YOLO-V3 把玩 image.c demo.cdetector.cimage.cdemo.c

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detector.c

复制代码

#include "network.h"
#include "region_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
#include "demo.h"
#include "option_list.h"

#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/core/core_c.h"
//#include "opencv2/core/core.hpp"
#include "opencv2/core/version.hpp"
#include "opencv2/imgproc/imgproc_c.h"

#ifndef CV_VERSION_EPOCH
#include "opencv2/videoio/videoio_c.h"
#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION)
#pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib")
#else
#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)
#pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
#endif

IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size);
void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches);
#endif    // OPENCV

#include "http_stream.h"

int check_mistakes;

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, int dont_show)
{
    list *options = read_data_cfg(datacfg);
    char *train_images = option_find_str(options, "train", "data/train.list");
    char *backup_directory = option_find_str(options, "backup", "/backup/");

srand(time(0));
    char *base = basecfg(cfgfile);
    printf("%sn", 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] = parse_network_cfg(cfgfile);
        if(weightfile){
            load_weights(&nets[i], weightfile);
        }
        if(clear) *nets[i].seen = 0;
        nets[i].learning_rate *= ngpus;
    }
    srand(time(0));
    network net = nets[0];

const int actual_batch_size = net.batch * net.subdivisions;
    if (actual_batch_size == 1) {
        printf("n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 n");
        getchar();
    }
    else if (actual_batch_size < 64) {
            printf("n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 n", actual_batch_size);
    }

int imgs = net.batch * net.subdivisions * ngpus;
    printf("Learning Rate: %g, Momentum: %g, Decay: %gn", 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);

int init_w = net.w;
    int init_h = net.h;
    int iter_save;
    iter_save = get_current_batch(net);

load_args args = {0};
    args.w = net.w;
    args.h = net.h;
    args.c = net.c;
    args.paths = paths;
    args.n = imgs;
    args.m = plist->size;
    args.classes = classes;
    args.flip = net.flip;
    args.jitter = jitter;
    args.num_boxes = l.max_boxes;
    args.small_object = net.small_object;
    args.d = &buffer;
    args.type = DETECTION_DATA;
    args.threads = 16;    // 64

args.angle = net.angle;
    args.exposure = net.exposure;
    args.saturation = net.saturation;
    args.hue = net.hue;

#ifdef OPENCV
    args.threads = 3 * ngpus;
    IplImage* img = NULL;
    float max_img_loss = 5;
    int number_of_lines = 100;
    int img_size = 1000;
    if (!dont_show)
        img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size);
#endif    //OPENCV

pthread_t load_thread = load_data(args);
    double time;
    int count = 0;
    //while(i*imgs < N*120){
    while(get_current_batch(net) < net.max_batches){
        if(l.random && count++%10 == 0){
            printf("Resizingn");
            //int dim = (rand() % 12 + (init_w/32 - 5)) * 32;    // +-160
            //int dim = (rand() % 4 + 16) * 32;
            //if (get_current_batch(net)+100 > net.max_batches) dim = 544;

//int random_val = rand() % 12;
            //int dim_w = (random_val + (init_w / 32 - 5)) * 32;    // +-160
            //int dim_h = (random_val + (init_h / 32 - 5)) * 32;    // +-160

float random_val = rand_scale(1.4);    // *x or /x
            int dim_w = roundl(random_val*init_w / 32) * 32;
            int dim_h = roundl(random_val*init_h / 32) * 32;

if (dim_w < 32) dim_w = 32;
            if (dim_h < 32) dim_h = 32;

printf("%d x %d n", dim_w, dim_h);
            args.w = dim_w;
            args.h = dim_h;

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_w, dim_h);
            }
            net = nets[0];
        }
        time=what_time_is_it_now();
        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 %fn", b.x, b.y, b.w, b.h);
           }
           image im = float_to_image(448, 448, 3, train.X.vals[10]);
           int k;
           for(k = 0; k < l.max_boxes; ++k){
           box b = float_to_box(train.y.vals[10] + 1 + k*5);
           printf("%d %d %d %dn", truth.x, truth.y, truth.w, truth.h);
           draw_bbox(im, b, 8, 1,0,0);
           }
           save_image(im, "truth11");
         */

printf("Loaded: %lf secondsn", (what_time_is_it_now()-time));

time=what_time_is_it_now();
        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 != avg_loss) avg_loss = loss;    // if(-inf or nan)
        avg_loss = avg_loss*.9 + loss*.1;

i = get_current_batch(net);
        printf("n %d: %f, %f avg loss, %f rate, %lf seconds, %d imagesn", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs);

#ifdef OPENCV
        if(!dont_show)
            draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches);
#endif    // OPENCV

//if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
        //if (i % 100 == 0) {
        if(i >= (iter_save + 100)) {
            iter_save = i;
#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);

#ifdef OPENCV
    cvReleaseImage(&img);
    cvDestroyAllWindows();
#endif

// free memory
    pthread_join(load_thread, 0);
    free_data(buffer);

free(base);
    free(paths);
    free_list_contents(plist);
    free_list(plist);

free_list_contents_kvp(options);
    free_list(options);

free(nets);
    free_network(net);
}

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

static void print_cocos(FILE *fp, char *image_path, detection *dets, 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 = dets[i].bbox.x - dets[i].bbox.w / 2.;
        float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
        float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
        float ymax = dets[i].bbox.y + dets[i].bbox.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 (dets[i].prob[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, dets[i].prob[j]);
        }
    }
}

void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
{
    int i, j;
    for (i = 0; i < total; ++i) {
        float xmin = dets[i].bbox.x - dets[i].bbox.w / 2. + 1;
        float xmax = dets[i].bbox.x + dets[i].bbox.w / 2. + 1;
        float ymin = dets[i].bbox.y - dets[i].bbox.h / 2. + 1;
        float ymax = dets[i].bbox.y + dets[i].bbox.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 (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %fn", id, dets[i].prob[j],
                xmin, ymin, xmax, ymax);
        }
    }
}

void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
{
    int i, j;
    for (i = 0; i < total; ++i) {
        float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
        float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
        float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
        float ymax = dets[i].bbox.y + dets[i].bbox.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 (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %fn", id, j + 1, dets[i].prob[class],
                xmin, ymin, xmax, ymax);
        }
    }
}

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", "data/train.list");
    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_custom(cfgfile, 1);    // set batch=1
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    //set_batch_network(&net, 1);
    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %gn", 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");
        }
    }

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.c = net.c;
    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, "%dn", 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;
            int nboxes = 0;
            int letterbox = (args.type == LETTERBOX_DATA);
            detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox);
            if (nms) do_nms_sort(dets, nboxes, classes, nms);
            if (coco) {
                print_cocos(fp, path, dets, nboxes, classes, w, h);
            }
            else if (imagenet) {
                print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h);
            }
            else {
                print_detector_detections(fps, id, dets, nboxes, classes, w, h);
            }
            free_detections(dets, nboxes);
            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 Secondsn", (double)time(0) - start);
}

void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
{
    network net = parse_network_cfg_custom(cfgfile, 1);    // set batch=1
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    //set_batch_network(&net, 1);
    fuse_conv_batchnorm(net);
    srand(time(0));

//list *plist = get_paths("data/coco_val_5k.list");
    list *options = read_data_cfg(datacfg);
    char *valid_images = option_find_str(options, "valid", "data/train.txt");
    list *plist = get_paths(valid_images);
    char **paths = (char **)list_to_array(plist);

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

int j, k;

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(path, 0, 0, net.c);
        image sized = resize_image(orig, net.w, net.h);
        char *id = basecfg(path);
        network_predict(net, sized.data);
        int nboxes = 0;
        int letterbox = 0;
        detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes, letterbox);
        if (nms) do_nms_obj(dets, nboxes, 1, nms);

char labelpath[4096];
        replace_image_to_label(path, labelpath);

int num_labels = 0;
        box_label *truth = read_boxes(labelpath, &num_labels);
        for (k = 0; k < nboxes; ++k) {
            if (dets[k].objectness > 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 < nboxes; ++k) {
                float iou = box_iou(dets[k].bbox, t);
                if (dets[k].objectness > thresh && iou > best_iou) {
                    best_iou = iou;
                }
            }
            avg_iou += best_iou;
            if (best_iou > iou_thresh) {
                ++correct;
            }
        }
        //fprintf(stderr, " %s - %s - ", paths[i], labelpath);
        fprintf(stderr, "%5d %5d %5dtRPs/Img: %.2ftIOU: %.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);
    }
}

typedef struct {
    box b;
    float p;
    int class_id;
    int image_index;
    int truth_flag;
    int unique_truth_index;
} box_prob;

int detections_comparator(const void *pa, const void *pb)
{
    box_prob a = *(box_prob *)pa;
    box_prob b = *(box_prob *)pb;
    float diff = a.p - b.p;
    if (diff < 0) return 1;
    else if (diff > 0) return -1;
    return 0;
}

void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou)
{
    int j;
    list *options = read_data_cfg(datacfg);
    char *valid_images = option_find_str(options, "valid", "data/train.txt");
    char *difficult_valid_images = option_find_str(options, "difficult", NULL);
    char *name_list = option_find_str(options, "names", "data/names.list");
    char **names = get_labels(name_list);
    char *mapf = option_find_str(options, "map", 0);
    int *map = 0;
    if (mapf) map = read_map(mapf);
    FILE* reinforcement_fd = NULL;

network net = parse_network_cfg_custom(cfgfile, 1);    // set batch=1
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    //set_batch_network(&net, 1);
    fuse_conv_batchnorm(net);
    calculate_binary_weights(net);
    srand(time(0));

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

char **paths_dif = NULL;
    if (difficult_valid_images) {
        list *plist_dif = get_paths(difficult_valid_images);
        paths_dif = (char **)list_to_array(plist_dif);
    }

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

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

const float thresh = .005;
    const float nms = .45;
    const float iou_thresh = 0.5;

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

//const float thresh_calc_avg_iou = 0.24;
    float avg_iou = 0;
    int tp_for_thresh = 0;
    int fp_for_thresh = 0;

box_prob *detections = calloc(1, sizeof(box_prob));
    int detections_count = 0;
    int unique_truth_count = 0;

int *truth_classes_count = calloc(classes, sizeof(int));

int nboxes = 0;
            float hier_thresh = 0;
            detection *dets;
            if (args.type == LETTERBOX_DATA) {
                int letterbox = 1;
                dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
            }
            else {
                int letterbox = 0;
                dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letterbox);
            }
            //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); // for letterbox=1
            if (nms) do_nms_sort(dets, nboxes, l.classes, nms);

char labelpath[4096];
            replace_image_to_label(path, labelpath);
            int num_labels = 0;
            box_label *truth = read_boxes(labelpath, &num_labels);
            int i, j;
            for (j = 0; j < num_labels; ++j) {
                truth_classes_count[truth[j].id]++;
            }

// difficult
            box_label *truth_dif = NULL;
            int num_labels_dif = 0;
            if (paths_dif)
            {
                char *path_dif = paths_dif[image_index];

char labelpath_dif[4096];
                replace_image_to_label(path_dif, labelpath_dif);

truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
            }

const int checkpoint_detections_count = detections_count;

for (i = 0; i < nboxes; ++i) {

int class_id;
                for (class_id = 0; class_id < classes; ++class_id) {
                    float prob = dets[i].prob[class_id];
                    if (prob > 0) {
                        detections_count++;
                        detections = realloc(detections, detections_count * sizeof(box_prob));
                        detections[detections_count - 1].b = dets[i].bbox;
                        detections[detections_count - 1].p = prob;
                        detections[detections_count - 1].image_index = image_index;
                        detections[detections_count - 1].class_id = class_id;
                        detections[detections_count - 1].truth_flag = 0;
                        detections[detections_count - 1].unique_truth_index = -1;

int truth_index = -1;
                        float max_iou = 0;
                        for (j = 0; j < num_labels; ++j)
                        {
                            box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
                            //printf(" IoU = %f, prob = %f, class_id = %d, truth[j].id = %d n",
                            //    box_iou(dets[i].bbox, t), prob, class_id, truth[j].id);
                            float current_iou = box_iou(dets[i].bbox, t);
                            if (current_iou > iou_thresh && class_id == truth[j].id) {
                                if (current_iou > max_iou) {
                                    max_iou = current_iou;
                                    truth_index = unique_truth_count + j;
                                }
                            }
                        }

// best IoU
                        if (truth_index > -1) {
                            detections[detections_count - 1].truth_flag = 1;
                            detections[detections_count - 1].unique_truth_index = truth_index;
                        }
                        else {
                            // if object is difficult then remove detection
                            for (j = 0; j < num_labels_dif; ++j) {
                                box t = { truth_dif[j].x, truth_dif[j].y, truth_dif[j].w, truth_dif[j].h };
                                float current_iou = box_iou(dets[i].bbox, t);
                                if (current_iou > iou_thresh && class_id == truth_dif[j].id) {
                                    --detections_count;
                                    break;
                                }
                            }
                        }

// calc avg IoU, true-positives, false-positives for required Threshold
                        if (prob > thresh_calc_avg_iou) {
                            int z, found = 0;
                            for (z = checkpoint_detections_count; z < detections_count-1; ++z)
                                if (detections[z].unique_truth_index == truth_index) {
                                    found = 1; break;
                                }

if(truth_index > -1 && found == 0) {
                                avg_iou += max_iou;
                                ++tp_for_thresh;
                            }
                            else
                                fp_for_thresh++;
                        }
                    }
                }
            }

unique_truth_count += num_labels;

//static int previous_errors = 0;
            //int total_errors = fp_for_thresh + (unique_truth_count - tp_for_thresh);
            //int errors_in_this_image = total_errors - previous_errors;
            //previous_errors = total_errors;
            //if(reinforcement_fd == NULL) reinforcement_fd = fopen("reinforcement.txt", "wb");
            //char buff[1000];
            //sprintf(buff, "%sn", path);
            //if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd);

free_detections(dets, nboxes);
            free(id);
            free_image(val[t]);
            free_image(val_resized[t]);
        }
    }

if((tp_for_thresh + fp_for_thresh) > 0)
        avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);

// SORT(detections)
    qsort(detections, detections_count, sizeof(box_prob), detections_comparator);

typedef struct {
        double precision;
        double recall;
        int tp, fp, fn;
    } pr_t;

// for PR-curve
    pr_t **pr = calloc(classes, sizeof(pr_t*));
    for (i = 0; i < classes; ++i) {
        pr[i] = calloc(detections_count, sizeof(pr_t));
    }
    printf("detections_count = %d, unique_truth_count = %d  n", detections_count, unique_truth_count);

int *truth_flags = calloc(unique_truth_count, sizeof(int));

int rank;
    for (rank = 0; rank < detections_count; ++rank) {
        if(rank % 100 == 0)
            printf(" rank = %d of ranks = %d r", rank, detections_count);

if (rank > 0) {
            int class_id;
            for (class_id = 0; class_id < classes; ++class_id) {
                pr[class_id][rank].tp = pr[class_id][rank - 1].tp;
                pr[class_id][rank].fp = pr[class_id][rank - 1].fp;
            }
        }

box_prob d = detections[rank];
        // if (detected && isn't detected before)
        if (d.truth_flag == 1) {
            if (truth_flags[d.unique_truth_index] == 0)
            {
                truth_flags[d.unique_truth_index] = 1;
                pr[d.class_id][rank].tp++;    // true-positive
            }
        }
        else {
            pr[d.class_id][rank].fp++;    // false-positive
        }

for (i = 0; i < classes; ++i)
        {
            const int tp = pr[i][rank].tp;
            const int fp = pr[i][rank].fp;
            const int fn = truth_classes_count[i] - tp;    // false-negative = objects - true-positive
            pr[i][rank].fn = fn;

if ((tp + fp) > 0) pr[i][rank].precision = (double)tp / (double)(tp + fp);
            else pr[i][rank].precision = 0;

if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn);
            else pr[i][rank].recall = 0;
        }
    }

free(truth_flags);

double mean_average_precision = 0;

for (i = 0; i < classes; ++i) {
        double avg_precision = 0;
        int point;
        for (point = 0; point < 11; ++point) {
            double cur_recall = point * 0.1;
            double cur_precision = 0;
            for (rank = 0; rank < detections_count; ++rank)
            {
                if (pr[i][rank].recall >= cur_recall) {    // > or >=
                    if (pr[i][rank].precision > cur_precision) {
                        cur_precision = pr[i][rank].precision;
                    }
                }
            }
            //printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f n", i, point, cur_recall, cur_precision);

avg_precision += cur_precision;
        }
        avg_precision = avg_precision / 11;
        printf("class_id = %d, name = %s, t ap = %2.2f %% n", i, names[i], avg_precision*100);
        mean_average_precision += avg_precision;
    }

const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh);
    const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh));
    const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall);
    printf(" for thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f n",
        thresh_calc_avg_iou, cur_precision, cur_recall, f1_score);

printf(" for thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% n",
        thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100);

mean_average_precision = mean_average_precision / classes;
    printf("n mean average precision (mAP) = %f, or %2.2f %% n", mean_average_precision, mean_average_precision*100);

for (i = 0; i < classes; ++i) {
        free(pr[i]);
    }
    free(pr);
    free(detections);
    free(truth_classes_count);

fprintf(stderr, "Total Detection Time: %f Secondsn", (double)(time(0) - start));
    if (reinforcement_fd != NULL) fclose(reinforcement_fd);
}

#ifdef OPENCV
typedef struct {
    float w, h;
} anchors_t;

int anchors_comparator(const void *pa, const void *pb)
{
    anchors_t a = *(anchors_t *)pa;
    anchors_t b = *(anchors_t *)pb;
    float diff = b.w*b.h - a.w*a.h;
    if (diff < 0) return 1;
    else if (diff > 0) return -1;
    return 0;
}

void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show)
{
    printf("n num_of_clusters = %d, width = %d, height = %d n", num_of_clusters, width, height);
    if (width < 0 || height < 0) {
        printf("Usage: darknet detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -height 416 n");
        printf("Error: set width and height n");
        return;
    }

//float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 };
    float *rel_width_height_array = calloc(1000, sizeof(float));

list *options = read_data_cfg(datacfg);
    char *train_images = option_find_str(options, "train", "data/train.list");
    list *plist = get_paths(train_images);
    int number_of_images = plist->size;
    char **paths = (char **)list_to_array(plist);

int number_of_boxes = 0;
    printf(" read labels from %d images n", number_of_images);

int i, j;
    for (i = 0; i < number_of_images; ++i) {
        char *path = paths[i];
        char labelpath[4096];
        replace_image_to_label(path, labelpath);

int num_labels = 0;
        box_label *truth = read_boxes(labelpath, &num_labels);
        //printf(" new path: %s n", labelpath);
        char buff[1024];
        for (j = 0; j < num_labels; ++j)
        {
            if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 ||
                truth[j].w > 1 || truth[j].w <= 0 || truth[j].h > 1 || truth[j].h <= 0)
            {
                printf("nnWrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f n",
                    labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
                sprintf(buff, "echo "Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f" >> bad_label.list",
                    labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
                system(buff);
                if (check_mistakes) getchar();
            }
            number_of_boxes++;
            rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float));
            rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width;
            rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height;
            printf("r loaded t image: %d t box: %d", i+1, number_of_boxes);
        }
    }
    printf("n all loaded. n");

CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1);
    CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1);
    CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1);

for (i = 0; i < number_of_boxes; ++i) {
        points->data.fl[i * 2] = rel_width_height_array[i * 2];
        points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1];
        //cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0));
        //cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0));
    }

const int attemps = 10;
    double compactness;

enum {
        KMEANS_RANDOM_CENTERS = 0,
        KMEANS_USE_INITIAL_LABELS = 1,
        KMEANS_PP_CENTERS = 2
    };

printf("n calculating k-means++ ...");
    // Should be used: distance(box, centroid) = 1 - IoU(box, centroid)
    cvKMeans2(points, num_of_clusters, labels,
        cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10000, 0), attemps,
        0, KMEANS_PP_CENTERS,
        centers, &compactness);

// sort anchors
    qsort(centers->data.fl, num_of_clusters, 2*sizeof(float), anchors_comparator);

//orig 2.0 anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
    //float orig_anch[] = { 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52 };
    // worse than ours (even for 19x19 final size - for input size 608x608)

//orig anchors = 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071
    //float orig_anch[] = { 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 };
    // orig (IoU=59.90%) better than ours (59.75%)

//gen_anchors.py = 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66
    //float orig_anch[] = { 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 };

// ours: anchors = 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595
    //float orig_anch[] = { 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 };
    //for (i = 0; i < num_of_clusters * 2; ++i) centers->data.fl[i] = orig_anch[i];

//for (i = 0; i < number_of_boxes; ++i)
    //    printf("%2.2f,%2.2f, ", points->data.fl[i * 2], points->data.fl[i * 2 + 1]);

printf("n");
    float avg_iou = 0;
    for (i = 0; i < number_of_boxes; ++i) {
        float box_w = points->data.fl[i * 2];
        float box_h = points->data.fl[i * 2 + 1];
        //int cluster_idx = labels->data.i[i];
        int cluster_idx = 0;
        float min_dist = FLT_MAX;
        for (j = 0; j < num_of_clusters; ++j) {
            float anchor_w = centers->data.fl[j * 2];
            float anchor_h = centers->data.fl[j * 2 + 1];
            float w_diff = anchor_w - box_w;
            float h_diff = anchor_h - box_h;
            float distance = sqrt(w_diff*w_diff + h_diff*h_diff);
            if (distance < min_dist) min_dist = distance, cluster_idx = j;
        }

float anchor_w = centers->data.fl[cluster_idx * 2];
        float anchor_h = centers->data.fl[cluster_idx * 2 + 1];
        float min_w = (box_w < anchor_w) ? box_w : anchor_w;
        float min_h = (box_h < anchor_h) ? box_h : anchor_h;
        float box_intersect = min_w*min_h;
        float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect;
        float iou = box_intersect / box_union;
        if (iou > 1 || iou < 0) { // || box_w > width || box_h > height) {
            printf(" Wrong label: i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f n",
                i, box_w, box_h, anchor_w, anchor_h, iou);
        }
        else avg_iou += iou;
    }
    avg_iou = 100 * avg_iou / number_of_boxes;
    printf("n avg IoU = %2.2f %% n", avg_iou);

char buff[1024];
    FILE* fw = fopen("anchors.txt", "wb");
    if (fw) {
        printf("nSaving anchors to the file: anchors.txt n");
        printf("anchors = ");
        for (i = 0; i < num_of_clusters; ++i) {
            sprintf(buff, "%2.4f,%2.4f", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]);
            printf("%s", buff);
            fwrite(buff, sizeof(char), strlen(buff), fw);
            if (i + 1 < num_of_clusters) {
                fwrite(", ", sizeof(char), 2, fw);
                printf(", ");
            }
        }
        printf("n");
        fclose(fw);
    }
    else {
        printf(" Error: file anchors.txt can't be open n");
    }

if (show) {
        size_t img_size = 700;
        IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3);
        cvZero(img);
        for (j = 0; j < num_of_clusters; ++j) {
            CvPoint pt1, pt2;
            pt1.x = pt1.y = 0;
            pt2.x = centers->data.fl[j * 2] * img_size / width;
            pt2.y = centers->data.fl[j * 2 + 1] * img_size / height;
            cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0);
        }

for (i = 0; i < number_of_boxes; ++i) {
            CvPoint pt;
            pt.x = points->data.fl[i * 2] * img_size / width;
            pt.y = points->data.fl[i * 2 + 1] * img_size / height;
            int cluster_idx = labels->data.i[i];
            int red_id = (cluster_idx * (uint64_t)123 + 55) % 255;
            int green_id = (cluster_idx * (uint64_t)321 + 33) % 255;
            int blue_id = (cluster_idx * (uint64_t)11 + 99) % 255;
            cvCircle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0);
            //if(pt.x > img_size || pt.y > img_size) printf("n pt.x = %d, pt.y = %d n", pt.x, pt.y);
        }
        cvShowImage("clusters", img);
        cvWaitKey(0);
        cvReleaseImage(&img);
        cvDestroyAllWindows();
    }

free(rel_width_height_array);
    cvReleaseMat(&points);
    cvReleaseMat(¢ers);
    cvReleaseMat(&labels);
}
#else
void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) {
    printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation n");
}
#endif // OPENCV

void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
                   float hier_thresh, int dont_show, int ext_output, int save_labels)
{
    list *options = read_data_cfg(datacfg);
    char *name_list = option_find_str(options, "names", "data/names.list");
    int names_size = 0;
    char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);

image **alphabet = load_alphabet();
    network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
    if(weightfile){
        load_weights(&net, weightfile);
    }
    //set_batch_network(&net, 1);
    fuse_conv_batchnorm(net);
    calculate_binary_weights(net);
    if (net.layers[net.n - 1].classes != names_size) {
        printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s n",
            name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
        if(net.layers[net.n - 1].classes > names_size) getchar();
    }
    srand(2222222);
    double time;
    char buff[256];
    char *input = buff;
    int j;
    float nms=.45;    // 0.4F
    while(1){
        if(filename){
            strncpy(input, filename, 256);
            if(strlen(input) > 0)
                if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
        } else {
            printf("****detector.c 1133****** Enter Image Path: ");					//dspeia
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "n");
        }
        image im = load_image(input,0,0,net.c);
		printf("****detector.c 1140****** input: %c",input);					//dspeia

int letterbox = 0;
        image sized = resize_image(im, net.w, net.h);
        //image sized = letterbox_image(im, net.w, net.h); letterbox = 1;
        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, sizeof(float *));

float *X = sized.data;

//time= what_time_is_it_now();
        double time = get_time_point();
        network_predict(net, X);
        //network_predict_image(&net, im); letterbox = 1;
		//图片载入完成
        printf("%s: Predicted in %lf milli-seconds. **detecotr.c 1162 n", input, ((double)get_time_point() - time) / 1000);  
        //printf("%s: Predicted in %f seconds.n", input, (what_time_is_it_now()-time));

int nboxes = 0;
        detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
		printf("****  l.classes == %c ** n", l.classes);  //test classes
		draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);		//draw_detections_v3,不是image.c的draw_detections
         
		save_image(im, "pre-img//predictions");

if (!dont_show) {
            show_image(im, "predictions");
        }

// pseudo labeling concept - fast.ai
        if(save_labels)
        {
            char labelpath[4096];
            replace_image_to_label(input, labelpath);

