563 lines
18 KiB
C++
563 lines
18 KiB
C++
#include "opencv2/core/core.hpp"
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#include "opencv2/ml/ml.hpp"
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#include <cstdio>
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#include <vector>
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#include <iostream>
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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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static void help()
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{
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printf("\nThe sample demonstrates how to train Random Trees classifier\n"
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"(or Boosting classifier, or MLP, or Knearest, or Nbayes, or Support Vector Machines - see main()) using the provided dataset.\n"
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"\n"
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"We use the sample database letter-recognition.data\n"
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"from UCI Repository, here is the link:\n"
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"\n"
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"Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).\n"
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"UCI Repository of machine learning databases\n"
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"[http://www.ics.uci.edu/~mlearn/MLRepository.html].\n"
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"Irvine, CA: University of California, Department of Information and Computer Science.\n"
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"\n"
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"The dataset consists of 20000 feature vectors along with the\n"
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"responses - capital latin letters A..Z.\n"
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"The first 16000 (10000 for boosting)) samples are used for training\n"
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"and the remaining 4000 (10000 for boosting) - to test the classifier.\n"
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"======================================================\n");
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printf("\nThis is letter recognition sample.\n"
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"The usage: letter_recog [-data=<path to letter-recognition.data>] \\\n"
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" [-save=<output XML file for the classifier>] \\\n"
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" [-load=<XML file with the pre-trained classifier>] \\\n"
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" [-boost|-mlp|-knearest|-nbayes|-svm] # to use boost/mlp/knearest/SVM classifier instead of default Random Trees\n" );
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}
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// This function reads data and responses from the file <filename>
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static bool
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read_num_class_data( const string& filename, int var_count,
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Mat* _data, Mat* _responses )
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{
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const int M = 1024;
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char buf[M+2];
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Mat el_ptr(1, var_count, CV_32F);
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int i;
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vector<int> responses;
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_data->release();
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_responses->release();
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FILE* f = fopen( filename.c_str(), "rt" );
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if( !f )
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{
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cout << "Could not read the database " << filename << endl;
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return false;
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}
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for(;;)
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{
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char* ptr;
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if( !fgets( buf, M, f ) || !strchr( buf, ',' ) )
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break;
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responses.push_back((int)buf[0]);
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ptr = buf+2;
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for( i = 0; i < var_count; i++ )
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{
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int n = 0;
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sscanf( ptr, "%f%n", &el_ptr.at<float>(i), &n );
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ptr += n + 1;
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}
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if( i < var_count )
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break;
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_data->push_back(el_ptr);
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}
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fclose(f);
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Mat(responses).copyTo(*_responses);
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cout << "The database " << filename << " is loaded.\n";
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return true;
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}
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template<typename T>
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static Ptr<T> load_classifier(const string& filename_to_load)
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{
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// load classifier from the specified file
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Ptr<T> model = StatModel::load<T>( filename_to_load );
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if( model.empty() )
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cout << "Could not read the classifier " << filename_to_load << endl;
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else
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cout << "The classifier " << filename_to_load << " is loaded.\n";
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return model;
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}
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static Ptr<TrainData>
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prepare_train_data(const Mat& data, const Mat& responses, int ntrain_samples)
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{
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Mat sample_idx = Mat::zeros( 1, data.rows, CV_8U );
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Mat train_samples = sample_idx.colRange(0, ntrain_samples);
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train_samples.setTo(Scalar::all(1));
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int nvars = data.cols;
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Mat var_type( nvars + 1, 1, CV_8U );
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var_type.setTo(Scalar::all(VAR_ORDERED));
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var_type.at<uchar>(nvars) = VAR_CATEGORICAL;
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return TrainData::create(data, ROW_SAMPLE, responses,
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noArray(), sample_idx, noArray(), var_type);
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}
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inline TermCriteria TC(int iters, double eps)
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{
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return TermCriteria(TermCriteria::MAX_ITER + (eps > 0 ? TermCriteria::EPS : 0), iters, eps);
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}
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static void test_and_save_classifier(const Ptr<StatModel>& model,
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const Mat& data, const Mat& responses,
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int ntrain_samples, int rdelta,
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const string& filename_to_save)
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{
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int i, nsamples_all = data.rows;
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double train_hr = 0, test_hr = 0;
