@ -12,6 +12,7 @@
using namespace std ;
using namespace cv ;
using namespace cv : : ml ;
const Scalar WHITE_COLOR = Scalar ( 255 , 255 , 255 ) ;
const string winName = " points " ;
@ -22,18 +23,20 @@ RNG rng;
vector < Point > trainedPoints ;
vector < int > trainedPointsMarkers ;
vector < Scalar > classColors ;
# define _NBC_ 0 // normal Bayessian classifier
# define _KNN_ 0 // k nearest neighbors classifier
# define _SVM_ 0 // support vectors machine
const int MAX_CLASSES = 2 ;
vector < Vec3b > classColors ( MAX_CLASSES ) ;
int currentClass = 0 ;
vector < int > classCounters ( MAX_CLASSES ) ;
# define _NBC_ 1 // normal Bayessian classifier
# define _KNN_ 1 // k nearest neighbors classifier
# define _SVM_ 1 // support vectors machine
# define _DT_ 1 // decision tree
# define _BT_ 0 // ADA Boost
# define _BT_ 1 // ADA Boost
# define _GBT_ 0 // gradient boosted trees
# define _RF_ 0 // random forest
# define _ERT_ 0 // extremely randomized trees
# define _ANN_ 0 // artificial neural networks
# define _EM_ 0 // expectation-maximization
# define _RF_ 1 // random forest
# define _ANN_ 1 // artificial neural networks
# define _EM_ 1 // expectation-maximization
static void on_mouse ( int event , int x , int y , int /*flags*/ , void * )
{
@ -44,76 +47,43 @@ static void on_mouse( int event, int x, int y, int /*flags*/, void* )
if ( event = = EVENT_LBUTTONUP )
{
if ( classColors . empty ( ) )
return ;
trainedPoints . push_back ( Point ( x , y ) ) ;
trainedPointsMarkers . push_back ( ( int ) ( classColors . size ( ) - 1 ) ) ;
trainedPointsMarkers . push_back ( currentClass ) ;
classCounters [ currentClass ] + + ;
updateFlag = true ;
}
else if ( event = = EVENT_RBUTTONUP )
{
# if _BT_
if ( classColors . size ( ) < 2 )
{
# endif
classColors . push_back ( Scalar ( ( uchar ) rng ( 256 ) , ( uchar ) rng ( 256 ) , ( uchar ) rng ( 256 ) ) ) ;
updateFlag = true ;
# if _BT_
}
else
cout < < " New class can not be added, because CvBoost can only be used for 2-class classification " < < endl ;
# endif
}
//draw
if ( updateFlag )
{
img = Scalar : : all ( 0 ) ;
// put the text
stringstream text ;
text < < " current class " < < classColors . size ( ) - 1 ;
putText ( img , text . str ( ) , Point ( 10 , 25 ) , FONT_HERSHEY_SIMPLEX , 0.8f , WHITE_COLOR , 2 ) ;
text . str ( " " ) ;
text < < " total classes " < < classColors . size ( ) ;
putText ( img , text . str ( ) , Point ( 10 , 50 ) , FONT_HERSHEY_SIMPLEX , 0.8f , WHITE_COLOR , 2 ) ;
text . str ( " " ) ;
text < < " total points " < < trainedPoints . size ( ) ;
putText ( img , text . str ( ) , Point ( 10 , 75 ) , FONT_HERSHEY_SIMPLEX , 0.8f , WHITE_COLOR , 2 ) ;
// draw points
for ( size_t i = 0 ; i < trainedPoints . size ( ) ; i + + )
circle ( img , trainedPoints [ i ] , 5 , classColors [ trainedPointsMarkers [ i ] ] , - 1 ) ;
{
Vec3b c = classColors [ trainedPointsMarkers [ i ] ] ;
circle ( img , trainedPoints [ i ] , 5 , Scalar ( c ) , - 1 ) ;
