请教基于opencv的轮廓提取问题

发布网友发布时间：2022-04-19 19:29

共1个回答

热心网友时间：2022-05-22 01:31

先灰度化->二值化,弄成二值图,你就会提取了

给你个例子
C/C++ code

#include <stdio.h>

#include "cv.h"

#include "cxcore.h"

#include "highgui.h"

#include <iostream>

using namespace std;

#pragma comment(lib,"cv.lib")

#pragma comment(lib,"cxcore.lib")

#pragma comment(lib,"highgui.lib")

struct Position

{

int x,y;

};

double per[256];// 保存灰度概率

IplImage *FindCountours(IplImage* src,IplImage *pContourImg);

int ImageStretchByHistogram(IplImage *src,IplImage *dst);

IplImage* Hist_Equalization(IplImage *srcimg);

void proBorder(IplImage *src); // 边界的处理

void GetBackImage(IplImage* src,IplImage* src_back);

void Threshold(IplImage *src);

int GetThreshold(double *const prob);

void Getprobability(IplImage *src);

double Eccentricity(IplImage *src);

void main()

{

//IplImage * src = cvLoadImage("C:\\image19\\A634.jpg",-1);//灰度图的方式载入

IplImage * src = cvLoadImage("C:\\image19\\A857.jpg",-1);

IplImage * dst = cvCreateImage(cvGetSize(src),IPL_DEPTH_8U,3);

IplImage *src_back = cvCreateImage(cvGetSize(src),IPL_DEPTH_8U,src->nChannels);

GetBackImage(src,src_back);

dst = FindCountours(src_back,dst);

cvNamedWindow("test",CV_WINDOW_AUTOSIZE);

cvShowImage("test",dst);

cvWaitKey(0);

cvReleaseImage(&src);

cvReleaseImage(&dst);

}

void GetBackImage(IplImage* src,IplImage* src_back)

{

//cvCvtColor(src,src,CV_RGB2GRAY);//灰度化

IplImage *tmp = cvCreateImage(cvGetSize(src),IPL_DEPTH_8U,3);

// 创建结构元素

IplConvKernel *element = cvCreateStructuringElementEx( 2, 2, 0, 0, CV_SHAPE_ELLIPSE,0);

//用该结构对源图象进行数学形态学的开操作后，估计背景亮度

cvErode(src,tmp,element,9);

//使用任意结构元素腐蚀图像

cvDilate(tmp,src_back, element,9);

//使用任意结构元素膨胀图像

}

IplImage *FindCountours(IplImage* src,IplImage *pContourImg)

{

CvMemStorage *storage = cvCreateMemStorage(0); //提取轮廓需要的储存容量为默认KB

CvSeq * pcontour = 0; //提取轮廓的序列指针

IplImage *temp = cvCreateImage(cvGetSize(src),src->depth,1);

//cvSmooth(src,temp,CV_GAUSSIAN,3,1,0);

cvSmooth(src,src,CV_GAUSSIAN,3,1,0);//平滑处理

cvCvtColor(src,temp,CV_RGB2GRAY);//灰度化

Getprobability(temp);

printf("最好的阈值:%d\n",GetThreshold(per));

//Threshold(temp);

proBorder(temp);

cvThreshold(temp,temp,GetThreshold(per),255,CV_THRESH_BINARY_INV);

int contoursNum = 0; // 轮廓数量

//int mode = CV_RETR_LIST;

int mode = CV_RETR_EXTERNAL;// 提取最外层轮廓

contoursNum = cvFindContours(temp,storage,&pcontour,sizeof(CvContour),mode,CV_CHAIN_APPROX_NONE);

// contoursNum = cvFindContours(temp,storage,&pcontour,sizeof(CvContour),CV_RETR_LIST,CV_CHAIN_APPROX_SIMPLE,cvPoint(0,0));

//二值图, 得到轮廓存储,轮廓指针序列,header_size,提取模式,*近方法

CvScalar externalColor;// 保存颜色值

CvScalar holeColor;

//————–画轮廓—————-//

for (; pcontour != 0; pcontour=pcontour -> h_next)

{

//holeColor=CV_RGB(rand()&255,rand()&255,rand()&255);

//externalColor=CV_RGB(rand()&255,rand()&255,rand()&255);

CvRect r = ((CvContour *)pcontour)->rect;

if(r.height * r.width < 800)

{

holeColor=CV_RGB(0,0,0);

externalColor=CV_RGB(0,0,0);

cvDrawContours(pContourImg,pcontour,externalColor,holeColor,1,1,8);

}

else

{

//取得轮廓面积

double contArea = fabs(cvContourArea(pcontour,CV_WHOLE_SEQ));

//取得轮廓长度

double contLenth = cvArcLength(pcontour,CV_WHOLE_SEQ,-1);

// 圆形度

double contcircularity = contLenth * contLenth / contArea;

double pxl =Eccentricity(temp);

cout<<"面积为:"<<contArea<<endl;

cout<<"周长为:"<<contLenth<<endl;

cout<<"圆形度为:"<<contcircularity<<endl;

holeColor=CV_RGB(255,255,255);

externalColor=CV_RGB(255,255,255);

cvDrawContours(pContourImg,pcontour,externalColor,holeColor,1,1,8);

