ZZ opencv 识别图像中的四边形的代码 -

睡着的兔子

浏览: 34939 次
性别:
来自: 北京

最近访客更多访客>>

luo5032

sbwfgihc

hnqq

weihong01267

博主相关

博客

微博

相册

留言

关于我

文章分类

社区版块

存档分类

ZZ opencv 识别图像中的四边形的代码

博客分类：

opencv

C C++C#Blog

#include "stdafx.h"

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


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;
	   //1e-10就是“aeb”的形式，表示a乘以10的b次方。
       //其中b必须是整数，a可以是小数。
	   //?余弦定理CosB=(a^2+c^2-b^2)/2ac？？所以这里的计算似乎有问题
       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
       //使用gaussian金字塔分解对输入图像向下采样，首先对它输入的图像用指定滤波器
	   //进行卷积，然后通过拒绝偶数的行与列来下采样
	   cvPyrDown( timg, pyr, 7 );
       //函数 cvPyrUp 使用Gaussian 金字塔分解对输入图像向上采样。首先通过在图像中插入 0 偶数行和偶数列，然后对得到的图像用指定的滤波器进行高斯卷积，其中滤波器乘以4做插值。所以输出图像是输入图像的 4 倍大小。
	   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
		   //函数 cvSetImageCOI 基于给定的值设置感兴趣的通道。值 0 意味着所有的通道都被选定, 1 意味着第一个通道被选定等等。
           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 
                   //使用任意结构元素膨胀图像
                   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
				   //cvContourArea 计算整个轮廓或部分轮廓的面积
                   //cvCheckContourConvexity测试轮廓的凸性                  
				   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[] = { "E:\\1.jpg", "E:\\2.jpg", "E:\\3.jpg",
                     "E:\\4.jpg", "E:\\5.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;
}

这个程序的基本思想是：对输入的图像进行滤波去掉噪音，然后进行canny边缘检测，之后进行膨胀，然后寻找轮廓，对轮廓进行多边形的逼近，检测多边形的点数是否是4而且各个角的的余弦是否是小于某个值，程序中认为是0.3，然后就判断该多边形是四边形，之后根据这四个点画出该图像。

ps：我对程序中余弦定理的使用感觉公式用错了

转自：http://hi.baidu.com/%B7%E7%B4%B5%B2%DD%B5%CD%BC%FB%C5%A3%B1%C6/blog/item/e8a54ff48ed4626fddc474a5.html

分享到：