}
imshow ( winName , img ) ;
}
}
static void prepare_train_data ( Mat & samples , Mat & classes )
static Mat prepare_train_samples ( const vector < Point > & pts )
{
Mat ( trainedPoints ) . copyTo ( samples ) ;
Mat ( trainedPointsMarkers ) . copyTo ( classes ) ;
// reshape trainData and change its type
samples = samples . reshape ( 1 , samples . rows ) ;
samples . convertTo ( samples , CV_32FC1 ) ;
Mat samples ;
Mat ( pts ) . reshape ( 1 , ( int ) pts . size ( ) ) . convertTo ( samples , CV_32F ) ;
return samples ;
}
# if _NBC_
static void find_decision_boundary_NBC ( )
static Ptr < TrainData > prepare_train_data ( )
{
img . copyTo ( imgDst ) ;
Mat trainSamples , trainClasses ;
prepare_train_data ( trainSamples , trainClasses ) ;
// learn classifier
CvNormalBayesClassifier normalBayesClassifier ( trainSamples , trainClasses ) ;
Mat samples = prepare_train_samples ( trainedPoints ) ;
return TrainData : : create ( samples , ROW_SAMPLE , Mat ( trainedPointsMarkers ) ) ;
}
static void predict_and_paint ( const Ptr < StatModel > & model , Mat & dst )
{
Mat testSample ( 1 , 2 , CV_32FC1 ) ;
for ( int y = 0 ; y < img . rows ; y + = testStep )
{
@ -122,328 +92,146 @@ static void find_decision_boundary_NBC()
testSample . at < float > ( 0 ) = ( float ) x ;
testSample . at < float > ( 1 ) = ( float ) y ;
int response = ( int ) normalBayesClassifier . predict ( testSample ) ;
circle ( imgDst , Point ( x , y ) , 1 , classColors [ response ] ) ;
int response = ( int ) model - > predict ( testSample ) ;
dst . at < Vec3b > ( y , x ) = classColors [ response ] ;
}
}
}
# endif
# if _KN N_
static void find_decision_boundary_KN N ( int K )
# if _NBC_
static void find_decision_boundary_NBC ( )
{
img . copyTo ( imgDst ) ;
Mat trainSamples , trainClasses ;
prepare_train_data ( trainSamples , trainClasses ) ;
// learn classifier
# if defined HAVE_OPENCV_OCL && _OCL_KNN_
cv : : ocl : : KNearestNeighbour knnClassifier ;
Mat temp , result ;
knnClassifier . train ( trainSamples , trainClasses , temp , false , K ) ;
cv : : ocl : : oclMat testSample_ocl , reslut_ocl ;
# else
CvKNearest knnClassifier ( trainSamples , trainClasses , Mat ( ) , false , K ) ;
# endif
Ptr < NormalBayesClassifier > normalBayesClassifier = NormalBayesClassifier : : create ( ) ;
normalBayesClassifier - > train ( prepare_train_data ( ) ) ;
Mat testSample ( 1 , 2 , CV_32FC1 ) ;
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 ;
# if defined HAVE_OPENCV_OCL && _OCL_KNN_
testSample_ocl . upload ( testSample ) ;
predict_and_paint ( normalBayesClassifier , imgDst ) ;
}
# endif
knnClassifier . find_nearest ( testSample_ocl , K , reslut_ocl ) ;
reslut_ocl . download ( result ) ;
int response = saturate_cast < int > ( result . at < float > ( 0 ) ) ;
circle ( imgDst , Point ( x , y ) , 1 , classColors [ response ] ) ;
# else
# if _KNN_
static void find_decision_boundary_KNN ( int K )
{
Ptr < KNearest > knn = KNearest : : create ( true ) ;
