opencv学习之路（37）、运动物体检测（二）
一、运动物体轮廓椭圆拟合及中心
#include "opencv2/opencv.hpp"
#include<iostream>
using namespace std;
using namespace cv; Mat MoveDetect(Mat frame1, Mat frame2)
{
Mat result = frame2.clone();
Mat gray1, gray2;
cvtColor(frame1, gray1, CV_BGR2GRAY);
cvtColor(frame2, gray2, CV_BGR2GRAY); Mat diff;
absdiff(gray1, gray2, diff);
imshow("absdiss", diff);
threshold(diff, diff, , , CV_THRESH_BINARY);
imshow("threshold", diff); Mat element = getStructuringElement(MORPH_RECT, Size(, ));
Mat element2 = getStructuringElement(MORPH_RECT, Size(, ));
erode(diff, diff, element);
imshow("erode", diff); dilate(diff, diff, element2);
imshow("dilate", diff); vector<vector<Point>> contours;
vector<Vec4i> hierarcy;
//画椭圆及中心
findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
cout<<"num="<<contours.size()<<endl;
vector<RotatedRect> box(contours.size());
for(int i=; i<contours.size(); i++)
{
box[i] = fitEllipse(Mat(contours[i]));
ellipse(result, box[i], Scalar(, , ), , );
circle(result, box[i].center, , Scalar(, , ), -, );
}
return result;
} void main()
{
VideoCapture cap("E://man.avi");
if(!cap.isOpened()) //检查打开是否成功
return;
Mat frame;
Mat result;
Mat background;
int count=;
while()
{
cap>>frame;
if(frame.empty())
break;
else{
count++;
if(count==)
background = frame.clone(); //提取第一帧为背景帧
imshow("video", frame);
result = MoveDetect(background, frame);
imshow("result", result);
if(waitKey()==)
break;
}
}
cap.release();
}
和上一篇文章代码的不同点在30-38行，天台行人视频适合用背景减法处理，自行车视频适合帧差法处理
二、滤波方法去除噪声
上篇文章中使用腐蚀膨胀消除噪声，这次使用滤波方法去除噪声
中值滤波
//二值化后使用中值滤波+膨胀
Mat element = getStructuringElement(MORPH_RECT, Size(, ));
medianBlur(diff, diff, );//中值滤波
imshow("medianBlur", diff);
dilate(diff, diff, element);
imshow("dilate", diff);
均值滤波
#include "opencv2/opencv.hpp"
#include<iostream>
using namespace std;
using namespace cv; //int to string helper function
string intToString(int number)
{
stringstream ss;
ss << number;
return ss.str();
} Mat MoveDetect(Mat background, Mat img)
{
Mat result = img.clone();
Mat gray1, gray2;
cvtColor(background, gray1, CV_BGR2GRAY);
cvtColor(img, gray2, CV_BGR2GRAY); Mat diff;
absdiff(gray1, gray2, diff);
threshold(diff, diff, , , CV_THRESH_BINARY);
imshow("threshold", diff);
blur(diff, diff, Size(, ));//均值滤波
imshow("blur", diff); vector<vector<Point>> contours;
vector<Vec4i> hierarcy;
findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CHAIN_APPROX_NONE); //查找轮廓
vector<Rect> boundRect(contours.size()); //定义外接矩形集合
//drawContours(img2, contours, -1, Scalar(0, 0, 255), 1, 8); //绘制轮廓
int x0=, y0=, w0=, h0=;
for(int i=; i<contours.size(); i++)
{
boundRect[i] = boundingRect((Mat)contours[i]); //查找每个轮廓的外接矩形 x0 = boundRect[i].x; //获得第i个外接矩形的左上角的x坐标
y0 = boundRect[i].y; //获得第i个外接矩形的左上角的y坐标
w0 = boundRect[i].width; //获得第i个外接矩形的宽度
h0 = boundRect[i].height; //获得第i个外接矩形的高度
//rectangle(result, Point(x0, y0), Point(x0+w0, y0+h0), Scalar(0, 255, 0), 2, 8); //绘制第i个外接矩形
circle(result, Point(x0+w0/, y0+h0/), , Scalar(, , ), , );
