class_docstr: Args:
      in_channels (int): the channel number of input to CenterNetHead.
      num_classes (int): the number of classes, 1 (MOT17 dataset) by default.
      head_planes (int): the channel number in all head, 256 by default.
      task (str): the type of task for regression, 'tracking' by default.
      loss_weight (dict): the weight of each loss.
      add_ltrb_amodal (bool): whether to add ltrb_amodal branch, False by default.
class_docstr_tok: ["Args",":","in_channels","(","int",")",":","the","channel","number","of","input","to","CenterNetHead",".","num_classes","(","int",")",":","the","number","of","classes",",","1","(","MOT17","dataset",")","by","default",".","head_planes","(","int",")",":","the","channel","number","in","all","head",",","256","by","default",".","task","(","str",")",":","the","type","of","task","for","regression",",","'tracking","'","by","default",".","loss_weight","(","dict",")",":","the","weight","of","each","loss",".","add_ltrb_amodal","(","bool",")",":","whether","to","add","ltrb_amodal","branch",",","False","by","default","."]
code: def centertrack_post_process(self, dets, meta, out_thresh):
      if not ("bboxes" in dets):
          return [{}]
  
      preds = []
      c, s = meta["center"].numpy(), meta["scale"].numpy()
      h, w = meta["out_height"].numpy(), meta["out_width"].numpy()
      trans = get_affine_transform(
          center=c[0],
          input_size=s[0],
          rot=0,
          output_size=[w[0], h[0]],
          shift=(0.0, 0.0),
          inv=True,
      ).astype(np.float32)
      for i, dets_bbox in enumerate(dets["bboxes"]):
          if dets_bbox[1] < out_thresh:
              break
          item = {}
          item["score"] = dets_bbox[1]
          item["class"] = int(dets_bbox[0]) + 1
          item["ct"] = transform_preds_with_trans(
              dets["cts"][i].reshape([1, 2]), trans
          ).reshape(2)
  
          if "tracking" in dets:
              tracking = transform_preds_with_trans(
                  (dets["tracking"][i] + dets["cts"][i]).reshape(
                      [1, 2]
                  ),
                  trans,
              ).reshape(2)
              item["tracking"] = tracking - item["ct"]
  
          if "bboxes" in dets:
              bbox = transform_preds_with_trans(
                  dets_bbox[2:6].reshape([2, 2]), trans
              ).reshape(4)
              item["bbox"] = bbox
  
          preds.append(item)
      return preds
  
file_path: PaddleDetection/ppdet/modeling/heads/centertrack_head.py
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from .centernet_head import ConvLayer
  from ..keypoint_utils import get_affine_transform
label_desc: Use image node_id 6 for calling the CenterTrackHead obj's underlying member method code with example usage: obj.centertrack_post_process(dets, meta, out_thresh) and returns: preds, list