knn - > setDefaultK ( K ) ;
knn - > train ( prepare_train_data ( ) ) ;
int response = ( int ) knnClassifier . find_nearest ( testSample , K ) ;
circle ( imgDst , Point ( x , y ) , 1 , classColors [ response ] ) ;
# endif
}
}
predict_and_paint ( knn , imgDst ) ;
}
# endif
# if _SVM_
static void find_decision_boundary_SVM ( Cv SVMParamsparams )
static void find_decision_boundary_SVM ( SVM : : Params params )
{
img . copyTo ( imgDst ) ;
Mat trainSamples , trainClasses ;
prepare_train_data ( trainSamples , trainClasses ) ;
Ptr < SVM > svm = SVM : : create ( params ) ;
svm - > train ( prepare_train_data ( ) ) ;
// learn classifier
# if defined HAVE_OPENCV_OCL && _OCL_SVM_
cv : : ocl : : CvSVM_OCL svmClassifier ( trainSamples , trainClasses , Mat ( ) , Mat ( ) , params ) ;
# else
CvSVM svmClassifier ( trainSamples , trainClasses , Mat ( ) , Mat ( ) , params ) ;
# endif
predict_and_paint ( svm , imgDst ) ;
Mat testSample ( 1 , 2 , CV_32FC1 ) ;
for ( int y = 0 ; y < img . rows ; y + = testStep )
Mat sv = svm - > getSupportVectors ( ) ;
for ( int i = 0 ; i < sv . rows ; i + + )
{
for ( int x = 0 ; x < img . cols ; x + = testStep )
{
testSample . at < float > ( 0 ) = ( float ) x ;
testSample . at < float > ( 1 ) = ( float ) y ;
int response = ( int ) svmClassifier . predict ( testSample ) ;
circle ( imgDst , Point ( x , y ) , 2 , classColors [ response ] , 1 ) ;
}
const float * supportVector = sv . ptr < float > ( i ) ;
circle ( imgDst , Point ( saturate_cast < int > ( supportVector [ 0 ] ) , saturate_cast < int > ( supportVector [ 1 ] ) ) , 5 , Scalar ( 255 , 255 , 255 ) , - 1 ) ;
}
for ( int i = 0 ; i < svmClassifier . get_support_vector_count ( ) ; i + + )
{
const float * supportVector = svmClassifier . get_support_vector ( i ) ;
circle ( imgDst , Point ( saturate_cast < int > ( supportVector [ 0 ] ) , saturate_cast < int > ( supportVector [ 1 ] ) ) , 5 , CV_RGB ( 255 , 255 , 255 ) , - 1 ) ;
}
}
# endif
# if _DT_
static void find_decision_boundary_DT ( )
{
img . copyTo ( imgDst ) ;
Mat trainSamples , trainClasses ;
prepare_train_data ( trainSamples , trainClasses ) ;
// learn classifier
CvDTree dtree ;
Mat var_types ( 1 , trainSamples . cols + 1 , CV_8UC1 , Scalar ( CV_VAR_ORDERED ) ) ;
var_types . at < uchar > ( trainSamples . cols ) = CV_VAR_CATEGORICAL ;
CvDTreeParams params ;
params . max_depth = 8 ;
params . min_sample_count = 2 ;
params . use_surrogates = false ;
params . cv_folds = 0 ; // the number of cross-validation folds
params . use_1se_rule = false ;
params . truncate_pruned_tree = false ;
dtree . train ( trainSamples , CV_ROW_SAMPLE , trainClasses ,
Mat ( ) , Mat ( ) , var_types , Mat ( ) , params ) ;
Mat testSample ( 1 , 2 , CV_32FC1 ) ;
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 ;
int response = ( int ) dtree . predict ( testSample ) - > value ;
circle ( imgDst , Point ( x , y ) , 2 , classColors [ response ] , 1 ) ;
}
}
DTrees : : Params params ;
