OpenCV2.4.10之samples_cpp_tutorial-code_learn-----ml(SVM支持向量机一)

#include "stdafx.h"    //预编译头文件    

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>

using namespace cv;

int main()
{
	//定义一个512*512像素大小的图像进行SVM可视化呈现
	int width = 512, height = 512;
	Mat image = Mat::zeros(height, width, CV_8UC3);

	//设置训练数据。训练数据有两个类1和-1 这两个类分别有一个和三个数据。
	float labels[4] = {1.0, -1.0, -1.0, -1.0};
	Mat labelsMat(4, 1, CV_32FC1, labels);

	float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
	Mat trainingDataMat(4, 2, CV_32FC1, trainingData);

	//设置SVM参数
	CvSVMParams params;
	params.svm_type    = CvSVM::C_SVC;
	params.kernel_type = CvSVM::LINEAR;
	params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);

	// 训练SVM
	CvSVM SVM;
	SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);

	Vec3b green(0,255,0), blue (255,0,0);
	// 将图像中的每个点进行SVM分类并着色
	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);

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

		//显示训练数据
		int thickness = -1;
		int lineType = 8;
		circle(	image, Point(501,  10), 5, Scalar(  0,   0,   0), thickness, lineType);
		circle(	image, Point(255,  10), 5, Scalar(255, 255, 255), 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);

		//显示支持向量
		thickness = 2;
		lineType  = 8;
		int c     = SVM.get_support_vector_count();

		for (int i = 0; i < c; ++i)
		{
			const float* v = SVM.get_support_vector(i);
			circle(	image,  Point( (int) v[0], (int) v[1]),   6,  Scalar(128, 128, 128), thickness, lineType);
		}

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

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

}