text: #include "darknet.h"
  #include "yolo_v2_class.hpp"
  
  #include "network.h"
  
  extern "C" {
  #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"
  #include "option_list.h"
  #include "stb_image.h"
  }
  //#include <sys/time.h>
  
  #include <vector>
  #include <iostream>
  #include <algorithm>
  #include <cmath>
  
  #define NFRAMES 3
  
  //static Detector* detector = NULL;
  static std::unique_ptr<Detector> detector;
  
  int init(const char *configurationFilename, const char *weightsFilename, int gpu)
  {
      detector.reset(new Detector(configurationFilename, weightsFilename, gpu));
      return 1;
  }
  
  int detect_image(const char *filename, bbox_t_container &container)
  {
      std::vector<bbox_t> detection = detector->detect(filename);
      for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i)
          container.candidates[i] = detection[i];
      return detection.size();
  }
  
  int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container) {
  #ifdef OPENCV
      std::vector<char> vdata(data, data + data_length);
      cv::Mat image = imdecode(cv::Mat(vdata), 1);
  
      std::vector<bbox_t> detection = detector->detect(image);
      for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i)
          container.candidates[i] = detection[i];
      return detection.size();
  #else
      return -1;
  #endif    // OPENCV
  }
  
  int dispose() {
      //if (detector != NULL) delete detector;
      //detector = NULL;
      detector.reset();
      return 1;
  }
  
  int get_device_count() {
  #ifdef GPU
      int count = 0;
      cudaGetDeviceCount(&count);
      return count;
  #else
      return -1;
  #endif	// GPU
  }
  
  bool built_with_cuda(){
  #ifdef GPU
      return true;
  #else
      return false;
  #endif
  }
  
  bool built_with_cudnn(){
  #ifdef CUDNN
      return true;
  #else
      return false;
  #endif
  }
  
  bool built_with_opencv(){
  #ifdef OPENCV
      return true;
  #else
      return false;
  #endif
  }
  
  
  int get_device_name(int gpu, char* deviceName) {
  #ifdef GPU
      cudaDeviceProp prop;
      cudaGetDeviceProperties(&prop, gpu);
      std::string result = prop.name;
      std::copy(result.begin(), result.end(), deviceName);
      return 1;
  #else
      return -1;
  #endif	// GPU
  }
  
  #ifdef GPU
  void check_cuda(cudaError_t status) {
      if (status != cudaSuccess) {
          const char *s = cudaGetErrorString(status);
          printf("CUDA Error Prev: %s\n", s);
      }
  }
  #endif
  
  struct detector_gpu_t {
      network net;
      image images[NFRAMES];
      float *avg;
      float* predictions[NFRAMES];
      int demo_index;
      unsigned int *track_id;
  };
  
  LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id)
  {
      wait_stream = 0;
  #ifdef GPU
      int old_gpu_index;
      check_cuda( cudaGetDevice(&old_gpu_index) );
  #endif
  
      detector_gpu_ptr = std::make_shared<detector_gpu_t>();
      detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
  
  #ifdef GPU
      //check_cuda( cudaSetDevice(cur_gpu_id) );
      cuda_set_device(cur_gpu_id);
      printf(" Used GPU %d \n", cur_gpu_id);
  #endif
      network &net = detector_gpu.net;
      net.gpu_index = cur_gpu_id;
      //gpu_index = i;
  
      _cfg_filename = cfg_filename;
      _weight_filename = weight_filename;
  
      char *cfgfile = const_cast<char *>(_cfg_filename.c_str());
      char *weightfile = const_cast<char *>(_weight_filename.c_str());
  
      net = parse_network_cfg_custom(cfgfile, 1, 1);
      if (weightfile) {
          load_weights(&net, weightfile);
      }
      set_batch_network(&net, 1);
      net.gpu_index = cur_gpu_id;
      fuse_conv_batchnorm(net);
  
      layer l = net.layers[net.n - 1];
      int j;
  
      detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float));
      for (j = 0; j < NFRAMES; ++j) detector_gpu.predictions[j] = (float*)calloc(l.outputs, sizeof(float));
      for (j = 0; j < NFRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3);
  
      detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int));
      for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1;
  
  #ifdef GPU
      check_cuda( cudaSetDevice(old_gpu_index) );
  #endif
  }
  
  
  LIB_API Detector::~Detector()
  {
      detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
      //layer l = detector_gpu.net.layers[detector_gpu.net.n - 1];
  
      free(detector_gpu.track_id);
  
      free(detector_gpu.avg);
      for (int j = 0; j < NFRAMES; ++j) free(detector_gpu.predictions[j]);
      for (int j = 0; j < NFRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data);
  
  #ifdef GPU
      int old_gpu_index;
      cudaGetDevice(&old_gpu_index);
      cuda_set_device(detector_gpu.net.gpu_index);
  #endif
  
      free_network(detector_gpu.net);
  
  #ifdef GPU
      cudaSetDevice(old_gpu_index);
  #endif
  }
  
  LIB_API int Detector::get_net_width() const {
      detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
      return detector_gpu.net.w;
  }
  LIB_API int Detector::get_net_height() const {
      detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
      return detector_gpu.net.h;
  }
  LIB_API int Detector::get_net_color_depth() const {
      detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
      return detector_gpu.net.c;
  }
  
