diff --git a/modules/ml/src/ann_mlp.cpp b/modules/ml/src/ann_mlp.cpp
index 750b3e67db..8b7ec3aad2 100644
--- a/modules/ml/src/ann_mlp.cpp
+++ b/modules/ml/src/ann_mlp.cpp
@@ -228,9 +228,8 @@ public:
int n = inputs.rows, dn0 = n;
CV_Assert( (type == CV_32F || type == CV_64F) && inputs.cols == layer_sizes[0] );
- _outputs.create(n, layer_sizes[l_count-1], type);
-
- Mat outputs = _outputs.getMat();
+ int noutputs = layer_sizes[l_count-1];
+ Mat outputs;
int min_buf_sz = 2*max_lsize;
int buf_sz = n*min_buf_sz;
@@ -242,9 +241,20 @@ public:
buf_sz = dn0*min_buf_sz;
}
- cv::AutoBuffer<double> _buf(buf_sz);
+ cv::AutoBuffer<double> _buf(buf_sz+noutputs);
double* buf = _buf;
+ if( !_outputs.needed() )
+ {
+ CV_Assert( n == 1 );
+ outputs = Mat(n, noutputs, type, buf + buf_sz);
+ }
+ else
+ {
+ _outputs.create(n, noutputs, type);
+ outputs = _outputs.getMat();
+ }
+
int dn = 0;
for( int i = 0; i < n; i += dn )
{
@@ -273,6 +283,13 @@ public:
scale_output( layer_in, layer_out );
}
+ if( n == 1 )
+ {
+ int maxIdx[] = {0, 0};
+ minMaxIdx(outputs, 0, 0, 0, maxIdx);
+ return maxIdx[0] + maxIdx[1];
+ }
+
return 0.f;
}
diff --git a/samples/cpp/letter_recog.cpp b/samples/cpp/letter_recog.cpp
index ddbe676291..a07aae8fe9 100644
--- a/samples/cpp/letter_recog.cpp
+++ b/samples/cpp/letter_recog.cpp
@@ -1,11 +1,13 @@
-#include "opencv2/core/core_c.h"
+#include "opencv2/core/core.hpp"
#include "opencv2/ml/ml.hpp"
#include <cstdio>
#include <vector>
-/*
+#include <iostream>
-*/
+using namespace std;
+using namespace cv;
+using namespace cv::ml;
static void help()
{
@@ -33,142 +35,101 @@ static void help()
}
// 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 )
+static bool
+read_num_class_data( const string& filename, int var_count,
+ Mat* _data, Mat* _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;
+ Mat el_ptr(1, var_count, CV_32F);
+ int i;
+ vector<int> responses;
- el_ptr = new float[var_count+1];
- storage = cvCreateMemStorage();
- seq = cvCreateSeq( 0, sizeof(*seq), (var_count+1)*sizeof(float), storage );
+ _data->release();
+ _responses->release();
+
+ FILE* f = fopen( filename.c_str(), "rt" );
+ if( !f )
+ {
+ cout << "Could not read the database " << filename << endl;
+ return false;
+ }
for(;;)
{
char* ptr;
if( !fgets( buf, M, f ) || !strchr( buf, ',' ) )
break;
- el_ptr[0] = buf[0];
+ responses.push_back((int)buf[0]);
ptr = buf+2;
- for( i = 1; i <= var_count; i++ )
+ for( i = 0; i < var_count; i++ )
{
int n = 0;
- sscanf( ptr, "%f%n", el_ptr + i, &n );
+ sscanf( ptr, "%f%n", &el_ptr.at<float>(i), &n );
ptr += n + 1;
}
- if( i <= var_count )
+ if( i < var_count )
break;
- cvSeqPush( seq, el_ptr );
+ _data->push_back(el_ptr);
}
fclose(f);
+ Mat(responses).copyTo(*_responses);
- *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 );
- }
+ cout << "The database " << filename << " is loaded.\n";
- cvReleaseMemStorage( &storage );
- delete[] el_ptr;
- return 1;
+ return true;
}
-static
-int build_rtrees_classifier( char* data_filename,
- char* filename_to_save, char* filename_to_load )
+template<typename T>
+static Ptr<T> load_classifier(const string& 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;
- }
+ // load classifier from the specified file
+ Ptr<T> model = StatModel::load<T>( filename_to_load );
+ if( model.empty() )
+ cout << "Could not read the classifier " << filename_to_load << endl;
+ else
