400 lines
10 KiB
C++
400 lines
10 KiB
C++
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#include "opencv2/core.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/ml.hpp"
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#include "opencv2/highgui.hpp"
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#include <stdio.h>
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using namespace std;
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using namespace cv;
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using namespace cv::ml;
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const Scalar WHITE_COLOR = Scalar(255,255,255);
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const string winName = "points";
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const int testStep = 5;
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Mat img, imgDst;
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RNG rng;
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vector<Point> trainedPoints;
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vector<int> trainedPointsMarkers;
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const int MAX_CLASSES = 2;
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vector<Vec3b> classColors(MAX_CLASSES);
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int currentClass = 0;
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vector<int> classCounters(MAX_CLASSES);
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#define _NBC_ 1 // normal Bayessian classifier
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#define _KNN_ 1 // k nearest neighbors classifier
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#define _SVM_ 1 // support vectors machine
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#define _DT_ 1 // decision tree
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#define _BT_ 1 // ADA Boost
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#define _GBT_ 0 // gradient boosted trees
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#define _RF_ 1 // random forest
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#define _ANN_ 1 // artificial neural networks
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#define _EM_ 1 // expectation-maximization
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static void on_mouse( int event, int x, int y, int /*flags*/, void* )
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{
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if( img.empty() )
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return;
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int updateFlag = 0;
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if( event == EVENT_LBUTTONUP )
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{
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trainedPoints.push_back( Point(x,y) );
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trainedPointsMarkers.push_back( currentClass );
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classCounters[currentClass]++;
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updateFlag = true;
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}
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//draw
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if( updateFlag )
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{
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img = Scalar::all(0);
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// draw points
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for( size_t i = 0; i < trainedPoints.size(); i++ )
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{
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Vec3b c = classColors[trainedPointsMarkers[i]];
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circle( img, trainedPoints[i], 5, Scalar(c), -1 );
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}
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imshow( winName, img );
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}
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}
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static Mat prepare_train_samples(const vector<Point>& pts)
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{
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Mat samples;
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Mat(pts).reshape(1, (int)pts.size()).convertTo(samples, CV_32F);
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return samples;
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}
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static Ptr<TrainData> prepare_train_data()
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{
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Mat samples = prepare_train_samples(trainedPoints);
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return TrainData::create(samples, ROW_SAMPLE, Mat(trainedPointsMarkers));
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}
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static void predict_and_paint(const Ptr<StatModel>& model, Mat& dst)
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{
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Mat testSample( 1, 2, CV_32FC1 );
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for( int y = 0; y < img.rows; y += testStep )
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{
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for( int x = 0; x < img.cols; x += testStep )
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{
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testSample.at<float>(0) = (float)x;
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testSample.at<float>(1) = (float)y;
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int response = (int)model->predict( testSample );
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dst.at<Vec3b>(y, x) = classColors[response];
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}
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}
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}
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#if _NBC_
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static void find_decision_boundary_NBC()
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{
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// learn classifier
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Ptr<NormalBayesClassifier> normalBayesClassifier = StatModel::train<NormalBayesClassifier>(prepare_train_data());
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predict_and_paint(normalBayesClassifier, imgDst);
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}
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#endif
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#if _KNN_
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static void find_decision_boundary_KNN( int K )
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{
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Ptr<KNearest> knn = KNearest::create();
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knn->setDefaultK(K);
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knn->setIsClassifier(true);
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knn->train(prepare_train_data());
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predict_and_paint(knn, imgDst);
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}
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#endif
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#if _SVM_
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static void find_decision_boundary_SVM( double C )
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{
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Ptr<SVM> svm = SVM::create();
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svm->setType(SVM::C_SVC);
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svm->setKernel(SVM::POLY); //SVM::LINEAR;
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svm->setDegree(0.5);
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svm->setGamma(1);
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svm->setCoef0(1);
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svm->setNu(0.5);
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svm->setP(0);
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svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01));
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svm->setC(C);
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svm->train(prepare_train_data());
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predict_and_paint(svm, imgDst);
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Mat sv = svm->getSupportVectors();
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for( int i = 0; i < sv.rows; i++ )
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{