}

}

//IplConvKernel *element = cvCreateStructuringElementEx( 2, 2, 0, 0, CV_SHAPE_ELLIPSE,0);

//cvDilate(pContourImg,pContourImg, element,9);

return pContourImg;

}

double Eccentricity(IplImage *src)//偏心率

{

Position pos[4];

int width = src->width;

int height = src->height;

int i,j;

for(i = 0; i < height; i++)

{

for(j = 0; j < width; j++)

{

int pixel = (int)cvGet2D(src,i,j).val[0];

if(pixel != 0)

{

pos[0].x = j;

pos[0].y = i;//

goto s;

}

}

}

s:

for(i = height – 1; i >= 0; i–)

{

for(j = 0; j < width ; j++)

{

int pixel = (int)cvGet2D(src,i,j).val[0];

if(pixel != 0)

{

pos[1].x = j;

pos[1].y = i;//

goto w;

}

}

}

w:

for(i = 0 ; i < width ; i++)

{

for(j = 0;j < height; j++)

{

int pixel = (int)cvGet2D(src,j,i).val[0];

if(pixel != 0)

{

pos[2].x = j;//

pos[2].y = i;

goto e;

}

}

}

e:

for(i = width – 1; i >= 0; i–)

{

for(j = 0 ; j < height ; j++)

{

int pixel = (int)cvGet2D(src,j,i).val[0];

if(pixel != 0)

{

pos[3].x = j;//

pos[3].y = i;

goto f;

}

}

}

f:

int l_dis = abs(pos[0].y – pos[1].y);

int s_dis = abs(pos[2].x – pos[3].x);

int tmp_dis;

if(l_dis > s_dis)

{

printf("偏心率:%f\n",l_dis*1.0/s_dis);

}

else

{

tmp_dis = l_dis;

l_dis = s_dis;

s_dis = tmp_dis;

printf("偏心率:%f\n",l_dis*1.0/s_dis);

}

return 0;

}

void Getprobability(IplImage *src)

{

memset(per,0,sizeof(per));

int width = src->width;

int height = src->height;

for(int i = 0; i < height; i++) {

for(int j = 0; j < width; j++) {

per[(int)cvGet2D(src,i,j).val[0]]++;

}

}

int PixlNum = width * height;

for(i = 0; i < 256; i++)

per[i] = per[i] / PixlNum;

}

int GetThreshold(double *const prob)

{

int threshold = 0;

double maxf = 0;

for (int crrctThrshld = 1; crrctThrshld < 256 – 1; ++crrctThrshld) {

double W0 = 0, W1 = 0, U0 = 0, U1 = 0;

int i = 0;

for (i = 0; i <= crrctThrshld; ++i) {

U0 += i * prob[i];

W0 += prob[i];

}

for (; i < 256; ++i) {

U1 += i * prob[i];

W1 += prob[i];

}

if (W1 == 0 || W1 == 0)

continue;

U0 /= W0;

U1 /= W1;

double D0 = 0, D1= 0;

for (i = 0; i <= crrctThrshld; ++i)

D0 += pow((i – U0) * prob[i], 2.0);

for (; i < 256; ++i)

D1 += pow((i – U1) * prob[i], 2.0);

D0 /= W0;

D1 /= W1;

double Dw = pow(D0, 2.0) * W0 + pow(D1, 2.0) * W1;

double Db = W0 * W1 * pow((U1 – U0), 2.0);

double f = Db / (Db + Dw);

if (maxf < f) {

maxf = f;

threshold = crrctThrshld;

}

}

return threshold;

}

void proBorder(IplImage *src) // 边界的处理

{

int i,j;

int height = src->height;

int width = src->width;

int N = 100;

for(i = 0; i < N * width; i += width) // i表示向下走左上角

{

for(j = 0; j < N ; j++)

{

int index = i + j;

src->imageData[index] = (char)255;

}

}

int NN = 150;

int sw = width * (height – NN);// 左下角三角形

int t = 1;

for(i = sw; i < sw + NN * width; i += width,t++)

{

for(j = 0; j < t; j++)

{

int index = i + j;

src->imageData[index] = (char)255;

}

}

int se = (height – NN – 1) * width; // 右下角

t = 0;

for(i = se; i < width * height ; i += width,t++)

{

for(j = 0; j < t; j++)

{

int index = i + j – t;

src->imageData[index] = (char)255;

}

}

int ne = width – NN; // 右上角三角形剪切

t = 0;

for(i = ne; i < NN * width; i +=width,t++)

{

for(j = 0; j < NN – t; j++)

{

int index = i + j + t;

src->imageData[index] = (char)255;

}

}

}

void Threshold(IplImage *src)

{

int width = src->width;

int height = src->height;

float minpixel = cvGet2D(src,0,0).val[0];

float maxpixel = cvGet2D(src,0,0).val[0];

CvScalar s;

for(int i = 0; i < height; i++){

for(int j = 0; j < width; j++){

s = cvGet2D(src,i,j);

if(s.val[0] > maxpixel)

maxpixel = s.val[0];

if(s.val[0] < minpixel)

minpixel = s.val[0];

}

}

float firstgrey = (maxpixel + minpixel) / 2;

printf("%f\n",firstgrey);

float lastgrey;

float sum1 = 0,sum2 = 0;

int num1 = 0,num2 = 0;

int result = 0;

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请教基于opencv的轮廓提取问题