OpenCV SVM 内核示例

Question

OpenCV 文档 give the following SVM kernel type example :

A comparison of different kernels on the following 2D test case with four classes. Four SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score. Bright means max-score > 0, dark means max-score < 0.

在哪里可以找到生成此示例的示例代码？

具体来说，SVM predict() 方法大概会返回一个标签值，而不是 max-score。它如何返回最高分数？

请注意，引用声明它使用 SVM::C_SVC，这是一个分类，而不是一个回归类型。

Answer 1

你可以用2-class SVM得到分数，如果你通过RAW_OUTPUT来预测:

// svm.cpp, SVMImpl::predict(...) , line 1917 
bool returnDFVal = (flags & RAW_OUTPUT) != 0;

// svm.cpp, PredictBody::operator(), line 1896,
float result = returnDFVal && class_count == 2 ?
                    (float)sum : (float)(svm->class_labels.at<int>(k));

然后你需要训练 4 个不同的 2 类 SVM，一个针对休息。

这些是我在这些样本上得到的结果:

INTER 与 trainAuto

CHI2 与 trainAuto

RBF with train (C = 0.1, gamma = 0.001)(trainAuto 在这种情况下过度拟合)

这是代码。您可以使用 AUTO_TRAIN_ENABLED bool 变量启用 trainAuto，并且可以设置 KERNEL 以及图像尺寸等。

#include <opencv2/opencv.hpp>
#include <vector>
#include <algorithm>
using namespace std;
using namespace cv;
using namespace cv::ml;

int main()
{
    const int WIDTH = 512;
    const int HEIGHT = 512;
    const int N_SAMPLES_PER_CLASS = 10;
    const float NON_LINEAR_SAMPLES_RATIO = 0.1;
    const int KERNEL = SVM::CHI2;
    const bool AUTO_TRAIN_ENABLED = false;


    int N_NON_LINEAR_SAMPLES = N_SAMPLES_PER_CLASS * NON_LINEAR_SAMPLES_RATIO;
    int N_LINEAR_SAMPLES = N_SAMPLES_PER_CLASS - N_NON_LINEAR_SAMPLES;



    vector<Scalar> colors{Scalar(255,0,0), Scalar(0,255,0), Scalar(0,0,255), Scalar(0,255,255)};
    vector<Vec3b> colorsv{ Vec3b(255, 0, 0), Vec3b(0, 255, 0), Vec3b(0, 0, 255), Vec3b(0, 255, 255) };
    vector<Vec3b> colorsv_shaded{ Vec3b(200, 0, 0), Vec3b(0, 200, 0), Vec3b(0, 0, 200), Vec3b(0, 200, 200) };

    Mat1f data(4 * N_SAMPLES_PER_CLASS, 2);
    Mat1i labels(4 * N_SAMPLES_PER_CLASS, 1);

    RNG rng(0);

    ////////////////////////
    // Set training data
    ////////////////////////

    // Class 1
    Mat1f class1 = data.rowRange(0, 0.5 * N_LINEAR_SAMPLES);
    Mat1f x1 = class1.colRange(0, 1);
    Mat1f y1 = class1.colRange(1, 2);
    rng.fill(x1, RNG::UNIFORM, Scalar(1), Scalar(WIDTH));
    rng.fill(y1, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT / 8));

    class1 = data.rowRange(0.5 * N_LINEAR_SAMPLES, 1 * N_LINEAR_SAMPLES);
    x1 = class1.colRange(0, 1);
    y1 = class1.colRange(1, 2);
    rng.fill(x1, RNG::UNIFORM, Scalar(1), Scalar(WIDTH));
    rng.fill(y1, RNG::UNIFORM, Scalar(7*HEIGHT / 8), Scalar(HEIGHT));

    class1 = data.rowRange(N_LINEAR_SAMPLES, 1 * N_SAMPLES_PER_CLASS);
    x1 = class1.colRange(0, 1);
    y1 = class1.colRange(1, 2);
    rng.fill(x1, RNG::UNIFORM, Scalar(1), Scalar(WIDTH));
    rng.fill(y1, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));



