使用opencv的SVM和神经网络实现车牌识别(高质量的文章******)

本文转载自：http://blog.csdn.net/ap1005834/article/details/51340831

一、前言

本文参考自《深入理解OpenCV 实用计算机视觉项目解析》中的自动车牌识别项目，并对其中的方法理解后，再进行实践。深刻认识到实际上要完成车牌区域准确定位、车牌区域中字符的准确分割，字符准确识别这一系列步骤的困难。所以最后的识别效果也是有待进一步提高。

二、程序流程

程序流程如下所示：

相应的main函数如下

 #include "carID_Detection.h"


 int main()
 {
     Mat img_input = imread("testCarID.jpg");
     //如果读入图像失败
     if(img_input.empty())
     {
         fprintf(stderr, "Can not load image %s\n", "testCarID.jpg");
         return -1;
     }

     Mat hsvImg ;
     cvtColor(img_input,hsvImg,CV_BGR2HSV);
     vector planes;
     split(hsvImg,planes);
     Mat sImg;
     sImg = planes[1];  //获得红色分量
     blur(sImg,sImg,Size(3,3));   //3*3高斯滤波
     vector   rects_sImg;
     posDetect(sImg ,rects_sImg);


     Mat grayImg;
     RgbConvToGray(img_input ,grayImg);
     medianBlur(grayImg,grayImg,3);   //3*3中值滤波

     vector   rects_grayImg;
     posDetect(grayImg ,rects_grayImg);

     vector   rects_closeImg;  //车牌区域较为贴近
     posDetect_closeImg(sImg ,rects_closeImg);

     vector   rects_optimal;
     optimPosDetect(rects_sImg,rects_grayImg,rects_closeImg,rects_optimal);

     vector  output_area;
     normalPosArea(img_input ,rects_optimal,output_area);  //获得144*33的候选车牌区域output_area

     CvSVM  svmClassifier;

     svm_train(svmClassifier);  //使用SVM对正负样本进行训练

     vector plates_svm;   //需要把候选车牌区域output_area图像中每个像素点作为一行特征向量，后进行预测
     for(int i=0;i< output_area.size(); ++i)
     {
         Mat img = output_area[i];
         Mat p = img.reshape(1,1);
         p.convertTo(p,CV_32FC1);
         int response = (int)svmClassifier.predict( p );
         if (response == 1)
             plates_svm.push_back(output_area[i]);    //保存预测结果
     }

     if(plates_svm.size() != 0)
     {
         imshow("Test", plates_svm[0]);     //正确预测的话，就只有一个结果plates_svm[0]
         waitKey(0);
     }
     else
     {
         std::cout<<"定位失败";
         return -1;
     }

     //从SVM预测获得得车牌区域中分割得字符区域
     vector  char_seg;
     char_segment(plates_svm[0],char_seg);

     //获得7个字符矩阵的相应特征矩阵
     vector  char_feature;
     char_feature.resize(7);
     for (int i =0;i
         features(char_seg[i], char_feature[i],5);

     //神经网络训练
     CvANN_MLP ann_classify;
     ann_train(ann_classify, 34 ,48);   //34为样本的类别数，48为隐藏层的神经元数

     //字符预测
     vector<int>  char_result;
     classify(ann_classify,char_feature,char_result);


     //此函数等待按键，按键盘任意键就返回
     svmClassifier.clear();

     return 0;
 }

三、代码简介

本文编程没有使用类的概念，纯属面向过程编程，以下为主要使用的函数

 //carID_Detection.h
 #include "opencv2/core/core.hpp"
 #include "opencv2//highgui/highgui.hpp"
 #include "opencv2/imgproc/imgproc.hpp"
 #include "opencv2/ml/ml.hpp"
 #include
 #include

 #include
 using namespace std;

 using namespace cv;


 void RgbConvToGray(const Mat& inputImage,Mat & outpuImage); //rgb转为灰度


 void posDetect(Mat &, vector  &); //粗步选取候选车牌区域
 bool verifySizes(const RotatedRect & );  //车牌区域需要满足的条件
 void posDetect_closeImg(Mat &inputImage , vector  & rects  ) ;  //考虑到车牌距离非常近的时候的情况
 bool verifySizes_closeImg(const RotatedRect & candidate); //距离近时的车牌区域需要满足的条件


 void optimPosDetect(vector  & rects_sImg , vector  & rects_grayImg, //车牌区域进一步定位
     vector  & rects_closeImg,vector  & rects_optimal );
 float calOverlap(const Rect& box1,const Rect& box2);  //计算2个矩阵的重叠比例


