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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 RetinaFace(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a RetinaFace model exported by RetinaFace.
:param model_file: (str)Path of model file, e.g ./retinaface.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(RetinaFace, self).__init__(runtime_option)
self._model = C.vision.facedet.RetinaFace(
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, "RetinaFace initialize failed."
[docs] def predict(self, input_image, conf_threshold=0.7, nms_iou_threshold=0.3):
"""Detect the location and key points of human faces from an input image
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:param conf_threshold: confidence threashold for postprocessing, default is 0.7
:param nms_iou_threshold: iou threashold for NMS, default is 0.3
:return: FaceDetectionResult
"""
return self._model.predict(input_image, conf_threshold,
nms_iou_threshold)
# 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 = [640, 480]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 (640, 640)
"""
return self._model.size
@property
def variance(self):
"""
Argument for image postprocessing step, variance in RetinaFace's prior-box(anchor) generate process, default (0.1, 0.2)
"""
return self._model.variance
@property
def downsample_strides(self):
"""
Argument for image postprocessing step, downsample strides (namely, steps) for RetinaFace to generate anchors, will take (8,16,32) as default values
"""
return self._model.downsample_strides
@property
def min_sizes(self):
"""
Argument for image postprocessing step, min sizes, width and height for each anchor, default min_sizes = [[16, 32], [64, 128], [256, 512]]
"""
return self._model.min_sizes
@property
def landmarks_per_face(self):
"""
Argument for image postprocessing step, landmarks_per_face, default 5 in RetinaFace
"""
return self._model.landmarks_per_face
@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
@variance.setter
def variance(self, value):
assert isinstance(v, (list, tuple)),\
"The value to set `variance` must be type of tuple or list."
assert len(value) == 2,\
"The value to set `variance` must contatins 2 elements".format(
len(value))
self._model.variance = value
@downsample_strides.setter
def downsample_strides(self, value):
assert isinstance(
value,
list), "The value to set `downsample_strides` must be type of list."
self._model.downsample_strides = value
@min_sizes.setter
def min_sizes(self, value):
assert isinstance(
value, list), "The value to set `min_sizes` must be type of list."
self._model.min_sizes = value
@landmarks_per_face.setter
def landmarks_per_face(self, value):
assert isinstance(
value,
int), "The value to set `landmarks_per_face` must be type of int."
self._model.landmarks_per_face = value