看效果 下面源代码
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50Object.h #pragma once #include <string> #include <cv.h> #include <highgui.h> using namespace std; using namespace cv; class Object { public: Object(); ~Object(void); Object(string name); int getXPos(); void setXPos(int x); int getYPos(); void setYPos(int y); Scalar getHSVmin(); Scalar getHSVmax(); void setHSVmin(Scalar min); void setHSVmax(Scalar max); string getType(){return type;} void setType(string t){type = t;} Scalar getColor(){ return Color; } void setColor(Scalar c){ Color = c; } private: int xPos, yPos; string type; Scalar HSVmin, HSVmax; Scalar Color; };
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114Object.cpp #include "Object.h" Object::Object() { //为默认构造函数设置值 setType("Object"); setColor(Scalar(0,0,0)); } Object::Object(string name){ setType(name); if(name=="blue"){ //TODO:使用“校准模式”查找HSV最小值和最大值 setHSVmin(Scalar(92,0,0)); setHSVmax(Scalar(124,256,256)); //BGR value for Blue: setColor(Scalar(255,0,0)); } if(name=="green"){ //TODO:使用“校准模式”查找HSV最小值和最大值 setHSVmin(Scalar(34,50,50)); setHSVmax(Scalar(80,220,200)); //绿色的BGR值: setColor(Scalar(0,255,0)); } if(name=="yellow"){ //TODO:使用“校准模式”查找HSV最小值和HSV最大值 setHSVmin(Scalar(20,124,123)); setHSVmax(Scalar(30,256,256)); //黄色的BGR值： setColor(Scalar(0,255,255)); } if(name=="red"){ //TODO:使用“校准模式”查找HSV最小值和HSV最大值 setHSVmin(Scalar(0,200,0)); setHSVmax(Scalar(19,255,255)); //BGR value for Red: setColor(Scalar(0,0,255)); } } Object::~Object(void) { } int Object::getXPos(){ return Object::xPos; } void Object::setXPos(int x){ Object::xPos = x; } int Object::getYPos(){ return Object::yPos; } void Object::setYPos(int y){ Object::yPos = y; } Scalar Object::getHSVmin(){ return Object::HSVmin; } Scalar Object::getHSVmax(){ return Object::HSVmax; } void Object::setHSVmin(Scalar min){ Object::HSVmin = min; } void Object::setHSVmax(Scalar max){ Object::HSVmax = max; }
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321multipleObjectTracking.cpp #include <sstream> #include <string> #include <iostream> #include <vector> #include "Object.h" //初始最小和最大HSV过滤器值。 //更改轨迹栏 int H_MIN = 0; int H_MAX = 256; int S_MIN = 0; int S_MAX = 256; int V_MIN = 0; int V_MAX = 256; //默认capture 宽度和高度 const int FRAME_WIDTH = 640; const int FRAME_HEIGHT = 480; //帧中要检测的最大对象数 const int MAX_NUM_OBJECTS=50; //最小和最大物体面积 const int MIN_OBJECT_AREA = 20*20; const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5; //每个窗口顶部的名称 const string windowName = "Original Image"; const string windowName1 = "HSV Image"; const string windowName2 = "Thresholded Image"; const string windowName3 = "After Morphological Operations"; const string trackbarWindowName = "Trackbars"; //下面是canny边缘检测的示例： Mat dst, detected_edges; Mat src, src_gray; int edgeThresh = 1; int lowThreshold; int const max_lowThreshold = 100; int ratio = 3; int kernel_size = 3; const char* window_name = "Edge Map"; void on_trackbar( int, void* ) {//每当发生错误时，就调用此函数 } string intToString(int number){ std::stringstream ss; ss << number; return ss.str(); } void createTrackbars(){ //创建窗口轨迹栏 //下面注释有些写英文了 方便理解 （ 输入法切换太麻烦） namedWindow(trackbarWindowName,0); //create memory to store trackbar name on window char TrackbarName[50]; sprintf( TrackbarName, "H_MIN", H_MIN); sprintf( TrackbarName, "H_MAX", H_MAX); sprintf( TrackbarName, "S_MIN", S_MIN); sprintf( TrackbarName, "S_MAX", S_MAX); sprintf( TrackbarName, "V_MIN", V_MIN); sprintf( TrackbarName, "V_MAX", V_MAX); //创建轨迹栏并将其插入窗口 //3个参数为：移动轨迹栏时发生变化的变量地址（如H_LOW）， //轨迹栏可以移动的最大值（例如H_高）， //以及每当移动轨迹栏时调用的函数（例如，在轨迹栏上） // ----> ----> ----> createTrackbar( "H_MIN", trackbarWindowName, &H_MIN, H_MAX, on_trackbar ); createTrackbar( "H_MAX", trackbarWindowName, &H_MAX, H_MAX, on_trackbar ); createTrackbar( "S_MIN", trackbarWindowName, &S_MIN, S_MAX, on_trackbar ); createTrackbar( "S_MAX", trackbarWindowName, &S_MAX, S_MAX, on_trackbar ); createTrackbar( "V_MIN", trackbarWindowName, &V_MIN, V_MAX, on_trackbar ); createTrackbar( "V_MAX", trackbarWindowName, &V_MAX, V_MAX, on_trackbar ); } void drawObject(vector<Object> theObjects,Mat &frame, Mat &temp, vector< vector<Point> > contours, vector<Vec4i> hierarchy){ for(int i =0; i<theObjects.size(); i++){ cv::drawContours(frame,contours,i,theObjects.at(i).getColor(),3,8,hierarchy); cv::circle(frame,cv::Point(theObjects.at(i).getXPos(),theObjects.at(i).getYPos()),5,theObjects.at(i).getColor()); cv::putText(frame,intToString(theObjects.at(i).getXPos())+ " , " + intToString(theObjects.at(i).getYPos()),cv::Point(theObjects.at(i).getXPos(),theObjects.at(i).getYPos()+20),1,1,theObjects.at(i).getColor()); cv::putText(frame,theObjects.at(i).getType(),cv::Point(theObjects.at(i).getXPos(),theObjects.at(i).getYPos()-20),1,2,theObjects.at(i).getColor()); } } void drawObject(vector<Object> theObjects,Mat &frame){ for(int i =0; i<theObjects.size(); i++){ cv::circle(frame,cv::Point(theObjects.at(i).getXPos(),theObjects.at(i).getYPos()),10,cv::Scalar(0,0,255)); cv::putText(frame,intToString(theObjects.at(i).getXPos())+ " , " + intToString(theObjects.at(i).getYPos()),cv::Point(theObjects.at(i).getXPos(),theObjects.at(i).getYPos()+20),1,1,Scalar(0,255,0)); cv::putText(frame,theObjects.at(i).getType(),cv::Point(theObjects.at(i).getXPos(),theObjects.at(i).getYPos()-30),1,2,theObjects.at(i).getColor()); } } void morphOps(Mat &thresh){ //创建用于“扩张”和“侵蚀”形象的结构元素。 //the element chosen here is a 3px by 3px rectangle Mat erodeElement = getStructuringElement( MORPH_RECT,Size(3,3)); //dilate with larger element so make sure object is nicely visible Mat dilateElement = getStructuringElement( MORPH_RECT,Size(8,8)); erode(thresh,thresh,erodeElement); erode(thresh,thresh,erodeElement); dilate(thresh,thresh,dilateElement); dilate(thresh,thresh,dilateElement); } void trackFilteredObject(Mat threshold,Mat HSV, Mat &cameraFeed) { vector <Object> objects; Mat temp; threshold.copyTo(temp); //these two vectors needed for output of findContours vector< vector<Point> > contours; vector<Vec4i> hierarchy; //find contours of filtered image using openCV findContours function findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE ); //use moments method to find our filtered object double refArea = 0; bool objectFound = false; if (hierarchy.size() > 0) { int numObjects = hierarchy.size(); //if number of objects greater than MAX_NUM_OBJECTS we have a noisy filter if(numObjects<MAX_NUM_OBJECTS) { for (int index = 0; index >= 0; index = hierarchy[index][0]) { Moments moment = moments((cv::Mat)contours[index]); double area = moment.m00; //如果面积小于20px乘以20px，则可能只是噪声 //如果面积与图像大小的3/2相同，可能只是一个坏的过滤器 //只需要具有最大面积的对象，因此每个对象都有一个安全的参考区域 //迭代并将其与下一次迭代中的区域进行比较。 if(area>MIN_OBJECT_AREA) { Object object; object.setXPos(moment.m10/area); object.setYPos(moment.m01/area); objects.push_back(object); objectFound = true; } else objectFound = false; } //let user know you found an object if(objectFound ==true) { //draw object location on screen drawObject(objects,cameraFeed); } } else putText(cameraFeed,"TOO MUCH NOISE! ADJUST FILTER",Point(0,50),1,2,Scalar(0,0,255),2); } } void trackFilteredObject(Object theObject,Mat threshold,Mat HSV, Mat &cameraFeed){ vector <Object> objects; Mat temp; threshold.copyTo(temp); //这两个向量是FindContentours输出所需的 vector< vector<Point> > contours; vector<Vec4i> hierarchy; //使用openCV findContours函数查找过滤图像的轮廓 findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE ); //使用矩方法查找过滤对象 double refArea = 0; bool objectFound = false; if (hierarchy.size() > 0) { int numObjects = hierarchy.size(); //if number of objects greater than MAX_NUM_OBJECTS we have a noisy filter if(numObjects<MAX_NUM_OBJECTS){ for (int index = 0; index >= 0; index = hierarchy[index][0]) { Moments moment = moments((cv::Mat)contours[index]); double area = moment.m00; //如果面积小于20px乘以20px，则可能只是噪声 //如果面积与图像大小的3/2相同，可能只是一个坏的过滤器 //只需要具有最大面积的对象，因此每个对象都有一个安全的参考区域 //迭代并将其与下一次迭代中的区域进行比较。 