params . maxDepth = 8 ;
params . minSampleCount = 2 ;
params . useSurrogates = false ;
params . CVFolds = 0 ; // the number of cross-validation folds
params . use1SERule = false ;
params . truncatePrunedTree = false ;
Ptr < DTrees > dtree = DTrees : : create ( params ) ;
dtree - > train ( prepare_train_data ( ) ) ;
predict_and_paint ( dtree , imgDst ) ;
}
# endif
# if _BT_
void find_decision_boundary_BT ( )
static void find_decision_boundary_BT ( )
{
img . copyTo ( imgDst ) ;
Mat trainSamples , trainClasses ;
prepare_train_data ( trainSamples , trainClasses ) ;
// learn classifier
CvBoost boost ;
Mat var_types ( 1 , trainSamples . cols + 1 , CV_8UC1 , Scalar ( CV_VAR_ORDERED ) ) ;
var_types . at < uchar > ( trainSamples . cols ) = CV_VAR_CATEGORICAL ;
CvBoostParams params ( CvBoost : : DISCRETE , // boost_type
100 , // weak_count
0.95 , // weight_trim_rate
2 , // max_depth
false , //use_surrogates
0 // priors
) ;
boost . train ( trainSamples , CV_ROW_SAMPLE , trainClasses , Mat ( ) , Mat ( ) , var_types , Mat ( ) , params ) ;
Mat testSample ( 1 , 2 , CV_32FC1 ) ;
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 ;
int response = ( int ) boost . predict ( testSample ) ;
circle ( imgDst , Point ( x , y ) , 2 , classColors [ response ] , 1 ) ;
}
}
Boost : : Params params ( Boost : : DISCRETE , // boost_type
100 , // weak_count
0.95 , // weight_trim_rate
2 , // max_depth
false , //use_surrogates
Mat ( ) // priors
) ;
Ptr < Boost > boost = Boost : : create ( params ) ;
boost - > train ( prepare_train_data ( ) ) ;
predict_and_paint ( boost , imgDst ) ;
}
# endif
# if _GBT_
void find_decision_boundary_GBT ( )
static void find_decision_boundary_GBT ( )
{
img . copyTo ( imgDst ) ;
Mat trainSamples , trainClasses ;
prepare_train_data ( trainSamples , trainClasses ) ;
// learn classifier
CvGBTrees gbtrees ;
Mat var_types ( 1 , trainSamples . cols + 1 , CV_8UC1 , Scalar ( CV_VAR_ORDERED ) ) ;
var_types . at < uchar > ( trainSamples . cols ) = CV_VAR_CATEGORICAL ;
CvGBTreesParams params ( CvGBTrees : : DEVIANCE_LOSS , // loss_function_type
100 , // weak_count
0.1f , // shrinkage
1.0f , // subsample_portion
2 , // max_depth
false // use_surrogates )
) ;
gbtrees . train ( trainSamples , CV_ROW_SAMPLE , trainClasses , Mat ( ) , Mat ( ) , var_types , Mat ( ) , params ) ;
Mat testSample ( 1 , 2 , CV_32FC1 ) ;
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 ;
GBTrees : : Params params ( GBTrees : : DEVIANCE_LOSS , // loss_function_type
100 , // weak_count
0.1f , // shrinkage
1.0f , // subsample_portion
2 , // max_depth
false // use_surrogates )
) ;
int response = ( int ) gbtrees . predict ( testSample ) ;
circle ( imgDst , Point ( x , y ) , 2 , classColors [ response ] , 1 ) ;
}
}
Ptr < GBTrees > gbtrees = GBTrees : : create ( params ) ;
gbtrees - > train ( prepare_train_data ( ) ) ;
predict_and_paint ( gbtrees , imgDst ) ;
}
# endif
# if _RF_
void find_decision_boundary_RF ( )
