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#define HAVE_CAFFE
#include <opencv2/cnn_3dobj.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <iomanip>
using namespace cv;
using namespace std;
using namespace cv::cnn_3dobj;

/* Get the file name from a root dictionary. */
void listDir(const char *path, std::vector<string>& files, bool r)
{
    DIR *pDir;
    struct dirent *ent;
    char childpath[512];
    pDir = opendir(path);
    memset(childpath, 0, sizeof(childpath));
    while ((ent = readdir(pDir)) != NULL)
    {
        if (ent->d_type & DT_DIR)
        {
            if (strcmp(ent->d_name, ".") == 0 || strcmp(ent->d_name, "..") == 0)
            {
                continue;
            }
            if(r)
            {
                sprintf(childpath, "%s/%s", path, ent->d_name);
                listDir(childpath,files,false);
            }
        }
        else
        {
            files.push_back(ent->d_name);
        }
    }
    sort(files.begin(),files.end());
};

int main(int argc, char** argv)
{
    const String keys = "{help | | this demo will convert a set of images in a particular path into leveldb database for feature extraction using Caffe. If there little variance in data such as human faces, you can add a mean_file, otherwise it is not so useful}"
"{src_dir | ../data/images_all/ | Source direction of the images ready for being used for extract feature as gallery.}"
"{caffemodel | ../data/3d_triplet_iter_20000.caffemodel | caffe model for feature exrtaction.}"
"{network_forIMG | ../data/3d_triplet_testIMG.prototxt | Network definition file used for extracting feature from a single image and making a classification}"
"{mean_file | no | The mean file generated by Caffe from all gallery images, this could be used for mean value substraction from all images. If you want to use the mean file, you can set this as ../data/images_mean/triplet_mean.binaryproto.}"
"{target_img | ../data/images_all/3_13.png | Path of image waiting to be classified.}"
"{feature_blob | feat | Name of layer which will represent as the feature, in this network, ip1 or feat is well.}"
"{num_candidate | 15 | Number of candidates in gallery as the prediction result.}"
"{device | CPU | device}"
"{dev_id | 0 | dev_id}";
    cv::CommandLineParser parser(argc, argv, keys);
    parser.about("Demo for object data classification and pose estimation");
    if (parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }
    string src_dir = parser.get<string>("src_dir");
    string caffemodel = parser.get<string>("caffemodel");
    string network_forIMG   = parser.get<string>("network_forIMG");
    string mean_file    = parser.get<string>("mean_file");
    string target_img   = parser.get<string>("target_img");
    string feature_blob = parser.get<string>("feature_blob");
    int num_candidate = parser.get<int>("num_candidate");
    string device = parser.get<string>("device");
    int dev_id = parser.get<int>("dev_id");

    cv::cnn_3dobj::descriptorExtractor descriptor(device, dev_id);
    std::vector<string> device_info = descriptor.getter();
    std::cout << "Using" << device_info[0] << std::endl;
    if (strcmp(mean_file.c_str(), "no") == 0)
        descriptor.loadNet(network_forIMG, caffemodel);
    else
        descriptor.loadNet(network_forIMG, caffemodel, mean_file);
    std::vector<string> name_gallery;
    listDir(src_dir.c_str(), name_gallery, false);
    for (unsigned int i = 0; i < name_gallery.size(); i++) {
        name_gallery[i] = src_dir + name_gallery[i];
    }
    std::vector<cv::Mat> img_gallery;
    cv::Mat feature_reference;
    for (unsigned int i = 0; i < name_gallery.size(); i++) {
        img_gallery.push_back(cv::imread(name_gallery[i], -1));
    }
    descriptor.extract(img_gallery, feature_reference, feature_blob);

    std::cout << std::endl << "---------- Prediction for " << target_img << " ----------" << std::endl;

    cv::Mat img = cv::imread(target_img, -1);
    // CHECK(!img.empty()) << "Unable to decode image " << target_img;
    std::cout << std::endl << "---------- Featrue of gallery images ----------" << std::endl;
    std::vector<std::pair<string, float> > prediction;
    for (unsigned int i = 0; i < feature_reference.rows; i++)
        std::cout << feature_reference.row(i) << endl;
    cv::Mat feature_test;
    descriptor.extract(img, feature_test, feature_blob);
    cv::BFMatcher matcher(NORM_L2);
    std::vector<std::vector<cv::DMatch> > matches;
    matcher.knnMatch(feature_test, feature_reference, matches, num_candidate);
    std::cout << std::endl << "---------- Featrue of target image: " << target_img << "----------" << endl << feature_test << std::endl;
    // Print the top N prediction.
    std::cout << std::endl << "---------- Prediction result(Distance - File Name in Gallery) ----------" << std::endl;
    for (size_t i = 0; i < matches[0].size(); ++i) {
        std::cout << i << " - " << std::fixed << std::setprecision(2) << name_gallery[matches[0][i].trainIdx] << " - \""  << matches[0][i].distance << "\"" << std::endl;
    }
    return 0;
}