# 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, ModelFormat
from .... import c_lib_wrap as C
[docs]class MODNet(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a MODNet model exported by MODNet.
:param model_file: (str)Path of model file, e.g ./modnet.onnx
:param params_file: (str)Path of parameters file, e.g yolox/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
"""
# 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(MODNet, self).__init__(runtime_option)
self._model = C.vision.matting.MODNet(
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, "MODNet initialize failed."
[docs] def predict(self, input_image):
""" Predict the matting result for an input image
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: MattingResult
"""
return self._model.predict(input_image)
# 一些跟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 = [256, 256]改变预处理时resize的大小（前提是模型支持）
@property
def size(self):
"""
Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [256,256]
"""
return self._model.size
@property
def alpha(self):
"""
Argument for image preprocessing step, alpha value for normalization, default alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f}
"""
return self._model.alpha
@property
def beta(self):
"""
Argument for image preprocessing step, beta value for normalization, default beta = {-1.f, -1.f, -1.f}
"""
return self._model.beta
@property
def swap_rb(self):
"""
Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
"""
return self._model.swap_rb
@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