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// compute prediction error on train and test data
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for( i = 0; i < nsamples_all; i++ )
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{
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Mat sample = data.row(i);
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float r = model->predict( sample );
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r = std::abs(r + rdelta - responses.at<int>(i)) <= FLT_EPSILON ? 1.f : 0.f;
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if( i < ntrain_samples )
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train_hr += r;
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else
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test_hr += r;
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}
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test_hr /= nsamples_all - ntrain_samples;
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train_hr = ntrain_samples > 0 ? train_hr/ntrain_samples : 1.;
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printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
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train_hr*100., test_hr*100. );
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if( !filename_to_save.empty() )
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{
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model->save( filename_to_save );
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}
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}
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static bool
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build_rtrees_classifier( const string& data_filename,
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const string& filename_to_save,
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const string& filename_to_load )
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{
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Mat data;
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Mat responses;
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bool ok = read_num_class_data( data_filename, 16, &data, &responses );
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if( !ok )
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return ok;
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Ptr<RTrees> model;
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int nsamples_all = data.rows;
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int ntrain_samples = (int)(nsamples_all*0.8);
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// Create or load Random Trees classifier
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if( !filename_to_load.empty() )
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{
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model = load_classifier<RTrees>(filename_to_load);
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if( model.empty() )
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return false;
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ntrain_samples = 0;
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}
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else
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{
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// create classifier by using <data> and <responses>
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cout << "Training the classifier ...\n";
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// Params( int maxDepth, int minSampleCount,
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// double regressionAccuracy, bool useSurrogates,
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// int maxCategories, const Mat& priors,
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// bool calcVarImportance, int nactiveVars,
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// TermCriteria termCrit );
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Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
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model = RTrees::create();
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model->setMaxDepth(10);
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model->setMinSampleCount(10);
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model->setRegressionAccuracy(0);
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model->setUseSurrogates(false);
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model->setMaxCategories(15);
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model->setPriors(Mat());
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model->setCalculateVarImportance(true);
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model->setActiveVarCount(4);
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model->setTermCriteria(TC(100,0.01f));
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model->train(tdata);
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cout << endl;
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}
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test_and_save_classifier(model, data, responses, ntrain_samples, 0, filename_to_save);
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cout << "Number of trees: " << model->getRoots().size() << endl;
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// Print variable importance
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Mat var_importance = model->getVarImportance();
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if( !var_importance.empty() )
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{
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double rt_imp_sum = sum( var_importance )[0];
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printf("var#\timportance (in %%):\n");
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int i, n = (int)var_importance.total();
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for( i = 0; i < n; i++ )
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printf( "%-2d\t%-4.1f\n", i, 100.f*var_importance.at<float>(i)/rt_imp_sum);
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}
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return true;
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}
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static bool
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build_boost_classifier( const string& data_filename,
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const string& filename_to_save,
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const string& filename_to_load )
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{
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const int class_count = 26;
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Mat data;
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Mat responses;
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Mat weak_responses;
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bool ok = read_num_class_data( data_filename, 16, &data, &responses );
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if( !ok )
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return ok;
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int i, j, k;
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Ptr<Boost> model;
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int nsamples_all = data.rows;
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int ntrain_samples = (int)(nsamples_all*0.5);
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int var_count = data.cols;
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// Create or load Boosted Tree classifier
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if( !filename_to_load.empty() )
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{
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model = load_classifier<Boost>(filename_to_load);
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if( model.empty() )
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return false;
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ntrain_samples = 0;
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}
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else
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{
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// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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//
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// As currently boosted tree classifier in MLL can only be trained
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// for 2-class problems, we transform the training database by
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// "unrolling" each training sample as many times as the number of
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// classes (26) that we have.
