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samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp

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+#include <iostream>
+#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;
+using namespace std;
+
+static void help()
+{
+    cout<< "\n--------------------------------------------------------------------------" << endl
+        << "This program shows Support Vector Machines for Non-Linearly Separable Data. " << endl
+        << "--------------------------------------------------------------------------"   << endl
+        << endl;
+}
+
+int main()
+{
+    help();
+
+    const int NTRAINING_SAMPLES = 100;         // Number of training samples per class
+    const float FRAC_LINEAR_SEP = 0.9f;        // Fraction of samples which compose the linear separable part
+
+    // 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_32F);
+    Mat labels   (2*NTRAINING_SAMPLES, 1, CV_32S);
+
+    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);
+
+    //! [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(0), 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(0), 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(0), 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(0), 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 --------------------
+    cout << "Starting training process" << endl;
+    //! [init]
+    Ptr<SVM> svm = SVM::create();
+    svm->setType(SVM::C_SVC);
+    svm->setC(0.1);
+    svm->setKernel(SVM::LINEAR);
+    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));
+    //! [init]
+
+    //------------------------ 3. Train the svm ----------------------------------------------------
+    //! [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) << j, i);
+            float response = svm->predict(sampleMat);
+
+            if      (response == 1) I.at<Vec3b>(i,j) = green;
+            else if (response == 2) I.at<Vec3b>(i,j) = blue;
+        }
+    }
+    //! [show]
+
+    //----------------------- 5. Show the training data --------------------------------------------
+    //! [show_data]
+    int thick = -1;
+    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);
+    }
+    // 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);
+    }
+    //! [show_data]
+
+    //------------------------- 6. Show support vectors --------------------------------------------
+    //! [show_vectors]
+    thick = 2;
+    Mat sv = svm->getUncompressedSupportVectors();
+
+    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);
+    }
+    //! [show_vectors]
+
+    imwrite("result.png", I);                      // save the Image
+    imshow("SVM for Non-Linear Training Data", I); // show it to the user
+    waitKey();
+    return 0;
+}