1. 程式人生 > >opencv 檢測直線 線段 圓 矩形

opencv 檢測直線 線段 圓 矩形

ali 精確 parameter who als 起點 closed 註意 內存空間

轉自:http://blog.csdn.net/byxdaz/archive/2009/12/01/4912136.aspx

檢測直線:cvHoughLines,cvHoughLines2

檢測圓:cvHoughCircles

檢測矩形:opencv中沒有對應的函數,下面有段代碼可以檢測矩形,是通過先找直線,然後找到直線平行與垂直的四根線。

檢測直線代碼:

/* This is a standalone program. Pass an image name as a first parameter of the program.

Switch between standard and probabilistic Hough transform by changing "#if 1" to "#if 0" and back */

#include <cv.h>

#include <highgui.h>

#include <math.h>

int main(int argc, char** argv)

{

const char* filename = argc >= 2 ? argv[1] : "pic1.png";

IplImage* src = cvLoadImage( filename, 0 );

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, 200, 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, 50, 10 );

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;

}

檢測圓代碼:

#include <cv.h>

#include <highgui.h>

#include <math.h>

int main(int argc, char** argv)

{

IplImage* img;

if( argc == 2 && (img=cvLoadImage(argv[1], 1))!= 0)

{

IplImage* gray = cvCreateImage( cvGetSize(img), 8, 1 );

CvMemStorage* storage = cvCreateMemStorage(0);

cvCvtColor( img, gray, CV_BGR2GRAY );

cvSmooth( gray, gray, CV_GAUSSIAN, 9, 9 ); // smooth it, otherwise a lot of false circles may be detected

CvSeq* circles = cvHoughCircles( gray, storage, CV_HOUGH_GRADIENT, 2, gray->height/4, 200, 100 );

int i;

for( i = 0; i < circles->total; i++ )

{

float* p = (float*)cvGetSeqElem( circles, i );

cvCircle( img, cvPoint(cvRound(p[0]),cvRound(p[1])), 3, CV_RGB(0,255,0), -1, 8, 0 );

cvCircle( img, cvPoint(cvRound(p[0]),cvRound(p[1])), cvRound(p[2]), CV_RGB(255,0,0), 3, 8, 0 );

}

cvNamedWindow( "circles", 1 );

cvShowImage( "circles", img );

}

return 0;

}

檢測矩形代碼:

/*在程序裏找尋矩形*/

#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, 0, thresh, 5 );

// 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 );

}

// 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, 1 );

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

其它參考博客:

1、http://blog.csdn.net/superdont/article/details/6664254

2、http://hi.baidu.com/%CE%C4%BF%A1%B5%C4%CF%A3%CD%FB/blog/item/3a5cb2079158b304738b65f2.html

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

int main()
{
IplImage* src;
if( (src=cvLoadImage("5.bmp", 1)) != 0)
{
IplImage* dst = cvCreateImage( cvGetSize(src), 8, 1 );
IplImage* color_dst = cvCreateImage( cvGetSize(src), 8, 3 );
CvMemStorage* storage = cvCreateMemStorage(0);//存儲檢測到線段,當然可以是N*1的矩陣數列,如果

實際的直線數量多余N,那麽最大可能數目的線段被返回
CvSeq* lines = 0;
int i;
IplImage* src1=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,1);

cvCvtColor(src, src1, CV_BGR2GRAY); //把src轉換成灰度圖像保存在src1中,註意進行邊緣檢測一定要

換成灰度圖
cvCanny( src1, dst, 50, 200, 3 );//參數50,200的灰度變換

cvCvtColor( dst, color_dst, CV_GRAY2BGR );
#if 1
lines = cvHoughLines2( dst, storage, CV_HOUGH_STANDARD, 1, CV_PI/180, 150, 0, 0 );//標準霍夫變