 void normalPosArea(Mat &intputImg, vector &rects_optimal, vector & output_area );  //车牌裁剪，标准化为144*33


 void svm_train(CvSVM & );  //取出SVM.xml中的特征矩阵和标签矩阵进行训练


 void char_segment(const Mat & inputImg,vector &); //对车牌区域中的字符进行分割
 bool char_verifySizes(const RotatedRect &);   //字符区域需要满足的条件
 void char_sort(vector  & in_char ); //对字符区域进行排序


 void features(const Mat & in , Mat & out ,int sizeData);  //获得一个字符矩阵对应的特征向量
 Mat projectHistogram(const Mat& img ,int t);             //计算水平或累计直方图，取决于t为0还是1


 void ann_train(CvANN_MLP &ann ,int numCharacters, int nlayers); //取出ann_xml中的数据，并进行神经网络训练
 void classify(CvANN_MLP& ann, vector &char_feature , vector<int> & char_result); //使用神经网络模型预测车牌字符，并打印至屏幕

 //carID_Detection.cpp

 #include "carID_Detection.h"



 void RgbConvToGray(const Mat& inputImage,Mat & outpuImage)  //g = 0.3R+0.59G+0.11B
 {
     outpuImage = Mat(inputImage.rows ,inputImage.cols ,CV_8UC1);

     for (int i = 0 ;i
     {
         uchar *ptrGray = outpuImage.ptr(i);
         const Vec3b * ptrRgb = inputImage.ptr(i);
         for (int j = 0 ;j
         {
             ptrGray[j] = 0.3*ptrRgb[j][2]+0.59*ptrRgb[j][1]+0.11*ptrRgb[j][0];
         }
     }
 }

 void posDetect_closeImg(Mat &inputImage , vector  & rects  )   //初步找到候选区域 rects
 {

     Mat img_canny;
     Canny(inputImage, img_canny, 150, 220);
     Mat img_threshold;
     threshold(img_canny , img_threshold,0,255 , CV_THRESH_OTSU+CV_THRESH_BINARY); //otsu算法自动获得阈值

     Mat element = getStructuringElement(MORPH_RECT ,Size(15 ,3));  //闭形态学的结构元素
     morphologyEx(img_threshold ,img_threshold,CV_MOP_CLOSE,element);  //形态学处理

     //寻找车牌区域的轮廓
     vector< vector  > contours;
     findContours(img_threshold ,contours,CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);//只检测外轮廓
     //对候选的轮廓进行进一步筛选
     vector< vector  > ::iterator itc = contours.begin();

     while( itc != contours.end())
     {
         RotatedRect mr = minAreaRect(Mat( *itc )); //返回每个轮廓的最小有界矩形区域
         if(!verifySizes_closeImg(mr))  //判断矩形轮廓是否符合要求
         {
             itc = contours.erase(itc);
         }
         else
         {

             rects.push_back(mr);
             ++itc;
         }
     }

 }


 bool verifySizes_closeImg(const RotatedRect & candidate)
 {
     float error = 0.4;
     const float aspect = 44/14; //长宽比
     int min = 100*aspect*100; //最小区域
     int max = 180*aspect*180;  //最大区域
     float rmin = aspect - aspect*error; //考虑误差后的最小长宽比
     float rmax = aspect + aspect*error; //考虑误差后的最大长宽比

     int area = candidate.size.height * candidate.size.width;
     float r = (float)candidate.size.width/(float)candidate.size.height;
     if(r <1)
         r = 1/r;

     if( (area < min || area > max) || (r< rmin || r > rmax)  )
         return false;
     else
         return true;
 }

 void posDetect(Mat &inputImage , vector  & rects  )   //初步找到候选区域 rects
 {
     Mat img_sobel;
     Sobel(inputImage , img_sobel , CV_8U, 1,0,3,1,0);

     Mat img_threshold;
     threshold(img_sobel , img_threshold,0,255 , CV_THRESH_OTSU+CV_THRESH_BINARY); //otsu算法自动获得阈值

     Mat element = getStructuringElement(MORPH_RECT ,Size(15 ,3));  //闭形态学的结构元素
     morphologyEx(img_threshold ,img_threshold,CV_MOP_CLOSE,element);

