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适用小白,大佬勿喷
个人配置:vs2013 ; opencv 3.0 ;
直接上效果图
注意:右下角的水印把中心点挡住了,要仔细看才能看到
下面是代码:
#include <iostream> #include<opencv2\opencv.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <opencv2/highgui/highgui.hpp> #define PI 3.1415926 using namespace cv; using namespace std; void RGB2HSV(double red, double green, double blue, double& hue, double& saturation, double& intensity) { double r, g, b; double h, s, i; double sum; double minRGB, maxRGB; double theta; r = red / 255.0; g = green / 255.0; b = blue / 255.0; minRGB = ((r<g) "," << S << "," << V << endl;*/ } } /*imshow("hsv", vec_rgb);*/ return vec_rgb; } void O_x1y1(Mat in, double *x1, double *y1, double *x2, double *y2) { Mat matSrc = in; /*Mat matSrc = imread("qwer9.png", 0);*/ GaussianBlur(matSrc, matSrc, Size(5, 5), 0);//高斯滤波,除噪点 vector<vector<Point> > contours;//contours的类型,双重的vector vector<Vec4i> hierarchy;//Vec4i是指每一个vector元素中有四个int型数据。 //阈值 threshold(matSrc, matSrc, 100, 255, THRESH_BINARY);//图像二值化 //寻找轮廓,这里注意,findContours的输入参数要求是二值图像,二值图像的来源大致有两种,第一种用threshold,第二种用canny findContours(matSrc.clone(), contours, hierarchy, CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0)); /// 计算矩 vector<Moments> mu(contours.size()); for (int i = 0; i < contours.size(); i++) { mu[i] = moments(contours[i], false); } /// 计算矩中心: vector<Point2f> mc(contours.size()); for (int i = 0; i < contours.size(); i++) { mc[i] = Point2f(mu[i].m10 / mu[i].m00, mu[i].m01 / mu[i].m00); } /// 绘制轮廓 Mat drawing = Mat::zeros(matSrc.size(), CV_8UC1); for (int i = 0; i < contours.size(); i++) { Scalar color = Scalar(255); //drawContours(drawing, contours, i, color, 2, 8, hierarchy, 0, Point());//绘制轮廓函数 circle(drawing, mc[i], 4, color, -1, 8, 0); } *x1 = mc[0].x; *y1 = mc[0].y; *x2 = mc[contours.size()-1].x; *y2 = mc[contours.size() - 1].y; imshow("outImage", drawing); } int main() { double xx1, yy1, xx2, yy2; double x1, y1, x2, y2; Mat matSrc = imread("qwer4.png"); Mat middle = picture_red(matSrc); O_x1y1(middle, &xx1, &yy1, &xx2, &yy2); x1 = xx1; y1 = yy1; x2 = xx2; y2 = yy2; imshow("原图", matSrc); imshow("red", picture_red(matSrc)); cout << "红点:" << x1 << ", " << y1 << "; " << "红点1:" << x2 << ", " << y2 << endl; waitKey(); return 0; }
如有不足,望指点!
补充知识:opencv 识别网球 ,或者绿色的小球 输出重心坐标
我就废话不多说了,大家还是直接看代码吧!
void image_process(IplImage *image) { int iLowH =26; int iHighH = 69; int iLowS = 42; int iHighS = 206; int iLowV = 0; int iHighV = 198; CvMemStorage* storage2 = cvCreateMemStorage(); CvSeq* contour3 = NULL; CvMoments moments; CvMat *region; CvPoint pt1,pt2; double m00 = 0, m10, m01, mu20, mu11, mu02, inv_m00; double a, b, c; int xc, yc; CvMemStorage* storage = cvCreateMemStorage(); CvSeq * circles=NULL; // Circle cir[6]; CvPoint P0; CvPoint CenterPoint; // cvNamedWindow("win1"); //cvShowImage("win1",image); //cvNamedWindow("image",CV_WINDOW_AUTOSIZE);//用于显示图像的窗口 //cvNamedWindow("hsv",CV_WINDOW_AUTOSIZE); //cvNamedWindow("saturation",CV_WINDOW_AUTOSIZE); //cvNamedWindow("value",CV_WINDOW_AUTOSIZE); //cvNamedWindow("pImg8u",1); IplImage *hsv=cvCreateImage(cvGetSize(image),8,3);//给hsv色系的图像申请空间 IplImage *hue=cvCreateImage(cvGetSize(image),8,1); //色调 IplImage *saturation=cvCreateImage(cvGetSize(image),8,1);//饱和度 IplImage *value=cvCreateImage(cvGetSize(image),8,1);//亮度 IplImage *imgThresholded=cvCreateImage(cvGetSize(hue),8,1); cvNamedWindow("yuan",1); cvCvtColor(image,hsv,CV_BGR2HSV);//将RGB色系转为HSV色系 cvShowImage("yuan",image); //cvShowImage("hsv",hsv); cvSplit(hsv, hue, 0, 0, 0 );//分离三个通道 cvSplit(hsv, 0, saturation, 0, 0 ); cvSplit(hsv, 0, 0, value, 0 ); int value_1=0; cvInRangeS( hsv, cvScalar(iLowH, iLowS, iLowV), cvScalar(iHighH, iHighS, iHighV), imgThresholded ); cvNamedWindow("imgThresholded",1); cvShowImage("imgThresholded",imgThresholded); IplImage*pContourImg= cvCreateImage( cvGetSize(image), 8, 1 ); cvCopy(imgThresholded,pContourImg); cvNamedWindow("pContourImg",1); cvShowImage("pContourImg",pContourImg); IplImage* dst = cvCreateImage( cvGetSize(image), 8, 3 ); CvMemStorage* storage3 = cvCreateMemStorage(0); CvSeq* contour = 0; // 提取轮廓 int contour_num = cvFindContours(pContourImg, storage3, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE); cvZero(dst); // 清空数组 CvSeq *_contour = contour; double maxarea = 100; double minarea = 10; int m = 0; for( ; contour != 0; contour = contour->h_next ) { double tmparea = fabs(cvContourArea(contour)); if(tmparea < minarea) { cvSeqRemove(contour, 0); // 删除面积小于设定值的轮廓 continue; } CvRect aRect = cvBoundingRect( contour, 0 ); if ((aRect.width/aRect.height)<1) { cvSeqRemove(contour, 0); //删除宽高比例小于设定值的轮廓 continue; } if(tmparea > maxarea) { maxarea = tmparea; } m++; // 创建一个色彩值 // CvScalar color = CV_RGB( 0, 0, 255 ); /* max_level 绘制轮廓的最大等级。如果等级为0,绘制单独的轮廓。如果为1,绘制轮廓及在其后的相同的级别下轮廓 如果值为2,所有的轮廓。如果等级为2,绘制所有同级轮廓及所有低一级轮廓,诸此种种 如果值为负数,函数不绘制同级轮廓,但会升序绘制直到级别为abs(max_level)-1的子轮廓 */ // cvDrawContours(dst, contour, color, color, 0, 1, 8); //绘制外部和内部的轮廓 } contour = _contour; int count = 0; double tmparea=0; for(; contour != 0; contour = contour->h_next) { count++; tmparea = fabs(cvContourArea(contour)); if (tmparea >= maxarea) { CvScalar color = CV_RGB( 0, 255, 0); cvDrawContours(dst, contour, color, color, -1, 1, 8); cout<<"222"<<endl; cout<<"面积为"<<tmparea<<endl; cout<<endl; CvRect aRect = cvBoundingRect( contour, 0 ); //找重心 { CvPoint2D32f center = cvPoint2D32f(0, 0); int countOfPoint = 0; for(int i = aRect.x; i < aRect.x + aRect.width; ++i){ for(int j = aRect.y; j < aRect.y + aRect.height; ++j){ if(*(image->imageData + image->widthStep * j + i) != 0){ center.x += i; center.y += j; countOfPoint++; } } } center.x /= countOfPoint; center.y /= countOfPoint; cout<<"重心坐标为x:"<<center.x<<endl; cout<<"重心坐标为y:"<<center.y<<endl; cvCircle(dst, cvPoint(center.x, center.y), 5, cvScalar(0, 255), 2); } } // //Threshold the image // cvErode(imgThresholded,imgThresholded); // cvErode(imgThresholded,imgThresholded); //cvErode(imgThresholded,imgThresholded); //cvErode(imgThresholded,imgThresholded); //IplImage* pImg8u=cvCloneImage(imgThresholded); //cvCanny(pImg8u, pImg8u,40, 50, 5); //cvShowImage("pImg8u",pImg8u); //circles=cvHoughCircles(pImg8u,storage,CV_HOUGH_GRADIENT, //2, //最小分辨率,应当>=1 //pImg8u->height/15, //该参数是让算法能明显区分的两个不同圆之间的最小距离 //80, //用于Canny的边缘阀值上限,下限被置为上限的一半 //65, //累加器的阀值 //25, //最小圆半径 //50 //最大圆半径 //); } cvShowImage( "contour", dst ); }
以上这篇使用opencv识别图像红色区域,并输出红色区域中心点坐标就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。
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白云岛资源网 Design By www.pvray.com
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P70系列延期,华为新旗舰将在下月发布
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