FILE* fw = fopen(labelpath, "wb");
            int i;
            for (i = 0; i < nboxes; ++i) {
                char buff[1024];
                int class_id = -1;
                float prob = 0;
                for (j = 0; j < l.classes; ++j) {
                    if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) {
                        prob = dets[i].prob[j];
                        class_id = j;
                    }
                }
                if (class_id >= 0) {
                    sprintf(buff, "%d %2.4f %2.4f %2.4f %2.4fn", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h);
                    fwrite(buff, sizeof(char), strlen(buff), fw);
                }
            }
            fclose(fw);
        }

free_detections(dets, nboxes);
        free_image(im);
        free_image(sized);
        //free(boxes);
        //free_ptrs((void **)probs, l.w*l.h*l.n);
#ifdef OPENCV
        if (!dont_show) {
            cvWaitKey(0);
            cvDestroyAllWindows();
        }
#endif
        if (filename) break;
    }

// free memory
    free_ptrs(names, net.layers[net.n - 1].classes);
    free_list_contents_kvp(options);
    free_list(options);

int i;
    const int nsize = 8;
    for (j = 0; j < nsize; ++j) {
        for (i = 32; i < 127; ++i) {
            free_image(alphabet[j][i]);
        }
        free(alphabet[j]);
    }
    free(alphabet);

free_network(net);
}

void run_detector(int argc, char **argv)
{	//输入中有第四位参数的函数，要求cmd中跟上参数如："-out_filename result/out.mp4" 其他的不跟参数
    int dont_show = find_arg(argc, argv, "-dont_show");	//不展示窗口，find_arg()有匹配为1，无匹配则0
    int show = find_arg(argc, argv, "-show");
    check_mistakes = find_arg(argc, argv, "-check_mistakes");
    int http_stream_port = find_int_arg(argc, argv, "-http_port", -1);		//浏览器展示结果的端口号
    char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);		//如：-out_filename out.mp4，有视频文件输入才能有输出，无法用摄像头保存输出
    char *outfile = find_char_arg(argc, argv, "-out", 0);
    char *prefix = find_char_arg(argc, argv, "-prefix", 0);
    float thresh = find_float_arg(argc, argv, "-thresh", .25);    // 0.24	//单一阈值
    float hier_thresh = find_float_arg(argc, argv, "-hier", .5);	//多类别显示阈值
    int cam_index = find_int_arg(argc, argv, "-c", 0);		//摄像头选择
    int frame_skip = find_int_arg(argc, argv, "-s", 0);		//在框中跳帧显示
    int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);	//簇个数？
    int width = find_int_arg(argc, argv, "-width", -1);
    int height = find_int_arg(argc, argv, "-height", -1);
    // extended output in test mode (output of rect bound coords)
    // and for recall mode (extended output table-like format with results for best_class fit)
    int ext_output = find_arg(argc, argv, "-ext_output");		//输出目标坐标
    int save_labels = find_arg(argc, argv, "-save_labels");
    if(argc < 4){
        fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]n", argv[0], argv[1]);
        return;
    }
    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);		//选择gpu
    int *gpus = 0;
    int gpu = 0;
    int ngpus = 0;
    if(gpu_list){
        printf("%sn", 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");

char *datacfg = argv[3];
    char *cfg = argv[4];
    char *weights = (argc > 5) ? argv[5] : 0;
    if(weights)
        if(strlen(weights) > 0)
            if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
    char *filename = (argc > 6) ? argv[6]: 0;
    if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels);
    else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show);
    else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
    else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
    else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh);
    else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);
    else if(0==strcmp(argv[2], "demo")) {
        list *options = read_data_cfg(datacfg);
        int classes = option_find_int(options, "classes", 20);	//class可能是一帧最大类别数？？
        char *name_list = option_find_str(options, "names", "data/names.list");
        char **names = get_labels(name_list);
        if(filename)
            if(strlen(filename) > 0)
                if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;
        demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename,
            http_stream_port, dont_show, ext_output);

free_list_contents_kvp(options);
        free_list(options);
    }
    else printf(" There isn't such command: %s", argv[2]);
}

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#include "network.h"
#include "region_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
#include "demo.h"
#include "option_list.h"

#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/core/core_c.h"
//#include "opencv2/core/core.hpp"
#include "opencv2/core/version.hpp"
#include "opencv2/imgproc/imgproc_c.h"

#ifndef CV_VERSION_EPOCH
#include "opencv2/videoio/videoio_c.h"
#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION)
#pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib")
#else
#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)
#pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
#endif

IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size);
void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches);
#endif    // OPENCV

#include "http_stream.h"

int check_mistakes;

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, int dont_show)
{
    list *options = read_data_cfg(datacfg);
    char *train_images = option_find_str(options, "train", "data/train.list");
    char *backup_directory = option_find_str(options, "backup", "/backup/");

    srand(time(0));
    char *base = basecfg(cfgfile);
    printf("%sn", 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] = parse_network_cfg(cfgfile);
        if(weightfile){
            load_weights(&nets[i], weightfile);
        }
        if(clear) *nets[i].seen = 0;
        nets[i].learning_rate *= ngpus;
    }
    srand(time(0));
    network net = nets[0];

    const int actual_batch_size = net.batch * net.subdivisions;
    if (actual_batch_size == 1) {
        printf("n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 n");
        getchar();
    }
    else if (actual_batch_size < 64) {
            printf("n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 n", actual_batch_size);
    }

    int imgs = net.batch * net.subdivisions * ngpus;
    printf("Learning Rate: %g, Momentum: %g, Decay: %gn", 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);

    int init_w = net.w;
    int init_h = net.h;
    int iter_save;
    iter_save = get_current_batch(net);

    load_args args = {0};
    args.w = net.w;
    args.h = net.h;
    args.c = net.c;
    args.paths = paths;
    args.n = imgs;
    args.m = plist->size;
    args.classes = classes;
    args.flip = net.flip;
    args.jitter = jitter;
    args.num_boxes = l.max_boxes;
    args.small_object = net.small_object;
    args.d = &buffer;
    args.type = DETECTION_DATA;
    args.threads = 16;    // 64

    args.angle = net.angle;
    args.exposure = net.exposure;
    args.saturation = net.saturation;
    args.hue = net.hue;

#ifdef OPENCV
    args.threads = 3 * ngpus;
    IplImage* img = NULL;
    float max_img_loss = 5;
    int number_of_lines = 100;
    int img_size = 1000;
    if (!dont_show)
        img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size);
#endif    //OPENCV

    pthread_t load_thread = load_data(args);
    double time;
    int count = 0;
    //while(i*imgs < N*120){
    while(get_current_batch(net) < net.max_batches){
        if(l.random && count++%10 == 0){
            printf("Resizingn");
            //int dim = (rand() % 12 + (init_w/32 - 5)) * 32;    // +-160
            //int dim = (rand() % 4 + 16) * 32;
            //if (get_current_batch(net)+100 > net.max_batches) dim = 544;

            //int random_val = rand() % 12;
            //int dim_w = (random_val + (init_w / 32 - 5)) * 32;    // +-160
            //int dim_h = (random_val + (init_h / 32 - 5)) * 32;    // +-160

            float random_val = rand_scale(1.4);    // *x or /x
            int dim_w = roundl(random_val*init_w / 32) * 32;
            int dim_h = roundl(random_val*init_h / 32) * 32;

            if (dim_w < 32) dim_w = 32;
            if (dim_h < 32) dim_h = 32;

            printf("%d x %d n", dim_w, dim_h);
            args.w = dim_w;
            args.h = dim_h;

            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_w, dim_h);
            }
            net = nets[0];
        }
        time=what_time_is_it_now();
        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 %fn", b.x, b.y, b.w, b.h);
           }
           image im = float_to_image(448, 448, 3, train.X.vals[10]);
           int k;
           for(k = 0; k < l.max_boxes; ++k){
           box b = float_to_box(train.y.vals[10] + 1 + k*5);
           printf("%d %d %d %dn", truth.x, truth.y, truth.w, truth.h);
           draw_bbox(im, b, 8, 1,0,0);
           }
           save_image(im, "truth11");
         */

        printf("Loaded: %lf secondsn", (what_time_is_it_now()-time));

        time=what_time_is_it_now();
        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 != avg_loss) avg_loss = loss;    // if(-inf or nan)
        avg_loss = avg_loss*.9 + loss*.1;

        i = get_current_batch(net);
        printf("n %d: %f, %f avg loss, %f rate, %lf seconds, %d imagesn", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs);

#ifdef OPENCV
        if(!dont_show)
            draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches);
#endif    // OPENCV

        //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
        //if (i % 100 == 0) {
        if(i >= (iter_save + 100)) {
            iter_save = i;
#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);

#ifdef OPENCV
    cvReleaseImage(&img);
    cvDestroyAllWindows();
#endif

    // free memory
    pthread_join(load_thread, 0);
    free_data(buffer);

    free(base);
    free(paths);
    free_list_contents(plist);
    free_list(plist);

    free_list_contents_kvp(options);
    free_list(options);

    free(nets);
    free_network(net);
}


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

static void print_cocos(FILE *fp, char *image_path, detection *dets, 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 = dets[i].bbox.x - dets[i].bbox.w / 2.;
        float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
        float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
        float ymax = dets[i].bbox.y + dets[i].bbox.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 (dets[i].prob[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, dets[i].prob[j]);
        }
    }
}

void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
{
    int i, j;
    for (i = 0; i < total; ++i) {
        float xmin = dets[i].bbox.x - dets[i].bbox.w / 2. + 1;
        float xmax = dets[i].bbox.x + dets[i].bbox.w / 2. + 1;
        float ymin = dets[i].bbox.y - dets[i].bbox.h / 2. + 1;
        float ymax = dets[i].bbox.y + dets[i].bbox.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 (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %fn", id, dets[i].prob[j],
                xmin, ymin, xmax, ymax);
        }
    }
}

void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
{
    int i, j;
    for (i = 0; i < total; ++i) {
        float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
        float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
        float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
        float ymax = dets[i].bbox.y + dets[i].bbox.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 (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %fn", id, j + 1, dets[i].prob[class],
                xmin, ymin, xmax, ymax);
        }
    }
}

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", "data/train.list");
    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_custom(cfgfile, 1);    // set batch=1
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    //set_batch_network(&net, 1);
    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %gn", 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");
        }
    }


    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.c = net.c;
    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, "%dn", 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;
            int nboxes = 0;
            int letterbox = (args.type == LETTERBOX_DATA);
            detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox);
            if (nms) do_nms_sort(dets, nboxes, classes, nms);
            if (coco) {
                print_cocos(fp, path, dets, nboxes, classes, w, h);
            }
            else if (imagenet) {
                print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h);
            }
            else {
                print_detector_detections(fps, id, dets, nboxes, classes, w, h);
            }
            free_detections(dets, nboxes);
            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 Secondsn", (double)time(0) - start);
}

void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
{
    network net = parse_network_cfg_custom(cfgfile, 1);    // set batch=1
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    //set_batch_network(&net, 1);
    fuse_conv_batchnorm(net);
    srand(time(0));

    //list *plist = get_paths("data/coco_val_5k.list");
    list *options = read_data_cfg(datacfg);
    char *valid_images = option_find_str(options, "valid", "data/train.txt");
    list *plist = get_paths(valid_images);
    char **paths = (char **)list_to_array(plist);

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

    int j, k;

    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(path, 0, 0, net.c);
        image sized = resize_image(orig, net.w, net.h);
        char *id = basecfg(path);
        network_predict(net, sized.data);
        int nboxes = 0;
        int letterbox = 0;
        detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes, letterbox);
        if (nms) do_nms_obj(dets, nboxes, 1, nms);

        char labelpath[4096];
        replace_image_to_label(path, labelpath);

        int num_labels = 0;
        box_label *truth = read_boxes(labelpath, &num_labels);
        for (k = 0; k < nboxes; ++k) {
            if (dets[k].objectness > 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 < nboxes; ++k) {
                float iou = box_iou(dets[k].bbox, t);
                if (dets[k].objectness > thresh && iou > best_iou) {
                    best_iou = iou;
                }
            }
            avg_iou += best_iou;
            if (best_iou > iou_thresh) {
                ++correct;
            }
        }
        //fprintf(stderr, " %s - %s - ", paths[i], labelpath);
        fprintf(stderr, "%5d %5d %5dtRPs/Img: %.2ftIOU: %.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);
    }
}

typedef struct {
    box b;
    float p;
    int class_id;
    int image_index;
    int truth_flag;
    int unique_truth_index;
} box_prob;

int detections_comparator(const void *pa, const void *pb)
{
    box_prob a = *(box_prob *)pa;
    box_prob b = *(box_prob *)pb;
    float diff = a.p - b.p;
    if (diff < 0) return 1;
    else if (diff > 0) return -1;
    return 0;
}

void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou)
{
    int j;
    list *options = read_data_cfg(datacfg);
    char *valid_images = option_find_str(options, "valid", "data/train.txt");
    char *difficult_valid_images = option_find_str(options, "difficult", NULL);
    char *name_list = option_find_str(options, "names", "data/names.list");
    char **names = get_labels(name_list);
    char *mapf = option_find_str(options, "map", 0);
    int *map = 0;
    if (mapf) map = read_map(mapf);
    FILE* reinforcement_fd = NULL;

    network net = parse_network_cfg_custom(cfgfile, 1);    // set batch=1
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    //set_batch_network(&net, 1);
    fuse_conv_batchnorm(net);
    calculate_binary_weights(net);
    srand(time(0));

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

    char **paths_dif = NULL;
    if (difficult_valid_images) {
        list *plist_dif = get_paths(difficult_valid_images);
        paths_dif = (char **)list_to_array(plist_dif);
    }


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

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

    const float thresh = .005;
    const float nms = .45;
    const float iou_thresh = 0.5;

    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.c = net.c;
    args.type = IMAGE_DATA;
    //args.type = LETTERBOX_DATA;

    //const float thresh_calc_avg_iou = 0.24;
    float avg_iou = 0;
    int tp_for_thresh = 0;
    int fp_for_thresh = 0;

    box_prob *detections = calloc(1, sizeof(box_prob));
    int detections_count = 0;
    int unique_truth_count = 0;

    int *truth_classes_count = calloc(classes, sizeof(int));

    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, "%dn", 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) {
            const int image_index = i + t - nthreads;
            char *path = paths[image_index];
            char *id = basecfg(path);
            float *X = val_resized[t].data;
            network_predict(net, X);

            int nboxes = 0;
            float hier_thresh = 0;
            detection *dets;
            if (args.type == LETTERBOX_DATA) {
                int letterbox = 1;
                dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
            }
            else {
                int letterbox = 0;
                dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letterbox);
            }
            //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); // for letterbox=1
            if (nms) do_nms_sort(dets, nboxes, l.classes, nms);

            char labelpath[4096];
            replace_image_to_label(path, labelpath);
            int num_labels = 0;
            box_label *truth = read_boxes(labelpath, &num_labels);
            int i, j;
            for (j = 0; j < num_labels; ++j) {
                truth_classes_count[truth[j].id]++;
            }

            // difficult
            box_label *truth_dif = NULL;
            int num_labels_dif = 0;
            if (paths_dif)
            {
                char *path_dif = paths_dif[image_index];

                char labelpath_dif[4096];
                replace_image_to_label(path_dif, labelpath_dif);

                truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
            }

            const int checkpoint_detections_count = detections_count;

            for (i = 0; i < nboxes; ++i) {

                int class_id;
                for (class_id = 0; class_id < classes; ++class_id) {
                    float prob = dets[i].prob[class_id];
                    if (prob > 0) {
                        detections_count++;
                        detections = realloc(detections, detections_count * sizeof(box_prob));
                        detections[detections_count - 1].b = dets[i].bbox;
                        detections[detections_count - 1].p = prob;
                        detections[detections_count - 1].image_index = image_index;
                        detections[detections_count - 1].class_id = class_id;
                        detections[detections_count - 1].truth_flag = 0;
                        detections[detections_count - 1].unique_truth_index = -1;

                        int truth_index = -1;
                        float max_iou = 0;
                        for (j = 0; j < num_labels; ++j)
                        {
                            box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
                            //printf(" IoU = %f, prob = %f, class_id = %d, truth[j].id = %d n",
                            //    box_iou(dets[i].bbox, t), prob, class_id, truth[j].id);
                            float current_iou = box_iou(dets[i].bbox, t);
                            if (current_iou > iou_thresh && class_id == truth[j].id) {
                                if (current_iou > max_iou) {
                                    max_iou = current_iou;
                                    truth_index = unique_truth_count + j;
                                }
                            }
                        }

                        // best IoU
                        if (truth_index > -1) {
                            detections[detections_count - 1].truth_flag = 1;
                            detections[detections_count - 1].unique_truth_index = truth_index;
                        }
                        else {
                            // if object is difficult then remove detection
                            for (j = 0; j < num_labels_dif; ++j) {
                                box t = { truth_dif[j].x, truth_dif[j].y, truth_dif[j].w, truth_dif[j].h };
                                float current_iou = box_iou(dets[i].bbox, t);
                                if (current_iou > iou_thresh && class_id == truth_dif[j].id) {
                                    --detections_count;
                                    break;
                                }
                            }
                        }

                        // calc avg IoU, true-positives, false-positives for required Threshold
                        if (prob > thresh_calc_avg_iou) {
                            int z, found = 0;
                            for (z = checkpoint_detections_count; z < detections_count-1; ++z)
                                if (detections[z].unique_truth_index == truth_index) {
                                    found = 1; break;
                                }

                            if(truth_index > -1 && found == 0) {
                                avg_iou += max_iou;
                                ++tp_for_thresh;
                            }
                            else
                                fp_for_thresh++;
                        }
                    }
                }
            }

            unique_truth_count += num_labels;

            //static int previous_errors = 0;
            //int total_errors = fp_for_thresh + (unique_truth_count - tp_for_thresh);
            //int errors_in_this_image = total_errors - previous_errors;
            //previous_errors = total_errors;
            //if(reinforcement_fd == NULL) reinforcement_fd = fopen("reinforcement.txt", "wb");
            //char buff[1000];
            //sprintf(buff, "%sn", path);
            //if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd);

            free_detections(dets, nboxes);
            free(id);
            free_image(val[t]);
            free_image(val_resized[t]);
        }
    }

    if((tp_for_thresh + fp_for_thresh) > 0)
        avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);


    // SORT(detections)
    qsort(detections, detections_count, sizeof(box_prob), detections_comparator);

    typedef struct {
        double precision;
        double recall;
        int tp, fp, fn;
    } pr_t;

    // for PR-curve
    pr_t **pr = calloc(classes, sizeof(pr_t*));
    for (i = 0; i < classes; ++i) {
        pr[i] = calloc(detections_count, sizeof(pr_t));
    }
    printf("detections_count = %d, unique_truth_count = %d  n", detections_count, unique_truth_count);


    int *truth_flags = calloc(unique_truth_count, sizeof(int));

    int rank;
    for (rank = 0; rank < detections_count; ++rank) {
        if(rank % 100 == 0)
            printf(" rank = %d of ranks = %d r", rank, detections_count);

        if (rank > 0) {
            int class_id;
            for (class_id = 0; class_id < classes; ++class_id) {
                pr[class_id][rank].tp = pr[class_id][rank - 1].tp;
                pr[class_id][rank].fp = pr[class_id][rank - 1].fp;
            }
        }

        box_prob d = detections[rank];
        // if (detected && isn't detected before)
        if (d.truth_flag == 1) {
            if (truth_flags[d.unique_truth_index] == 0)
            {
                truth_flags[d.unique_truth_index] = 1;
                pr[d.class_id][rank].tp++;    // true-positive
            }
        }
        else {
            pr[d.class_id][rank].fp++;    // false-positive
        }

        for (i = 0; i < classes; ++i)
        {
            const int tp = pr[i][rank].tp;
            const int fp = pr[i][rank].fp;
            const int fn = truth_classes_count[i] - tp;    // false-negative = objects - true-positive
            pr[i][rank].fn = fn;

            if ((tp + fp) > 0) pr[i][rank].precision = (double)tp / (double)(tp + fp);
            else pr[i][rank].precision = 0;

            if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn);
            else pr[i][rank].recall = 0;
        }
    }

    free(truth_flags);


    double mean_average_precision = 0;

    for (i = 0; i < classes; ++i) {
        double avg_precision = 0;
        int point;
        for (point = 0; point < 11; ++point) {
            double cur_recall = point * 0.1;
            double cur_precision = 0;
            for (rank = 0; rank < detections_count; ++rank)
            {
                if (pr[i][rank].recall >= cur_recall) {    // > or >=
                    if (pr[i][rank].precision > cur_precision) {
                        cur_precision = pr[i][rank].precision;
                    }
                }
            }
            //printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f n", i, point, cur_recall, cur_precision);

            avg_precision += cur_precision;
        }
        avg_precision = avg_precision / 11;
        printf("class_id = %d, name = %s, t ap = %2.2f %% n", i, names[i], avg_precision*100);
        mean_average_precision += avg_precision;
    }

    const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh);
    const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh));
    const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall);
    printf(" for thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f n",
        thresh_calc_avg_iou, cur_precision, cur_recall, f1_score);

    printf(" for thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% n",
        thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100);

    mean_average_precision = mean_average_precision / classes;
    printf("n mean average precision (mAP) = %f, or %2.2f %% n", mean_average_precision, mean_average_precision*100);


    for (i = 0; i < classes; ++i) {
        free(pr[i]);
    }
    free(pr);
    free(detections);
    free(truth_classes_count);

    fprintf(stderr, "Total Detection Time: %f Secondsn", (double)(time(0) - start));
    if (reinforcement_fd != NULL) fclose(reinforcement_fd);
}

#ifdef OPENCV
typedef struct {
    float w, h;
} anchors_t;

int anchors_comparator(const void *pa, const void *pb)
{
    anchors_t a = *(anchors_t *)pa;
    anchors_t b = *(anchors_t *)pb;
    float diff = b.w*b.h - a.w*a.h;
    if (diff < 0) return 1;
    else if (diff > 0) return -1;
    return 0;
}

void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show)
{
    printf("n num_of_clusters = %d, width = %d, height = %d n", num_of_clusters, width, height);
    if (width < 0 || height < 0) {
        printf("Usage: darknet detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -height 416 n");
        printf("Error: set width and height n");
        return;
    }

    //float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 };
    float *rel_width_height_array = calloc(1000, sizeof(float));

    list *options = read_data_cfg(datacfg);
    char *train_images = option_find_str(options, "train", "data/train.list");
    list *plist = get_paths(train_images);
    int number_of_images = plist->size;
    char **paths = (char **)list_to_array(plist);

    int number_of_boxes = 0;
    printf(" read labels from %d images n", number_of_images);

    int i, j;
    for (i = 0; i < number_of_images; ++i) {
        char *path = paths[i];
        char labelpath[4096];
        replace_image_to_label(path, labelpath);

        int num_labels = 0;
        box_label *truth = read_boxes(labelpath, &num_labels);
        //printf(" new path: %s n", labelpath);
        char buff[1024];
        for (j = 0; j < num_labels; ++j)
        {
            if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 ||
                truth[j].w > 1 || truth[j].w <= 0 || truth[j].h > 1 || truth[j].h <= 0)
            {
                printf("nnWrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f n",
                    labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
                sprintf(buff, "echo "Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f" >> bad_label.list",
                    labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
                system(buff);
                if (check_mistakes) getchar();
            }
            number_of_boxes++;
            rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float));
            rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width;
            rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height;
            printf("r loaded t image: %d t box: %d", i+1, number_of_boxes);
        }
    }
    printf("n all loaded. n");

    CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1);
    CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1);
    CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1);

    for (i = 0; i < number_of_boxes; ++i) {
        points->data.fl[i * 2] = rel_width_height_array[i * 2];
        points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1];
        //cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0));
        //cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0));
    }


    const int attemps = 10;
    double compactness;

    enum {
        KMEANS_RANDOM_CENTERS = 0,
        KMEANS_USE_INITIAL_LABELS = 1,
        KMEANS_PP_CENTERS = 2
    };

    printf("n calculating k-means++ ...");
    // Should be used: distance(box, centroid) = 1 - IoU(box, centroid)
    cvKMeans2(points, num_of_clusters, labels,
        cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10000, 0), attemps,
        0, KMEANS_PP_CENTERS,
        centers, &compactness);

    // sort anchors
    qsort(centers->data.fl, num_of_clusters, 2*sizeof(float), anchors_comparator);

    //orig 2.0 anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
    //float orig_anch[] = { 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52 };
    // worse than ours (even for 19x19 final size - for input size 608x608)

    //orig anchors = 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071
    //float orig_anch[] = { 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 };
    // orig (IoU=59.90%) better than ours (59.75%)

    //gen_anchors.py = 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66
    //float orig_anch[] = { 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 };

    // ours: anchors = 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595
    //float orig_anch[] = { 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 };
    //for (i = 0; i < num_of_clusters * 2; ++i) centers->data.fl[i] = orig_anch[i];

