說明

檢測到的物體被box所標記，輸出box所在的位置，並存儲到txt中。
上次是在src/image.c的程式碼中修改的，相當於是在畫出檢測出的物體的框的同時把座標存入了txt，這次修改的是examples/detector.c檔案。

先來看一下主函式 examples/darknet.c

官網上執行的示例測試程式碼為：

其中執行的主函式的原始碼就是examples/darknet.c，後面的detector test等引數通過命令列傳給了主函式。

darknet.c的程式碼為(程式碼比較長，省略了其他只看main)：

#include "darknet.h"
 


#include <time.h>
#include <stdlib.h>
#include <stdio.h>
int main(int argc, char **argv)
{
    //test_resize("data/bad.jpg");
    //test_box();
    //test_convolutional_layer();
    if(argc < 2){
        fprintf(stderr, "usage: %s <function>\n", argv[0]);
        return 0;
    }
    gpu_index = find_int_arg(argc, argv, "-i"
 
, 0);
    if(find_arg(argc, argv, "-nogpu")) {
        gpu_index = -1;
    }

#ifndef GPU
    gpu_index = -1;
#else
    if(gpu_index >= 0){
        cuda_set_device(gpu_index);
    }
#endif

    if (0 == strcmp(argv[1], "average")){
        average(argc, argv);
    } else if (0 == strcmp(argv[1], "yolo"
 
)){
        run_yolo(argc, argv);
    } else if (0 == strcmp(argv[1], "voxel")){
        run_voxel(argc, argv);
    } else if (0 == strcmp(argv[1], "super")){
        run_super(argc, argv);
    } else if (0 == strcmp(argv[1], "lsd")){
        run_lsd(argc, argv);
    } else if (0 == strcmp(argv[1], "detector")){ //*********************************
        run_detector(argc, argv);
    } else if (0 == strcmp(argv[1], "detect")){
        float thresh = find_float_arg(argc, argv, "-thresh", .24);
        char *filename = (argc > 4) ? argv[4]: 0;
        char *outfile = find_char_arg(argc, argv, "-out", 0);
        int fullscreen = find_arg(argc, argv, "-fullscreen");
        test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5, outfile, fullscreen);
    } else if (0 == strcmp(argv[1], "cifar")){
        run_cifar(argc, argv);
    } else if (0 == strcmp(argv[1], "go")){
        run_go(argc, argv);
    } else if (0 == strcmp(argv[1], "rnn")){
        run_char_rnn(argc, argv);
    } else if (0 == strcmp(argv[1], "vid")){
        run_vid_rnn(argc, argv);
    } else if (0 == strcmp(argv[1], "coco")){
        run_coco(argc, argv);
    } else if (0 == strcmp(argv[1], "classify")){
        predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
    } else if (0 == strcmp(argv[1], "classifier")){
        run_classifier(argc, argv);
    } else if (0 == strcmp(argv[1], "regressor")){
        run_regressor(argc, argv);
    } else if (0 == strcmp(argv[1], "segmenter")){
        run_segmenter(argc, argv);
    } else if (0 == strcmp(argv[1], "art")){
        run_art(argc, argv);
    } else if (0 == strcmp(argv[1], "tag")){
        run_tag(argc, argv);
    } else if (0 == strcmp(argv[1], "compare")){
        run_compare(argc, argv);
    } else if (0 == strcmp(argv[1], "dice")){
        run_dice(argc, argv);
    } else if (0 == strcmp(argv[1], "writing")){
        run_writing(argc, argv);
    } else if (0 == strcmp(argv[1], "3d")){
        composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
    } else if (0 == strcmp(argv[1], "test")){
        test_resize(argv[2]);
    } else if (0 == strcmp(argv[1], "captcha")){
        run_captcha(argc, argv);
    } else if (0 == strcmp(argv[1], "nightmare")){
        run_nightmare(argc, argv);
    } else if (0 == strcmp(argv[1], "rgbgr")){
        rgbgr_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "reset")){
        reset_normalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "denormalize")){
        denormalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "statistics")){
        statistics_net(argv[2], argv[3]);
    } else if (0 == strcmp(argv[1], "normalize")){
        normalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "rescale")){
        rescale_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "ops")){
        operations(argv[2]);
    } else if (0 == strcmp(argv[1], "speed")){
        speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
    } else if (0 == strcmp(argv[1], "oneoff")){
        oneoff(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "oneoff2")){
        oneoff2(argv[2], argv[3], argv[4], atoi(argv[5]));
    } else if (0 == strcmp(argv[1], "partial")){
        partial(argv[2], argv[3], argv[4], atoi(argv[5]));
    } else if (0 == strcmp(argv[1], "average")){
        average(argc, argv);
    } else if (0 == strcmp(argv[1], "visualize")){
        visualize(argv[2], (argc > 3) ? argv[3] : 0);
    } else if (0 == strcmp(argv[1], "mkimg")){
        mkimg(argv[2], argv[3], atoi(argv[4]), atoi(argv[5]), atoi(argv[6]), argv[7]);
    } else if (0 == strcmp(argv[1], "imtest")){
        test_resize(argv[2]);
    } else {
        fprintf(stderr, "Not an option: %s\n", argv[1]);
    }
    return 0;
}