static void find_decision_boundary_RF ( )
{
img . copyTo ( imgDst ) ;
Mat trainSamples , trainClasses ;
prepare_train_data ( trainSamples , trainClasses ) ;
// learn classifier
CvRTrees rtrees ;
CvRTParams params ( 4 , // max_depth,
RTrees : : Params params ( 4 , // max_depth,
2 , // min_sample_count,
0.f , // regression_accuracy,
false , // use_surrogates,
16 , // max_categories,
0 , // priors,
Mat ( ) , // priors,
false , // calc_var_importance,
1 , // nactive_vars,
5 , // max_num_of_trees_in_the_forest,
0 , // forest_accuracy,
CV_TERMCRIT_ITER // termcrit_type
TermCriteria ( TermCriteria : : MAX_ITER , 5 , 0 ) // max_num_of_trees_in_the_forest,
) ;
rtrees . train ( trainSamples , CV_ROW_SAMPLE , trainClasses , Mat ( ) , Mat ( ) , Mat ( ) , Mat ( ) , params ) ;
Mat testSample ( 1 , 2 , CV_32FC1 ) ;
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 ;
int response = ( int ) rtrees . predict ( testSample ) ;
circle ( imgDst , Point ( x , y ) , 2 , classColors [ response ] , 1 ) ;
}
}
Ptr < RTrees > rtrees = RTrees : : create ( params ) ;
rtrees - > train ( prepare_train_data ( ) ) ;
predict_and_paint ( rtrees , imgDst ) ;
}
# endif
# if _ERT_
void find_decision_boundary_ERT ( )
{
img . copyTo ( imgDst ) ;
Mat trainSamples , trainClasses ;
prepare_train_data ( trainSamples , trainClasses ) ;
// learn classifier
CvERTrees ertrees ;
Mat var_types ( 1 , trainSamples . cols + 1 , CV_8UC1 , Scalar ( CV_VAR_ORDERED ) ) ;
var_types . at < uchar > ( trainSamples . cols ) = CV_VAR_CATEGORICAL ;
CvRTParams params ( 4 , // max_depth,
2 , // min_sample_count,
0.f , // regression_accuracy,
false , // use_surrogates,
16 , // max_categories,
0 , // priors,
false , // calc_var_importance,
1 , // nactive_vars,
5 , // max_num_of_trees_in_the_forest,
0 , // forest_accuracy,
CV_TERMCRIT_ITER // termcrit_type
) ;
ertrees . train ( trainSamples , CV_ROW_SAMPLE , trainClasses , Mat ( ) , Mat ( ) , var_types , Mat ( ) , params ) ;
Mat testSample ( 1 , 2 , CV_32FC1 ) ;
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 ;
int response = ( int ) ertrees . predict ( testSample ) ;
circle ( imgDst , Point ( x , y ) , 2 , classColors [ response ] , 1 ) ;
}
}
}
# endif
# if _ANN_
void find_decision_boundary_ANN ( const Mat & layer_sizes )
static void find_decision_boundary_ANN ( const Mat & layer_sizes )
{
img . copyTo ( imgDst ) ;
ANN_MLP : : Params params ( TermCriteria ( TermCriteria : : MAX_ITER + TermCriteria : : EPS , 300 , FLT_EPSILON ) ,
ANN_MLP : : Params : : BACKPROP , 0.001 ) ;
Ptr < ANN_MLP > ann = ANN_MLP : : create ( layer_sizes , params , ANN_MLP : : SIGMOID_SYM , 1 , 1 ) ;
Mat trainSamples , trainClasses ;
prepare_train_data ( trainSamples , trainClasses ) ;
// prerare trainClasses
trainClasses . create ( trainedPoints . size ( ) , classColors . size ( ) , CV_32FC1 ) ;
for ( int i = 0 ; i < trainClasses . rows ; i + + )
