opencv的svm学习_1

概述

　　本篇是对opencv的svm学习笔记，基于对opencv官方svm教程的修改和记录。opencv的svm教程如下：
官网原版：http://docs.opencv.org/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html#introductiontosvms
汉语翻译版：http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html#introductiontosvms

效果演示

　　本文代码运行效果如下：
                                          

代码讲解

具体代码

　　具体代码如下：

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv/cv.h>
#include <stdio.h>
#include <opencv2/ml/ml.hpp>

using namespace cv;

int main()
{
	// Data for visual representation
	int width = 512, height = 512;
	Mat image = Mat::zeros(height, width, CV_8UC3);

	float labels[3];
	Mat labelsMat(3, 1, CV_32FC1, labels);
	labelsMat.rowRange(0,1).setTo(1);
	labelsMat.rowRange(1,2).setTo(2);
	labelsMat.rowRange(2,3).setTo(3);

	float trainingData[3][2] = { {501, 10}, {255, 10}, {501, 255} };
	Mat trainingDataMat(3, 2, CV_32FC1, trainingData);

	// Set up SVM's parameters
	CvSVMParams params;
	params.svm_type    = CvSVM::C_SVC;
	params.kernel_type = CvSVM::LINEAR;
	params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);

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

	Vec3b green(0,255,0), blue (255,0,0), red(0,0,255);
	// Show the decision regions given by the SVM
	for (int i = 0; i < image.rows; ++i){
		for (int j = 0; j < image.cols; ++j)
		{
			Mat sampleMat = (Mat_<float>(1,2) << i,j);
			float response = SVM.predict(sampleMat);

			if (response == 1)
				image.at<Vec3b>(j, i)  = green;
			else if (response == 2)
				image.at<Vec3b>(j, i)  = blue;
			else if (response == 3)
				image.at<Vec3b>(j, i)  = red;
		}
         }
	// Show the training data
	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, 0, 255), thickness, lineType);
	circle( image, Point(501, 255), 5, Scalar(255, 255, 0), thickness, lineType);

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

}

建立训练样本

float labels[3];
	Mat labelsMat(3, 1, CV_32FC1, labels);
	labelsMat.rowRange(0,1).setTo(1);
	labelsMat.rowRange(1,2).setTo(2);
	labelsMat.rowRange(2,3).setTo(3);

	float trainingData[3][2] = { {501, 10}, {255, 10}, {501, 255} };
	Mat trainingDataMat(3, 2, CV_32FC1, trainingData);

　　这里对labelsMat设置了分类标签1、2、3,对应到训练数据中，也就是将trainingData中的三组数据正好分成了三类。

训练支持向量机

　　用函数SVM.train进行训练。

// Set up SVM's parameters
	CvSVMParams params;
	params.svm_type    = CvSVM::C_SVC;
	params.kernel_type = CvSVM::LINEAR;
	params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);

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

SVM区域分割

　　使用CvSVM::predict 通过重建训练完毕的支持向量机来将输入的样本分类。

Vec3b green(0,255,0), blue (255,0,0), red(0,0,255);
// Show the decision regions given by the SVM
for (int i = 0; i < image.rows; ++i){
	for (int j = 0; j < image.cols; ++j)
	{
		Mat sampleMat = (Mat_<float>(1,2) << i,j);
		float response = SVM.predict(sampleMat);

		if (response == 1)
			image.at<Vec3b>(j, i)  = green;
		else if (response == 2)
			image.at<Vec3b>(j, i)  = blue;
		else if (response == 3)
			image.at<Vec3b>(j, i)  = red;
	}
}

　　这是一个大循环，遍历整个imgge图像，在每一次循环中:首先创建一个sampleMat，将本次循环中对应的i、j，以float的形式作为作为sampleMat的初始化参数。
接着使用SVM.predict对这个sampleMat进行分类。通过response返回数据，判断出本次的sampleMat属于第一、第二还是第三类，然后分别用红、绿、蓝在图像对应
位置，对分类进行标记。