class_docstr: Shuffle detection information for corresponding input image.
class_docstr_tok: ["Shuffle","detection","information","for","corresponding","input","image","."]
code: def __call__(self, img, data):
      gt = np.concatenate(
          [
              data["gt_boxes"],
              data["gt_labels"][:, np.newaxis],
              data["gt_scores"][:, np.newaxis],
          ],
          axis=1,
      )
      idx = np.arange(gt.shape[0])
      np.random.shuffle(idx)
      gt = gt[idx, :]
      data["gt_boxes"], data["gt_labels"], data["gt_scores"] = (
          gt[:, :4],
          gt[:, 4],
          gt[:, 5],
      )
      return img, data
  
imports: import os
  import random
  from typing import Callable
  import cv2
  import PIL
  import numpy
  import paddlehub.vision.transforms.functional
  from paddlehub.vision.utils import box_crop, box_iou_xywh
label_desc: Use image node_id 1 for calling the ShuffleBox obj's underlying member method code with example usage: obj.__call__(img, data) and returns: img,  data