OpenCV中的SVM

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    #include "stdafx.h"    #include <opencv2/opencv.hpp>    #include <iostream>    #include <fstream>    #include <sstream>    #include <math.h>    #include <vector>    #include <windows.h>    #include <io.h>    #include <time.h>    using namespace cv;    using namespace std;    #define HORIZONTAL    1    #define VERTICAL    0    CvSVM svm;    const char strCharacters[] = { '0', '1', '2', '3', '4', '5', \    '6', '7', '8', '9' };    const int numCharacter = 10;    const int numNeurons = 40;    const int predictSize = 10;    void generateRandom(int n, int test_num, int min, int max, vector<int>*mark_samples)    {        int range = max - min;        int index = rand() % range + min;        if (mark_samples->at(index) == 0)        {            mark_samples->at(index) = 1;            n++;        }        if (n < test_num)            generateRandom(n, test_num, min, max, mark_samples);    }    vector<string> getFiles(const string &folder,        const bool all /* = true */) {        vector<string> files;        list<string> subfolders;        subfolders.push_back(folder);        while (!subfolders.empty()) {            string current_folder(subfolders.back());            if (*(current_folder.end() - 1) != '/') {                current_folder.append("/*");            }            else {                current_folder.append("*");            }            subfolders.pop_back();            struct _finddata_t file_info;            long file_handler = _findfirst(current_folder.c_str(), &file_info);            while (file_handler != -1) {                if (all &&                    (!strcmp(file_info.name, ".") || !strcmp(file_info.name, ".."))) {                    if (_findnext(file_handler, &file_info) != 0) break;                    continue;                }                if (file_info.attrib & _A_SUBDIR) {                    // it's a sub folder                    if (all) {                        // will search sub folder                        string folder(current_folder);                        folder.pop_back();                        folder.append(file_info.name);                        subfolders.push_back(folder.c_str());                    }                }                else {                    // it's a file                    string file_path;                    // current_folder.pop_back();                    file_path.assign(current_folder.c_str()).pop_back();                    file_path.append(file_info.name);                    files.push_back(file_path);                }                if (_findnext(file_handler, &file_info) != 0) break;            }  // while            _findclose(file_handler);        }        return files;    }    void AppendText(string filename, string text)    {        fstream ftxt;        ftxt.open(filename, ios::out | ios::app);        if (ftxt.fail())        {            cout << "创建文件失败!" << endl;            getchar();        }        ftxt << text << endl;        ftxt.close();    }    // ！获取垂直和水平方向直方图    Mat ProjectedHistogram(Mat img, int t)    {        int sz = (t) ? img.rows : img.cols;        Mat mhist = Mat::zeros(1, sz, CV_32F);        for (int j = 0; j<sz; j++) {            Mat data = (t) ? img.row(j) : img.col(j);            mhist.at<float>(j) = countNonZero(data);    //统计这一行或一列中，非零元素的个数，并保存到mhist中        }        //Normalize histogram        double min, max;        minMaxLoc(mhist, &min, &max);        if (max>0)            mhist.convertTo(mhist, -1, 1.0f / max, 0);//用mhist直方图中的最大值，归一化直方图        return mhist;    }    Mat features(Mat in, int sizeData)    {        Mat lowData;        resize(in, lowData, Size(sizeData, sizeData));        //Last 10 is the number of moments components        int numCols =  lowData.cols*lowData.cols;        //int numCols = vhist.cols + hhist.cols;        Mat out = Mat::zeros(1, numCols, CV_32F);        //Asign values to feature,ANN的样本特征为水平、垂直直方图和低分辨率图像所组成的矢量        int j = 0;        for (int x = 