     //寻找车牌区域的轮廓
     vector< vector  > contours;
     findContours(img_threshold ,contours,CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);//只检测外轮廓
     //对候选的轮廓进行进一步筛选
     vector< vector  > ::iterator itc = contours.begin();

     while( itc != contours.end())
     {
         RotatedRect mr = minAreaRect(Mat( *itc )); //返回每个轮廓的最小有界矩形区域
         if(!verifySizes(mr))  //判断矩形轮廓是否符合要求
         {
             itc = contours.erase(itc);
         }
         else
         {
             rects.push_back(mr);
             ++itc;
         }
     }
 }

 bool verifySizes(const RotatedRect & candidate)
 {
     float error = 0.4;
     const float aspect = 44/14; //长宽比
     int min = 20*aspect*20; //最小区域
     int max = 180*aspect*180;  //最大区域
     float rmin = aspect - 2*aspect*error; //考虑误差后的最小长宽比
     float rmax = aspect + 2*aspect*error; //考虑误差后的最大长宽比

     int area = candidate.size.height * candidate.size.width;
     float r = (float)candidate.size.width/(float)candidate.size.height;
     if(r <1)
         r = 1/r;

     if( (area < min || area > max) || (r< rmin || r > rmax)  ) //满足该条件才认为该candidate为车牌区域
         return false;
     else
         return true;
 }

 void optimPosDetect(vector  & rects_sImg , vector  & rects_grayImg,
     vector  & rects_closeImg,vector  & rects_optimal )
 {
     for (int i=0;i
     {
         for (int j=0;j
         {
             if (calOverlap(rects_sImg[i].boundingRect() , rects_grayImg[j].boundingRect()) > 0.2)
             {
                 if(rects_sImg[i].boundingRect().width * rects_sImg[i].boundingRect().height
                     >= rects_grayImg[j].boundingRect().width * rects_grayImg[j].boundingRect().height)
                     rects_optimal.push_back(rects_sImg[i]);
                 else
                     rects_optimal.push_back(rects_grayImg[j]);
             }
         }
     }

     if (rects_closeImg.size()<2 )  //只考虑1个，为了速度
     {
         for (int i =0;i < rects_optimal.size();++ i )
             for (int j =0;j < rects_closeImg.size();++ j)
             {
                 if ((    calOverlap(rects_optimal[i].boundingRect() , rects_closeImg[j].boundingRect()) < 0.2 &&
                     calOverlap(rects_optimal[i].boundingRect() , rects_closeImg[j].boundingRect()) > 0.05))
                 {
                     rects_optimal.push_back(rects_closeImg[j]);
                 }
             }
     }

 }

 float calOverlap(const Rect& box1,const Rect& box2)
 {
     if (box1.x > box2.x+box2.width) { return 0.0; }
     if (box1.y > box2.y+box2.height) { return 0.0; }
     if (box1.x+box1.width < box2.x) { return 0.0; }
     if (box1.y+box1.height < box2.y) { return 0.0; }
     float colInt =  min(box1.x+box1.width,box2.x+box2.width) - max(box1.x, box2.x);
     float rowInt =  min(box1.y+box1.height,box2.y+box2.height) - max(box1.y,box2.y);
     float intersection = colInt * rowInt;
     float area1 = box1.width*box1.height;
     float area2 = box2.width*box2.height;
     return intersection / (area1 + area2 - intersection);
 }

 void normalPosArea(Mat &intputImg, vector &rects_optimal, vector & output_area )
 {
     float r,angle;
     for (int i = 0 ;i< rects_optimal.size() ; ++i)
     {
         //旋转区域
         angle = rects_optimal[i].angle;
         r = (float)rects_optimal[i].size.width / (float) (float)rects_optimal[i].size.height;
         if(r<1)
             angle = 90 + angle;
         Mat rotmat = getRotationMatrix2D(rects_optimal[i].center , angle,1);//获得变形矩阵对象
         Mat img_rotated;
         warpAffine(intputImg ,img_rotated,rotmat, intputImg.size(),CV_INTER_CUBIC);

         //裁剪图像
         Size rect_size = rects_optimal[i].size;
         if(r<1)
             swap(rect_size.width, rect_size.height);
         Mat  img_crop;
         getRectSubPix(img_rotated ,rect_size,rects_optimal[i].center , img_crop );

         //用光照直方图调整所有裁剪得到的图像，使具有相同宽度和高度，适用于训练和分类
         Mat resultResized;
         resultResized.create(33,144,CV_8UC3);
         resize(img_crop , resultResized,resultResized.size() , 0,0,INTER_CUBIC);
         Mat grayResult;
         RgbConvToGray(resultResized ,grayResult);
         //blur(grayResult ,grayResult,Size(3,3));
         equalizeHist(grayResult,grayResult);

         output_area.push_back(grayResult);
     }
 }

 void svm_train(CvSVM & svmClassifier)
 {
     FileStorage fs;

     fs.open("SVM.xml" , FileStorage::READ);
     Mat SVM_TrainningData;
     Mat SVM_Classes;

