text: #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
  
  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("%s\n", base);
      float avg_loss = -1;
      network *nets = calloc(ngpus, sizeof(network));
  
      srand(time(0));
      int seed = rand();
      int i;
      for(i = 0; i < ngpus; ++i){
          srand(seed);
  #ifdef GPU
          cuda_set_device(gpus[i]);
  #endif
          nets[i] = 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];
  
      int imgs = net.batch * net.subdivisions * ngpus;
      printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
      data train, buffer;
  
      layer l = net.layers[net.n - 1];
  
      int classes = l.classes;
      float jitter = l.jitter;
  
      list *plist = get_paths(train_images);
      //int N = plist->size;
      char **paths = (char **)list_to_array(plist);
  
  	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.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;
  	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("Resizing\n");
  			int dim = (rand() % 12 + (init_w/32 - 5)) * 32;	// +-160
              //if (get_current_batch(net)+100 > net.max_batches) dim = 544;
              //int dim = (rand() % 4 + 16) * 32;
              printf("%d\n", dim);
              args.w = dim;
              args.h = dim;
  
              pthread_join(load_thread, 0);
              train = buffer;
              free_data(train);
              load_thread = load_data(args);
  
              for(i = 0; i < ngpus; ++i){
                  resize_network(nets + i, dim, dim);
              }
              net = nets[0];
          }
          time=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 %f\n", 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 %d\n", truth.x, truth.y, truth.w, truth.h);
             draw_bbox(im, b, 8, 1,0,0);
             }
             save_image(im, "truth11");
           */
  
          printf("Loaded: %lf seconds\n", (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, %f rate, %lf seconds, %d images\n", 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);
  
  	//cvReleaseImage(&img);
  	//cvDestroyAllWindows();
  }
  
  
  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 %f\n", 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 %f\n", 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: %g\n", net.learning_rate, net.momentum, net.decay);
  	srand(time(0));
  
  	list *plist = get_paths(valid_images);
  	char **paths = (char **)list_to_array(plist);
  
  	layer l = net.layers[net.n - 1];
  	int classes = l.classes;
  
  	char buff[1024];
  	char *type = option_find_str(options, "eval", "voc");
  	FILE *fp = 0;
  	FILE **fps = 0;
  	int coco = 0;
  	int imagenet = 0;
  	if (0 == strcmp(type, "coco")) {
  		if (!outfile) outfile = "coco_results";
  		snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
  		fp = fopen(buff, "w");
  		fprintf(fp, "[\n");
  		coco = 1;
  	}
  	else if (0 == strcmp(type, "imagenet")) {
  		if (!outfile) outfile = "imagenet-detection";
  		snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
  		fp = fopen(buff, "w");
  		imagenet = 1;
  		classes = 200;
  	}
  	else {
  		if (!outfile) outfile = "comp4_det_test_";
  		fps = calloc(classes, sizeof(FILE *));
  		for (j = 0; j < classes; ++j) {
  			snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
  			fps[j] = fopen(buff, "w");
  		}
  	}
  
  
  	int m = plist->size;
  	int i = 0;
  	int t;
  
  	float thresh = .005;
  	float nms = .45;
  
  	int nthreads = 4;
  	image *val = calloc(nthreads, sizeof(image));
  	image *val_resized = calloc(nthreads, sizeof(image));
  	image *buf = calloc(nthreads, sizeof(image));
  	image *buf_resized = calloc(nthreads, sizeof(image));
  	pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
  
  	load_args args = { 0 };
  	args.w = net.w;
  	args.h = net.h;
  	args.type = IMAGE_DATA;
  	//args.type = LETTERBOX_DATA;
  
  	for (t = 0; t < nthreads; ++t) {
  		args.path = paths[i + t];
  		args.im = &buf[t];
  		args.resized = &buf_resized[t];
  		thr[t] = load_data_in_thread(args);
  	}
  	time_t start = time(0);
  	for (i = nthreads; i < m + nthreads; i += nthreads) {
  		fprintf(stderr, "%d\n", i);
  		for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
  			pthread_join(thr[t], 0);
  			val[t] = buf[t];
  			val_resized[t] = buf_resized[t];
  		}
  		for (t = 0; t < nthreads && i + t < m; ++t) {
  			args.path = paths[i + t];
  			args.im = &buf[t];
  			args.resized = &buf_resized[t];
  			thr[t] = load_data_in_thread(args);
  		}
  		for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
  			char *path = paths[i + t - nthreads];
  			char *id = basecfg(path);
  			float *X = val_resized[t].data;
  			network_predict(net, X);
  			int w = val[t].w;
  			int h = val[t].h;
  			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 Seconds\n", 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_color(path, 0, 0);
  		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];
  		find_replace(path, "images", "labels", labelpath);
  		find_replace(labelpath, "JPEGImages", "labels", labelpath);
  		find_replace(labelpath, ".jpg", ".txt", labelpath);
  		find_replace(labelpath, ".png", ".txt", labelpath);
  		find_replace(labelpath, ".bmp", ".txt", labelpath);
  		find_replace(labelpath, ".JPG", ".txt", labelpath);
  		find_replace(labelpath, ".JPEG", ".txt", labelpath);
  
  		int num_labels = 0;
  		box_label *truth = read_boxes(labelpath, &num_labels);
  		for (k = 0; k < 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 %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals / (i + 1), avg_iou * 100 / total, 100.*correct / total);
  		free(id);
  		free_image(orig);
  		free_image(sized);
  	}
  }
  
