text: ﻿#include "network.h"
  #include "detection_layer.h"
  #include "cost_layer.h"
  #include "utils.h"
  #include "parser.h"
  #include "box.h"
  #include "demo.h"
  
  #ifdef OPENCV
  #include "opencv2/opencv.hpp"
  #endif
  #endif
  
  char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
  
  void train_yolo(char *cfgfile, char *weightfile)
  {
      char *train_images = "/data/voc/train.txt";
      char *backup_directory = "/home/pjreddie/backup/";
      srand(time(0));
      char *base = basecfg(cfgfile);
      printf("%s\n", base);
      float avg_loss = -1;
      network net = parse_network_cfg(cfgfile);
      if(weightfile){
          load_weights(&net, weightfile);
      }
      printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
      int imgs = net.batch*net.subdivisions;
      int i = *net.seen/imgs;
      data train, buffer;
  
  
      layer l = net.layers[net.n - 1];
  
      int side = l.side;
      int classes = l.classes;
      float jitter = l.jitter;
  
      list *plist = get_paths(train_images);
      //int N = plist->size;
      char **paths = (char **)list_to_array(plist);
  
      load_args args = {0};
      args.w = net.w;
      args.h = net.h;
      args.paths = paths;
      args.n = imgs;
      args.m = plist->size;
      args.classes = classes;
      args.jitter = jitter;
      args.num_boxes = side;
      args.d = &buffer;
      args.type = REGION_DATA;
  
      args.angle = net.angle;
      args.exposure = net.exposure;
      args.saturation = net.saturation;
      args.hue = net.hue;
  
      pthread_t load_thread = load_data_in_thread(args);
      clock_t time;
      //while(i*imgs < N*120){
      while(get_current_batch(net) < net.max_batches){
          i += 1;
          time=clock();
          pthread_join(load_thread, 0);
          train = buffer;
          load_thread = load_data_in_thread(args);
  
          printf("Loaded: %lf seconds\n", sec(clock()-time));
  
          time=clock();
          float loss = train_network(net, train);
          if (avg_loss < 0) avg_loss = loss;
          avg_loss = avg_loss*.9 + loss*.1;
  
          printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
          if(i%1000==0 || (i < 1000 && i%100 == 0)){
              char buff[256];
              sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
              save_weights(net, buff);
          }
          free_data(train);
      }
      char buff[256];
      sprintf(buff, "%s/%s_final.weights", backup_directory, base);
      save_weights(net, buff);
  }
  
  void print_yolo_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
  {
      int i, j;
      for(i = 0; i < total; ++i){
          float xmin = boxes[i].x - boxes[i].w/2.;
          float xmax = boxes[i].x + boxes[i].w/2.;
          float ymin = boxes[i].y - boxes[i].h/2.;
          float ymax = boxes[i].y + boxes[i].h/2.;
  
          if (xmin < 0) xmin = 0;
          if (ymin < 0) ymin = 0;
          if (xmax > w) xmax = w;
          if (ymax > h) ymax = h;
  
          for(j = 0; j < classes; ++j){
              if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],
                      xmin, ymin, xmax, ymax);
          }
      }
  }
  
  void validate_yolo(char *cfgfile, char *weightfile)
  {
      network net = parse_network_cfg(cfgfile);
      if(weightfile){
          load_weights(&net, weightfile);
      }
      set_batch_network(&net, 1);
      fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
      srand(time(0));
  
      char *base = "results/comp4_det_test_";
      //list *plist = get_paths("data/voc.2007.test");
      list *plist = get_paths("/home/pjreddie/data/voc/2007_test.txt");
      //list *plist = get_paths("data/voc.2012.test");
      char **paths = (char **)list_to_array(plist);
  
      layer l = net.layers[net.n-1];
      int classes = l.classes;
  
      int j;
      FILE **fps = calloc(classes, sizeof(FILE *));
      for(j = 0; j < classes; ++j){
          char buff[1024];
          snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
          fps[j] = fopen(buff, "w");
      }
      box *boxes = calloc(l.side*l.side*l.n, sizeof(box));
      float **probs = calloc(l.side*l.side*l.n, sizeof(float *));
      for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
  
      int m = plist->size;
      int i=0;
      int t;
  
      float thresh = .001;
      int nms = 1;
      float iou_thresh = .5;
  
      int nthreads = 8;
      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;
  
      for(t = 0; t < nthreads; ++t){
          args.path = paths[i+t];
          args.im = &buf[t];
          args.resized = &buf_resized[t];
          thr[t] = load_data_in_thread(args);
      }
      time_t start = time(0);
      for(i = nthreads; i < m+nthreads; i += nthreads){
          fprintf(stderr, "%d\n", i);
          for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
              pthread_join(thr[t], 0);
              val[t] = buf[t];
              val_resized[t] = buf_resized[t];
          }
          for(t = 0; t < nthreads && i+t < m; ++t){
              args.path = paths[i+t];
              args.im = &buf[t];
              args.resized = &buf_resized[t];
              thr[t] = load_data_in_thread(args);
          }
          for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
              char *path = paths[i+t-nthreads];
              char *id = basecfg(path);
              float *X = val_resized[t].data;
              network_predict(net, X);
              int w = val[t].w;
              int h = val[t].h;
              get_detection_boxes(l, w, h, thresh, probs, boxes, 0);
              if (nms) do_nms_sort_v2(boxes, probs, l.side*l.side*l.n, classes, iou_thresh);
              print_yolo_detections(fps, id, boxes, probs, l.side*l.side*l.n, classes, w, h);
              free(id);
              free_image(val[t]);
              free_image(val_resized[t]);
          }
      }
      fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
  }
  
