rajat99's profile - activity

I am reading 4 video files using OpenCV C++ and then apply red and green color position tracking to get coordinates (for red and green LED) for each video and then do some other complex analysis on coordinates thus extracted. I was doing it sequentially just now like I have shown in the code below. I thought of making the code fast using multi-threading and read about TBB, std::thread, but kind of novice in multi-threading. Can someone suggest me how to go about using either TBB or std::thread?

#include <iostream>
#include <fstream>
#include <string>
#include <cstdio>
#include "opencv2/opencv_modules.hpp"
#include <opencv2/core/utility.hpp>
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/stitching/detail/autocalib.hpp"
#include "opencv2/stitching/detail/blenders.hpp"
#include "opencv2/stitching/detail/timelapsers.hpp"
#include "opencv2/stitching/detail/camera.hpp"
#include "opencv2/stitching/detail/exposure_compensate.hpp"
#include "opencv2/stitching/detail/matchers.hpp"
#include "opencv2/stitching/detail/motion_estimators.hpp"
#include "opencv2/stitching/detail/seam_finders.hpp"
#include "opencv2/stitching/detail/warpers.hpp"
#include "opencv2/stitching/warpers.hpp"
#include <numeric>
#include <thread>

#define ENABLE_LOG 1
#define LOG(msg) std::cout << msg
#define LOGLN(msg) std::cout << msg << std::endl 

vector<Point2f> getLedCoordinates(Mat frame)
{
    Point2f redLedPos = Point2f(-1,-1);
    Point2f greenLedPos = Point2f(-1,-1);
    vector<Point2f> ledPos;
    Mat thresholdedImage;

    //thresholded image
    threshold(frame, thresholdedImage, 160, 255,THRESH_BINARY);

    //remove small noise from the red and green colro thesholded image
    Mat str_el = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(2,2));
    morphologyEx(thresholdedImage, thresholdedImage, cv::MORPH_OPEN, str_el);
    morphologyEx(thresholdedImage, thresholdedImage, cv::MORPH_CLOSE, str_el);

    // Convert input image to HSV
    Mat hsv_image;
    cvtColor(thresholdedImage, hsv_image, cv::COLOR_BGR2HSV);

    // Threshold the HSV image, keep only the red pixels
    Mat lower_red_hue_range, upper_red_hue_range;
    inRange(hsv_image, cv::Scalar(0, 100, 100), cv::Scalar(10, 255, 255), lower_red_hue_range);
    inRange(hsv_image, cv::Scalar(160, 100, 100), cv::Scalar(179, 255, 255), upper_red_hue_range);

    // Combine the above two image
    Mat red_hue_image;
    addWeighted(lower_red_hue_range, 1.0, upper_red_hue_range, 1.0, 0.0, red_hue_image);
    //blur the image to avoid false positives
    GaussianBlur(red_hue_image, red_hue_image, cv::Size(9, 9), 2, 2);

    // Threshold the HSV image, keep only the green pixels
    Mat green_hue_image;
    inRange(hsv_image, cv::Scalar(50, 50, 120), cv::Scalar(70, 255, 255), green_hue_image);

    //blur the image to avoid false positives
    GaussianBlur(green_hue_image, green_hue_image, cv::Size(9, 9), 2, 2);

    //find center of red contours and green contours with max area
    vector<vector<Point> > redContours, greenContours;
    vector<Vec4i> redHeirarchy, greenHeirarchy;

    //find contours
    findContours(red_hue_image.clone(), redContours, redHeirarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
    findContours(green_hue_image.clone(), greenContours, greenHeirarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);

    //iterate through each contour and find the centroid of max area contour for red LED
    double largest_area = 0;
    int largest_contour_index = 0;
    size_t count = (int)redContours.size();
    if(count>0){
        for(unsigned int i = 0; i< count; i++ )
        {
            //  Find the area of contour
            double a=contourArea(redContours[i],false);
            if(a>largest_area){
                largest_area=a;
                // Store the index of largest contour
                largest_contour_index=i;
        }
        }
        Moments redMoment = moments(redContours[largest_contour_index], false);
        redLedPos = Point2f(redMoment.m10/redMoment.m00, redMoment.m01/redMoment.m00);
    }

    //iterate through each contour and find the centroid ...