+ cout << "The classifier " << filename_to_load << " is loaded.\n";
- printf( "The database %s is loaded.\n", data_filename );
- nsamples_all = data->rows;
- ntrain_samples = (int)(nsamples_all*0.8);
+ return model;
+}
- // 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", filename_to_load );
- }
- else
- {
- // create classifier by using <data> and <responses>
- printf( "Training the classifier ...\n");
+static Ptr<TrainData>
+prepare_train_data(const Mat& data, const Mat& responses, int ntrain_samples)
+{
+ Mat sample_idx = Mat::zeros( 1, data.rows, CV_8U );
+ Mat train_samples = sample_idx.colRange(0, ntrain_samples);
+ train_samples.setTo(Scalar::all(1));
- // 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 );
+ int nvars = data.cols;
+ Mat var_type( nvars + 1, 1, CV_8U );
+ var_type.setTo(Scalar::all(VAR_ORDERED));
+ var_type.at<uchar>(nvars) = 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) );
+ return TrainData::create(data, ROW_SAMPLE, responses,
+ noArray(), sample_idx, noArray(), var_type);
+}
- cvGetCols( sample_idx, &mat, ntrain_samples, nsamples_all );
- cvSetZero( &mat );
- }
+inline TermCriteria TC(int iters, double eps)
+{
+ return TermCriteria(TermCriteria::MAX_ITER + (eps > 0 ? TermCriteria::EPS : 0), iters, eps);
+}
- // 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");
- }
+static void test_and_save_classifier(const Ptr<StatModel>& model,
+ const Mat& data, const Mat& responses,
+ int ntrain_samples, int rdelta,
+ const string& filename_to_save)
+{
+ int i, nsamples_all = data.rows;
+ double train_hr = 0, test_hr = 0;
// compute prediction error on train and test data
for( i = 0; i < nsamples_all; i++ )
{
- double r;
- CvMat sample;
- cvGetRow( data, &sample, i );
+ Mat sample = data.row(i);
- r = forest.predict( &sample );
- r = fabs((double)r - responses->data.fl[i]) <= FLT_EPSILON ? 1 : 0;
+ float r = model->predict( sample );
+ r = std::abs(r + rdelta - responses.at<int>(i)) <= FLT_EPSILON ? 1 : 0;
if( i < ntrain_samples )
train_hr += r;
@@ -176,93 +137,101 @@ int build_rtrees_classifier( char* data_filename,
test_hr += r;
}
- test_hr /= (double)(nsamples_all-ntrain_samples);
- train_hr /= (double)ntrain_samples;
+ test_hr /= nsamples_all - ntrain_samples;
+ train_hr = ntrain_samples > 0 ? train_hr/ntrain_samples : 1.;
+
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 )
+ if( !filename_to_save.empty() )
{
- 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);
+ model->save( filename_to_save );
}
+}
+
- //Print some proximitites
- printf( "Proximities between some samples corresponding to the letter 'T':\n" );
+static bool
+build_rtrees_classifier( const string& data_filename,
+ const string& filename_to_save,
+ const string& filename_to_load )
+{
+ Mat data;
+ Mat responses;
+ bool ok = read_num_class_data( data_filename, 16, &data, &responses );
+ if( !ok )
+ return ok;
+
+ Ptr<RTrees> model;
+
+ int nsamples_all = data.rows;
+ int ntrain_samples = (int)(nsamples_all*0.8);
+
+ // Create or load Random Trees classifier
+ if( !filename_to_load.empty() )
{
- CvMat sample1, sample2;
- const int pairs[][2] = {{0,103}, {0,106}, {106,103}, {-1,-1}};
+ model = load_classifier<RTrees>(filename_to_load);
+ if( model.empty() )
+ return false;
+ ntrain_samples = 0;
+ }
+ else
+ {
+ // create classifier by using <data> and <responses>
+ cout << "Training the classifier ...\n";
+ Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
