1 /*////////////////////////////////////////////////////////////////////////////////////// 2 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 3 4 // By downloading, copying, installing or using the software you agree to this license. 5 // If you do not agree to this license, do not download, install, 6 // copy or use the software. 7 8 // This is a implementation of the Logistic Regression algorithm in C++ in OpenCV. 9 10 // AUTHOR: 11 // Rahul Kavi rahulkavi[at]live[at]com 12 // 13 14 // contains a subset of data from the popular Iris Dataset (taken from 15 // "http://archive.ics.uci.edu/ml/datasets/Iris") 16 17 // # You are free to use, change, or redistribute the code in any way you wish for 18 // # non-commercial purposes, but please maintain the name of the original author. 19 // # This code comes with no warranty of any kind. 20 21 // # 22 // # You are free to use, change, or redistribute the code in any way you wish for 23 // # non-commercial purposes, but please maintain the name of the original author. 24 // # This code comes with no warranty of any kind. 25 26 // # Logistic Regression ALGORITHM 27 28 // License Agreement 29 // For Open Source Computer Vision Library 30 31 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. 32 // Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved. 33 // Third party copyrights are property of their respective owners. 34 35 // Redistribution and use in source and binary forms, with or without modification, 36 // are permitted provided that the following conditions are met: 37 38 // * Redistributions of source code must retain the above copyright notice, 39 // this list of conditions and the following disclaimer. 40 41 // * Redistributions in binary form must reproduce the above copyright notice, 42 // this list of conditions and the following disclaimer in the documentation 43 // and/or other materials provided with the distribution. 44 45 // * The name of the copyright holders may not be used to endorse or promote products 46 // derived from this software without specific prior written permission. 47 48 // This software is provided by the copyright holders and contributors "as is" and 49 // any express or implied warranties, including, but not limited to, the implied 50 // warranties of merchantability and fitness for a particular purpose are disclaimed. 51 // In no event shall the Intel Corporation or contributors be liable for any direct, 52 // indirect, incidental, special, exemplary, or consequential damages 53 // (including, but not limited to, procurement of substitute goods or services; 54 // loss of use, data, or profits; or business interruption) however caused 55 // and on any theory of liability, whether in contract, strict liability, 56 // or tort (including negligence or otherwise) arising in any way out of 57 // the use of this software, even if advised of the possibility of such damage.*/ 58 59 #include <iostream> 60 61 #include <opencv2/core.hpp> 62 #include <opencv2/ml.hpp> 63 #include <opencv2/highgui.hpp> 64 65 using namespace std; 66 using namespace cv; 67 using namespace cv::ml; 68 69 static void showImage(const Mat &data, int columns, const String &name) 70 { 71 Mat bigImage; 72 for(int i = 0; i < data.rows; ++i) 73 { 74 bigImage.push_back(data.row(i).reshape(0, columns)); 75 } 76 imshow(name, bigImage.t()); 77 } 78 79 static float calculateAccuracyPercent(const Mat &original, const Mat &predicted) 80 { 81 return 100 * (float)countNonZero(original == predicted) / predicted.rows; 82 } 83 84 int main() 85 { 86 const String filename = "../data/data01.xml"; 87 cout << "**********************************************************************" << endl; 88 cout << filename 89 << " contains digits 0 and 1 of 20 samples each, collected on an Android device" << endl; 90 cout << "Each of the collected images are of size 28 x 28 re-arranged to 1 x 784 matrix" 91 << endl; 92 cout << "**********************************************************************" << endl; 93 94 Mat data, labels; 95 { 96 cout << "loading the dataset..."; 97 FileStorage f; 98 if(f.open(filename, FileStorage::READ)) 99 { 100 f["datamat"] >> data; 101 f["labelsmat"] >> labels; 102 f.release(); 103 } 104 else 105 { 106 cerr << "file can not be opened: " << filename << endl; 107 return 1; 108 } 109 data.convertTo(data, CV_32F); 110 labels.convertTo(labels, CV_32F); 111 cout << "read " << data.rows << " rows of data" << endl; 112 } 113 114 Mat data_train, data_test; 115 Mat labels_train, labels_test; 116 for(int i = 0; i < data.rows; i++) 117 { 118 if(i % 2 == 0) 119 { 120 data_train.push_back(data.row(i)); 121 labels_train.push_back(labels.row(i)); 122 } 123 else 124 { 125 data_test.push_back(data.row(i)); 126 labels_test.push_back(labels.row(i)); 127 } 128 } 129 cout << "training/testing samples count: " << data_train.rows << "/" << data_test.rows << endl; 130 131 // display sample image 132 showImage(data_train, 28, "train data"); 133 showImage(data_test, 28, "test data"); 134 135 // simple case with batch gradient 136 cout << "training..."; 137 //! [init] 138 Ptr<LogisticRegression> lr1 = LogisticRegression::create(); 139 lr1->setLearningRate(0.001); 140 lr1->setIterations(10); 141 lr1->setRegularization(LogisticRegression::REG_L2); 142 lr1->setTrainMethod(LogisticRegression::BATCH); 143 lr1->setMiniBatchSize(1); 144 //! [init] 145 lr1->train(data_train, ROW_SAMPLE, labels_train); 146 cout << "done!" << endl; 147 148 cout << "predicting..."; 149 Mat responses; 150 lr1->predict(data_test, responses); 151 cout << "done!" << endl; 152 153 // show prediction report 154 cout << "original vs predicted:" << endl; 155 labels_test.convertTo(labels_test, CV_32S); 156 cout << labels_test.t() << endl; 157 cout << responses.t() << endl; 158 cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses) << "%" << endl; 159 160 // save the classfier 161 const String saveFilename = "NewLR_Trained.xml"; 162 cout << "saving the classifier to " << saveFilename << endl; 163 lr1->save(saveFilename); 164 165 // load the classifier onto new object 166 cout << "loading a new classifier from " << saveFilename << endl; 167 Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(saveFilename); 168 169 // predict using loaded classifier 170 cout << "predicting the dataset using the loaded classfier..."; 171 Mat responses2; 172 lr2->predict(data_test, responses2); 173 cout << "done!" << endl; 174 175 // calculate accuracy 176 cout << labels_test.t() << endl; 177 cout << responses2.t() << endl; 178 cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses2) << "%" << endl; 179 180 waitKey(0); 181 return 0; 182 } 183
