(转) 学习OpenCV——车牌检测(定位)

原文:http://blog.csdn.net/sangni007/article/details/7444470

这两天在做关于车牌识别的实验，用了几种方式：

1.车牌颜色分布（HSV空间，YCrCb空间的没有颜色分布图谱，无法实验）；利用HSV的H通道，效果一般，受环境影响大。

 #include "highgui.h"
 #include "cv.h"
 #include <stdio.h>
 #include <math.h>
 #include <string>
 #include<iostream>
 using namespace std;

 CvPoint Point;
 IplImage* img=0;
 // skin region location using rgb limitation
 void SkinRGB(IplImage* rgb,IplImage* _dst)
 {
     assert(rgb->nChannels==3&& _dst->nChannels==3);

     static const int R=2;
     static const int G=1;
     static const int B=0;

     IplImage* dst=cvCreateImage(cvGetSize(_dst),8,3);
     cvZero(dst);

     for (int h=0;h<rgb->height;h++) {
         unsigned char* prgb=(unsigned char*)rgb->imageData+h*rgb->widthStep;
         unsigned char* pdst=(unsigned char*)dst->imageData+h*dst->widthStep;
         for (int w=0;w<rgb->width;w++) {
             if ((prgb[R]>95 && prgb[G]>40 && prgb[B]>20 &&
                 prgb[R]-prgb[B]>15 && prgb[R]-prgb[G]>15/*&&
                 !(prgb[R]>170&&prgb[G]>170&&prgb[B]>170)*/)||//uniform illumination
                 (prgb[R]>200 && prgb[G]>210 && prgb[B]>170 &&
                 abs(prgb[R]-prgb[B])<=15 && prgb[R]>prgb[B]&& prgb[G]>prgb[B])//lateral illumination
                 ) {
                     memcpy(pdst,prgb,3);
             }
             prgb+=3;
             pdst+=3;
         }
     }
     cvCopyImage(dst,_dst);
     cvReleaseImage(&dst);
 }
 // skin detection in rg space
 void cvSkinRG(IplImage* rgb,IplImage* gray)
 {
     assert(rgb->nChannels==3&&gray->nChannels==1);

     const int R=2;
     const int G=1;
     const int B=0;

     double Aup=-1.8423;
     double Bup=1.5294;
     double Cup=0.0422;
     double Adown=-0.7279;
     double Bdown=0.6066;
     double Cdown=0.1766;
     for (int h=0;h<rgb->height;h++) {
         unsigned char* pGray=(unsigned char*)gray->imageData+h*gray->widthStep;
         unsigned char* pRGB=(unsigned char* )rgb->imageData+h*rgb->widthStep;
         for (int w=0;w<rgb->width;w++)
         {
             int s=pRGB[R]+pRGB[G]+pRGB[B];
             double r=(double)pRGB[R]/s;
             double g=(double)pRGB[G]/s;
             double Gup=Aup*r*r+Bup*r+Cup;
             double Gdown=Adown*r*r+Bdown*r+Cdown;
             double Wr=(r-0.33)*(r-0.33)+(g-0.33)*(g-0.33);
             if (g<Gup && g>Gdown && Wr>0.004)
             {
                 *pGray=255;
             }
             else
             {
                 *pGray=0;
             }
             pGray++;
             pRGB+=3;
         }
     }

 }
 // implementation of otsu algorithm
 // author: onezeros#yahoo.cn
 // reference: Rafael C. Gonzalez. Digital Image Processing Using MATLAB
 void cvThresholdOtsu(IplImage* src, IplImage* dst)
 {
     int height=src->height;
     int width=src->width;

     //histogram
     float histogram[256]={0};
     for(int i=0;i<height;i++) {
         unsigned char* p=(unsigned char*)src->imageData+src->widthStep*i;
         for(int j=0;j<width;j++) {
             histogram[*p++]++;
         }
     }
     //normalize histogram
     int size=height*width;
     for(int i=0;i<256;i++) {
         histogram[i]=histogram[i]/size;
     }