  
  LIB_API std::vector<bbox_t> Detector::detect(std::string image_filename, float thresh, bool use_mean)
  {
      std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; });
      *image_ptr = load_image(image_filename);
      return detect(*image_ptr, thresh, use_mean);
  }
  
  static image load_image_stb(char *filename, int channels)
  {
      int w, h, c;
      unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
      if (!data)
          throw std::runtime_error("file not found");
      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;
  }
  
  LIB_API image_t Detector::load_image(std::string image_filename)
  {
      char *input = const_cast<char *>(image_filename.c_str());
      image im = load_image_stb(input, 3);
  
      image_t img;
      img.c = im.c;
      img.data = im.data;
      img.h = im.h;
      img.w = im.w;
  
      return img;
  }
  
  
  LIB_API void Detector::free_image(image_t m)
  {
      if (m.data) {
          free(m.data);
      }
  }
  
  LIB_API std::vector<bbox_t> Detector::detect(image_t img, float thresh, bool use_mean)
  {
      detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
      network &net = detector_gpu.net;
  #ifdef GPU
      int old_gpu_index;
      cudaGetDevice(&old_gpu_index);
      if(cur_gpu_id != old_gpu_index)
          cudaSetDevice(net.gpu_index);
  
      net.wait_stream = wait_stream;    // 1 - wait CUDA-stream, 0 - not to wait
  #endif
      //std::cout << "net.gpu_index = " << net.gpu_index << std::endl;
  
      image im;
      im.c = img.c;
      im.data = img.data;
      im.h = img.h;
      im.w = img.w;
  
      image sized;
  
      if (net.w == im.w && net.h == im.h) {
          sized = make_image(im.w, im.h, im.c);
          memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float));
      }
      else
          sized = resize_image(im, net.w, net.h);
  
      layer l = net.layers[net.n - 1];
  
      float *X = sized.data;
  
      float *prediction = network_predict(net, X);
  
      if (use_mean) {
          memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float));
          mean_arrays(detector_gpu.predictions, NFRAMES, l.outputs, detector_gpu.avg);
          l.output = detector_gpu.avg;
          detector_gpu.demo_index = (detector_gpu.demo_index + 1) % NFRAMES;
      }
      //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0);
      //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms);
  
      int nboxes = 0;
      int letterbox = 0;
      float hier_thresh = 0.5;
      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);
  
      std::vector<bbox_t> bbox_vec;
  
      for (int i = 0; i < nboxes; ++i) {
          box b = dets[i].bbox;
          int const obj_id = max_index(dets[i].prob, l.classes);
          float const prob = dets[i].prob[obj_id];
  
          if (prob > thresh)
          {
              bbox_t bbox;
              bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w);
              bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h);
              bbox.w = b.w*im.w;
              bbox.h = b.h*im.h;
              bbox.obj_id = obj_id;
              bbox.prob = prob;
              bbox.track_id = 0;
              bbox.frames_counter = 0;
              bbox.x_3d = NAN;
              bbox.y_3d = NAN;
              bbox.z_3d = NAN;
  
              bbox_vec.push_back(bbox);
          }
      }
  
      free_detections(dets, nboxes);
      if(sized.data)
          free(sized.data);
  
  #ifdef GPU
      if (cur_gpu_id != old_gpu_index)
          cudaSetDevice(old_gpu_index);
  #endif
  
      return bbox_vec;
  }
  
  LIB_API std::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history,
      int const frames_story, int const max_dist)
  {
      detector_gpu_t &det_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
  
      bool prev_track_id_present = false;
      for (auto &i : prev_bbox_vec_deque)
          if (i.size() > 0) prev_track_id_present = true;
  
      if (!prev_track_id_present) {
          for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
              cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
          prev_bbox_vec_deque.push_front(cur_bbox_vec);
          if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
          return cur_bbox_vec;
      }
  
      std::vector<unsigned int> dist_vec(cur_bbox_vec.size(), std::numeric_limits<unsigned int>::max());
  
      for (auto &prev_bbox_vec : prev_bbox_vec_deque) {
          for (auto &i : prev_bbox_vec) {
              int cur_index = -1;
              for (size_t m = 0; m < cur_bbox_vec.size(); ++m) {
                  bbox_t const& k = cur_bbox_vec[m];
                  if (i.obj_id == k.obj_id) {
                      float center_x_diff = (float)(i.x + i.w/2) - (float)(k.x + k.w/2);
                      float center_y_diff = (float)(i.y + i.h/2) - (float)(k.y + k.h/2);
                      unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff*center_y_diff);
                      if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) {
                          dist_vec[m] = cur_dist;
                          cur_index = m;
                      }
                  }
              }
  
              bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(),
                  [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; });
  
              if (cur_index >= 0 && track_id_absent){
                  cur_bbox_vec[cur_index].track_id = i.track_id;
                  cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2;
                  cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2;
              }
          }
      }
  
      for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
          if (cur_bbox_vec[i].track_id == 0)
              cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
  
      if (change_history) {
          prev_bbox_vec_deque.push_front(cur_bbox_vec);
          if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
      }
  
      return cur_bbox_vec;
  }
  
  
  void *Detector::get_cuda_context()
  {
  #ifdef GPU
      int old_gpu_index;
      cudaGetDevice(&old_gpu_index);
      if (cur_gpu_id != old_gpu_index)
          cudaSetDevice(cur_gpu_id);
  
      void *cuda_context = cuda_get_context();
  
      if (cur_gpu_id != old_gpu_index)
          cudaSetDevice(old_gpu_index);
  
      return cuda_context;
  #else   // GPU
      return NULL;
  #endif  // GPU
  }