yolo2 检测到的物体输出2

说明

检测到的物体被box所标记，输出box所在的位置，并存储到txt中。上次是在src/image.c的代码中修改的，相当于是在画出检测出的物体的框的同时把坐标存入了txt，这次修改的是examples/detector.c文件。

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先来看一下主函数 examples/darknet.c

官网上运行的示例测试代码为：

其中运行的主函数的源码就是examples/darknet.c，后面的detector test等参数通过命令行传给了主函数。

darknet.c的代码为(代码比较长，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;}

可以发现调用了run_detector(argc, argv);这个函数，这个函数位于 examples/detector.c中。查看detector.c,发现调用了test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)。

于是考虑在这个函数中存储输出文件。

编写了一个sava_txt函数。

#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");//这里应该是train还是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"); //测试？？？数据    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 && 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);    }}//保存船的位置和数量void save_txt(char *filename,box *boxes,int num,float **probs,float thresh,image im,int classes){    char a[100];    //存储每行的字符串    char name[100]; //存储txt的路径    strcpy(name,filename);    int k=0;    while(name[k]!='\0')    {        k++;    }    k=k-3;    name[k]='t';k++;    name[k]='x';k++;    name[k]='t';k++;    printf("txt_name:%s\n",name);    int shipnum=0;   //船的数量    FILE *fp;    printf("save_txt filename:%s \n",filename);    if((fp=fopen(name,"w"))==NULL){        printf("%s can't open\n",name);        exit(1);    }    int left;//距离图片左边界的值  right-left为box宽度    int right;    int top;//距离图片上边界的值  bot-top为box高度    int bot;    int i;    for(i=0;i<num;i++)    {            int class = max_index(probs[i], classes);                 float prob = probs[i][class];            if(prob > thresh)        {            left  = (boxes[i].x-boxes[i].w/2.)*im.w;//距离图片左边界的值  right-left为box宽度                right = (boxes[i].x+boxes[i].w/2.)*im.w;                top   = (boxes[i].y-boxes[i].h/2.)*im.h;//距离图片上边界的值  bot-top为box高度                bot   = (boxes[i].y+boxes[i].h/2.)*im.h;            if(left < 0) left = 0;                if(right > im.w-1) right = im.w-1;                if(top < 0) top = 0;                if(bot > im.h-1) bot = im.h-1;        ++shipnum;            printf("boxes[%d]: left:%d  right:%d  top:%d  bot:%d prob:%f \n",i,left,right,top,bot,prob);  //只有1类的话概率存在probs[i][1]中            sprintf(a,"%d %d %d %d\n",left,right,top,bot);//将每个box的数据打印到 a中            fputs(a,fp);     //写入文件        }    }    sprintf(a,"%d\n",shipnum);    printf("save shipnum:%s \n",a);    fputs(a,fp);    fclose(fp);      //关闭文件}void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen){    list *options = read_data_cfg(datacfg);    char *name_list = option_find_str(options, "names", "data/names.list");    char **names = get_labels(name_list);    image **alphabet = load_alphabet();    network net = parse_network_cfg(cfgfile);    if(weightfile){        load_weights(&net, weightfile);    }    set_batch_network(&net, 1);    srand(2222222);    double time;    char buff[256];    char *input = buff;    int j;    float nms=.3;    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 = letterbox_image(im, net.w, net.h);        //image sized = resize_image(im, net.w, net.h);        //image sized2 = resize_max(im, net.w);        //image sized = crop_image(sized2, -((net.w - sized2.w)/2), -((net.h - sized2.h)/2), net.w, net.h);        //resize_network(&net, sized.w, sized.h);        layer l = net.layers[net.n-1];        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(l.classes + 1, sizeof(float *));        float **masks = 0;        if (l.coords > 4){            masks = calloc(l.w*l.h*l.n, sizeof(float*));            for(j = 0; j < l.w*l.h*l.n; ++j) masks[j] = calloc(l.coords-4, sizeof(float *));        }        float *X = sized.data;        time=what_time_is_it_now();        network_predict(net, X);        printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);        get_region_boxes(l, im.w, im.h, net.w, net.h, thresh, probs, boxes, masks, 0, 0, hier_thresh, 1);   //这里得到了box未转化前的区域以及probs    //maqy添加，打印出boxes    //打印出l.w,l.h,l.n    printf("l.w:%d   l.h:%d   l.n:%d   l.classes:%d  \n",l.w,l.h,l.n,l.classes);    printf("maqy filename:%s\n",filename);    //for(kkk=0;kkk<l.w*l.h*l.n;kkk++)    //{    //    printf("boxes[%d]: x:%f  y:%f  w:%f  h:%f prob:%f ---%f\n",kkk,boxes[kkk].x,boxes[kkk].y,boxes[kkk].w,boxes[kkk].h,probs[kkk][0],probs[kkk][1]);    //}//  int left;//距离图片左边界的值  right-left为box宽度//       int right;//        int top;//距离图片上边界的值  bot-top为box高度//        int bot;//        int kkk;//  for(kkk=0;kkk<l.w*l.h*l.n;kkk++)//  {//      left  = (boxes[kkk].x-boxes[kkk].w/2.)