Mat trainClasses = Mat : : zeros ( trainedPoints . size ( ) , classColors . size ( ) , CV_32FC1 ) ;
for ( int i = 0 ; i < trainClasses . rows ; i + + )
{
for ( int k = 0 ; k < trainClasses . cols ; k + + )
{
if ( k = = trainedPointsMarkers [ i ] )
trainClasses . at < float > ( i , k ) = 1 ;
else
trainClasses . at < float > ( i , k ) = 0 ;
}
trainClasses . at < float > ( i , trainedPointsMarkers [ i ] ) = 1.f ;
}
Mat weights ( 1 , trainedPoints . size ( ) , CV_32FC1 , Scalar : : all ( 1 ) ) ;
Mat samples = prepare_train_samples ( trainedPoints ) ;
Ptr < TrainData > tdata = TrainData : : create ( samples , ROW_SAMPLE , trainClasses ) ;
// learn classifier
CvANN_MLP ann ( layer_sizes , CvANN_MLP : : SIGMOID_SYM , 1 , 1 ) ;
ann . train ( trainSamples , trainClasses , weights ) ;
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 )
@ -451,49 +239,50 @@ void find_decision_boundary_ANN( const Mat& layer_sizes )
testSample . at < float > ( 0 ) = ( float ) x ;
testSample . at < float > ( 1 ) = ( float ) y ;
Mat outputs ( 1 , classColors . size ( ) , CV_32FC1 , testSample . data ) ;
ann . predict ( testSample , outputs ) ;
ann - > predict ( testSample , outputs ) ;
Point maxLoc ;
minMaxLoc ( outputs , 0 , 0 , 0 , & maxLoc ) ;
circle ( imgDst , Point ( x , y ) , 2 , classColors [ maxLoc . x ] , 1 ) ;
imgDst . at < Vec3b > ( y , x ) = classColors [ maxLoc . x ] ;
}
}
}
# endif
# if _EM_
void find_decision_boundary_EM ( )
static void find_decision_boundary_EM ( )
{
img . copyTo ( imgDst ) ;
Mat trainSamples , trainClasses ;
prepare_train_data ( trainSamples , trainClasses ) ;
vector < cv : : EM > em_models ( classColors . size ( ) ) ;
Mat samples = prepare_train_samples ( trainedPoints ) ;
CV_Assert ( ( int ) trainClasses . total ( ) = = trainSamples . rows ) ;
CV_Assert ( ( int ) trainClasses . type ( ) = = CV_32SC1 ) ;
int i , j , nmodels = ( int ) classColors . size ( ) ;
vector < Ptr < EM > > em_models ( nmodels ) ;
Mat modelSamples ;
for ( size_t modelIndex = 0 ; modelIndex < em_models . size ( ) ; modelIndex + + )
for ( i = 0 ; i < nmodels ; i + + )
{
const int componentCount = 3 ;
em_models [ modelIndex ] = EM ( componentCount , cv : : EM : : COV_MAT_DIAGONAL ) ;
Mat modelSamples ;
for ( int sampleIndex = 0 ; sampleIndex < trainS amples. rows ; sampleIndex + + )
modelSamples . release ( ) ;
for ( j = 0 ; j < s amples . rows ; j + + )
{
if ( trainClasses . at < int > ( sampleIndex ) = = ( int ) modelIndex )
modelSamples . push_back ( trainS amples. row ( sampleIndex ) ) ;
if ( trainedPointsMarkers [ j ] = = i )
modelSamples . push_back ( s amples. row ( j ) ) ;
}
// learn models
if ( ! modelSamples . empty ( ) )
em_models [ modelIndex ] . train ( modelSamples ) ;
if ( ! modelSamples . empty ( ) )
{
em_models [ i ] = EM : : train ( modelSamples , noArray ( ) , noArray ( ) , noArray ( ) ,
EM : : Params ( componentCount , EM : : COV_MAT_DIAGONAL ) ) ;
}
}
// classify coordinate plane points using the bayes classifier, i.e.
