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/* Change output weight folders here */
char *backup_directory = "/Guanghan/dev/darknet-video-2class/backup/cpu_3class/";

srand(time(0));
data_seed = time(0);
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
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 = net.batch*net.subdivisions;
int i = *net.seen/imgs;
printf("\n\n\n\n\n\n\nimgs = %d,\n i= %d\n", imgs, i);
data train, buffer;


layer l = net.layers[net.n - 1];

int side = l.side;
int classes = l.classes;
float jitter = l.jitter;

list *plist = get_paths(train_images);
//int N = plist->size;
char **paths = (char **)list_to_array(plist);

load_args args = {0};
args.w = net.w;
args.h = net.h;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.classes = classes;
args.jitter = jitter;
args.num_boxes = side;
args.d = &buffer;
args.type = REGION_DATA;

pthread_t load_thread = load_data_in_thread(args);
clock_t time;
//while(i*imgs < N*120){
while(get_current_batch(net) < net.max_batches){
    i += 1;
    time=clock();
    pthread_join(load_thread, 0);
    train = buffer;
    load_thread = load_data_in_thread(args);

    printf("Loaded: %lf seconds\n", sec(clock()-time));

    time=clock();
    float loss = train_network(net, train);
    if (avg_loss < 0) avg_loss = loss;
    avg_loss = avg_loss*.9 + loss*.1;

    printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
    if(i%1000==0 || i == 600){
        char buff[256];
        sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
        save_weights(net, buff);
    }
    free_data(train);
}
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);

    if (xmin < 0) xmin = 0;
    if (ymin < 0) ymin = 0;
    if (xmax > w) xmax = w;
    if (ymax > h) ymax = h;

    for(j = 0; j < classes; ++j){
        if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],
                xmin, ymin, xmax, ymax);
    }
}

char *base = "results/comp4_det_test_";
list *plist = get_paths("data/voc.2007.test");
//list *plist = get_paths("data/voc.2012.test");
char **paths = (char **)list_to_array(plist);

layer l = net.layers[net.n-1];
int classes = l.classes;
int square = l.sqrt;
int side = l.side;

int j;
FILE **fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
    char buff[1024];
    snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
    fps[j] = fopen(buff, "w");
}
box *boxes = calloc(side*side*l.n, sizeof(box));
float **probs = calloc(side*side*l.n, sizeof(float *));
for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));

int m = plist->size;
int i=0;
int t;

float thresh = .001;
int nms = 1;
float iou_thresh = .5;

int nthreads = 2;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
image *buf = calloc(nthreads, sizeof(image));
image *buf_resized = calloc(nthreads, sizeof(image));
pthread_t *thr = calloc(nthreads, sizeof(pthread_t));

load_args args = {0};
args.w = net.w;
args.h = net.h;
args.type = IMAGE_DATA;

for(t = 0; t < nthreads; ++t){
    args.path = paths[i+t];
    args.im = &buf[t];
    args.resized = &buf_resized[t];
    thr[t] = load_data_in_thread(args);
}
time_t start = time(0);
for(i = nthreads; i < m+nthreads; i += nthreads){
    fprintf(stderr, "%d\n", i);
    for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
        pthread_join(thr[t], 0);
        val[t] = buf[t];
        val_resized[t] = buf_resized[t];
    }
    for(t = 0; t < nthreads && i+t < m; ++t){
        args.path = paths[i+t];
        args.im = &buf[t];
        args.resized = &buf_resized[t];
        thr[t] = load_data_in_thread(args);
    }
    for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
        char *path = paths[i+t-nthreads];
        char *id = basecfg(path);
        float *X = val_resized[t].data;
        float *predictions = network_predict(net, X);
        int w = val[t].w;
        int h = val[t].h;
        convert_yolo_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0);
        if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh);
        print_yolo_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h);
        free(id);
        free_image(val[t]);
        free_image(val_resized[t]);
    }
}
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));

char *base = "results/comp4_det_test_";
list *plist = get_paths("data/voc.2007.test");
char **paths = (char **)list_to_array(plist);

layer l = net.layers[net.n-1];
int classes = l.classes;
int square = l.sqrt;
int side = l.side;

int j, k;
FILE **fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
    char buff[1024];
    snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
    fps[j] = fopen(buff, "w");
}
box *boxes = calloc(side*side*l.n, sizeof(box));
float **probs = calloc(side*side*l.n, sizeof(float *));
for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));

int m = plist->size;
int i=0;

float thresh = .001;
int nms = 0;
float iou_thresh = .5;
float nms_thresh = .5;

int total = 0;
int correct = 0;
int proposals = 0;
float avg_iou = 0;

for(i = 0; i < m; ++i){
    char *path = paths[i];
    image orig = load_image_color(path, 0, 0);
    image sized = resize_image(orig, net.w, net.h);
    char *id = basecfg(path);
    float *predictions = network_predict(net, sized.data);
    convert_yolo_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1);
    if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh);

    char *labelpath = find_replace(path, "images", "labels");
    labelpath = find_replace(labelpath, "JPEGImages", "labels");
    labelpath = find_replace(labelpath, ".jpg", ".txt");
    labelpath = find_replace(labelpath, ".JPEG", ".txt");

    int num_labels = 0;
    box_label *truth = read_boxes(labelpath, &num_labels);
    for(k = 0; k < side*side*l.n; ++k){
        if(probs[k][0] > thresh){
            ++proposals;
        }
    }
    for (j = 0; j < num_labels; ++j) {
        ++total;
        box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
        float best_iou = 0;
        for(k = 0; k < side*side*l.n; ++k){
            float iou = box_iou(boxes[k], t);
            if(probs[k][0] > thresh && iou > best_iou){
                best_iou = iou;
            }
        }
        avg_iou += best_iou;
        if(best_iou > iou_thresh){
            ++correct;
        }
    }

    fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
    free(id);
    free_image(orig);
    free_image(sized);
}

network net = parse_network_cfg(cfgfile);
if(weightfile){
    load_weights(&net, weightfile);
}
detection_layer l = net.layers[net.n-1];
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int j;
float nms=.5;
box *boxes = calloc(l.side*l.side*l.n, sizeof(box));
float **probs = calloc(l.side*l.side*l.n, sizeof(float *));
for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
while(1){
    if(filename){
        strncpy(input, filename, 256);
    } else {
        printf("Enter Image Path: ");
        fflush(stdout);
        input = fgets(input, 256, stdin);
        if(!input) return;
        strtok(input, "\n");
    }
    image im = load_image_color(input,0,0);
    image sized = resize_image(im, net.w, net.h);
    float *X = sized.data;


    time=clock();
    float *predictions = network_predict(net, X);
    printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));

    time=clock();
    predictions = network_predict(net, X);
    printf("The 2nd time, %s: Predicted in %f seconds.\n", input, sec(clock()-time));


    convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
    if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
    draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, CLASSNUM);
    show_image(im, "predictions");

    show_image(sized, "resized");
    free_image(im);
    free_image(sized);

float thresh = find_float_arg(argc, argv, "-thresh", .2);
int cam_index = find_int_arg(argc, argv, "-c", 0);
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 *filename = (argc > 5) ? argv[5]: 0;
if(0==strcmp(argv[2], "test")) test_yolo(cfg, weights, filename, thresh);
else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights);
else if(0==strcmp(argv[2], "demo_cam")) demo_yolo(cfg, weights, thresh, cam_index, "NULL");
else if(0==strcmp(argv[2], "demo_vid")) demo_yolo(cfg, weights, thresh, -1, filename);