class_docstr: The post process for Differentiable Binarization (DB).
class_docstr_tok: ["The","post","process","for","Differentiable","Binarization","(","DB",")","."]
code: def __call__(self, outs_dict, shape_list):
      pred = outs_dict["maps"]
      if isinstance(pred, paddle.Tensor):
          pred = pred.numpy()
      pred = pred[:, 0, :, :]
      segmentation = pred > self.thresh
  
      boxes_batch = []
      for batch_index in range(pred.shape[0]):
          src_h, src_w = shape_list[batch_index]
          if self.dilation_kernel is not None:
              mask = cv2.dilate(
                  np.array(segmentation[batch_index]).astype(np.uint8),
                  self.dilation_kernel,
              )
          else:
              mask = segmentation[batch_index]
          boxes, scores = self.boxes_from_bitmap(
              pred[batch_index], mask, src_w, src_h
          )
  
          boxes_batch.append({"points": boxes})
      return boxes_batch
  
imports: import numpy
  import paddle
  from paddle.nn import functional
  import re
  from shapely.geometry import Polygon
  import cv2
  import copy
label_desc: Use image node_id 7 for calling the DBPostProcess obj's underlying member method code with example usage: obj.__call__(outs_dict, shape_list) and returns: boxes_batch