opencv ML¶
class¶
class cv::ml::ANN_MLP Artificial Neural Networks - Multi-Layer Perceptrons. More... class cv::ml::Boost Boosted tree classifier derived from DTrees. More... class cv::ml::DTrees The class represents a single decision tree or a collection of decision trees. More... class cv::ml::EM The class implements the Expectation Maximization algorithm. More... class cv::ml::KNearest The class implements K-Nearest Neighbors model. More... class cv::ml::LogisticRegression Implements Logistic Regression classifier. More... class cv::ml::NormalBayesClassifier Bayes classifier for normally distributed data. More... // class cv::ml::ParamGrid // The structure represents the logarithmic grid range of statmodel parameters. More... class cv::ml::RTrees The class implements the random forest predictor. More... struct cv::ml::SimulatedAnnealingSolverSystem This class declares example interface for system state used in simulated annealing optimization algorithm. More... // class cv::ml::StatModel // Base class for statistical models in OpenCV ML. More... class cv::ml::SVM Support Vector Machines. More... class cv::ml::SVMSGD Stochastic Gradient Descent SVM classifier. More... // class cv::ml::TrainData // Class encapsulating training data. More...
算法¶
MLP¶
MLP(Multilayer PerceptronP) 多层感知机, 也叫人工神经网络（ANN，Artificial Neural Network），除了输入输出层，它中间可以有多个隐层，最简单的MLP只含一个隐层，即三层的结构
letter recong¶
#include "opencv2/core.hpp" #include "opencv2/ml.hpp" #include <cstdio> #include <vector> #include <iostream> using namespace std; using namespace cv; using namespace cv::ml; static void help() { printf("\nThe sample demonstrates how to train Random Trees classifier\n" "(or Boosting classifier, or MLP, or Knearest, or Nbayes, or Support Vector Machines - see main()) using the provided dataset.\n" "\n" "We use the sample database letter-recognition.data\n" "from UCI Repository, here is the link:\n" "\n" "Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).\n" "UCI Repository of machine learning databases\n" "[http://www.ics.uci.edu/~mlearn/MLRepository.html].\n" "Irvine, CA: University of California, Department of Information and Computer Science.\n" "\n" "The dataset consists of 20000 feature vectors along with the\n" "responses - capital latin letters A..Z.\n" "The first 16000 (10000 for boosting)) samples are used for training\n" "and the remaining 4000 (10000 for boosting) - to test the classifier.\n" "======================================================\n"); printf("\nThis is letter recognition sample.\n" "The usage: letter_recog [-data=<path to letter-recognition.data>] \\\n" " [-save=<output XML file for the classifier>] \\\n" " [-load=<XML file with the pre-trained classifier>] \\\n" " [-boost|-mlp|-knearest|-nbayes|-svm] # to use boost/mlp/knearest/SVM classifier instead of default Random Trees\n" ); } // This function reads data and responses from the file <filename> static bool read_num_class_data( const string& filename, int var_count, Mat* _data, Mat* _responses ) { const int M = 1024; char buf[M+2]; Mat el_ptr(1, var_count, CV_32F); int i; vector<int> responses; _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; responses.push_back((int)buf[0]); ptr = buf+2; for( i = 0; i < var_count; i++ ) { int n = 0; sscanf( ptr, "%f%n", &el_ptr.at<float>(i), &n ); ptr += n + 1; } if( i < var_count ) break; _data->push_back(el_ptr); } fclose(f); Mat(responses).copyTo(*_responses); cout << "The database " << filename << " is loaded.\n"; return true; } template<typename T> static Ptr<T> load_classifier(const string& filename_to_load) { // 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"; return model; } 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)); 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; return TrainData::create(data, ROW_SAMPLE, responses, noArray(), sample_idx, noArray(), var_type); } inline TermCriteria TC(int iters, double eps) { return TermCriteria(TermCriteria::MAX_ITER + (eps > 0 ? TermCriteria::EPS : 0), iters, eps); } 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++ ) { Mat sample = data.row(i); float r = model->predict( sample ); r = std::abs(r + rdelta - responses.at<int>(i)) <= FLT_EPSILON ? 