line(result, Point(x0+w0/-, y0+h0/), Point(x0+w0/+, y0+h0/), Scalar(, , ), , );
line(result, Point(x0+w0/, y0+h0/-), Point(x0+w0/, y0+h0/+), Scalar(, , ), , );
putText(result,"(" + intToString(x0+w0/)+","+intToString(y0+h0/)+")",Point(x0+w0/+, y0+h0/), , ,Scalar(,,),);
}
return result;
} void main()
{
VideoCapture cap("E://ball.avi");
if(!cap.isOpened()) //检查打开是否成功
return;
Mat frame;
Mat result;
Mat background;
int count=;
while()
{
cap>>frame;
if(frame.empty())
break;
else{
count++;
if(count==)
background = frame.clone(); //提取第一帧为背景帧
imshow("video", frame);
result = MoveDetect(background, frame);
imshow("result", result);
if(waitKey()==)
break;
}
}
cap.release();
}
三、轮廓筛选去除噪声(效果挺好的)
//其余代码相同
int x0=, y0=, w0=, h0=;
for(int i=; i<contours.size(); i++)
{
boundRect[i] = boundingRect((Mat)contours[i]); //查找每个轮廓的外接矩形 x0 = boundRect[i].x; //获得第i个外接矩形的左上角的x坐标
y0 = boundRect[i].y; //获得第i个外接矩形的左上角的y坐标
w0 = boundRect[i].width; //获得第i个外接矩形的宽度
h0 = boundRect[i].height; //获得第i个外接矩形的高度
//筛选
if(w0> && h0>)
rectangle(result, Point(x0, y0), Point(x0+w0, y0+h0), Scalar(, , ), , ); //绘制第i个外接矩形
}
四、运动轨迹绘制
#include "opencv2/opencv.hpp"
#include<iostream>
using namespace std;
using namespace cv; Point center;
Point fre_center;//存储前一帧中心坐标
int num=;
vector<Point> points; Mat MoveDetect(Mat background, Mat img)
{
Mat result = img.clone();
Mat gray1, gray2;
cvtColor(background, gray1, CV_BGR2GRAY);
cvtColor(img, gray2, CV_BGR2GRAY); Mat diff;
absdiff(gray1, gray2, diff);
imshow("absdiss", diff);
threshold(diff, diff, , , CV_THRESH_BINARY);
imshow("threshold", diff); Mat element = getStructuringElement(MORPH_RECT, Size(, ));
Mat element2 = getStructuringElement(MORPH_RECT, Size(, ));
erode(diff, diff, element);
imshow("erode", diff);
dilate(diff, diff, element2);
imshow("dilate", diff); vector<vector<Point>> contours;
vector<Vec4i> hierarcy;
findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CHAIN_APPROX_NONE); //查找轮廓
vector<Rect> boundRect(contours.size()); //定义外接矩形集合
//drawContours(img2, contours, -1, Scalar(0, 0, 255), 1, 8); //绘制轮廓
vector<RotatedRect> box(contours.size());
int x0=, y0=, w0=, h0=;
for(int i=; i<contours.size(); i++)
{
boundRect[i] = boundingRect((Mat)contours[i]); //查找每个轮廓的外接矩形 x0 = boundRect[i].x; //获得第i个外接矩形的左上角的x坐标
y0 = boundRect[i].y; //获得第i个外接矩形的左上角的y坐标
w0 = boundRect[i].width; //获得第i个外接矩形的宽度
h0 = boundRect[i].height; //获得第i个外接矩形的高度
if(w0> && h0>)//筛选长宽大于30的轮廓
{
num++;
//rectangle(result, Point(x0, y0), Point(x0+w0, y0+h0), Scalar(0, 255, 0), 2, 8); //绘制第i个外接矩形
box[i] = fitEllipse(Mat(contours[i]));
ellipse(result, box[i], Scalar(, , ), , ); //椭圆轮廓
circle(result, box[i].center, , Scalar(, , ), -, ); //画中心
center = box[i].center;//当前帧的中心坐标
points.push_back(center);//中心塞进points向量集
if(num !=)
{
//line(result, fre_center, center, Scalar(255, 0, 0), 2, 8);
for(int j=; j<points.size()-; j++)
line(result, points[j], points[j+], Scalar(, , ), , );
}
//fre_center = center;
}
}
return result;
} void main()
{
