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added new ML models to points_classifier sample

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This commit is contained in:
Maria Dimashova 2011-04-30 18:04:33 +00:00
parent 5c9e6b7059
commit 92852ca06e
2 changed files with 233 additions and 39 deletions
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6
samples/c/tree_engine.cpp
View File

@ -124,9 +124,9 @@ int main(int argc, char** argv)
ertrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data, CV_TEST_ERROR ), ertrees.get_var_importance() );
printf("======GBTREES=====\n");
gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.05f, 0.6f, 10, true));
print_result( gbtrees.calc_error( &data, CV_TRAIN_ERROR), gbtrees.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance
printf("======GBTREES=====\n");
gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.05f, 0.6f, 10, true));
print_result( gbtrees.calc_error( &data, CV_TRAIN_ERROR), gbtrees.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance
}
else
printf("File can not be read");

266
samples/cpp/points_classifier.cpp
View File

@ -12,19 +12,23 @@ const string winName = "points";
const int testStep = 5;
Mat img, img_dst;
Mat img, imgDst;
RNG rng;
vector<Point> trainedPoints;
vector<int> trainedPointsMarkers;
vector<Scalar> classColors;
#define KNN 0
#define SVM 0
#define DT 1
#define RF 0
#define ANN 0
#define GMM 0
#define NBC 0 // normal Bayessian classifier
#define KNN 0 // k nearest neighbors classifier
#define SVM 0 // support vectors machine
#define DT 1 // decision tree
#define BT 0 // ADA Boost
#define GBT 1 // 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
void on_mouse( int event, int x, int y, int /*flags*/, void* )
{
@ -44,8 +48,18 @@ void on_mouse( int event, int x, int y, int /*flags*/, void* )
}
else if( event == CV_EVENT_RBUTTONUP )
{
classColors.push_back( Scalar((uchar)rng(256), (uchar)rng(256), (uchar)rng(256)) );
updateFlag = true;
#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
@ -84,10 +98,37 @@ void prepare_train_data( Mat& samples, Mat& classes )
samples.convertTo( samples, CV_32FC1 );
}
#if NBC
void find_decision_boundary_NBC()
{
img.copyTo( imgDst );
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
// learn classifier
CvNormalBayesClassifier normalBayesClassifier( trainSamples, trainClasses );
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)normalBayesClassifier.predict( testSample );
circle( imgDst, Point(x,y), 1, classColors[response] );
}
}
}
#endif
#if KNN
void find_decision_boundary_KNN( int K )
{
img.copyTo( img_dst );
img.copyTo( imgDst );
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
@ -104,7 +145,7 @@ void find_decision_boundary_KNN( int K )
testSample.at<float>(1) = (float)y;
int response = (int)knnClassifier.find_nearest( testSample, K );
circle( img_dst, Point(x,y), 1, classColors[response] );
circle( imgDst, Point(x,y), 1, classColors[response] );
}
}
}
@ -113,7 +154,7 @@ void find_decision_boundary_KNN( int K )
#if SVM
void find_decision_boundary_SVM( CvSVMParams params )
{
img.copyTo( img_dst );
img.copyTo( imgDst );
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
@ -130,7 +171,7 @@ void find_decision_boundary_SVM( CvSVMParams params )
testSample.at<float>(1) = (float)y;
int response = (int)svmClassifier.predict( testSample );
circle( img_dst, Point(x,y), 2, classColors[response], 1 );
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
}
}
@ -138,7 +179,7 @@ void find_decision_boundary_SVM( CvSVMParams params )
for( int i = 0; i < svmClassifier.get_support_vector_count(); i++ )
{
const float* supportVector = svmClassifier.get_support_vector(i);
circle( img_dst, Point(supportVector[0],supportVector[1]), 5, CV_RGB(255,255,255), -1 );
circle( imgDst, Point(supportVector[0],supportVector[1]), 5, CV_RGB(255,255,255), -1 );
}
}
@ -147,7 +188,7 @@ void find_decision_boundary_SVM( CvSVMParams params )
#if DT
void find_decision_boundary_DT()
{
img.copyTo( img_dst );
img.copyTo( imgDst );
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
@ -178,16 +219,96 @@ void find_decision_boundary_DT()
testSample.at<float>(1) = (float)y;
