text: from ctypes import *
  import math
  import random
  
  def sample(probs):
      s = sum(probs)
      probs = [a/s for a in probs]
      r = random.uniform(0, 1)
      for i in range(len(probs)):
          r = r - probs[i]
          if r <= 0:
              return i
      return len(probs)-1
  
  def c_array(ctype, values):
      arr = (ctype*len(values))()
      arr[:] = values
      return arr
  
  class BOX(Structure):
      _fields_ = [("x", c_float),
                  ("y", c_float),
                  ("w", c_float),
                  ("h", c_float)]
  
  class DETECTION(Structure):
      _fields_ = [("bbox", BOX),
                  ("classes", c_int),
                  ("prob", POINTER(c_float)),
                  ("mask", POINTER(c_float)),
                  ("objectness", c_float),
                  ("sort_class", c_int)]
  
  
  class IMAGE(Structure):
      _fields_ = [("w", c_int),
                  ("h", c_int),
                  ("c", c_int),
                  ("data", POINTER(c_float))]
  
  class METADATA(Structure):
      _fields_ = [("classes", c_int),
                  ("names", POINTER(c_char_p))]
  
      
  
  lib = CDLL("./libdarknet.so", RTLD_GLOBAL)
  lib.network_width.argtypes = [c_void_p]
  lib.network_width.restype = c_int
  lib.network_height.argtypes = [c_void_p]
  lib.network_height.restype = c_int
  
  predict = lib.network_predict
  predict.argtypes = [c_void_p, POINTER(c_float)]
  predict.restype = POINTER(c_float)
  
  set_gpu = lib.cuda_set_device
  set_gpu.argtypes = [c_int]
  
  make_image = lib.make_image
  make_image.argtypes = [c_int, c_int, c_int]
  make_image.restype = IMAGE
  
  get_network_boxes = lib.get_network_boxes
  get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]
  get_network_boxes.restype = POINTER(DETECTION)
  
  make_network_boxes = lib.make_network_boxes
  make_network_boxes.argtypes = [c_void_p]
  make_network_boxes.restype = POINTER(DETECTION)
  
  free_detections = lib.free_detections
  free_detections.argtypes = [POINTER(DETECTION), c_int]
  
  free_ptrs = lib.free_ptrs
  free_ptrs.argtypes = [POINTER(c_void_p), c_int]
  
  network_predict = lib.network_predict
  network_predict.argtypes = [c_void_p, POINTER(c_float)]
  
  reset_rnn = lib.reset_rnn
  reset_rnn.argtypes = [c_void_p]
  
  load_net = lib.load_network
  load_net.argtypes = [c_char_p, c_char_p, c_int]
  load_net.restype = c_void_p
  
  do_nms_obj = lib.do_nms_obj
  do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
  
  do_nms_sort = lib.do_nms_sort
  do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
  
  free_image = lib.free_image
  free_image.argtypes = [IMAGE]
  
  letterbox_image = lib.letterbox_image
  letterbox_image.argtypes = [IMAGE, c_int, c_int]
  letterbox_image.restype = IMAGE
  
  load_meta = lib.get_metadata
  lib.get_metadata.argtypes = [c_char_p]
  lib.get_metadata.restype = METADATA
  
  load_image = lib.load_image_color
  load_image.argtypes = [c_char_p, c_int, c_int]
  load_image.restype = IMAGE
  
  rgbgr_image = lib.rgbgr_image
  rgbgr_image.argtypes = [IMAGE]
  
  predict_image = lib.network_predict_image
  predict_image.argtypes = [c_void_p, IMAGE]
  predict_image.restype = POINTER(c_float)
  
  def classify(net, meta, im):
      out = predict_image(net, im)
      res = []
      for i in range(meta.classes):
          res.append((meta.names[i], out[i]))
      res = sorted(res, key=lambda x: -x[1])
      return res
  
  def detect(net, meta, image, thresh=0.9, hier_thresh=.5, nms=.45):
      im = load_image(image, 0, 0)
      num = c_int(0)
      pnum = pointer(num)
      predict_image(net, im)
      dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
      num = pnum[0]
      if (nms): do_nms_obj(dets, num, meta.classes, nms);
  
      res = []
      for j in range(num):
          for i in range(meta.classes):
              if dets[j].prob[i] > 0:
                  b = dets[j].bbox
                  res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
      res = sorted(res, key=lambda x: -x[1])
      free_image(im)
      free_detections(dets, num)
      return res
      
  
  