[OpenCV] BOW trainer

#include <iostream>
#include <string>
#include <iostream>
#include <fstream>
#include <vector>

#include <opencv2/highgui.hpp>
#include <opencv2/xfeatures2d.hpp>
#include <opencv2/core.hpp>
#include <opencv2/features2d.hpp>
#include <opencv2/imgproc.hpp>

#include "dlib/image_processing.h"
#include "dlib/string.h"
#include "dlib/opencv.h"

std::vector<std::string> labels{ "dolphin", "ibis" };
    std::string filename;
    std::vector<cv::KeyPoint> kpts;
    cv::Mat desc;
    cv::Mat feats_unclustered;
    cv::Ptr<cv::Feature2D> sift_detector = cv::xfeatures2d::SIFT::create();

    cv::Mat image;

    // Get the keypoints and compute descriptors for all the images in the dataset.
    for (int class_id = 0; class_id < labels.size(); class_id++) {
        for (int image_id = 1; image_id < 51; image_id++) {
            filename = "./data/" + labels[class_id] + "/image_" + dlib::pad_int_with_zeros(image_id, 4) + ".jpg";
            image = cv::imread(filename, cv::IMREAD_GRAYSCALE);
            if (image.empty()) {
                std::cout << filename << " cannot read\n";
            }
            sift_detector->detectAndCompute(image, cv::Mat(), kpts, desc);
            feats_unclustered.push_back(desc);
        }
    }

    // Build the vocabulary by clustering the obtained descriptors and obtain 200 words.
    int dict_size = 200;
    cv::TermCriteria tc(cv::TermCriteria::MAX_ITER, 100, 0.01);
    int retires = 1;
    int flag = cv::KMEANS_PP_CENTERS;
    cv::BOWKMeansTrainer bow_trainer(dict_size, tc, retires, flag);
    cv::Mat dictionary = bow_trainer.cluster(feats_unclustered);

    // Save dictionary to YML file
    cv::FileStorage fs("dictionary.yml", cv::FileStorage::WRITE);
    fs << "vocabulary" << dictionary;
    fs.release();

    //// write to CSV file
    //std::ofstream ofs;
    //ofs.open("dictionary_trained.csv");
    //ofs << cv::format(dict_trained, cv::Formatter::FMT_CSV) << std::endl;
    return 0;
}