    // Class 2
    Mat1f class2 = data.rowRange(N_SAMPLES_PER_CLASS, N_SAMPLES_PER_CLASS + N_LINEAR_SAMPLES);
    Mat1f x2 = class2.colRange(0, 1);
    Mat1f y2 = class2.colRange(1, 2);
    rng.fill(x2, RNG::NORMAL, Scalar(3 * WIDTH / 4), Scalar(WIDTH/16));
    rng.fill(y2, RNG::NORMAL, Scalar(HEIGHT / 2), Scalar(HEIGHT/4));

    class2 = data.rowRange(N_SAMPLES_PER_CLASS + N_LINEAR_SAMPLES, 2 * N_SAMPLES_PER_CLASS);
    x2 = class2.colRange(0, 1);
    y2 = class2.colRange(1, 2);
    rng.fill(x2, RNG::UNIFORM, Scalar(1), Scalar(WIDTH));
    rng.fill(y2, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));



    // Class 3
    Mat1f class3 = data.rowRange(2 * N_SAMPLES_PER_CLASS, 2 * N_SAMPLES_PER_CLASS + N_LINEAR_SAMPLES);
    Mat1f x3 = class3.colRange(0, 1);
    Mat1f y3 = class3.colRange(1, 2);
    rng.fill(x3, RNG::NORMAL, Scalar(WIDTH / 4), Scalar(WIDTH/8));
    rng.fill(y3, RNG::NORMAL, Scalar(HEIGHT / 2), Scalar(HEIGHT/8));

    class3 = data.rowRange(2*N_SAMPLES_PER_CLASS + N_LINEAR_SAMPLES, 3 * N_SAMPLES_PER_CLASS);
    x3 = class3.colRange(0, 1);
    y3 = class3.colRange(1, 2);
    rng.fill(x3, RNG::UNIFORM, Scalar(1), Scalar(WIDTH));
    rng.fill(y3, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));



    // Class 4
    Mat1f class4 = data.rowRange(3 * N_SAMPLES_PER_CLASS, 3 * N_SAMPLES_PER_CLASS + 0.5 * N_LINEAR_SAMPLES);
    Mat1f x4 = class4.colRange(0, 1);
    Mat1f y4 = class4.colRange(1, 2);
    rng.fill(x4, RNG::NORMAL, Scalar(WIDTH / 2), Scalar(WIDTH / 16));
    rng.fill(y4, RNG::NORMAL, Scalar(HEIGHT / 4), Scalar(HEIGHT / 16));

    class4 = data.rowRange(3 * N_SAMPLES_PER_CLASS + 0.5 * N_LINEAR_SAMPLES, 3 * N_SAMPLES_PER_CLASS + N_LINEAR_SAMPLES);
    x4 = class4.colRange(0, 1);
    y4 = class4.colRange(1, 2);
    rng.fill(x4, RNG::NORMAL, Scalar(WIDTH / 2), Scalar(WIDTH / 16));
    rng.fill(y4, RNG::NORMAL, Scalar(3 * HEIGHT / 4), Scalar(HEIGHT / 16));

    class4 = data.rowRange(3 * N_SAMPLES_PER_CLASS + N_LINEAR_SAMPLES, 4 * N_SAMPLES_PER_CLASS);
    x4 = class4.colRange(0, 1);
    y4 = class4.colRange(1, 2);
    rng.fill(x4, RNG::UNIFORM, Scalar(1), Scalar(WIDTH));
    rng.fill(y4, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));



    // Labels
    labels.rowRange(0*N_SAMPLES_PER_CLASS, 1*N_SAMPLES_PER_CLASS).setTo(1);
    labels.rowRange(1*N_SAMPLES_PER_CLASS, 2*N_SAMPLES_PER_CLASS).setTo(2);
    labels.rowRange(2*N_SAMPLES_PER_CLASS, 3*N_SAMPLES_PER_CLASS).setTo(3);
    labels.rowRange(3*N_SAMPLES_PER_CLASS, 4*N_SAMPLES_PER_CLASS).setTo(4);



    // Draw training data
    Mat3b samples(HEIGHT, WIDTH, Vec3b(0,0,0));
    for (int i = 0; i < labels.rows; ++i)
    {
        circle(samples, Point(data(i, 0), data(i, 1)), 3, colors[labels(i,0) - 1], CV_FILLED);
    }



