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 paddle_serving_client import Client
  from paddle_serving_app.reader import OCRReader
  import cv2
  import sys
  import numpy as np
  import os
  from paddle_serving_client import Client
  from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
  from paddle_serving_app.reader import Div, Normalize, Transpose
  from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
  if sys.argv[1] == 'gpu':
      from paddle_serving_server_gpu.web_service import WebService
  elif sys.argv[1] == 'cpu':
      from paddle_serving_server.web_service import WebService
  from paddle_serving_app.local_predict import Debugger
  import time
  import re
  import base64
  
  
  class OCRService(WebService):
      def init_det_debugger(self, det_model_config):
          self.det_preprocess = Sequential([
              ResizeByFactor(32, 960), Div(255),
              Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
                  (2, 0, 1))
          ])
          self.det_client = Debugger()
          if sys.argv[1] == 'gpu':
              self.det_client.load_model_config(
                  det_model_config, gpu=True, profile=False)
          elif sys.argv[1] == 'cpu':
              self.det_client.load_model_config(
                  det_model_config, gpu=False, profile=False)
          self.ocr_reader = OCRReader()
  
      def preprocess(self, feed=[], fetch=[]):
          data = base64.b64decode(feed[0]["image"].encode('utf8'))
          data = np.fromstring(data, np.uint8)
          im = cv2.imdecode(data, cv2.IMREAD_COLOR)
          ori_h, ori_w, _ = im.shape
          det_img = self.det_preprocess(im)
          _, new_h, new_w = det_img.shape
          det_img = det_img[np.newaxis, :]
          det_img = det_img.copy()
          det_out = self.det_client.predict(
              feed={"image": det_img}, fetch=["concat_1.tmp_0"])
          filter_func = FilterBoxes(10, 10)
          post_func = DBPostProcess({
              "thresh": 0.3,
              "box_thresh": 0.5,
              "max_candidates": 1000,
              "unclip_ratio": 1.5,
              "min_size": 3
          })
          sorted_boxes = SortedBoxes()
          ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
          dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list])
          dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
          dt_boxes = sorted_boxes(dt_boxes)
          get_rotate_crop_image = GetRotateCropImage()
          img_list = []
          max_wh_ratio = 0
          for i, dtbox in enumerate(dt_boxes):
              boximg = get_rotate_crop_image(im, dt_boxes[i])
              img_list.append(boximg)
              h, w = boximg.shape[0:2]
              wh_ratio = w * 1.0 / h
              max_wh_ratio = max(max_wh_ratio, wh_ratio)
          if len(img_list) == 0:
              return [], []
          _, w, h = self.ocr_reader.resize_norm_img(img_list[0],
                                                    max_wh_ratio).shape
          imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
          for id, img in enumerate(img_list):
              norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
              imgs[id] = norm_img
          feed = {"image": imgs.copy()}
          fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
          return feed, fetch
  
      def postprocess(self, feed={}, fetch=[], fetch_map=None):
          rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
          res_lst = []
          for res in rec_res:
              res_lst.append(res[0])
          res = {"res": res_lst}
          return res
  
  
  ocr_service = OCRService(name="ocr")
  ocr_service.load_model_config("ocr_rec_model")
  ocr_service.prepare_server(workdir="workdir", port=9292)
  ocr_service.init_det_debugger(det_model_config="ocr_det_model")
  if sys.argv[1] == 'gpu':
      ocr_service.run_debugger_service(gpu=True)
  elif sys.argv[1] == 'cpu':
      ocr_service.run_debugger_service()
  ocr_service.run_web_service()
  
apis: numpy.fromstring