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//
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// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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Mat new_data( ntrain_samples*class_count, var_count + 1, CV_32F );
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Mat new_responses( ntrain_samples*class_count, 1, CV_32S );
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// 1. unroll the database type mask
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printf( "Unrolling the database...\n");
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for( i = 0; i < ntrain_samples; i++ )
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{
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const float* data_row = data.ptr<float>(i);
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for( j = 0; j < class_count; j++ )
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{
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float* new_data_row = (float*)new_data.ptr<float>(i*class_count+j);
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memcpy(new_data_row, data_row, var_count*sizeof(data_row[0]));
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new_data_row[var_count] = (float)j;
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new_responses.at<int>(i*class_count + j) = responses.at<int>(i) == j+'A';
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}
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}
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Mat var_type( 1, var_count + 2, CV_8U );
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var_type.setTo(Scalar::all(VAR_ORDERED));
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var_type.at<uchar>(var_count) = var_type.at<uchar>(var_count+1) = VAR_CATEGORICAL;
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Ptr<TrainData> tdata = TrainData::create(new_data, ROW_SAMPLE, new_responses,
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noArray(), noArray(), noArray(), var_type);
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vector<double> priors(2);
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priors[0] = 1;
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priors[1] = 26;
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cout << "Training the classifier (may take a few minutes)...\n";
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model = Boost::create();
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model->setBoostType(Boost::GENTLE);
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model->setWeakCount(100);
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model->setWeightTrimRate(0.95);
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model->setMaxDepth(5);
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model->setUseSurrogates(false);
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model->setPriors(Mat(priors));
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model->train(tdata);
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cout << endl;
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}
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Mat temp_sample( 1, var_count + 1, CV_32F );
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float* tptr = temp_sample.ptr<float>();
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// compute prediction error on train and test data
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double train_hr = 0, test_hr = 0;
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for( i = 0; i < nsamples_all; i++ )
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{
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int best_class = 0;
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double max_sum = -DBL_MAX;
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const float* ptr = data.ptr<float>(i);
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for( k = 0; k < var_count; k++ )
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tptr[k] = ptr[k];
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for( j = 0; j < class_count; j++ )
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{
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tptr[var_count] = (float)j;
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float s = model->predict( temp_sample, noArray(), StatModel::RAW_OUTPUT );
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if( max_sum < s )
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{
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max_sum = s;
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best_class = j + 'A';
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}
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}
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double r = std::abs(best_class - responses.at<int>(i)) < FLT_EPSILON ? 1 : 0;
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if( i < ntrain_samples )
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train_hr += r;
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else
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test_hr += r;
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}
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test_hr /= nsamples_all-ntrain_samples;
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train_hr = ntrain_samples > 0 ? train_hr/ntrain_samples : 1.;
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printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
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train_hr*100., test_hr*100. );
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cout << "Number of trees: " << model->getRoots().size() << endl;
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// Save classifier to file if needed
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if( !filename_to_save.empty() )
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model->save( filename_to_save );
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return true;
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}
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static bool
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build_mlp_classifier( const string& data_filename,
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const string& filename_to_save,
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const string& filename_to_load )
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{
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const int class_count = 26;
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Mat data;
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Mat responses;
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bool ok = read_num_class_data( data_filename, 16, &data, &responses );
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if( !ok )
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return ok;
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Ptr<ANN_MLP> model;
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int nsamples_all = data.rows;
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int ntrain_samples = (int)(nsamples_all*0.8);
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// Create or load MLP classifier
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if( !filename_to_load.empty() )
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{
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model = load_classifier<ANN_MLP>(filename_to_load);
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if( model.empty() )
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return false;
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ntrain_samples = 0;
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}
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else
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{
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// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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//
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// MLP does not support categorical variables by explicitly.
