#include "network.h"
#include "utils.h"
#include "parser.h"

#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif

void train_writing(char *cfgfile, char *weightfile)
{
    char *backup_directory = "/home/pjreddie/backup/";
    data_seed = time(0);
    srand(time(0));
    float avg_loss = -1;
    char *base = basecfg(cfgfile);
    printf("%s\n", base);
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
    int imgs = 1024;
    int i = *net.seen/imgs;
    list *plist = get_paths("figures.list");
    char **paths = (char **)list_to_array(plist);
    printf("%d\n", plist->size);
    clock_t time;
    while(1){
        ++i;
        time=clock();
        data train = load_data_writing(paths, imgs, plist->size, 256, 256, 1);
        printf("Loaded %lf seconds\n",sec(clock()-time));
        time=clock();
        float loss = train_network(net, train);

        /*
        image pred = float_to_image(64, 64, 1, out);
        print_image(pred);
        */

        /*
           image im = float_to_image(256, 256, 3, train.X.vals[0]);
           image lab = float_to_image(64, 64, 1, train.y.vals[0]);
           image pred = float_to_image(64, 64, 1, out);
           show_image(im, "image");
           show_image(lab, "label");
           print_image(lab);
           show_image(pred, "pred");
           cvWaitKey(0);
         */

        if(avg_loss == -1) avg_loss = loss;
        avg_loss = avg_loss*.9 + loss*.1;
        printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen);
        free_data(train);
        //if(i%100 == 0 && net.learning_rate > .00001) net.learning_rate *= .97;
        if(i%1000==0){
            char buff[256];
            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
            save_weights(net, buff);
        }
    }
}

void test_writing(char *cfgfile, char *weightfile, char *outfile)
{
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);
    srand(2222222);
    clock_t time;
    char filename[256];

    fgets(filename, 256, stdin);
    strtok(filename, "\n");
    image im = load_image_color(filename, 0, 0);
    //image im = load_image_color("/home/pjreddie/darknet/data/figs/C02-1001-Figure-1.png", 0, 0);
    image sized = resize_image(im, net.w, net.h);
    printf("%d %d %d\n", im.h, im.w, im.c);
    float *X = sized.data;
    time=clock();
    network_predict(net, X);
    printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
    image pred = get_network_image(net);

    if (outfile) {
        printf("Save image as %s.png (shape: %d %d)\n", outfile, pred.w, pred.h);
        save_image(pred, outfile);
    } else {
        show_image(pred, "prediction");
#ifdef OPENCV
        cvWaitKey(0);
        cvDestroyAllWindows();
#endif
    }   

    free_image(im);
    free_image(sized);
}

void run_writing(int argc, char **argv)
{
    if(argc < 4){
        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
        return;
    }

    char *cfg = argv[3];
    char *weights = (argc > 4) ? argv[4] : 0;
    char *outfile = (argc > 5) ? argv[5] : 0;
    if(0==strcmp(argv[2], "train")) train_writing(cfg, weights);
    else if(0==strcmp(argv[2], "test")) test_writing(cfg, weights, outfile);
}