1.f : 0.f; if( i < ntrain_samples ) train_hr += r; else test_hr += r; } 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. ); if( !filename_to_save.empty() ) { model->save( filename_to_save ); } } 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() ) { 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"; // Params( int maxDepth, int minSampleCount, // double regressionAccuracy, bool useSurrogates, // int maxCategories, const Mat& priors, // bool calcVarImportance, int nactiveVars, // TermCriteria termCrit ); Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples); model = RTrees::create(); model->setMaxDepth(10); model->setMinSampleCount(10); model->setRegressionAccuracy(0); model->setUseSurrogates(false); model->setMaxCategories(15); model->setPriors(Mat()); model->setCalculateVarImportance(true); model->setActiveVarCount(4); model->setTermCriteria(TC(100,0.01f)); model->train(tdata); cout << endl; } test_and_save_classifier(model, data, responses, ntrain_samples, 0, filename_to_save); cout << "Number of trees: " << model->getRoots().size() << endl; // 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 true; } static bool build_boost_classifier( const string& data_filename, const string& filename_to_save, const string& filename_to_load ) { const int class_count = 26; Mat data; Mat responses; Mat weak_responses; bool ok = read_num_class_data( data_filename, 16, &data, &responses ); if( !ok ) return ok; 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.empty() ) { model = load_classifier<Boost>(filename_to_load); if( model.empty() ) return false; ntrain_samples = 0; } else { // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! // // As currently boosted tree classifier in MLL can only be trained // for 2-class problems, we transform the training database by // "unrolling" each training sample as many times as the number of // classes (26) that we have. // // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 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++ ) { const float* data_row = data.ptr<float>(i); for( j = 0; j < class_count; j++ ) { 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.at<int>(i*class_count + j) = responses.at<int>(i) == j+'A'; } } 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; Ptr<TrainData> tdata = TrainData::create(new_data, ROW_SAMPLE, new_responses, noArray(), noArray(), noArray(), var_type); vector<double> priors(2); priors[0] = 1; priors[1] = 26; cout << "Training the classifier (may take a few minutes)...\n"; model = Boost::create(); model->setBoostType(Boost::GENTLE); model->setWeakCount(100); model->setWeightTrimRate(0.95); model->setMaxDepth(5); model->setUseSurrogates(false); model->setPriors(Mat(priors)); model->train(tdata); cout << endl; } 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; const float* ptr = data.ptr<float>(i); for( k = 0; k < var_count; k++ ) tptr[k] = ptr[k]; for( j = 0; j < class_count; j++ ) { tptr[var_count] = (float)j; float s = model->predict( temp_sample, noArray(), StatModel::RAW_OUTPUT ); if( max_sum < s ) { max_sum = s; best_class = j + 'A'; } } 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 /= 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. ); cout << "Number of trees: " << model->getRoots().size() << endl; // Save classifier to file if needed if( !filename_to_save.empty() ) model->save( filename_to_save ); return true; } static bool build_mlp_classifier( const string& data_filename, const string& filename_to_save, const string& filename_to_load ) { const int class_count = 26; Mat data; Mat responses; bool ok = read_num_class_data( data_filename, 16, &data, &responses ); if( !ok ) return ok; 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.empty() ) { model = load_classifier<ANN_MLP>(filename_to_load); if( model.empty() ) return false; ntrain_samples = 0; } else { // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! // // MLP does not support categorical variables by explicitly. // So, instead of the output class label, we will use // a binary vector of <class_count> components for training and, // therefore, MLP will give us a vector of "probabilities" at the // prediction stage // // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Mat train_data = data.rowRange(0, ntrain_samples); Mat train_responses = Mat::zeros( ntrain_samples, class_count, CV_32F ); // 1. unroll the responses cout << "Unrolling the responses...