 20000 feature vectors along with the
responses - capital latin letters A..Z.
The first 16000 (10000 for boosting)) samples are used for training
and the remaining 4000 (10000 for boosting) - to test the classifier.
*/
// This function reads data and responses from the file <filename>
static int
read_num_class_data( const char* filename, int var_count,
CvMat** data, CvMat** responses )
{
const int M = 1024;
FILE* f = fopen( filename, "rt" );
CvMemStorage* storage;
CvSeq* seq;
char buf[M+2];
float* el_ptr;
CvSeqReader reader;
int i, j;
if( !f )
return 0;
el_ptr = new float[var_count+1];
storage = cvCreateMemStorage();
seq = cvCreateSeq( 0, sizeof(*seq), (var_count+1)*sizeof(float), storage );
for(;;)
{
char* ptr;
if( !fgets( buf, M, f ) || !strchr( buf, ',' ) )
break;
el_ptr[0] = buf[0];
ptr = buf+2;
for( i = 1; i <= var_count; i++ )
{
int n = 0;
sscanf( ptr, "%f%n", el_ptr + i, &n );
ptr += n + 1;
}
if( i <= var_count )
break;
cvSeqPush( seq, el_ptr );
}
fclose(f);
*data = cvCreateMat( seq->total, var_count, CV_32F );
*responses = cvCreateMat( seq->total, 1, CV_32F );
cvStartReadSeq( seq, &reader );
for( i = 0; i < seq->total; i++ )
{
const float* sdata = (float*)reader.ptr + 1;
float* ddata = data[0]->data.fl + var_count*i;
float* dr = responses[0]->data.fl + i;
for( j = 0; j < var_count; j++ )
ddata[j] = sdata[j];
*dr = sdata[-1];
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
}
cvReleaseMemStorage( &storage );
delete el_ptr;
return 1;
}
static
int build_rtrees_classifier( char* data_filename,
char* filename_to_save, char* filename_to_load )
{
CvMat* data = 0;
CvMat* responses = 0;
CvMat* var_type = 0;
CvMat* sample_idx = 0;
int ok = read_num_class_data( data_filename, 16, &data, &responses );
int nsamples_all = 0, ntrain_samples = 0;
int i = 0;
double train_hr = 0, test_hr = 0;
CvRTrees forest;
CvMat* var_importance = 0;
if( !ok )
{
printf( "Could not read the database %s\n", data_filename );
return -1;
}
printf( "The database %s is loaded.\n", data_filename );
nsamples_all = data->rows;
ntrain_samples = (int)(nsamples_all*0.8);
// Create or load Random Trees classifier
if( filename_to_load )
{
// load classifier from the specified file
forest.load( filename_to_load );
ntrain_samples = 0;
if( forest.get_tree_count() == 0 )
{
printf( "Could not read the classifier %s\n", filename_to_load );
return -1;
}
printf( "The classifier %s is loaded.\n", data_filename );
}
else
{
// create classifier by using <data> and <responses>
printf( "Training the classifier ...\n");
// 1. create type mask
var_type = cvCreateMat( data->cols + 1, 1, CV_8U );
cvSet( var_type, cvScalarAll(CV_VAR_ORDERED) );
cvSetReal1D( var_type, data->cols, CV_VAR_CATEGORICAL );
// 2. create sample_idx
sample_idx = cvCreateMat( 1, nsamples_all, CV_8UC1 );
{
CvMat mat;
cvGetCols( sample_idx, &mat, 0, ntrain_samples );
cvSet( &mat, cvRealScalar(1) );
cvGetCols( sample_idx, &mat, ntrain_samples, nsamples_all );
cvSetZero( &mat );
}
// 3. train classifier
forest.train( data, CV_ROW_SAMPLE, responses, 0, sample_idx, var_type, 0,
CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
printf( "\n");
}
// compute prediction error on train and test data
for( i = 0; i < nsamples_all; i++ )
{
double r;
CvMat sample;
cvGetRow( data, &sample, i );
r = forest.predict( &sample );
r = fabs((double)r - responses->data.fl[i]) <= FLT_EPSILON ? 1 : 0;
if( i < ntrain_samples )
train_hr += r;
else
test_hr += r;
}
test_hr /= (double)(nsamples_all-ntrain_samples);
train_hr /= (double)ntrain_samples;
printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
train_hr*100., test_hr*100. );
printf( "Number of trees: %d\n", forest.get_tree_count() );
// Print variable importance
var_importance = (CvMat*)forest.get_var_importance();
if( var_importance )
{
double rt_imp_sum = cvSum( var_importance ).val[0];
printf("var#\timportance (in %%):\n");
for( i = 0; i < var_importance->cols; i++ )
printf( "%-2d\t%-4.1f\n", i,
100.f*var_importance->data.fl[i]/rt_imp_sum);
}
//Print some proximitites
printf( "Proximities between some samples corresponding to the letter 'T':\n" );
{
CvMat sample1, sample2;
const int pairs[][2] = {{0,103}, {0,106}, {106,103}, {-1,-1}};
for( i = 0; pairs[i][0] >= 0; i++ )
{
cvGetRow( data, &sample1, pairs[i][0] );
cvGetRow( data, &sample2, pairs[i][1] );
printf( "proximity(%d,%d) = %.1f%%\n", pairs[i][0], pairs[i][1],
forest.get_proximity( &sample1, &sample2 )*100. );
}
}
// Save Random Trees classifier to file if needed
if( filename_to_save )
forest.save( filename_to_save );
cvReleaseMat( &sample_idx );
cvReleaseMat( &var_type );
cvReleaseMat( &data );
cvReleaseMat( &responses );
return 0;
}
static
int build_boost_classifier( char* data_filename,
char* filename_to_save, char* filename_to_load )
{
const int class_count = 26;
