Multi object segmentation support about cnos HOT 2 CLOSED

  def get_id_from_mask(self,color_image,mask_list,bbox_list):
      """
      Get mesh_id from cnos/data/obj_dta

      Args:
          color_image (_type_): _description_
          mask_list (_type_): should be a list with np.uint8, or np.array with (N*width*height)
          bbox_list (_type_): should be a list with [[x1,y1,x2,y2],[x1,y1,x2,y2],...]

      Returns:
          _type_: _description_
      """
      #1: init color_image and detections
      color_image=cv.cvtColor(color_image,cv.COLOR_BGR2RGB)
      if type(mask_list)==list:
          mask_list=np.array(mask_list).astype(np.float32)/255.0
      elif type(mask_list)==np.ndarray:
          mask_list=mask_list.astype(np.float32)/255.0
      bbox_list=np.array(bbox_list).astype(np.float32)
      masks_tensor=torch.tensor(mask_list).cuda()#num_bbox*H*W
      bbox_tensor=torch.tensor(bbox_list).cuda()#num_bbox*4
      detections={'masks':masks_tensor,'boxes':bbox_tensor}
      
      detections=Detections(detections)
      decriptors=self.descriptor_model.forward(np.array(color_image),detections)#num_bbox*1024
      
      #2: compare similarity
      metric=PairwiseSimilarity()
      scores=metric(decriptors,self.all_ref_feats)#(num_bbox*1024, N*42*1024)->(num_bbox*N*42)

      #select top-k detections and compute their mean score as the final score
      top5_scores,_=torch.topk(scores,k=5,dim=-1)#(num_bbox*N*42)->(num_bbox*N*5)
      avg_top5_scores=torch.mean(top5_scores,dim=-1)#(num_bbox*N*5)->(num_bbox*N)     
      
      #get final score and index
      max_value,best_match_idx=torch.max(avg_top5_scores,dim=1)#(num_bbox*N)->(num_bbox)
      
      confidence_list=max_value.cpu().numpy()
      best_match_idx=best_match_idx.cpu().numpy()
      
      #get class name from sellf._object_name_list
      class_list=[self._object_name_list[index] for index in best_match_idx]
      
      return class_list,confidence_list