     //average pixel value
     float avgValue=0;
     for(int i=0;i<256;i++) {
         avgValue+=i*histogram[i];
     }

     int threshold;
     float maxVariance=0;
     float w=0,u=0;
     for(int i=0;i<256;i++) {
         w+=histogram[i];
         u+=i*histogram[i];

         float t=avgValue*w-u;
         float variance=t*t/(w*(1-w));
         if(variance>maxVariance) {
             maxVariance=variance;
             threshold=i;
         }
     }

     cvThreshold(src,dst,threshold,255,CV_THRESH_BINARY);
 }

 void cvSkinOtsu(IplImage* src, IplImage* dst)
 {
     assert(dst->nChannels==1&& src->nChannels==3);

     IplImage* ycrcb=cvCreateImage(cvGetSize(src),8,3);
     IplImage* cr=cvCreateImage(cvGetSize(src),8,1);
     cvCvtColor(src,ycrcb,CV_BGR2YCrCb);
     cvSplit(ycrcb,0,cr,0,0);

     cvThresholdOtsu(cr,cr);
     cvCopyImage(cr,dst);
     cvReleaseImage(&cr);
     cvReleaseImage(&ycrcb);
 }

 void cvSkinYUV(IplImage* src,IplImage* dst)
 {
     IplImage* ycrcb=cvCreateImage(cvGetSize(src),8,3);
     //IplImage* cr=cvCreateImage(cvGetSize(src),8,1);
     //IplImage* cb=cvCreateImage(cvGetSize(src),8,1);
     cvCvtColor(src,ycrcb,CV_BGR2YCrCb);
     //cvSplit(ycrcb,0,cr,cb,0);

     static const int Cb=2;
     static const int Cr=1;
     static const int Y=0;

     //IplImage* dst=cvCreateImage(cvGetSize(_dst),8,3);
     cvZero(dst);

     for (int h=0;h<src->height;h++) {
         unsigned char* pycrcb=(unsigned char*)ycrcb->imageData+h*ycrcb->widthStep;
         unsigned char* psrc=(unsigned char*)src->imageData+h*src->widthStep;
         unsigned char* pdst=(unsigned char*)dst->imageData+h*dst->widthStep;
         for (int w=0;w<src->width;w++) {
             if ((pycrcb[Cr]<=126||pycrcb[Cr]>=130)&&(pycrcb[Cb]<=126||pycrcb[Cb]>=130))
             {
                     memcpy(pdst,psrc,3);
             }
             pycrcb+=3;
             psrc+=3;
             pdst+=3;
         }
     }
     //cvCopyImage(dst,_dst);
     //cvReleaseImage(&dst);
 }

 void cvSkinHSV(IplImage* src,IplImage* dst)
 {
     IplImage* hsv=cvCreateImage(cvGetSize(src),8,3);
     //IplImage* cr=cvCreateImage(cvGetSize(src),8,1);
     //IplImage* cb=cvCreateImage(cvGetSize(src),8,1);
     cvCvtColor(src,hsv,CV_BGR2HSV);
     //cvSplit(ycrcb,0,cr,cb,0);

     static const int V=2;
     static const int S=1;
     static const int H=0;

     //IplImage* dst=cvCreateImage(cvGetSize(_dst),8,3);
     cvZero(dst);

     for (int h=0;h<src->height;h++) {
         unsigned char* phsv=(unsigned char*)hsv->imageData+h*hsv->widthStep;
         unsigned char* psrc=(unsigned char*)src->imageData+h*src->widthStep;
         unsigned char* pdst=(unsigned char*)dst->imageData+h*dst->widthStep;
         for (int w=0;w<src->width;w++) {
             if (phsv[H]>=90&&phsv[H]<=135)
             {
                     memcpy(pdst,psrc,3);
             }
             phsv+=3;
             psrc+=3;
             pdst+=3;
         }
     }
     //cvCopyImage(dst,_dst);
     //cvReleaseImage(&dst);
 }

 void on_mouse(int event,int x,int y,int flags,void* param )
 {
      switch(event)
      {
      case CV_EVENT_LBUTTONUP:
          {
              Point=cvPoint(x,y);
          }
             cvCircle(img,Point,1,CV_RGB(255,0,0),1);
             CvScalar HSV=cvGet2D(img,x,y);
             cout<<"H:"<<HSV.val[0]<<"\t S:"<<HSV.val[1]<<"\t V:"<<HSV.val[2]<<endl;
          break;
      }

 //printf("( %d, %d) ",x,y);
 //printf("The Event is : %d ",event);
 //printf("The flags is : %d ",flags);
 //printf("The param is : %d\n",param);
 }
 int main()
 {