    //for (i = 0; i < number_of_boxes; ++i)
    //    printf("%2.2f,%2.2f, ", points->data.fl[i * 2], points->data.fl[i * 2 + 1]);

    printf("n");
    float avg_iou = 0;
    for (i = 0; i < number_of_boxes; ++i) {
        float box_w = points->data.fl[i * 2];
        float box_h = points->data.fl[i * 2 + 1];
        //int cluster_idx = labels->data.i[i];
        int cluster_idx = 0;
        float min_dist = FLT_MAX;
        for (j = 0; j < num_of_clusters; ++j) {
            float anchor_w = centers->data.fl[j * 2];
            float anchor_h = centers->data.fl[j * 2 + 1];
            float w_diff = anchor_w - box_w;
            float h_diff = anchor_h - box_h;
            float distance = sqrt(w_diff*w_diff + h_diff*h_diff);
            if (distance < min_dist) min_dist = distance, cluster_idx = j;
        }

        float anchor_w = centers->data.fl[cluster_idx * 2];
        float anchor_h = centers->data.fl[cluster_idx * 2 + 1];
        float min_w = (box_w < anchor_w) ? box_w : anchor_w;
        float min_h = (box_h < anchor_h) ? box_h : anchor_h;
        float box_intersect = min_w*min_h;
        float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect;
        float iou = box_intersect / box_union;
        if (iou > 1 || iou < 0) { // || box_w > width || box_h > height) {
            printf(" Wrong label: i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f n",
                i, box_w, box_h, anchor_w, anchor_h, iou);
        }
        else avg_iou += iou;
    }
    avg_iou = 100 * avg_iou / number_of_boxes;
    printf("n avg IoU = %2.2f %% n", avg_iou);

    char buff[1024];
    FILE* fw = fopen("anchors.txt", "wb");
    if (fw) {
        printf("nSaving anchors to the file: anchors.txt n");
        printf("anchors = ");
        for (i = 0; i < num_of_clusters; ++i) {
            sprintf(buff, "%2.4f,%2.4f", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]);
            printf("%s", buff);
            fwrite(buff, sizeof(char), strlen(buff), fw);
            if (i + 1 < num_of_clusters) {
                fwrite(", ", sizeof(char), 2, fw);
                printf(", ");
            }
        }
        printf("n");
        fclose(fw);
    }
    else {
        printf(" Error: file anchors.txt can't be open n");
    }

    if (show) {
        size_t img_size = 700;
        IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3);
        cvZero(img);
        for (j = 0; j < num_of_clusters; ++j) {
            CvPoint pt1, pt2;
            pt1.x = pt1.y = 0;
            pt2.x = centers->data.fl[j * 2] * img_size / width;
            pt2.y = centers->data.fl[j * 2 + 1] * img_size / height;
            cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0);
        }

        for (i = 0; i < number_of_boxes; ++i) {
            CvPoint pt;
            pt.x = points->data.fl[i * 2] * img_size / width;
            pt.y = points->data.fl[i * 2 + 1] * img_size / height;
            int cluster_idx = labels->data.i[i];
            int red_id = (cluster_idx * (uint64_t)123 + 55) % 255;
            int green_id = (cluster_idx * (uint64_t)321 + 33) % 255;
            int blue_id = (cluster_idx * (uint64_t)11 + 99) % 255;
            cvCircle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0);
            //if(pt.x > img_size || pt.y > img_size) printf("n pt.x = %d, pt.y = %d n", pt.x, pt.y);
        }
        cvShowImage("clusters", img);
        cvWaitKey(0);
        cvReleaseImage(&img);
        cvDestroyAllWindows();
    }

    free(rel_width_height_array);
    cvReleaseMat(&points);
    cvReleaseMat(&centers);
    cvReleaseMat(&labels);
}
#else
void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) {
    printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation n");
}
#endif // OPENCV

void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
                   float hier_thresh, int dont_show, int ext_output, int save_labels)
{
    list *options = read_data_cfg(datacfg);
    char *name_list = option_find_str(options, "names", "data/names.list");
    int names_size = 0;
    char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);

    image **alphabet = load_alphabet();
    network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
    if(weightfile){
        load_weights(&net, weightfile);
    }
    //set_batch_network(&net, 1);
    fuse_conv_batchnorm(net);
    calculate_binary_weights(net);
    if (net.layers[net.n - 1].classes != names_size) {
        printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s n",
            name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
        if(net.layers[net.n - 1].classes > names_size) getchar();
    }
    srand(2222222);
    double time;
    char buff[256];
    char *input = buff;
    int j;
    float nms=.45;    // 0.4F
    while(1){
        if(filename){
            strncpy(input, filename, 256);
            if(strlen(input) > 0)
                if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
        } else {
            printf("****detector.c 1133****** Enter Image Path: ");					//dspeia
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "n");
        }
        image im = load_image(input,0,0,net.c);
		printf("****detector.c 1140****** input: %c",input);					//dspeia

        int letterbox = 0;
        image sized = resize_image(im, net.w, net.h);
        //image sized = letterbox_image(im, net.w, net.h); letterbox = 1;
        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, sizeof(float *));

        float *X = sized.data;

        //time= what_time_is_it_now();
        double time = get_time_point();
        network_predict(net, X);
        //network_predict_image(&net, im); letterbox = 1;
		//图片载入完成
        printf("%s: Predicted in %lf milli-seconds. **detecotr.c 1162 n", input, ((double)get_time_point() - time) / 1000);  
        //printf("%s: Predicted in %f seconds.n", input, (what_time_is_it_now()-time));

        int nboxes = 0;
        detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
		printf("****  l.classes == %c ** n", l.classes);  //test classes
		draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);		//draw_detections_v3,不是image.c的draw_detections
         
		save_image(im, "pre-img//predictions");

        if (!dont_show) {
            show_image(im, "predictions");
        }

        // pseudo labeling concept - fast.ai
        if(save_labels)
        {
            char labelpath[4096];
            replace_image_to_label(input, labelpath);

            FILE* fw = fopen(labelpath, "wb");
            int i;
            for (i = 0; i < nboxes; ++i) {
                char buff[1024];
                int class_id = -1;
                float prob = 0;
                for (j = 0; j < l.classes; ++j) {
                    if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) {
                        prob = dets[i].prob[j];
                        class_id = j;
                    }
                }
                if (class_id >= 0) {
                    sprintf(buff, "%d %2.4f %2.4f %2.4f %2.4fn", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h);
                    fwrite(buff, sizeof(char), strlen(buff), fw);
                }
            }
            fclose(fw);
        }

        free_detections(dets, nboxes);
        free_image(im);
        free_image(sized);
        //free(boxes);
        //free_ptrs((void **)probs, l.w*l.h*l.n);
#ifdef OPENCV
        if (!dont_show) {
            cvWaitKey(0);
            cvDestroyAllWindows();
        }
#endif
        if (filename) break;
    }

    // free memory
    free_ptrs(names, net.layers[net.n - 1].classes);
    free_list_contents_kvp(options);
    free_list(options);

    int i;
    const int nsize = 8;
    for (j = 0; j < nsize; ++j) {
        for (i = 32; i < 127; ++i) {
            free_image(alphabet[j][i]);
        }
        free(alphabet[j]);
    }
    free(alphabet);

    free_network(net);
}

void run_detector(int argc, char **argv)
{	//输入中有第四位参数的函数，要求cmd中跟上参数如："-out_filename result/out.mp4" 其他的不跟参数
    int dont_show = find_arg(argc, argv, "-dont_show");	//不展示窗口，find_arg()有匹配为1，无匹配则0
    int show = find_arg(argc, argv, "-show");
    check_mistakes = find_arg(argc, argv, "-check_mistakes");
    int http_stream_port = find_int_arg(argc, argv, "-http_port", -1);		//浏览器展示结果的端口号
    char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);		//如：-out_filename out.mp4，有视频文件输入才能有输出，无法用摄像头保存输出
    char *outfile = find_char_arg(argc, argv, "-out", 0);
    char *prefix = find_char_arg(argc, argv, "-prefix", 0);
    float thresh = find_float_arg(argc, argv, "-thresh", .25);    // 0.24	//单一阈值
    float hier_thresh = find_float_arg(argc, argv, "-hier", .5);	//多类别显示阈值
    int cam_index = find_int_arg(argc, argv, "-c", 0);		//摄像头选择
    int frame_skip = find_int_arg(argc, argv, "-s", 0);		//在框中跳帧显示
    int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);	//簇个数？
    int width = find_int_arg(argc, argv, "-width", -1);
    int height = find_int_arg(argc, argv, "-height", -1);
    // extended output in test mode (output of rect bound coords)
    // and for recall mode (extended output table-like format with results for best_class fit)
    int ext_output = find_arg(argc, argv, "-ext_output");		//输出目标坐标
    int save_labels = find_arg(argc, argv, "-save_labels");
    if(argc < 4){
        fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]n", argv[0], argv[1]);
        return;
    }
    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);		//选择gpu
    int *gpus = 0;
    int gpu = 0;
    int ngpus = 0;
    if(gpu_list){
        printf("%sn", 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");

    char *datacfg = argv[3];
    char *cfg = argv[4];
    char *weights = (argc > 5) ? argv[5] : 0;
    if(weights)
        if(strlen(weights) > 0)
            if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
    char *filename = (argc > 6) ? argv[6]: 0;
    if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels);
    else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show);
    else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
    else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
    else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh);
    else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);
    else if(0==strcmp(argv[2], "demo")) {
        list *options = read_data_cfg(datacfg);
        int classes = option_find_int(options, "classes", 20);	//class可能是一帧最大类别数？？
        char *name_list = option_find_str(options, "names", "data/names.list");
        char **names = get_labels(name_list);
        if(filename)
            if(strlen(filename) > 0)
                if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;
        demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename,
            http_stream_port, dont_show, ext_output);

        free_list_contents_kvp(options);
        free_list(options);
    }
    else printf(" There isn't such command: %s", argv[2]);
}

image.c

复制代码

#include "image.h"
#include "utils.h"
#include "blas.h"
#include "cuda.h"
#include <stdio.h>
#include <math.h>

#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
#define STB_IMAGE_WRITE_IMPLEMENTATION
#include "stb_image_write.h"

#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/core/types_c.h"
#include "opencv2/core/version.hpp"
#ifndef CV_VERSION_EPOCH
#include "opencv2/videoio/videoio_c.h"
#include "opencv2/imgcodecs/imgcodecs_c.h"
#include "http_stream.h"

#endif
#include "http_stream.h"
#endif

extern int check_mistakes;
int windows = 0;

float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };

float get_color(int c, int x, int max)
{
    float ratio = ((float)x/max)*5;
    int i = floor(ratio);
    int j = ceil(ratio);
    ratio -= i;
    float r = (1-ratio) * colors[i][c] + ratio*colors[j][c];
    //printf("%fn", r);
    return r;
}

static float get_pixel(image m, int x, int y, int c)
{
    assert(x < m.w && y < m.h && c < m.c);
    return m.data[c*m.h*m.w + y*m.w + x];
}
static float get_pixel_extend(image m, int x, int y, int c)
{
    if (x < 0 || x >= m.w || y < 0 || y >= m.h) return 0;
    /*
    if(x < 0) x = 0;
    if(x >= m.w) x = m.w-1;
    if(y < 0) y = 0;
    if(y >= m.h) y = m.h-1;
    */
    if (c < 0 || c >= m.c) return 0;
    return get_pixel(m, x, y, c);
}
static void set_pixel(image m, int x, int y, int c, float val)
{
    if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return;
    assert(x < m.w && y < m.h && c < m.c);
    m.data[c*m.h*m.w + y*m.w + x] = val;
}
static void add_pixel(image m, int x, int y, int c, float val)
{
    assert(x < m.w && y < m.h && c < m.c);
    m.data[c*m.h*m.w + y*m.w + x] += val;
}

void composite_image(image source, image dest, int dx, int dy)
{
    int x,y,k;
    for(k = 0; k < source.c; ++k){
        for(y = 0; y < source.h; ++y){
            for(x = 0; x < source.w; ++x){
                float val = get_pixel(source, x, y, k);
                float val2 = get_pixel_extend(dest, dx+x, dy+y, k);
                set_pixel(dest, dx+x, dy+y, k, val * val2);
            }
        }
    }
}

image border_image(image a, int border)
{
    image b = make_image(a.w + 2*border, a.h + 2*border, a.c);
    int x,y,k;
    for(k = 0; k < b.c; ++k){
        for(y = 0; y < b.h; ++y){
            for(x = 0; x < b.w; ++x){
                float val = get_pixel_extend(a, x - border, y - border, k);
                if(x - border < 0 || x - border >= a.w || y - border < 0 || y - border >= a.h) val = 1;
                set_pixel(b, x, y, k, val);
            }
        }
    }
    return b;
}

image tile_images(image a, image b, int dx)
{
    if(a.w == 0) return copy_image(b);
    image c = make_image(a.w + b.w + dx, (a.h > b.h) ? a.h : b.h, (a.c > b.c) ? a.c : b.c);
    fill_cpu(c.w*c.h*c.c, 1, c.data, 1);
    embed_image(a, c, 0, 0);
    composite_image(b, c, a.w + dx, 0);
    return c;
}

image get_label(image **characters, char *string, int size)
{
    if(size > 7) size = 7;
    image label = make_empty_image(0,0,0);
    while(*string){
        image l = characters[size][(int)*string];
        image n = tile_images(label, l, -size - 1 + (size+1)/2);
        free_image(label);
        label = n;
        ++string;
    }
    image b = border_image(label, label.h*.25);
    free_image(label);
    return b;
}

image get_label_v3(image **characters, char *string, int size)
{
    size = size / 10;
    if (size > 7) size = 7;
    image label = make_empty_image(0, 0, 0);
    while (*string) {
        image l = characters[size][(int)*string];
        image n = tile_images(label, l, -size - 1 + (size + 1) / 2);
        free_image(label);
        label = n;
        ++string;
    }
    image b = border_image(label, label.h*.25);
    free_image(label);
    return b;
}

void draw_label(image a, int r, int c, image label, const float *rgb)
{
    int w = label.w;
    int h = label.h;
    if (r - h >= 0) r = r - h;

int i, j, k;
    for(j = 0; j < h && j + r < a.h; ++j){
        for(i = 0; i < w && i + c < a.w; ++i){
            for(k = 0; k < label.c; ++k){
                float val = get_pixel(label, i, j, k);
                set_pixel(a, i+c, j+r, k, rgb[k] * val);
            }
        }
    }
}

void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b)
{
    //normalize_image(a);
    int i;
    if(x1 < 0) x1 = 0;
    if(x1 >= a.w) x1 = a.w-1;
    if(x2 < 0) x2 = 0;
    if(x2 >= a.w) x2 = a.w-1;

if(y1 < 0) y1 = 0;
    if(y1 >= a.h) y1 = a.h-1;
    if(y2 < 0) y2 = 0;
    if(y2 >= a.h) y2 = a.h-1;

for(i = x1; i <= x2; ++i){
        a.data[i + y1*a.w + 0*a.w*a.h] = r;
        a.data[i + y2*a.w + 0*a.w*a.h] = r;

a.data[i + y1*a.w + 1*a.w*a.h] = g;
        a.data[i + y2*a.w + 1*a.w*a.h] = g;

a.data[i + y1*a.w + 2*a.w*a.h] = b;
        a.data[i + y2*a.w + 2*a.w*a.h] = b;
    }
    for(i = y1; i <= y2; ++i){
        a.data[x1 + i*a.w + 0*a.w*a.h] = r;
        a.data[x2 + i*a.w + 0*a.w*a.h] = r;

a.data[x1 + i*a.w + 1*a.w*a.h] = g;
        a.data[x2 + i*a.w + 1*a.w*a.h] = g;

a.data[x1 + i*a.w + 2*a.w*a.h] = b;
        a.data[x2 + i*a.w + 2*a.w*a.h] = b;
    }
}

void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b)
{
    int i;
    for(i = 0; i < w; ++i){
        draw_box(a, x1+i, y1+i, x2-i, y2-i, r, g, b);
    }
}

void draw_bbox(image a, box bbox, int w, float r, float g, float b)
{
    int left  = (bbox.x-bbox.w/2)*a.w;
    int right = (bbox.x+bbox.w/2)*a.w;
    int top   = (bbox.y-bbox.h/2)*a.h;
    int bot   = (bbox.y+bbox.h/2)*a.h;

int i;
    for(i = 0; i < w; ++i){
        draw_box(a, left+i, top+i, right-i, bot-i, r, g, b);
    }
}

image **load_alphabet()
{
    int i, j;
    const int nsize = 8;
    image **alphabets = calloc(nsize, sizeof(image));
    for(j = 0; j < nsize; ++j){
        alphabets[j] = calloc(128, sizeof(image));
        for(i = 32; i < 127; ++i){
            char buff[256];
            sprintf(buff, "data/labels/%d_%d.png", i, j);
            alphabets[j][i] = load_image_color(buff, 0, 0);
        }
    }
    return alphabets;
}

// Creates array of detections with prob > thresh and fills best_class for them
detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num)
{
    int selected_num = 0;
    detection_with_class* result_arr = calloc(dets_num, sizeof(detection_with_class));
    int i;
    for (i = 0; i < dets_num; ++i) {		//提取到的特征目标循环判断
        int best_class = -1;
        float best_class_prob = thresh;
        int j;
        for (j = 0; j < dets[i].classes; ++j) {		//对检测到的目标的类别概率判断，赋予最大的概率类别
            if (dets[i].prob[j] > best_class_prob ) {
                best_class = j;
                best_class_prob = dets[i].prob[j];
            }
        }
        if (best_class >= 0) {			//如果最大的类别概率大于0，该detction结构体赋给result_arr返回
            result_arr[selected_num].det = dets[i];
            result_arr[selected_num].best_class = best_class;
            ++selected_num;
        }
    }
    if (selected_detections_num)
        *selected_detections_num = selected_num;
    return result_arr;
}

// compare to sort detection** by bbox.x
int compare_by_lefts(const void *a_ptr, const void *b_ptr) {
    const detection_with_class* a = (detection_with_class*)a_ptr;
    const detection_with_class* b = (detection_with_class*)b_ptr;
    const float delta = (a->det.bbox.x - a->det.bbox.w/2) - (b->det.bbox.x - b->det.bbox.w/2);
    return delta < 0 ? -1 : delta > 0 ? 1 : 0;
}

// compare to sort detection** by best_class probability
int compare_by_probs(const void *a_ptr, const void *b_ptr) {
    const detection_with_class* a = (detection_with_class*)a_ptr;
    const detection_with_class* b = (detection_with_class*)b_ptr;
    float delta = a->det.prob[a->best_class] - b->det.prob[b->best_class];
    return delta < 0 ? -1 : delta > 0 ? 1 : 0;
}

void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output)
{
    int selected_detections_num;
	//实例化一个结构体并得到卷积后的各个特征的类名和最大概率
    detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num); 
    // text output
    qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts);
    int i;
    for (i = 0; i < selected_detections_num; ++i) {  //对于检测到的目标的循环
         int best_class = selected_detections[i].best_class;		//上面返回的结构体中的best_class   原为const int
		/*************dspeia 20181026 修改label上的显示***************/
		if (best_class != 1 && best_class != 2 && best_class !=  3 && best_class != 0){ 
			printf("%s: %.0f%%   ******* image.c 292 *******", names[79], selected_detections[i].det.prob[best_class] * 100);  //增加这一句，cmd上显示other，调用的coco.name文件，如何图片上显示other？？
		}
		else {
			printf("%s: %.0f%%   ******* image.c 292 *******", names[best_class], selected_detections[i].det.prob[best_class] * 100);
		}
        if (ext_output)				//如果cmd的命令该位置为真，可打印出各个目标的box位置
            printf("t(left_x: %4.0f   top_y: %4.0f   width: %4.0f   height: %4.0f)n",
                (selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w,
                (selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h,
                selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h);
        else
            printf("n");
        //int j;
        //for (j = 0; j < classes; ++j) {			//一个object有多个预测类别时进入，输出大于给定阈值的同一个目标的不同类别信息。控制是否进入for的是classes
        //    if (selected_detections[i].det.prob[j] > thresh && j != best_class) {
        //        printf("%s: %.0f%%    ******image.c 303 ***** n", names[j], selected_detections[i].det.prob[j] * 100);
        //    }
        //}
    }
	/*******************dspeia 20181017***********************/

// image output
    qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_probs);
    for (i = 0; i < selected_detections_num; ++i) {
            int width = im.h * .006;
            if (width < 1)
                width = 1;

/*
            if(0){
            width = pow(prob, 1./2.)*10+1;
            alphabet = 0;
            }
            */

//printf("%d %s: %.0f%%n", i, names[selected_detections[i].best_class], prob*100);
            int offset = selected_detections[i].best_class * 123457 % classes;
            float red = get_color(2, offset, classes);
            float green = get_color(1, offset, classes);
            float blue = get_color(0, offset, classes);
            float rgb[3];

//width = prob*20+2;
			//rgb值为了定义label框的颜色
            rgb[0] = red;
            rgb[1] = green;
            rgb[2] = blue;
            box b = selected_detections[i].det.bbox;
            //printf("%f %f %f %fn", b.x, b.y, b.w, b.h);

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;

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;

/*******************************/
			int the_class = selected_detections[i].best_class;

//char cut_class[20] = { 0 };		//为了传类名到cut函数的方法一
			//strcpy(cut_class, names[the_class]);
			//char cut_class = names[the_class];
			float cut_pro = selected_detections[i].det.prob[the_class] * 100;
			printf("******355 **cut_class  :%s  ...........cut_class:%.0f n", names[the_class], cut_pro);
			//printf(cut_class);
			//printf("%f", cut_pro);	//

/******dspeia    ****/
			int pre_x = left;
			int pre_y = top;
			int pre_h = bot - top;
			int pre_w = right - left;
			save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im, names, cut_pro, the_class);
			printf("/************  cut and save over *****************/ n");
			/********************************/

//int b_x_center = (left + right) / 2;
            //int b_y_center = (top + bot) / 2;
            //int b_width = right - left;
            //int b_height = bot - top;
            //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height);

draw_box_width(im, left, top, right, bot, width, red, green, blue);
            if (alphabet) {
                char labelstr[4096] = { 0 };
				if (selected_detections[i].best_class != 0 && selected_detections[i].best_class != 1 && selected_detections[i].best_class != 2 && selected_detections[i].best_class != 3){
					strcat(labelstr, names[79]);  //加入这一句if，在label上写other
				}
				else
				{
					strcat(labelstr, names[selected_detections[i].best_class]);

}
                //int j;
                //for (j = 0; j < classes; ++j) {  //同一个object多个预测时，画多个框,注释了图片上仍然画第二框，只是cmd上不打印第二预测
                //    if (selected_detections[i].det.prob[j] > thresh && j != selected_detections[i].best_class) {
                //        strcat(labelstr, ", ");
                //        strcat(labelstr, names[j]);
                //    }
                //}
                image label = get_label_v3(alphabet, labelstr, (im.h*.03));  //画出框，复制到im上
                draw_label(im, top + width, left, label, rgb);
				//image* pic -> label;
				//const CvArr* label_copy = (CvArr*)&label;          //**********************
				//cvShowImage("*ima 394 label", label_copy);        //***********dspeia plus test
                free_image(label);				
            }
            if (selected_detections[i].det.mask) {
                image mask = float_to_image(14, 14, 1, selected_detections[i].det.mask);
                image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h);
                image tmask = threshold_image(resized_mask, .5);
                embed_image(tmask, im, left, top);
                free_image(mask);
                free_image(resized_mask);
                free_image(tmask);
            }
    }
    free(selected_detections);
}

/********************dspeia  20181017********************/
/***********     darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights    */

void save_cut_image(int px, int py, int ph, int pw, int no, image m_img, char **names, float cut_pro, int the_class)
{
	//cvShowImage("the enter", *m_img);
	image copy = copy_image(m_img);
	if (m_img.c == 3) rgbgr_image(copy);
	int x, y, k;
	char buff[256];
	/*****************************/
	//printf("%s: %.0f%%   ******* image.c 292 *******", cut_clas, selected_detections[i].det.prob[best_class] * 100);
	/**********************************/
	sprintf(buff, "results//%s%.0f%%%d.jpg", names[the_class], cut_pro, no);
	printf("****411** cut_class  :%s  ...........cut_class:%.0f ", names[the_class], cut_pro);//
	printf(names[the_class]);
	printf("%f",cut_pro);	//
	IplImage *disp = cvCreateImage(cvSize(m_img.w, m_img.h), IPL_DEPTH_8U, m_img.c);
	//cvShowImage("**the enter", disp);   //disp 为黑框乱码
	int step = disp->widthStep;
	for (y = 0; y < m_img.h; ++y) {
		for (x = 0; x < m_img.w; ++x) {
			for (k = 0; k < m_img.c; ++k) {
				disp->imageData[y*step + x*m_img.c + k] = (unsigned char)(get_pixel(copy, x, y, k) * 255);
			}
		}
	}
	CvMat *pMat = cvCreateMatHeader(m_img.w, m_img.h, IPL_DEPTH_8U);
 
	//char rect_name[256];
 
	//sprintf(rect_name, "%d_rect", no);
 
	CvRect rect = cvRect(px, py, pw, ph);
 
	cvGetSubRect(disp, pMat, rect);
 
	IplImage *pSubImg = cvCreateImage(cvSize(pw, ph), IPL_DEPTH_8U, m_img.c);
 
	cvGetImage(pMat, pSubImg);
 
	//printf("x=%d,y=%d,h=%d,w=%dn", px, py, ph, pw);
 
	cvSaveImage(buff, pSubImg, 0);
	//cvShowImage("average loss", pSubImg);   //pSubImg为分割的子目标

//cvReleaseImage(&disp);
	//cvReleaseImage(&pMat);
	//cvReleaseImage(&rect);
 
	//memset(&rect, 0, sizeof(rect));
	//cvReleaseImage(&pSubImg);
	//free(&rect);
	
	
	free_image(copy);
}
/**********************20181017******************************/

void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes)
{
	
    int i;

for(i = 0; i < num; ++i){
        int class_id = max_index(probs[i], classes);
        float prob = probs[i][class_id];
        if(prob > thresh){

for comparison with OpenCV version of DNN Darknet Yolo v2
            //printf("n %f, %f, %f, %f, ", boxes[i].x, boxes[i].y, boxes[i].w, boxes[i].h);
            // int k;
            //for (k = 0; k < classes; ++k) {
            //    printf("%f, ", probs[i][k]);
            //}
            //printf("n");

int width = im.h * .012;

if(0){
                width = pow(prob, 1./2.)*10+1;
                alphabet = 0;
            }

int offset = class_id*123457 % classes;
            float red = get_color(2,offset,classes);
            float green = get_color(1,offset,classes);
            float blue = get_color(0,offset,classes);
            float rgb[3];

//width = prob*20+2;

rgb[0] = red;
            rgb[1] = green;
            rgb[2] = blue;
            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;
			/*******************dspeia 20181017***********************/
			int pre_x = left;
			int pre_y = top;
			int pre_h = bot - top;
			int pre_w = right - left;
			/*******************dspeia 20181017***********************/

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;
            printf("%s: %.0f%% test*******************", names[class_id], prob * 100);
			/**********************/
			printf("/**********image.c 505***  test和demo 出不来，不走这一步？****************/");
			//save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im);
			
