Object Detection and Tracking using Color Separation

Steps for Object Detection & Tracking | OpenCV with Visual Studio in Windows 10. In this Tutorial, we are going to Detect and Track a Yellow Ball using Object Detection (Color Separation) OpenCV.

1. First of all, Follow this tutorial to Install & Configure OpenCV with Visual Studio 2015


2. Copy the Full Source Code for Object Detection and Tracking from here:

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include <iostream>

/*
TRACK A YELLOW BALL - OBJECT DETECTION METHOD USING COLOR SEPERATION OPEN CV 3.1.0
author - Rachit Gulati
*/

int main() {

 cv::VideoCapture capWebcam(0);  // declare a VideoCapture object to associate webcam, 0 means use 1st (default) webcam

 if (capWebcam.isOpened() == false)  //  To check if object was associated to webcam successfully
 {    
  std::cout << "error: Webcam connect unsuccessful\n"; // if not then print error message
  return(0);            // and exit program
 }

 cv::Mat imgOriginal;  // Input image
 cv::Mat hsvImg;    // HSV Image
 cv::Mat threshImg;   // Thresh Image

 std::vector v3fCircles;  // 3 element vector of floats, this will be the pass by reference output of HoughCircles()

 char charCheckForEscKey = 0;

 int lowH = 21;       // Set Hue
 int highH = 30;

 int lowS = 200;       // Set Saturation
 int highS = 255;

 int lowV = 102;       // Set Value
 int highV = 225;
 // HUE for YELLOW is 21-30.
 // Adjust Saturation and Value depending on the lighting condition of the environment as well as the surface of the object.

 while (charCheckForEscKey != 27 && capWebcam.isOpened()) {    // until the Esc is pressed or webcam connection is lost
  bool blnFrameReadSuccessfully = capWebcam.read(imgOriginal);  // get next frame

  if (!blnFrameReadSuccessfully || imgOriginal.empty()) {    // if frame read unsuccessfully
   std::cout << "error: frame can't read \n";      // print error message
   break;               // jump out of loop
  }

  cv::cvtColor(imgOriginal, hsvImg, CV_BGR2HSV);      // Convert Original Image to HSV Thresh Image

  cv::inRange(hsvImg, cv::Scalar(lowH, lowS, lowV), cv::Scalar(highH, highS, highV), threshImg);

  cv::GaussianBlur(threshImg, threshImg, cv::Size(3, 3), 0);   //Blur Effect
  cv::dilate(threshImg, threshImg, 0);        // Dilate Filter Effect
  cv::erode(threshImg, threshImg, 0);         // Erode Filter Effect

  // fill circles vector with all circles in processed image
  cv::HoughCircles(threshImg,v3fCircles,CV_HOUGH_GRADIENT,2,threshImg.rows / 4,100,50,10,800);  // algorithm for detecting circles  

  for (int i = 0; i < v3fCircles.size(); i++) {      // for each circle
               
   std::cout << "Ball position X = "<< v3fCircles[i][0]   // x position of center point of circle
    <<",\tY = "<< v3fCircles[i][1]        // y position of center point of circle
    <<",\tRadius = "<< v3fCircles[i][2]<< "\n";     // radius of circle

                    // draw small green circle at center of object detected
   cv::circle(imgOriginal,            // draw on original image
    cv::Point((int)v3fCircles[i][0], (int)v3fCircles[i][1]),  // center point of circle
    3,                // radius of circle in pixels
    cv::Scalar(0, 255, 0),           // draw green
    CV_FILLED);              // thickness

                    // draw red circle around object detected 
   cv::circle(imgOriginal,            // draw on original image
    cv::Point((int)v3fCircles[i][0], (int)v3fCircles[i][1]),  // center point of circle
    (int)v3fCircles[i][2],           // radius of circle in pixels
    cv::Scalar(0, 0, 255),           // draw red
    3);                // thickness
  } 

  // declare windows
  cv::namedWindow("imgOriginal", CV_WINDOW_AUTOSIZE);
  cv::namedWindow("threshImg", CV_WINDOW_AUTOSIZE); 

     /* Create trackbars in "threshImg" window to adjust according to object and environment.*/
  cv::createTrackbar("LowH", "threshImg", &lowH, 179); //Hue (0 - 179)
  cv::createTrackbar("HighH", "threshImg", &highH, 179);

  cv::createTrackbar("LowS", "threshImg", &lowS, 255); //Saturation (0 - 255)
  cv::createTrackbar("HighS", "threshImg", &highS, 255);

  cv::createTrackbar("LowV", "threshImg", &lowV, 255); //Value (0 - 255)
  cv::createTrackbar("HighV", "threshImg", &highV, 255);
  

  cv::imshow("imgOriginal", imgOriginal);     // show windows
  cv::imshow("threshImg", threshImg);

  charCheckForEscKey = cv::waitKey(1);     // delay and get key press
 }
 
 return(0);           
}

OR
Get ObjectTracker.cpp from Github:
Download ObjectTracker.cpp


3. Paste full source code and Run it (Ctrl+F5 or F5).

OUTPUT

Yellow Ball Detection and Tracker OpenCV

Yellow Ball Threshold Image

X-Coord, Y-Coord and Radius Of Yellow Ball

Explaination: You can use trackbars in "threshImg" window to adjust Hue, Saturation, Value according to object and environment(lightning), Hue values of basic colors:

• Yellow 21- 30
• Blue 75-130
• Violet 130-160
• Orange 0-21
• Green 38-75
• Red 160-179

You have to find the exact range of HUE values according to the color of the object. The SATURATION and VALUE is depend on the lighting condition of the environment as well as the surface of the object.

---

Like, Share and Comment Below

---