     IplImage* img0= cvLoadImage("D:/image/car/00.jpg"); //随便放一张jpg图片在D盘或另行设置目录
     img=cvCreateImage(cvSize(400,300),8,3);
     cvResize(img0,img);
     IplImage* dstRGB=cvCreateImage(cvGetSize(img),8,3);
     IplImage* dstRG=cvCreateImage(cvGetSize(img),8,1);
     IplImage* dst_crotsu=cvCreateImage(cvGetSize(img),8,1);
     IplImage* dst_YUV=cvCreateImage(cvGetSize(img),8,3);
     IplImage* dst_HSV=cvCreateImage(cvGetSize(img),8,3);


     cvNamedWindow("inputimage", CV_WINDOW_AUTOSIZE);
     cvShowImage("inputimage", img);
     cvWaitKey(0);
     /*
     SkinRGB(img,dstRGB);
     cvNamedWindow("outputimage1", CV_WINDOW_AUTOSIZE);
     cvShowImage("outputimage1", dstRGB);
     cvWaitKey(0);
     cvSkinRG(img,dstRG);
     cvNamedWindow("outputimage2", CV_WINDOW_AUTOSIZE);
     cvShowImage("outputimage2", dstRG);
     cvWaitKey(0);
     cvSkinOtsu(img,dst_crotsu);
     cvNamedWindow("outputimage3", CV_WINDOW_AUTOSIZE);
     cvShowImage("outputimage3", dst_crotsu);
     cvWaitKey(0);

     cvSkinYUV(img,dst_YUV);
     cvNamedWindow("outputimage4", CV_WINDOW_AUTOSIZE);
     cvShowImage("outputimage4", dst_YUV);
     //cvSaveImage("D:/skin04.jpg",dst_YUV);
     cvWaitKey(0);
     */
     cvSkinHSV(img,dst_HSV);
     cvNamedWindow("outputimage5", CV_WINDOW_AUTOSIZE);
     cvShowImage("outputimage5", dst_HSV);
     cvSaveImage("D:/image/car/car00.jpg",dst_HSV);
     cvWaitKey(0);

     return 0;
 }

 

2.Canny+Hough；效果也不好，但学习了hough变换的有关内容。

 

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

 int main(int argc, char** argv)
 {
     const char* filename = argc >= 2 ? argv[1] : "D:/image/car/car04.jpg";
     IplImage* src = cvLoadImage( filename, 0 );
     cvDilate(src,src);
     IplImage* dst;
     IplImage* color_dst;
     CvMemStorage* storage = cvCreateMemStorage(0);
     CvSeq* lines = 0;
     int i;

     if( !src )
         return -1;

     dst = cvCreateImage( cvGetSize(src), 8, 1 );
     color_dst = cvCreateImage( cvGetSize(src), 8, 3 );

     cvCanny( src, dst, 50, 150, 3 );
     cvCvtColor( dst, color_dst, CV_GRAY2BGR );

 #if 0
     lines = cvHoughLines2( dst, storage, CV_HOUGH_STANDARD, 1, CV_PI/180, 100, 0, 0 );

     for( i = 0; i < MIN(lines->total,100); i++ )
     {
         float* line = (float*)cvGetSeqElem(lines,i);
         float rho = line[0];
         float theta = line[1];
         CvPoint pt1, pt2;
         double a = cos(theta), b = sin(theta);
         double x0 = a*rho, y0 = b*rho;
         pt1.x = cvRound(x0 + 1000*(-b));
         pt1.y = cvRound(y0 + 1000*(a));
         pt2.x = cvRound(x0 - 1000*(-b));
         pt2.y = cvRound(y0 - 1000*(a));
         cvLine( color_dst, pt1, pt2, CV_RGB(255,0,0), 3, CV_AA, 0 );
     }
 #else

     lines = cvHoughLines2( dst, storage, CV_HOUGH_PROBABILISTIC, 1, CV_PI/180, 50, 5, 3 );
     for( i = 0; i < lines->total; i++ )
     {
         CvPoint* line = (CvPoint*)cvGetSeqElem(lines,i);
         cvLine( color_dst, line[0], line[1], CV_RGB(255,0,0), 3, CV_AA, 0 );
     }
 //#endif
     cvNamedWindow( "Source", 1 );
     cvShowImage( "Source", src );

     cvNamedWindow( "Hough", 1 );
     cvShowImage( "Hough", color_dst );

     cvWaitKey(0);

     return 0;
 }

 

