学习OpenCV——车牌检测(定位)
2012-11-07 21:36
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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。
转载自：http://blog.csdn.net/sangni007/article/details/7444470
[cpp]
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#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>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。
[b][cpp]
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#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。
[cpp]
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#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
[cpp]
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#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
[cpp]
view plaincopyprint?
#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
{
*/
}
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。
转载自：http://blog.csdn.net/sangni007/article/details/7444470
[cpp]
view plaincopyprint?
#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>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;
}
#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。
[b][cpp]
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#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;
}
#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。
[cpp]
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#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;
}
#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
[cpp]
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#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
#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");
#endif5.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
[cpp]
view plaincopyprint?
#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
{
*/
}
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