code: # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  import os
  import sys
  import json
  
  __dir__ = os.path.dirname(os.path.abspath(__file__))
  sys.path.append(__dir__)
  sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
  
  os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
  
  import paddle
  from paddle.jit import to_static
  
  from ppocr.data import create_operators, transform
  from ppocr.modeling.architectures import build_model
  from ppocr.postprocess import build_post_process
  from ppocr.utils.save_load import init_model
  from ppocr.utils.utility import get_image_file_list
  import tools.program as program
  import cv2
  
  def main(config, device, logger, vdl_writer):
      global_config = config['Global']
  
      # build post process
      post_process_class = build_post_process(config['PostProcess'],
                                              global_config)
  
      # build model
      if hasattr(post_process_class, 'character'):
          config['Architecture']["Head"]['out_channels'] = len(
              getattr(post_process_class, 'character'))
  
      model = build_model(config['Architecture'])
  
      init_model(config, model, logger)
  
      # create data ops
      transforms = []
      use_padding = False
      for op in config['Eval']['dataset']['transforms']:
          op_name = list(op)[0]
          if 'Label' in op_name:
              continue
          if op_name == 'KeepKeys':
              op[op_name]['keep_keys'] = ['image']
          if op_name == "ResizeTableImage":
              use_padding = True
              padding_max_len = op['ResizeTableImage']['max_len']
          transforms.append(op)
  
      global_config['infer_mode'] = True
      ops = create_operators(transforms, global_config)
  
      model.eval()
      for file in get_image_file_list(config['Global']['infer_img']):
          logger.info("infer_img: {}".format(file))
          with open(file, 'rb') as f:
              img = f.read()
              data = {'image': img}
          batch = transform(data, ops)
          images = np.expand_dims(batch[0], axis=0)
          images = paddle.to_tensor(images)
          preds = model(images)
          post_result = post_process_class(preds)
          res_html_code = post_result['res_html_code']
          res_loc = post_result['res_loc']
          img = cv2.imread(file)
          imgh, imgw = img.shape[0:2]
          res_loc_final = []
          for rno in range(len(res_loc[0])):
              x0, y0, x1, y1 = res_loc[0][rno]
              left = max(int(imgw * x0), 0)
              top = max(int(imgh * y0), 0)
              right = min(int(imgw * x1), imgw - 1)
              bottom = min(int(imgh * y1), imgh - 1)
              cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2)
              res_loc_final.append([left, top, right, bottom])
          res_loc_str = json.dumps(res_loc_final)
          logger.info("result: {}, {}".format(res_html_code, res_loc_final))
      logger.info("success!")
  
  
  if __name__ == '__main__':
      config, device, logger, vdl_writer = program.preprocess()
      main(config, device, logger, vdl_writer)
  
   # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """
  This code is refer from:
  https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/iaa_augment.py
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  import numpy as np
  import imgaug
  import imgaug.augmenters as iaa
  
  
  class AugmenterBuilder(object):
      def __init__(self):
          pass
  
      def build(self, args, root=True):
          if args is None or len(args) == 0:
              return None
          elif isinstance(args, list):
              if root:
                  sequence = [self.build(value, root=False) for value in args]
                  return iaa.Sequential(sequence)
              else:
                  return getattr(iaa, args[0])(
                      *[self.to_tuple_if_list(a) for a in args[1:]])
          elif isinstance(args, dict):
              cls = getattr(iaa, args['type'])
              return cls(**{
                  k: self.to_tuple_if_list(v)
                  for k, v in args['args'].items()
              })
          else:
              raise RuntimeError('unknown augmenter arg: ' + str(args))
  
      def to_tuple_if_list(self, obj):
          if isinstance(obj, list):
              return tuple(obj)
          return obj
  
  
  class IaaAugment():
      def __init__(self, augmenter_args=None, **kwargs):
          if augmenter_args is None:
              augmenter_args = [{
                  'type': 'Fliplr',
                  'args': {
                      'p': 0.5
                  }
              }, {
                  'type': 'Affine',
                  'args': {
                      'rotate': [-10, 10]
                  }
              }, {
                  'type': 'Resize',
                  'args': {
                      'size': [0.5, 3]
                  }
              }]
          self.augmenter = AugmenterBuilder().build(augmenter_args)
  
      def __call__(self, data):
          image = data['image']
          shape = image.shape
  
          if self.augmenter:
              aug = self.augmenter.to_deterministic()
              data['image'] = aug.augment_image(image)
              data = self.may_augment_annotation(aug, data, shape)
          return data
  
      def may_augment_annotation(self, aug, data, shape):
          if aug is None:
              return data
  
          line_polys = []
          for poly in data['polys']:
              new_poly = self.may_augment_poly(aug, shape, poly)
              line_polys.append(new_poly)
          data['polys'] = np.array(line_polys)
          return data
  
      def may_augment_poly(self, aug, img_shape, poly):
          keypoints = [imgaug.Keypoint(p[0], p[1]) for p in poly]
          keypoints = aug.augment_keypoints(
              [imgaug.KeypointsOnImage(
                  keypoints, shape=img_shape)])[0].keypoints
          poly = [(p.x, p.y) for p in keypoints]
          return poly
  
apis: numpy.expand_dims
apis: numpy.array