0; x<lowData.cols; x++)        {            for (int y = 0; y<lowData.rows; y++) {                out.at<float>(j) = (float)lowData.at<unsigned char>(x, y);                j++;            }        }        //if(DEBUG)        //  cout << out << "\n===========================================\n";        return out;    }    Mat features2(Mat in, int sizeData)    {        //Histogram features        Mat vhist = ProjectedHistogram(in, VERTICAL);        Mat hhist = ProjectedHistogram(in, HORIZONTAL);        //Low data feature        Mat lowData;        resize(in, lowData, Size(sizeData, sizeData));        //Last 10 is the number of moments components        int numCols = vhist.cols + hhist.cols + lowData.cols*lowData.cols;        //int numCols = vhist.cols + hhist.cols;        Mat out = Mat::zeros(1, numCols, CV_32F);        //Asign values to feature,ANN的样本特征为水平、垂直直方图和低分辨率图像所组成的矢量        int j = 0;        for (int i = 0; i<vhist.cols; i++)        {            out.at<float>(j) = vhist.at<float>(i);            j++;        }        for (int i = 0; i<hhist.cols; i++)        {            out.at<float>(j) = hhist.at<float>(i);            j++;        }        for (int x = 0; x<lowData.cols; x++)        {            for (int y = 0; y<lowData.rows; y++) {                out.at<float>(j) = (float)lowData.at<unsigned char>(x, y);                j++;            }        }        //if(DEBUG)        //  cout << out << "\n===========================================\n";        return out;    }    void Svm_Train(Mat TrainData, Mat classes, int sizeData)    {        CvTermCriteria criteria;        criteria = cvTermCriteria(CV_TERMCRIT_EPS, 10000, FLT_EPSILON);        CvSVMParams SVM_params;        SVM_params = CvSVMParams(CvSVM::C_SVC, CvSVM::LINEAR, 0, 1, 0, 1, 0, 0, NULL, criteria);        //SVM_params  = CvSVMParams(CvSVM::C_SVC, CvSVM::RBF, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, NULL, criteria);        svm.train(TrainData, classes, Mat(), Mat(), SVM_params);        /*svm.train_auto(TrainData, classes, cv::Mat(),            cv::Mat(),            SVM_params,            10,            CvSVM::get_default_grid(CvSVM::C),            CvSVM::get_default_grid(CvSVM::GAMMA),            CvSVM::get_default_grid(CvSVM::P),            CvSVM::get_default_grid(CvSVM::NU),            CvSVM::get_default_grid(CvSVM::COEF),            CvSVM::get_default_grid(CvSVM::DEGREE),            true);*/        string file_path;        stringstream ss(stringstream::in | stringstream::out);        ss << "train/" << sizeData;        file_path = ss.str();        FileStorage fsTo(file_path, cv::FileStorage::WRITE);        svm.write(*fsTo, "svm");    }    int recog(Mat features)    {        int result = -1;        result = svm.predict(features);        return result;    }    float SVM_test(Mat samples_set, Mat sample_labels)    {        int correctNum = 0;        float accurate = 0;        for (int i = 0; i < samples_set.rows; i++)        {            int result = recog(samples_set.row(i));            if (result == sample_labels.at<int>(i))                correctNum++;        }        accurate = (float)correctNum / samples_set.rows;        return accurate;    }    int saveTrainData()    {        cout << "Begin saveTrainData" << endl;        Mat classes;        Mat trainingDataf5;        Mat trainingDataf10;        Mat trainingDataf15;        Mat trainingDataf20;        vector<int> trainingLabels;        string path = "charSamples";        for (int i = 0; i < numCharacter; i++)        {            cout << "Character: " << strCharacters[i] << "\n";            stringstream ss(stringstream::in | stringstream::out);            ss << path << "/" << strCharacters[i];            auto files = getFiles(ss.str(), 1);            int size = files.size();            for (int j = 0; j < size; j++)            {                cout << files[j].c_str() << endl;                Mat img = imread(files[j].c_str(), 0);                Mat f5 =  features(img, 5);                Mat f10 = features(img, 10);                Mat