可以發現呼叫了run_detector(argc, argv);這個函式，這個函式位於 examples/detector.c中。檢視detector.c,發現呼叫了test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)。

於是考慮在這個函式中儲存輸出檔案。

編寫了一個sava_txt函式。

#include "darknet.h"

static 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 train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
    list *options = read_data_cfg(datacfg);
    char *train_images = option_find_str(options, "train", "/home/maqy/darknet/darknet-master/scripts/train.txt");
    char *backup_directory = option_find_str(options, "backup", "/backup/");

    srand(time(0));
    char *base = basecfg(cfgfile);
    printf("%s\n", base);
    float avg_loss = -1;
    network *nets = calloc(ngpus, sizeof(network));

    srand(time(0));
    int seed = rand();
    int i;
    for(i = 0; i < ngpus; ++i){
        srand(seed);
#ifdef GPU
        cuda_set_device(gpus[i]);
#endif
        nets[i] = load_network(cfgfile, weightfile, clear);
        nets[i].learning_rate *= ngpus;
    }
    srand(time(0));
    network net = nets[0];

    int imgs = net.batch * net.subdivisions * ngpus;
    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
    data train, buffer;

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

    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 = get_base_args(net);
    args.coords = l.coords;
    args.paths = paths;
    args.n = imgs;
    args.m = plist->size;
    args.classes = classes;
    args.jitter = jitter;
    args.num_boxes = l.max_boxes;
    args.d = &buffer;
    args.type = DETECTION_DATA;
    //args.type = INSTANCE_DATA;
    args.threads = 8;

    pthread_t load_thread = load_data(args);
    clock_t time;
    int count = 0;
    //while(i*imgs < N*120){
    while(get_current_batch(net) < net.max_batches){
        if(l.random && count++%10 == 0){
            printf("Resizing\n");
            int dim = (rand() % 10 + 10) * 32;
            if (get_current_batch(net)+200 > net.max_batches) dim = 608;
            //int dim = (rand() % 4 + 16) * 32;
            printf("%d\n", dim);
            args.w = dim;
            args.h = dim;

            pthread_join(load_thread, 0);
            train = buffer;
            free_data(train);
            load_thread = load_data(args);

            for(i = 0; i < ngpus; ++i){
                resize_network(nets + i, dim, dim);
            }
            net = nets[0];
        }
        time=clock();
        pthread_join(load_thread, 0);
        train = buffer;
        load_thread = load_data(args);

        /*
        int k;
        for(k = 0; k < l.max_boxes; ++k){
            box b = float_to_box(train.y.vals[10] + 1 + k*5);
            if(!b.x) break;
            printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
        }
        */
        /*
        int zz;
        for(zz = 0; zz < train.X.cols; ++zz){
            image im = float_to_image(net.w, net.h, 3, train.X.vals[zz]);
            int k;
            for(k = 0; k < l.max_boxes; ++k){
                box b = float_to_box(train.y.vals[zz] + k*5, 1);
                printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
                draw_bbox(im, b, 1, 1,0,0);
            }
            show_image(im, "truth11");
            cvWaitKey(0);
            save_image(im, "truth11");
        }
        */

