机器学习 C++ 的opencv实现SVM图像二分类的训练 (二)【附源码】

本节讲机器学习 C++ 的opencv实现SVM图像二分类的训练，下节讲测试：
数据集合data内容如下：
下载地址为：https://download.csdn.net/download/hgaohr1021/89506900

#include <stdio.h>  
#include <time.h>  
#include <opencv2/opencv.hpp>  

#include <iostream> 
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/core/utils/logger.hpp>
#include <opencv2/ml/ml.hpp>  
#include <io.h>

using namespace std;
using namespace cv;
using namespace cv::ml;

void getFiles(string path, vector<string>& files);
void get_1(Mat& trainingImages, vector<int>& trainingLabels);
void get_0(Mat& trainingImages, vector<int>& trainingLabels);

int main()
{
	//获取训练数据
	Mat classes;
	Mat trainingData;
	Mat trainingImages;
	vector<int> trainingLabels;
	get_1(trainingImages, trainingLabels);
	//waitKey(2000);
	get_0(trainingImages, trainingLabels);
	Mat(trainingImages).copyTo(trainingData);
	trainingData.convertTo(trainingData, CV_32FC1);
	Mat(trainingLabels).copyTo(classes);
	//配置SVM训练器参数
	Ptr<SVM> svm = SVM::create();
	svm->setType(SVM::C_SVC);
	svm->setKernel(SVM::LINEAR);
	svm->setDegree(0);
	svm->setGamma(1);
	svm->setCoef0(0);
	svm->setC(1);
	svm->setNu(0);
	svm->setP(0);
	svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 1000, 0.01));
	//训练
	svm->train(trainingData, ROW_SAMPLE, classes);
	//保存模型
	svm->save("svm.xml");

	cout << "训练好了！！！" << endl;

	getchar();
	return 0;
}
void getFiles(string path, vector<string>& files)
{
	long long  hFile = 0;
	struct _finddata_t fileinfo;
	string p;
	if ((hFile = _findfirst(p.assign(path).append("\\*").c_str(), &fileinfo)) != -1)
	{
		do
		{
			if ((fileinfo.attrib & _A_SUBDIR))
			{
				if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0)
					getFiles(p.assign(path).append("\\").append(fileinfo.name), files);
			}
			else
			{
				files.push_back(p.assign(path).append("\\").append(fileinfo.name));
			}
		} while (_findnext(hFile, &fileinfo) == 0);

		_findclose(hFile);
	}
}
void get_1(Mat& trainingImages, vector<int>& trainingLabels)
{
	string filePath = "data\\train_image\\1";
	vector<string> files;
	getFiles(filePath, files);
	int number = files.size();
	for (int i = 0; i < number; i++)
	{
		Mat  SrcImage = imread(files[i].c_str());
		resize(SrcImage, SrcImage, cv::Size(60, 256), (0, 0), (0, 0), cv::INTER_LINEAR);  //将图片调整为相同的大小
		SrcImage = SrcImage.reshape(1, 1);
		trainingImages.push_back(SrcImage);
		trainingLabels.push_back(1);
	}
}
void get_0(Mat& trainingImages, vector<int>& trainingLabels)
{
	string filePath = "data\\train_image\\0";
	vector<string> files;
	getFiles(filePath, files);
	int number = files.size();
	for (int i = 0; i < number; i++)
	{
		Mat  SrcImage = imread(files[i].c_str());
		resize(SrcImage, SrcImage, cv::Size(60, 256), (0, 0), (0, 0), cv::INTER_LINEAR);  //将图片调整为相同的大小
		SrcImage = SrcImage.reshape(1, 1);
		trainingImages.push_back(SrcImage);
		trainingLabels.push_back(0);
	}
}

运行结果为：

运行玩，在根目录里面出现，svm.xml文件，为下一节，测试图片用。
数据集下载地址为：https://download.csdn.net/download/hgaohr1021/89506900