*im.w;//距离图片左边界的值  right-left为box宽度//          right = (boxes[kkk].x+boxes[kkk].w/2.)*im.w;//          top   = (boxes[kkk].y-boxes[kkk].h/2.)*im.h;//距离图片上边界的值  bot-top为box高度//          bot   = (boxes[kkk].y+boxes[kkk].h/2.)*im.h;//      if(left < 0) left = 0;//          if(right > im.w-1) right = im.w-1;//          if(top < 0) top = 0;//          if(bot > im.h-1) bot = im.h-1;//      if(probs[kkk][1]>thresh)//      {//          printf("boxes[%d]: left:%d  right:%d  top:%d  bot:%d prob:%f \n",kkk,left,right,top,bot,probs[kkk][1]);  //只有1类的话概率存在probs[kkk][1]中//      }//  }        if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);        //else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);    save_txt(filename, boxes, l.w*l.h*l.n, probs, thresh,im,l.classes);//必须放在上个if后面，不然会有重复的框出现        draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, masks, names, alphabet, l.classes);//maqy        if(outfile){            save_image(im, outfile);        }        else{            save_image(im, "predictions");#ifdef OPENCV            cvNamedWindow("predictions", CV_WINDOW_NORMAL);             if(fullscreen){                cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);            }            show_image(im, "predictions");            cvWaitKey(0);            cvDestroyAllWindows();#endif        }        free_image(im);        free_image(sized);        free(boxes);        free_ptrs((void **)probs, l.w*l.h*l.n);        if (filename) break;    }}void run_detector(int argc, char **argv){    char *prefix = find_char_arg(argc, argv, "-prefix", 0);    float thresh = find_float_arg(argc, argv, "-thresh", .24);    float hier_thresh = find_float_arg(argc, argv, "-hier", .5);    int cam_index = find_int_arg(argc, argv, "-c", 0);    int frame_skip = find_int_arg(argc, argv, "-s", 0);    int avg = find_int_arg(argc, argv, "-avg", 3);    if(argc < 4){        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);        return;    }    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);    char *outfile = find_char_arg(argc, argv, "-out", 0);    int *gpus = 0;    int gpu = 0;    int ngpus = 0;    if(gpu_list){        printf("%s\n", gpu_list);        int len = strlen(gpu_list);        ngpus = 1;        int i;        for(i = 0; i < len; ++i){            if (gpu_list[i] == ',') ++ngpus;        }        gpus = calloc(ngpus, sizeof(int));        for(i = 0; i < ngpus; ++i){            gpus[i] = atoi(gpu_list);            gpu_list = strchr(gpu_list, ',')+1;        }    } else {        gpu = gpu_index;        gpus = &gpu;        ngpus = 1;    }    int clear = find_arg(argc, argv, "-clear");    int fullscreen = find_arg(argc, argv, "-fullscreen");    int width = find_int_arg(argc, argv, "-w", 0);    int height = find_int_arg(argc, argv, "-h", 0);    int fps = find_int_arg(argc, argv, "-fps", 0);    char *datacfg = argv[3];    char *cfg = argv[4];    char *weights = (argc > 5) ? argv[5] : 0;    char *filename = (argc > 6) ? argv[6]: 0;    if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, outfile, fullscreen);    else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);    else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);    else if(0==strcmp(argv[2], "valid2")) validate_detector_flip(datacfg, cfg, weights, outfile);    else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights);    else if(0==strcmp(argv[2], "demo")) {        list *options = read_data_cfg(datacfg);        int classes = option_find_int(options, "classes", 1);   //class        char *name_list = option_find_str(options, "names", "data/names.list");        char **names = get_labels(name_list);        demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, avg, hier_thresh, width, height, fps, fullscreen);    }}

写的有点乱，以后再做修改。
功能就是在待检测图片路径下多了一个同名的txt文件，存储的是框的位置和数量