opencv SVM分類Demo

train.cpp:

#define  _CRT_SECURE_NO_WARNINGS
#include <stdio.h>  
#include <time.h>  
#include <opencv2/opencv.hpp>  
#include <opencv/cv.h>  
#include <iostream> 
#include <opencv2/core/core.hpp>  
#include <opencv2/highgui/highgui.hpp>  
#include <opencv2/ml/ml.hpp>  
#include <io.h>

using namespace std;
using namespace cv;

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()
{
	//獲取訓練資料
	Mat classes;
	Mat trainingData;
	Mat trainingImages;
	vector<int> trainingLabels;
	get_1(trainingImages, trainingLabels);
	get_0(trainingImages, trainingLabels);
	Mat(trainingImages).copyTo(trainingData);
	trainingData.convertTo(trainingData, CV_32FC1);
	Mat(trainingLabels).copyTo(classes);
	//配置SVM訓練器引數
	CvSVMParams SVM_params;
	SVM_params.svm_type = CvSVM::C_SVC;
	SVM_params.kernel_type = CvSVM::LINEAR;
	SVM_params.degree = 0;
	SVM_params.gamma = 1;
	SVM_params.coef0 = 0;
	SVM_params.C = 1;
	SVM_params.nu = 0;
	SVM_params.p = 0;
	SVM_params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.01);
	//訓練
	CvSVM svm;
	svm.train(trainingData, classes, Mat(), Mat(), SVM_params);
	//儲存模型
	svm.save("svm.xml");
	cout << "訓練好了！！！" << endl;
	getchar();
	return 0;
}
void getFiles(string path, vector<string>& files)
{
	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)
{
	char * 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);  //將圖片調整為相同的大小
		SrcImage = SrcImage.reshape(1, 1);
		trainingImages.push_back(SrcImage);
		trainingLabels.push_back(1);
	}
}
void get_0(Mat& trainingImages, vector<int>& trainingLabels)
{
	char * 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);  //將圖片調整為相同的大小
		SrcImage = SrcImage.reshape(1, 1);
		trainingImages.push_back(SrcImage);
		trainingLabels.push_back(0);
	}
}

由於我的資料集不是同樣大小的，所以在reshape前進行resize操作。

test.cpp:

#define  _CRT_SECURE_NO_WARNINGS
#include <stdio.h>  
#include <time.h>  
#include <opencv2/opencv.hpp>  
#include <opencv/cv.h>  
#include <iostream> 
#include <opencv2/core/core.hpp>  
#include <opencv2/highgui/highgui.hpp>  
#include <opencv2/ml/ml.hpp>  
#include <io.h>
#include<iomanip>

using namespace std;
using namespace cv;

void getFiles(string path, vector<string>& files);

int main()
{
	int result = 0;
	char * filePath = "data\\test_image\\0";
	vector<string> files;
	getFiles(filePath, files);
	int number = files.size();
	cout << "圖片個數:"<<number << endl;
	CvSVM svm;
	svm.clear();
	string modelpath = "svm.xml";
	if (modelpath.length() == 0)
	{
		cout << "匯入模型錯誤" << endl;
		return 0;
	}
	FileStorage svm_fs(modelpath, FileStorage::READ);
	if (svm_fs.isOpened())
	{
		svm.load(modelpath.c_str());
	}
	for (int i = 0; i < number; i++)
	{
		Mat inMat = imread(files[i].c_str());
		resize(inMat, inMat, cv::Size(60, 256), (0, 0), (0, 0), cv::INTER_LINEAR);  //將圖片調整為相同的大小
		Mat p = inMat.reshape(1, 1);
		p.convertTo(p, CV_32FC1);
		int response = (int)svm.predict(p);
		if (response == 0)
		{
			result++;
		}
	}
	cout << "識別個數："<<result << endl;
	cout << "識別率：" <<setprecision(2)<<fixed<< double(result) / (double)(number) << endl;

	getchar();
	return  0;
}
void getFiles(string path, vector<string>& files)
{
	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);
	}
}

 在進行預測時，predict中的圖片大小需要和train中的圖片大小保持一致。否則會報錯。

程式碼已上傳

參考自https://blog.csdn.net/chaipp0607/article/details/68067098