if(area>MIN_OBJECT_AREA){ Object object; object.setXPos(moment.m10/area); object.setYPos(moment.m01/area); object.setType(theObject.getType()); object.setColor(theObject.getColor()); objects.push_back(object); objectFound = true; }else objectFound = false; } //找到了一个对象 if(objectFound ==true){ //draw object location on screen drawObject(objects,cameraFeed,temp,contours,hierarchy);} }else putText(cameraFeed,"TOO MUCH NOISE! ADJUST FILTER",Point(0,50),1,2,Scalar(0,0,255),2); } } int main(int argc, char* argv[]) { //如果要校准过滤器值，请设置为true。 bool calibrationMode = true; //Matrix to store each frame of the webcam feed Mat cameraFeed; Mat threshold; Mat HSV; if(calibrationMode){ //create slider bars for HSV filtering createTrackbars(); } //用于获取网络摄像头提要的视频捕获对象 VideoCapture capture; //open capture object at location zero (default location for webcam) capture.open(0); //set height and width of capture frame capture.set(CV_CAP_PROP_FRAME_WIDTH,FRAME_WIDTH); capture.set(CV_CAP_PROP_FRAME_HEIGHT,FRAME_HEIGHT); //启动一个无限循环，将网络摄像头提要复制到cameraFeed矩阵 //all of our operations will be 在这个循环中执行 waitKey(1000); while(1){ //store image to matrix capture.read(cameraFeed); src = cameraFeed; if( !src.data ) { return -1; } //将帧从BGR转换为HSV颜色空间 cvtColor(cameraFeed,HSV,COLOR_BGR2HSV); if(calibrationMode==true){ //需要找到合适的颜色范围值 // 校准模式必须为false //如果处于校准模式，根据HSV滑块值跟踪对象。 cvtColor(cameraFeed,HSV,COLOR_BGR2HSV); inRange(HSV,Scalar(H_MIN,S_MIN,V_MIN),Scalar(H_MAX,S_MAX,V_MAX),threshold); morphOps(threshold); imshow(windowName2,threshold); //canny边缘检测的后续步骤 /// 创建一个与src（用于dst）类型和大小相同的矩阵 dst.create( src.size(), src.type() ); /// 图像转换为灰度 cvtColor( src, src_gray, CV_BGR2GRAY ); /// Create a window namedWindow( window_name, CV_WINDOW_AUTOSIZE ); // 创建一个追踪栏供用户输入阈值 createTrackbar( "Min Threshold:", window_name, &lowThreshold, max_lowThreshold); /// Show the image trackFilteredObject(threshold,HSV,cameraFeed); } else{ //创建一些临时姐果对象，以便可以使用他们的成员功能/信息 Object blue("blue"), yellow("yellow"), red("red"), green("green"); //首先找到蓝色的物体 cvtColor(cameraFeed,HSV,COLOR_BGR2HSV); inRange(HSV,blue.getHSVmin(),blue.getHSVmax(),threshold); morphOps(threshold); trackFilteredObject(blue,threshold,HSV,cameraFeed); //then yellows cvtColor(cameraFeed,HSV,COLOR_BGR2HSV); inRange(HSV,yellow.getHSVmin(),yellow.getHSVmax(),threshold); morphOps(threshold); trackFilteredObject(yellow,threshold,HSV,cameraFeed); //then reds cvtColor(cameraFeed,HSV,COLOR_BGR2HSV); inRange(HSV,red.getHSVmin(),red.getHSVmax(),threshold); morphOps(threshold); trackFilteredObject(red,threshold,HSV,cameraFeed); //then greens cvtColor(cameraFeed,HSV,COLOR_BGR2HSV); inRange(HSV,green.getHSVmin(),green.getHSVmax(),threshold); morphOps(threshold); trackFilteredObject(green,threshold,HSV,cameraFeed); } //show frames //imshow(windowName2,threshold); imshow(windowName,cameraFeed); //imshow(windowName1,HSV); //延迟30毫秒，以便屏幕可以刷新。 //没有此waitKey（）命令，图像将不会显示 waitKey(30); } return 0; }
工程项目：
链接：https://pan.baidu.com/s/1BUnslDd-FK2WFeVeLYp5Yw
提取码：1rwn
工程文件虽然跑起来了但是可能有点问题 需要自行调试：目前我没有细调试
如果不清楚怎么配置 参考这个博客
https://blog.csdn.net/weixin_39276851/article/details/106720387
邮箱：okjokull@gmail.com
最后
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