libsvm Minist Hog 手写体识别（源码文件）
以上是我上一篇文章中的代码实现，里面分别用了opencv中的SVM和LibSVM，opencv的SVM用起来更方便，但貌似内部其实也是基于Libsvm，同样的参数训练出来的结果是一致的，里面有Libsvm的调用过程，如果用libsvm需要在工程里面添加libsvm的源码文件分别是svm.h和svm.cpp，林智仁的库里自带的那两个核心文件即可。
libsvm的用法让人更感觉是在用C的写法，opencv封装过的易用性更好，稍后我会把工程文件放到github上供大家下载，若有什么错误，还请批评指教~，这里用的是Minist的数据集，图像时我经过处理后的单张图像，大小为20*20，里面有详细的函数使用说明
[https://github.com/YihangLou/SVM-Minist-HandWriting-Recognition] Github上的工程链接
// DigitsRec_HOG_SVM.cpp : 定义控制台应用程序的入口点。
#include "opencv2/opencv.hpp"
#include "fstream"
#include "svm.h"
using namespace std;
using namespace cv;
#define srcfeature
vector<string> trainImageList;//训练图像列表，此处路径
vector<int> trainLabelList; //标签
vector<string> testImageList;//训练图像列表，此处路径
string trainImageFile= "D:\\WorkSpace\\homework\\PatternRecognization\\第一次作业\\minist\\train_image\\imagelist.txt";
string testImageFile = "D:\\WorkSpace\\homework\\PatternRecognization\\第一次作业\\minist\\test_image\\imagelist.txt";
string testBasePath = "D:\\WorkSpace\\homework\\PatternRecognization\\第一次作业\\minist\\test_image\\";
string trainBasePath = "D:\\WorkSpace\\homework\\PatternRecognization\\第一次作业\\minist\\train_image\\";
string SVMModel ="svm_model.xml";
CvMat * dataMat;
CvMat * labelMat;
//***************************************************************
// 名称: readTrainFileList
// 功能: 读取训练的图像列表和图像的位置
// 权限: public
// 返回值: void
// 参数: string trainImageFile 文件列表
// 参数: string basePath 基地址
// 参数: vector<string> & trainImageList 图像路径list
// 参数: vector<int> & trainLabelList 图像标签list
//***************************************************************
void readTrainFileList(string trainImageFile, string basePath, vector<string> &trainImageList, vector<int> &trainLabelList)
{
ifstream readData( trainImageFile );
string buffer;
while( readData )
{
if( getline( readData, buffer))
{
int label = int((buffer[0])-'0');//在我这里路径中第一个文件夹就是类别
trainLabelList.push_back( label);
trainImageList.push_back( buffer );//图像路径
}
}
readData.close();
cout<<"Read Train Data Complete"<<endl;
}
//***************************************************************
// 名称: readTestFileList
// 功能: 读测试文件
// 权限: public
// 返回值: void
// 参数: string testImageFile
// 参数: string basePath
// 参数: vector<string> & testImageList 测试图像列表
//***************************************************************
void readTestFileList(string testImageFile, string basePath, vector<string> &testImageList)
{
ifstream readData( testImageFile ); //加载测试图片集合
string buffer;
while( readData )
{
if( getline( readData, buffer))
{
testImageList.push_back( buffer );//图像路径
}
}
readData.close();
cout<<"Read Test Data Complete"<<endl;
}
//***************************************************************
// 名称: processHogFeature
// 功能: 计算Hog特征
// 权限: public
// 返回值: void
// 参数: vector<string> trainImageList
// 参数: vector<int> trainLabelList
// 参数: CvMat * & dataMat
// 参数: CvMat * & labelMat
//***************************************************************
void processHogFeature(vector<string> trainImageList,vector<int> trainLabelList, CvMat * &dataMat,CvMat * &labelMat)
{
int trainSampleNum = trainImageList.size();