- 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. );
- }
+ // 3. train classifier
+ model = RTrees::create(RTrees::Params(10,10,0,false,15,Mat(),true,4,TC(100,0.01f)));
+ model->train( tdata );
+ cout << endl;
}
- // Save Random Trees classifier to file if needed
- if( filename_to_save )
- forest.save( filename_to_save );
+ test_and_save_classifier(model, data, responses, ntrain_samples, 0, filename_to_save);
+ cout << "Number of trees: " << model->getRoots().size() << endl;
- cvReleaseMat( &sample_idx );
- cvReleaseMat( &var_type );
- cvReleaseMat( &data );
- cvReleaseMat( &responses );
+ // Print variable importance
+ Mat var_importance = model->getVarImportance();
+ if( !var_importance.empty() )
+ {
+ double rt_imp_sum = sum( var_importance )[0];
+ printf("var#\timportance (in %%):\n");
+ int i, n = (int)var_importance.total();
+ for( i = 0; i < n; i++ )
+ printf( "%-2d\t%-4.1f\n", i, 100.f*var_importance.at<float>(i)/rt_imp_sum);
+ }
- return 0;
+ return true;
}
-static
-int build_boost_classifier( char* data_filename,
- char* filename_to_save, char* filename_to_load )
+static bool
+build_boost_classifier( const string& data_filename,
+ const string& filename_to_save,
+ const string& 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;
+ Mat data;
+ Mat responses;
+ Mat weak_responses;
+ bool ok = read_num_class_data( data_filename, 16, &data, &responses );
if( !ok )
- {
- printf( "Could not read the database %s\n", data_filename );
- return -1;
- }
+ return ok;
- printf( "The database %s is loaded.\n", data_filename );
- nsamples_all = data->rows;
- ntrain_samples = (int)(nsamples_all*0.5);
- var_count = data->cols;
+ int i, j, k;
+ Ptr<Boost> model;
+
+ int nsamples_all = data.rows;
+ int ntrain_samples = (int)(nsamples_all*0.5);
+ int var_count = data.cols;
// Create or load Boosted Tree classifier
- if( filename_to_load )
+ if( !filename_to_load.empty() )
{
- // load classifier from the specified file
- boost.load( filename_to_load );
+ model = load_classifier<Boost>(filename_to_load);
+ if( model.empty() )
+ return false;
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", filename_to_load );
}
else
{
@@ -275,135 +244,108 @@ int build_boost_classifier( char* data_filename,
//
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
- CvMat* new_data = cvCreateMat( ntrain_samples*class_count, var_count + 1, CV_32F );
- CvMat* new_responses = cvCreateMat( ntrain_samples*class_count, 1, CV_32S );
+ Mat new_data( ntrain_samples*class_count, var_count + 1, CV_32F );
+ Mat new_responses( 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);
+ const float* data_row = data.ptr<float>(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];
+ float* new_data_row = (float*)new_data.ptr<float>(i*class_count+j);
+ memcpy(new_data_row, data_row, var_count*sizeof(data_row[0]));
new_data_row[var_count] = (float)j;
- new_responses->data.i[i*class_count + j] = responses->data.fl[i] == j+'A';
+ new_responses.at<int>(i*class_count + j) = responses.at<int>(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 );
+ Mat var_type( 1, var_count + 2, CV_8U );
+ var_type.setTo(Scalar::all(VAR_ORDERED));
+ var_type.at<uchar>(var_count) = var_type.at<uchar>(var_count+1) = 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");
+ Ptr<TrainData> tdata = TrainData::create(new_data, ROW_SAMPLE, new_responses,
