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* * 新增scrfd rknpu2代码 * * 新增scrfd python代码 * 修正文档 * 修正文档以及部分错误 * 修改文档 * 修复部分错误 * 修复部分错误 * 修复部分错误 * scrfd更新代码 * scrfd更新代码
205 lines
7.3 KiB
Python
205 lines
7.3 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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import logging
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from .... import FastDeployModel, ModelFormat
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from .... import c_lib_wrap as C
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class SCRFD(FastDeployModel):
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def __init__(self,
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model_file,
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params_file="",
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runtime_option=None,
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model_format=ModelFormat.ONNX):
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"""Load a SCRFD model exported by SCRFD.
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:param model_file: (str)Path of model file, e.g ./scrfd.onnx
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: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
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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# 调用基函数进行backend_option的初始化
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# 初始化后的option保存在self._runtime_option
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super(SCRFD, self).__init__(runtime_option)
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self._model = C.vision.facedet.SCRFD(
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model_file, params_file, self._runtime_option, model_format)
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# 通过self.initialized判断整个模型的初始化是否成功
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assert self.initialized, "SCRFD initialize failed."
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def predict(self, input_image, conf_threshold=0.7, nms_iou_threshold=0.3):
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"""Detect the location and key points of human faces from an input image
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:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
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:param conf_threshold: confidence threashold for postprocessing, default is 0.7
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:param nms_iou_threshold: iou threashold for NMS, default is 0.3
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:return: FaceDetectionResult
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"""
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return self._model.predict(input_image, conf_threshold,
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nms_iou_threshold)
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def disable_normalize_and_permute(self):
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"""
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This function will disable normalize and hwc2chw in preprocessing step.
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"""
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self._model.disable_normalize_and_permute()
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# 一些跟SCRFD模型有关的属性封装
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# 多数是预处理相关，可通过修改如model.size = [640, 640]改变预处理时resize的大小（前提是模型支持）
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@property
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def size(self):
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"""
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Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (640, 640)
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"""
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return self._model.size
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@property
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def padding_value(self):
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# padding value, size should be the same as channels
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return self._model.padding_value
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@property
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def is_no_pad(self):
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# while is_mini_pad = false and is_no_pad = true, will resize the image to the set size
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return self._model.is_no_pad
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@property
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def is_mini_pad(self):
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# only pad to the minimum rectange which height and width is times of stride
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return self._model.is_mini_pad
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@property
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def is_scale_up(self):
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# if is_scale_up is false, the input image only can be zoom out, the maximum resize scale cannot exceed 1.0
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return self._model.is_scale_up
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@property
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def stride(self):
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# padding stride, for is_mini_pad
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return self._model.stride
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@property
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def downsample_strides(self):
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"""
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Argument for image postprocessing step,
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downsample strides (namely, steps) for SCRFD to generate anchors,
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will take (8,16,32) as default values
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"""
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return self._model.downsample_strides
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@property
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def landmarks_per_face(self):
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"""
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Argument for image postprocessing step, landmarks_per_face, default 5 in SCRFD
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"""
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return self._model.landmarks_per_face
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@property
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def use_kps(self):
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"""
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Argument for image postprocessing step,
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the outputs of onnx file with key points features or not, default true
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"""
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return self._model.use_kps
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@property
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def max_nms(self):
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"""
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Argument for image postprocessing step, the upperbond number of boxes processed by nms, default 30000
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"""
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return self._model.max_nms
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@property
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def num_anchors(self):
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"""
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Argument for image postprocessing step, anchor number of each stride, default 2
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"""
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return self._model.num_anchors
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@size.setter
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def size(self, wh):
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assert isinstance(wh, (list, tuple)),\
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"The value to set `size` must be type of tuple or list."
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assert len(wh) == 2,\
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"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
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len(wh))
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self._model.size = wh
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@padding_value.setter
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def padding_value(self, value):
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assert isinstance(
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value,
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list), "The value to set `padding_value` must be type of list."
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self._model.padding_value = value
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@is_no_pad.setter
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def is_no_pad(self, value):
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assert isinstance(
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value, bool), "The value to set `is_no_pad` must be type of bool."
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self._model.is_no_pad = value
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@is_mini_pad.setter
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def is_mini_pad(self, value):
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assert isinstance(
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value,
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bool), "The value to set `is_mini_pad` must be type of bool."
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self._model.is_mini_pad = value
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@is_scale_up.setter
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def is_scale_up(self, value):
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assert isinstance(
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value,
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bool), "The value to set `is_scale_up` must be type of bool."
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self._model.is_scale_up = value
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@stride.setter
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def stride(self, value):
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assert isinstance(
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value, int), "The value to set `stride` must be type of int."
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self._model.stride = value
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@downsample_strides.setter
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def downsample_strides(self, value):
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assert isinstance(
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value,
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list), "The value to set `downsample_strides` must be type of list."
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self._model.downsample_strides = value
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@landmarks_per_face.setter
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def landmarks_per_face(self, value):
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assert isinstance(
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value,
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int), "The value to set `landmarks_per_face` must be type of int."
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self._model.landmarks_per_face = value
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@use_kps.setter
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def use_kps(self, value):
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assert isinstance(
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value, bool), "The value to set `use_kps` must be type of bool."
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self._model.use_kps = value
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@max_nms.setter
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def max_nms(self, value):
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assert isinstance(
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value, int), "The value to set `max_nms` must be type of int."
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self._model.max_nms = value
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@num_anchors.setter
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def num_anchors(self, value):
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assert isinstance(
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value, int), "The value to set `num_anchors` must be type of int."
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self._model.num_anchors = value
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