opencv中svm原始碼

線性SVM

#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui.hpp>
#include <opencv2/ml.hpp>

using namespace cv;
using namespace cv::ml;

int main(int, char**)
{
    // Data for visual representation
    int width = 512, height = 512;
    Mat image = Mat::zeros(height, width, CV_8UC3);    

    // Set up training data
    //! [setup1]
    int labels[5] = {1, 1, -1, -1,-1};                 
	float trainingData[5][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501},{10,10} }; //建立訓練資料，必須是線性可分的。
    //! [setup1]
    //! [setup2]
    Mat trainingDataMat(5, 2, CV_32FC1, trainingData);                          //32位float型
    Mat labelsMat(5, 1, CV_32SC1, labels);                                      //32位int型
    //! [setup2]


    // Train the SVM
    //! [init]
    Ptr<SVM> svm = SVM::create();                                               //採用智慧指標調研SVM
    svm->setType(SVM::C_SVC);
    svm->setKernel(SVM::LINEAR);                                               //線性可分因此採用線性核
    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));     終止準則函式：當迭代次數達到最大值時終止
    //! [init]
    //! [train]
    svm->train(trainingDataMat, ROW_SAMPLE, labelsMat);                   
    //! [train]

    // Show the decision regions given by the SVM
    //! [show]
    Vec3b green(0,255,0), blue (255,0,0);
    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);//predict是用來預測的，引數為：樣本、返回值型別（如果值為ture而且是一個2類問題則返回判決函式值，否則返回類標籤）

            if (response == 1)
                image.at<Vec3b>(i,j)  = green;
            else if (response == -1)
                image.at<Vec3b>(i,j)  = blue;
        }
    //! [show]

    // Show the training data
    //! [show_data]
    int thickness = -1;
    int lineType = 8;            //線形
    circle(	image, Point(501,  10), 5, Scalar(  0,   0,   0), thickness, lineType );
    circle(	image, Point(255,  10), 5, Scalar(0, 0, 0), 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 );
	circle(image, Point(10, 10), 5, Scalar(255, 255, 255), thickness, lineType);
    //! [show_data]

    // Show support vectors
    //! [show_vectors]
    thickness = 2;
    lineType  = 8;
    Mat sv = svm->getUncompressedSupportVectors();

    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);
    }
    //! [show_vectors]

    imwrite("result.png", image);        // save the image

    imshow("SVM Simple Example", image); // show it to the user
    waitKey(0);

}

非線性SVM

opencv 原始碼中SVM核型別還是LINEAR ，要改成非線性型比如POLY 多項式型

#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui.hpp>
#include <opencv2/ml.hpp>

#define	NTRAINING_SAMPLES	100			// Number of training samples per class
#define FRAC_LINEAR_SEP		0.9f	    // Fraction of samples which compose the linear separable part

using namespace cv;
using namespace cv::ml;
using namespace std;

static void help()
{
    cout<< "\n--------------------------------------------------------------------------" << endl
        << "This program shows Support Vector Machines for Non-Linearly Separable Data. " << endl
        << "Usage:"                                                               << endl
        << "./non_linear_svms" << endl
        << "--------------------------------------------------------------------------"   << endl
        << endl;
}

int main()
{
    help();

    // Data for visual representation
    const int WIDTH = 512, HEIGHT = 512;
    Mat I = Mat::zeros(HEIGHT, WIDTH, CV_8UC3);   //建立畫布

    //--------------------- 1. Set up training data randomly ---------------------------------------
    Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1);
    Mat labels   (2*NTRAINING_SAMPLES, 1, CV_32SC1);

    RNG rng(100); // Random value generation class

    // Set up the linearly separable part of the training data
    int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);  //float型強制轉換為int

    //! [setup1]
    // Generate random points for the class 1
    Mat trainClass = trainData.rowRange(0, nLinearSamples);
    // The x coordinate of the points is in [0, 0.4)
    Mat c = trainClass.colRange(0, 1);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * WIDTH));
    // The y coordinate of the points is in [0, 1)
    c = trainClass.colRange(1,2);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));

    // Generate random points for the class 2
    trainClass = trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES);
    // The x coordinate of the points is in [0.6, 1]
    c = trainClass.colRange(0 , 1);
    rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH));
    // The y coordinate of the points is in [0, 1)
    c = trainClass.colRange(1,2);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
    //! [setup1]

    //------------------ Set up the non-linearly separable part of the training data ---------------
    //! [setup2]
    // Generate random points for the classes 1 and 2
    trainClass = trainData.rowRange(  nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples);
    // The x coordinate of the points is in [0.4, 0.6)
    c = trainClass.colRange(0,1);
    rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH));
    // The y coordinate of the points is in [0, 1)
    c = trainClass.colRange(1,2);
    rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
    //! [setup2]
    //------------------------- Set up the labels for the classes ---------------------------------
    labels.rowRange(                0,   NTRAINING_SAMPLES).setTo(1);  // Class 1
    labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2);  // Class 2

    //------------------------ 2. Set up the support vector machines parameters --------------------
    //------------------------ 3. Train the svm ----------------------------------------------------
    cout << "Starting training process" << endl;
    //! [init]
    Ptr<SVM> svm = SVM::create();
    svm->setType(SVM::C_SVC);
    /*svm->setC(0.1);
    svm->setKernel(SVM::LINEAR);//不應該用線性核*/
	
	svm->setKernel(SVM::POLY); //採用多項式核
	svm->setGamma(3);
	svm->setDegree(1);
    //svm->setKernel(SVM::RBF);
    //svm->setKernel(SVM::SIGMOID);
    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));//個最大迭代次數和容許誤差，
    //! [init]
    //! [train]
    svm->train(trainData, ROW_SAMPLE, labels);
    //! [train]
    cout << "Finished training process" << endl;

    //------------------------ 4. Show the decision regions ----------------------------------------
    //! [show]
    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;
        }
    //! [show]

    //----------------------- 5. Show the training data --------------------------------------------
    //! [show_data]
    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);
    }
    //! [show_data]

    //------------------------- 6. Show support vectors --------------------------------------------
    //! [show_vectors]
    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);
    }
    //! [show_vectors]

    imwrite("result.png", I);	                   // save the Image
    imshow("SVM for Non-Linear Training Data", I); // show it to the user
    waitKey(0);
}

在二維顯示分錯的情況。原因是在在高維空間是可分的，低維空間只是這樣表示表示？  還是用了軟間隔的原因？

degree設為2情況

SVM的引數意義

SVM經常選用的核函式

高斯核

多項式核

sigmoid