OpenCV码源笔记——（letter_recog.cpp）随机Forest部分

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 );//response的类型;
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 打印自变量重要性;
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 打印相似度;
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( "forest.xml" );

cvReleaseMat( &sample_idx );
cvReleaseMat( &var_type );
cvReleaseMat( &data );
cvReleaseMat( &responses );

return 0;
}