使用opencv识别图像红色区域,并输出红色区域中心点坐标

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) ? (r) : (g));
	minRGB = (minRGB<b) ? (minRGB) : (b);

	maxRGB = ((r>g) ? (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<Vec3b>(x, y)[0];
			G = srcImg.at<Vec3b>(x, y)[1];
			R = srcImg.at<Vec3b>(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<uchar>(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<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 );
	}

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