OpenCV下车牌定位算法实现代码

转自http://www.rosoo.net/a/201005/9390.html

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>
#endif

int 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->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.
// the sequence is stored in the specified memory storage
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
    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 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[] = { "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 _EiC
main(1,"squares.c");
#endif

原文：http://blog.csdn.net/heihei723/