Speeding up my code for object recognition

edit

I am working on a multi-object recognition program. I have succeeded recognising two objects. However, the speed of my program is really slow and laggy. Can somebody please tell a way to speed the program up?

So far, I have seen that ORB is quite fast at matching features. However, my program is still too slow. I have also noticed that there are three for loops, possibly making the program slow down. Interestingly on YouTube, I have seen videos where the output is really smooth.

Is there a way to fix this? Thanks.

This is my code:

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"

using namespace std;
using namespace cv;

FlannBasedMatcher matcher(new flann::LshIndexParams(20, 10, 2));

void ttea(vector<KeyPoint> bwImgKP, Mat objectDS, Mat bwImgDS, vector<KeyPoint> objectKP, vector<Point2f> objectCRN, Mat colorImg, string text)
{
    vector<vector<DMatch>> matches;
    vector<DMatch> matchesGD;
    vector<Point2f> obj;
    vector<Point2f> scene;
    vector<Point2f> sceneCRN(4);
    Mat H;

    if (bwImgKP.empty() || objectDS.empty() || bwImgDS.empty())
    {
        return;
    }

    matcher.knnMatch(objectDS, bwImgDS, matches, 2);

    for (int i = 0; i < min(bwImgDS.rows - 1, (int)matches.size()); i++)
    {
        if ((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int)matches[i].size() <= 2 && (int)matches[i].size() > 0))
        {
            matchesGD.push_back(matches[i][0]);
        }
    }

    if (matchesGD.size() >= 4)
    {
        for (int i = 0; i < matchesGD.size(); i++)
        {
            obj.push_back(objectKP[matchesGD[i].queryIdx].pt);
            scene.push_back(bwImgKP[matchesGD[i].trainIdx].pt);
        }

        H = findHomography(obj, scene, CV_RANSAC);

        perspectiveTransform(objectCRN, sceneCRN, H);

        line(colorImg, sceneCRN[0], sceneCRN[1], Scalar(255, 0, 0), 4);
        line(colorImg, sceneCRN[1], sceneCRN[2], Scalar(255, 0, 0), 4);
        line(colorImg, sceneCRN[2], sceneCRN[3], Scalar(255, 0, 0), 4);
        line(colorImg, sceneCRN[3], sceneCRN[0], Scalar(255, 0, 0), 4);
        putText(colorImg, text, sceneCRN[1], FONT_HERSHEY_DUPLEX, 1, Scalar(0, 0, 255), 1, 8);
    }
}

int main()
{
    OrbFeatureDetector detector;
    OrbDescriptorExtractor extractor;

    VideoCapture capture(0);

    Mat object0 = imread("Much Ado About Nothing.jpg", CV_LOAD_IMAGE_GRAYSCALE);

    vector<KeyPoint> object0KP;
    detector.detect(object0, object0KP);

    Mat object0DS;
    extractor.compute(object0, object0KP, object0DS);

    vector<Point2f> object0CRN(4);
    object0CRN[0] = (cvPoint(0, 0));
    object0CRN[1] = (cvPoint(object0.cols, 0));
    object0CRN[2] = (cvPoint(object0.cols, object0.rows));
    object0CRN[3] = (cvPoint(0, object0.rows));

    Mat object1 = imread("Popular Science.jpg", CV_LOAD_IMAGE_GRAYSCALE);

    vector<KeyPoint> object1KP;
    detector.detect(object1, object1KP);

    Mat object1DS;
    extractor.compute(object1, object1KP, object1DS);

    vector<Point2f> object1CRN(4);
    object1CRN[0] = (cvPoint(0, 0));
    object1CRN[1] = (cvPoint(object1.cols, 0));
    object1CRN[2] = (cvPoint(object1.cols, object1.rows));
    object1CRN[3] = (cvPoint(0, object1.rows));

    while (true)
    {
        Mat bwImg;
        Mat bwImgDS;
        vector<KeyPoint> bwImgKP;

        Mat colorImg;
        capture.read(colorImg);

        cvtColor(colorImg, bwImg, CV_BGR2GRAY);

        detector.detect(bwImg, bwImgKP);
        extractor.compute(bwImg, bwImgKP, bwImgDS);

        ttea(bwImgKP, object0DS, bwImgDS, object0KP, object0CRN, colorImg, "Play");
        ttea(bwImgKP, object1DS, bwImgDS, object1KP, object1CRN, colorImg, "Magazine");

        imshow("Fish Smart", colorImg);

        if (waitKey(1) == 27)
        {
            return 0;
        }
    }
}