dataMat = cvCreateMat( trainSampleNum, 324, CV_32FC1 ); //324为Hog feature Size
cvSetZero( dataMat );
labelMat = cvCreateMat( trainSampleNum, 1, CV_32FC1 );
cvSetZero( labelMat );
IplImage* src;
IplImage* trainImg=cvCreateImage(cvSize(20,20),8,3);//20 20
for( int i = 0; i != trainImageList.size(); i++ )
{
src=cvLoadImage( (trainBasePath + trainImageList[i]).c_str(),1);
if( src == NULL )
{
cout<<" can not load the image: "<<(trainBasePath + trainImageList[i]).c_str()<<endl;
continue;
}
//cout<<"Calculate Hog Feature "<<(trainBasePath + trainImageList[i]).c_str()<<endl;
cvResize(src,trainImg);
HOGDescriptor *hog=new HOGDescriptor(cvSize(20,20),cvSize(10,10),cvSize(5,5),cvSize(5,5),9);
vector<float>descriptors;
hog->compute(trainImg, descriptors,Size(1,1), Size(0,0));
int j =0;
for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
{
cvmSet(dataMat,i,j,*iter);//存储HOG特征
j++;
}
cvmSet( labelMat, i, 0, trainLabelList[i] );
//cout<<"Image and label "<<trainImageList[i].c_str()<<" "<<trainLabelList[i]<<endl;
}
cout<<"Calculate Hog Feature Complete"<<endl;
cout<<dataMat<<endl;
}
void processNonFeature(vector<string> trainImageList,vector<int> trainLabelList, CvMat * &dataMat,CvMat * &labelMat)
{
int trainSampleNum = trainImageList.size();
dataMat = cvCreateMat( trainSampleNum, 400, CV_32FC1 ); //324为Hog feature 大小，需提前设置
cvSetZero( dataMat );
labelMat = cvCreateMat( trainSampleNum, 1, CV_32FC1 );
cvSetZero( labelMat );
IplImage* src;
IplImage* resizeImg=cvCreateImage(cvSize(20,20),8,3);//20 20是训练样本的大小
for( int i = 0; i != trainImageList.size(); i++ )
{
src=cvLoadImage( (trainBasePath + trainImageList[i]).c_str(),1);
if( src == NULL )
{
cout<<" can not load the image: "<<(trainBasePath + trainImageList[i]).c_str()<<endl;
continue;
}
//cout<<"Calculate Hog Feature "<<(trainBasePath + trainImageList[i]).c_str()<<endl;
cvResize(src,resizeImg);
IplImage * grayImage = cvCreateImage(cvGetSize(resizeImg), IPL_DEPTH_8U, 1);
cvCvtColor(resizeImg,grayImage,CV_BGR2GRAY);
//二值化图像
IplImage * binaryImage = cvCreateImage(cvGetSize(grayImage),IPL_DEPTH_8U,1);
cvThreshold(grayImage,binaryImage,25,255,CV_THRESH_BINARY);
//cvNamedWindow("src");
//cvShowImage("src", src);
//cvNamedWindow("show");
//cvShowImage("show", binaryImage);
//cvWaitKey(0);//这里是看一下二值化的效果怎么样
HOGDescriptor *hog=new HOGDescriptor(cvSize(20,20),cvSize(10,10),cvSize(5,5),cvSize(5,5),9);
vector<float>descriptors;
int j =0; //j为矩阵的水平坐标，要把特征从vector中拷贝过来
uchar * tmp = new uchar;
for(int n=0;n<binaryImage->height;n++)
{ for(int m=0;m<binaryImage->width;m++)
{
*tmp=((uchar *)(binaryImage->imageData + n*binaryImage->widthStep))[m];
cvmSet(dataMat,i,j,*tmp);//存储HOG特征
j++;
}
}
cvmSet( labelMat, i, 0, trainLabelList[i] );
//cout<<"Image and label "<<trainImageList[i].c_str()<<" "<<trainLabelList[i]<<endl;
}
cout<<"Calculate Hog Feature Complete"<<endl;
}
//***************************************************************
// 名称: trainSVM
// 功能: 此处用的是opencv的SVM训练
// 权限: public
// 返回值: void
// 参数: CvMat * & dataMat
// 参数: CvMat * & labelMat