VideoCapture cap("E://man.avi");
if(!cap.isOpened()) //检查打开是否成功
return;
Mat frame;
Mat background;
Mat result;
int count=;
while()
{
cap>>frame;
if(!frame.empty())
{
count++;
if(count==)
background = frame.clone(); //提取第一帧为背景帧
imshow("video", frame);
result = MoveDetect(background, frame);
imshow("result", result);
if(waitKey()==)
break;
}
else
break;
}
cap.release();
}
五、车辆数量检测
1.帧差法检测运动目标
2.预处理：a.转灰度图，绝对值做差 b.二值化，腐蚀，中值滤波，膨胀 c.查找轮廓，筛选轮廓，绘制外接矩形，计数，输出
#include "opencv2/opencv.hpp"
#include<iostream>
using namespace std;
using namespace cv; int CarNum = ;
//int to string helper function
string intToString(int number)
{
//this function has a number input and string output
stringstream ss;
ss << number;
return ss.str();
} Mat MoveDetect(Mat frame1, Mat frame2) {
Mat result = frame2.clone();
Mat gray1, gray2;
cvtColor(frame1, gray1, CV_BGR2GRAY);
cvtColor(frame2, gray2, CV_BGR2GRAY); Mat diff;
absdiff(gray1, gray2, diff);
//imshow("absdiss", diff);
threshold(diff, diff, , , CV_THRESH_BINARY);
imshow("threshold", diff); Mat element = getStructuringElement(MORPH_RECT, Size(, ));
Mat element2 = getStructuringElement(MORPH_RECT, Size(, ));
erode(diff, diff, element);
//imshow("erode", dst);
medianBlur(diff, diff, );
imshow("medianBlur", diff);
dilate(diff, diff, element2);
imshow("dilate", diff); vector<vector<Point>> contours;
vector<Vec4i> hierarcy;
findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(, ));//查找轮廓
vector<vector<Point>>contours_poly(contours.size());
vector<Rect> boundRect(contours.size()); //定义外接矩形集合
//drawContours(img2, contours, -1, Scalar(0, 0, 255), 1, 8); //绘制轮廓
int x0 = , y0 = , w0 = , h0 = ;
for (int i = ; i<contours.size(); i++)
{
approxPolyDP(Mat(contours[i]), contours_poly[i], , true);//对图像轮廓点进行多边形拟合：轮廓点组成的点集，输出的多边形点集，精度（即两个轮廓点之间的距离），输出多边形是否封闭
boundRect[i] = boundingRect(Mat(contours_poly[i]));
if (boundRect[i].width> && boundRect[i].width< && boundRect[i].height> && boundRect[i].height<) {//轮廓筛选
x0 = boundRect[i].x;
y0 = boundRect[i].y;
w0 = boundRect[i].width;
h0 = boundRect[i].height; rectangle(result, Point(x0, y0), Point(x0 + w0, y0 + h0), Scalar(, , ), , , );
if ((y0 + h0 / + ) >= && (y0 + h0 / - ) <= ) {//经过这条线（区间），车辆数量+1
CarNum++;
}
}
line(result, Point(, ), Point(, ), Scalar(, , ), , );//画红线
Point org(, );
putText(result, "CarNum=" + intToString(CarNum), org, CV_FONT_HERSHEY_SIMPLEX, 0.8f, Scalar(, , ), );
}
return result;
} void main()
{
VideoCapture cap("E://2.avi");
if (!cap.isOpened()) //检查打开是否成功
return;
Mat frame;
Mat tmp;
Mat result;
int count = ;
while ()
{
cap >> frame;
if(frame.empty())//检查视频是否结束
break;
else{
count++;
if (count == )
result = MoveDetect(frame, frame);
else result = MoveDetect(tmp, frame);
imshow("video", frame);
imshow("result", result);
tmp = frame.clone();
if (waitKey() == )
break;
}
}
cap.release();
}
opencv学习之路（37）、运动物体检测（二）的更多相关文章
- opencv学习之路【四】——opencv文件结构介绍
这里要感谢这篇博主的文章 部分内容转载自此 opencv在2.3版本之前 都是用的c语言实现的 而在2.3以后的版本 做了很多重大的改变 其中最主要的是用c++重写大部分结构 然后文件的结构和2.0之 ...
