# 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
from ..... import FastDeployModel, ModelFormat
from ..... import c_lib_wrap as C
class InsightFaceRecognitionPreprocessor:
def __init__(self):
"""Create a preprocessor for InsightFaceRecognition Model
"""
self._preprocessor = C.vision.faceid.InsightFaceRecognitionPreprocessor(
)
def run(self, input_ims):
"""Preprocess input images for InsightFaceRecognition Model
:param: input_ims: (list of numpy.ndarray)The input image
:return: list of FDTensor, include image, scale_factor, im_shape
"""
return self._preprocessor.run(input_ims)
@property
def size(self):
"""
Argument for image preprocessing step, tuple of (width, height),
decide the target size after resize, default (112, 112)
"""
return self._preprocessor.size
@property
def alpha(self):
"""
Argument for image preprocessing step, alpha values for normalization,
default alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
"""
return self._preprocessor.alpha
@property
def beta(self):
"""
Argument for image preprocessing step, beta values for normalization,
default beta = {-1.f, -1.f, -1.f}
"""
return self._preprocessor.beta
def disable_normalize(self):
"""
This function will disable normalize in preprocessing step.
"""
self._preprocessor.disable_normalize()
def disable_permute(self):
"""
This function will disable hwc2chw in preprocessing step.
"""
self._preprocessor.disable_permute()
class InsightFaceRecognitionPostprocessor:
def __init__(self):
"""Create a postprocessor for InsightFaceRecognition Model
"""
self._postprocessor = C.vision.faceid.InsightFaceRecognitionPostprocessor(
)
def run(self, runtime_results):
"""Postprocess the runtime results for PaddleClas Model
:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
:return: list of FaceRecognitionResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
"""
return self._postprocessor.run(runtime_results)
@property
def l2_normalize(self):
"""
confidence threshold for postprocessing, default is 0.5
"""
return self._postprocessor.l2_normalize
class InsightFaceRecognitionBase(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a InsightFaceRecognitionBase model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g InsightFaceRecognitionBase/model.pdmodel
:param params_file: (str)Path of parameters file, e.g InsightFaceRecognitionBase/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(InsightFaceRecognitionBase, self).__init__(runtime_option)
self._model = C.vision.faceid.InsightFaceRecognitionBase(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "InsightFaceRecognitionBase model initialize failed."
def predict(self, im):
"""Detect an input image
:param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: DetectionResult
"""
assert im is not None, "The input image data is None."
return self._model.predict(im)
def batch_predict(self, images):
"""Detect a batch of input image list
:param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
:return list of DetectionResult
"""
return self._model.batch_predict(images)
@property
def preprocessor(self):
"""Get InsightFaceRecognitionPreprocessor object of the loaded model
:return InsightFaceRecognitionPreprocessor
"""
return self._model.preprocessor
@property
def postprocessor(self):
"""Get InsightFaceRecognitionPostprocessor object of the loaded model
:return InsightFaceRecognitionPostprocessor
"""
return self._model.postprocessor
[docs]class ArcFace(InsightFaceRecognitionBase):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a ArcFace model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g ArcFace/model.pdmodel
:param params_file: (str)Path of parameters file, e.g ArcFace/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(InsightFaceRecognitionBase, self).__init__(runtime_option)
self._model = C.vision.faceid.ArcFace(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "ArcFace model initialize failed."
[docs]class CosFace(InsightFaceRecognitionBase):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a CosFace model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g CosFace/model.pdmodel
:param params_file: (str)Path of parameters file, e.g CosFace/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(InsightFaceRecognitionBase, self).__init__(runtime_option)
self._model = C.vision.faceid.CosFace(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "CosFace model initialize failed."
[docs]class PartialFC(InsightFaceRecognitionBase):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a PartialFC model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g PartialFC/model.pdmodel
:param params_file: (str)Path of parameters file, e.g PartialFC/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(InsightFaceRecognitionBase, self).__init__(runtime_option)
self._model = C.vision.faceid.PartialFC(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "PartialFC model initialize failed."
[docs]class VPL(InsightFaceRecognitionBase):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a VPL model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g VPL/model.pdmodel
:param params_file: (str)Path of parameters file, e.g VPL/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(InsightFaceRecognitionBase, self).__init__(runtime_option)
self._model = C.vision.faceid.VPL(model_file, params_file,
self._runtime_option, model_format)
assert self.initialized, "VPL model initialize failed."