  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);
  
  	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(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.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, "%d\n", i);
  		for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
  			pthread_join(thr[t], 0);
  			val[t] = buf[t];
  			val_resized[t] = buf_resized[t];
  		}
  		for (t = 0; t < nthreads && i + t < m; ++t) {
  			args.path = paths[i + t];
  			args.im = &buf[t];
  			args.resized = &buf_resized[t];
  			thr[t] = load_data_in_thread(args);
  		}
  		for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
  			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;
  			int letterbox = (args.type == LETTERBOX_DATA);
  			float hier_thresh = 0;
  			detection *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];
  			find_replace(path, "images", "labels", labelpath);
  			find_replace(labelpath, "JPEGImages", "labels", labelpath);
  			find_replace(labelpath, ".jpg", ".txt", labelpath);
  			find_replace(labelpath, ".png", ".txt", labelpath);
  			find_replace(labelpath, ".bmp", ".txt", labelpath);
  			find_replace(labelpath, ".JPG", ".txt", labelpath);
  			find_replace(labelpath, ".JPEG", ".txt", labelpath);
  			int num_labels = 0;
  			box_label *truth = read_boxes(labelpath, &num_labels);
  			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];
  				find_replace(path_dif, "images", "labels", labelpath_dif);
  				find_replace(labelpath_dif, "JPEGImages", "labels", labelpath_dif);
  				find_replace(labelpath_dif, ".jpg", ".txt", labelpath_dif);
  				find_replace(labelpath_dif, ".JPEG", ".txt", labelpath_dif);
  				find_replace(labelpath_dif, ".png", ".txt", 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;
  
  			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 Seconds\n", (double)(time(0) - start));
  }
  
  #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];
  		find_replace(path, "images", "labels", labelpath);
  		find_replace(labelpath, "JPEGImages", "labels", labelpath);
  		find_replace(labelpath, ".jpg", ".txt", labelpath);
  		find_replace(labelpath, ".png", ".txt", labelpath);
  		find_replace(labelpath, ".bmp", ".txt", labelpath);
  		find_replace(labelpath, ".JPG", ".txt", labelpath);
  		find_replace(labelpath, ".JPEG", ".txt", labelpath);
  		int num_labels = 0;
  		box_label *truth = read_boxes(labelpath, &num_labels);
  		//printf(" new path: %s \n", labelpath);
  		for (j = 0; j < num_labels; ++j)
  		{
  			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]);
  
  	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) {
  			printf(" 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");
  	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);
  
  	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)
  {
      list *options = read_data_cfg(datacfg);
      char *name_list = option_find_str(options, "names", "data/names.list");
      char **names = get_labels(name_list);
  
      image **alphabet = load_alphabet();
      network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
      if(weightfile){
          load_weights(&net, weightfile);
      }
      //set_batch_network(&net, 1);
  	fuse_conv_batchnorm(net);
      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("Enter Image Path: ");
              fflush(stdout);
              input = fgets(input, 256, stdin);
              if(!input) return;
              strtok(input, "\n");
          }
          image im = load_image_color(input,0,0);
  		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();
          network_predict(net, X);
  		//network_predict_image(&net, im); letterbox = 1;
          printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time));
          //get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0);
  		// if (nms) do_nms_sort_v2(boxes, probs, l.w*l.h*l.n, l.classes, nms);
  		//draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
  		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);
  		draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes);
  		free_detections(dets, nboxes);
          save_image(im, "predictions");
  		if (!dont_show) {
  			show_image(im, "predictions");
  		}
  
          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;
      }
  }
  
  void run_detector(int argc, char **argv)
  {
  	int dont_show = find_arg(argc, argv, "-dont_show");
  	int show = find_arg(argc, argv, "-show");
  	int http_stream_port = find_int_arg(argc, argv, "-http_port", -1);
  	char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
  	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
  	float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
      int cam_index = find_int_arg(argc, argv, "-c", 0);
      int frame_skip = find_int_arg(argc, argv, "-s", 0);
  	int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);
  	int width = find_int_arg(argc, argv, "-width", -1);
  	int height = find_int_arg(argc, argv, "-height", -1);
      if(argc < 4){
          fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
          return;
      }
      char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
      int *gpus = 0;
      int gpu = 0;
      int ngpus = 0;
      if(gpu_list){
          printf("%s\n", gpu_list);
          int len = strlen(gpu_list);
          ngpus = 1;
          int i;
          for(i = 0; i < len; ++i){
              if (gpu_list[i] == ',') ++ngpus;
          }
          gpus = calloc(ngpus, sizeof(int));
          for(i = 0; i < ngpus; ++i){
              gpus[i] = atoi(gpu_list);
              gpu_list = strchr(gpu_list, ',')+1;
          }
      } else {
          gpu = gpu_index;
          gpus = &gpu;
          ngpus = 1;
      }
  
      int clear = find_arg(argc, argv, "-clear");
  
      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);
      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);
          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);
      }
  	else printf(" There isn't such command: %s", argv[2]);
  }
  