/*////////////////////////////////////////////////////////////////////////////////////// // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // This is a implementation of the Logistic Regression algorithm in C++ in OpenCV. // AUTHOR: // Rahul Kavi rahulkavi[at]live[at]com // // contains a subset of data from the popular Iris Dataset (taken from // "http://archive.ics.uci.edu/ml/datasets/Iris") // # You are free to use, change, or redistribute the code in any way you wish for // # non-commercial purposes, but please maintain the name of the original author. // # This code comes with no warranty of any kind. // # // # You are free to use, change, or redistribute the code in any way you wish for // # non-commercial purposes, but please maintain the name of the original author. // # This code comes with no warranty of any kind. // # Logistic Regression ALGORITHM // License Agreement // For Open Source Computer Vision Library // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage.*/ #include <iostream> #include <opencv2/core.hpp> #include <opencv2/ml.hpp> #include <opencv2/highgui.hpp> using namespace std; using namespace cv; using namespace cv::ml; static void showImage(const Mat &data, int columns, const String &name) { Mat bigImage; for(int i = 0; i < data.rows; ++i) { bigImage.push_back(data.row(i).reshape(0, columns)); } imshow(name, bigImage.t()); } static float calculateAccuracyPercent(const Mat &original, const Mat &predicted) { return 100 * (float)countNonZero(original == predicted) / predicted.rows; } int main() { const String filename = samples::findFile("data01.xml"); cout << "**********************************************************************" << endl; cout << filename << " contains digits 0 and 1 of 20 samples each, collected on an Android device" << endl; cout << "Each of the collected images are of size 28 x 28 re-arranged to 1 x 784 matrix" << endl; cout << "**********************************************************************" << endl; Mat data, labels; { cout << "loading the dataset..."; FileStorage f; if(f.open(filename, FileStorage::READ)) { f["datamat"] >> data; f["labelsmat"] >> labels; f.release(); } else { cerr << "file can not be opened: " << filename << endl; return 1; } data.convertTo(data, CV_32F); labels.convertTo(labels, CV_32F); cout << "read " << data.rows << " rows of data" << endl; } Mat data_train, data_test; Mat labels_train, labels_test; for(int i = 0; i < data.rows; i++) { if(i % 2 == 0) { data_train.push_back(data.row(i)); labels_train.push_back(labels.row(i)); } else { data_test.push_back(data.row(i)); labels_test.push_back(labels.row(i)); } } cout << "training/testing samples count: " << data_train.rows << "/" << data_test.rows << endl; // display sample image showImage(data_train, 28, "train data"); showImage(data_test, 28, "test data"); // simple case with batch gradient cout << "training..."; //! [init] Ptr<LogisticRegression> lr1 = LogisticRegression::create(); lr1->setLearningRate(0.001); lr1->setIterations(10); lr1->setRegularization(LogisticRegression::REG_L2); lr1->setTrainMethod(LogisticRegression::BATCH); lr1->setMiniBatchSize(1); //! [init] lr1->train(data_train, ROW_SAMPLE, labels_train); cout << "done!" << endl; cout << "predicting..."; Mat responses; lr1->predict(data_test, responses); cout << "done!" << endl; // show prediction report cout << "original vs predicted:" << endl; labels_test.convertTo(labels_test, CV_32S); cout << labels_test.t() << endl; cout << responses.t() << endl; cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses) << "%" << endl; // save the classifier const String saveFilename = "NewLR_Trained.xml"; cout << "saving the classifier to " << saveFilename << endl; lr1->save(saveFilename); // load the classifier onto new object cout << "loading a new classifier from " << saveFilename << endl; Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(saveFilename); // predict using loaded classifier cout << "predicting the dataset using the loaded classifier..."; Mat responses2; lr2->predict(data_test, responses2); cout << "done!" << endl; // calculate accuracy cout << labels_test.t() << endl; cout << responses2.t() << endl; cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses2) << "%" << endl; waitKey(0); return 0; }