\n"; for( int i = 0; i < ntrain_samples; i++ ) { int cls_label = responses.at<int>(i) - 'A'; train_responses.at<float>(i, cls_label) = 1.f; } // 2. train classifier 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 = ANN_MLP::BACKPROP; double method_param = 0.001; int max_iter = 300; #else int method = ANN_MLP::RPROP; double method_param = 0.1; int max_iter = 1000; #endif Ptr<TrainData> tdata = TrainData::create(train_data, ROW_SAMPLE, train_responses); cout << "Training the classifier (may take a few minutes)...\n"; model = ANN_MLP::create(); model->setLayerSizes(layer_sizes); model->setActivationFunction(ANN_MLP::SIGMOID_SYM, 0, 0); model->setTermCriteria(TC(max_iter,0)); model->setTrainMethod(method, method_param); model->train(tdata); cout << endl; } test_and_save_classifier(model, data, responses, ntrain_samples, 'A', filename_to_save); return true; } static bool build_knearest_classifier( const string& data_filename, int K ) { Mat data; Mat responses; bool ok = read_num_class_data( data_filename, 16, &data, &responses ); if( !ok ) return ok; int nsamples_all = data.rows; int ntrain_samples = (int)(nsamples_all*0.8); // create classifier by using <data> and <responses> cout << "Training the classifier ...\n"; Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples); Ptr<KNearest> model = KNearest::create(); model->setDefaultK(K); model->setIsClassifier(true); model->train(tdata); cout << endl; test_and_save_classifier(model, data, responses, ntrain_samples, 0, string()); return true; } static bool build_nbayes_classifier( const string& data_filename ) { Mat data; Mat responses; bool ok = read_num_class_data( data_filename, 16, &data, &responses ); if( !ok ) return ok; Ptr<NormalBayesClassifier> model; int nsamples_all = data.rows; int ntrain_samples = (int)(nsamples_all*0.8); // create classifier by using <data> and <responses> cout << "Training the classifier ...\n"; Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples); model = NormalBayesClassifier::create(); model->train(tdata); cout << endl; test_and_save_classifier(model, data, responses, ntrain_samples, 0, string()); return true; } static bool build_svm_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<SVM> 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() ) { model = load_classifier<SVM>(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); model = SVM::create(); model->setType(SVM::C_SVC); model->setKernel(SVM::LINEAR); model->setC(1); model->train(tdata); cout << endl; } test_and_save_classifier(model, data, responses, ntrain_samples, 0, filename_to_save); return true; } int main( int argc, char *argv[] ) { string filename_to_save = ""; string filename_to_load = ""; string data_filename; int method = 0; cv::CommandLineParser parser(argc, argv, "{data|letter-recognition.data|}{save||}{load||}{boost||}" "{mlp||}{knn knearest||}{nbayes||}{svm||}"); data_filename = samples::findFile(parser.get<string>("data")); if (parser.has("save")) filename_to_save = parser.get<string>("save"); if (parser.has("load")) filename_to_load = samples::findFile(parser.get<string>("load")); if (parser.has("boost")) method = 1; else if (parser.has("mlp")) method = 2; else if (parser.has("knearest")) method = 3; else if (parser.has("nbayes")) method = 4; else if (parser.has("svm")) method = 5; help(); if( (method == 0 ? build_rtrees_classifier( data_filename, filename_to_save, filename_to_load ) : method == 1 ? build_boost_classifier( data_filename, filename_to_save, filename_to_load ) : method == 2 ? build_mlp_classifier( data_filename, filename_to_save, filename_to_load ) : method == 3 ? build_knearest_classifier( data_filename, 10 ) : method == 4 ? build_nbayes_classifier( data_filename) : method == 5 ? build_svm_classifier( data_filename, filename_to_save, filename_to_load ): -1) < 0) return 0; }