 

3.Coutour检测；效果勉强。

 

 #include "cv.h"
 #include "highgui.h"
 #include <cxcore.h>
 #include <stdio.h>

 int BinarizeImageByOTSU (IplImage * src)
 {
     assert(src != NULL);

     //get the ROI
     CvRect rect = cvGetImageROI(src);

     //information of the source image
     int x = rect.x;
     int y = rect.y;
     int width = rect.width;
     int height = rect.height;
     int ws = src->widthStep;

     int thresholdValue=1;//阈值
     int ihist [256] ; // 图像直方图, 256个点
     int i, j, k,n, n1, n2, Color=0;
     double m1, m2, sum, csum, fmax, sb;
     memset (ihist, 0, sizeof (ihist)) ; // 对直方图置 零...

     for (i=y;i< y+height;i++) // 生成直方图
     {
         int mul =  i*ws;
         for (j=x;j<x+width;j++)
         {
             //Color=Point (i,j) ;
             Color = (int)(unsigned char)*(src->imageData + mul+ j);
             ihist [Color] +=1;
         }
     }
     sum=csum=0.0;
     n=0;
     for (k = 0; k <= 255; k++)
     {
         sum+= (double) k* (double) ihist [k] ; // x*f (x) 质量矩
         n +=ihist [k]; //f (x) 质量
     }
     // do the otsu global thresholding method
     fmax = - 1.0;
     n1 = 0;
     for (k=0;k<255;k++)
     {
         n1+=ihist [k] ;
         if (! n1)
         {
             continue;
         }
         n2=n- n1;
         if (n2==0)
         {
             break;
         }
         csum+= (double) k*ihist [k] ;
         m1=csum/ n1;
         m2= (sum- csum) /n2;
         sb = ( double) n1* ( double) n2* ( m1 - m2) * (m1- m2) ;

         if (sb>fmax)
         {
             fmax=sb;
             thresholdValue=k;
         }
     }

     //binarize the image
     cvThreshold( src, src ,thresholdValue, 255, CV_THRESH_BINARY );
     return 0;
 }

 int main( int argc, char* argv[])
 {
     IplImage* src;
     if((src=cvLoadImage("D:/image/car/05sobel.jpg", 0)))//载入图像
     {
         //为轮廓显示图像申请空间,3通道图像，以便用彩色显示
         IplImage* dst = cvCreateImage( cvGetSize(src), 8, 3);
         //创建内存块，将该块设置成默认值,当前默认大小为64k
         CvMemStorage* storage = cvCreateMemStorage(0);
         //可动态增长元素序列
         CvSeq* contour = 0;
         //对图像进行自适二值化
         BinarizeImageByOTSU(src);
         //图像膨胀
         cvDilate(src,src);
         //图像腐蚀
         cvErode(src,src);
         //显示源图像的二值图
         cvNamedWindow( "Source", 1 );
         cvShowImage( "Source", src );
         //在二值图像中寻找轮廓
         cvFindContours( src, storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
         cvZero( dst );//清空数组
         cvCvtColor(src,dst,CV_GRAY2BGR);
         //目标轮廓最小下限
         int mix_area = 2500;
         //目标轮廓最大上限
         int max_area = 3500;
         //可存放在1-，2-，3-，4-TUPLE类型的捆绑数据的容器
         CvScalar color = CV_RGB( 255, 0, 0);
         //在图像中绘制外部和内部的轮廓
         for( ; contour != 0; contour = contour->h_next)
         {
             //取得轮廓的最小矩形
             CvRect aRect = cvBoundingRect( contour, 1 );
             //取得矩形的面积
             int tmparea=aRect.height*aRect.height;
             //用车牌的形态做判断
             if (((double)aRect.width/(double)aRect.height>3)
                 && ((double)aRect.width/(double)aRect.height<6))
             {
                 cvRectangle(dst,cvPoint(aRect.x,aRect.y),cvPoint(aRect.x+aRect.width ,aRect.y+aRect.height),color,2);
                 //cvDrawContours( dst, contour, color, color, -1, 1, 8 );
             }
         }

         cvNamedWindow( "Components", 1 );
         cvShowImage( "Components", dst );
         cvWaitKey(0);
         cvDestroyWindow("Components");
         cvReleaseImage(&dst);
         cvDestroyWindow("Source");
         cvReleaseImage(&src);

         return 0;
     }
     return 1;
 }

 

