text: import numpy as np
  import torch
  
  from .deep.feature_extractor import Extractor
  from .sort.nn_matching import NearestNeighborDistanceMetric
  from .sort.preprocessing import non_max_suppression
  from .sort.detection import Detection
  from .sort.tracker import Tracker
  
  
  __all__ = ['DeepSort']
  
  
  class DeepSort(object):
      def __init__(self, model_path, max_dist=0.2, min_confidence=0.3, nms_max_overlap=1.0, max_iou_distance=0.7, max_age=70, n_init=3, nn_budget=100, use_cuda=True):
          self.min_confidence = min_confidence
          self.nms_max_overlap = nms_max_overlap
  
          self.extractor = Extractor(model_path, use_cuda=use_cuda)
  
          max_cosine_distance = max_dist
          nn_budget = 100
          metric = NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
          self.tracker = Tracker(metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init)
  
      def update(self, bbox_xywh, confidences, ori_img):
          self.height, self.width = ori_img.shape[:2]
          # generate detections
          features = self._get_features(bbox_xywh, ori_img)
          bbox_tlwh = self._xywh_to_tlwh(bbox_xywh)
          detections = [Detection(bbox_tlwh[i], conf, features[i]) for i,conf in enumerate(confidences) if conf>self.min_confidence]
  
          # run on non-maximum supression
          boxes = np.array([d.tlwh for d in detections])
          scores = np.array([d.confidence for d in detections])
          indices = non_max_suppression(boxes, self.nms_max_overlap, scores)
          detections = [detections[i] for i in indices]
  
          # update tracker
          self.tracker.predict()
          self.tracker.update(detections)
  
          # output bbox identities
          outputs = []
          for track in self.tracker.tracks:
              if not track.is_confirmed() or track.time_since_update > 1:
                  continue
              box = track.to_tlwh()
              x1,y1,x2,y2 = self._tlwh_to_xyxy2(box)
              track_id = track.track_id
              outputs.append(np.array([x1,y1,x2,y2,track_id], dtype=np.float))
          if len(outputs) > 0:
              outputs = np.stack(outputs,axis=0)
          return outputs
  
  
      """
      TODO:
          Convert bbox from xc_yc_w_h to xtl_ytl_w_h
      Thanks JieChen91@github.com for reporting this bug!
      """
      @staticmethod
      def _xywh_to_tlwh(bbox_xywh):
          if isinstance(bbox_xywh, np.ndarray):
              bbox_tlwh = bbox_xywh.copy()
          elif isinstance(bbox_xywh, torch.Tensor):
              bbox_tlwh = bbox_xywh.clone()
          bbox_tlwh[:,0] = bbox_xywh[:,0] - bbox_xywh[:,2]/2.
          bbox_tlwh[:,1] = bbox_xywh[:,1] - bbox_xywh[:,3]/2.
          return bbox_tlwh
  
  
      def _xywh_to_xyxy(self, bbox_xywh):
          x,y,w,h = bbox_xywh
          x1 = max(int(x-w/2),0)
          x2 = min(int(x+w/2),self.width-1)
          y1 = max(int(y-h/2),0)
          y2 = min(int(y+h/2),self.height-1)
          return x1,y1,x2,y2
  
      def _tlwh_to_xyxy(self, bbox_tlwh):
          """
          TODO:
              Convert bbox from xtl_ytl_w_h to xc_yc_w_h
          Thanks JieChen91@github.com for reporting this bug!
          """
          x,y,w,h = bbox_tlwh
          x1 = max(int(x),0)
          x2 = min(int(x+w),self.width-1)
          y1 = max(int(y),0)
          y2 = min(int(y+h),self.height-1)
          return x1,y1,x2,y2
  
      def _tlwh_to_xyxy2(self, bbox_tlwh):
          """
          TODO:
              Convert bbox from xtl_ytl_w_h to xc_yc_w_h
          Thanks JieChen91@github.com for reporting this bug!
          """
          x,y,w,h = bbox_tlwh
          x1 = max((x),0)
          x2 = min((x+w),self.width-1)
          y1 = max((y),0)
          y2 = min((y+h),self.height-1)
          return x1,y1,x2,y2
          
      def _xyxy_to_tlwh(self, bbox_xyxy):
          x1,y1,x2,y2 = bbox_xyxy
  
          t = x1
          l = y1
          w = int(x2-x1)
          h = int(y2-y1)
          return t,l,w,h
      
      def _get_features(self, bbox_xywh, ori_img):
          im_crops = []
          for box in bbox_xywh:
              x1,y1,x2,y2 = self._xywh_to_xyxy(box)
              im = ori_img[y1:y2,x1:x2]
              im_crops.append(im)
          if im_crops:
              features = self.extractor(im_crops)
          else:
              features = np.array([])
          return features
  
  
  