// y(x) = arg max_i=1_modelsCount likelihoods_i(x)
Mat testSample ( 1 , 2 , CV_32FC1 ) ;
Mat logLikelihoods ( 1 , nmodels , CV_64FC1 , Scalar ( - DBL_MAX ) ) ;
for ( int y = 0 ; y < img . rows ; y + = testStep )
{
for ( int x = 0 ; x < img . cols ; x + = testStep )
@ -501,17 +290,14 @@ void find_decision_boundary_EM()
testSample . at < float > ( 0 ) = ( float ) x ;
testSample . at < float > ( 1 ) = ( float ) y ;
Mat logLikelihoods ( 1 , em_models . size ( ) , CV_64FC1 , Scalar ( - DBL_MAX ) ) ;
for ( size_t modelIndex = 0 ; modelIndex < em_models . size ( ) ; modelIndex + + )
for ( i = 0 ; i < nmodels ; i + + )
{
if ( em_models [ modelIndex ] . isTrained ( ) )
logLikelihoods . at < double > ( modelIndex ) = em_models [ modelIndex ] . predict ( testSample ) [ 0 ] ;
if ( ! em_models [ i ] . empty ( ) )
logLikelihoods . at < double > ( i ) = em_models [ i ] - > predict2 ( testSample , noArray ( ) ) [ 0 ] ;
}
Point maxLoc ;
minMaxLoc ( logLikelihoods , 0 , 0 , 0 , & maxLoc ) ;
int response = maxLoc . x ;
circle ( imgDst , Point ( x , y ) , 2 , classColors [ response ] , 1 ) ;
imgDst . at < Vec3b > ( y , x ) = classColors [ maxLoc . x ] ;
}
}
}
@ -520,7 +306,7 @@ void find_decision_boundary_EM()
int main ( )
{
cout < < " Use: " < < endl
< < " right mouse button - to add new class; " < < endl
< < " key '0' .. '1' - switch to class #n " < < endl
< < " left mouse button - to add new point; " < < endl
< < " key 'r' - to run the ML model; " < < endl
< < " key 'i' - to init (clear) the data. " < < endl < < endl ;
@ -532,6 +318,9 @@ int main()
imshow ( " points " , img ) ;
setMouseCallback ( " points " , on_mouse ) ;
classColors [ 0 ] = Vec3b ( 0 , 255 , 0 ) ;
classColors [ 1 ] = Vec3b ( 0 , 0 , 255 ) ;
for ( ; ; )
{
uchar key = ( uchar ) waitKey ( ) ;
@ -542,15 +331,28 @@ int main()
{
img = Scalar : : all ( 0 ) ;
classColors . clear ( ) ;
trainedPoints . clear ( ) ;
trainedPointsMarkers . clear ( ) ;
classCounters . assign ( MAX_CLASSES , 0 ) ;
imshow ( winName , img ) ;
}
if ( key = = ' 0 ' | | key = = ' 1 ' )
{
currentClass = key - ' 0 ' ;
}
if ( key = = ' r ' ) // run
{
double minVal = 0 ;
minMaxLoc ( classCounters , & minVal , 0 , 0 , 0 ) ;
if ( minVal = = 0 )
{
printf ( " each class should have at least 1 point \n " ) ;
continue ;
}
img . copyTo ( imgDst ) ;
# if _NBC_
find_decision_boundary_NBC ( ) ;
namedWindow ( " NormalBayesClassifier " , WINDOW_AUTOSIZE ) ;
@ -570,16 +372,16 @@ int main()
# if _SVM_
//(1)-(2)separable and not sets
Cv SVMParamsparams ;
params . svm_t ype = Cv SVM: : C_SVC ;
params . kernel_t ype = Cv SVM: : POLY ; //CvSVM::LINEAR;
SVM : : Params params ;
params . svmT ype = SVM : : C_SVC ;
params . kernelT ype = SVM : : POLY ; //CvSVM::LINEAR;
params . degree = 0.5 ;
params . gamma = 1 ;
params . coef0 = 1 ;
params . C = 1 ;
params . nu = 0.5 ;
params . p = 0 ;
params . term_c rit = cv TermCriteria( CV_TERMCRIT_ITER , 1000 , 0.01 ) ;
params . termC rit = TermCriteria ( TermCriteria : : MAX_ITER + TermCriteria : : EPS , 1000 , 0.01 ) ;
find_decision_boundary_SVM ( params ) ;
namedWindow ( " classificationSVM1 " , WINDOW_AUTOSIZE ) ;
@ -615,12 +417,6 @@ int main()
imshow ( " RF " , imgDst ) ;
# endif
# if _ERT_
find_decision_boundary_ERT ( ) ;
namedWindow ( " ERT " , WINDOW_AUTOSIZE ) ;
imshow ( " ERT " , imgDst ) ;
# endif
# if _ANN_
Mat layer_sizes1 ( 1 , 3 , CV_32SC1 ) ;
layer_sizes1 . at < int > ( 0 ) = 2 ;
Vadim Pisarevsky
11 years ago