OpenCV Tutorials —— Support Vector Machines for Non-Linearly Separable Data

与上一篇类似 ~~ 只不过是非线性数据

#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 part

using 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);
}