OpenCV下车牌定位算法实现代码（一）

   车牌定位算法在车牌识别技术中占有很重要地位，一个车牌识别系统的识别率往往取决于车牌定位的成功率及准确度。

      车牌定位有很多种算法，从最简单的来，车牌在图像中一般被认为是长方形，由于图像摄取角度不同也可能是四边形。我们可以使用OpenCV中的实例： C:/Program Files/OpenCV/samples/c.squares.c 这是一个搜索图片中矩形的一个算法。我们只要稍微修改一下就可以实现定位车牌。

      在这个实例中使用了canny算法进行边缘检测，然后二值化，接着用cvFindContours搜索轮廓，最后从找到的轮廓中根据角点的个数，角的度数和轮廓大小确定，矩形位置。以下是效果图：

这个算法可以找到一些车牌位置，但在复杂噪声背景下，或者车牌图像灰度与背景相差不大就很难定位车牌

所以我们需要寻找更好的定位算法。下面是squares的代码：

#ifdef _CH_#pragma package <opencv>#endif#ifndef _EiC#include "cv.h"#include "highgui.h"#include <stdio.h>#include <math.h>#include <string.h>#endifint thresh = 50;IplImage* img = 0;IplImage* img0 = 0;CvMemStorage* storage = 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->pt2double 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.// the sequence is stored in the specified memory storageCvSeq* 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    IplImage* gray = cvCreateImage( sz, 8, 1 );    IplImage* pyr = cvCreateImage( cvSize(sz.width/2, sz.height/2), 8, 3 );    IplImage* tgray;    CvSeq* result;    double s, t;    // create empty sequence that will contain points -    // 4 points per square (the square's vertices)    CvSeq* squares = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvPoint), storage );       // select the maximum ROI in the image    // with the width and height divisible by 2    cvSetImageROI( timg, cvRect( 0, 0, sz.width, sz.height ));       // down-scale and upscale the image to filter out the noise    cvPyrDown( timg, pyr, 7 );    cvPyrUp( pyr, timg, 7 );    tgray = cvCreateImage( sz, 8, 1 );       // find squares in every color plane of the image    for( c = 0; c < 3; c++ )    {        // extract the c-th color plane        cvSetImageCOI( timg, c+1 );        cvCopy( timg, tgray, 0 );               // try several threshold levels        for( l = 0; l < N; l++ )        {            // hack: use Canny instead of zero threshold level.            // Canny helps to catch squares with gradient shading              if( l == 0 )            {                // apply Canny. Take the upper threshold from slider                // and set the lower to 0 (which forces edges merging)                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                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 &&                    cvCheckContourConvexity(result) )                {                    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 imagevoid 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[] = { "pic1.png", "pic2.png", "pic3.png",                  "pic4.png", "pic5.png", "pic6.png", 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 _EiCmain(1,"squares.c");#endif