//***************************************************************
void trainSVM(CvMat * & dataMat,CvMat * & labelMat )
{
cout<<"train svm start"<<endl;
cout<<dataMat<<endl;
CvSVM svm;
CvSVMParams param;//这里是SVM训练相关参数
CvTermCriteria criteria;
criteria = cvTermCriteria( CV_TERMCRIT_EPS, 1000, FLT_EPSILON );
param = CvSVMParams( CvSVM::C_SVC, CvSVM::RBF, 10.0, 0.09, 1.0, 10.0, 0.5, 1.0, NULL, criteria );
svm.train( dataMat, labelMat, NULL, NULL, param );//训练数据
svm.save( SVMModel.c_str());
cout<<"SVM Training Complete"<<endl;
}
//***************************************************************
// 名称: trainLibSVM
// 功能: 此处用的是LibSVM库的SVM训练
// 权限: public
// 返回值: void
// 参数: CvMat * & dataMat
// 参数: CvMat * & labelMat
//***************************************************************
void trainLibSVM(CvMat *& dataMat, CvMat * & labelMat)
{
cout<<"LibSVM start"<<endl;
//配置SVM参数
svm_parameter param;
//param.svm_type = C_SVC;
param.svm_type = EPSILON_SVR;
param.kernel_type = RBF;
param.degree = 10.0;
param.gamma = 0.09;
param.coef0 = 1.0;
param.nu = 0.5;
param.cache_size = 1000;
param.C = 10.0;
param.eps = 1e-3;
param.p = 1.0;
//svm_prob读取
svm_problem svm_prob;
int sampleNum = dataMat->rows;
int vectorLength = dataMat->cols;
svm_prob.l = sampleNum;
svm_prob.y = new double [sampleNum];
for (int i = 0; i < sampleNum; i++)
{
svm_prob.y[i] = cvmGet(labelMat,i,0);
}
cout<<"LibSVM middle"<<endl;
svm_prob.x = new svm_node * [sampleNum];
for (int i = 0; i < sampleNum; i++)
{
svm_node * x_space = new svm_node [vectorLength + 1];
for (int j = 0; j < vectorLength; j++)
{
x_space[j].index = j;
x_space[j].value = cvmGet(dataMat,i,j);
}
x_space[vectorLength].index = -1;//注意，结束符号，一开始忘记加了
svm_prob.x[i] = x_space;
}
cout<<"LibSVM end"<<endl;
svm_model * svm_model = svm_train(&svm_prob, ¶m);
#ifdef srcfeature
svm_save_model("libsvm_minist_src_feature_model_.model",svm_model);
#else
svm_save_model("libsvm_minist_model.model",svm_model);
#endif
for (int i=0 ; i < sampleNum; i++)
{
delete [] svm_prob.x[i];
}
delete [] svm_prob.y;
svm_free_model_content(svm_model);
}
//***************************************************************
// 名称: testSVM
// 功能: 测试opencv训练的SVM准确率
// 权限: public
// 返回值: void
// 参数: vector<string> testImageList
// 参数: string SVMModel
//***************************************************************
void testSVM(vector<string> testImageList, string SVMModel)
{
CvSVM svm;
svm.load(SVMModel.c_str());//加载模型文件
IplImage* testImage;
IplImage* tempImage;
char buffer[512];
ofstream ResultOutput( "predict_result.txt" );//把预测结果存储在这个文本中
for( int j = 0; j != testImageList.size(); j++ )//依次遍历所有的待检测图片
{
testImage = cvLoadImage( (testBasePath+testImageList[j]).c_str(), 1);
if( testImage == NULL )
{
cout<<" can not load the image: "<<(testBasePath+testImageList[j]).c_str()<<endl;
continue;
}
tempImage =cvCreateImage(cvSize(20,20),8,3);
cvZero(tempImage);
cvResize(testImage,tempImage);
HOGDescriptor *hog=new HOGDescriptor(cvSize(20,20),cvSize(10,10),cvSize(5,5),cvSize(5,5),9);
vector<float>descriptors;
hog->compute(tempImage, descriptors,Size(1,1), Size(0,0));
