From: Maria Dimashova Date: Sat, 30 Apr 2011 18:04:33 +0000 (+0000) Subject: added new ML models to points_classifier sample X-Git-Tag: accepted/2.0/20130307.220821~3257 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=92852ca06eccefe1bf3a23e433d23cae9af78faf;p=profile%2Fivi%2Fopencv.git added new ML models to points_classifier sample --- diff --git a/samples/c/tree_engine.cpp b/samples/c/tree_engine.cpp index 3822840..4f41884 100644 --- a/samples/c/tree_engine.cpp +++ b/samples/c/tree_engine.cpp @@ -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"); diff --git a/samples/cpp/points_classifier.cpp b/samples/cpp/points_classifier.cpp index c665608..66aec92 100644 --- a/samples/cpp/points_classifier.cpp +++ b/samples/cpp/points_classifier.cpp @@ -12,19 +12,23 @@ const string winName = "points"; const int testStep = 5; -Mat img, img_dst; +Mat img, imgDst; RNG rng; vector trainedPoints; vector trainedPointsMarkers; vector 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(0) = (float)x; + testSample.at(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(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(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(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( 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(0) = (float)x; + testSample.at(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( 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(0) = (float)x; + testSample.at(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(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( 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(0) = (float)x; + testSample.at(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(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(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 } }