#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);
}
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ObjectTracker.cpp from Github:
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