检测特定颜色的物体 —— 基于OpenCV

1. 需求

利用USB摄像头实时检测一个红色的小球

2. 原料/前期准备

OpenCV2.4.11，VS2010

摄像头模块（可以用罗技的USB摄像头代替）

3. 原理/实施步骤

#include <iostream>   
  
#include "opencv2/highgui/highgui.hpp"   
  
#include "opencv2/imgproc/imgproc.hpp"   
  
using namespace cv;   
  
using namespace std;   
  
int main( int argc, char** argv )   
  
 {   
  
       VideoCapture cap(0); //capture the video from web cam   
  
       if ( !cap.isOpened() )  // if not success, exit program   
  
       {   
  
         cout << "Cannot open the  cam" << endl;   
  
         return -1;   
  
        }  
  
        namedWindow("Control", CV_WINDOW_AUTOSIZE);  
  
//create a window called "Control"   
  
        int iLowH = 156;   
  
        int iHighH = 180;   
  
        int iLowS = 43;    
  
        int iHighS = 255;   
  
        int iLowV = 46;   
  
        int iHighV = 255;   
  
        //Create trackbars in "Control" window   
  
        cvCreateTrackbar("LowH", "Control", &iLowH, 179); //Hue (0 - 179)   
  
        cvCreateTrackbar("HighH", "Control", &iHighH, 179);   
  
        cvCreateTrackbar("LowS", "Control", &iLowS, 255);  
  
//Saturation (0 - 255)   
  
        cvCreateTrackbar("HighS", "Control", &iHighS, 255);   
  
        cvCreateTrackbar("LowV", "Control", &iLowV, 255);  
  
//Value (0 - 255)   
  
        cvCreateTrackbar("HighV", "Control", &iHighV, 255);   
  
        while (true)   
  
        {   
  
          Mat imgOriginal;   
  
          bool bSuccess = cap.read(imgOriginal);  
  
// read a new frame from video   
  
          if (!bSuccess) //if not success, break loop   
  
           {   
  
             cout << "Cannot read a frame from video stream" << endl;   
  
             break;   
  
            }   
  
        Mat imgHSV;   
  
        vector<Mat> hsvSplit;   
  
        cvtColor(imgOriginal, imgHSV, COLOR_BGR2HSV);  
  
//Convert the captured frame from BGR to HSV   
  
         //因为我们读取的是彩色图，直方图均衡化需要在HSV空间做   
  
        split(imgHSV, hsvSplit);   
  
        equalizeHist(hsvSplit[2],hsvSplit[2]);   
  
        merge(hsvSplit,imgHSV);   
  
        Mat imgThresholded;   
  
        inRange(imgHSV, Scalar(iLowH, iLowS, iLowV), Scalar(iHighH, iHighS, iHighV), imgThresholded); //Threshold the image   
  
        /* //开操作 (去除一些噪点)  
 
        Mat element = getStructuringElement(MORPH_RECT, Size(5, 5));  
 
        morphologyEx(imgThresholded, imgThresholded, MORPH_OPEN, element);  
 
        //闭操作 (连接一些连通域)  
 
        morphologyEx(imgThresholded, imgThresholded, MORPH_CLOSE, element);  
 
          */  
  
        vector<Vec3f> circles;   
  
        HoughCircles( imgThresholded, circles, CV_HOUGH_GRADIENT,1.5, 10, 200, 100, 0, 0 );   
  
        //依次在图中绘制出圆   
  
        for( size_t i = 0; i < circles.size(); i++ )   
  
         {   
  
            Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));   
  
            int radius = cvRound(circles[i][2]);   
  
             //绘制圆心   
  
            circle( imgThresholded, center, 3, Scalar(0,255,0), -1, 8, 0 );   
  
             //绘制圆轮廓   
  
            circle( imgThresholded, center, radius, Scalar(155,50,255), 3, 8, 0 );   
  
           }  
  
         imshow("Thresholded Image", img); //show the thresholded image   
  
         imshow("Original", imgOriginal); //show the original image   
  
         char key = (char) waitKey(300);   
  
         if(key == 27)   
  
         break;   
  
         }   
  
    return 0;   
  
}

说明：

1.二值化是将彩色图片转换文纯黑纯白，cvCvtColor可以将图片转为灰度图，但是不是二值图。CvThreshold是二值化函数。获得的图片原红色部分是白色，其余部分是黑色。

2.通过控制RGB通道很难找到红色的准确范围，于是我们选用HSV空间。HSV(Hue, Saturation,Value)是根据颜色的直观特性而创建的一种颜色空间， H：色调 S：饱和度 V：明度

imgHSV是将imgOriginal的RGB空间转换为HSV空间后的得到的图像，之后是分离HSV三通道。cvEqualizeHist函数用于将直方图均衡化，可以将比较淡的图像变换为比较深的图像（即增强图像的亮度及对比度）。cvMerge：把几个单通道图像合并为一个多通道图像，这样我们就获得了一个HSV空间的图像。并且通过调节HSV的值基本可以滤掉所有非红色区域。

效果图：

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检测特定颜色的物体 —— 基于OpenCV

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