     fs["TrainingData"] >>SVM_TrainningData;
     fs["classes"] >>SVM_Classes;
     CvSVMParams SVM_params;
     SVM_params.kernel_type = CvSVM::LINEAR;

     svmClassifier.train(SVM_TrainningData,SVM_Classes ,Mat(),Mat(),SVM_params); //SVM训练模型
     fs.release();
 }

 void char_segment(const Mat & inputImg,vector & dst_mat)//得到20*20的标准字符分割图像
 {
     Mat img_threshold;
     threshold(inputImg ,img_threshold , 180,255 ,CV_THRESH_BINARY );
     Mat img_contours;
     img_threshold.copyTo(img_contours);

     vector < vector  > contours;
     findContours(img_contours ,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_NONE);

     vector< vector  > ::iterator itc = contours.begin();
     vector char_rects;

     while( itc != contours.end())
     {
         RotatedRect minArea = minAreaRect(Mat( *itc )); //返回每个轮廓的最小有界矩形区域

         Point2f vertices[4];
         minArea.points(vertices);

         if(!char_verifySizes(minArea))  //判断矩形轮廓是否符合要求
         {
             itc = contours.erase(itc);
         }
         else
         {
             ++itc;
             char_rects.push_back(minArea);

         }
     }
     char_sort(char_rects); //对字符排序

     vector  char_mat;
     for (int i = 0; i
     {
         char_mat.push_back( Mat(img_threshold,char_rects[i].boundingRect())) ;

     }

     Mat train_mat(2,3,CV_32FC1);
     int length ;
     dst_mat.resize(7);
     Point2f srcTri[3];
     Point2f dstTri[3];

     for (int i = 0; i< char_mat.size();++i)
     {
         srcTri[0] = Point2f( 0,0 );
         srcTri[1] = Point2f( char_mat[i].cols - 1, 0 );
         srcTri[2] = Point2f( 0, char_mat[i].rows - 1 );
         length = char_mat[i].rows > char_mat[i].cols?char_mat[i].rows:char_mat[i].cols;
         dstTri[0] = Point2f( 0.0, 0.0 );
         dstTri[1] = Point2f( length, 0.0 );
         dstTri[2] = Point2f( 0.0, length );
         train_mat = getAffineTransform( srcTri, dstTri );
         dst_mat[i]=Mat::zeros(length,length,char_mat[i].type());
         warpAffine(char_mat[i],dst_mat[i],train_mat,dst_mat[i].size(),INTER_LINEAR,BORDER_CONSTANT,Scalar(0));
         resize(dst_mat[i],dst_mat[i],Size(20,20));  //尺寸调整为20*20

     }

 }

 bool char_verifySizes(const RotatedRect & candidate)
 {
     float aspect = 33.0f/20.0f;
     float charAspect = (float) candidate.size.width/ (float)candidate.size.height; //宽高比
     float error = 0.35;
     float minHeight = 11;  //最小高度11
     float maxHeight = 33;  //最大高度33

     float minAspect = 0.20;  //考虑到数字1，最小长宽比为0.15
     float maxAspect = aspect + aspect*error;

     if( charAspect > minAspect && charAspect < maxAspect
         && candidate.size.height >= minHeight && candidate.size.width< maxHeight) //非0像素点数、长宽比、高度需满足条件
         return true;
     else
         return false;
 }

 void char_sort(vector  & in_char ) //对字符区域进行排序
 {
     vector   out_char;
     const int length = 7;           //7个字符
     int index[length] = {0,1,2,3,4,5,6};
     float centerX[length];
     for (int i=0;i < length ; ++ i)
     {
         centerX[i] = in_char[i].center.x;
     }

     for (int j=0;j
         for (int i=length-2;i >= j;i--)
             if (centerX[i] > centerX[i+1])
             {
                 float t=centerX[i];
                 centerX[i]=centerX[i+1];
                 centerX[i+1]=t;

                 int tt = index[i];
                 index[i] = index[i+1];
                 index[i+1] = tt;
             }
     }

     for(int i=0;i
         out_char.push_back(in_char[(index[i])]);

     in_char.clear();     //清空in_char
     in_char = out_char; //将排序好的字符区域向量重新赋值给in_char
 }

 void features(const Mat & in , Mat & out ,int sizeData)
 {
     Mat vhist = projectHistogram(in , 1); //水平直方图
     Mat hhist = projectHistogram(in , 0);  //垂直直方图

