1、c++ opencv轮廓检测
opencv3/C++轮廓的提取与筛选，轮廓提取
findContours发现轮廓
1 findcontours（
2 InputoutputArray binImg，/输入8bit图像，0值酸素值不变，非0的酸素看成1；（变为二值图象）
3outputArrayofArrays contours，/新出转到的轮对象4 outputArray，hierachy//图像的拓扑结构5int mode，/轮家返回的模式YRETR TREE等）
6int method，//发现方法（CHAIN APPROX SIMPLE等）
7Point offset=Point0/轮廓像素的位移（默认没有位移（0，0））
8）
2、请教基于opencv的轮廓提取问题
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,逼近方法
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;
3、opencv 填充大致轮廓
这人形轮廓没有统一到一个轮廓下呀，opencv只能对单个轮廓进行填充，函数为cvDrawContours
( img, c, cvScalar(0,255,0,0),cvScalar(255,0,0,0), -1,2, 8, cvPoint(0, 0) );
4、在opencv下 如何将边缘检测后得到的车辆轮廓进行填充
?
5、请问，用opencv进行边缘检测，求轮廓后，怎么求轮廓间的最小距离（如下图）呢？请求详细的解答。谢谢
首先找到各点的质心，然后要是轮廓不多可以直接用简单的搜索来找就好
6、怎么用opencv在输入的原图上面已经寻找出来的轮廓
二值化，然后用countour那个函数就可以得到物体的轮廓。
7、opencv 怎样提取运动物体的轮廓？
你可以在提取的轮廓中进行识别，一般来说，车得轮廓比较大
8、opencv实现图像轮廓提取并矢量化
整个项目的结构图：
编写DetectFaceDemo.java,代码如下：
[java] view
plaincopyprint?
package com.njupt.zhb.test;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.highgui.Highgui;
import org.opencv.objdetect.CascadeClassifier;
//
// Detects faces in an image, draws boxes around them, and writes the results
// to "faceDetection.png".
//
public class DetectFaceDemo {
public void run() {
System.out.println("\nRunning DetectFaceDemo");
System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath());
// Create a face detector from the cascade file in the resources
// directory.
//CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath());
//Mat image = Highgui.imread(getClass().getResource("lena.png").getPath());
//注意：源程序的路径会多打印一个‘/’，因此总是出现如下错误
/*
* Detected 0 faces Writing faceDetection.png libpng warning: Image
* width is zero in IHDR libpng warning: Image height is zero in IHDR
* libpng error: Invalid IHDR data
*/
//因此，我们将第一个字符去掉
String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1);
CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath);
Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1));
// Detect faces in the image.
// MatOfRect is a special container class for Rect.
MatOfRect faceDetections = new MatOfRect();
faceDetector.detectMultiScale(image, faceDetections);
System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));
// Draw a bounding box around each face.
for (Rect rect : faceDetections.toArray()) {
Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
}
// Save the visualized detection.
String filename = "faceDetection.png";
System.out.println(String.format("Writing %s", filename));
Highgui.imwrite(filename, image);
}
}
package com.njupt.zhb.test;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.highgui.Highgui;
import org.opencv.objdetect.CascadeClassifier;
//
// Detects faces in an image, draws boxes around them, and writes the results
// to "faceDetection.png".
//
public class DetectFaceDemo {
public void run() {
System.out.println("\nRunning DetectFaceDemo");
System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath());
// Create a face detector from the cascade file in the resources
// directory.
//CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath());
//Mat image = Highgui.imread(getClass().getResource("lena.png").getPath());
//注意：源程序的路径会多打印一个‘/’，因此总是出现如下错误
/*
* Detected 0 faces Writing faceDetection.png libpng warning: Image
* width is zero in IHDR libpng warning: Image height is zero in IHDR
* libpng error: Invalid IHDR data
*/
//因此，我们将第一个字符去掉
String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1);
CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath);
Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1));
// Detect faces in the image.
// MatOfRect is a special container class for Rect.
MatOfRect faceDetections = new MatOfRect();
faceDetector.detectMultiScale(image, faceDetections);
System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));
// Draw a bounding box around each face.
for (Rect rect : faceDetections.toArray()) {
Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
}
// Save the visualized detection.
String filename = "faceDetection.png";
System.out.println(String.format("Writing %s", filename));
Highgui.imwrite(filename, image);
}
}
3.编写测试类：
[java] view
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package com.njupt.zhb.test;
public class TestMain {
public static void main(String[] args) {
System.out.println("Hello, OpenCV");
// Load the native library.
System.loadLibrary("opencv_java246");
new DetectFaceDemo().run();
}
}
//运行结果:
//Hello, OpenCV
//
//Running DetectFaceDemo
///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml
//Detected 8 faces
//Writing faceDetection.png
package com.njupt.zhb.test;
public class TestMain {
public static void main(String[] args) {
System.out.println("Hello, OpenCV");
// Load the native library.
System.loadLibrary("opencv_java246");
new DetectFaceDemo().run();
}
}
//运行结果:
//Hello, OpenCV
//
//Running DetectFaceDemo
///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml
//Detected 8 faces
//Writing faceDetection.png
9、如何利用OPENCV的matchShapes进行轮廓匹配
如何利用OPENCV的matchShapes进行轮廓匹配？主要步骤1.读取一幅图片，并且对其进行二值化。2.对其进行形态学处理，减少孔洞等次要特征，保留其主要特征。3.进行边缘提取。4.进行形状轮廓匹配，得到其匹配值，从而判断是否是同一个形状。
下面是演示代码:
#include <iostream>
#include "opencv2/opencv.hpp"
using namespace std;
using namespace cv;
int main()
{
Mat k=imread("E:/TestGit/8.jpg",0);
Mat f;
Mat k1=imread("E:/TestGit/9.jpg",0);
Mat f1;
threshold(k,f,50,255,THRESH_BINARY);//对图像进行二值化
threshold(k1,f1,50,255,THRESH_BINARY);
Mat closerect=getStructuringElement(MORPH_RECT,Size(3,3)); //进行结构算子生成
morphologyEx(f,f,MORPH_OPEN,closerect);
morphologyEx(f1,f1,MORPH_OPEN,closerect);//进行形态学开运算
Mat dst = Mat::zeros(k.rows, k.cols, CV_8UC3);
Mat dst1 = Mat::zeros(k1.rows, k1.cols, CV_8UC3);
vector<vector<Point>> w,w1;
vector<Vec4i> hierarchy,hierarchy1 ;
findContours(f,w,hierarchy,RETR_CCOMP,CHAIN_APPROX_SIMPLE);//提取轮廓元素