opencv svm分类

void svm(){    // 视觉表达数据的设置    int width = 512, height = 512;    Mat image = Mat::zeros(height, width, CV_8UC3);    //建立训练数据    int labels[4] = { 1, -1, -1, -1 };    Mat labelsMat(4, 1, CV_32SC1, labels);    InputArray svmOutput(labelsMat);    float trainingData[4][2] = { { 501, 10 },{ 255, 10 },{ 501, 255 },{ 10, 501 } };    Mat trainingDataMat(4, 2, CV_32FC1, trainingData);    InputArray svmInput(trainingDataMat);    //设置支持向量机的参数    Ptr<SVM> svm = SVM::create();    svm->setType(SVM::C_SVC);    svm->setKernel(SVM::LINEAR);    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));    // 训练支持向量机    svm->train(svmInput, ROW_SAMPLE, svmOutput);    Vec3b green(0, 255, 0), blue(255, 0, 0);    //显示由SVM给出的决定区域    for (int i = 0; i < image.rows; ++i)        for (int j = 0; j < image.cols; ++j)        {            Mat sampleMat = (Mat_<float>(1, 2) << j, i);            float response = svm->predict(sampleMat);            if (response == 1)                image.at<Vec3b>(i, j) = green;            else if (response == -1)                image.at<Vec3b>(i, j) = blue;        }    //显示训练数据    int thickness = -1;    int lineType = 8;    circle(image, Point(501, 10), 5, Scalar(0, 0, 0), thickness, lineType);    circle(image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType);    circle(image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);    circle(image, Point(10, 501), 5, Scalar(255, 255, 255), thickness, lineType);    //显示支持向量    thickness = 2;    lineType = 8;    Mat sv = svm->getSupportVectors();    for (int i = 0; i < sv.rows; ++i)    {        const float* v = sv.ptr<float>(i);        circle(image, Point((int)v[0], (int)v[1]), 6, Scalar(128, 128, 128), thickness, lineType);    }    imwrite("result.png", image);        // 保存图像    imshow("SVM Simple Example", image); // 显示图像    waitKey(0);    return ;}void svm2(){    #define NTRAINING_SAMPLES   100         // 每类训练样本的数量    #define FRAC_LINEAR_SEP     0.9f        // 部分(Fraction)线性可分的样本组成部分    //设置视觉表达的参数    const int WIDTH = 512, HEIGHT = 512;    Mat I = Mat::zeros(HEIGHT, WIDTH, CV_8UC3);    //随机建立训练数据    Mat trainData(2 * NTRAINING_SAMPLES, 2, CV_32FC1);    InputArray svmInput(trainData);    Mat labels(2 * NTRAINING_SAMPLES, 1, CV_32SC1);    InputArray svmOutput(labels);    RNG rng(100); // 随机生成值    //建立训练数据的线性可分的组成部分    int nLinearSamples = (int)(FRAC_LINEAR_SEP * NTRAINING_SAMPLES);    // 为Class1生成随机点    Mat trainClass = trainData.rowRange(0, nLinearSamples);    // 点的x坐标为[0,0.4)    Mat c = trainClass.colRange(0, 1);    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * WIDTH));    // 点的Y坐标为[0,1)    c = trainClass.colRange(1, 2);    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));    // 为Class2生成随机点    trainClass = trainData.rowRange(2 * NTRAINING_SAMPLES - nLinearSamples, 2 * NTRAINING_SAMPLES);    // 点的x坐标为[0.6, 1]    c = trainClass.colRange(0, 1);    rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH));    // 点的Y坐标为[0, 1)    c = trainClass.colRange(1, 2);    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));    //建立训练数据的非线性可分组成部分    // 随机生成Class1和Class2的点    trainClass = trainData.rowRange(nLinearSamples, 2 * NTRAINING_SAMPLES - nLinearSamples);    // 点的x坐标为[0.4, 0.6)    c = trainClass.colRange(0, 1);    rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH));    // 点的y坐标为[0, 1)    c = trainClass.colRange(1, 2);    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));    //为类设置标签    labels.rowRange(0, NTRAINING_SAMPLES).setTo(1);  // Class 1    labels.rowRange(NTRAINING_SAMPLES, 2 * NTRAINING_SAMPLES).setTo(2);  // Class 2    //设置支持向量机的参数    Ptr<SVM> svm = SVM::create();    svm->setType(SVM::C_SVC);    svm->setKernel(SVM::LINEAR);    svm->setC(0.1);    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));    //训练支持向量机    cout << "Starting training process" << endl;    svm->train(svmInput, ROW_SAMPLE, svmOutput);    cout << "Finished training process" << endl;    //标出决策区域    Vec3b green(0, 100, 0), blue(100, 0, 0);    for (int i = 0; i < I.rows; ++i)        for (int j = 0; j < I.cols; ++j)        {            Mat sampleMat = (Mat_<float>(1, 2) << i, j);            float response = svm->predict(sampleMat);            if (response == 1)    I.at<Vec3b>(j, i) = green;            else if (response == 2)    I.at<Vec3b>(j, i) = blue;        }    //显示训练数据    int thick = -1;    int lineType = 8;    float px, py;    // Class 1    for (int i = 0; i < NTRAINING_SAMPLES; ++i)    {        px = trainData.at<float>(i, 0);        py = trainData.at<float>(i, 1);        circle(I, Point((int)px, (int)py), 3, Scalar(0, 255, 0), thick, lineType);    }    // Class 2    for (int i = NTRAINING_SAMPLES; i <2 * NTRAINING_SAMPLES; ++i)    {        px = trainData.at<float>(i, 0);        py = trainData.at<float>(i, 1);        circle(I, Point((int)px, (int)py), 3, Scalar(255, 0, 0), thick, lineType);    }    //显示支持向量    thick = 2;    lineType = 8;    Mat sv = svm->getSupportVectors();    for (int i = 0; i < sv.rows; ++i)    {        const float* v = sv.ptr<float>(i);        circle(I, Point((int)v[0], (int)v[1]), 6, Scalar(128, 128, 128), thick, lineType);    }    imwrite("result.png", I);                      //保存图像到文件    imshow("SVM for Non-Linear Training Data", I); // 显示最终窗口    waitKey(0);    return ;}