OpenCV例子学习轮廓检测

最新推荐文章于 2024-06-10 10:40:14 发布

原创最新推荐文章于 2024-06-10 10:40:14 发布 · 708 阅读

0 ·

CC 4.0 BY-SA版权

文章标签：

#hierarchy #360 #image #tree

本文介绍如何使用OpenCV库绘制具有不同复杂度的图像，并通过调整轮廓级别来实现图像的视觉效果变化。包括椭圆、圆形、多边形等元素的绘制，以及如何利用轮廓追踪和逼近技术优化图像细节。

//contours.c

#ifdef _CH_
#pragma package <opencv>
#endif

#ifndef _EiC
#include "cv.h"
#include "highgui.h"
#endif

#define w 500
int levels = 3;
CvSeq* contours = 0;

void on_trackbar(int pos)
{
    IplImage* cnt_img = cvCreateImage( cvSize(w,w), 8, 3 );
    CvSeq* _contours = contours;
    int _levels = levels - 3;
    if( _levels <= 0 ) // get to the nearest face to make it look more funny
        _contours = _contours->h_next->h_next->h_next;
    cvZero( cnt_img );
    cvDrawContours( cnt_img, _contours, CV_RGB(255,0,0), CV_RGB(0,255,0), _levels, 3, CV_AA, cvPoint(0,0) );/*在图像中绘制简单的和复杂的轮廓*/
    cvShowImage( "contours", cnt_img );
    cvReleaseImage( &cnt_img );
}

int main( int argc, char** argv )
{
    int i;
    CvMemStorage* storage = cvCreateMemStorage(0);   //创建一内存块并返回指向块首的指针，参数表示块大小（0表示默认大小64k）
    IplImage* img = cvCreateImage( cvSize(w,w), 8, 1 );   //创建W＊W大小的图像，8位深度，每个像素的信道号为1

    cvZero( img );   //清空数组
    for( i=0; i < 6; i++ )
    {
        int dx = (i%2)*250 - 30;
        int dy = (i/2)*150;
        CvScalar white = cvRealScalar(255);   /*构造一个可存放在1-，2-，3-，4-TUPLE类型的捆绑数据的容器，用255初始化val[0],val[1]…val[3]用0初始化 */

        CvScalar black = cvRealScalar(0);

        cvEllipse( img, cvPoint(dx+150, dy+100), cvSize(100,70), 0, 0, 360, white, -1, 8, 0 );//绘制椭圆圆弧和椭圆扇形
        cvEllipse( img, cvPoint(dx+115, dy+70), cvSize(30,20), 0, 0, 360, black, -1, 8, 0 );
        cvEllipse( img, cvPoint(dx+185, dy+70), cvSize(30,20), 0, 0, 360, black, -1, 8, 0 );
        cvEllipse( img, cvPoint(dx+115, dy+70), cvSize(15,15), 0, 0, 360, white, -1, 8, 0 );
        cvEllipse( img, cvPoint(dx+185, dy+70), cvSize(15,15), 0, 0, 360, white, -1, 8, 0 );
        cvEllipse( img, cvPoint(dx+115, dy+70), cvSize(5,5), 0, 0, 360, black, -1, 8, 0 );
        cvEllipse( img, cvPoint(dx+185, dy+70), cvSize(5,5), 0, 0, 360, black, -1, 8, 0 );
        cvEllipse( img, cvPoint(dx+150, dy+100), cvSize(10,5), 0, 0, 360, black, -1, 8, 0 );
        cvEllipse( img, cvPoint(dx+150, dy+150), cvSize(40,10), 0, 0, 360, black, -1, 8, 0 );
        cvEllipse( img, cvPoint(dx+27, dy+100), cvSize(20,35), 0, 0, 360, white, -1, 8, 0 );
        cvEllipse( img, cvPoint(dx+273, dy+100), cvSize(20,35), 0, 0, 360, white, -1, 8, 0 );
    }

    cvNamedWindow( "image", 1 );
    cvShowImage( "image", img );

    cvFindContours( img, storage, &contours, sizeof(CvContour),
                    CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );   /*在二值图像中寻找轮廓,        * 提取所有轮廓，并且重构嵌套轮廓的全部 hierarchy*/

    // comment this out if you do not want approximation
    contours = cvApproxPoly( contours, sizeof(CvContour), storage, CV_POLY_APPROX_DP, 3, 1 );   //用指定精度逼近多边形曲线

    cvNamedWindow( "contours", 1 );
    cvCreateTrackbar( "levels+3", "contours", &levels, 7, on_trackbar );

    on_trackbar(0);
    cvWaitKey(0);

    return 0;
}

#ifdef _EiC
main(1,"");
#endif