mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 00:57:33 +08:00
100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
# Copyright (c) 2022 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
|
||
import logging
|
||
from .... import FastDeployModel, Frontend
|
||
from .... import c_lib_wrap as C
|
||
|
||
|
||
class InsightFaceRecognitionModel(FastDeployModel):
|
||
def __init__(self,
|
||
model_file,
|
||
params_file="",
|
||
runtime_option=None,
|
||
model_format=Frontend.ONNX):
|
||
# 调用基函数进行backend_option的初始化
|
||
# 初始化后的option保存在self._runtime_option
|
||
super(InsightFaceRecognitionModel, self).__init__(runtime_option)
|
||
|
||
self._model = C.vision.faceid.InsightFaceRecognitionModel(
|
||
model_file, params_file, self._runtime_option, model_format)
|
||
# 通过self.initialized判断整个模型的初始化是否成功
|
||
assert self.initialized, "InsightFaceRecognitionModel initialize failed."
|
||
|
||
def predict(self, input_image):
|
||
return self._model.predict(input_image)
|
||
|
||
# 一些跟InsightFaceRecognitionModel模型有关的属性封装
|
||
# 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
|
||
@property
|
||
def size(self):
|
||
return self._model.size
|
||
|
||
@property
|
||
def alpha(self):
|
||
return self._model.alpha
|
||
|
||
@property
|
||
def beta(self):
|
||
return self._model.beta
|
||
|
||
@property
|
||
def swap_rb(self):
|
||
return self._model.swap_rb
|
||
|
||
@property
|
||
def l2_normalize(self):
|
||
return self._model.l2_normalize
|
||
|
||
@size.setter
|
||
def size(self, wh):
|
||
assert isinstance(wh, (list, tuple)),\
|
||
"The value to set `size` must be type of tuple or list."
|
||
assert len(wh) == 2,\
|
||
"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
|
||
len(wh))
|
||
self._model.size = wh
|
||
|
||
@alpha.setter
|
||
def alpha(self, value):
|
||
assert isinstance(value, (list, tuple)),\
|
||
"The value to set `alpha` must be type of tuple or list."
|
||
assert len(value) == 3,\
|
||
"The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
|
||
len(value))
|
||
self._model.alpha = value
|
||
|
||
@beta.setter
|
||
def beta(self, value):
|
||
assert isinstance(value, (list, tuple)),\
|
||
"The value to set `beta` must be type of tuple or list."
|
||
assert len(value) == 3,\
|
||
"The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
|
||
len(value))
|
||
self._model.beta = value
|
||
|
||
@swap_rb.setter
|
||
def swap_rb(self, value):
|
||
assert isinstance(
|
||
value, bool), "The value to set `swap_rb` must be type of bool."
|
||
self._model.swap_rb = value
|
||
|
||
@l2_normalize.setter
|
||
def l2_normalize(self, value):
|
||
assert isinstance(
|
||
value,
|
||
bool), "The value to set `l2_normalize` must be type of bool."
|
||
self._model.l2_normalize = value
|