換後兩個參數為0,由於line_storage是內存空間,所以返回一個CvSeq序列結構的指針

for( i = 0; i < lines->total; i++ )
{
float* line = (float*)cvGetSeqElem(lines,i);//用GetSeqElem得到直線
float rho = line[0];
float theta = line[1];//對於SHT和MSHT(標準變換)這裏line[0],line[1]是rho(與像素相關單位的距

離精度)和theta(弧度測量的角度精度)
CvPoint pt1, pt2;
double a = cos(theta), b = sin(theta);
if( fabs(a) < 0.001 )
{
pt1.x = pt2.x = cvRound(rho);
pt1.y = 0;
pt2.y = color_dst->height;
}
else if( fabs(b) < 0.001 )
{
pt1.y = pt2.y = cvRound(rho);
pt1.x = 0;
pt2.x = color_dst->width;
}
else
{
pt1.x = 0;
pt1.y = cvRound(rho/b);
pt2.x = cvRound(rho/a);
pt2.y = 0;
}
cvLine( color_dst, pt1, pt2, CV_RGB(255,0,0), 3, 8 );
}
#else
lines = cvHoughLines2( dst, storage, CV_HOUGH_PROBABILISTIC, 1, CV_PI/180, 80, 30, 10 );
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, 8 );
}
#endif
cvNamedWindow( "Source", 1 );
cvShowImage( "Source", src );

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

cvWaitKey(0);
}
}

line_storage
檢測到的線段存儲倉. 可以是內存存儲倉 (此種情況下,一個線段序列在存儲倉中被創建,並且由函數返回),或者是包含線段參數的特殊類型(見下面)的具有單行/單列的矩陣(CvMat*)。矩陣頭為函數所修改,使得它的 cols/rows 將包含一組檢測到的線段。如果 line_storage 是矩陣,而實際線段的數目超過矩陣尺寸,那麽最大可能數目的線段被返回(線段沒有按照長度、可信度或其它指標排序).
method
Hough 變換變量,是下面變量的其中之一:
CV_HOUGH_STANDARD - 傳統或標準 Hough 變換. 每一個線段由兩個浮點數 (ρ, θ) 表示,其中 ρ 是直線與原點 (0,0) 之間的距離,θ 線段與 x-軸之間的夾角。因此,矩陣類型必須是 CV_32FC2 type.
CV_HOUGH_PROBABILISTIC - 概率 Hough 變換(如果圖像包含一些長的線性分割,則效率更高). 它返回線段分割而不是整個線段。每個分割用起點和終點來表示,所以矩陣(或創建的序列)類型是 CV_32SC4.
CV_HOUGH_MULTI_SCALE - 傳統 Hough 變換的多尺度變種。線段的編碼方式與 CV_HOUGH_STANDARD 的一致。
rho
與象素相關單位的距離精度
theta
弧度測量的角度精度
threshold
閾值參數。如果相應的累計值大於 threshold, 則函數返回的這個線段.
param1
第一個方法相關的參數:
對傳統 Hough 變換,不使用(0).
對概率 Hough 變換,它是最小線段長度.
對多尺度 Hough 變換,它是距離精度 rho 的分母 (大致的距離精度是 rho 而精確的應該是 rho / param1 ).
param2
第二個方法相關參數:
對傳統 Hough 變換,不使用 (0).
對概率 Hough 變換,這個參數表示在同一條直線上進行碎線段連接的最大間隔值(gap), 即當同一條直線上的兩條碎線段之間的間隔小於param2時,將其合二為一。
對多尺度 Hough 變換,它是角度精度 theta 的分母 (大致的角度精度是 theta 而精確的角度應該是 theta / param2).
函數 cvHoughLines2 實現了用於線段檢測的不同 Hough 變換方法. Example. 用 Hough transform 檢測線段

3、http://www.opencv.org.cn/index.php/Hough%E7%BA%BF%E6%AE%B5%E6%A3%80%E6%B5%8B

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opencv 檢測直線 線段 圓 矩形