void KMeansClustering() {
	Mat imgSrc = imread("points1.bmp", CV_LOAD_IMAGE_GRAYSCALE);

	// Creating a color image
	Mat img(imgSrc.rows, imgSrc.cols, CV_8UC3);

	std::uniform_int_distribution<int> dist_img(0, 255);
	std::default_random_engine gen;
	int randInt;

	vector<Point2i> vec;
	vector<int> vecClusters;
	vector<Point2i> centre;

	// Assigning random colors to clusters
	const int K = 3;
	Vec3b colors[K];
	for (int i = 0; i < K; i++) {
		colors[i] = Vec3b(dist_img(gen), dist_img(gen), dist_img(gen));
	}

	for (int i = 0;  i < img.rows;  i++) {
		for (int j = 0; j < img.cols; j++) {
			if (imgSrc.at<uchar>(i, j) == 0) {
				vec.push_back(Point2i(j, i));
				vecClusters.push_back(-1);
			}
		}
	}

	std::uniform_int_distribution<int> dist_vec(0, vec.size());
	int index;
	for (int i = 0; i < K; i++) {
		index = dist_vec(gen);
		centre.push_back(vec.at(index));
	}

	bool updateStatus = true;
	int eq = 0; int min;
	int idxMin;
	while (updateStatus) {
		for (int i = 0; i < img.rows; i++) {
			for (int j = 0; j < img.cols; j++) {
				img.at<Vec3b>(i, j) = Vec3b(255, 255, 255);
			}
		}

		for (int i = 0; i < vec.size(); i++) {
			Point2i currentPoint = vec.at(i);
			min = INT_MAX;
			int idx = 0;
			for (Point2i p : centre) {
				eq = sqrt((p.x - currentPoint.x)*(p.x - currentPoint.x) + (p.y - currentPoint.y)*(p.y - currentPoint.y));
				if (eq < min) {
					min = eq;
					idxMin = i;
				}
				idx++;
			}
			vecClusters[i] = idxMin;
		}

		for (int i = 0; i < vec.size(); i++) {

		}
	}
}