 

4.Squares方式：Canny||Threshold+cvFindContours+cvApproxPoly；效果一般

 

 #ifdef _CH_
 #pragma package <opencv>
 #endif

 #ifndef _EiC
 #include "cv.h"
 #include "highgui.h"
 #include <stdio.h>
 #include <math.h>
 #include <string.h>
 #endif

 int thresh = 50;
 IplImage* img = 0;
 IplImage* img0 = 0;
 CvMemStorage* storage =  cvCreateMemStorage(0);
 CvPoint pt[4];
 const char* wndname = "Square Detection Demo";

 // helper function:
 // finds a cosine of angle between vectors
 // from pt0->pt1 and from pt0->pt2
 double angle( CvPoint* pt1, CvPoint* pt2, CvPoint* pt0 )
 {
     double dx1 = pt1->x - pt0->x;
     double dy1 = pt1->y - pt0->y;
     double dx2 = pt2->x - pt0->x;
     double dy2 = pt2->y - pt0->y;
     return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
 }

 // returns sequence of squares detected on the image.
 //返回图像中的四边形序列
 // the sequence is stored in the specified memory storage
 //序列存储在特定的storage中
 CvSeq* findSquares4( IplImage* img, CvMemStorage* storage )
 {

     CvSeq* contours;
     int i, c, l, N = 11;
     CvSize sz = cvSize( img->width & -2, img->height & -2 );
     IplImage* timg = cvCloneImage( img ); // make a copy of input image复制输入图像
     IplImage* gray = cvCreateImage( sz, 8, 1 );
     IplImage* pyr = cvCreateImage( cvSize(sz.width/2, sz.height/2), 8, 3 );//尺度减小为1/2
     IplImage* tgray;
     CvSeq* result;
     double s, t;
     // create empty sequence that will contain points -
     // 4 points per square (the square's vertices)
     //建立一个空序列存储每个四边形的四个顶点
     CvSeq* squares = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvPoint), storage );

     // select the maximum ROI in the image
     // with the width and height divisible by 2
     //设定timg的ROI为最大值（）
     cvSetImageROI( timg, cvRect( 0, 0, sz.width, sz.height ));

     // down-scale and upscale the image to filter out the noise
     //金字塔方式升和降来滤波去除噪声
     //cvPyrDown( timg, pyr, 7 );
     //cvPyrUp( pyr, timg, 7 );
     tgray = cvCreateImage( sz, 8, 1 );

     // find squares in every color plane of the image
     //寻找每个通道的四边形
     for( c = 0; c < 3; c++ )
     {
         // extract the c-th color plane
         //提取第c个通道
         cvSetImageCOI( timg, c+1 );
         cvCopy( timg, tgray, 0 );

         // try several threshold levels
         //尝试每个阈值等级
         for( l = 0; l < N; l++ )
         {
             // hack: use Canny instead of zero threshold level.
             // Canny helps to catch squares with gradient shading
             //Canny代替零阈值，Canny通过梯度变化程度大来寻找四边形

             if( l == 0 )
             {
                 // apply Canny. Take the upper threshold from slider
                 // and set the lower to 0 (which forces edges merging)
                 // l=0使用Canny
                 cvCanny( tgray, gray,60, 180, 3 );
                 //
                 // dilate canny output to remove potential
                 // holes between edge segments
                 cvDilate( gray, gray, 0, 1 );
             }
             else
             {
                 // apply threshold if l!=0:
                 // tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
                 //cvThreshold( tgray, gray, (l+1)*255/N, 255, CV_THRESH_BINARY );
                 cvThreshold( tgray, gray, 50, 255, CV_THRESH_BINARY );
             }

             // find contours and store them all as a list
             cvFindContours( gray, storage, &contours, sizeof(CvContour),
                 CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );

             // test each contour
             while( contours )
             {
                 // approximate contour with accuracy proportional
                 // to the contour perimeter
                 //用指定精度逼近多边形曲线
                 result = cvApproxPoly( contours, sizeof(CvContour), storage,
                     CV_POLY_APPROX_DP, cvContourPerimeter(contours)*0.02, 0 );
                 // square contours should have 4 vertices after approximation
                 // relatively large area (to filter out noisy contours)
                 // and be convex.
                 // Note: absolute value of an area is used because
                 // area may be positive or negative - in accordance with the
                 // contour orientation
                 if( result->total == 4 &&
                     fabs(cvContourArea(result,CV_WHOLE_SEQ)) > 1000 &&   //cvContourArea计算整个轮廓或部分轮廓的面积
                     cvCheckContourConvexity(result) )                   //CheckContourConvexity
                 {
                     s = 0;

                     for( i = 0; i < 5; i++ )
                     {
                         // find minimum angle between joint
                         // edges (maximum of cosine)
                         if( i >= 2 )
                         {
                             t = fabs(angle(
                             (CvPoint*)cvGetSeqElem( result, i ),
                             (CvPoint*)cvGetSeqElem( result, i-2 ),
                             (CvPoint*)cvGetSeqElem( result, i-1 )));
                             s = s > t ? s : t;
                         }
                     }

                     // if cosines of all angles are small
                     // (all angles are ~90 degree) then write quandrange
                     // vertices to resultant sequence
                     if( s < 0.3 )
                         for( i = 0; i < 4; i++ )
                             cvSeqPush( squares,
                                 (CvPoint*)cvGetSeqElem( result, i ));
                 }

                 // take the next contour
                 contours = contours->h_next;
             }
         }
     }

     // release all the temporary images
     cvReleaseImage( &gray );
     cvReleaseImage( &pyr );
     cvReleaseImage( &tgray );
     cvReleaseImage( &timg );

     return squares;
 }


 // the function draws all the squares in the image
 void drawSquares( IplImage* img, CvSeq* squares )
 {
     CvSeqReader reader;
     IplImage* cpy = cvCloneImage( img );
     int i;

     // initialize reader of the sequence
     cvStartReadSeq( squares, &reader, 0 );

     // read 4 sequence elements at a time (all vertices of a square)
     for( i = 0; i < squares->total; i += 4 )
     {
         CvPoint* rect = pt;
         int count = 4;

         // read 4 vertices
         memcpy( pt, reader.ptr, squares->elem_size );
         CV_NEXT_SEQ_ELEM( squares->elem_size, reader );
         memcpy( pt + 1, reader.ptr, squares->elem_size );
         CV_NEXT_SEQ_ELEM( squares->elem_size, reader );
         memcpy( pt + 2, reader.ptr, squares->elem_size );
         CV_NEXT_SEQ_ELEM( squares->elem_size, reader );
         memcpy( pt + 3, reader.ptr, squares->elem_size );
         CV_NEXT_SEQ_ELEM( squares->elem_size, reader );

         // draw the square as a closed polyline
         cvPolyLine( cpy, &rect, &count, 1, 1, CV_RGB(0,255,0), 3, CV_AA, 0 );
     }

     // show the resultant image
     cvShowImage( wndname, cpy );
     cvReleaseImage( &cpy );
 }


