text: # 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_server.web_service import WebService, Op
  import logging
  import numpy as np
  import sys
  import cv2
  from paddle_serving_app.reader import *
  import base64
  
  class Yolov3Op(Op):
      def init_op(self):
          self.img_preprocess = Sequential([
              BGR2RGB(), Div(255.0),
              Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], False),
              Resize((640, 640)), Transpose((2, 0, 1))
          ])
          self.img_postprocess = RCNNPostprocess("label_list.txt", "output")
  
      def preprocess(self, input_dicts, data_id, log_id):
          (_, input_dict), = input_dicts.items()
          imgs = []
          #print("keys", input_dict.keys())
          for key in input_dict.keys():
              data = base64.b64decode(input_dict[key].encode('utf8'))
              data = np.fromstring(data, np.uint8)
              im = cv2.imdecode(data, cv2.IMREAD_COLOR)
              im = self.img_preprocess(im)
              imgs.append({
                "image": im[np.newaxis,:],
                "im_shape": np.array(list(im.shape[1:])).reshape(-1)[np.newaxis,:],
                "scale_factor": np.array([1.0, 1.0]).reshape(-1)[np.newaxis,:],
              })
          feed_dict = {
              "image": np.concatenate([x["image"] for x in imgs], axis=0),
              "im_shape": np.concatenate([x["im_shape"] for x in imgs], axis=0),
              "scale_factor": np.concatenate([x["scale_factor"] for x in imgs], axis=0)
          }
          #for key in feed_dict.keys():
          #    print(key, feed_dict[key].shape)
          return feed_dict, False, None, ""
  
      def postprocess(self, input_dicts, fetch_dict, log_id):
          #print(fetch_dict)
          res_dict = {"bbox_result": str(self.img_postprocess(fetch_dict, visualize=False))}
          return res_dict, None, ""
  
  
  class Yolov3Service(WebService):
      def get_pipeline_response(self, read_op):
          yolov3_op = Yolov3Op(name="yolov3", input_ops=[read_op])
          return yolov3_op
  
  
  yolov3_service = Yolov3Service(name="yolov3")
  yolov3_service.prepare_pipeline_config("config.yml")
  yolov3_service.run_service()
  