CvMat* data = 0;
CvMat* responses = 0;
CvMat* var_type = 0;
CvMat* temp_sample = 0;
CvMat* weak_responses = 0;
int ok = read_num_class_data( data_filename, 16, &data, &responses );
int nsamples_all = 0, ntrain_samples = 0;
int var_count;
int i, j, k;
double train_hr = 0, test_hr = 0;
CvBoost boost;
if( !ok )
{
printf( "Could not read the database %s\n", data_filename );
return -1;
}
printf( "The database %s is loaded.\n", data_filename );
nsamples_all = data->rows;
ntrain_samples = (int)(nsamples_all*0.5);
var_count = data->cols;
// Create or load Boosted Tree classifier
if( filename_to_load )
{
// load classifier from the specified file
boost.load( filename_to_load );
ntrain_samples = 0;
if( !boost.get_weak_predictors() )
{
printf( "Could not read the classifier %s\n", filename_to_load );
return -1;
}
printf( "The classifier %s is loaded.\n", data_filename );
}
else
{
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
//
// As currently boosted tree classifier in MLL can only be trained
// for 2-class problems, we transform the training database by
// "unrolling" each training sample as many times as the number of
// classes (26) that we have.
//
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
CvMat* new_data = cvCreateMat( ntrain_samples*class_count, var_count + 1, CV_32F );
CvMat* new_responses = cvCreateMat( ntrain_samples*class_count, 1, CV_32S );
// 1. unroll the database type mask
printf( "Unrolling the database...\n");
for( i = 0; i < ntrain_samples; i++ )
{
float* data_row = (float*)(data->data.ptr + data->step*i);
for( j = 0; j < class_count; j++ )
{
float* new_data_row = (float*)(new_data->data.ptr +
new_data->step*(i*class_count+j));
for( k = 0; k < var_count; k++ )
new_data_row[k] = data_row[k];
new_data_row[var_count] = (float)j;
new_responses->data.i[i*class_count + j] = responses->data.fl[i] == j+'A';
}
}
// 2. create type mask
var_type = cvCreateMat( var_count + 2, 1, CV_8U );
cvSet( var_type, cvScalarAll(CV_VAR_ORDERED) );
// the last indicator variable, as well
// as the new (binary) response are categorical
cvSetReal1D( var_type, var_count, CV_VAR_CATEGORICAL );
cvSetReal1D( var_type, var_count+1, CV_VAR_CATEGORICAL );
// 3. train classifier
printf( "Training the classifier (may take a few minutes)...\n");
boost.train( new_data, CV_ROW_SAMPLE, new_responses, 0, 0, var_type, 0,
CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 ));
cvReleaseMat( &new_data );
cvReleaseMat( &new_responses );
printf("\n");
}
temp_sample = cvCreateMat( 1, var_count + 1, CV_32F );
weak_responses = cvCreateMat( 1, boost.get_weak_predictors()->total, CV_32F );
// compute prediction error on train and test data
for( i = 0; i < nsamples_all; i++ )
{
int best_class = 0;
double max_sum = -DBL_MAX;
double r;
CvMat sample;
cvGetRow( data, &sample, i );
for( k = 0; k < var_count; k++ )
temp_sample->data.fl[k] = sample.data.fl[k];
for( j = 0; j < class_count; j++ )
{
temp_sample->data.fl[var_count] = (float)j;
boost.predict( temp_sample, 0, weak_responses );
double sum = cvSum( weak_responses ).val[0];
if( max_sum < sum )
{
max_sum = sum;
best_class = j + 'A';
}
}
r = fabs(best_class - responses->data.fl[i]) < FLT_EPSILON ? 1 : 0;
if( i < ntrain_samples )
train_hr += r;
else
test_hr += r;
}
test_hr /= (double)(nsamples_all-ntrain_samples);
train_hr /= (double)ntrain_samples;
printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
train_hr*100., test_hr*100. );
printf( "Number of trees: %d\n", boost.get_weak_predictors()->total );
// Save classifier to file if needed
if( filename_to_save )
boost.save( filename_to_save );
cvReleaseMat( &temp_sample );
cvReleaseMat( &weak_responses );
cvReleaseMat( &var_type );
cvReleaseMat( &data );
cvReleaseMat( &responses );
return 0;
}
static
int build_mlp_classifier( char* data_filename,
char* filename_to_save, char* filename_to_load )
{
const int class_count = 26;
CvMat* data = 0;
CvMat train_data;
CvMat* responses = 0;
CvMat* mlp_response = 0;
int ok = read_num_class_data( data_filename, 16, &data, &responses );
int nsamples_all = 0, ntrain_samples = 0;
int i, j;
double train_hr = 0, test_hr = 0;
CvANN_MLP mlp;
if( !ok )
{
printf( "Could not read the database %s\n", data_filename );
return -1;
}
printf( "The database %s is loaded.\n", data_filename );
nsamples_all = data->rows;
ntrain_samples = (int)(nsamples_all*0.8);
// Create or load MLP classifier
if( filename_to_load )
{
// load classifier from the specified file
mlp.load( filename_to_load );
ntrain_samples = 0;
if( !mlp.get_layer_count() )
{
printf( "Could not read the classifier %s\n", filename_to_load );
return -1;
}
printf( "The classifier %s is loaded.\n", data_filename );
}
else
{
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
//
// MLP does not support categorical variables by explicitly.