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// So, instead of the output class label, we will use
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// a binary vector of <class_count> components for training and,
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// therefore, MLP will give us a vector of "probabilities" at the
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// prediction stage
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//
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// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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Mat train_data = data.rowRange(0, ntrain_samples);
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Mat train_responses = Mat::zeros( ntrain_samples, class_count, CV_32F );
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// 1. unroll the responses
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cout << "Unrolling the responses...\n";
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for( int i = 0; i < ntrain_samples; i++ )
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{
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int cls_label = responses.at<int>(i) - 'A';
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train_responses.at<float>(i, cls_label) = 1.f;
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}
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// 2. train classifier
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int layer_sz[] = { data.cols, 100, 100, class_count };
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int nlayers = (int)(sizeof(layer_sz)/sizeof(layer_sz[0]));
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Mat layer_sizes( 1, nlayers, CV_32S, layer_sz );
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#if 1
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int method = ANN_MLP::BACKPROP;
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double method_param = 0.001;
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int max_iter = 300;
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#else
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int method = ANN_MLP::RPROP;
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double method_param = 0.1;
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int max_iter = 1000;
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#endif
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Ptr<TrainData> tdata = TrainData::create(train_data, ROW_SAMPLE, train_responses);
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cout << "Training the classifier (may take a few minutes)...\n";
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model = ANN_MLP::create();
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model->setLayerSizes(layer_sizes);
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model->setActivationFunction(ANN_MLP::SIGMOID_SYM, 0, 0);
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model->setTermCriteria(TC(max_iter,0));
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model->setTrainMethod(method, method_param);
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model->train(tdata);
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cout << endl;
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}
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test_and_save_classifier(model, data, responses, ntrain_samples, 'A', filename_to_save);
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return true;
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}
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static bool
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build_knearest_classifier( const string& data_filename, int K )
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{
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Mat data;
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Mat responses;
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bool ok = read_num_class_data( data_filename, 16, &data, &responses );
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if( !ok )
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return ok;
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int nsamples_all = data.rows;
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int ntrain_samples = (int)(nsamples_all*0.8);
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// create classifier by using <data> and <responses>
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cout << "Training the classifier ...\n";
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Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
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Ptr<KNearest> model = KNearest::create();
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model->setDefaultK(K);
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model->setIsClassifier(true);
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model->train(tdata);
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cout << endl;
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test_and_save_classifier(model, data, responses, ntrain_samples, 0, string());
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return true;
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}
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static bool
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build_nbayes_classifier( const string& data_filename )
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{
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Mat data;
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Mat responses;
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bool ok = read_num_class_data( data_filename, 16, &data, &responses );
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if( !ok )
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return ok;
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Ptr<NormalBayesClassifier> model;
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int nsamples_all = data.rows;
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int ntrain_samples = (int)(nsamples_all*0.8);
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// create classifier by using <data> and <responses>
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cout << "Training the classifier ...\n";
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Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
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model = NormalBayesClassifier::create();
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model->train(tdata);
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cout << endl;
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test_and_save_classifier(model, data, responses, ntrain_samples, 0, string());
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return true;
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}
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static bool
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build_svm_classifier( const string& data_filename,
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const string& filename_to_save,
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const string& filename_to_load )
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{
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Mat data;
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Mat responses;
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bool ok = read_num_class_data( data_filename, 16, &data, &responses );
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if( !ok )
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return ok;
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Ptr<SVM> model;
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int nsamples_all = data.rows;
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int ntrain_samples = (int)(nsamples_all*0.8);
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// Create or load Random Trees classifier
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if( !filename_to_load.empty() )
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{
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model = load_classifier<SVM>(filename_to_load);
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if( model.empty() )
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return false;
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ntrain_samples = 0;
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}
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else
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{
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// create classifier by using <data> and <responses>
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cout << "Training the classifier ...\n";
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Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
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model = SVM::create();
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model->setType(SVM::C_SVC);
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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|../data/letter-recognition.data|}{save||}{load||}{boost||}"
|
|
"{mlp||}{knn knearest||}{nbayes||}{svm||}{help h||}");
|
|
data_filename = parser.get<string>("data");
|
|
if (parser.has("save"))
|
|
filename_to_save = parser.get<string>("save");
|
|
if (parser.has("load"))
|
|
filename_to_load = 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;
|
|
if (parser.has("help"))
|
|
{
|
|
help();
|
|
return 0;
|
|
}
|
|
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)
|
|
{
|
|
help();
|
|
}
|
|
return 0;
|
|
}
|