#include "stdafx.h"#include <iostream>#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/ml/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 partusing namespace cv;using namespace std;int main(){	// 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);}