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#
# 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, ModelFormat
from .... import c_lib_wrap as C
[docs]class PIPNet(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a face alignment model exported by PIPNet.
:param model_file: (str)Path of model file, e.g ./PIPNet.onnx
:param params_file: (str)Path of parameters file, 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, default is ONNX
"""
super(PIPNet, self).__init__(runtime_option)
assert model_format == ModelFormat.ONNX, "PIPNet only support model format of ModelFormat.ONNX now."
self._model = C.vision.facealign.PIPNet(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "PIPNet initialize failed."
[docs] def predict(self, input_image):
"""Detect an input image landmarks
:param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: FaceAlignmentResult
"""
return self._model.predict(input_image)
@property
def size(self):
"""
Returns the preprocess image size, default (256, 256)
"""
return self._model.size
@property
def mean_vals(self):
"""
Returns the mean value of normlization, default mean_vals = [0.485f, 0.456f, 0.406f];
"""
return self._model.mean_vals
@property
def std_vals(self):
"""
Returns the std value of normlization, default std_vals = [0.229f, 0.224f, 0.225f];
"""
return self._model.std_vals
@property
def num_landmarks(self):
"""
Returns the number of landmarks
"""
return self._model.num_landmarks
@size.setter
def size(self, wh):
"""
Set the preprocess image size, default (256, 256)
"""
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
@mean_vals.setter
def mean_vals(self, value):
assert isinstance(
value, list), "The value to set `mean_vals` must be type of list."
self._model.mean_vals = value
@std_vals.setter
def std_vals(self, value):
assert isinstance(
value, list), "The value to set `std_vals` must be type of list."
self._model.std_vals = value
@num_landmarks.setter
def num_landmarks(self, value):
assert isinstance(
value, int), "The value to set `std_vals` must be type of int."
self._model.num_landmarks = value