code: def postprocess(
      paths,
      images,
      data_out,
      score_thresh,
      label_names,
      output_dir,
      handle_id,
      visualization=True,
  ):
      """
      postprocess the lod_tensor produced by fluid.Executor.run
  
      Args:
          paths (list[str]): The paths of images.
          images (list(numpy.ndarray)): images data, shape of each is [H, W, C]
          data_out (lod_tensor): data output of predictor.
          batch_size (int): batch size.
          use_gpu (bool): Whether to use gpu.
          output_dir (str): The path to store output images.
          visualization (bool): Whether to save image or not.
          score_thresh (float): the low limit of bounding box.
          label_names (list[str]): label names.
          handle_id (int): The number of images that have been handled.
  
      Returns:
          res (list[dict]): The result of vehicles detecion. keys include 'data', 'save_path', the corresponding value is:
              data (dict): the result of object detection, keys include 'left', 'top', 'right', 'bottom', 'label', 'confidence', the corresponding value is:
                  left (float): The X coordinate of the upper left corner of the bounding box;
                  top (float): The Y coordinate of the upper left corner of the bounding box;
                  right (float): The X coordinate of the lower right corner of the bounding box;
                  bottom (float): The Y coordinate of the lower right corner of the bounding box;
                  label (str): The label of detection result;
                  confidence (float): The confidence of detection result.
              save_path (str): The path to save output images.
      """
      lod_tensor = data_out[0]
      lod = lod_tensor.lod[0]
      results = lod_tensor.as_ndarray()
  
      check_dir(output_dir)
  
      assert type(paths) is list, "type(paths) is not list."
      if handle_id < len(paths):
          unhandled_paths = paths[handle_id:]
          unhandled_paths_num = len(unhandled_paths)
      else:
          unhandled_paths_num = 0
  
      output = list()
      for index in range(len(lod) - 1):
          output_i = {"data": []}
          if index < unhandled_paths_num:
              org_img_path = unhandled_paths[index]
              org_img = Image.open(org_img_path)
          else:
              org_img = images[index - unhandled_paths_num]
              org_img = org_img.astype(np.uint8)
              org_img = Image.fromarray(org_img[:, :, ::-1])
              if visualization:
                  org_img_path = get_save_image_name(
                      org_img,
                      output_dir,
                      "image_numpy_{}".format((handle_id + index)),
                  )
                  org_img.save(org_img_path)
          org_img_height = org_img.height
          org_img_width = org_img.width
          result_i = results[lod[index] : lod[index + 1]]
          for row in result_i:
              if len(row) != 6:
                  continue
              if row[1] < score_thresh:
                  continue
              category_id = int(row[0])
              confidence = row[1]
              bbox = row[2:]
              dt = {}
              dt["label"] = label_names[category_id]
              dt["confidence"] = confidence
              (
                  dt["left"],
                  dt["top"],
                  dt["right"],
                  dt["bottom"],
              ) = clip_bbox(bbox, org_img_width, org_img_height)
              output_i["data"].append(dt)
  
          output.append(output_i)
          if visualization:
              output_i["save_path"] = draw_bounding_box_on_image(
                  org_img_path, output_i["data"], output_dir
              )
  
      return output
  
file_path: PaddleHub/modules/image/object_detection/yolov3_darknet53_venus/processor.py
imports: import base64
  import os
  import cv2
  import numpy
  from PIL import Image, ImageDraw
label_desc: Use image node_id 7 for calling a global function with example usage: postprocess(paths, images, data_out, score_thresh, label_names, output_dir, handle_id, visualization) and returns: output