基于opencv的物体定位

opencv是一个很强大的机器视觉库，利用它我们可以开发出丰富多彩的使用项目。近日，我在研究一个图中物体定位系统。本程序用的是OpenCV2.4.9，附带OpenCV3.0。

程序中的原图为我随手拍的一张图片



图中有三个物体，都是蓝色的，我首先取原图的蓝色通道变为灰度图



灰度图经过中值滤波后可以得到去噪后的图片



根据原图的蓝色通道和红色通道的大概取值范围，我们可得到比较满意的二值图



为了去掉物体中少量的黑色部分，我用闭运算



然而，图中最上面的那个物体里面还有一块很大的黑色（目前我也不知道怎么去掉，如果有大神知道望告知~~）

接下来就是找出物体的轮廓



最后找到能包围轮廓的最小矩形



好了，占时就这么多了

下面是配套的程序

OpenCV2.4.9半根

#include<opencv2\opencv.hpp>
#include<iostream>
#define BIN_DIV 110

using namespace std;
using namespace cv;

int main()
{
Mat srcImg, midImg, dstImg;
srcImg = imread("hehe.jpg");
Mat xianshi = srcImg.clone();
Mat redChannel;
namedWindow("【原图】", WINDOW_NORMAL);
imshow("【原图】", srcImg);
Mat grayImg;
vector<Mat> channels;
split(srcImg, channels);
//cvtColor(srcImg,grayImg,COLOR_BGR2GRAY);
grayImg = channels.at(0);
redChannel = channels.at(2);
namedWindow("【灰度图】", WINDOW_NORMAL);
imshow("【灰度图】", grayImg);
//均值滤波
blur(grayImg, grayImg, Size(20, 20), Point(-1, -1));
namedWindow("【均值滤波后】", WINDOW_NORMAL);
imshow("【均值滤波后】", grayImg);
//转化为二值图
Mat midImg1 = grayImg.clone();
int rowNumber = midImg1.rows;
int colNumber = midImg1.cols;

for (int i = 0; i<rowNumber; i++)
{
uchar* data = midImg1.ptr<uchar>(i);  //取第i行的首地址
uchar* redData = redChannel.ptr<uchar>(i);
for (int j = 0; j<colNumber; j++)
{
if (data[j]>BIN_DIV&&redData[j]<BIN_DIV *2/ 3)
data[j] = 255;
else
data[j] = 0;
}
}
namedWindow("【二值图】", WINDOW_NORMAL);
imshow("【二值图】", midImg1);
Mat midImg2 = midImg1.clone();
Mat element = getStructuringElement(MORPH_RECT, Size(40, 40));
morphologyEx(midImg1, midImg2, MORPH_CLOSE, element);
namedWindow("【闭运算后】", WINDOW_NORMAL);
imshow("【闭运算后】", midImg2);
cout << "midImg1.channel=" << midImg1.channels() << endl;
cout << "mdiImg1.depth" << midImg1.depth() << endl;
//查找图像轮廓
Mat midImg3 = Mat::zeros(midImg2.rows, midImg2.cols, CV_8UC3);
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(midImg2, contours, hierarchy, RETR_CCOMP, CHAIN_APPROX_SIMPLE);
int index = 0;
for (; index >= 0; index = hierarchy[index][0])
{
Scalar color(255, 255, 255);
drawContours(midImg3, contours, index, color, NULL, 8, hierarchy);
}
namedWindow("【轮廓图】", WINDOW_NORMAL);
imshow("【轮廓图】", midImg3);
Mat midImg4 = midImg3.clone();
//创建包围轮廓的矩形边界
for (int i = 0; i<contours.size(); i++)
{
//每个轮廓
vector<Point> points = contours[i];
//对给定的2D点集，寻找最小面积的包围矩形
RotatedRect box = minAreaRect(Mat(points));
Point2f vertex[4];
box.points(vertex);
//绘制出最小面积的包围矩形
line(xianshi, vertex[0], vertex[1], Scalar(100, 200, 211), 6, CV_AA);
line(xianshi, vertex[1], vertex[2], Scalar(100, 200, 211), 6, CV_AA);
line(xianshi, vertex[2], vertex[3], Scalar(100, 200, 211), 6, CV_AA);
line(xianshi, vertex[3], vertex[0], Scalar(100, 200, 211), 6, CV_AA);
//绘制中心的光标
Point s1, l, r, u, d;
s1.x = (vertex[0].x + vertex[2].x) / 2.0;
s1.y = (vertex[0].y + vertex[2].y) / 2.0;
l.x = s1.x - 10;
l.y = s1.y;

r.x = s1.x + 10;
r.y = s1.y;

u.x = s1.x;
u.y = s1.y - 10;

d.x = s1.x;
d.y = s1.y + 10;
line(xianshi, l, r, Scalar(100, 200, 211), 2, CV_AA);
line(xianshi, u, d, Scalar(100, 200, 211), 2, CV_AA);
}
namedWindow("【绘制的最小面积矩形】", WINDOW_NORMAL);
imshow("【绘制的最小面积矩形】", xianshi);
waitKey(0);
return 0;
}