+ noArray(), noArray(), noArray(), var_type);
+ model = Boost::create(Boost::Params(Boost::REAL, 100, 0.95, 5, false, Mat() ));
+
+ cout << "Training the classifier (may take a few minutes)...\n";
+ model->train(tdata);
+ cout << endl;
}
- temp_sample = cvCreateMat( 1, var_count + 1, CV_32F );
- weak_responses = cvCreateMat( 1, boost.get_weak_predictors()->total, CV_32F );
+ Mat temp_sample( 1, var_count + 1, CV_32F );
+ float* tptr = temp_sample.ptr<float>();
// compute prediction error on train and test data
+ double train_hr = 0, test_hr = 0;
for( i = 0; i < nsamples_all; i++ )
{
int best_class = 0;
double max_sum = -DBL_MAX;
- double r;
- CvMat sample;
- cvGetRow( data, &sample, i );
+ const float* ptr = data.ptr<float>(i);
for( k = 0; k < var_count; k++ )
- temp_sample->data.fl[k] = sample.data.fl[k];
+ tptr[k] = ptr[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 )
+ tptr[var_count] = (float)j;
+ float s = model->predict( temp_sample, noArray(), StatModel::RAW_OUTPUT );
+ if( max_sum < s )
{
- max_sum = sum;
+ max_sum = s;
best_class = j + 'A';
}
}
- r = fabs(best_class - responses->data.fl[i]) < FLT_EPSILON ? 1 : 0;
-
+ double r = std::abs(best_class - responses.at<int>(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;
+ test_hr /= nsamples_all-ntrain_samples;
+ train_hr = ntrain_samples > 0 ? train_hr/ntrain_samples : 1.;
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 );
+ cout << "Number of trees: " << model->getRoots().size() << endl;
// Save classifier to file if needed
- if( filename_to_save )
- boost.save( filename_to_save );
+ if( !filename_to_save.empty() )
+ model->save( filename_to_save );
- cvReleaseMat( &temp_sample );
- cvReleaseMat( &weak_responses );
- cvReleaseMat( &var_type );
- cvReleaseMat( &data );
- cvReleaseMat( &responses );
-
- return 0;
+ return true;
}
-static
-int build_mlp_classifier( char* data_filename,
- char* filename_to_save, char* filename_to_load )
+static bool
+build_mlp_classifier( const string& data_filename,
+ const string& filename_to_save,
+ const string& 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;
+ Mat data;
+ Mat responses;
+ bool ok = read_num_class_data( data_filename, 16, &data, &responses );
if( !ok )
- {
- printf( "Could not read the database %s\n", data_filename );
- return -1;
- }
+ return ok;
- printf( "The database %s is loaded.\n", data_filename );
- nsamples_all = data->rows;
- ntrain_samples = (int)(nsamples_all*0.8);
+ int i, j;
+ Ptr<ANN_MLP> model;
+
+ int nsamples_all = data.rows;
+ int ntrain_samples = (int)(nsamples_all*0.8);
// Create or load MLP classifier
- if( filename_to_load )
+ if( !filename_to_load.empty() )
{
- // load classifier from the specified file
- mlp.load( filename_to_load );
+ model = load_classifier<ANN_MLP>(filename_to_load);
+ if( model.empty() )
+ return false;
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", filename_to_load );
}
else
{
@@ -417,45 +359,44 @@ int build_mlp_classifier( char* data_filename,
//
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
- CvMat* new_responses = cvCreateMat( ntrain_samples, class_count, CV_32F );
+ Mat train_data = data.rowRange(0, ntrain_samples);
+ Mat new_responses = Mat::zeros( ntrain_samples, class_count, CV_32F );
// 1. unroll the responses
- printf( "Unrolling the responses...\n");
+ cout << "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;
+ int cls_label = responses.at<int>(i) - 'A'