- opencv学习之路（36）、运动物体检测（一）
一.简介 二.背景减法 图片说明 #include "opencv2/opencv.hpp"using namespace cv; void main() { Mat img1 = ...
- opencv学习之路（32）、角点检测
一.角点检测的相关概念 二.Harris角点检测——cornerHarris() 参考网址: http://www.cnblogs.com/ronny/p/4009425.html #include ...
- OpenCV 学习笔记03 直线和圆检测
检测边缘和轮廓不仅重要,还经常用到,它们也是构成其他复杂操作的基础. 直线和形状检测与边缘和轮廓检测有密切的关系. 霍夫hough 变换是直线和形状检测背后的理论基础.霍夫变化是基于极坐标和向量开展的 ...
- Opencv学习之路—Opencv下基于HOG特征的KNN算法分类训练
在计算机视觉研究当中,HOG算法和LBP算法算是基础算法,但是却十分重要.后期很多图像特征提取的算法都是基于HOG和LBP,所以了解和掌握HOG,是学习计算机视觉的前提和基础. HOG算法的原理很多资 ...
- opencv学习之路（41）、人脸识别
一.人脸检测并采集个人图像 //take_photo.cpp #include<opencv2/opencv.hpp> using namespace cv; using namespac ...
- opencv学习之路（35）、SURF特征点提取与匹配（三）
一.简介 二.opencv中的SURF算法接口 三.特征点匹配方法 四.代码 1.特征点提取 #include "opencv2/opencv.hpp" #include < ...
- opencv学习之路（34）、SIFT特征匹配（二）
一.特征匹配简介 二.暴力匹配 1.nth_element筛选 #include "opencv2/opencv.hpp" #include <opencv2/nonfree ...
- opencv学习之路（33）、SIFT特征点提取（一）
一.简介 二.OpenCV中的SIFT算法接口 #include "opencv2/opencv.hpp" #include <opencv2/nonfree/nonfree ...
随机推荐
- POJ 2533 - Longest Ordered Subsequence - [最长递增子序列长度][LIS问题]
题目链接:http://poj.org/problem?id=2533 Time Limit: 2000MS Memory Limit: 65536K Description A numeric se ...
- hue,kylin,ambari
apache-kylin https://ambari.apache.org/ https://www.jianshu.com/p/c49c61b654da docker pull sequencei ...
- 安装Nginx到linux服务器（Ubuntu）详解
先去下载一个nginx放到服务器. 然后解压(可参考前面安装tomcat)编译(./configure --prefix=/usr/local/nginx/server/ && mak ...
- VUE 安装&创建一个项目
1,安装node.js vue依赖nodejs,所以首先要安装node.js 然后打开cmd,输入命令, node -v.正常出现版本号,说明你已经安装成功了 下载地址:http://nodejs.c ...
- msmq访问格式
//集群测试,以下格式不行(应是Host映射之类没配置OK) //_MSMQPath = @"FormatName:DIRECT=TCP:msmq496-ha\private$\496-10 ...
- HSRP(Hot Standby Router Protocol)
一.简介 HSRP(Hot Standby Router Protocol 热备份路由器协议)是Cisco的专有协议.HSRP把多台路由器组成一个“热备份组”,形成一个虚拟路由器.这个组内 ...
- opencart3图片Google Merchant Center验证通过不了的解决方法
最近在做一个opencart项目,有对接Google Merchant Center,但是一直提示产品图片验证无法通过,ytkah看了一下图片路径,/image/cache/catalog/demo/ ...
- jenkins深入学习
一.jenkins深入学习 一.jenkins项目配置 1.Jenkins Gitlab持续集成打包平台搭建 http://blog.csdn.net/zgzhaobo/article/details ...
- Github上36893颗星！这个被称为下一代企业级应用首选技术你学了么？
用一句话概括:这个技术,是JAVA后端框架的龙头老大,执牛耳者.这个技术就是: Spring Boot春靴. Spring Boot到底凭什么成为Java社区最具影响力的项目?说直白点,他爹Spr ...
- OO第一单元表达式求导作业总结
第一次作业 功能描述: 对输入的表达式进行求导计算和格式正误判断 思路: 一开始的想法是想写一个大正则找到一个通项式,通过这个多项式来判断WRONG FORMAT,结果发现正则写的总是不完善,会漏 ...