labeling images to recognize in SURF object detector - c++

Question

i am trying to implement a SURF object recognition software, so, i found this code in the internet that implements SURF and it works great (slow, but works), with this i can compare the template image to objects in a scene from my webcam and find them, what i am seeking now is how can i label this template so when the code finds it in the camera feed, the software would return the name of the object appearing., here is the code i have, sorry for no source, could not remember it.
Edit: I have been reading about Bag of Words, but it seems rather complicated to implement, i would like to know if there is a simpler way to do this.
Thank You All.
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
using namespace cv;
int main()
{
Mat object = imread("C:\\teste.png", CV_LOAD_IMAGE_GRAYSCALE);
if (!object.data)
{
std::cout << "Error reading object " << std::endl;
return -1;
}
//Detect the keypoints using SURF Detector
int minHessian = 400;
SurfFeatureDetector detector(minHessian);
std::vector<KeyPoint> kp_object;
detector.detect(object, kp_object);
//Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat des_object;
extractor.compute(object, kp_object, des_object);
FlannBasedMatcher matcher;
CvCapture* cap = cvCreateCameraCapture(0);
cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, 320);
cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, 240);
namedWindow("Good Matches");
std::vector<Point2f> obj_corners(4);
//Get the corners from the object
obj_corners[0] = cvPoint(0, 0);
obj_corners[1] = cvPoint(object.cols, 0);
obj_corners[2] = cvPoint(object.cols, object.rows);
obj_corners[3] = cvPoint(0, object.rows);
char key = 'a';
int framecount = 0;
while (key != 27)
{
Mat frame;
frame = cvQueryFrame(cap);
if (framecount < 5)
{
framecount++;
continue;
}
Mat des_image, img_matches;
std::vector<KeyPoint> kp_image;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
Mat H;
Mat image;
cvtColor(frame, image, CV_RGB2GRAY);
detector.detect(image, kp_image);
extractor.compute(image, kp_image, des_image);
matcher.knnMatch(des_object, des_image, matches, 2);
for (int i = 0; i < min(des_image.rows - 1, (int)matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if ((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int)matches[i].size() <= 2 && (int)matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
//Draw only "good" matches
drawMatches(object, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
if (good_matches.size() >= 4)
{
for (int i = 0; i < good_matches.size(); i++)
{
//Get the keypoints from the good matches
obj.push_back(kp_object[good_matches[i].queryIdx].pt);
scene.push_back(kp_image[good_matches[i].trainIdx].pt);
}
H = findHomography(obj, scene, CV_RANSAC);
perspectiveTransform(obj_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image )
line(img_matches, scene_corners[0] + Point2f(object.cols, 0), scene_corners[1] + Point2f(object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[1] + Point2f(object.cols, 0), scene_corners[2] + Point2f(object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[2] + Point2f(object.cols, 0), scene_corners[3] + Point2f(object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[3] + Point2f(object.cols, 0), scene_corners[0] + Point2f(object.cols, 0), Scalar(0, 255, 0), 4);
}
//Show detected matches
imshow("Good Matches", img_matches);
key = waitKey(1);
}
return 0;
}