I hate deepmind

Joseph Redmon

2016-03-15 09fd5c8c84eeae711f49d3a52d8bf4b65f43970b

 src/coco.c

@@ -15,35 +15,13 @@

int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};

void draw_coco(image im, int num, float thresh, box *boxes, float **probs)
{
    int classes = 80;
    int i;

    for(i = 0; i < num; ++i){
        int class = max_index(probs[i], classes);
        float prob = probs[i][class];
        if(prob > thresh){
            int width = sqrt(prob)*5 + 1;
            printf("%f %s\n", prob, coco_classes[class]);
            float red = get_color(0,class,classes);
            float green = get_color(1,class,classes);
            float blue = get_color(2,class,classes);
            box b = boxes[i];

            int left  = (b.x-b.w/2.)*im.w;
            int right = (b.x+b.w/2.)*im.w;
            int top   = (b.y-b.h/2.)*im.h;
            int bot   = (b.y+b.h/2.)*im.h;
            draw_box_width(im, left, top, right, bot, width, red, green, blue);
        }
    }
}
image coco_labels[80];

void train_coco(char *cfgfile, char *weightfile)
{
    //char *train_images = "/home/pjreddie/data/voc/test/train.txt";
    char *train_images = "/home/pjreddie/data/coco/train.txt";
    //char *train_images = "/home/pjreddie/data/coco/train.txt";
    char *train_images = "data/coco.trainval.txt";
    char *backup_directory = "/home/pjreddie/backup/";
    srand(time(0));
    data_seed = time(0);
@@ -368,6 +346,7 @@
    detection_layer l = net.layers[net.n-1];
    set_batch_network(&net, 1);
    srand(2222222);
    float nms = .4;
    clock_t time;
    char buff[256];
    char *input = buff;
@@ -392,7 +371,8 @@
        float *predictions = network_predict(net, X);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        convert_coco_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
        draw_coco(im, l.side*l.side*l.n, thresh, boxes, probs);
        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, coco_classes, coco_labels, 80);
        show_image(im, "predictions");

        show_image(sized, "resized");
@@ -406,9 +386,28 @@
    }
}

void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index, char *filename);
static void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, char* filename)
{
    #if defined(OPENCV)
        demo_coco(cfgfile, weightfile, thresh, cam_index, filename);
    #else
        fprintf(stderr, "Need to compile with OpenCV for demo.\n");
    #endif
}

void run_coco(int argc, char **argv)
{
    int i;
    for(i = 0; i < 80; ++i){
        char buff[256];
        sprintf(buff, "data/labels/%s.png", coco_classes[i]);
        coco_labels[i] = load_image_color(buff, 0, 0);
    }
    float thresh = find_float_arg(argc, argv, "-thresh", .2);
    int cam_index = find_int_arg(argc, argv, "-c", 0);
    char *file = find_char_arg(argc, argv, "-file", 0);

    if(argc < 4){
        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
        return;
@@ -421,4 +420,5 @@
    else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
    else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights);
    else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights);
    else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, file);
}