Files
insightface/recognition/arcface_paddle/deploy/pdserving/web_service.py
2021-11-04 14:16:17 +00:00

55 lines
1.9 KiB
Python

# Copyright (c) 2021 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 numpy as np
import cv2
import base64
class ArcFaceOp(Op):
def init_op(self):
pass
def preprocess(self, input_dicts, data_id, log_id):
(_, input_dict), = input_dicts.items()
data = base64.b64decode(input_dict["image"])
data = np.frombuffer(data, np.uint8)
# Note: class variables(self.var) can only be used in process op mode
img = cv2.imdecode(data, cv2.IMREAD_COLOR)
img = cv2.resize(img,(112,112))
# normalize to mean 0.5, std 0.5
img = (img - 127.5) * 0.00784313725
# BGR2RGB
img = img[:, :, ::-1]
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, 0)
img = img.astype('float32')
return {"x":img.copy()}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
out = fetch_dict["save_infer_model/scale_0.tmp_1"]
out_dict = {"out": out}
return out_dict, None, ""
class ArcFaceService(WebService):
def get_pipeline_response(self, read_op):
arcface_op = ArcFaceOp(name="ArcFace", input_ops=[read_op])
return arcface_op
arcface_service = ArcFaceService(name="ArcFace")
arcface_service.prepare_pipeline_config("config.yml")
arcface_service.run_service()