			/*******************/
            //printf(" - id: %d, x_center: %d, y_center: %d, width: %d, height: %d",
            //    class_id, (right + left) / 2, (bot - top) / 2, right - left, bot - top);

printf("n");
            draw_box_width(im, left, top, right, bot, width, red, green, blue);
            if (alphabet) {
                image label = get_label(alphabet, names[class_id], (im.h*.03)/10);
                draw_label(im, top + width, left, label, rgb);
            }
			/*****************dspeia 20181017************************/
			/*save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im);
			printf("/");*/
			/*****************dspeia 20181017************************/
			/***********     darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights    */

}
    }
}

#ifdef OPENCV
				**************1019最新版本，改动此函数,demo 视频用
void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output)
{
    int i, j;
    if (!show_img) return;
    static int frame_id = 0;
    frame_id++;
	/**********dspeia 20181019***** 新加视频裁剪小目标 *******/
	/*int selected_detections_num;
	detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num);*/
	/***********************/
    for (i = 0; i < num; ++i) {
        char labelstr[4096] = { 0 };
        int class_id = -1;
        for (j = 0; j < classes; ++j) {
            if (dets[i].prob[j] > thresh) {
                if (class_id < 0) {
                    strcat(labelstr, names[j]);
                    class_id = j;
                }
                else {
                    strcat(labelstr, ", ");
                    strcat(labelstr, names[j]);
                }
                printf(" **** image.c 562 ***** %s: %.0f%% ", names[j], dets[i].prob[j] * 100);

///***************************新加，视频裁剪小目标*************/
				IplImage* cut = show_img;
				//box b = selected_detections[i].det.bbox;
				//int left = (b.x - b.w / 2.)* show_img->width;
				//int right = (b.x + b.w / 2.)*show_img->width;
				//int top = (b.y - b.h / 2.)*show_img->height;
				//int bot = (b.y + b.h / 2.)*show_img->height;

//if (left < 0) left = 0;
				//if (right > show_img->width - 1) right = show_img->width - 1;
				//if (top < 0) top = 0;
				//if (bot > show_img->height - 1) bot = show_img->height - 1;
				//int pre_x = left;
				//int pre_y = top;
				//int pre_h = bot - top;
				//int pre_w = right - left;
				image m = show_img;
				network net = parse_network_cfg_custom(cfgfile, 1);
				//image im = load_image(show_img, 0, 0, 3);
				//int the_class = names[j];

char cut_class[20] = { 0 };		//为了传类名到cut函数的方法一
				strcpy(cut_class, names[the_class]);
				char cut_class = names[the_class];
				//float cut_pro = dets[i].prob[j] * 100;
				//save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im, names, cut_pro, the_class);
				//printf("/************  cut and save over *****************/");
				///********************************/
            }
        }
        if (class_id >= 0) {
            int width = show_img->height * .006;

//if(0){
            //width = pow(prob, 1./2.)*10+1;
            //alphabet = 0;
            //}

//printf("%d %s: %.0f%%n", i, names[class_id], prob*100);
            int offset = class_id * 123457 % classes;
            float red = get_color(2, offset, classes);
            float green = get_color(1, offset, classes);
            float blue = get_color(0, offset, classes);
            float rgb[3];

//width = prob*20+2;

rgb[0] = red;
            rgb[1] = green;
            rgb[2] = blue;
            box b = dets[i].bbox;
            b.w = (b.w < 1) ? b.w : 1;
            b.h = (b.h < 1) ? b.h : 1;
            b.x = (b.x < 1) ? b.x : 1;
            b.y = (b.y < 1) ? b.y : 1;
            //printf("%f %f %f %fn", b.x, b.y, b.w, b.h);

int left = (b.x - b.w / 2.)*show_img->width;
            int right = (b.x + b.w / 2.)*show_img->width;
            int top = (b.y - b.h / 2.)*show_img->height;
            int bot = (b.y + b.h / 2.)*show_img->height;

if (left < 0) left = 0;
            if (right > show_img->width - 1) right = show_img->width - 1;
            if (top < 0) top = 0;
            if (bot > show_img->height - 1) bot = show_img->height - 1;

float const font_size = show_img->height / 1000.F;
            CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2;
            pt1.x = left;
            pt1.y = top;
            pt2.x = right;
            pt2.y = bot;
            pt_text.x = left;
            pt_text.y = top - 12;
            pt_text_bg1.x = left;
            pt_text_bg1.y = top - (10 + 25 * font_size);
            pt_text_bg2.x = right;
            pt_text_bg2.y = top;
            CvScalar color;
            color.val[0] = red * 256;
            color.val[1] = green * 256;
            color.val[2] = blue * 256;

// you should create directory: result_img
            //static int copied_frame_id = -1;
            //static IplImage* copy_img = NULL;
            //if (copied_frame_id != frame_id) {
            //    copied_frame_id = frame_id;
            //    if(copy_img == NULL) copy_img = cvCreateImage(cvSize(show_img->width, show_img->height), show_img->depth, show_img->nChannels);
            //    cvCopy(show_img, copy_img, 0);
            //}
            //static int img_id = 0;
            //img_id++;
            //char image_name[1024];
            //sprintf(image_name, "result_img/img_%d_%d_%d.jpg", frame_id, img_id, class_id);
            //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y);
            //cvSetImageROI(copy_img, rect);
            //cvSaveImage(image_name, copy_img, 0);
            //cvResetImageROI(copy_img);

cvRectangle(show_img, pt1, pt2, color, width, 8, 0);
            if (ext_output)
                printf("t(****image.c 644**left_x: %4.0f   top_y: %4.0f   width: %4.0f   height: %4.0f)n",
                    (float)left, (float)top, b.w*show_img->width, b.h*show_img->height);
            else
                printf("n");

cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0);
            cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0);    // filled
            CvScalar black_color;
            black_color.val[0] = 0;
            CvFont font;
            cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8);
            cvPutText(show_img, labelstr, pt_text, &font, black_color);
        }
    }
    if (ext_output) {
        fflush(stdout);
    }
}

void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes)
{
    int i;

for (i = 0; i < num; ++i) {
        int class_id = max_index(probs[i], classes);
        float prob = probs[i][class_id];
        if (prob > thresh) {

int width = show_img->height * .012;

if (0) {
                width = pow(prob, 1. / 2.) * 10 + 1;
                alphabet = 0;
            }

printf("%s: %.0f%%n", names[class_id], prob * 100);
            int offset = class_id * 123457 % classes;
            float red = get_color(2, offset, classes);
            float green = get_color(1, offset, classes);
            float blue = get_color(0, offset, classes);
            float rgb[3];

//width = prob*20+2;

rgb[0] = red;
            rgb[1] = green;
            rgb[2] = blue;
            box b = boxes[i];

float const font_size = show_img->height / 1000.F;
            CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2;
            pt1.x = left;
            pt1.y = top;
            pt2.x = right;
            pt2.y = bot;
            pt_text.x = left;
            pt_text.y = top - 12;
            pt_text_bg1.x = left;
            pt_text_bg1.y = top - (10+25*font_size);
            pt_text_bg2.x = right;
            pt_text_bg2.y = top;
            CvScalar color;
            color.val[0] = red * 256;
            color.val[1] = green * 256;
            color.val[2] = blue * 256;

cvRectangle(show_img, pt1, pt2, color, width, 8, 0);
            //printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s n", left, right, top, bot, class_id, names[class_id]);
            cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0);
            cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0);    // filled
            CvScalar black_color;
            black_color.val[0] = 0;
            CvFont font;
            cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8);
            cvPutText(show_img, names[class_id], pt_text, &font, black_color);
        }
    }
}

IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size)
{
    int img_offset = 50;
    int draw_size = img_size - img_offset;
    IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3);
    cvSet(img, CV_RGB(255, 255, 255), 0);
    CvPoint pt1, pt2, pt_text;
    CvFont font;
    cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA);
    char char_buff[100];
    int i;
    // vertical lines
    pt1.x = img_offset; pt2.x = img_size, pt_text.x = 10;
    for (i = 1; i <= number_of_lines; ++i) {
        pt1.y = pt2.y = (float)i * draw_size / number_of_lines;
        cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0);
        if (i % 10 == 0) {
            sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines);
            pt_text.y = pt1.y + 5;
            cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0));
            cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0);
        }
    }
    // horizontal lines
    pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15;
    for (i = 0; i <= number_of_lines; ++i) {
        pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines;
        cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0);
        if (i % 10 == 0) {
            sprintf(char_buff, "%d", max_batches * i / number_of_lines);
            pt_text.x = pt1.x - 20;
            cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0));
            cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0);
        }
    }
    cvPutText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), &font, CV_RGB(0, 0, 0));
    cvPutText(img, "Press 's' to save: chart.jpg", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0));
    printf(" If error occurs - run training with flag: -dont_show n");
    cvNamedWindow("average loss", CV_WINDOW_NORMAL);
    cvMoveWindow("average loss", 0, 0);
    cvResizeWindow("average loss", img_size, img_size);
    cvShowImage("average loss", img);
    cvWaitKey(20);
    return img;
}

void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches)
{
    int img_offset = 50;
    int draw_size = img_size - img_offset;
    CvFont font;
    cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA);
    char char_buff[100];
    CvPoint pt1, pt2;
    pt1.x = img_offset + draw_size * (float)current_batch / max_batches;
    pt1.y = draw_size * (1 - avg_loss / max_img_loss);
    if (pt1.y < 0) pt1.y = 1;
    cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0);

sprintf(char_buff, "current avg loss = %2.4f", avg_loss);
    pt1.x = img_size / 2, pt1.y = 30;
    pt2.x = pt1.x + 250, pt2.y = pt1.y + 20;
    cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0);
    pt1.y += 15;
    cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0));
    cvShowImage("average loss", img);
    int k = cvWaitKey(20);
    if (k == 's' || current_batch == (max_batches-1)) cvSaveImage("chart.jpg", img, 0);
}
#endif    // OPENCV

void transpose_image(image im)
{
    assert(im.w == im.h);
    int n, m;
    int c;
    for(c = 0; c < im.c; ++c){
        for(n = 0; n < im.w-1; ++n){
            for(m = n + 1; m < im.w; ++m){
                float swap = im.data[m + im.w*(n + im.h*c)];
                im.data[m + im.w*(n + im.h*c)] = im.data[n + im.w*(m + im.h*c)];
                im.data[n + im.w*(m + im.h*c)] = swap;
            }
        }
    }
}

void rotate_image_cw(image im, int times)
{
    assert(im.w == im.h);
    times = (times + 400) % 4;
    int i, x, y, c;
    int n = im.w;
    for(i = 0; i < times; ++i){
        for(c = 0; c < im.c; ++c){
            for(x = 0; x < n/2; ++x){
                for(y = 0; y < (n-1)/2 + 1; ++y){
                    float temp = im.data[y + im.w*(x + im.h*c)];
                    im.data[y + im.w*(x + im.h*c)] = im.data[n-1-x + im.w*(y + im.h*c)];
                    im.data[n-1-x + im.w*(y + im.h*c)] = im.data[n-1-y + im.w*(n-1-x + im.h*c)];
                    im.data[n-1-y + im.w*(n-1-x + im.h*c)] = im.data[x + im.w*(n-1-y + im.h*c)];
                    im.data[x + im.w*(n-1-y + im.h*c)] = temp;
                }
            }
        }
    }
}

void flip_image(image a)
{
    int i,j,k;
    for(k = 0; k < a.c; ++k){
        for(i = 0; i < a.h; ++i){
            for(j = 0; j < a.w/2; ++j){
                int index = j + a.w*(i + a.h*(k));
                int flip = (a.w - j - 1) + a.w*(i + a.h*(k));
                float swap = a.data[flip];
                a.data[flip] = a.data[index];
                a.data[index] = swap;
            }
        }
    }
}

image image_distance(image a, image b)
{
    int i,j;
    image dist = make_image(a.w, a.h, 1);
    for(i = 0; i < a.c; ++i){
        for(j = 0; j < a.h*a.w; ++j){
            dist.data[j] += pow(a.data[i*a.h*a.w+j]-b.data[i*a.h*a.w+j],2);
        }
    }
    for(j = 0; j < a.h*a.w; ++j){
        dist.data[j] = sqrt(dist.data[j]);
    }
    return dist;
}

void embed_image(image source, image dest, int dx, int dy)
{
    int x,y,k;
    for(k = 0; k < source.c; ++k){
        for(y = 0; y < source.h; ++y){
            for(x = 0; x < source.w; ++x){
                float val = get_pixel(source, x,y,k);
                set_pixel(dest, dx+x, dy+y, k, val);
            }
        }
    }
}

image collapse_image_layers(image source, int border)
{
    int h = source.h;
    h = (h+border)*source.c - border;
    image dest = make_image(source.w, h, 1);
    int i;
    for(i = 0; i < source.c; ++i){
        image layer = get_image_layer(source, i);
        int h_offset = i*(source.h+border);
        embed_image(layer, dest, 0, h_offset);
        free_image(layer);
    }
    return dest;
}

void constrain_image(image im)
{
    int i;
    for(i = 0; i < im.w*im.h*im.c; ++i){
        if(im.data[i] < 0) im.data[i] = 0;
        if(im.data[i] > 1) im.data[i] = 1;
    }
}

void normalize_image(image p)
{
    int i;
    float min = 9999999;
    float max = -999999;

for(i = 0; i < p.h*p.w*p.c; ++i){
        float v = p.data[i];
        if(v < min) min = v;
        if(v > max) max = v;
    }
    if(max - min < .000000001){
        min = 0;
        max = 1;
    }
    for(i = 0; i < p.c*p.w*p.h; ++i){
        p.data[i] = (p.data[i] - min)/(max-min);
    }
}

void normalize_image2(image p)
{
    float *min = calloc(p.c, sizeof(float));
    float *max = calloc(p.c, sizeof(float));
    int i,j;
    for(i = 0; i < p.c; ++i) min[i] = max[i] = p.data[i*p.h*p.w];

for(j = 0; j < p.c; ++j){
        for(i = 0; i < p.h*p.w; ++i){
            float v = p.data[i+j*p.h*p.w];
            if(v < min[j]) min[j] = v;
            if(v > max[j]) max[j] = v;
        }
    }
    for(i = 0; i < p.c; ++i){
        if(max[i] - min[i] < .000000001){
            min[i] = 0;
            max[i] = 1;
        }
    }
    for(j = 0; j < p.c; ++j){
        for(i = 0; i < p.w*p.h; ++i){
            p.data[i+j*p.h*p.w] = (p.data[i+j*p.h*p.w] - min[j])/(max[j]-min[j]);
        }
    }
    free(min);
    free(max);
}

image copy_image(image p)
{
    image copy = p;
    copy.data = calloc(p.h*p.w*p.c, sizeof(float));
    memcpy(copy.data, p.data, p.h*p.w*p.c*sizeof(float));
    return copy;
}

void rgbgr_image(image im)
{
    int i;
    for(i = 0; i < im.w*im.h; ++i){
        float swap = im.data[i];
        im.data[i] = im.data[i+im.w*im.h*2];
        im.data[i+im.w*im.h*2] = swap;
    }
}

#ifdef OPENCV
void show_image_cv(image p, const char *name)
{
    int x,y,k;
    image copy = copy_image(p);
    constrain_image(copy);
    if(p.c == 3) rgbgr_image(copy);
    //normalize_image(copy);

char buff[256];
    //sprintf(buff, "%s (%d)", name, windows);
    sprintf(buff, "%s", name);

IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c);
    int step = disp->widthStep;
    cvNamedWindow(buff, CV_WINDOW_NORMAL);
    //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10));
    ++windows;
    for(y = 0; y < p.h; ++y){
        for(x = 0; x < p.w; ++x){
            for(k= 0; k < p.c; ++k){
                disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255);
            }
        }
    }
    free_image(copy);
    if(0){
        int w = 448;
        int h = w*p.h/p.w;
        if(h > 1000){
            h = 1000;
            w = h*p.w/p.h;
        }
        IplImage *buffer = disp;
        disp = cvCreateImage(cvSize(w, h), buffer->depth, buffer->nChannels);
        cvResize(buffer, disp, CV_INTER_LINEAR);
        cvReleaseImage(&buffer);
    }
    cvShowImage(buff, disp);

cvReleaseImage(&disp);
}

void show_image_cv_ipl(IplImage *disp, const char *name)		//demo.c 传过来的，*disp为检测后的图片 *name为窗口名称字符串
{
    if (disp == NULL) return;
    char buff[256];
    //sprintf(buff, "%s (%d)", name, windows);
    sprintf(buff, "%s", name);							//name写入buff 显示合成视频在demo窗口上
    cvNamedWindow(buff, CV_WINDOW_NORMAL);
    //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10));
    ++windows;
    cvShowImage(buff, disp);									//框合成到demo，展示
    //cvReleaseImage(&disp);
}
#endif

void show_image(image p, const char *name)
{
#ifdef OPENCV
    show_image_cv(p, name);
#else
    fprintf(stderr, "Not compiled with OpenCV, saving to %s.png insteadn", name);
    save_image(p, name);
#endif
}

#ifdef OPENCV

image ipl_to_image(IplImage* src)
{
    unsigned char *data = (unsigned char *)src->imageData;
    int h = src->height;
    int w = src->width;
    int c = src->nChannels;
    int step = src->widthStep;
    image out = make_image(w, h, c);
    int i, j, k, count=0;;

for(k= 0; k < c; ++k){
        for(i = 0; i < h; ++i){
            for(j = 0; j < w; ++j){
                out.data[count++] = data[i*step + j*c + k]/255.;
            }
        }
    }
    return out;
}

image load_image_cv(char *filename, int channels)
{
    IplImage* src = 0;
    int flag = -1;
    if (channels == 0) flag = 1;
    else if (channels == 1) flag = 0;
    else if (channels == 3) flag = 1;
    else {
        fprintf(stderr, "OpenCV can't force load with %d channelsn", channels);
    }

if( (src = cvLoadImage(filename, flag)) == 0 )
    {
        char shrinked_filename[1024];
        if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long");
        else sprintf(shrinked_filename, "%s", filename);
        fprintf(stderr, "*image.c 1132*Cannot load image "%s"n", shrinked_filename);
        FILE* fw = fopen("bad.list", "a");
        fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw);
        char *new_line = "n";
        fwrite(new_line, sizeof(char), strlen(new_line), fw);
        fclose(fw);
        if (check_mistakes) getchar();
        return make_image(10,10,3);
        //exit(EXIT_FAILURE);
    }
    image out = ipl_to_image(src);
    cvReleaseImage(&src);
    if (out.c > 1)
        rgbgr_image(out);
    return out;
}

image get_image_from_stream(CvCapture *cap)
{
    IplImage* src = cvQueryFrame(cap);
    if (!src) return make_empty_image(0,0,0);
    image im = ipl_to_image(src);
    rgbgr_image(im);
    return im;
}

image get_image_from_stream_cpp(CvCapture *cap)
{
    //IplImage* src = cvQueryFrame(cap);
    IplImage* src;
    static int once = 1;
    if (once) {
        once = 0;
        do {
            src = get_webcam_frame(cap);
            if (!src) return make_empty_image(0, 0, 0);
        } while (src->width < 1 || src->height < 1 || src->nChannels < 1);
        printf("Video stream: %d x %d n", src->width, src->height);
    }
    else
        src = get_webcam_frame(cap);

if (!src) return make_empty_image(0, 0, 0);
    image im = ipl_to_image(src);
    rgbgr_image(im);
    return im;
}

int wait_for_stream(CvCapture *cap, IplImage* src, int dont_close) {
    if (!src) {
        if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3);
        else return 0;
    }
    if (src->width < 1 || src->height < 1 || src->nChannels < 1) {
        if (dont_close) {
            cvReleaseImage(&src);
            int z = 0;
            for (z = 0; z < 20; ++z) {
                get_webcam_frame(cap);
                cvReleaseImage(&src);
            }
            src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3);
        }
        else return 0;
    }
    return 1;
}

image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close)
{
    c = c ? c : 3;
    IplImage* src;
    if (cpp_video_capture) {
        static int once = 1;
        if (once) {
            once = 0;
            do {
                src = get_webcam_frame(cap);
                if (!src) return make_empty_image(0, 0, 0);
            } while (src->width < 1 || src->height < 1 || src->nChannels < 1);
            printf("Video stream: %d x %d n", src->width, src->height);
        } else
            src = get_webcam_frame(cap);
    }
    else src = cvQueryFrame(cap);

if (cpp_video_capture)
        if(!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0);
    IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c);
    *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c);
    cvResize(src, *in_img, CV_INTER_LINEAR);
    cvResize(src, new_img, CV_INTER_LINEAR);
    image im = ipl_to_image(new_img);
    cvReleaseImage(&new_img);
    if (cpp_video_capture) cvReleaseImage(&src);
    if (c>1)
        rgbgr_image(im);
    return im;
}

image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close)
{
    c = c ? c : 3;
    IplImage* src;
    if (cpp_video_capture) {
        static int once = 1;
        if (once) {
            once = 0;
            do {
                src = get_webcam_frame(cap);
                if (!src) return make_empty_image(0, 0, 0);
            } while (src->width < 1 || src->height < 1 || src->nChannels < 1);
            printf("Video stream: %d x %d n", src->width, src->height);
        }
        else
            src = get_webcam_frame(cap);
    }
    else src = cvQueryFrame(cap);

if (cpp_video_capture)
        if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0);
    *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c);
    cvResize(src, *in_img, CV_INTER_LINEAR);
    image tmp = ipl_to_image(src);
    image im = letterbox_image(tmp, w, h);
    free_image(tmp);
    if (cpp_video_capture) cvReleaseImage(&src);
    if (c>1) rgbgr_image(im);
    return im;
}

int get_stream_fps(CvCapture *cap, int cpp_video_capture)
{
    int fps = 25;
    if (cpp_video_capture) {
        fps = get_stream_fps_cpp(cap);
    }
    else {
        fps = cvGetCaptureProperty(cap, CV_CAP_PROP_FPS);
    }
    return fps;
}

void save_image_jpg(image p, const char *name)
{
    image copy = copy_image(p);
    if(p.c == 3) rgbgr_image(copy);
    int x,y,k;

char buff[256];
    sprintf(buff, "%s.jpg", name);

IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c);
    int step = disp->widthStep;
    for(y = 0; y < p.h; ++y){
        for(x = 0; x < p.w; ++x){
            for(k= 0; k < p.c; ++k){
                disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255);
            }
        }
    }
    cvSaveImage(buff, disp,0);
    cvReleaseImage(&disp);
    free_image(copy);
}
#endif

void save_image_png(image im, const char *name)
{
    char buff[256];
    //sprintf(buff, "%s (%d)", name, windows);
    sprintf(buff, "%s.png", name);
    unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char));
    int i,k;
    for(k = 0; k < im.c; ++k){
        for(i = 0; i < im.w*im.h; ++i){
            data[i*im.c+k] = (unsigned char) (255*im.data[i + k*im.w*im.h]);
        }
    }
    int success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c);
    free(data);
    if(!success) fprintf(stderr, "Failed to write image %sn", buff);
}

void save_image(image im, const char *name)
{
#ifdef OPENCV
    save_image_jpg(im, name);
#else
    save_image_png(im, name);
#endif
}

void show_image_layers(image p, char *name)
{
    int i;
    char buff[256];
    for(i = 0; i < p.c; ++i){
        sprintf(buff, "%s - Layer %d", name, i);
        image layer = get_image_layer(p, i);
        show_image(layer, buff);
        free_image(layer);
    }
}

void show_image_collapsed(image p, char *name)
{
    image c = collapse_image_layers(p, 1);
    show_image(c, name);
    free_image(c);
}

image make_empty_image(int w, int h, int c)
{
    image out;
    out.data = 0;
    out.h = h;
    out.w = w;
    out.c = c;
    return out;
}

image make_image(int w, int h, int c)
{
    image out = make_empty_image(w,h,c);
    out.data = calloc(h*w*c, sizeof(float));
    return out;
}

image make_random_image(int w, int h, int c)
{
    image out = make_empty_image(w,h,c);
    out.data = calloc(h*w*c, sizeof(float));
    int i;
    for(i = 0; i < w*h*c; ++i){
        out.data[i] = (rand_normal() * .25) + .5;
    }
    return out;
}

image float_to_image(int w, int h, int c, float *data)
{
    image out = make_empty_image(w,h,c);
    out.data = data;
    return out;
}

image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect)
{
    int x, y, c;
    float cx = im.w/2.;
    float cy = im.h/2.;
    image rot = make_image(w, h, im.c);
    for(c = 0; c < im.c; ++c){
        for(y = 0; y < h; ++y){
            for(x = 0; x < w; ++x){
                float rx = cos(rad)*((x - w/2.)/s*aspect + dx/s*aspect) - sin(rad)*((y - h/2.)/s + dy/s) + cx;
                float ry = sin(rad)*((x - w/2.)/s*aspect + dx/s*aspect) + cos(rad)*((y - h/2.)/s + dy/s) + cy;
                float val = bilinear_interpolate(im, rx, ry, c);
                set_pixel(rot, x, y, c, val);
            }
        }
    }
    return rot;
}

image rotate_image(image im, float rad)
{
    int x, y, c;
    float cx = im.w/2.;
    float cy = im.h/2.;
    image rot = make_image(im.w, im.h, im.c);
    for(c = 0; c < im.c; ++c){
        for(y = 0; y < im.h; ++y){
            for(x = 0; x < im.w; ++x){
                float rx = cos(rad)*(x-cx) - sin(rad)*(y-cy) + cx;
                float ry = sin(rad)*(x-cx) + cos(rad)*(y-cy) + cy;
                float val = bilinear_interpolate(im, rx, ry, c);
                set_pixel(rot, x, y, c, val);
            }
        }
    }
    return rot;
}

void translate_image(image m, float s)
{
    int i;
    for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] += s;
}

void scale_image(image m, float s)
{
    int i;
    for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] *= s;
}

image crop_image(image im, int dx, int dy, int w, int h)
{
    image cropped = make_image(w, h, im.c);
    int i, j, k;
    for(k = 0; k < im.c; ++k){
        for(j = 0; j < h; ++j){
            for(i = 0; i < w; ++i){
                int r = j + dy;
                int c = i + dx;
                float val = 0;
                r = constrain_int(r, 0, im.h-1);
                c = constrain_int(c, 0, im.w-1);
                if (r >= 0 && r < im.h && c >= 0 && c < im.w) {
                    val = get_pixel(im, c, r, k);
                }
                set_pixel(cropped, i, j, k, val);
            }
        }
    }
    return cropped;
}