     Mat lowData;
     resize(in , lowData ,Size(sizeData ,sizeData ));
     int numCols = vhist.cols + hhist.cols + lowData.cols * lowData.cols;
     out = Mat::zeros(1, numCols , CV_32F);

     int j = 0;
     for (int i =0 ;i
     {
         out.at<float>(j) = vhist.at<float>(i);
         j++;
     }
     for (int i=0 ; i < hhist.cols ;++i)
     {
         out.at<float>(j) = hhist.at<float>(i);
     }
     for(int x =0 ;x
     {
         for (int y =0 ;y < lowData.cols ;++ y)
         {
             out.at<float>(j) = (float)lowData.atchar>(x,y);
             j++;
         }
     }

 }

 Mat projectHistogram(const Mat& img ,int t)  //水平或垂直直方图,0为按列统计
 {                                            //1为按行统计
     int sz = (t)? img.rows: img.cols;
     Mat mhist = Mat::zeros(1, sz ,CV_32F);

     for(int j = 0 ;j < sz; j++ )
     {
         Mat data = (t)?img.row(j):img.col(j);
         mhist.at<float>(j) = countNonZero(data);
     }

     double min,max;
     minMaxLoc(mhist , &min ,&max);

     if(max > 0)
         mhist.convertTo(mhist ,-1,1.0f/max , 0);

     return mhist;
 }

 void ann_train(CvANN_MLP &ann ,int numCharacters, int nlayers)
 {
     Mat trainData ,classes;
     FileStorage fs;
     fs.open("ann_xml.xml" , FileStorage::READ);

     fs["TrainingData"] >>trainData;
     fs["classes"] >>classes;

     Mat layerSizes(1,3,CV_32SC1);
     layerSizes.at<int>( 0 ) = trainData.cols;
     layerSizes.at<int>( 1 ) = nlayers; //隐藏神经元数，可设为3
     layerSizes.at<int>( 2 ) = numCharacters; //样本类数为34
     ann.create(layerSizes , CvANN_MLP::SIGMOID_SYM ,1,1);  //初始化ann

     Mat trainClasses;
     trainClasses.create(trainData.rows , numCharacters ,CV_32FC1);
     for (int i =0;i< trainData.rows; i++)
     {
         for (int k=0 ; k< trainClasses.cols ; k++ )
         {
             if ( k == (int)classes.at (i))
             {
                 trainClasses.at<float>(i,k)  = 1 ;
             }
             else
                 trainClasses.at<float>(i,k)  = 0;
         }
     }

     Mat weights(1 , trainData.rows , CV_32FC1 ,Scalar::all(1) );
     ann.train( trainData ,trainClasses , weights);
 }

 void classify(CvANN_MLP& ann, vector &char_feature , vector<int> & char_result)
 {
     char_result.resize(char_feature.size());
     for (int i=0;i
     {
         Mat output(1 ,34 , CV_32FC1); //1*34矩阵
         ann.predict(char_feature[i] ,output);
         Point maxLoc;
         double maxVal;
         minMaxLoc(output , 0 ,&maxVal , 0 ,&maxLoc);
         char_result[i] =  maxLoc.x;
     }

     std::cout<<"该车牌后6位为：";
     char  s[] = {'0','1','2','3','4','5','6','7','8','9','A','B',
         'C','D','E','F','G','H','J','K','L','M','N','P','Q',
         'R','S','T','U','V','W','X','Y','Z'};
     for (int i=1;i//第一位是汉字，这里没实现对汉字的预测
     {
         std::cout<
     }
     std::cout<<'\n';
 }

四、关于SVM.xml与ann_xml.xml

SVM.xml中保存的是用于SVM训练的训练矩阵和类别矩阵数据，标签为"TrainingData"对应训练矩阵，为195*4752大小，195表示有195个训练样本，4752表示每个样本的特征向量维度，因为每个图片大小为144*33，将其转为一行，也即将每个像素值都作为一个特征值，则可得到4752个特征值，保存为1行，所使用的样本图片有75个正样本和120个负样本，保存如下：

标签为"classes"对应类别矩阵，为195*1矩阵，前75个值对应正样本为1.0，后120个值对应负样本为-1.0。

       同理，ann_xml.xml保存的是用于神经网络训练的数据，标签为"TrainingData"对应训练矩阵，为1700*65大小，1700表示训练样本数，因为车牌字符有34类，每类有50个，故总数1700，65为每个样本提取的特征向量。标签为"classes"对应类别矩阵，为1700*1大小，标记负样本数是哪一类的，训练样本取自

五、结果及分析

       勉强找到2张图片，可以完整地识别出车牌的后6为字符，效果如下。

故该系统的性能仍有待提升，不过我认为在车牌定位方面可以有所改进，采用其他更好的车牌定位算法会更好，以及可以增加神经网络的训练样本。

六、完整代码文件下载

下载链接为：http://download.csdn.NET/detail/ap1005834/9513328