 void on_trackbar( int a )
 {
     if( img )
         drawSquares( img, findSquares4( img, storage ) );
 }
 //char* names[] = { "D:/image/car/00.jpg", "D:/image/car/01.jpg", "D:/image/car/02.jpg",
 //                  "D:/image/car/03.jpg", "D:/image/car/04.jpg", "D:/image/car/05.jpg", 0 };
 //char* names[] = { "D:/image/car/car00.jpg", "D:/image/car/car01.jpg", "D:/image/car/car02.jpg",
 //                  "D:/image/car/car03.jpg", "D:/image/car/car04.jpg", "D:/image/car/car05.jpg", 0 };
 //char* names[] = { "D:/image/car/00sobel.jpg", "D:/image/car/01sobel.jpg", "D:/image/car/02sobel.jpg",
 //                "D:/image/car/03sobel.jpg", "D:/image/car/04sobel.jpg", "D:/image/car/05sobel.jpg", 0 };
 char* names[] = { "D:/image/car/06sobel_normal.jpg",
                   "D:/image/car/0sobel_normal.jpg",
                   "D:/image/car/08sobel_normal.jpg",
                   "D:/image/car/09sobel_normal.jpg",
                   "D:/image/car/10sobel_normal.jpg",
                   "D:/image/car/11sobel_normal.jpg",
                   "D:/image/car/12sobel_normal.jpg",
                   "D:/image/car/13sobel_normal.jpg",
                   "D:/image/car/14sobel_normal.jpg",
                   "D:/image/car/15sobel_normal.jpg",
                   "D:/image/car/16sobel_normal.jpg",
                   "D:/image/car/17sobel_normal.jpg",
                   "D:/image/car/18sobel_normal.jpg",
                   "D:/image/car/19sobel_normal.jpg",
                   "D:/image/car/20sobel_normal.jpg",
                   "D:/image/car/21sobel_normal.jpg",
                   "D:/image/car/22sobel_normal.jpg",
                   "D:/image/car/23sobel_normal.jpg",
                   "D:/image/car/00sobel_normal.jpg",
                   "D:/image/car/01sobel_normal.jpg",
                   "D:/image/car/02sobel_normal.jpg",
                   "D:/image/car/03sobel_normal.jpg",
                   "D:/image/car/04sobel_normal.jpg",
                   "D:/image/car/05sobel_normal.jpg",
                   0 };
 int main(int argc, char** argv)
 {
     int i, c;
     // create memory storage that will contain all the dynamic data
     storage = cvCreateMemStorage(0);

     for( i = 0; names[i] != 0; i++ )
     {
         // load i-th image
         img0 = cvLoadImage( names[i], 1 );
         if( !img0 )
         {
             printf("Couldn't load %s/n", names[i] );
             continue;
         }
         img = cvCloneImage( img0 );

         // create window and a trackbar (slider) with parent "image" and set callback
         // (the slider regulates upper threshold, passed to Canny edge detector)
         cvNamedWindow( wndname,0 );
         cvCreateTrackbar( "canny thresh", wndname, &thresh, 1000, on_trackbar );

         // force the image processing
         on_trackbar(0);
         // wait for key.
         // Also the function cvWaitKey takes care of event processing
         c = cvWaitKey(0);
         // release both images
         cvReleaseImage( &img );
         cvReleaseImage( &img0 );
         // clear memory storage - reset free space position
         cvClearMemStorage( storage );
         if( c == 27 )
             break;
     }

     cvDestroyWindow( wndname );

     return 0;
 }

 #ifdef _EiC
 main(1,"squares.c");
 #endif

 

 

5.Sobel（横向求导，保留纵向纹理）+（颜色反向）+cvMorphologyEx（Close操作，IplConvKernel*（3x1）横向闭运算）+FindContours+cvBoundingRect+cvRectangle(满足一定条件)

正确率65% 主要由于没有加入仿射变换或变形

 

 #include "cv.h"
 #include "highgui.h"
 #include "cxcore.h"
 #include <stdio.h>
 #include <math.h>
 #include <string.h>
 #include <string>
 using namespace std;


 CvPoint pt[4];
 IplImage* img = 0;
 IplImage* img0 = 0;
 const char* wndname = "Demo";

 char* names[] = { "D:/image/car/06.jpg",
                   "D:/image/car/07.jpg",
                   "D:/image/car/08.jpg",
                   "D:/image/car/09.jpg",
                   "D:/image/car/10.jpg",
                   "D:/image/car/11.jpg",
                   "D:/image/car/12.jpg",
                   "D:/image/car/13.jpg",
                   "D:/image/car/14.jpg",
                   "D:/image/car/15.jpg",
                   "D:/image/car/16.jpg",
                   "D:/image/car/17.jpg",
                   "D:/image/car/18.jpg",
                   "D:/image/car/19.jpg",
                   "D:/image/car/20.jpg",
                   "D:/image/car/21.jpg",
                   "D:/image/car/22.jpg",
                   "D:/image/car/23.jpg",
                   "D:/image/car/00.jpg",
                   "D:/image/car/01.jpg",
                   "D:/image/car/02.jpg",
                   "D:/image/car/03.jpg",
                   "D:/image/car/04.jpg",
                   "D:/image/car/05.jpg",
                   0 };

 void FindContours(IplImage* src);

 int main(int argc, char** argv)
 {
     int i;

     for( i = 0; names[i] != 0; i++ )
     {
         // load i-th image
         img0 = cvLoadImage( names[i], 0 );
         if( !img0 )
         {
             printf("Couldn't load %s/n", names[i] );
             continue;
         }
         img=cvCreateImage(cvSize(400,300),8,1);
         IplImage* pyr=cvCreateImage(cvSize(img->width/2,img->height/2),IPL_DEPTH_8U,1);
         cvResize(img0,img);
         cvNamedWindow("input",1);
         cvShowImage("input",img);
         cvSmooth(img,img,CV_MEDIAN);
         //cvPyrDown( img, pyr, 7 );
         //cvPyrUp( pyr, img, 7 );