int best_3d_shift_r(image a, image b, int min, int max)
{
    if(min == max) return min;
    int mid = floor((min + max) / 2.);
    image c1 = crop_image(b, 0, mid, b.w, b.h);
    image c2 = crop_image(b, 0, mid+1, b.w, b.h);
    float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 10);
    float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 10);
    free_image(c1);
    free_image(c2);
    if(d1 < d2) return best_3d_shift_r(a, b, min, mid);
    else return best_3d_shift_r(a, b, mid+1, max);
}

int best_3d_shift(image a, image b, int min, int max)
{
    int i;
    int best = 0;
    float best_distance = FLT_MAX;
    for(i = min; i <= max; i += 2){
        image c = crop_image(b, 0, i, b.w, b.h);
        float d = dist_array(c.data, a.data, a.w*a.h*a.c, 100);
        if(d < best_distance){
            best_distance = d;
            best = i;
        }
        printf("%d %fn", i, d);
        free_image(c);
    }
    return best;
}

void composite_3d(char *f1, char *f2, char *out, int delta)
{
    if(!out) out = "out";
    image a = load_image(f1, 0,0,0);
    image b = load_image(f2, 0,0,0);
    int shift = best_3d_shift_r(a, b, -a.h/100, a.h/100);

image c1 = crop_image(b, 10, shift, b.w, b.h);
    float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 100);
    image c2 = crop_image(b, -10, shift, b.w, b.h);
    float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 100);

if(d2 < d1 && 0){
        image swap = a;
        a = b;
        b = swap;
        shift = -shift;
        printf("swapped, %dn", shift);
    }
    else{
        printf("%dn", shift);
    }

image c = crop_image(b, delta, shift, a.w, a.h);
    int i;
    for(i = 0; i < c.w*c.h; ++i){
        c.data[i] = a.data[i];
    }
#ifdef OPENCV
    save_image_jpg(c, out);
#else
    save_image(c, out);
#endif
}

void fill_image(image m, float s)
{
    int i;
    for (i = 0; i < m.h*m.w*m.c; ++i) m.data[i] = s;
}

void letterbox_image_into(image im, int w, int h, image boxed)
{
    int new_w = im.w;
    int new_h = im.h;
    if (((float)w / im.w) < ((float)h / im.h)) {
        new_w = w;
        new_h = (im.h * w) / im.w;
    }
    else {
        new_h = h;
        new_w = (im.w * h) / im.h;
    }
    image resized = resize_image(im, new_w, new_h);
    embed_image(resized, boxed, (w - new_w) / 2, (h - new_h) / 2);
    free_image(resized);
}

image letterbox_image(image im, int w, int h)
{
    int new_w = im.w;
    int new_h = im.h;
    if (((float)w / im.w) < ((float)h / im.h)) {
        new_w = w;
        new_h = (im.h * w) / im.w;
    }
    else {
        new_h = h;
        new_w = (im.w * h) / im.h;
    }
    image resized = resize_image(im, new_w, new_h);
    image boxed = make_image(w, h, im.c);
    fill_image(boxed, .5);
    //int i;
    //for(i = 0; i < boxed.w*boxed.h*boxed.c; ++i) boxed.data[i] = 0;
    embed_image(resized, boxed, (w - new_w) / 2, (h - new_h) / 2);
    free_image(resized);
    return boxed;
}

image resize_max(image im, int max)
{
    int w = im.w;
    int h = im.h;
    if(w > h){
        h = (h * max) / w;
        w = max;
    } else {
        w = (w * max) / h;
        h = max;
    }
    if(w == im.w && h == im.h) return im;
    image resized = resize_image(im, w, h);
    return resized;
}

image resize_min(image im, int min)
{
    int w = im.w;
    int h = im.h;
    if(w < h){
        h = (h * min) / w;
        w = min;
    } else {
        w = (w * min) / h;
        h = min;
    }
    if(w == im.w && h == im.h) return im;
    image resized = resize_image(im, w, h);
    return resized;
}

image random_crop_image(image im, int w, int h)
{
    int dx = rand_int(0, im.w - w);
    int dy = rand_int(0, im.h - h);
    image crop = crop_image(im, dx, dy, w, h);
    return crop;
}

image random_augment_image(image im, float angle, float aspect, int low, int high, int size)
{
    aspect = rand_scale(aspect);
    int r = rand_int(low, high);
    int min = (im.h < im.w*aspect) ? im.h : im.w*aspect;
    float scale = (float)r / min;

float rad = rand_uniform(-angle, angle) * TWO_PI / 360.;

float dx = (im.w*scale/aspect - size) / 2.;
    float dy = (im.h*scale - size) / 2.;
    if(dx < 0) dx = 0;
    if(dy < 0) dy = 0;
    dx = rand_uniform(-dx, dx);
    dy = rand_uniform(-dy, dy);

image crop = rotate_crop_image(im, rad, scale, size, size, dx, dy, aspect);

return crop;
}

float three_way_max(float a, float b, float c)
{
    return (a > b) ? ( (a > c) ? a : c) : ( (b > c) ? b : c) ;
}

float three_way_min(float a, float b, float c)
{
    return (a < b) ? ( (a < c) ? a : c) : ( (b < c) ? b : c) ;
}

// http://www.cs.rit.edu/~ncs/color/t_convert.html
void rgb_to_hsv(image im)
{
    assert(im.c == 3);
    int i, j;
    float r, g, b;
    float h, s, v;
    for(j = 0; j < im.h; ++j){
        for(i = 0; i < im.w; ++i){
            r = get_pixel(im, i , j, 0);
            g = get_pixel(im, i , j, 1);
            b = get_pixel(im, i , j, 2);
            float max = three_way_max(r,g,b);
            float min = three_way_min(r,g,b);
            float delta = max - min;
            v = max;
            if(max == 0){
                s = 0;
                h = 0;
            }else{
                s = delta/max;
                if(r == max){
                    h = (g - b) / delta;
                } else if (g == max) {
                    h = 2 + (b - r) / delta;
                } else {
                    h = 4 + (r - g) / delta;
                }
                if (h < 0) h += 6;
                h = h/6.;
            }
            set_pixel(im, i, j, 0, h);
            set_pixel(im, i, j, 1, s);
            set_pixel(im, i, j, 2, v);
        }
    }
}

void hsv_to_rgb(image im)
{
    assert(im.c == 3);
    int i, j;
    float r, g, b;
    float h, s, v;
    float f, p, q, t;
    for(j = 0; j < im.h; ++j){
        for(i = 0; i < im.w; ++i){
            h = 6 * get_pixel(im, i , j, 0);
            s = get_pixel(im, i , j, 1);
            v = get_pixel(im, i , j, 2);
            if (s == 0) {
                r = g = b = v;
            } else {
                int index = floor(h);
                f = h - index;
                p = v*(1-s);
                q = v*(1-s*f);
                t = v*(1-s*(1-f));
                if(index == 0){
                    r = v; g = t; b = p;
                } else if(index == 1){
                    r = q; g = v; b = p;
                } else if(index == 2){
                    r = p; g = v; b = t;
                } else if(index == 3){
                    r = p; g = q; b = v;
                } else if(index == 4){
                    r = t; g = p; b = v;
                } else {
                    r = v; g = p; b = q;
                }
            }
            set_pixel(im, i, j, 0, r);
            set_pixel(im, i, j, 1, g);
            set_pixel(im, i, j, 2, b);
        }
    }
}

image grayscale_image(image im)
{
    assert(im.c == 3);
    int i, j, k;
    image gray = make_image(im.w, im.h, 1);
    float scale[] = {0.587, 0.299, 0.114};
    for(k = 0; k < im.c; ++k){
        for(j = 0; j < im.h; ++j){
            for(i = 0; i < im.w; ++i){
                gray.data[i+im.w*j] += scale[k]*get_pixel(im, i, j, k);
            }
        }
    }
    return gray;
}

image threshold_image(image im, float thresh)
{
    int i;
    image t = make_image(im.w, im.h, im.c);
    for(i = 0; i < im.w*im.h*im.c; ++i){
        t.data[i] = im.data[i]>thresh ? 1 : 0;
    }
    return t;
}

image blend_image(image fore, image back, float alpha)
{
    assert(fore.w == back.w && fore.h == back.h && fore.c == back.c);
    image blend = make_image(fore.w, fore.h, fore.c);
    int i, j, k;
    for(k = 0; k < fore.c; ++k){
        for(j = 0; j < fore.h; ++j){
            for(i = 0; i < fore.w; ++i){
                float val = alpha * get_pixel(fore, i, j, k) +
                    (1 - alpha)* get_pixel(back, i, j, k);
                set_pixel(blend, i, j, k, val);
            }
        }
    }
    return blend;
}

void scale_image_channel(image im, int c, float v)
{
    int i, j;
    for(j = 0; j < im.h; ++j){
        for(i = 0; i < im.w; ++i){
            float pix = get_pixel(im, i, j, c);
            pix = pix*v;
            set_pixel(im, i, j, c, pix);
        }
    }
}

void translate_image_channel(image im, int c, float v)
{
    int i, j;
    for(j = 0; j < im.h; ++j){
        for(i = 0; i < im.w; ++i){
            float pix = get_pixel(im, i, j, c);
            pix = pix+v;
            set_pixel(im, i, j, c, pix);
        }
    }
}

image binarize_image(image im)
{
    image c = copy_image(im);
    int i;
    for(i = 0; i < im.w * im.h * im.c; ++i){
        if(c.data[i] > .5) c.data[i] = 1;
        else c.data[i] = 0;
    }
    return c;
}

void saturate_image(image im, float sat)
{
    rgb_to_hsv(im);
    scale_image_channel(im, 1, sat);
    hsv_to_rgb(im);
    constrain_image(im);
}

void hue_image(image im, float hue)
{
    rgb_to_hsv(im);
    int i;
    for(i = 0; i < im.w*im.h; ++i){
        im.data[i] = im.data[i] + hue;
        if (im.data[i] > 1) im.data[i] -= 1;
        if (im.data[i] < 0) im.data[i] += 1;
    }
    hsv_to_rgb(im);
    constrain_image(im);
}

void exposure_image(image im, float sat)
{
    rgb_to_hsv(im);
    scale_image_channel(im, 2, sat);
    hsv_to_rgb(im);
    constrain_image(im);
}

void distort_image(image im, float hue, float sat, float val)
{
    if (im.c >= 3)
    {
        rgb_to_hsv(im);
        scale_image_channel(im, 1, sat);
        scale_image_channel(im, 2, val);
        int i;
        for(i = 0; i < im.w*im.h; ++i){
            im.data[i] = im.data[i] + hue;
            if (im.data[i] > 1) im.data[i] -= 1;
            if (im.data[i] < 0) im.data[i] += 1;
        }
        hsv_to_rgb(im);
    }
    else
    {
        scale_image_channel(im, 0, val);
    }
    constrain_image(im);
}

void random_distort_image(image im, float hue, float saturation, float exposure)
{
    float dhue = rand_uniform_strong(-hue, hue);
    float dsat = rand_scale(saturation);
    float dexp = rand_scale(exposure);
    distort_image(im, dhue, dsat, dexp);
}

void saturate_exposure_image(image im, float sat, float exposure)
{
    rgb_to_hsv(im);
    scale_image_channel(im, 1, sat);
    scale_image_channel(im, 2, exposure);
    hsv_to_rgb(im);
    constrain_image(im);
}

float bilinear_interpolate(image im, float x, float y, int c)
{
    int ix = (int) floorf(x);
    int iy = (int) floorf(y);

float dx = x - ix;
    float dy = y - iy;

float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) +
        dy     * (1-dx) * get_pixel_extend(im, ix, iy+1, c) +
        (1-dy) *   dx   * get_pixel_extend(im, ix+1, iy, c) +
        dy     *   dx   * get_pixel_extend(im, ix+1, iy+1, c);
    return val;
}

image resize_image(image im, int w, int h)
{
    image resized = make_image(w, h, im.c);
    image part = make_image(w, im.h, im.c);
    int r, c, k;
    float w_scale = (float)(im.w - 1) / (w - 1);
    float h_scale = (float)(im.h - 1) / (h - 1);
    for(k = 0; k < im.c; ++k){
        for(r = 0; r < im.h; ++r){
            for(c = 0; c < w; ++c){
                float val = 0;
                if(c == w-1 || im.w == 1){
                    val = get_pixel(im, im.w-1, r, k);
                } else {
                    float sx = c*w_scale;
                    int ix = (int) sx;
                    float dx = sx - ix;
                    val = (1 - dx) * get_pixel(im, ix, r, k) + dx * get_pixel(im, ix+1, r, k);
                }
                set_pixel(part, c, r, k, val);
            }
        }
    }
    for(k = 0; k < im.c; ++k){
        for(r = 0; r < h; ++r){
            float sy = r*h_scale;
            int iy = (int) sy;
            float dy = sy - iy;
            for(c = 0; c < w; ++c){
                float val = (1-dy) * get_pixel(part, c, iy, k);
                set_pixel(resized, c, r, k, val);
            }
            if(r == h-1 || im.h == 1) continue;
            for(c = 0; c < w; ++c){
                float val = dy * get_pixel(part, c, iy+1, k);
                add_pixel(resized, c, r, k, val);
            }
        }
    }

free_image(part);
    return resized;
}

void test_resize(char *filename)
{
    image im = load_image(filename, 0,0, 3);
    float mag = mag_array(im.data, im.w*im.h*im.c);
    printf("L2 Norm: %fn", mag);
    image gray = grayscale_image(im);

image c1 = copy_image(im);
    image c2 = copy_image(im);
    image c3 = copy_image(im);
    image c4 = copy_image(im);
    distort_image(c1, .1, 1.5, 1.5);
    distort_image(c2, -.1, .66666, .66666);
    distort_image(c3, .1, 1.5, .66666);
    distort_image(c4, .1, .66666, 1.5);

show_image(im,   "Original");
    show_image(gray, "Gray");
    show_image(c1, "C1");
    show_image(c2, "C2");
    show_image(c3, "C3");
    show_image(c4, "C4");
#ifdef OPENCV
    while(1){
        image aug = random_augment_image(im, 0, .75, 320, 448, 320);
        show_image(aug, "aug");
        free_image(aug);

float exposure = 1.15;
        float saturation = 1.15;
        float hue = .05;

image c = copy_image(im);

float dexp = rand_scale(exposure);
        float dsat = rand_scale(saturation);
        float dhue = rand_uniform(-hue, hue);

distort_image(c, dhue, dsat, dexp);
        show_image(c, "rand");
        printf("%f %f %fn", dhue, dsat, dexp);
        free_image(c);
        cvWaitKey(0);
    }
#endif
}

image load_image_stb(char *filename, int channels)
{
    int w, h, c;
    unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
    if (!data) {
        char shrinked_filename[1024];
        if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long");
        else sprintf(shrinked_filename, "%s", filename);
        fprintf(stderr, "*image.c 1979*Cannot load image "%s"nSTB Reason: %sn", shrinked_filename, stbi_failure_reason());
        FILE* fw = fopen("bad.list", "a");
        fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw);
        char *new_line = "n";
        fwrite(new_line, sizeof(char), strlen(new_line), fw);
        fclose(fw);
        if (check_mistakes) getchar();
        return make_image(10, 10, 3);
        //exit(EXIT_FAILURE);
    }
    if(channels) c = channels;
    int i,j,k;
    image im = make_image(w, h, c);
    for(k = 0; k < c; ++k){
        for(j = 0; j < h; ++j){
            for(i = 0; i < w; ++i){
                int dst_index = i + w*j + w*h*k;
                int src_index = k + c*i + c*w*j;
                im.data[dst_index] = (float)data[src_index]/255.;
            }
        }
    }
    free(data);
    return im;
}

image load_image(char *filename, int w, int h, int c)
{
#ifdef OPENCV

#ifndef CV_VERSION_EPOCH
    //image out = load_image_stb(filename, c);    // OpenCV 3.x
    image out = load_image_cv(filename, c);
#else
    image out = load_image_cv(filename, c);        // OpenCV 2.4.x
#endif

#else
    image out = load_image_stb(filename, c);    // without OpenCV
#endif

if((h && w) && (h != out.h || w != out.w)){
        image resized = resize_image(out, w, h);
        free_image(out);
        out = resized;
    }
    return out;
}

image load_image_color(char *filename, int w, int h)
{
    return load_image(filename, w, h, 3);
}

image get_image_layer(image m, int l)
{
    image out = make_image(m.w, m.h, 1);
    int i;
    for(i = 0; i < m.h*m.w; ++i){
        out.data[i] = m.data[i+l*m.h*m.w];
    }
    return out;
}

void print_image(image m)
{
    int i, j, k;
    for(i =0 ; i < m.c; ++i){
        for(j =0 ; j < m.h; ++j){
            for(k = 0; k < m.w; ++k){
                printf("%.2lf, ", m.data[i*m.h*m.w + j*m.w + k]);
                if(k > 30) break;
            }
            printf("n");
            if(j > 30) break;
        }
        printf("n");
    }
    printf("n");
}

image collapse_images_vert(image *ims, int n)
{
    int color = 1;
    int border = 1;
    int h,w,c;
    w = ims[0].w;
    h = (ims[0].h + border) * n - border;
    c = ims[0].c;
    if(c != 3 || !color){
        w = (w+border)*c - border;
        c = 1;
    }

image filters = make_image(w, h, c);
    int i,j;
    for(i = 0; i < n; ++i){
        int h_offset = i*(ims[0].h+border);
        image copy = copy_image(ims[i]);
        //normalize_image(copy);
        if(c == 3 && color){
            embed_image(copy, filters, 0, h_offset);
        }
        else{
            for(j = 0; j < copy.c; ++j){
                int w_offset = j*(ims[0].w+border);
                image layer = get_image_layer(copy, j);
                embed_image(layer, filters, w_offset, h_offset);
                free_image(layer);
            }
        }
        free_image(copy);
    }
    return filters;
}

image collapse_images_horz(image *ims, int n)
{
    int color = 1;
    int border = 1;
    int h,w,c;
    int size = ims[0].h;
    h = size;
    w = (ims[0].w + border) * n - border;
    c = ims[0].c;
    if(c != 3 || !color){
        h = (h+border)*c - border;
        c = 1;
    }

image filters = make_image(w, h, c);
    int i,j;
    for(i = 0; i < n; ++i){
        int w_offset = i*(size+border);
        image copy = copy_image(ims[i]);
        //normalize_image(copy);
        if(c == 3 && color){
            embed_image(copy, filters, w_offset, 0);
        }
        else{
            for(j = 0; j < copy.c; ++j){
                int h_offset = j*(size+border);
                image layer = get_image_layer(copy, j);
                embed_image(layer, filters, w_offset, h_offset);
                free_image(layer);
            }
        }
        free_image(copy);
    }
    return filters;
}

void show_image_normalized(image im, const char *name)
{
    image c = copy_image(im);
    normalize_image(c);
    show_image(c, name);
    free_image(c);
}

void show_images(image *ims, int n, char *window)
{
    image m = collapse_images_vert(ims, n);
    /*
       int w = 448;
       int h = ((float)m.h/m.w) * 448;
       if(h > 896){
       h = 896;
       w = ((float)m.w/m.h) * 896;
       }
       image sized = resize_image(m, w, h);
     */
    normalize_image(m);
    save_image(m, window);
    show_image(m, window);
    free_image(m);
}

void free_image(image m)
{
    if(m.data){
        free(m.data);
    }
}

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#include "image.h"
#include "utils.h"
#include "blas.h"
#include "cuda.h"
#include <stdio.h>
#include <math.h>

#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
#define STB_IMAGE_WRITE_IMPLEMENTATION
#include "stb_image_write.h"


#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/core/types_c.h"
#include "opencv2/core/version.hpp"
#ifndef CV_VERSION_EPOCH
#include "opencv2/videoio/videoio_c.h"
#include "opencv2/imgcodecs/imgcodecs_c.h"
#include "http_stream.h"


#endif
#include "http_stream.h"
#endif

extern int check_mistakes;
int windows = 0;

float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };

float get_color(int c, int x, int max)
{
    float ratio = ((float)x/max)*5;
    int i = floor(ratio);
    int j = ceil(ratio);
    ratio -= i;
    float r = (1-ratio) * colors[i][c] + ratio*colors[j][c];
    //printf("%fn", r);
    return r;
}

static float get_pixel(image m, int x, int y, int c)
{
    assert(x < m.w && y < m.h && c < m.c);
    return m.data[c*m.h*m.w + y*m.w + x];
}
static float get_pixel_extend(image m, int x, int y, int c)
{
    if (x < 0 || x >= m.w || y < 0 || y >= m.h) return 0;
    /*
    if(x < 0) x = 0;
    if(x >= m.w) x = m.w-1;
    if(y < 0) y = 0;
    if(y >= m.h) y = m.h-1;
    */
    if (c < 0 || c >= m.c) return 0;
    return get_pixel(m, x, y, c);
}
static void set_pixel(image m, int x, int y, int c, float val)
{
    if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return;
    assert(x < m.w && y < m.h && c < m.c);
    m.data[c*m.h*m.w + y*m.w + x] = val;
}
static void add_pixel(image m, int x, int y, int c, float val)
{
    assert(x < m.w && y < m.h && c < m.c);
    m.data[c*m.h*m.w + y*m.w + x] += val;
}

void composite_image(image source, image dest, int dx, int dy)
{
    int x,y,k;
    for(k = 0; k < source.c; ++k){
        for(y = 0; y < source.h; ++y){
            for(x = 0; x < source.w; ++x){
                float val = get_pixel(source, x, y, k);
                float val2 = get_pixel_extend(dest, dx+x, dy+y, k);
                set_pixel(dest, dx+x, dy+y, k, val * val2);
            }
        }
    }
}

image border_image(image a, int border)
{
    image b = make_image(a.w + 2*border, a.h + 2*border, a.c);
    int x,y,k;
    for(k = 0; k < b.c; ++k){
        for(y = 0; y < b.h; ++y){
            for(x = 0; x < b.w; ++x){
                float val = get_pixel_extend(a, x - border, y - border, k);
                if(x - border < 0 || x - border >= a.w || y - border < 0 || y - border >= a.h) val = 1;
                set_pixel(b, x, y, k, val);
            }
        }
    }
    return b;
}

image tile_images(image a, image b, int dx)
{
    if(a.w == 0) return copy_image(b);
    image c = make_image(a.w + b.w + dx, (a.h > b.h) ? a.h : b.h, (a.c > b.c) ? a.c : b.c);
    fill_cpu(c.w*c.h*c.c, 1, c.data, 1);
    embed_image(a, c, 0, 0);
    composite_image(b, c, a.w + dx, 0);
    return c;
}

image get_label(image **characters, char *string, int size)
{
    if(size > 7) size = 7;
    image label = make_empty_image(0,0,0);
    while(*string){
        image l = characters[size][(int)*string];
        image n = tile_images(label, l, -size - 1 + (size+1)/2);
        free_image(label);
        label = n;
        ++string;
    }
    image b = border_image(label, label.h*.25);
    free_image(label);
    return b;
}

image get_label_v3(image **characters, char *string, int size)
{
    size = size / 10;
    if (size > 7) size = 7;
    image label = make_empty_image(0, 0, 0);
    while (*string) {
        image l = characters[size][(int)*string];
        image n = tile_images(label, l, -size - 1 + (size + 1) / 2);
        free_image(label);
        label = n;
        ++string;
    }
    image b = border_image(label, label.h*.25);
    free_image(label);
    return b;
}

void draw_label(image a, int r, int c, image label, const float *rgb)
{
    int w = label.w;
    int h = label.h;
    if (r - h >= 0) r = r - h;

    int i, j, k;
    for(j = 0; j < h && j + r < a.h; ++j){
        for(i = 0; i < w && i + c < a.w; ++i){
            for(k = 0; k < label.c; ++k){
                float val = get_pixel(label, i, j, k);
                set_pixel(a, i+c, j+r, k, rgb[k] * val);
            }
        }
    }
}

void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b)
{
    //normalize_image(a);
    int i;
    if(x1 < 0) x1 = 0;
    if(x1 >= a.w) x1 = a.w-1;
    if(x2 < 0) x2 = 0;
    if(x2 >= a.w) x2 = a.w-1;

    if(y1 < 0) y1 = 0;
    if(y1 >= a.h) y1 = a.h-1;
    if(y2 < 0) y2 = 0;
    if(y2 >= a.h) y2 = a.h-1;

    for(i = x1; i <= x2; ++i){
        a.data[i + y1*a.w + 0*a.w*a.h] = r;
        a.data[i + y2*a.w + 0*a.w*a.h] = r;

        a.data[i + y1*a.w + 1*a.w*a.h] = g;
        a.data[i + y2*a.w + 1*a.w*a.h] = g;

        a.data[i + y1*a.w + 2*a.w*a.h] = b;
        a.data[i + y2*a.w + 2*a.w*a.h] = b;
    }
    for(i = y1; i <= y2; ++i){
        a.data[x1 + i*a.w + 0*a.w*a.h] = r;
        a.data[x2 + i*a.w + 0*a.w*a.h] = r;

        a.data[x1 + i*a.w + 1*a.w*a.h] = g;
        a.data[x2 + i*a.w + 1*a.w*a.h] = g;

        a.data[x1 + i*a.w + 2*a.w*a.h] = b;
        a.data[x2 + i*a.w + 2*a.w*a.h] = b;
    }
}

void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b)
{
    int i;
    for(i = 0; i < w; ++i){
        draw_box(a, x1+i, y1+i, x2-i, y2-i, r, g, b);
    }
}

void draw_bbox(image a, box bbox, int w, float r, float g, float b)
{
    int left  = (bbox.x-bbox.w/2)*a.w;
    int right = (bbox.x+bbox.w/2)*a.w;
    int top   = (bbox.y-bbox.h/2)*a.h;
    int bot   = (bbox.y+bbox.h/2)*a.h;

    int i;
    for(i = 0; i < w; ++i){
        draw_box(a, left+i, top+i, right-i, bot-i, r, g, b);
    }
}

image **load_alphabet()
{
    int i, j;
    const int nsize = 8;
    image **alphabets = calloc(nsize, sizeof(image));
    for(j = 0; j < nsize; ++j){
        alphabets[j] = calloc(128, sizeof(image));
        for(i = 32; i < 127; ++i){
            char buff[256];
            sprintf(buff, "data/labels/%d_%d.png", i, j);
            alphabets[j][i] = load_image_color(buff, 0, 0);
        }
    }
    return alphabets;
}