// So, instead of the output class label, we will use
// a binary vector of <class_count> components for training and,
// therefore, MLP will give us a vector of "probabilities" at the
// prediction stage
//
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
CvMat* new_responses = cvCreateMat( ntrain_samples, class_count, CV_32F );
// 1. unroll the responses
printf( "Unrolling the responses...\n");
for( i = 0; i < ntrain_samples; i++ )
{
int cls_label = cvRound(responses->data.fl[i]) - 'A';
float* bit_vec = (float*)(new_responses->data.ptr + i*new_responses->step);
for( j = 0; j < class_count; j++ )
bit_vec[j] = 0.f;
bit_vec[cls_label] = 1.f;
}
cvGetRows( data, &train_data, 0, ntrain_samples );
// 2. train classifier
int layer_sz[] = { data->cols, 100, 100, class_count };
CvMat layer_sizes =
cvMat( 1, (int)(sizeof(layer_sz)/sizeof(layer_sz[0])), CV_32S, layer_sz );
mlp.create( &layer_sizes );
printf( "Training the classifier (may take a few minutes)...\n");
mlp.train( &train_data, new_responses, 0, 0,
CvANN_MLP_TrainParams(cvTermCriteria(CV_TERMCRIT_ITER,300,0.01),
#if 1
CvANN_MLP_TrainParams::BACKPROP,0.001));
#else
CvANN_MLP_TrainParams::RPROP,0.05));
#endif
cvReleaseMat( &new_responses );
printf("\n");
}
mlp_response = cvCreateMat( 1, class_count, CV_32F );
// compute prediction error on train and test data
for( i = 0; i < nsamples_all; i++ )
{
int best_class;
CvMat sample;
cvGetRow( data, &sample, i );
CvPoint max_loc = {0,0};
mlp.predict( &sample, mlp_response );
cvMinMaxLoc( mlp_response, 0, 0, 0, &max_loc, 0 );
best_class = max_loc.x + 'A';
int r = fabs((double)best_class - responses->data.fl[i]) < FLT_EPSILON ? 1 : 0;
if( i < ntrain_samples )
train_hr += r;
else
test_hr += r;
}
test_hr /= (double)(nsamples_all-ntrain_samples);
train_hr /= (double)ntrain_samples;
printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
train_hr*100., test_hr*100. );
// Save classifier to file if needed
if( filename_to_save )
mlp.save( filename_to_save );
cvReleaseMat( &mlp_response );
cvReleaseMat( &data );
cvReleaseMat( &responses );
return 0;
}
int main( int argc, char *argv[] )
{
char* filename_to_save = 0;
char* filename_to_load = 0;
char default_data_filename[] = "./letter-recognition.data";
char* data_filename = default_data_filename;
int method = 0;
int i;
for( i = 1; i < argc; i++ )
{
if( strcmp(argv[i],"-data") == 0 ) // flag "-data letter_recognition.xml"
{
i++;
data_filename = argv[i];
}
else if( strcmp(argv[i],"-save") == 0 ) // flag "-save filename.xml"
{
i++;
filename_to_save = argv[i];
}
else if( strcmp(argv[i],"-load") == 0) // flag "-load filename.xml"
{
i++;
filename_to_load = argv[i];
}
else if( strcmp(argv[i],"-boost") == 0)
{
method = 1;
}
else if( strcmp(argv[i],"-mlp") == 0 )
{
method = 2;
}
else
break;
}
if( i < argc ||
(method == 0 ?
build_rtrees_classifier( data_filename, filename_to_save, filename_to_load ) :
method == 1 ?
build_boost_classifier( data_filename, filename_to_save, filename_to_load ) :
method == 2 ?
build_mlp_classifier( data_filename, filename_to_save, filename_to_load ) :
-1) < 0)
{
printf("This is letter recognition sample.\n"
"The usage: letter_recog [-data <path to letter-recognition.data>] \\\n"
" [-save <output XML file for the classifier>] \\\n"
" [-load <XML file with the pre-trained classifier>] \\\n"
" [-boost|-mlp] # to use boost/mlp classifier instead of default Random Trees\n" );
}
return 0;
}
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