Opencv Machine Learning Library MLL

When learning machine learning, we basically use MATLAB and python to write algorithms and perform tests;

Recently, thanks to the use of opencv to write homework, we have taken a look at the Machine Learning Library (MLL) of opencv ).

Let's take a look at the main components of MLL: the statistical model is a base class, and the following K-nn and SVM are their subclasses.

I don't like this statistical attribute very much. Statistics has been rampant in the ml field for many years and it feels that the temperature has dropped.

Let's take a look at the statistical model:

class CV_EXPORTS_W CvStatModel{public:    CvStatModel();    virtual ~CvStatModel();    virtual void clear();    CV_WRAP virtual void save( const char* filename, const char* name=0 ) const;    CV_WRAP virtual void load( const char* filename, const char* name=0 );    virtual void write( CvFileStorage* storage, const char* name ) const;    virtual void read( CvFileStorage* storage, CvFileNode* node );    virtual bool train(const Mat& train_data, const Mat& responses, Mat(), Mat(), CVParms params );    virtual float predict(const Mat& sample, ...);protected:    const char* default_model_name;};

Void cvstatmodel: clear () clear memory reset model status;

Void cvstatmodel: Save ()/load () Save/load files and models;

Void cvstatmodel: Read ()
/Write () reads and writes files and models;

Bool cvstatmodel: train () training model;

Float cvstatmodel: predict () prediction sample result;

Naive Bayes, k-nn, SVM, and decision tree inherit cvstatmodel;

The basic framework for using these methods is:

Method. Train (train_data, responses, MAT (), MAT (), Params );

Method. predict (samplemat );

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A specific example <Support
Vector Machines for non-linearly separable data>

#include <iostream>#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/ml/ml.hpp>#defineNTRAINING_SAMPLES100// Number of training samples per class#define FRAC_LINEAR_SEP0.9f    // Fraction of samples which compose the linear separable partusing namespace cv;using namespace std;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);    //--------------------- 1. Set up training data randomly ---------------------------------------    Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1);    Mat labels   (2*NTRAINING_SAMPLES, 1, CV_32FC1);        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);    // 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));    //------------------ Set up the non-linearly separable part of the training data ---------------    // 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));        //------------------------- 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 --------------------    CvSVMParams params;    params.svm_type    = SVM::C_SVC;    params.C    = 0.1;    params.kernel_type = SVM::LINEAR;    params.term_crit   = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);    //------------------------ 3. Train the svm ----------------------------------------------------    cout << "Starting training process" << endl;    CvSVM svm;    svm.train(trainData, labels, Mat(), Mat(), params);    cout << "Finished training process" << endl;        //------------------------ 4. Show the decision regions ----------------------------------------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;        }    //----------------------- 5. Show the training 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);    }    //------------------------- 6. Show support vectors --------------------------------------------    thick = 2;    lineType  = 8;    int x     = svm.get_support_vector_count();    for (int i = 0; i < x; ++i)    {        const float* v = svm.get_support_vector(i);        circle(I,  Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick, lineType);    }    imwrite("result.png", I);                   // save the Image    imshow("SVM for Non-Linear Training Data", I); // show it to the user    waitKey(0);}

Result: