SVM 支持向量机

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最新推荐文章于 2025-08-11 21:23:32 发布

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分类专栏： C++ opencv 文章标签：图像处理

本文链接：https://blog.youkuaiyun.com/u014037422/article/details/39007617

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本文介绍了一种使用支持向量机（SVM）和神经网络的自动车牌识别方法，包括数据集准备、特征提取和模型训练过程，并通过实际案例展示了识别效果。

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/*****************************************************************************
* Number Plate Recognition using SVM and Neural Networks
******************************************************************************
* by David Mill醤 Escriv? 5th Dec 2012
* http://blog.damiles.com
******************************************************************************
* Ch5 of the book "Mastering OpenCV with Practical Computer Vision Projects"
* Copyright Packt Publishing 2012.
* http://www.packtpub.com/cool-projects-with-opencv/book
*****************************************************************************/
TRAINSVM.CPP

// Main entry code OpenCV

#include <cv.h>
#include <highgui.h>
#include <cvaux.h>

#include <iostream>
#include <vector>

using namespace std;
using namespace cv;

int main ( int argc, char** argv )
{
cout << "OpenCV Training SVM Automatic Number Plate Recognition\n";
cout << "\n";

char* path_Plates;
char* path_NoPlates;
int numPlates;
int numNoPlates;
int imageWidth=144;
int imageHeight=33;

//Check if user specify image to process
if(argc >= 5 )
{
numPlates= atoi(argv[1]);
numNoPlates= atoi(argv[2]);
path_Plates= argv[3];
path_NoPlates= argv[4];

}else{
cout << "Usage:\n" << argv[0] << " <num Plate Files> <num Non Plate Files> <path to plate folder files> <path to non plate files> \n";
return 0;
}

Mat classes;//(numPlates+numNoPlates, 1, CV_32FC1);
Mat trainingData;//(numPlates+numNoPlates, imageWidth*imageHeight, CV_32FC1 );

Mat trainingImages;
vector<int> trainingLabels;
//车牌
for(int i=0; i< numPlates; i++)
{

stringstream ss(stringstream::in | stringstream::out);
ss << path_Plates << i << ".jpg";
Mat img=imread(ss.str(), 0);
img= img.reshape(1, 1);
trainingImages.push_back(img);
trainingLabels.push_back(1);
}
// 非车牌
for(int i=0; i< numNoPlates; i++)
{
stringstream ss(stringstream::in | stringstream::out);
ss << path_NoPlates << i << ".jpg";
Mat img=imread(ss.str(), 0);
img= img.reshape(1, 1);
trainingImages.push_back(img);
trainingLabels.push_back(0);

}

Mat(trainingImages).copyTo(trainingData);
//trainingData = trainingData.reshape(1,trainingData.rows);
trainingData.convertTo(trainingData, CV_32FC1);
Mat(trainingLabels).copyTo(classes);

FileStorage fs("SVM.xml", FileStorage::WRITE);
fs << "TrainingData" << trainingData;
fs << "classes" << classes;
fs.release();

return 0;

}

main.cpp

//SVM for each plate region to get valid car plates
//Read file storage.
FileStorage fs;
fs.open("SVM.xml", FileStorage::READ);
Mat SVM_TrainingData;
Mat SVM_Classes;
fs["TrainingData"] >> SVM_TrainingData;
fs["classes"] >> SVM_Classes;
//Set SVM params
CvSVMParams SVM_params;
SVM_params.svm_type = CvSVM::C_SVC;
SVM_params.kernel_type = CvSVM::LINEAR; //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);
//Train SVM
CvSVM svmClassifier(SVM_TrainingData, SVM_Classes, Mat(), Mat(), SVM_params);

//For each possible plate, classify with svm if it's a plate or no
vector<Plate> plates;
for(int i=0; i< posible_regions.size(); i++)
{
Mat img=posible_regions[i].plateImg;
Mat p= img.reshape(1, 1);
p.convertTo(p, CV_32FC1);

int response = (int)svmClassifier.predict( p );
if(response==1)
plates.push_back(posible_regions[i]);
}