f15 = features(img, 15);                Mat f20 = features(img, 20);                trainingDataf5.push_back(f5);                trainingDataf10.push_back(f10);                trainingDataf15.push_back(f15);                trainingDataf20.push_back(f20);                trainingLabels.push_back(i);            //每一幅字符图片所对应的字符类别索引下标            }        }        trainingDataf5.convertTo(trainingDataf5, CV_32FC1);        trainingDataf10.convertTo(trainingDataf10, CV_32FC1);        trainingDataf15.convertTo(trainingDataf15, CV_32FC1);        trainingDataf20.convertTo(trainingDataf20, CV_32FC1);        Mat(trainingLabels).copyTo(classes);        FileStorage fs("train/features_data.xml", FileStorage::WRITE);        fs << "TrainingDataF5" << trainingDataf5;        fs << "TrainingDataF10" << trainingDataf10;        fs << "TrainingDataF15" << trainingDataf15;        fs << "TrainingDataF20" << trainingDataf20;        fs << "classes" << classes;        fs.release();        cout << "End saveTrainData" << endl;        return 0;    }    void SVM_Cross_Train_and_Test(int Imagsize)    {        String training;        Mat TrainingData;        Mat Classes;        FileStorage fs;        fs.open("train/features_data.xml", FileStorage::READ);        cout << "Begin to SVM_Cross_Train_and_Test " << endl;        char *txt = new char[50];        sprintf(txt, "交叉训练，特征维度%d,", 40 + Imagsize * Imagsize);        AppendText("output.txt", txt);        cout << txt << endl;        stringstream ss(stringstream::in | stringstream::out);        ss << "TrainingDataF" << Imagsize;        training = ss.str();        fs[training] >> TrainingData;        fs["classes"] >> Classes;        fs.release();        float result = 0.0;        srand(time(NULL));        vector<int> markSample(TrainingData.rows, 0);        generateRandom(0, 50, 0, TrainingData.rows - 1, &markSample);        Mat train_set, train_labels;        Mat sample_set, sample_labels;        for (int i = 0; i < TrainingData.rows; i++)        {            if (markSample[i] == 1)            {                sample_set.push_back(TrainingData.row(i));                sample_labels.push_back(Classes.row(i));            }            else            {                train_set.push_back(TrainingData.row(i));                train_labels.push_back(Classes.row(i));            }        }        Svm_Train(train_set, train_labels);        result = SVM_test(sample_set, sample_labels);        sprintf(txt, "正确率%f\n", result);        cout << txt << endl;        AppendText("output.txt", txt);        cout << "End the SVM_Cross_Train_and_Test" << endl;        cout << endl;    }    void SVM_saveModel(int sizeData)    {        FileStorage fs;        fs.open("train/features_data.xml", FileStorage::READ);        Mat TrainingData;        Mat Classes;        string training;        if (1)        {            stringstream ss(stringstream::in | stringstream::out);            ss << "TrainingDataF" << sizeData;            training = ss.str();        }        fs[training] >> TrainingData;        fs["classes"] >> Classes;        cout << "Begin to saveModelChar predictSize:" << sizeData            << endl;        Svm_Train(TrainingData, Classes);        cout << "End the saveModelChar" << endl;        string model_name = "train/svm.xml";        FileStorage fsTo(model_name, cv::FileStorage::WRITE);        svm.write(*fsTo, "svm");    }    void test_img(const string& filename)    {        Mat src_img = imread(filename, 0);        Mat feat = features(src_img, 10);        int ret = svm.predict(feat);        cout << ret << endl;    }    int main()    {        int DigitSize[4] = { 5, 10, 15, 20};        cout << "To be begin." << endl;        saveTrainData();        for (int i = 0; i < 4; i++)        {            SVM_Cross_Train_and_Test(DigitSize[i]);        }        cout << "To be end." << endl;        /*SVM_saveModel(10);        svm.load("train/svm_auto_rbf.xml");        test_img("91.jpg");    */        return 0;    }
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