         //img = cvCloneImage( img0 );
         IplImage* imgS=cvCreateImage(cvGetSize(img),IPL_DEPTH_16S,1);
         IplImage* imgTh=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);
         IplImage* temp=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);


         cvSobel(img,imgS,2,0,3);
         cvNormalize(imgS,imgTh,255,0,CV_MINMAX);

         cvNamedWindow( wndname,1);

         cvNamedWindow("Sobel",1);
         cvShowImage("Sobel",imgTh);


         //cvAdaptiveThreshold(imgTh,imgTh,255,0,0,5,5);
         cvThreshold( imgTh, imgTh, 100, 255, CV_THRESH_BINARY );

         for (int k=0; k<img->height; k++)

             for(int j=0; j<img->width; j++)

             {

                 imgTh->imageData[k*img->widthStep+j] = 255 - imgTh->imageData[k*img->widthStep+j];

             }

         cvNamedWindow("Th",1);
         cvShowImage("Th",imgTh);
         IplConvKernel* K=cvCreateStructuringElementEx(3,1,0,0,CV_SHAPE_RECT);
         IplConvKernel* K1=cvCreateStructuringElementEx(3,3,0,0,CV_SHAPE_RECT);

         cvMorphologyEx(imgTh,imgTh,temp,K,CV_MOP_CLOSE,10);
         cvMorphologyEx(imgTh,imgTh,temp,K1,CV_MOP_OPEN,1);
         //cvDilate(imgTh,imgTh,K,15);
         //cvErode(imgTh,imgTh,K,15);
         cvShowImage(wndname,imgTh);
         string a=names[i];
         a.insert(15,"sobel_normal");
         //cvSaveImage(a.c_str(),imgTh);
         //cvWaitKey(0);
         FindContours(imgTh);

         //cvShowImage(wndname,imgTh);




     }
 }

 void FindContours(IplImage* src)
 {
     CvMemStorage* storage = cvCreateMemStorage(0);
     IplImage* dst = cvCreateImage( cvGetSize(src), 8, 3);
     cvCvtColor(src,dst,CV_GRAY2BGR);
     CvScalar color = CV_RGB( 255, 0, 0);
     CvSeq* contours=0;

      //建立一个空序列存储每个四边形的四个顶点
    // CvSeq* squares = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvPoint), storage );

     //cvFindContours( src, storage, &contours, sizeof(CvContour),CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );//外界边界h_next 和 孔用v_next连接
     cvFindContours( src, storage, &contours, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
      for( ; contours != 0; contours = contours->h_next)
             {
                 //使用边界框的方式
                 CvRect aRect = cvBoundingRect( contours, 1 );
                 int tmparea=aRect.height*aRect.height;
                 if (((double)aRect.width/(double)aRect.height>3)
                 && ((double)aRect.width/(double)aRect.height<6)&& tmparea>=200&&tmparea<=2500)
             {
                 cvRectangle(dst,cvPoint(aRect.x,aRect.y),cvPoint(aRect.x+aRect.width ,aRect.y+aRect.height),color,2);
                 //cvDrawContours( dst, contours, color, color, -1, 1, 8 );
             }
         }
      cvNamedWindow("contour",1);
      cvShowImage("contour",dst);
      cvWaitKey(0);
                 //多边形曲线逼近方法
                 /*
                 //用指定精度逼近多边形曲线
                 result = cvApproxPoly( contours, sizeof(CvContour), storage,
                     CV_POLY_APPROX_DP, cvContourPerimeter(contours)*0.02, 0 );

                 if( result->total == 4 &&
                     fabs(cvContourArea(result,CV_WHOLE_SEQ)) > 1000 &&   //cvContourArea计算整个轮廓或部分轮廓的面积
                     cvCheckContourConvexity(result) )                   //CheckContourConvexity
                 {
                 */

 }