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const float* supportVector = sv.ptr<float>(i);
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circle( imgDst, Point(saturate_cast<int>(supportVector[0]),saturate_cast<int>(supportVector[1])), 5, Scalar(255,255,255), -1 );
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}
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}
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#endif
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#if _DT_
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static void find_decision_boundary_DT()
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{
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Ptr<DTrees> dtree = DTrees::create();
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dtree->setMaxDepth(8);
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dtree->setMinSampleCount(2);
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dtree->setUseSurrogates(false);
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dtree->setCVFolds(0); // the number of cross-validation folds
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dtree->setUse1SERule(false);
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dtree->setTruncatePrunedTree(false);
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dtree->train(prepare_train_data());
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predict_and_paint(dtree, imgDst);
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}
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#endif
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#if _BT_
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static void find_decision_boundary_BT()
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{
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Ptr<Boost> boost = Boost::create();
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boost->setBoostType(Boost::DISCRETE);
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boost->setWeakCount(100);
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boost->setWeightTrimRate(0.95);
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boost->setMaxDepth(2);
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boost->setUseSurrogates(false);
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boost->setPriors(Mat());
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boost->train(prepare_train_data());
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predict_and_paint(boost, imgDst);
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}
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#endif
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#if _GBT_
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static void find_decision_boundary_GBT()
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{
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GBTrees::Params params( GBTrees::DEVIANCE_LOSS, // loss_function_type
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100, // weak_count
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0.1f, // shrinkage
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1.0f, // subsample_portion
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2, // max_depth
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false // use_surrogates )
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);
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Ptr<GBTrees> gbtrees = StatModel::train<GBTrees>(prepare_train_data(), params);
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predict_and_paint(gbtrees, imgDst);
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}
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#endif
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#if _RF_
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static void find_decision_boundary_RF()
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{
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Ptr<RTrees> rtrees = RTrees::create();
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rtrees->setMaxDepth(4);
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rtrees->setMinSampleCount(2);
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rtrees->setRegressionAccuracy(0.f);
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rtrees->setUseSurrogates(false);
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rtrees->setMaxCategories(16);
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rtrees->setPriors(Mat());
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rtrees->setCalculateVarImportance(false);
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rtrees->setActiveVarCount(1);
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rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 5, 0));
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rtrees->train(prepare_train_data());
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predict_and_paint(rtrees, imgDst);
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}
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#endif
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#if _ANN_
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static void find_decision_boundary_ANN( const Mat& layer_sizes )
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{
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Mat trainClasses = Mat::zeros( (int)trainedPoints.size(), (int)classColors.size(), CV_32FC1 );
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for( int i = 0; i < trainClasses.rows; i++ )
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{
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trainClasses.at<float>(i, trainedPointsMarkers[i]) = 1.f;
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}
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Mat samples = prepare_train_samples(trainedPoints);
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Ptr<TrainData> tdata = TrainData::create(samples, ROW_SAMPLE, trainClasses);
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Ptr<ANN_MLP> ann = ANN_MLP::create();
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ann->setLayerSizes(layer_sizes);
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ann->setActivationFunction(ANN_MLP::SIGMOID_SYM, 1, 1);
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ann->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, FLT_EPSILON));
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ann->setTrainMethod(ANN_MLP::BACKPROP, 0.001);
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ann->train(tdata);
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predict_and_paint(ann, imgDst);
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}
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#endif
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#if _EM_
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static void find_decision_boundary_EM()
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{
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img.copyTo( imgDst );
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Mat samples = prepare_train_samples(trainedPoints);
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int i, j, nmodels = (int)classColors.size();
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vector<Ptr<EM> > em_models(nmodels);
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Mat modelSamples;
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for( i = 0; i < nmodels; i++ )
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{
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const int componentCount = 3;
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modelSamples.release();
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for( j = 0; j < samples.rows; j++ )
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{
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if( trainedPointsMarkers[j] == i )
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modelSamples.push_back(samples.row(j));
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}
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// learn models
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if( !modelSamples.empty() )
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{
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Ptr<EM> em = EM::create();
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em->setClustersNumber(componentCount);
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em->setCovarianceMatrixType(EM::COV_MAT_DIAGONAL);
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em->trainEM(modelSamples, noArray(), noArray(), noArray());
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em_models[i] = em;
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}
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}
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// classify coordinate plane points using the bayes classifier, i.e.
