opencv 检测直线 线段 圆 矩形

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

检测直线：cvHoughLines，cvHoughLines2

检测圆：cvHoughCircles

检测矩形：opencv中没有对应的函数，下面有段代码可以检测矩形，是通过先找直线，然后找到直线平行与垂直的四根线。

检测直线代码：

/* 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;

}

检测圆代码：

#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;

}

检测矩形代码：

/*在程序里找寻矩形*/

#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

其它参考博客：

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);//存储检测到线段，当然可以是N*1的矩阵数列，如果

实际的直线数量多余N，那么最大可能数目的线段被返回

CvSeq* lines = 0;

int i;

IplImage* src1=cvCreateImage(cvSize(src->width,src->height),IPL_DEPTH_8U,1);

cvCvtColor(src, src1, CV_BGR2GRAY); //把src转换成灰度图像保存在src1中，注意进行边缘检测一定要

换成灰度图

cvCanny( src1, dst, 50, 200, 3 );//参数50，200的灰度变换

cvCvtColor( dst, color_dst, CV_GRAY2BGR );

#if 1

lines = cvHoughLines2( dst, storage, CV_HOUGH_STANDARD, 1, CV_PI/180, 150, 0, 0 );//标准霍夫变

换后两个参数为0，由于line_storage是内存空间，所以返回一个CvSeq序列结构的指针

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

{

float* line = (float*)cvGetSeqElem(lines,i);//用GetSeqElem得到直线

float rho = line[0];

float theta = line[1];//对于SHT和MSHT(标准变换)这里line[0],line[1]是rho(与像素相关单位的距

离精度)和theta(弧度测量的角度精度)

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

检测到的线段存储仓. 可以是内存存储仓 (此种情况下，一个线段序列在存储仓中被创建，并且由函数返回），或者是包含线段参数的特殊类型（见下面）的具有单行/单列的矩阵(CvMat*)。矩阵头为函数所修改，使得它的 cols/rows 将包含一组检测到的线段。如果 line_storage 是矩阵，而实际线段的数目超过矩阵尺寸，那么最大可能数目的线段被返回(线段没有按照长度、可信度或其它指标排序).

method

Hough 变换变量，是下面变量的其中之一：

CV_HOUGH_STANDARD - 传统或标准 Hough 变换. 每一个线段由两个浮点数 (ρ, θ) 表示，其中 ρ 是直线与原点 (0,0) 之间的距离，θ 线段与 x-轴之间的夹角。因此，矩阵类型必须是 CV_32FC2 type.

CV_HOUGH_PROBABILISTIC - 概率 Hough 变换(如果图像包含一些长的线性分割，则效率更高). 它返回线段分割而不是整个线段。每个分割用起点和终点来表示，所以矩阵（或创建的序列）类型是 CV_32SC4.

CV_HOUGH_MULTI_SCALE - 传统 Hough 变换的多尺度变种。线段的编码方式与 CV_HOUGH_STANDARD 的一致。

rho

与象素相关单位的距离精度

theta

弧度测量的角度精度

threshold

阈值参数。如果相应的累计值大于 threshold， 则函数返回的这个线段.

param1

第一个方法相关的参数:

对传统 Hough 变换，不使用(0).

对概率 Hough 变换，它是最小线段长度.

对多尺度 Hough 变换，它是距离精度 rho 的分母 (大致的距离精度是 rho 而精确的应该是 rho / param1 ).

param2

第二个方法相关参数:

对传统 Hough 变换，不使用 (0).

对概率 Hough 变换，这个参数表示在同一条直线上进行碎线段连接的最大间隔值(gap), 即当同一条直线上的两条碎线段之间的间隔小于param2时，将其合二为一。

对多尺度 Hough 变换，它是角度精度 theta 的分母 (大致的角度精度是 theta 而精确的角度应该是 theta / param2).

函数 cvHoughLines2 实现了用于线段检测的不同 Hough 变换方法. Example. 用 Hough transform 检测线段

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

再分享一下我老师大神的人工智能教程吧。零基础！通俗易懂！风趣幽默！还带黄段子！希望你也加入到我们人工智能的队伍中来！https://blog.csdn.net/jiangjunshow

原文地址：https://www.cnblogs.com/xkiwnchwhd/p/10315972.html

时间： 2024-10-11 00:50:00

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(I)直线篇 1 直线是如何表示的? 对于平面中的一条直线,在笛卡尔坐标系中,常见的有点斜式,两点式两种表示方法.然而在hough变换中,考虑的是另外一种表示方式:使用(r,theta)来表示一条直线.其中r为该直线到原点的距离,theta为该直线的垂线与x轴的夹角.如下图所示. 2 如果坐标系中有多个点,又怎样识别出哪些点在一条直线上呢? 使用hough变换来检测直线的思想就是:为每一个点假设n个方向的直线,通常n=180,此时检测的直线的角度精度为1°,分别计算这n条直线的(r,theta)