识别图像红色区域,并输出红色区域中心点坐标 并输出红色区域中心点坐标 使用使用opencv识别图像红色区域 主要介绍了使用opencv识别图像红色区域,并输出红色区域中心点坐标，具有很好的参考价值，希望对大家有所 帮助。一起跟随小编过来看看吧 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 #include #include #include #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 = ((rg) ? (r) : (g)); maxRGB = (maxRGB>b) ? (maxRGB) : (b); sum = r + g + b; i = sum / 3.0; if (i<0.001 || maxRGB - minRGB<0.001) { h = 0.0; s = 0.0; } else { s = 1.0 - 3.0*minRGB / sum; theta = sqrt((r - g)*(r - g) + (r - b)*(g - b)); theta = acos((r - g + r - b)*0.5 / theta); 

if (b <= g) h = theta; else h = 2 * PI - theta; if (s <= 0.01) h = 0; } hue = (int)(h * 180 / PI); saturation = (int)(s * 100); intensity = (int)(i * 100); } Mat picture_red(Mat input) { Mat frame; Mat srcImg = input; frame = srcImg; waitKey(1); int width = srcImg.cols; int height = srcImg.rows; int x, y; double B = 0.0, G = 0.0, R = 0.0, H = 0.0, S = 0.0, V = 0.0; Mat vec_rgb = Mat::zeros(srcImg.size(), CV_8UC1); for (x = 0; x < height; x++) { for (y = 0; y < width; y++) { B = srcImg.at(x, y)[0]; G = srcImg.at(x, y)[1]; R = srcImg.at(x, y)[2]; RGB2HSV(R, G, B, H, S, V); //红色范围，范围参考的网上。可以自己调 if ((H >= 312 && H <= 360) && (S >= 17 && S <= 100) && (V>18 && V < 100)) vec_rgb.at(x, y) = 255; /*cout << H << "," << 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 > contours;//contours的类型，双重的vector vector 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 mu(contours.size()); for (int i = 0; i < contours.size(); i++) { mu[i] = moments(contours[i], false); } /// 计算矩中心: vector 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 补充知识： 识别网球 ，或者绿色的小球 ，或者绿色的小球 输出重心坐标 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"<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："<=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我们。