CvMat* TempMat=cvCreateMat(1,descriptors.size(),CV_32FC1);
int n=0;
for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
{
cvmSet(TempMat,0,n,*iter);
n++;
}
int resultLabel = svm.predict(TempMat);//检测结果
sprintf( buffer, "%s %d\r\n",testImageList[j].c_str(),resultLabel );
ResultOutput<<buffer;
}
cvReleaseImage(&testImage);
cvReleaseImage(&tempImage);
ResultOutput.close();
cout<<"SVM Predict Complete"<<endl;
}
//***************************************************************
// 名称: testLibSVM
// 功能: 测试LisbSVM训练的模型的分类性能
// 权限: public
// 返回值: void
// 参数: string LibSVMModelFile
// 参数: vector<string> testImageList
// 参数: string SVMModel
//***************************************************************
void testLibSVM(string LibSVMModelFile, vector<string> testImageList, string SVMModel)
{
svm_model * svm = svm_load_model(LibSVMModelFile.c_str());
IplImage* testImage;
IplImage* tempImage;
char buffer[512];
ofstream ResultOutput( "libsvm_predict_result.txt" );
for( int j = 0; j != testImageList.size(); j++ )//依次遍历所有的待检测图片
{
testImage = cvLoadImage( (testBasePath+testImageList[j]).c_str(), 1);
if( testImage == NULL )
{
cout<<" can not load the image: "<<(testBasePath+testImageList[j]).c_str()<<endl;
continue;
}
tempImage =cvCreateImage(cvSize(20,20),8,3);
cvZero(tempImage);
cvResize(testImage,tempImage);
HOGDescriptor *hog=new HOGDescriptor(cvSize(20,20),cvSize(10,10),cvSize(5,5),cvSize(5,5),9);
vector<float>descriptors;
hog->compute(tempImage, descriptors,Size(1,1), Size(0,0));
svm_node * inputVector = new svm_node [ descriptors.size()+1];
int n = 0;
for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
{
inputVector[n].index = n;
inputVector[n].value = *iter;
n++;
}
inputVector[n].index = -1;
int resultLabel = svm_predict(svm,inputVector);//分类结果
sprintf( buffer, "%s %d\r\n",testImageList[j].c_str(),resultLabel );
ResultOutput<<buffer;
delete [] inputVector;
}
svm_free_model_content(svm);
cvReleaseImage(&testImage);
cvReleaseImage(&tempImage);
ResultOutput.close();
cout<<"SVM Predict Complete"<<endl;
}
//***************************************************************
// 名称: releaseAll
// 功能: 释放相应的资源
// 权限: public
// 返回值: void
//***************************************************************
void releaseAll()
{
cvReleaseMat( &dataMat );
cvReleaseMat( &labelMat);
cout<<"Release All Complete"<<endl;
}
//***************************************************************
// 名称: main
// 功能: 这里用了两种SVM，一种是opencv中的，一种是libsvm中的，训练测试需要选择相对应的svm
// 权限: public
// 返回值: int
//***************************************************************
int main()
{
readTrainFileList(trainImageFile,trainBasePath,trainImageList,trainLabelList);
processHogFeature(trainImageList,trainLabelList, dataMat,labelMat);
//trainSVM(dataMat,labelMat );
//processNonFeature(trainImageList,trainLabelList, dataMat,labelMat);
trainLibSVM(dataMat,labelMat);
//readTestFileList( testImageFile, testBasePath, testImageList);
testLibSVM("libsvm_minist_model.model",testImageList,SVMModel);
//testSVM( testImageList, SVMModel);
releaseAll();
return 0;
}