int response = (int)dtree.predict( testSample )->value;
circle( img_dst, Point(x,y), 2, classColors[response], 1 );
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
}
}
}
#endif
#if BT
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 );
}
}
}
#endif
#if GBT
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::SQUARED_LOSS, // loss_function_type
100, // weak_count
0.05f, // shrinkage
0.6f, // subsample_portion
2, // max_depth
true // 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;
int response = (int)gbtrees.predict( testSample );
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
}
}
}
#endif
#if RF
void find_decision_boundary_RF()
{
img.copyTo( img_dst );
img.copyTo( imgDst );
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
@ -222,17 +343,61 @@ void find_decision_boundary_RF()
testSample.at<float>(1) = (float)y;
int response = (int)rtrees.predict( testSample );
circle( img_dst, Point(x,y), 2, classColors[response], 1 );
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
}
}
}
#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 )
{
img.copyTo( img_dst );
img.copyTo( imgDst );
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
@ -268,16 +433,16 @@ void find_decision_boundary_ANN( const Mat& layer_sizes )
ann.predict( testSample, outputs );
Point maxLoc;
minMaxLoc( outputs, 0, 0, 0, &maxLoc );
circle( img_dst, Point(x,y), 2, classColors[maxLoc.x], 1 );
circle( imgDst, Point(x,y), 2, classColors[maxLoc.x], 1 );
}
}
}
#endif
#if GMM
void find_decision_boundary_GMM()
#if EM
void find_decision_boundary_EM()
{
img.copyTo( img_dst );
img.copyTo( imgDst );
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
@ -308,7 +473,7 @@ void find_decision_boundary_GMM()
testSample.at<float>(1) = (float)y;
int response = (int)em.predict( testSample );
circle( img_dst, Point(x,y), 2, classColors[response], 1 );
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
}
}
}
@ -316,9 +481,15 @@ void find_decision_boundary_GMM()
int main()
{
cout << "Use:" << endl
<< " right mouse button - to add new class;" << 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;
cv::namedWindow( "points", 1 );
img.create( 480, 640, CV_8UC3 );
img_dst.create( 480, 640, CV_8UC3 );
imgDst.create( 480, 640, CV_8UC3 );
imshow( "points", img );
cvSetMouseCallback( "points", on_mouse );
@ -342,16 +513,21 @@ int main()
if( key == 'r' ) // run
{
#if NBC
find_decision_boundary_NBC();
cvNamedWindow( "NormalBayesClassifier", WINDOW_AUTOSIZE );
imshow( "NormalBayesClassifier", imgDst );
#endif
#if KNN
int K = 3;
find_decision_boundary_KNN( K );
namedWindow( "kNN", WINDOW_AUTOSIZE );
imshow( "kNN", img_dst );
imshow( "kNN", imgDst );
K = 15;
find_decision_boundary_KNN( K );
namedWindow( "kNN2", WINDOW_AUTOSIZE );
imshow( "kNN2", img_dst );
imshow( "kNN2", imgDst );
#endif
#if SVM
@ -369,24 +545,42 @@ int main()
find_decision_boundary_SVM( params );
namedWindow( "classificationSVM1", WINDOW_AUTOSIZE );
imshow( "classificationSVM1", img_dst );
imshow( "classificationSVM1", imgDst );
params.C = 10;
find_decision_boundary_SVM( params );
cvNamedWindow( "classificationSVM2", WINDOW_AUTOSIZE );
imshow( "classificationSVM2", img_dst );
imshow( "classificationSVM2", imgDst );
#endif
#if DT
find_decision_boundary_DT();
namedWindow( "DT", 1 );
imshow( "DT", img_dst );
imshow( "DT", imgDst );
#endif
#if BT
find_decision_boundary_BT();
namedWindow( "BT", 1 );
imshow( "BT", imgDst);
#endif
#if GBT
find_decision_boundary_GBT();
namedWindow( "GBT", 1 );
imshow( "GBT", imgDst);
#endif
#if RF
find_decision_boundary_RF();
namedWindow( "RF", 1 );
imshow( "RF", img_dst);
imshow( "RF", imgDst);
#endif
#if ERT
find_decision_boundary_ERT();
namedWindow( "ERT", 1 );
imshow( "ERT", imgDst);
#endif
#if ANN
@ -396,13 +590,13 @@ int main()
layer_sizes1.at<int>(2) = classColors.size();
find_decision_boundary_ANN( layer_sizes1 );
namedWindow( "ANN", WINDOW_AUTOSIZE );
imshow( "ANN", img_dst );
imshow( "ANN", imgDst );
#endif
#if GMM
find_decision_boundary_GMM();
namedWindow( "GMM", WINDOW_AUTOSIZE );
imshow( "GMM", img_dst );
#if EM
find_decision_boundary_EM();
namedWindow( "EM", WINDOW_AUTOSIZE );
imshow( "EM", imgDst );
#endif
}
}