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

        time=clock();
        float loss = 0;
#ifdef GPU
        if(ngpus == 1){
            loss = train_network(net, train);
        } else {
            loss = train_networks(nets, ngpus, train, 4);
        }
#else
        loss = train_network(net, train);
#endif
        if (avg_loss < 0) avg_loss = loss;
        avg_loss = avg_loss*.9 + loss*.1;

        i = get_current_batch(net);
        printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
        if(i%100==0){
#ifdef GPU
            if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
            char buff[256];
            sprintf(buff, "%s/%s.backup", backup_directory, base);
            save_weights(net, buff);
        }
        if(i%10000==0 || (i < 1000 && i%100 == 0)){
#ifdef GPU
            if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
            char buff[256];
            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
            save_weights(net, buff);
        }
        free_data(train);
    }
#ifdef GPU
    if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
    char buff[256];
    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
    save_weights(net, buff);
}


static int get_coco_image_id(char *filename)
{
    char *p = strrchr(filename, '_');
    return atoi(p+1);
}

static void print_cocos(FILE *fp, char *image_path, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
{
    int i, j;
    int image_id = get_coco_image_id(image_path);
    for(i = 0; i < num_boxes; ++i){
        float xmin = boxes[i].x - boxes[i].w/2.;
        float xmax = boxes[i].x + boxes[i].w/2.;
        float ymin = boxes[i].y - boxes[i].h/2.;
        float ymax = boxes[i].y + boxes[i].h/2.;

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

        float bx = xmin;
        float by = ymin;
        float bw = xmax - xmin;
        float bh = ymax - ymin;

        for(j = 0; j < classes; ++j){
            if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]);
        }
    }
}

void print_detector_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
{
    int i, j;
    for(i = 0; i < total; ++i){
        float xmin = boxes[i].x - boxes[i].w/2. + 1;
        float xmax = boxes[i].x + boxes[i].w/2. + 1;
        float ymin = boxes[i].y - boxes[i].h/2. + 1;
        float ymax = boxes[i].y + boxes[i].h/2. + 1;

        if (xmin < 1) xmin = 1;
        if (ymin < 1) ymin = 1;
        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);
        }
    }
}

void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int total, int classes, int w, int h)
{
    int i, j;
    for(i = 0; i < total; ++i){
        float xmin = boxes[i].x - boxes[i].w/2.;
        float xmax = boxes[i].x + boxes[i].w/2.;
        float ymin = boxes[i].y - boxes[i].h/2.;
        float ymax = boxes[i].y + boxes[i].h/2.;

        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){
            int class = j;
            if (probs[i][class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class],
                    xmin, ymin, xmax, ymax);
        }
    }
}

void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
{
    int j;
    list *options = read_data_cfg(datacfg);
    char *valid_images = option_find_str(options, "valid", "/home/maqy/darknet/darknet-master/scripts/2007_test.txt");
    char *name_list = option_find_str(options, "names", "data/names.list");
    char *prefix = option_find_str(options, "results", "results");
    char **names = get_labels(name_list);
    char *mapf = option_find_str(options, "map", 0);
    int *map = 0;
    if (mapf) map = read_map(mapf);

    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 2);
    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
    srand(time(0));

    list *plist = get_paths(valid_images);
    char **paths = (char **)list_to_array(plist);

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

    char buff[1024];
    char *type = option_find_str(options, "eval", "voc");
    FILE *fp = 0;
    FILE **fps = 0;
    int coco = 0;
    int imagenet = 0;
    if(0==strcmp(type, "coco")){
        if(!outfile) outfile = "coco_results";
        snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
        fp = fopen(buff, "w");
        fprintf(fp, "[\n");
        coco = 1;
    } else if(0==strcmp(type, "imagenet")){
        if(!outfile) outfile = "imagenet-detection";
        snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
        fp = fopen(buff, "w");
        imagenet = 1;
        classes = 200;
    } else {
        if(!outfile) outfile = "comp4_det_test_";
        fps = calloc(classes, sizeof(FILE *));
        for(j = 0; j < classes; ++j){
            snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
            fps[j] = fopen(buff, "w");
        }
    }


    box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
    float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
    for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes+1, sizeof(float *));