// Creates array of detections with prob > thresh and fills best_class for them
detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num)
{
    int selected_num = 0;
    detection_with_class* result_arr = calloc(dets_num, sizeof(detection_with_class));
    int i;
    for (i = 0; i < dets_num; ++i) {		//提取到的特征目标循环判断
        int best_class = -1;
        float best_class_prob = thresh;
        int j;
        for (j = 0; j < dets[i].classes; ++j) {		//对检测到的目标的类别概率判断，赋予最大的概率类别
            if (dets[i].prob[j] > best_class_prob ) {
                best_class = j;
                best_class_prob = dets[i].prob[j];
            }
        }
        if (best_class >= 0) {			//如果最大的类别概率大于0，该detction结构体赋给result_arr返回
            result_arr[selected_num].det = dets[i];
            result_arr[selected_num].best_class = best_class;
            ++selected_num;
        }
    }
    if (selected_detections_num)
        *selected_detections_num = selected_num;
    return result_arr;
}

// compare to sort detection** by bbox.x
int compare_by_lefts(const void *a_ptr, const void *b_ptr) {
    const detection_with_class* a = (detection_with_class*)a_ptr;
    const detection_with_class* b = (detection_with_class*)b_ptr;
    const float delta = (a->det.bbox.x - a->det.bbox.w/2) - (b->det.bbox.x - b->det.bbox.w/2);
    return delta < 0 ? -1 : delta > 0 ? 1 : 0;
}

// compare to sort detection** by best_class probability
int compare_by_probs(const void *a_ptr, const void *b_ptr) {
    const detection_with_class* a = (detection_with_class*)a_ptr;
    const detection_with_class* b = (detection_with_class*)b_ptr;
    float delta = a->det.prob[a->best_class] - b->det.prob[b->best_class];
    return delta < 0 ? -1 : delta > 0 ? 1 : 0;
}

void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output)
{
    int selected_detections_num;
	//实例化一个结构体并得到卷积后的各个特征的类名和最大概率
    detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num); 
    // text output
    qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts);
    int i;
    for (i = 0; i < selected_detections_num; ++i) {  //对于检测到的目标的循环
         int best_class = selected_detections[i].best_class;		//上面返回的结构体中的best_class   原为const int
		/*************dspeia 20181026 修改label上的显示***************/
		if (best_class != 1 && best_class != 2 && best_class !=  3 && best_class != 0){ 
			printf("%s: %.0f%%   ******* image.c 292 *******", names[79], selected_detections[i].det.prob[best_class] * 100);  //增加这一句，cmd上显示other，调用的coco.name文件，如何图片上显示other？？
		}
		else {
			printf("%s: %.0f%%   ******* image.c 292 *******", names[best_class], selected_detections[i].det.prob[best_class] * 100);
		}
        if (ext_output)				//如果cmd的命令该位置为真，可打印出各个目标的box位置
            printf("t(left_x: %4.0f   top_y: %4.0f   width: %4.0f   height: %4.0f)n",
                (selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w,
                (selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h,
                selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h);
        else
            printf("n");
        //int j;
        //for (j = 0; j < classes; ++j) {			//一个object有多个预测类别时进入，输出大于给定阈值的同一个目标的不同类别信息。控制是否进入for的是classes
        //    if (selected_detections[i].det.prob[j] > thresh && j != best_class) {
        //        printf("%s: %.0f%%    ******image.c 303 ***** n", names[j], selected_detections[i].det.prob[j] * 100);
        //    }
        //}
    }
	/*******************dspeia 20181017***********************/
	

    // image output
    qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_probs);
    for (i = 0; i < selected_detections_num; ++i) {
            int width = im.h * .006;
            if (width < 1)
                width = 1;

            /*
            if(0){
            width = pow(prob, 1./2.)*10+1;
            alphabet = 0;
            }
            */

            //printf("%d %s: %.0f%%n", i, names[selected_detections[i].best_class], prob*100);
            int offset = selected_detections[i].best_class * 123457 % classes;
            float red = get_color(2, offset, classes);
            float green = get_color(1, offset, classes);
            float blue = get_color(0, offset, classes);
            float rgb[3];

            //width = prob*20+2;
			//rgb值为了定义label框的颜色
            rgb[0] = red;
            rgb[1] = green;
            rgb[2] = blue;
            box b = selected_detections[i].det.bbox;
            //printf("%f %f %f %fn", b.x, b.y, b.w, b.h);

            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;

            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;



			/*******************************/
			int the_class = selected_detections[i].best_class;

			//char cut_class[20] = { 0 };		//为了传类名到cut函数的方法一
			//strcpy(cut_class, names[the_class]);
			//char cut_class = names[the_class];
			float cut_pro = selected_detections[i].det.prob[the_class] * 100;
			printf("******355 **cut_class  :%s  ...........cut_class:%.0f n", names[the_class], cut_pro);
			//printf(cut_class);
			//printf("%f", cut_pro);	//

			/******dspeia    ****/
			int pre_x = left;
			int pre_y = top;
			int pre_h = bot - top;
			int pre_w = right - left;
			save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im, names, cut_pro, the_class);
			printf("/************  cut and save over *****************/ n");
			/********************************/

            //int b_x_center = (left + right) / 2;
            //int b_y_center = (top + bot) / 2;
            //int b_width = right - left;
            //int b_height = bot - top;
            //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height);

            draw_box_width(im, left, top, right, bot, width, red, green, blue);
            if (alphabet) {
                char labelstr[4096] = { 0 };
				if (selected_detections[i].best_class != 0 && selected_detections[i].best_class != 1 && selected_detections[i].best_class != 2 && selected_detections[i].best_class != 3){
					strcat(labelstr, names[79]);  //加入这一句if，在label上写other
				}
				else
				{
					strcat(labelstr, names[selected_detections[i].best_class]);

				}
                //int j;
                //for (j = 0; j < classes; ++j) {  //同一个object多个预测时，画多个框,注释了图片上仍然画第二框，只是cmd上不打印第二预测
                //    if (selected_detections[i].det.prob[j] > thresh && j != selected_detections[i].best_class) {
                //        strcat(labelstr, ", ");
                //        strcat(labelstr, names[j]);
                //    }
                //}
                image label = get_label_v3(alphabet, labelstr, (im.h*.03));  //画出框，复制到im上
                draw_label(im, top + width, left, label, rgb);
				//image* pic -> label;
				//const CvArr* label_copy = (CvArr*)&label;          //**********************
				//cvShowImage("*ima 394 label", label_copy);        //***********dspeia plus test
                free_image(label);				
            }
            if (selected_detections[i].det.mask) {
                image mask = float_to_image(14, 14, 1, selected_detections[i].det.mask);
                image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h);
                image tmask = threshold_image(resized_mask, .5);
                embed_image(tmask, im, left, top);
                free_image(mask);
                free_image(resized_mask);
                free_image(tmask);
            }
    }
    free(selected_detections);
}

/********************dspeia  20181017********************/
/***********     darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights    */

void save_cut_image(int px, int py, int ph, int pw, int no, image m_img, char **names, float cut_pro, int the_class)
{
	//cvShowImage("the enter", *m_img);
	image copy = copy_image(m_img);
	if (m_img.c == 3) rgbgr_image(copy);
	int x, y, k;
	char buff[256];
	/*****************************/
	//printf("%s: %.0f%%   ******* image.c 292 *******", cut_clas, selected_detections[i].det.prob[best_class] * 100);
	/**********************************/
	sprintf(buff, "results//%s%.0f%%%d.jpg", names[the_class], cut_pro, no);
	printf("****411** cut_class  :%s  ...........cut_class:%.0f ", names[the_class], cut_pro);//
	printf(names[the_class]);
	printf("%f",cut_pro);	//
	IplImage *disp = cvCreateImage(cvSize(m_img.w, m_img.h), IPL_DEPTH_8U, m_img.c);
	//cvShowImage("**the enter", disp);   //disp 为黑框乱码
	int step = disp->widthStep;
	for (y = 0; y < m_img.h; ++y) {
		for (x = 0; x < m_img.w; ++x) {
			for (k = 0; k < m_img.c; ++k) {
				disp->imageData[y*step + x*m_img.c + k] = (unsigned char)(get_pixel(copy, x, y, k) * 255);
			}
		}
	}
	CvMat *pMat = cvCreateMatHeader(m_img.w, m_img.h, IPL_DEPTH_8U);
 
	//char rect_name[256];
 
	//sprintf(rect_name, "%d_rect", no);
 
	CvRect rect = cvRect(px, py, pw, ph);
 
	cvGetSubRect(disp, pMat, rect);
 
	IplImage *pSubImg = cvCreateImage(cvSize(pw, ph), IPL_DEPTH_8U, m_img.c);
 
	cvGetImage(pMat, pSubImg);
 
	//printf("x=%d,y=%d,h=%d,w=%dn", px, py, ph, pw);
 
	cvSaveImage(buff, pSubImg, 0);
	//cvShowImage("average loss", pSubImg);   //pSubImg为分割的子目标

	//cvReleaseImage(&disp);
	//cvReleaseImage(&pMat);
	//cvReleaseImage(&rect);
 
	//memset(&rect, 0, sizeof(rect));
	//cvReleaseImage(&pSubImg);
	//free(&rect);
	
	
	free_image(copy);
}
/**********************20181017******************************/

void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes)
{
	
    int i;

    for(i = 0; i < num; ++i){
        int class_id = max_index(probs[i], classes);
        float prob = probs[i][class_id];
        if(prob > thresh){

             for comparison with OpenCV version of DNN Darknet Yolo v2
            //printf("n %f, %f, %f, %f, ", boxes[i].x, boxes[i].y, boxes[i].w, boxes[i].h);
            // int k;
            //for (k = 0; k < classes; ++k) {
            //    printf("%f, ", probs[i][k]);
            //}
            //printf("n");

            int width = im.h * .012;

            if(0){
                width = pow(prob, 1./2.)*10+1;
                alphabet = 0;
            }

            int offset = class_id*123457 % classes;
            float red = get_color(2,offset,classes);
            float green = get_color(1,offset,classes);
            float blue = get_color(0,offset,classes);
            float rgb[3];

            //width = prob*20+2;

            rgb[0] = red;
            rgb[1] = green;
            rgb[2] = blue;
            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;
			/*******************dspeia 20181017***********************/
			int pre_x = left;
			int pre_y = top;
			int pre_h = bot - top;
			int pre_w = right - left;
			/*******************dspeia 20181017***********************/

            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;
            printf("%s: %.0f%% test*******************", names[class_id], prob * 100);
			/**********************/
			printf("/**********image.c 505***  test和demo 出不来，不走这一步？****************/");
			//save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im);
			
			/*******************/
            //printf(" - id: %d, x_center: %d, y_center: %d, width: %d, height: %d",
            //    class_id, (right + left) / 2, (bot - top) / 2, right - left, bot - top);

            printf("n");
            draw_box_width(im, left, top, right, bot, width, red, green, blue);
            if (alphabet) {
                image label = get_label(alphabet, names[class_id], (im.h*.03)/10);
                draw_label(im, top + width, left, label, rgb);
            }
			/*****************dspeia 20181017************************/
			/*save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im);
			printf("/");*/
			/*****************dspeia 20181017************************/
			/***********     darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights    */

        }
    }
}

#ifdef OPENCV
				**************1019最新版本，改动此函数,demo 视频用
void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output)
{
    int i, j;
    if (!show_img) return;
    static int frame_id = 0;
    frame_id++;
	/**********dspeia 20181019***** 新加视频裁剪小目标 *******/
	/*int selected_detections_num;
	detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num);*/
	/***********************/
    for (i = 0; i < num; ++i) {
        char labelstr[4096] = { 0 };
        int class_id = -1;
        for (j = 0; j < classes; ++j) {
            if (dets[i].prob[j] > thresh) {
                if (class_id < 0) {
                    strcat(labelstr, names[j]);
                    class_id = j;
                }
                else {
                    strcat(labelstr, ", ");
                    strcat(labelstr, names[j]);
                }
                printf(" **** image.c 562 ***** %s: %.0f%% ", names[j], dets[i].prob[j] * 100);

				///***************************新加，视频裁剪小目标*************/
				IplImage* cut = show_img;
				//box b = selected_detections[i].det.bbox;
				//int left = (b.x - b.w / 2.)* show_img->width;
				//int right = (b.x + b.w / 2.)*show_img->width;
				//int top = (b.y - b.h / 2.)*show_img->height;
				//int bot = (b.y + b.h / 2.)*show_img->height;

				//if (left < 0) left = 0;
				//if (right > show_img->width - 1) right = show_img->width - 1;
				//if (top < 0) top = 0;
				//if (bot > show_img->height - 1) bot = show_img->height - 1;
				//int pre_x = left;
				//int pre_y = top;
				//int pre_h = bot - top;
				//int pre_w = right - left;
				image m = show_img;
				network net = parse_network_cfg_custom(cfgfile, 1);
				//image im = load_image(show_img, 0, 0, 3);
				//int the_class = names[j];

				char cut_class[20] = { 0 };		//为了传类名到cut函数的方法一
				strcpy(cut_class, names[the_class]);
				char cut_class = names[the_class];
				//float cut_pro = dets[i].prob[j] * 100;
				//save_cut_image(pre_x, pre_y, pre_h, pre_w, i, im, names, cut_pro, the_class);
				//printf("/************  cut and save over *****************/");
				///********************************/
            }
        }
        if (class_id >= 0) {
            int width = show_img->height * .006;

            //if(0){
            //width = pow(prob, 1./2.)*10+1;
            //alphabet = 0;
            //}

            //printf("%d %s: %.0f%%n", i, names[class_id], prob*100);
            int offset = class_id * 123457 % classes;
            float red = get_color(2, offset, classes);
            float green = get_color(1, offset, classes);
            float blue = get_color(0, offset, classes);
            float rgb[3];

            //width = prob*20+2;

            rgb[0] = red;
            rgb[1] = green;
            rgb[2] = blue;
            box b = dets[i].bbox;
            b.w = (b.w < 1) ? b.w : 1;
            b.h = (b.h < 1) ? b.h : 1;
            b.x = (b.x < 1) ? b.x : 1;
            b.y = (b.y < 1) ? b.y : 1;
            //printf("%f %f %f %fn", b.x, b.y, b.w, b.h);

            int left = (b.x - b.w / 2.)*show_img->width;
            int right = (b.x + b.w / 2.)*show_img->width;
            int top = (b.y - b.h / 2.)*show_img->height;
            int bot = (b.y + b.h / 2.)*show_img->height;

            if (left < 0) left = 0;
            if (right > show_img->width - 1) right = show_img->width - 1;
            if (top < 0) top = 0;
            if (bot > show_img->height - 1) bot = show_img->height - 1;

            //int b_x_center = (left + right) / 2;
            //int b_y_center = (top + bot) / 2;
            //int b_width = right - left;
            //int b_height = bot - top;
            //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height);

            float const font_size = show_img->height / 1000.F;
            CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2;
            pt1.x = left;
            pt1.y = top;
            pt2.x = right;
            pt2.y = bot;
            pt_text.x = left;
            pt_text.y = top - 12;
            pt_text_bg1.x = left;
            pt_text_bg1.y = top - (10 + 25 * font_size);
            pt_text_bg2.x = right;
            pt_text_bg2.y = top;
            CvScalar color;
            color.val[0] = red * 256;
            color.val[1] = green * 256;
            color.val[2] = blue * 256;

            // you should create directory: result_img
            //static int copied_frame_id = -1;
            //static IplImage* copy_img = NULL;
            //if (copied_frame_id != frame_id) {
            //    copied_frame_id = frame_id;
            //    if(copy_img == NULL) copy_img = cvCreateImage(cvSize(show_img->width, show_img->height), show_img->depth, show_img->nChannels);
            //    cvCopy(show_img, copy_img, 0);
            //}
            //static int img_id = 0;
            //img_id++;
            //char image_name[1024];
            //sprintf(image_name, "result_img/img_%d_%d_%d.jpg", frame_id, img_id, class_id);
            //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y);
            //cvSetImageROI(copy_img, rect);
            //cvSaveImage(image_name, copy_img, 0);
            //cvResetImageROI(copy_img);

            cvRectangle(show_img, pt1, pt2, color, width, 8, 0);
            if (ext_output)
                printf("t(****image.c 644**left_x: %4.0f   top_y: %4.0f   width: %4.0f   height: %4.0f)n",
                    (float)left, (float)top, b.w*show_img->width, b.h*show_img->height);
            else
                printf("n");

            cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0);
            cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0);    // filled
            CvScalar black_color;
            black_color.val[0] = 0;
            CvFont font;
            cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8);
            cvPutText(show_img, labelstr, pt_text, &font, black_color);
        }
    }
    if (ext_output) {
        fflush(stdout);
    }
}

void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes)
{
    int i;

    for (i = 0; i < num; ++i) {
        int class_id = max_index(probs[i], classes);
        float prob = probs[i][class_id];
        if (prob > thresh) {

            int width = show_img->height * .012;

            if (0) {
                width = pow(prob, 1. / 2.) * 10 + 1;
                alphabet = 0;
            }

            printf("%s: %.0f%%n", names[class_id], prob * 100);
            int offset = class_id * 123457 % classes;
            float red = get_color(2, offset, classes);
            float green = get_color(1, offset, classes);
            float blue = get_color(0, offset, classes);
            float rgb[3];

            //width = prob*20+2;

            rgb[0] = red;
            rgb[1] = green;
            rgb[2] = blue;
            box b = boxes[i];

            int left = (b.x - b.w / 2.)*show_img->width;
            int right = (b.x + b.w / 2.)*show_img->width;
            int top = (b.y - b.h / 2.)*show_img->height;
            int bot = (b.y + b.h / 2.)*show_img->height;

            if (left < 0) left = 0;
            if (right > show_img->width - 1) right = show_img->width - 1;
            if (top < 0) top = 0;
            if (bot > show_img->height - 1) bot = show_img->height - 1;

            float const font_size = show_img->height / 1000.F;
            CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2;
            pt1.x = left;
            pt1.y = top;
            pt2.x = right;
            pt2.y = bot;
            pt_text.x = left;
            pt_text.y = top - 12;
            pt_text_bg1.x = left;
            pt_text_bg1.y = top - (10+25*font_size);
            pt_text_bg2.x = right;
            pt_text_bg2.y = top;
            CvScalar color;
            color.val[0] = red * 256;
            color.val[1] = green * 256;
            color.val[2] = blue * 256;

            cvRectangle(show_img, pt1, pt2, color, width, 8, 0);
            //printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s n", left, right, top, bot, class_id, names[class_id]);
            cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0);
            cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0);    // filled
            CvScalar black_color;
            black_color.val[0] = 0;
            CvFont font;
            cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8);
            cvPutText(show_img, names[class_id], pt_text, &font, black_color);
        }
    }
}

IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size)
{
    int img_offset = 50;
    int draw_size = img_size - img_offset;
    IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3);
    cvSet(img, CV_RGB(255, 255, 255), 0);
    CvPoint pt1, pt2, pt_text;
    CvFont font;
    cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA);
    char char_buff[100];
    int i;
    // vertical lines
    pt1.x = img_offset; pt2.x = img_size, pt_text.x = 10;
    for (i = 1; i <= number_of_lines; ++i) {
        pt1.y = pt2.y = (float)i * draw_size / number_of_lines;
        cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0);
        if (i % 10 == 0) {
            sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines);
            pt_text.y = pt1.y + 5;
            cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0));
            cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0);
        }
    }
    // horizontal lines
    pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15;
    for (i = 0; i <= number_of_lines; ++i) {
        pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines;
        cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0);
        if (i % 10 == 0) {
            sprintf(char_buff, "%d", max_batches * i / number_of_lines);
            pt_text.x = pt1.x - 20;
            cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0));
            cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0);
        }
    }
    cvPutText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), &font, CV_RGB(0, 0, 0));
    cvPutText(img, "Press 's' to save: chart.jpg", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0));
    printf(" If error occurs - run training with flag: -dont_show n");
    cvNamedWindow("average loss", CV_WINDOW_NORMAL);
    cvMoveWindow("average loss", 0, 0);
    cvResizeWindow("average loss", img_size, img_size);
    cvShowImage("average loss", img);
    cvWaitKey(20);
    return img;
}

void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches)
{
    int img_offset = 50;
    int draw_size = img_size - img_offset;
    CvFont font;
    cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA);
    char char_buff[100];
    CvPoint pt1, pt2;
    pt1.x = img_offset + draw_size * (float)current_batch / max_batches;
    pt1.y = draw_size * (1 - avg_loss / max_img_loss);
    if (pt1.y < 0) pt1.y = 1;
    cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0);

    sprintf(char_buff, "current avg loss = %2.4f", avg_loss);
    pt1.x = img_size / 2, pt1.y = 30;
    pt2.x = pt1.x + 250, pt2.y = pt1.y + 20;
    cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0);
    pt1.y += 15;
    cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0));
    cvShowImage("average loss", img);
    int k = cvWaitKey(20);
    if (k == 's' || current_batch == (max_batches-1)) cvSaveImage("chart.jpg", img, 0);
}
#endif    // OPENCV

void transpose_image(image im)
{
    assert(im.w == im.h);
    int n, m;
    int c;
    for(c = 0; c < im.c; ++c){
        for(n = 0; n < im.w-1; ++n){
            for(m = n + 1; m < im.w; ++m){
                float swap = im.data[m + im.w*(n + im.h*c)];
                im.data[m + im.w*(n + im.h*c)] = im.data[n + im.w*(m + im.h*c)];
                im.data[n + im.w*(m + im.h*c)] = swap;
            }
        }
    }
}

void rotate_image_cw(image im, int times)
{
    assert(im.w == im.h);
    times = (times + 400) % 4;
    int i, x, y, c;
    int n = im.w;
    for(i = 0; i < times; ++i){
        for(c = 0; c < im.c; ++c){
            for(x = 0; x < n/2; ++x){
                for(y = 0; y < (n-1)/2 + 1; ++y){
                    float temp = im.data[y + im.w*(x + im.h*c)];
                    im.data[y + im.w*(x + im.h*c)] = im.data[n-1-x + im.w*(y + im.h*c)];
                    im.data[n-1-x + im.w*(y + im.h*c)] = im.data[n-1-y + im.w*(n-1-x + im.h*c)];
                    im.data[n-1-y + im.w*(n-1-x + im.h*c)] = im.data[x + im.w*(n-1-y + im.h*c)];
                    im.data[x + im.w*(n-1-y + im.h*c)] = temp;
                }
            }
        }
    }
}

void flip_image(image a)
{
    int i,j,k;
    for(k = 0; k < a.c; ++k){
        for(i = 0; i < a.h; ++i){
            for(j = 0; j < a.w/2; ++j){
                int index = j + a.w*(i + a.h*(k));
                int flip = (a.w - j - 1) + a.w*(i + a.h*(k));
                float swap = a.data[flip];
                a.data[flip] = a.data[index];
                a.data[index] = swap;
            }
        }
    }
}

image image_distance(image a, image b)
{
    int i,j;
    image dist = make_image(a.w, a.h, 1);
    for(i = 0; i < a.c; ++i){
        for(j = 0; j < a.h*a.w; ++j){
            dist.data[j] += pow(a.data[i*a.h*a.w+j]-b.data[i*a.h*a.w+j],2);
        }
    }
    for(j = 0; j < a.h*a.w; ++j){
        dist.data[j] = sqrt(dist.data[j]);
    }
    return dist;
}

void embed_image(image source, image dest, int dx, int dy)
{
    int x,y,k;
    for(k = 0; k < source.c; ++k){
        for(y = 0; y < source.h; ++y){
            for(x = 0; x < source.w; ++x){
                float val = get_pixel(source, x,y,k);
                set_pixel(dest, dx+x, dy+y, k, val);
            }
        }
    }
}

image collapse_image_layers(image source, int border)
{
    int h = source.h;
    h = (h+border)*source.c - border;
    image dest = make_image(source.w, h, 1);
    int i;
    for(i = 0; i < source.c; ++i){
        image layer = get_image_layer(source, i);
        int h_offset = i*(source.h+border);
        embed_image(layer, dest, 0, h_offset);
        free_image(layer);
    }
    return dest;
}

void constrain_image(image im)
{
    int i;
    for(i = 0; i < im.w*im.h*im.c; ++i){
        if(im.data[i] < 0) im.data[i] = 0;
        if(im.data[i] > 1) im.data[i] = 1;
    }
}

void normalize_image(image p)
{
    int i;
    float min = 9999999;
    float max = -999999;

    for(i = 0; i < p.h*p.w*p.c; ++i){
        float v = p.data[i];
        if(v < min) min = v;
        if(v > max) max = v;
    }
    if(max - min < .000000001){
        min = 0;
        max = 1;
    }
    for(i = 0; i < p.c*p.w*p.h; ++i){
        p.data[i] = (p.data[i] - min)/(max-min);
    }
}

void normalize_image2(image p)
{
    float *min = calloc(p.c, sizeof(float));
    float *max = calloc(p.c, sizeof(float));
    int i,j;
    for(i = 0; i < p.c; ++i) min[i] = max[i] = p.data[i*p.h*p.w];

    for(j = 0; j < p.c; ++j){
        for(i = 0; i < p.h*p.w; ++i){
            float v = p.data[i+j*p.h*p.w];
            if(v < min[j]) min[j] = v;
            if(v > max[j]) max[j] = v;
        }
    }
    for(i = 0; i < p.c; ++i){
        if(max[i] - min[i] < .000000001){
            min[i] = 0;
            max[i] = 1;
        }
    }
    for(j = 0; j < p.c; ++j){
        for(i = 0; i < p.w*p.h; ++i){
            p.data[i+j*p.h*p.w] = (p.data[i+j*p.h*p.w] - min[j])/(max[j]-min[j]);
        }
    }
    free(min);
    free(max);
}

image copy_image(image p)
{
    image copy = p;
    copy.data = calloc(p.h*p.w*p.c, sizeof(float));
    memcpy(copy.data, p.data, p.h*p.w*p.c*sizeof(float));
    return copy;
}

void rgbgr_image(image im)
{
    int i;
    for(i = 0; i < im.w*im.h; ++i){
        float swap = im.data[i];
        im.data[i] = im.data[i+im.w*im.h*2];
        im.data[i+im.w*im.h*2] = swap;
    }
}

