基于qt和opencv3实现机器学习之：利用逻辑斯谛回归（logistic regression)分类

本文用它来进行手写数字分类。在opencv3.0中提供了一个xml文件，里面存放了40个样本，分别是20个数字0的手写体和20个数字1的手写体。本来每个数字的手写体是一张28*28的小图片，但opencv把它reshape了一下，变成了1*784 的向量，然后放在xml文件中。这个文件的位置：

第一步：在pro文件里面设置路径

INCLUDEPATH += /usr/local/include \                /usr/local/include/opencv \                /usr/local/include/opencv2LIBS += /usr/local/lib/libopencv_highgui.so \        /usr/local/lib/libopencv_core.so    \        /usr/local/lib/libopencv_imgproc.so \        /usr/local/lib/libopencv_imgcodecs.so \        /usr/local/lib/libopencv_ml.so

第二步：建立cpp

#include "opencv2/opencv.hpp"#include "opencv2/imgproc.hpp"#include "opencv2/highgui.hpp"#include "opencv2/ml.hpp"#include <iostream>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 = "/home/xyl/opencv/samples/data/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 classfier    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 classfier...";    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;}

第三步：运行程序