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

    float thresh = .005;
    float nms = .45;

    int nthreads = 4;
    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));

    image input = make_image(net.w, net.h, net.c*2);

    load_args args = {0};
    args.w = net.w;
    args.h = net.h;
    //args.type = IMAGE_DATA;
    args.type = LETTERBOX_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);
            copy_cpu(net.w*net.h*net.c, val_resized[t].data, 1, input.data, 1);
            flip_image(val_resized[t]);
            copy_cpu(net.w*net.h*net.c, val_resized[t].data, 1, input.data + net.w*net.h*net.c, 1);

            network_predict(net, input.data);
            int w = val[t].w;
            int h = val[t].h;
            get_region_boxes(l, w, h, net.w, net.h, thresh, probs, boxes, 0, 0, map, .5, 0);
            if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
            if (coco){
                print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
            } else if (imagenet){
                print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h);
            } else {
                print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);
            }
            free(id);
            free_image(val[t]);
            free_image(val_resized[t]);
        }
    }
    for(j = 0; j < classes; ++j){
        if(fps) fclose(fps[j]);
    }
    if(coco){
        fseek(fp, -2, SEEK_CUR); 
        fprintf(fp, "\n]\n");
        fclose(fp);
    }
    fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}


void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
{
    int j;
    list *options = read_data_cfg(datacfg);
    char *valid_images = option_find_str(options, "valid", "/home/maqy/darknet/darknet-master/scripts/2007_test.txt");//這裡應該是train還是test？？
    char *name_list = option_find_str(options, "names", "data/names.list");
    char *prefix = option_find_str(options, "results", "results");
    char **names = get_labels(name_list);
    char *mapf = option_find_str(options, "map", 0);
    int *map = 0;
    if (mapf) map = read_map(mapf);

    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);
    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
    srand(time(0));

    list *plist = get_paths(valid_images);
    char **paths = (char **)list_to_array(plist);

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

    char buff[1024];
    char *type = option_find_str(options, "eval", "voc");
    FILE *fp = 0;
    FILE **fps = 0;
    int coco = 0;
    int imagenet = 0;
    if(0==strcmp(type, "coco")){
        if(!outfile) outfile = "coco_results";
        snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
        fp = fopen(buff, "w");
        fprintf(fp, "[\n");
        coco = 1;
    } else if(0==strcmp(type, "imagenet")){
        if(!outfile) outfile = "imagenet-detection";
        snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
        fp = fopen(buff, "w");
        imagenet = 1;
        classes = 200;
    } else {
        if(!outfile) outfile = "comp4_det_test_";
        fps = calloc(classes, sizeof(FILE *));
        for(j = 0; j < classes; ++j){
            snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
            fps[j] = fopen(buff, "w");
        }
    }


    box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
    float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
    for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes+1, sizeof(float *));

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

    float thresh = .005;
    float nms = .45;

    int nthreads = 4;
    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;
    args.type = LETTERBOX_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;
            network_predict(net, X);
            int w = val[t].w;
            int h = val[t].h;
            get_region_boxes(l, w, h, net.w, net.h, thresh, probs, boxes, 0, 0, map, .5, 0);
            if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
            if (coco){
                print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
            } else if (imagenet){
                print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h);
            } else {
                print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);
            }
            free(id);
            free_image(val[t]);
            free_image(val_resized[t]);
        }
    }
    for(j = 0; j < classes; ++j){
        if(fps) fclose(fps[j]);
    }
    if(coco){
        fseek(fp, -2, SEEK_CUR); 
        fprintf(fp, "\n]\n");
        fclose(fp);
    }
    fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}

void validate_detector_recall(char *cfgfile, char *weightfile)
{
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);
    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
    srand(time(0));

    list *plist = get_paths("/home/maqy/darknet/darknet-master/scripts/2007_test.txt"); //測試？？？資料
    char **paths = (char **)list_to_array(plist);

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

    int j, k;
    box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
    float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
    for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes+1, sizeof(float *));

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

    float thresh = .001;
    float iou_thresh = .5;
    float nms = .4;

    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);
        network_predict(net, sized.data);
        get_region_boxes(l, sized.w, sized.h, net.w, net.h, thresh, probs, boxes, 0, 1, 0, .5, 1);
        if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);

        char labelpath[4096];
        find_replace(path, "images", "labels", labelpath);
        find_replace(labelpath, "JPEGImages", "labels", labelpath);
        find_replace(labelpath, ".jpg", ".txt", labelpath);
        find_replace(labelpath, ".JPEG", ".txt", labelpath);

        int num_labels = 0;
        box_label *truth = read_boxes(labelpath, &num_labels);
        for(k = 0; k < l.w*l.h*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 < l.w*l.h*l.n; ++k){
                float iou = box_iou(boxes[k], t);
                if(probs[k][0] > thresh && io