    //////////////////////////
    // SVM
    //////////////////////////

    // SVM label 1
    Ptr<SVM> svm1 = SVM::create();
    svm1->setType(SVM::C_SVC);
    svm1->setKernel(KERNEL);

    Mat1i labels1 = (labels != 1) / 255;

    if (AUTO_TRAIN_ENABLED)
    {
        Ptr<TrainData> td1 = TrainData::create(data, ROW_SAMPLE, labels1);
        svm1->trainAuto(td1);
    }
    else
    {
        svm1->setC(0.1);
        svm1->setGamma(0.001);
        svm1->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));
        svm1->train(data, ROW_SAMPLE, labels1);
    }

    // SVM label 2
    Ptr<SVM> svm2 = SVM::create();
    svm2->setType(SVM::C_SVC);
    svm2->setKernel(KERNEL);

    Mat1i labels2 = (labels != 2) / 255;

    if (AUTO_TRAIN_ENABLED)
    {
        Ptr<TrainData> td2 = TrainData::create(data, ROW_SAMPLE, labels2);
        svm2->trainAuto(td2);
    }
    else
    {
        svm2->setC(0.1);
        svm2->setGamma(0.001);
        svm2->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));
        svm2->train(data, ROW_SAMPLE, labels2);
    }

    // SVM label 3
    Ptr<SVM> svm3 = SVM::create();
    svm3->setType(SVM::C_SVC);
    svm3->setKernel(KERNEL);

    Mat1i labels3 = (labels != 3) / 255;

    if (AUTO_TRAIN_ENABLED)
    {
        Ptr<TrainData> td3 = TrainData::create(data, ROW_SAMPLE, labels3);
        svm3->trainAuto(td3);
    }
    else
    {
        svm3->setC(0.1);
        svm3->setGamma(0.001);
        svm3->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));
        svm3->train(data, ROW_SAMPLE, labels3);
    }


    // SVM label 4
    Ptr<SVM> svm4 = SVM::create();
    svm4->setType(SVM::C_SVC);
    svm4->setKernel(KERNEL);

    Mat1i labels4 = (labels != 4) / 255;

    if (AUTO_TRAIN_ENABLED)
    {
        Ptr<TrainData> td4 = TrainData::create(data, ROW_SAMPLE, labels4);
        svm4->trainAuto(td4);
    }
    else
    {
        svm4->setC(0.1);
        svm4->setGamma(0.001);
        svm4->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));
        svm4->train(data, ROW_SAMPLE, labels4);
    }



    //////////////////////////
    // Show regions
    //////////////////////////

    Mat3b regions(HEIGHT, WIDTH);
    Mat1f R(HEIGHT, WIDTH);

    Mat1f R1(HEIGHT, WIDTH);
    Mat1f R2(HEIGHT, WIDTH);
    Mat1f R3(HEIGHT, WIDTH);
    Mat1f R4(HEIGHT, WIDTH);


    for (int r = 0; r < HEIGHT; ++r)
    {
        for (int c = 0; c < WIDTH; ++c)
        {
            Mat1f sample = (Mat1f(1,2) << c, r);

            vector<float> responses(4);

            responses[0] = svm1->predict(sample, noArray(), StatModel::RAW_OUTPUT);
            responses[1] = svm2->predict(sample, noArray(), StatModel::RAW_OUTPUT);
            responses[2] = svm3->predict(sample, noArray(), StatModel::RAW_OUTPUT);
            responses[3] = svm4->predict(sample, noArray(), StatModel::RAW_OUTPUT);

            int best_class = distance(responses.begin(), max_element(responses.begin(), responses.end()));
            float best_response = responses[best_class];

            // View responses for each SVM, and the best responses
            R(r,c) = best_response;
            R1(r, c) = responses[0];
            R2(r, c) = responses[1];
            R3(r, c) = responses[2];
            R4(r, c) = responses[3];

            if (best_response >= 0) {
                regions(r, c) = colorsv[best_class];
            }
            else {
                regions(r, c) = colorsv_shaded[best_class];
            }


        }
    }


    imwrite("svm_samples.png", samples);
    imwrite("svm_x.png", regions);

    imshow("Samples", samples);
    imshow("Regions", regions);
    waitKey();

    return 0;
}