基於opencv的物體定位
阿新 • • 發佈：2019-02-03
opencv是一個很強大的機器視覺庫，利用它我們可以開發出豐富多彩的使用專案。近日，我在研究一個圖中物體定位系統。本程式用的是OpenCV2.4.9，附帶OpenCV3.0。
程式中的原圖為我隨手拍的一張圖片
圖中有三個物體，都是藍色的，我首先取原圖的藍色通道變為灰度圖
灰度圖經過中值濾波後可以得到去噪後的圖片
根據原圖的藍色通道和紅色通道的大概取值範圍，我們可得到比較滿意的二值圖
為了去掉物體中少量的黑色部分，我用閉運算
然而，圖中最上面的那個物體裡面還有一塊很大的黑色（目前我也不知道怎麼去掉，如果有大神知道望告知~~）
接下來就是找出物體的輪廓
最後找到能包圍輪廓的最小矩形
好了，佔時就這麼多了
下面是配套的程式
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("【原圖】", WINDOW_NORMAL); imshow("【原圖】", srcImg); Mat grayImg; vector<Mat> channels; split(srcImg, channels); //cvtColor(srcImg,grayImg,COLOR_BGR2GRAY); grayImg = channels.at(0); redChannel = channels.at(2); namedWindow("【灰度圖】", WINDOW_NORMAL); imshow("【灰度圖】", grayImg); //均值濾波 blur(grayImg, grayImg, Size(20, 20), Point(-1, -1)); namedWindow("【均值濾波後】", WINDOW_NORMAL); imshow("【均值濾波後】", grayImg); //轉化為二值圖 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("【二值圖】", WINDOW_NORMAL); imshow("【二值圖】", midImg1); Mat midImg2 = midImg1.clone(); Mat element = getStructuringElement(MORPH_RECT, Size(40, 40)); morphologyEx(midImg1, midImg2, MORPH_CLOSE, element); namedWindow("【閉運算後】", WINDOW_NORMAL); imshow("【閉運算後】", midImg2); cout << "midImg1.channel=" << midImg1.channels() << endl; cout << "mdiImg1.depth" << midImg1.depth() << endl; //查詢影象輪廓 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("【輪廓圖】", WINDOW_NORMAL); imshow("【輪廓圖】", midImg3); Mat midImg4 = midImg3.clone(); //建立包圍輪廓的矩形邊界 for (int i = 0; i<contours.size(); i++) { //每個輪廓 vector<Point> points = contours[i]; //對給定的2D點集，尋找最小面積的包圍矩形 RotatedRect box = minAreaRect(Mat(points)); Point2f vertex[4]; box.points(vertex); //繪製出最小面積的包圍矩形 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); //繪製中心的游標 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("【繪製的最小面積矩形】", WINDOW_NORMAL); imshow("【繪製的最小面積矩形】", 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("【原圖】",WINDOW_NORMAL); imshow("【原圖】",srcImg); Mat grayImg; vector<Mat> channels; split(srcImg,channels); //cvtColor(srcImg,grayImg,COLOR_BGR2GRAY); grayImg=channels.at(0); redChannel=channels.at(2); namedWindow("【灰度圖】",WINDOW_NORMAL); imshow("【灰度圖】",grayImg); //均值濾波 blur(grayImg,grayImg,Size(20,20),Point(-1,-1)); namedWindow("【均值濾波後】",WINDOW_NORMAL); imshow("【均值濾波後】",grayImg); //轉化為二值圖 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("【二值圖】",WINDOW_NORMAL); imshow("【二值圖】",midImg1); Mat midImg2=midImg1.clone(); Mat element=getStructuringElement(MORPH_RECT,Size(20,20)); morphologyEx(midImg1,midImg2,MORPH_OPEN,element); namedWindow("【開運算後】",WINDOW_NORMAL); imshow("【開運算後】",midImg2); cout<<"midImg1.channel="<<midImg1.channels()<<endl; cout<<"mdiImg1.depth"<<midImg1.depth()<<endl; //查詢影象輪廓 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("【輪廓圖】",WINDOW_NORMAL); imshow("【輪廓圖】",midImg3); Mat midImg4=midImg3.clone(); //建立包圍輪廓的矩形邊界 for(int i=0;i<contours.size();i++) { //每個輪廓 vector<Point> points=contours[i]; //對給定的2D點集，尋找最小面積的包圍矩形 RotatedRect box=minAreaRect(Mat(points)); Point2f vertex[4]; box.points(vertex); //繪製出最小面積的包圍矩形 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); //繪製中心的游標 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("【繪製的最小面積矩形】",WINDOW_NORMAL); imshow("【繪製的最小面積矩形】",xianshi); waitKey(0); return 0; }