#ifdef OPENCV
void show_image_cv(image p, const char *name)
{
    int x,y,k;
    image copy = copy_image(p);
    constrain_image(copy);
    if(p.c == 3) rgbgr_image(copy);
    //normalize_image(copy);

    char buff[256];
    //sprintf(buff, "%s (%d)", name, windows);
    sprintf(buff, "%s", name);

    IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c);
    int step = disp->widthStep;
    cvNamedWindow(buff, CV_WINDOW_NORMAL);
    //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10));
    ++windows;
    for(y = 0; y < p.h; ++y){
        for(x = 0; x < p.w; ++x){
            for(k= 0; k < p.c; ++k){
                disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255);
            }
        }
    }
    free_image(copy);
    if(0){
        int w = 448;
        int h = w*p.h/p.w;
        if(h > 1000){
            h = 1000;
            w = h*p.w/p.h;
        }
        IplImage *buffer = disp;
        disp = cvCreateImage(cvSize(w, h), buffer->depth, buffer->nChannels);
        cvResize(buffer, disp, CV_INTER_LINEAR);
        cvReleaseImage(&buffer);
    }
    cvShowImage(buff, disp);

    cvReleaseImage(&disp);
}


void show_image_cv_ipl(IplImage *disp, const char *name)		//demo.c 传过来的，*disp为检测后的图片 *name为窗口名称字符串
{
    if (disp == NULL) return;
    char buff[256];
    //sprintf(buff, "%s (%d)", name, windows);
    sprintf(buff, "%s", name);							//name写入buff 显示合成视频在demo窗口上
    cvNamedWindow(buff, CV_WINDOW_NORMAL);
    //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10));
    ++windows;
    cvShowImage(buff, disp);									//框合成到demo，展示
    //cvReleaseImage(&disp);
}
#endif

void show_image(image p, const char *name)
{
#ifdef OPENCV
    show_image_cv(p, name);
#else
    fprintf(stderr, "Not compiled with OpenCV, saving to %s.png insteadn", name);
    save_image(p, name);
#endif
}

#ifdef OPENCV

image ipl_to_image(IplImage* src)
{
    unsigned char *data = (unsigned char *)src->imageData;
    int h = src->height;
    int w = src->width;
    int c = src->nChannels;
    int step = src->widthStep;
    image out = make_image(w, h, c);
    int i, j, k, count=0;;

    for(k= 0; k < c; ++k){
        for(i = 0; i < h; ++i){
            for(j = 0; j < w; ++j){
                out.data[count++] = data[i*step + j*c + k]/255.;
            }
        }
    }
    return out;
}

image load_image_cv(char *filename, int channels)
{
    IplImage* src = 0;
    int flag = -1;
    if (channels == 0) flag = 1;
    else if (channels == 1) flag = 0;
    else if (channels == 3) flag = 1;
    else {
        fprintf(stderr, "OpenCV can't force load with %d channelsn", channels);
    }

    if( (src = cvLoadImage(filename, flag)) == 0 )
    {
        char shrinked_filename[1024];
        if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long");
        else sprintf(shrinked_filename, "%s", filename);
        fprintf(stderr, "*image.c 1132*Cannot load image "%s"n", shrinked_filename);
        FILE* fw = fopen("bad.list", "a");
        fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw);
        char *new_line = "n";
        fwrite(new_line, sizeof(char), strlen(new_line), fw);
        fclose(fw);
        if (check_mistakes) getchar();
        return make_image(10,10,3);
        //exit(EXIT_FAILURE);
    }
    image out = ipl_to_image(src);
    cvReleaseImage(&src);
    if (out.c > 1)
        rgbgr_image(out);
    return out;
}

image get_image_from_stream(CvCapture *cap)
{
    IplImage* src = cvQueryFrame(cap);
    if (!src) return make_empty_image(0,0,0);
    image im = ipl_to_image(src);
    rgbgr_image(im);
    return im;
}

image get_image_from_stream_cpp(CvCapture *cap)
{
    //IplImage* src = cvQueryFrame(cap);
    IplImage* src;
    static int once = 1;
    if (once) {
        once = 0;
        do {
            src = get_webcam_frame(cap);
            if (!src) return make_empty_image(0, 0, 0);
        } while (src->width < 1 || src->height < 1 || src->nChannels < 1);
        printf("Video stream: %d x %d n", src->width, src->height);
    }
    else
        src = get_webcam_frame(cap);

    if (!src) return make_empty_image(0, 0, 0);
    image im = ipl_to_image(src);
    rgbgr_image(im);
    return im;
}

int wait_for_stream(CvCapture *cap, IplImage* src, int dont_close) {
    if (!src) {
        if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3);
        else return 0;
    }
    if (src->width < 1 || src->height < 1 || src->nChannels < 1) {
        if (dont_close) {
            cvReleaseImage(&src);
            int z = 0;
            for (z = 0; z < 20; ++z) {
                get_webcam_frame(cap);
                cvReleaseImage(&src);
            }
            src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3);
        }
        else return 0;
    }
    return 1;
}

image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close)
{
    c = c ? c : 3;
    IplImage* src;
    if (cpp_video_capture) {
        static int once = 1;
        if (once) {
            once = 0;
            do {
                src = get_webcam_frame(cap);
                if (!src) return make_empty_image(0, 0, 0);
            } while (src->width < 1 || src->height < 1 || src->nChannels < 1);
            printf("Video stream: %d x %d n", src->width, src->height);
        } else
            src = get_webcam_frame(cap);
    }
    else src = cvQueryFrame(cap);

    if (cpp_video_capture)
        if(!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0);
    IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c);
    *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c);
    cvResize(src, *in_img, CV_INTER_LINEAR);
    cvResize(src, new_img, CV_INTER_LINEAR);
    image im = ipl_to_image(new_img);
    cvReleaseImage(&new_img);
    if (cpp_video_capture) cvReleaseImage(&src);
    if (c>1)
        rgbgr_image(im);
    return im;
}

image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close)
{
    c = c ? c : 3;
    IplImage* src;
    if (cpp_video_capture) {
        static int once = 1;
        if (once) {
            once = 0;
            do {
                src = get_webcam_frame(cap);
                if (!src) return make_empty_image(0, 0, 0);
            } while (src->width < 1 || src->height < 1 || src->nChannels < 1);
            printf("Video stream: %d x %d n", src->width, src->height);
        }
        else
            src = get_webcam_frame(cap);
    }
    else src = cvQueryFrame(cap);

    if (cpp_video_capture)
        if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0);
    *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c);
    cvResize(src, *in_img, CV_INTER_LINEAR);
    image tmp = ipl_to_image(src);
    image im = letterbox_image(tmp, w, h);
    free_image(tmp);
    if (cpp_video_capture) cvReleaseImage(&src);
    if (c>1) rgbgr_image(im);
    return im;
}

int get_stream_fps(CvCapture *cap, int cpp_video_capture)
{
    int fps = 25;
    if (cpp_video_capture) {
        fps = get_stream_fps_cpp(cap);
    }
    else {
        fps = cvGetCaptureProperty(cap, CV_CAP_PROP_FPS);
    }
    return fps;
}

void save_image_jpg(image p, const char *name)
{
    image copy = copy_image(p);
    if(p.c == 3) rgbgr_image(copy);
    int x,y,k;

    char buff[256];
    sprintf(buff, "%s.jpg", name);

    IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c);
    int step = disp->widthStep;
    for(y = 0; y < p.h; ++y){
        for(x = 0; x < p.w; ++x){
            for(k= 0; k < p.c; ++k){
                disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255);
            }
        }
    }
    cvSaveImage(buff, disp,0);
    cvReleaseImage(&disp);
    free_image(copy);
}
#endif

void save_image_png(image im, const char *name)
{
    char buff[256];
    //sprintf(buff, "%s (%d)", name, windows);
    sprintf(buff, "%s.png", name);
    unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char));
    int i,k;
    for(k = 0; k < im.c; ++k){
        for(i = 0; i < im.w*im.h; ++i){
            data[i*im.c+k] = (unsigned char) (255*im.data[i + k*im.w*im.h]);
        }
    }
    int success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c);
    free(data);
    if(!success) fprintf(stderr, "Failed to write image %sn", buff);
}

void save_image(image im, const char *name)
{
#ifdef OPENCV
    save_image_jpg(im, name);
#else
    save_image_png(im, name);
#endif
}


void show_image_layers(image p, char *name)
{
    int i;
    char buff[256];
    for(i = 0; i < p.c; ++i){
        sprintf(buff, "%s - Layer %d", name, i);
        image layer = get_image_layer(p, i);
        show_image(layer, buff);
        free_image(layer);
    }
}

void show_image_collapsed(image p, char *name)
{
    image c = collapse_image_layers(p, 1);
    show_image(c, name);
    free_image(c);
}

image make_empty_image(int w, int h, int c)
{
    image out;
    out.data = 0;
    out.h = h;
    out.w = w;
    out.c = c;
    return out;
}

image make_image(int w, int h, int c)
{
    image out = make_empty_image(w,h,c);
    out.data = calloc(h*w*c, sizeof(float));
    return out;
}

image make_random_image(int w, int h, int c)
{
    image out = make_empty_image(w,h,c);
    out.data = calloc(h*w*c, sizeof(float));
    int i;
    for(i = 0; i < w*h*c; ++i){
        out.data[i] = (rand_normal() * .25) + .5;
    }
    return out;
}

image float_to_image(int w, int h, int c, float *data)
{
    image out = make_empty_image(w,h,c);
    out.data = data;
    return out;
}


image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect)
{
    int x, y, c;
    float cx = im.w/2.;
    float cy = im.h/2.;
    image rot = make_image(w, h, im.c);
    for(c = 0; c < im.c; ++c){
        for(y = 0; y < h; ++y){
            for(x = 0; x < w; ++x){
                float rx = cos(rad)*((x - w/2.)/s*aspect + dx/s*aspect) - sin(rad)*((y - h/2.)/s + dy/s) + cx;
                float ry = sin(rad)*((x - w/2.)/s*aspect + dx/s*aspect) + cos(rad)*((y - h/2.)/s + dy/s) + cy;
                float val = bilinear_interpolate(im, rx, ry, c);
                set_pixel(rot, x, y, c, val);
            }
        }
    }
    return rot;
}

image rotate_image(image im, float rad)
{
    int x, y, c;
    float cx = im.w/2.;
    float cy = im.h/2.;
    image rot = make_image(im.w, im.h, im.c);
    for(c = 0; c < im.c; ++c){
        for(y = 0; y < im.h; ++y){
            for(x = 0; x < im.w; ++x){
                float rx = cos(rad)*(x-cx) - sin(rad)*(y-cy) + cx;
                float ry = sin(rad)*(x-cx) + cos(rad)*(y-cy) + cy;
                float val = bilinear_interpolate(im, rx, ry, c);
                set_pixel(rot, x, y, c, val);
            }
        }
    }
    return rot;
}

void translate_image(image m, float s)
{
    int i;
    for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] += s;
}

void scale_image(image m, float s)
{
    int i;
    for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] *= s;
}

image crop_image(image im, int dx, int dy, int w, int h)
{
    image cropped = make_image(w, h, im.c);
    int i, j, k;
    for(k = 0; k < im.c; ++k){
        for(j = 0; j < h; ++j){
            for(i = 0; i < w; ++i){
                int r = j + dy;
                int c = i + dx;
                float val = 0;
                r = constrain_int(r, 0, im.h-1);
                c = constrain_int(c, 0, im.w-1);
                if (r >= 0 && r < im.h && c >= 0 && c < im.w) {
                    val = get_pixel(im, c, r, k);
                }
                set_pixel(cropped, i, j, k, val);
            }
        }
    }
    return cropped;
}

int best_3d_shift_r(image a, image b, int min, int max)
{
    if(min == max) return min;
    int mid = floor((min + max) / 2.);
    image c1 = crop_image(b, 0, mid, b.w, b.h);
    image c2 = crop_image(b, 0, mid+1, b.w, b.h);
    float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 10);
    float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 10);
    free_image(c1);
    free_image(c2);
    if(d1 < d2) return best_3d_shift_r(a, b, min, mid);
    else return best_3d_shift_r(a, b, mid+1, max);
}

int best_3d_shift(image a, image b, int min, int max)
{
    int i;
    int best = 0;
    float best_distance = FLT_MAX;
    for(i = min; i <= max; i += 2){
        image c = crop_image(b, 0, i, b.w, b.h);
        float d = dist_array(c.data, a.data, a.w*a.h*a.c, 100);
        if(d < best_distance){
            best_distance = d;
            best = i;
        }
        printf("%d %fn", i, d);
        free_image(c);
    }
    return best;
}

void composite_3d(char *f1, char *f2, char *out, int delta)
{
    if(!out) out = "out";
    image a = load_image(f1, 0,0,0);
    image b = load_image(f2, 0,0,0);
    int shift = best_3d_shift_r(a, b, -a.h/100, a.h/100);

    image c1 = crop_image(b, 10, shift, b.w, b.h);
    float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 100);
    image c2 = crop_image(b, -10, shift, b.w, b.h);
    float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 100);

    if(d2 < d1 && 0){
        image swap = a;
        a = b;
        b = swap;
        shift = -shift;
        printf("swapped, %dn", shift);
    }
    else{
        printf("%dn", shift);
    }

    image c = crop_image(b, delta, shift, a.w, a.h);
    int i;
    for(i = 0; i < c.w*c.h; ++i){
        c.data[i] = a.data[i];
    }
#ifdef OPENCV
    save_image_jpg(c, out);
#else
    save_image(c, out);
#endif
}

void fill_image(image m, float s)
{
    int i;
    for (i = 0; i < m.h*m.w*m.c; ++i) m.data[i] = s;
}

void letterbox_image_into(image im, int w, int h, image boxed)
{
    int new_w = im.w;
    int new_h = im.h;
    if (((float)w / im.w) < ((float)h / im.h)) {
        new_w = w;
        new_h = (im.h * w) / im.w;
    }
    else {
        new_h = h;
        new_w = (im.w * h) / im.h;
    }
    image resized = resize_image(im, new_w, new_h);
    embed_image(resized, boxed, (w - new_w) / 2, (h - new_h) / 2);
    free_image(resized);
}

image letterbox_image(image im, int w, int h)
{
    int new_w = im.w;
    int new_h = im.h;
    if (((float)w / im.w) < ((float)h / im.h)) {
        new_w = w;
        new_h = (im.h * w) / im.w;
    }
    else {
        new_h = h;
        new_w = (im.w * h) / im.h;
    }
    image resized = resize_image(im, new_w, new_h);
    image boxed = make_image(w, h, im.c);
    fill_image(boxed, .5);
    //int i;
    //for(i = 0; i < boxed.w*boxed.h*boxed.c; ++i) boxed.data[i] = 0;
    embed_image(resized, boxed, (w - new_w) / 2, (h - new_h) / 2);
    free_image(resized);
    return boxed;
}

image resize_max(image im, int max)
{
    int w = im.w;
    int h = im.h;
    if(w > h){
        h = (h * max) / w;
        w = max;
    } else {
        w = (w * max) / h;
        h = max;
    }
    if(w == im.w && h == im.h) return im;
    image resized = resize_image(im, w, h);
    return resized;
}

image resize_min(image im, int min)
{
    int w = im.w;
    int h = im.h;
    if(w < h){
        h = (h * min) / w;
        w = min;
    } else {
        w = (w * min) / h;
        h = min;
    }
    if(w == im.w && h == im.h) return im;
    image resized = resize_image(im, w, h);
    return resized;
}

image random_crop_image(image im, int w, int h)
{
    int dx = rand_int(0, im.w - w);
    int dy = rand_int(0, im.h - h);
    image crop = crop_image(im, dx, dy, w, h);
    return crop;
}

image random_augment_image(image im, float angle, float aspect, int low, int high, int size)
{
    aspect = rand_scale(aspect);
    int r = rand_int(low, high);
    int min = (im.h < im.w*aspect) ? im.h : im.w*aspect;
    float scale = (float)r / min;

    float rad = rand_uniform(-angle, angle) * TWO_PI / 360.;

    float dx = (im.w*scale/aspect - size) / 2.;
    float dy = (im.h*scale - size) / 2.;
    if(dx < 0) dx = 0;
    if(dy < 0) dy = 0;
    dx = rand_uniform(-dx, dx);
    dy = rand_uniform(-dy, dy);

    image crop = rotate_crop_image(im, rad, scale, size, size, dx, dy, aspect);

    return crop;
}

float three_way_max(float a, float b, float c)
{
    return (a > b) ? ( (a > c) ? a : c) : ( (b > c) ? b : c) ;
}

float three_way_min(float a, float b, float c)
{
    return (a < b) ? ( (a < c) ? a : c) : ( (b < c) ? b : c) ;
}

// http://www.cs.rit.edu/~ncs/color/t_convert.html
void rgb_to_hsv(image im)
{
    assert(im.c == 3);
    int i, j;
    float r, g, b;
    float h, s, v;
    for(j = 0; j < im.h; ++j){
        for(i = 0; i < im.w; ++i){
            r = get_pixel(im, i , j, 0);
            g = get_pixel(im, i , j, 1);
            b = get_pixel(im, i , j, 2);
            float max = three_way_max(r,g,b);
            float min = three_way_min(r,g,b);
            float delta = max - min;
            v = max;
            if(max == 0){
                s = 0;
                h = 0;
            }else{
                s = delta/max;
                if(r == max){
                    h = (g - b) / delta;
                } else if (g == max) {
                    h = 2 + (b - r) / delta;
                } else {
                    h = 4 + (r - g) / delta;
                }
                if (h < 0) h += 6;
                h = h/6.;
            }
            set_pixel(im, i, j, 0, h);
            set_pixel(im, i, j, 1, s);
            set_pixel(im, i, j, 2, v);
        }
    }
}

void hsv_to_rgb(image im)
{
    assert(im.c == 3);
    int i, j;
    float r, g, b;
    float h, s, v;
    float f, p, q, t;
    for(j = 0; j < im.h; ++j){
        for(i = 0; i < im.w; ++i){
            h = 6 * get_pixel(im, i , j, 0);
            s = get_pixel(im, i , j, 1);
            v = get_pixel(im, i , j, 2);
            if (s == 0) {
                r = g = b = v;
            } else {
                int index = floor(h);
                f = h - index;
                p = v*(1-s);
                q = v*(1-s*f);
                t = v*(1-s*(1-f));
                if(index == 0){
                    r = v; g = t; b = p;
                } else if(index == 1){
                    r = q; g = v; b = p;
                } else if(index == 2){
                    r = p; g = v; b = t;
                } else if(index == 3){
                    r = p; g = q; b = v;
                } else if(index == 4){
                    r = t; g = p; b = v;
                } else {
                    r = v; g = p; b = q;
                }
            }
            set_pixel(im, i, j, 0, r);
            set_pixel(im, i, j, 1, g);
            set_pixel(im, i, j, 2, b);
        }
    }
}

image grayscale_image(image im)
{
    assert(im.c == 3);
    int i, j, k;
    image gray = make_image(im.w, im.h, 1);
    float scale[] = {0.587, 0.299, 0.114};
    for(k = 0; k < im.c; ++k){
        for(j = 0; j < im.h; ++j){
            for(i = 0; i < im.w; ++i){
                gray.data[i+im.w*j] += scale[k]*get_pixel(im, i, j, k);
            }
        }
    }
    return gray;
}

image threshold_image(image im, float thresh)
{
    int i;
    image t = make_image(im.w, im.h, im.c);
    for(i = 0; i < im.w*im.h*im.c; ++i){
        t.data[i] = im.data[i]>thresh ? 1 : 0;
    }
    return t;
}

image blend_image(image fore, image back, float alpha)
{
    assert(fore.w == back.w && fore.h == back.h && fore.c == back.c);
    image blend = make_image(fore.w, fore.h, fore.c);
    int i, j, k;
    for(k = 0; k < fore.c; ++k){
        for(j = 0; j < fore.h; ++j){
            for(i = 0; i < fore.w; ++i){
                float val = alpha * get_pixel(fore, i, j, k) +
                    (1 - alpha)* get_pixel(back, i, j, k);
                set_pixel(blend, i, j, k, val);
            }
        }
    }
    return blend;
}

void scale_image_channel(image im, int c, float v)
{
    int i, j;
    for(j = 0; j < im.h; ++j){
        for(i = 0; i < im.w; ++i){
            float pix = get_pixel(im, i, j, c);
            pix = pix*v;
            set_pixel(im, i, j, c, pix);
        }
    }
}

void translate_image_channel(image im, int c, float v)
{
    int i, j;
    for(j = 0; j < im.h; ++j){
        for(i = 0; i < im.w; ++i){
            float pix = get_pixel(im, i, j, c);
            pix = pix+v;
            set_pixel(im, i, j, c, pix);
        }
    }
}

image binarize_image(image im)
{
    image c = copy_image(im);
    int i;
    for(i = 0; i < im.w * im.h * im.c; ++i){
        if(c.data[i] > .5) c.data[i] = 1;
        else c.data[i] = 0;
    }
    return c;
}

void saturate_image(image im, float sat)
{
    rgb_to_hsv(im);
    scale_image_channel(im, 1, sat);
    hsv_to_rgb(im);
    constrain_image(im);
}

void hue_image(image im, float hue)
{
    rgb_to_hsv(im);
    int i;
    for(i = 0; i < im.w*im.h; ++i){
        im.data[i] = im.data[i] + hue;
        if (im.data[i] > 1) im.data[i] -= 1;
        if (im.data[i] < 0) im.data[i] += 1;
    }
    hsv_to_rgb(im);
    constrain_image(im);
}

void exposure_image(image im, float sat)
{
    rgb_to_hsv(im);
    scale_image_channel(im, 2, sat);
    hsv_to_rgb(im);
    constrain_image(im);
}

void distort_image(image im, float hue, float sat, float val)
{
    if (im.c >= 3)
    {
        rgb_to_hsv(im);
        scale_image_channel(im, 1, sat);
        scale_image_channel(im, 2, val);
        int i;
        for(i = 0; i < im.w*im.h; ++i){
            im.data[i] = im.data[i] + hue;
            if (im.data[i] > 1) im.data[i] -= 1;
            if (im.data[i] < 0) im.data[i] += 1;
        }
        hsv_to_rgb(im);
    }
    else
    {
        scale_image_channel(im, 0, val);
    }
    constrain_image(im);
}

void random_distort_image(image im, float hue, float saturation, float exposure)
{
    float dhue = rand_uniform_strong(-hue, hue);
    float dsat = rand_scale(saturation);
    float dexp = rand_scale(exposure);
    distort_image(im, dhue, dsat, dexp);
}

void saturate_exposure_image(image im, float sat, float exposure)
{
    rgb_to_hsv(im);
    scale_image_channel(im, 1, sat);
    scale_image_channel(im, 2, exposure);
    hsv_to_rgb(im);
    constrain_image(im);
}

float bilinear_interpolate(image im, float x, float y, int c)
{
    int ix = (int) floorf(x);
    int iy = (int) floorf(y);

    float dx = x - ix;
    float dy = y - iy;

    float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) +
        dy     * (1-dx) * get_pixel_extend(im, ix, iy+1, c) +
        (1-dy) *   dx   * get_pixel_extend(im, ix+1, iy, c) +
        dy     *   dx   * get_pixel_extend(im, ix+1, iy+1, c);
    return val;
}

image resize_image(image im, int w, int h)
{
    image resized = make_image(w, h, im.c);
    image part = make_image(w, im.h, im.c);
    int r, c, k;
    float w_scale = (float)(im.w - 1) / (w - 1);
    float h_scale = (float)(im.h - 1) / (h - 1);
    for(k = 0; k < im.c; ++k){
        for(r = 0; r < im.h; ++r){
            for(c = 0; c < w; ++c){
                float val = 0;
                if(c == w-1 || im.w == 1){
                    val = get_pixel(im, im.w-1, r, k);
                } else {
                    float sx = c*w_scale;
                    int ix = (int) sx;
                    float dx = sx - ix;
                    val = (1 - dx) * get_pixel(im, ix, r, k) + dx * get_pixel(im, ix+1, r, k);
                }
                set_pixel(part, c, r, k, val);
            }
        }
    }
    for(k = 0; k < im.c; ++k){
        for(r = 0; r < h; ++r){
            float sy = r*h_scale;
            int iy = (int) sy;
            float dy = sy - iy;
            for(c = 0; c < w; ++c){
                float val = (1-dy) * get_pixel(part, c, iy, k);
                set_pixel(resized, c, r, k, val);
            }
            if(r == h-1 || im.h == 1) continue;
            for(c = 0; c < w; ++c){
                float val = dy * get_pixel(part, c, iy+1, k);
                add_pixel(resized, c, r, k, val);
            }
        }
    }

    free_image(part);
    return resized;
}


void test_resize(char *filename)
{
    image im = load_image(filename, 0,0, 3);
    float mag = mag_array(im.data, im.w*im.h*im.c);
    printf("L2 Norm: %fn", mag);
    image gray = grayscale_image(im);

    image c1 = copy_image(im);
    image c2 = copy_image(im);
    image c3 = copy_image(im);
    image c4 = copy_image(im);
    distort_image(c1, .1, 1.5, 1.5);
    distort_image(c2, -.1, .66666, .66666);
    distort_image(c3, .1, 1.5, .66666);
    distort_image(c4, .1, .66666, 1.5);


    show_image(im,   "Original");
    show_image(gray, "Gray");
    show_image(c1, "C1");
    show_image(c2, "C2");
    show_image(c3, "C3");
    show_image(c4, "C4");
#ifdef OPENCV
    while(1){
        image aug = random_augment_image(im, 0, .75, 320, 448, 320);
        show_image(aug, "aug");
        free_image(aug);


        float exposure = 1.15;
        float saturation = 1.15;
        float hue = .05;

        image c = copy_image(im);

        float dexp = rand_scale(exposure);
        float dsat = rand_scale(saturation);
        float dhue = rand_uniform(-hue, hue);

        distort_image(c, dhue, dsat, dexp);
        show_image(c, "rand");
        printf("%f %f %fn", dhue, dsat, dexp);
        free_image(c);
        cvWaitKey(0);
    }
#endif
}


image load_image_stb(char *filename, int channels)
{
    int w, h, c;
    unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
    if (!data) {
        char shrinked_filename[1024];
        if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long");
        else sprintf(shrinked_filename, "%s", filename);
        fprintf(stderr, "*image.c 1979*Cannot load image "%s"nSTB Reason: %sn", shrinked_filename, stbi_failure_reason());
        FILE* fw = fopen("bad.list", "a");
        fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw);
        char *new_line = "n";
        fwrite(new_line, sizeof(char), strlen(new_line), fw);
        fclose(fw);
        if (check_mistakes) getchar();
        return make_image(10, 10, 3);
        //exit(EXIT_FAILURE);
    }
    if(channels) c = channels;
    int i,j,k;
    image im = make_image(w, h, c);
    for(k = 0; k < c; ++k){
        for(j = 0; j < h; ++j){
            for(i = 0; i < w; ++i){
                int dst_index = i + w*j + w*h*k;
                int src_index = k + c*i + c*w*j;
                im.data[dst_index] = (float)data[src_index]/255.;
            }
        }
    }
    free(data);
    return im;
}

image load_image(char *filename, int w, int h, int c)
{
#ifdef OPENCV

#ifndef CV_VERSION_EPOCH
    //image out = load_image_stb(filename, c);    // OpenCV 3.x
    image out = load_image_cv(filename, c);
#else
    image out = load_image_cv(filename, c);        // OpenCV 2.4.x
#endif

