deepsort

class DeepSort(object): def __init__(self, model_path, model_config=None, 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 if model_config is None: self.extractor = Extractor(model_path, use_cuda=use_cuda) else: self.extractor = FastReIDExtractor(model_config, model_path, use_cuda=use_cuda)
        max_cosine_distance = max_dist
        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_xyxy(box)
            track_id = track.track_id
            outputs.append(np.array([x1,y1,x2,y2,track_id], dtype=np.int)) 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 _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