OpenCV3.0版本

#include<opencv2\opencv.hpp>
#include<iostream>
#define BIN_DIV 120

using namespace std;
using namespace cv;

int main()
{
Mat srcImg=imread("haha.jpg");
Mat xianshi=srcImg.clone();
Mat redChannel;
namedWindow("【原图】",WINDOW_NORMAL);
imshow("【原图】",srcImg);
Mat grayImg;
vector<Mat> channels;
split(srcImg,channels);
//cvtColor(srcImg,grayImg,COLOR_BGR2GRAY);
grayImg=channels.at(0);
redChannel=channels.at(2);
namedWindow("【灰度图】",WINDOW_NORMAL);
imshow("【灰度图】",grayImg);
//均值滤波
blur(grayImg,grayImg,Size(20,20),Point(-1,-1));
namedWindow("【均值滤波后】",WINDOW_NORMAL);
imshow("【均值滤波后】",grayImg);
//转化为二值图
Mat midImg1=grayImg.clone();
int rowNumber=midImg1.rows;
int colNumber=midImg1.cols;

for(int i=0;i<rowNumber;i++)
{
uchar* data=midImg1.ptr<uchar>(i);  //取第i行的首地址
uchar* redData=redChannel.ptr<uchar>(i);
for(int j=0;j<colNumber;j++)
{
if(data[j]>BIN_DIV&&redData[j]<BIN_DIV/2)
data[j]=0;
else
data[j]=255;
}
}
namedWindow("【二值图】",WINDOW_NORMAL);
imshow("【二值图】",midImg1);
Mat midImg2=midImg1.clone();
Mat element=getStructuringElement(MORPH_RECT,Size(20,20));
morphologyEx(midImg1,midImg2,MORPH_OPEN,element);
namedWindow("【开运算后】",WINDOW_NORMAL);
imshow("【开运算后】",midImg2);
cout<<"midImg1.channel="<<midImg1.channels()<<endl;
cout<<"mdiImg1.depth"<<midImg1.depth()<<endl;
//查找图像轮廓
Mat midImg3=Mat::zeros(midImg2.rows,midImg2.cols,CV_8UC3);
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(midImg2,contours,hierarchy,RETR_CCOMP,CHAIN_APPROX_SIMPLE);
int index=0;
for(;index>=0;index=hierarchy[index][0])
{
Scalar color(255,255,255);
drawContours(midImg3,contours,index,color,NULL,8,hierarchy);
}
namedWindow("【轮廓图】",WINDOW_NORMAL);
imshow("【轮廓图】",midImg3);
Mat
4000
midImg4=midImg3.clone();
//创建包围轮廓的矩形边界
for(int i=0;i<contours.size();i++)
{
//每个轮廓
vector<Point> points=contours[i];
//对给定的2D点集，寻找最小面积的包围矩形
RotatedRect box=minAreaRect(Mat(points));
Point2f vertex[4];
box.points(vertex);
//绘制出最小面积的包围矩形
line(xianshi,vertex[0],vertex[1],Scalar(100,200,211),6,LINE_AA);
line(xianshi,vertex[1],vertex[2],Scalar(100,200,211),6,LINE_AA);
line(xianshi,vertex[2],vertex[3],Scalar(100,200,211),6,LINE_AA);
line(xianshi,vertex[3],vertex[0],Scalar(100,200,211),6,LINE_AA);
//绘制中心的光标
Point s1,l,r,u,d;
s1.x=(vertex[0].x+vertex[2].x)/2.0;
s1.y=(vertex[0].y+vertex[2].y)/2.0;
l.x=s1.x-10;
l.y=s1.y;

r.x=s1.x+10;
r.y=s1.y;

u.x=s1.x;
u.y=s1.y-10;

d.x=s1.x;
d.y=s1.y+10;
line(xianshi,l,r,Scalar(100,200,211),2,LINE_AA);
line(xianshi,u,d,Scalar(100,200,211),2,LINE_AA);
}
namedWindow("【绘制的最小面积矩形】",WINDOW_NORMAL);
imshow("【绘制的最小面积矩形】",xianshi);
waitKey(0);
return 0;
}