  void validate_yolo_recall(char *cfgfile, char *weightfile)
  {
      network net = parse_network_cfg(cfgfile);
      if(weightfile){
          load_weights(&net, weightfile);
      }
      set_batch_network(&net, 1);
      fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
      srand(time(0));
  
      char *base = "results/comp4_det_test_";
      list *plist = get_paths("data/voc.2007.test");
      char **paths = (char **)list_to_array(plist);
  
      layer l = net.layers[net.n-1];
      int classes = l.classes;
      int side = l.side;
  
      int j, k;
      FILE **fps = calloc(classes, sizeof(FILE *));
      for(j = 0; j < classes; ++j){
          char buff[1024];
          snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
          fps[j] = fopen(buff, "w");
      }
      box *boxes = calloc(side*side*l.n, sizeof(box));
      float **probs = calloc(side*side*l.n, sizeof(float *));
      for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
  
      int m = plist->size;
      int i=0;
  
      float thresh = .001;
      float iou_thresh = .5;
      float nms = 0;
  
      int total = 0;
      int correct = 0;
      int proposals = 0;
      float avg_iou = 0;
  
      for(i = 0; i < m; ++i){
          char *path = paths[i];
          image orig = load_image_color(path, 0, 0);
          image sized = resize_image(orig, net.w, net.h);
          char *id = basecfg(path);
          network_predict(net, sized.data);
          get_detection_boxes(l, orig.w, orig.h, thresh, probs, boxes, 1);
          if (nms) do_nms(boxes, probs, side*side*l.n, 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 < side*side*l.n; ++k){
              if(probs[k][0] > thresh){
                  ++proposals;
              }
          }
          for (j = 0; j < num_labels; ++j) {
              ++total;
              box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
              float best_iou = 0;
              for(k = 0; k < side*side*l.n; ++k){
                  float iou = box_iou(boxes[k], t);
                  if(probs[k][0] > thresh && iou > best_iou){
                      best_iou = iou;
                  }
              }
              avg_iou += best_iou;
              if(best_iou > iou_thresh){
                  ++correct;
              }
          }
  
          fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
          free(id);
          free_image(orig);
          free_image(sized);
      }
  }
  
  void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
  {
      image **alphabet = load_alphabet();
      network net = parse_network_cfg(cfgfile);
      if(weightfile){
          load_weights(&net, weightfile);
      }
      detection_layer l = net.layers[net.n-1];
      set_batch_network(&net, 1);
      srand(2222222);
      clock_t time;
      char buff[256];
      char *input = buff;
      int j;
      float nms=.4;
      box *boxes = calloc(l.side*l.side*l.n, sizeof(box));
      float **probs = calloc(l.side*l.side*l.n, sizeof(float *));
      for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
      while(1){
          if(filename){
              strncpy(input, filename, 256);
          } else {
              printf("Enter Image Path: ");
              fflush(stdout);
              input = fgets(input, 256, stdin);
              if(!input) return;
              strtok(input, "\n");
          }
          image im = load_image_color(input,0,0);
          image sized = resize_image(im, net.w, net.h);
          float *X = sized.data;
          time=clock();
          network_predict(net, X);
          printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
          get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
          if (nms) do_nms_sort_v2(boxes, probs, l.side*l.side*l.n, l.classes, nms);
          //draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
          draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
          save_image(im, "predictions");
          show_image(im, "predictions");
  
          free_image(im);
          free_image(sized);
  #ifdef OPENCV
          cvWaitKey(0);
          cvDestroyAllWindows();
  #endif
          if (filename) break;
      }
  }
  
  void run_yolo(int argc, char **argv)
  {
  	int dont_show = find_arg(argc, argv, "-dont_show");
  	int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
      int json_port = find_int_arg(argc, argv, "-json_port", -1);
  	char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
      char *prefix = find_char_arg(argc, argv, "-prefix", 0);
      float thresh = find_float_arg(argc, argv, "-thresh", .2);
  	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 ext_output = find_arg(argc, argv, "-ext_output");
      if(argc < 4){
          fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
          return;
      }
  
      char *cfg = argv[3];
      char *weights = (argc > 4) ? argv[4] : 0;
      char *filename = (argc > 5) ? argv[5]: 0;
      if(0==strcmp(argv[2], "test")) test_yolo(cfg, weights, filename, thresh);
      else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights);
      else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights);
      else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights);
      else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, frame_skip,
  		prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output);
  }
  