+ new_responses.at<float>(i, 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");
+ int layer_sz[] = { data.cols, 100, 100, class_count };
+ int nlayers = (int)(sizeof(layer_sz)/sizeof(layer_sz[0]));
+ Mat layer_sizes( 1, nlayers, CV_32S, layer_sz );
#if 1
- int method = CvANN_MLP_TrainParams::BACKPROP;
+ int method = ANN_MLP::Params::BACKPROP;
double method_param = 0.001;
int max_iter = 300;
#else
- int method = CvANN_MLP_TrainParams::RPROP;
+ int method = ANN_MLP::Params::RPROP;
double method_param = 0.1;
int max_iter = 1000;
#endif
mlp.train( &train_data, new_responses, 0, 0,
- CvANN_MLP_TrainParams(cvTermCriteria(CV_TERMCRIT_ITER,max_iter,0.01),
- method, method_param));
+ ANN_MLP::Params(TC(max_iter,0), method, method_param));
+
+
+ model = ANN_MLP::create() mlp.create( &layer_sizes );
+ printf( "Training the classifier (may take a few minutes)...\n");
+
cvReleaseMat( &new_responses );
printf("\n");
}
- mlp_response = cvCreateMat( 1, class_count, CV_32F );
+ Mat mlp_response;
// compute prediction error on train and test data
for( i = 0; i < nsamples_all; i++ )
@@ -481,38 +422,26 @@ int build_mlp_classifier( char* data_filename,
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 );
+ if( !filename_to_save.empty() )
+ model->save( filename_to_save );
- cvReleaseMat( &mlp_response );
- cvReleaseMat( &data );
- cvReleaseMat( &responses );
-
- return 0;
+ return true;
}
-static
-int build_knearest_classifier( char* data_filename, int K )
+static bool
+build_knearest_classifier( const string& data_filename, int K )
{
const int var_count = 16;
- CvMat* data = 0;
+ Mat data;
CvMat train_data;
- CvMat* responses;
-
- 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;
+ Mat responses;
+ bool ok = read_num_class_data( data_filename, 16, &data, &responses );
if( !ok )
- {
- printf( "Could not read the database %s\n", data_filename );
- return -1;
- }
+ return ok;
+
+ int nsamples_all = 0, ntrain_samples = 0;
- printf( "The database %s is loaded.\n", data_filename );
nsamples_all = data->rows;
ntrain_samples = (int)(nsamples_all*0.8);
@@ -521,12 +450,13 @@ int build_knearest_classifier( char* data_filename, int K )
cvGetRows( data, &train_data, 0, ntrain_samples );
// 2. train classifier
- CvMat* train_resp = cvCreateMat( ntrain_samples, 1, CV_32FC1);
+ Mat train_resp = cvCreateMat( ntrain_samples, 1, CV_32FC1);
for (int i = 0; i < ntrain_samples; i++)
train_resp->data.fl[i] = responses->data.fl[i];
- CvKNearest knearest(&train_data, train_resp);
+ Ptr<KNearest> model = KNearest::create(true);
+ model->train(train_data, train_resp);
- CvMat* nearests = cvCreateMat( (nsamples_all - ntrain_samples), K, CV_32FC1);
+ Mat nearests = cvCreateMat( (nsamples_all - ntrain_samples), K, CV_32FC1);
float* _sample = new float[var_count * (nsamples_all - ntrain_samples)];
CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample );
float* true_results = new float[nsamples_all - ntrain_samples];
@@ -569,27 +499,20 @@ int build_knearest_classifier( char* data_filename, int K )
return 0;
}
-static
-int build_nbayes_classifier( char* data_filename )
+static bool
+build_nbayes_classifier( const string& data_filename )
{
const int var_count = 16;
- CvMat* data = 0;
+ Mat data;
CvMat train_data;
- CvMat* responses;
-
- 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;
+ Mat responses;
+ bool ok = read_num_class_data( data_filename, 16, &data, &responses );