#else
    image out = load_image_stb(filename, c);    // without OpenCV
#endif

    if((h && w) && (h != out.h || w != out.w)){
        image resized = resize_image(out, w, h);
        free_image(out);
        out = resized;
    }
    return out;
}

image load_image_color(char *filename, int w, int h)
{
    return load_image(filename, w, h, 3);
}

image get_image_layer(image m, int l)
{
    image out = make_image(m.w, m.h, 1);
    int i;
    for(i = 0; i < m.h*m.w; ++i){
        out.data[i] = m.data[i+l*m.h*m.w];
    }
    return out;
}

void print_image(image m)
{
    int i, j, k;
    for(i =0 ; i < m.c; ++i){
        for(j =0 ; j < m.h; ++j){
            for(k = 0; k < m.w; ++k){
                printf("%.2lf, ", m.data[i*m.h*m.w + j*m.w + k]);
                if(k > 30) break;
            }
            printf("n");
            if(j > 30) break;
        }
        printf("n");
    }
    printf("n");
}

image collapse_images_vert(image *ims, int n)
{
    int color = 1;
    int border = 1;
    int h,w,c;
    w = ims[0].w;
    h = (ims[0].h + border) * n - border;
    c = ims[0].c;
    if(c != 3 || !color){
        w = (w+border)*c - border;
        c = 1;
    }

    image filters = make_image(w, h, c);
    int i,j;
    for(i = 0; i < n; ++i){
        int h_offset = i*(ims[0].h+border);
        image copy = copy_image(ims[i]);
        //normalize_image(copy);
        if(c == 3 && color){
            embed_image(copy, filters, 0, h_offset);
        }
        else{
            for(j = 0; j < copy.c; ++j){
                int w_offset = j*(ims[0].w+border);
                image layer = get_image_layer(copy, j);
                embed_image(layer, filters, w_offset, h_offset);
                free_image(layer);
            }
        }
        free_image(copy);
    }
    return filters;
}

image collapse_images_horz(image *ims, int n)
{
    int color = 1;
    int border = 1;
    int h,w,c;
    int size = ims[0].h;
    h = size;
    w = (ims[0].w + border) * n - border;
    c = ims[0].c;
    if(c != 3 || !color){
        h = (h+border)*c - border;
        c = 1;
    }

    image filters = make_image(w, h, c);
    int i,j;
    for(i = 0; i < n; ++i){
        int w_offset = i*(size+border);
        image copy = copy_image(ims[i]);
        //normalize_image(copy);
        if(c == 3 && color){
            embed_image(copy, filters, w_offset, 0);
        }
        else{
            for(j = 0; j < copy.c; ++j){
                int h_offset = j*(size+border);
                image layer = get_image_layer(copy, j);
                embed_image(layer, filters, w_offset, h_offset);
                free_image(layer);
            }
        }
        free_image(copy);
    }
    return filters;
}

void show_image_normalized(image im, const char *name)
{
    image c = copy_image(im);
    normalize_image(c);
    show_image(c, name);
    free_image(c);
}

void show_images(image *ims, int n, char *window)
{
    image m = collapse_images_vert(ims, n);
    /*
       int w = 448;
       int h = ((float)m.h/m.w) * 448;
       if(h > 896){
       h = 896;
       w = ((float)m.w/m.h) * 896;
       }
       image sized = resize_image(m, w, h);
     */
    normalize_image(m);
    save_image(m, window);
    show_image(m, window);
    free_image(m);
}

void free_image(image m)
{
    if(m.data){
        free(m.data);
    }
}

demo.c

复制代码

#include "network.h"
#include "detection_layer.h"
#include "region_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
#include "image.h"
#include "demo.h"
#ifdef WIN32
#include <time.h>
#include <winsock.h>
#include "gettimeofday.h"
#else
#include <sys/time.h>
#endif

#define FRAMES 3

#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/core/version.hpp"
#ifndef CV_VERSION_EPOCH
#include "opencv2/videoio/videoio_c.h"
#endif
#include "http_stream.h"
image get_image_from_stream(CvCapture *cap);

static char **demo_names;
static image **demo_alphabet;
static int demo_classes;

static float **probs;
static box *boxes;
static network net;
static image in_s ;
static image det_s;
static CvCapture * cap;
static int cpp_video_capture = 0;
static float fps = 0;
static float demo_thresh = 0;
static int demo_ext_output = 0;

static float *predictions[FRAMES];
static int demo_index = 0;
static image images[FRAMES];
static IplImage* ipl_images[FRAMES];
static float *avg;

void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes);
void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output);
void show_image_cv_ipl(IplImage *disp, const char *name);
image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close);
image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close);
int get_stream_fps(CvCapture *cap, int cpp_video_capture);
IplImage* in_img;
IplImage* det_img;
IplImage* show_img;

static int flag_exit;
static int letter_box = 0;

void *fetch_in_thread(void *ptr)
{
    //in = get_image_from_stream(cap);
    int dont_close_stream = 0;    // set 1 if your IP-camera periodically turns off and turns on video-stream
    if(letter_box)
        in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream);
    else
        in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream);
    if(!in_s.data){
        //error("Stream closed.");
        printf("Stream closed.n");
        flag_exit = 1;
        return EXIT_FAILURE;
    }
    //in_s = resize_image(in, net.w, net.h);

return 0;
}

void *detect_in_thread(void *ptr)
{
    float nms = .45;    // 0.4F

layer l = net.layers[net.n-1];
    float *X = det_s.data;
    float *prediction = network_predict(net, X);

memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float));
    mean_arrays(predictions, FRAMES, l.outputs, avg);
    l.output = avg;

free_image(det_s);

int nboxes = 0;
    detection *dets = NULL;
    if (letter_box)
        dets = get_network_boxes(&net, in_img->width, in_img->height, demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box
    else
        dets = get_network_boxes(&net, det_s.w, det_s.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized
    //if (nms) do_nms_obj(dets, nboxes, l.classes, nms);    // bad results
    if (nms) do_nms_sort(dets, nboxes, l.classes, nms);

printf("33[2J");
    printf("33[1;1H");
    printf("nFPS:%.1fn",fps);
    printf("Objects:nn");

ipl_images[demo_index] = det_img;
    det_img = ipl_images[(demo_index + FRAMES / 2 + 1) % FRAMES];
    demo_index = (demo_index + 1)%FRAMES;

draw_detections_cv_v3(det_img, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output);
    free_detections(dets, nboxes);

return 0;
}

double get_wall_time()
{
    struct timeval time;
    if (gettimeofday(&time,NULL)){
        return 0;
    }
    return (double)time.tv_sec + (double)time.tv_usec * .000001;
}

void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes,
    int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output)
{
    //skip = frame_skip;
    image **alphabet = load_alphabet();
    int delay = frame_skip;
    demo_names = names;
    demo_alphabet = alphabet;
    demo_classes = classes;
    demo_thresh = thresh;
    demo_ext_output = ext_output;
    printf("Demon");
    net = parse_network_cfg_custom(cfgfile, 1);    // set batch=1
    if(weightfile){
        load_weights(&net, weightfile);
    }
    //set_batch_network(&net, 1);
    fuse_conv_batchnorm(net);
    calculate_binary_weights(net);
    srand(2222222);

if(filename){
        printf("video file: %sn", filename);
//#ifdef CV_VERSION_EPOCH    // OpenCV 2.x
//        cap = cvCaptureFromFile(filename);
//#else                    // OpenCV 3.x
        cpp_video_capture = 1;
        cap = get_capture_video_stream(filename);
//#endif
    }else{
        printf("Webcam index: %dn", cam_index);
//#ifdef CV_VERSION_EPOCH    // OpenCV 2.x
//        cap = cvCaptureFromCAM(cam_index);
//#else                    // OpenCV 3.x
        cpp_video_capture = 1;
        cap = get_capture_webcam(cam_index);
//#endif
    }

if (!cap) {
#ifdef WIN32
        printf("Check that you have copied file opencv_ffmpeg340_64.dll to the same directory where is darknet.exe n");
#endif
        error("Couldn't connect to webcam.n");
    }

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

avg = (float *) calloc(l.outputs, sizeof(float));
    for(j = 0; j < FRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float));
    for(j = 0; j < FRAMES; ++j) images[j] = make_image(1,1,3);
	//开辟存储空间
    boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box));
    probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *));
    for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));

if (l.classes != demo_classes) {
        printf("Parameters don't match: in cfg-file classes=%d, in data-file classes=%d n", l.classes, demo_classes);
        getchar();
        exit(0);
    }

flag_exit = 0;
	//多线程
    pthread_t fetch_thread;
    pthread_t detect_thread;

fetch_in_thread(0);
    det_img = in_img;
    det_s = in_s;

fetch_in_thread(0);
    detect_in_thread(0);
    det_img = in_img;
    det_s = in_s;

for(j = 0; j < FRAMES/2; ++j){
        fetch_in_thread(0);
        detect_in_thread(0);
        det_img = in_img;
        det_s = in_s;
    }

int count = 0;
    if(!prefix && !dont_show){		//不展示demo窗口
        cvNamedWindow("Demo", CV_WINDOW_NORMAL);
        cvMoveWindow("Demo", 0, 0);
        cvResizeWindow("Demo", 1352, 1013);
    }

CvVideoWriter* output_video_writer = NULL;    // cv::VideoWriter output_video;  可添加检测视频的结果保存函数，不用添加，参数有路径就自动保存？？？？？
    if (out_filename && !flag_exit)
    {
        CvSize size;
        size.width = det_img->width, size.height = det_img->height;
        int src_fps = 25;
        src_fps = get_stream_fps(cap, cpp_video_capture);

//const char* output_name = "test_dnn_out.avi";
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('H', '2', '6', '4'), src_fps, size, 1);
        output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('D', 'I', 'V', 'X'), src_fps, size, 1);
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'J', 'P', 'G'), src_fps, size, 1);
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', 'V'), src_fps, size, 1);
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', '2'), src_fps, size, 1);
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('X', 'V', 'I', 'D'), src_fps, size, 1);
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('W', 'M', 'V', '2'), src_fps, size, 1);
    }

double before = get_wall_time();

while(1){		//进入循环，进入算法，展示结果
        ++count;
        if(1){
            if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed");
            if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed");

if(!prefix){		//默认0进
                if (!dont_show) {
                    show_image_cv_ipl(show_img, "Demo");	//demo窗口，展示结果视频
					//save_cut_image();
					printf("/*************demo.c 252***************");
                    int c = cvWaitKey(1);
                    if (c == 10) {		//在demo窗口下按换行键Ctrl+Enter，暂停demo窗口的刷新，但是cmd后台仍然继续检测，再次按下有delay的显示（抽帧显示）第三次按下则还原实时显示。和在demo下长按enter一样的效果。demo下换行暂停后按enter会展示那一帧图片。到底是暂停还是delay展示？？？？？
                        if (frame_skip == 0) frame_skip = 60;
                        else if (frame_skip == 4) frame_skip = 0;
                        else if (frame_skip == 60) frame_skip = 4;
                        else frame_skip = 0;
                    }
                    else if (c == 27 || c == 1048603) // ESC - exit (OpenCV 2.x / 3.x)
                    {
                        flag_exit = 1;
                    }
                }
            }else{											//prefix有值就进。prefix为char真 类名？，dont_show为1，不展示demo，截图？？？默认为0不进
                char buff[256];
                sprintf(buff, "results//%s_%08d.jpg", prefix, count);
                cvSaveImage(buff, show_img, 0);			//—prefix 走此，不展示demo窗口，但没有存图
				printf("**********demo.c 269 save prefix************");
                //save_image(disp, buff);
            }

// if you run it with param -http_port 8090  then open URL in your web-browser: http://localhost:8090
            if (http_stream_port > 0 && show_img) {
                //int port = 8090;
                int port = http_stream_port;
                int timeout = 200;
                int jpeg_quality = 30;    // 1 - 100
                send_mjpeg(show_img, port, timeout, jpeg_quality);	//与正常摄像头无关
            }

// save video file										参数带有-out_filename就自动保存视频
            if (output_video_writer && show_img) {
                cvWriteFrame(output_video_writer, show_img);
                printf("n cvWriteFrame n");
            }

cvReleaseImage(&show_img);

pthread_join(fetch_thread, 0);
            pthread_join(detect_thread, 0);

if (flag_exit == 1) break;

if(delay == 0){
                show_img = det_img;
            }
            det_img = in_img;
            det_s = in_s;
        }else {				//应该进不来
            fetch_in_thread(0);
            det_img = in_img;
            det_s = in_s;
            detect_in_thread(0);

show_img = det_img;
            if (!dont_show) {
                show_image_cv_ipl(show_img, "Demo");				//展示视频的结果窗口，show_img为处理后的图片
				cvSaveImage("***********demo 307****", show_img, 0);
				printf("/***************demo.c 307******************/");
                cvWaitKey(1);
            }
            cvReleaseImage(&show_img);
        }
        --delay;
        if(delay < 0){
            delay = frame_skip;

double after = get_wall_time();
            float curr = 1./(after - before);
            fps = curr;
            before = after;
        }
    }
    printf("*****demo.c 321 ******  input video stream closed. n");
    if (output_video_writer) {
        cvReleaseVideoWriter(&output_video_writer);
        printf("output_video_writer closed. n");
    }

// free memory
    cvReleaseImage(&show_img);
    cvReleaseImage(&in_img);
    free_image(in_s);

free(avg);
    for (j = 0; j < FRAMES; ++j) free(predictions[j]);
    for (j = 0; j < FRAMES; ++j) free_image(images[j]);

for (j = 0; j < l.w*l.h*l.n; ++j) free(probs[j]);
    free(boxes);
    free(probs);

free_ptrs(names, net.layers[net.n - 1].classes);

free_network(net);
}
#else	//若没有opencv
void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes,
    int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output)
{
    fprintf(stderr, "Demo needs OpenCV for webcam images.n");
}
#endif

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#include "network.h"
#include "detection_layer.h"
#include "region_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
#include "image.h"
#include "demo.h"
#ifdef WIN32
#include <time.h>
#include <winsock.h>
#include "gettimeofday.h"
#else
#include <sys/time.h>
#endif

#define FRAMES 3

#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/core/version.hpp"
#ifndef CV_VERSION_EPOCH
#include "opencv2/videoio/videoio_c.h"
#endif
#include "http_stream.h"
image get_image_from_stream(CvCapture *cap);

static char **demo_names;
static image **demo_alphabet;
static int demo_classes;

static float **probs;
static box *boxes;
static network net;
static image in_s ;
static image det_s;
static CvCapture * cap;
static int cpp_video_capture = 0;
static float fps = 0;
static float demo_thresh = 0;
static int demo_ext_output = 0;

static float *predictions[FRAMES];
static int demo_index = 0;
static image images[FRAMES];
static IplImage* ipl_images[FRAMES];
static float *avg;

void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes);
void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output);
void show_image_cv_ipl(IplImage *disp, const char *name);
image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close);
image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close);
int get_stream_fps(CvCapture *cap, int cpp_video_capture);
IplImage* in_img;
IplImage* det_img;
IplImage* show_img;

static int flag_exit;
static int letter_box = 0;

void *fetch_in_thread(void *ptr)
{
    //in = get_image_from_stream(cap);
    int dont_close_stream = 0;    // set 1 if your IP-camera periodically turns off and turns on video-stream
    if(letter_box)
        in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream);
    else
        in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream);
    if(!in_s.data){
        //error("Stream closed.");
        printf("Stream closed.n");
        flag_exit = 1;
        return EXIT_FAILURE;
    }
    //in_s = resize_image(in, net.w, net.h);

    return 0;
}

void *detect_in_thread(void *ptr)
{
    float nms = .45;    // 0.4F

    layer l = net.layers[net.n-1];
    float *X = det_s.data;
    float *prediction = network_predict(net, X);

    memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float));
    mean_arrays(predictions, FRAMES, l.outputs, avg);
    l.output = avg;

    free_image(det_s);

    int nboxes = 0;
    detection *dets = NULL;
    if (letter_box)
        dets = get_network_boxes(&net, in_img->width, in_img->height, demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box
    else
        dets = get_network_boxes(&net, det_s.w, det_s.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized
    //if (nms) do_nms_obj(dets, nboxes, l.classes, nms);    // bad results
    if (nms) do_nms_sort(dets, nboxes, l.classes, nms);


    printf("33[2J");
    printf("33[1;1H");
    printf("nFPS:%.1fn",fps);
    printf("Objects:nn");

    ipl_images[demo_index] = det_img;
    det_img = ipl_images[(demo_index + FRAMES / 2 + 1) % FRAMES];
    demo_index = (demo_index + 1)%FRAMES;

    draw_detections_cv_v3(det_img, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output);
    free_detections(dets, nboxes);

    return 0;
}

double get_wall_time()
{
    struct timeval time;
    if (gettimeofday(&time,NULL)){
        return 0;
    }
    return (double)time.tv_sec + (double)time.tv_usec * .000001;
}

void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes,
    int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output)
{
    //skip = frame_skip;
    image **alphabet = load_alphabet();
    int delay = frame_skip;
    demo_names = names;
    demo_alphabet = alphabet;
    demo_classes = classes;
    demo_thresh = thresh;
    demo_ext_output = ext_output;
    printf("Demon");
    net = parse_network_cfg_custom(cfgfile, 1);    // set batch=1
    if(weightfile){
        load_weights(&net, weightfile);
    }
    //set_batch_network(&net, 1);
    fuse_conv_batchnorm(net);
    calculate_binary_weights(net);
    srand(2222222);

    if(filename){
        printf("video file: %sn", filename);
//#ifdef CV_VERSION_EPOCH    // OpenCV 2.x
//        cap = cvCaptureFromFile(filename);
//#else                    // OpenCV 3.x
        cpp_video_capture = 1;
        cap = get_capture_video_stream(filename);
//#endif
    }else{
        printf("Webcam index: %dn", cam_index);
//#ifdef CV_VERSION_EPOCH    // OpenCV 2.x
//        cap = cvCaptureFromCAM(cam_index);
//#else                    // OpenCV 3.x
        cpp_video_capture = 1;
        cap = get_capture_webcam(cam_index);
//#endif
    }

    if (!cap) {
#ifdef WIN32
        printf("Check that you have copied file opencv_ffmpeg340_64.dll to the same directory where is darknet.exe n");
#endif
        error("Couldn't connect to webcam.n");
    }

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

    avg = (float *) calloc(l.outputs, sizeof(float));
    for(j = 0; j < FRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float));
    for(j = 0; j < FRAMES; ++j) images[j] = make_image(1,1,3);
	//开辟存储空间
    boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box));
    probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *));
    for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));

    if (l.classes != demo_classes) {
        printf("Parameters don't match: in cfg-file classes=%d, in data-file classes=%d n", l.classes, demo_classes);
        getchar();
        exit(0);
    }


    flag_exit = 0;
	//多线程
    pthread_t fetch_thread;
    pthread_t detect_thread;

    fetch_in_thread(0);
    det_img = in_img;
    det_s = in_s;

    fetch_in_thread(0);
    detect_in_thread(0);
    det_img = in_img;
    det_s = in_s;

    for(j = 0; j < FRAMES/2; ++j){
        fetch_in_thread(0);
        detect_in_thread(0);
        det_img = in_img;
        det_s = in_s;
    }

    int count = 0;
    if(!prefix && !dont_show){		//不展示demo窗口
        cvNamedWindow("Demo", CV_WINDOW_NORMAL);
        cvMoveWindow("Demo", 0, 0);
        cvResizeWindow("Demo", 1352, 1013);
    }

    CvVideoWriter* output_video_writer = NULL;    // cv::VideoWriter output_video;  可添加检测视频的结果保存函数，不用添加，参数有路径就自动保存？？？？？
    if (out_filename && !flag_exit)
    {
        CvSize size;
        size.width = det_img->width, size.height = det_img->height;
        int src_fps = 25;
        src_fps = get_stream_fps(cap, cpp_video_capture);

        //const char* output_name = "test_dnn_out.avi";
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('H', '2', '6', '4'), src_fps, size, 1);
        output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('D', 'I', 'V', 'X'), src_fps, size, 1);
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'J', 'P', 'G'), src_fps, size, 1);
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', 'V'), src_fps, size, 1);
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', '2'), src_fps, size, 1);
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('X', 'V', 'I', 'D'), src_fps, size, 1);
        //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('W', 'M', 'V', '2'), src_fps, size, 1);
    }

    double before = get_wall_time();

    while(1){		//进入循环，进入算法，展示结果
        ++count;
        if(1){
            if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed");
            if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed");

            if(!prefix){		//默认0进
                if (!dont_show) {
                    show_image_cv_ipl(show_img, "Demo");	//demo窗口，展示结果视频
					//save_cut_image();
					printf("/*************demo.c 252***************");
                    int c = cvWaitKey(1);
                    if (c == 10) {		//在demo窗口下按换行键Ctrl+Enter，暂停demo窗口的刷新，但是cmd后台仍然继续检测，再次按下有delay的显示（抽帧显示）第三次按下则还原实时显示。和在demo下长按enter一样的效果。demo下换行暂停后按enter会展示那一帧图片。到底是暂停还是delay展示？？？？？
                        if (frame_skip == 0) frame_skip = 60;
                        else if (frame_skip == 4) frame_skip = 0;
                        else if (frame_skip == 60) frame_skip = 4;
                        else frame_skip = 0;
                    }
                    else if (c == 27 || c == 1048603) // ESC - exit (OpenCV 2.x / 3.x)
                    {
                        flag_exit = 1;
                    }
                }
            }else{											//prefix有值就进。prefix为char真 类名？，dont_show为1，不展示demo，截图？？？默认为0不进
                char buff[256];
                sprintf(buff, "results//%s_%08d.jpg", prefix, count);
                cvSaveImage(buff, show_img, 0);			//—prefix 走此，不展示demo窗口，但没有存图
				printf("**********demo.c 269 save prefix************");
                //save_image(disp, buff);
            }

            // if you run it with param -http_port 8090  then open URL in your web-browser: http://localhost:8090
            if (http_stream_port > 0 && show_img) {
                //int port = 8090;
                int port = http_stream_port;
                int timeout = 200;
                int jpeg_quality = 30;    // 1 - 100
                send_mjpeg(show_img, port, timeout, jpeg_quality);	//与正常摄像头无关
            }

            // save video file										参数带有-out_filename就自动保存视频
            if (output_video_writer && show_img) {
                cvWriteFrame(output_video_writer, show_img);
                printf("n cvWriteFrame n");
            }

            cvReleaseImage(&show_img);

            pthread_join(fetch_thread, 0);
            pthread_join(detect_thread, 0);

            if (flag_exit == 1) break;

            if(delay == 0){
                show_img = det_img;
            }
            det_img = in_img;
            det_s = in_s;
        }else {				//应该进不来
            fetch_in_thread(0);
            det_img = in_img;
            det_s = in_s;
            detect_in_thread(0);

            show_img = det_img;
            if (!dont_show) {
                show_image_cv_ipl(show_img, "Demo");				//展示视频的结果窗口，show_img为处理后的图片
				cvSaveImage("***********demo 307****", show_img, 0);
				printf("/***************demo.c 307******************/");
                cvWaitKey(1);
            }
            cvReleaseImage(&show_img);
        }
        --delay;
        if(delay < 0){
            delay = frame_skip;

            double after = get_wall_time();
            float curr = 1./(after - before);
            fps = curr;
            before = after;
        }
    }
    printf("*****demo.c 321 ******  input video stream closed. n");
    if (output_video_writer) {
        cvReleaseVideoWriter(&output_video_writer);
        printf("output_video_writer closed. n");
    }

    // free memory
    cvReleaseImage(&show_img);
    cvReleaseImage(&in_img);
    free_image(in_s);

    free(avg);
    for (j = 0; j < FRAMES; ++j) free(predictions[j]);
    for (j = 0; j < FRAMES; ++j) free_image(images[j]);

    for (j = 0; j < l.w*l.h*l.n; ++j) free(probs[j]);
    free(boxes);
    free(probs);

    free_ptrs(names, net.layers[net.n - 1].classes);

    int i;
    const int nsize = 8;
    for (j = 0; j < nsize; ++j) {
        for (i = 32; i < 127; ++i) {
            free_image(alphabet[j][i]);
        }
        free(alphabet[j]);
    }
    free(alphabet);

    free_network(net);
}
#else	//若没有opencv
void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes,
    int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output)
{
    fprintf(stderr, "Demo needs OpenCV for webcam images.n");
}
#endif