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// y(x) = arg max_i=1_modelsCount likelihoods_i(x)
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Mat testSample(1, 2, CV_32FC1 );
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Mat logLikelihoods(1, nmodels, CV_64FC1, Scalar(-DBL_MAX));
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for( int y = 0; y < img.rows; y += testStep )
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{
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for( int x = 0; x < img.cols; x += testStep )
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{
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testSample.at<float>(0) = (float)x;
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testSample.at<float>(1) = (float)y;
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for( i = 0; i < nmodels; i++ )
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{
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if( !em_models[i].empty() )
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logLikelihoods.at<double>(i) = em_models[i]->predict2(testSample, noArray())[0];
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}
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Point maxLoc;
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minMaxLoc(logLikelihoods, 0, 0, 0, &maxLoc);
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imgDst.at<Vec3b>(y, x) = classColors[maxLoc.x];
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}
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}
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}
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#endif
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int main()
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{
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cout << "Use:" << endl
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<< " key '0' .. '1' - switch to class #n" << endl
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<< " left mouse button - to add new point;" << endl
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<< " key 'r' - to run the ML model;" << endl
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<< " key 'i' - to init (clear) the data." << endl << endl;
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cv::namedWindow( "points", 1 );
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img.create( 480, 640, CV_8UC3 );
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imgDst.create( 480, 640, CV_8UC3 );
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imshow( "points", img );
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setMouseCallback( "points", on_mouse );
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classColors[0] = Vec3b(0, 255, 0);
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classColors[1] = Vec3b(0, 0, 255);
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for(;;)
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{
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char key = (char)waitKey();
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if( key == 27 ) break;
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if( key == 'i' ) // init
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{
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img = Scalar::all(0);
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trainedPoints.clear();
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trainedPointsMarkers.clear();
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classCounters.assign(MAX_CLASSES, 0);
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imshow( winName, img );
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}
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if( key == '0' || key == '1' )
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{
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currentClass = key - '0';
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}
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if( key == 'r' ) // run
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{
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double minVal = 0;
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minMaxLoc(classCounters, &minVal, 0, 0, 0);
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if( minVal == 0 )
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{
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printf("each class should have at least 1 point\n");
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continue;
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}
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img.copyTo( imgDst );
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#if _NBC_
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find_decision_boundary_NBC();
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imshow( "NormalBayesClassifier", imgDst );
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#endif
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#if _KNN_
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find_decision_boundary_KNN( 3 );
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imshow( "kNN", imgDst );
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find_decision_boundary_KNN( 15 );
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imshow( "kNN2", imgDst );
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#endif
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#if _SVM_
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//(1)-(2)separable and not sets
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find_decision_boundary_SVM( 1 );
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imshow( "classificationSVM1", imgDst );
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find_decision_boundary_SVM( 10 );
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imshow( "classificationSVM2", imgDst );
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#endif
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#if _DT_
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find_decision_boundary_DT();
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imshow( "DT", imgDst );
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#endif
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#if _BT_
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find_decision_boundary_BT();
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imshow( "BT", imgDst);
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#endif
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#if _GBT_
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find_decision_boundary_GBT();
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imshow( "GBT", imgDst);
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#endif
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#if _RF_
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find_decision_boundary_RF();
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imshow( "RF", imgDst);
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#endif
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|
#if _ANN_
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||
|
|
Mat layer_sizes1( 1, 3, CV_32SC1 );
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||
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|
layer_sizes1.at<int>(0) = 2;
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||
|
|
layer_sizes1.at<int>(1) = 5;
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||
|
|
layer_sizes1.at<int>(2) = (int)classColors.size();
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||
|
|
find_decision_boundary_ANN( layer_sizes1 );
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||
|
|
imshow( "ANN", imgDst );
|
||
|
|
#endif
|
||
|
|
|
||
|
|
#if _EM_
|
||
|
|
find_decision_boundary_EM();
|
||
|
|
imshow( "EM", imgDst );
|
||
|
|
#endif
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
return 0;
|
||
|
|
}
|