if( !ok )
- {
- printf( "Could not read the database %s\n", data_filename );
- return -1;
- }
+ return ok;
+
+ int nsamples_all = 0, ntrain_samples = 0;
- printf( "The database %s is loaded.\n", data_filename );
nsamples_all = data->rows;
ntrain_samples = (int)(nsamples_all*0.5);
@@ -598,7 +521,7 @@ int build_nbayes_classifier( char* data_filename )
cvGetRows( data, &train_data, 0, ntrain_samples );
// 2. train classifier
- CvMat* train_resp = cvCreateMat( ntrain_samples, 1, CV_32FC1);
+ Mat train_resp = cvCreateMat( ntrain_samples, 1, CV_32FC1);
for (int i = 0; i < ntrain_samples; i++)
train_resp->data.fl[i] = responses->data.fl[i];
CvNormalBayesClassifier nbayes(&train_data, train_resp);
@@ -638,23 +561,23 @@ int build_nbayes_classifier( char* data_filename )
return 0;
}
-static
-int build_svm_classifier( char* data_filename, const char* filename_to_save, const char* filename_to_load )
+static bool
+build_svm_classifier( const string& data_filename,
+ const string& filename_to_save,
+ const string& filename_to_load )
{
- CvMat* data = 0;
- CvMat* responses = 0;
- CvMat* train_resp = 0;
+ Mat data;
+ Mat responses;
+ Mat train_resp;
CvMat train_data;
int nsamples_all = 0, ntrain_samples = 0;
int var_count;
- CvSVM svm;
+ Ptr<SVM> model;
- int ok = read_num_class_data( data_filename, 16, &data, &responses );
+ bool ok = read_num_class_data( data_filename, 16, &data, &responses );
if( !ok )
- {
- printf( "Could not read the database %s\n", data_filename );
- return -1;
- }
+ return ok;
+
////////// SVM parameters ///////////////////////////////
CvSVMParams param;
param.kernel_type=CvSVM::LINEAR;
@@ -722,15 +645,10 @@ int build_svm_classifier( char* data_filename, const char* filename_to_save, con
printf("true_resp = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100);
- if( filename_to_save )
- svm.save( filename_to_save );
+ if( !filename_to_save.empty() )
+ model->save( filename_to_save );
- cvReleaseMat( &train_resp );
- cvReleaseMat( &result );
- cvReleaseMat( &data );
- cvReleaseMat( &responses );
-
- return 0;
+ return true;
}
int main( int argc, char *argv[] )
@@ -768,17 +686,17 @@ int main( int argc, char *argv[] )
method = 2;
}
else if ( strcmp(argv[i], "-knearest") == 0)
- {
- method = 3;
- }
- else if ( strcmp(argv[i], "-nbayes") == 0)
- {
- method = 4;
- }
- else if ( strcmp(argv[i], "-svm") == 0)
- {
- method = 5;
- }
+ {
+ method = 3;
+ }
+ else if ( strcmp(argv[i], "-nbayes") == 0)
+ {
+ method = 4;
+ }
+ else if ( strcmp(argv[i], "-svm") == 0)
+ {
+ method = 5;
+ }
else
break;
}
diff --git a/samples/cpp/points_classifier.cpp b/samples/cpp/points_classifier.cpp
index 0a742f34e9..ef0091526d 100644
--- a/samples/cpp/points_classifier.cpp
+++ b/samples/cpp/points_classifier.cpp
@@ -229,22 +229,7 @@ static void find_decision_boundary_ANN( const Mat& layer_sizes )
Ptr<TrainData> tdata = TrainData::create(samples, ROW_SAMPLE, trainClasses);
ann->train(tdata);
-
- Mat testSample( 1, 2, CV_32FC1 );
- Mat outputs;
- for( int y = 0; y < img.rows; y += testStep )
- {
- for( int x = 0; x < img.cols; x += testStep )
- {
- testSample.at<float>(0) = (float)x;
- testSample.at<float>(1) = (float)y;
-
- ann->predict( testSample, outputs );
- Point maxLoc;
- minMaxLoc( outputs, 0, 0, 0, &maxLoc );
- imgDst.at<Vec3b>(y, x) = classColors[maxLoc.x];
- }
- }
+ predict_and_paint(ann, imgDst);
}
#endif