95 lines
3.9 KiB
C
95 lines
3.9 KiB
C
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// 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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#pragma once
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#include "fastdeploy/fastdeploy_model.h"
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#include "fastdeploy/vision/common/processors/transform.h"
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#include "fastdeploy/vision/common/result.h"
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namespace fastdeploy {
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namespace vision {
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/** \brief All object face detection model APIs are defined inside this namespace
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*
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*/
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namespace facedet {
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/*! @brief RetinaFace model object used when to load a RetinaFace model exported by RetinaFace.
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*/
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class FASTDEPLOY_DECL RetinaFace : public FastDeployModel {
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public:
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/** \brief Set path of model file and the configuration of runtime.
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*
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* \param[in] model_file Path of model file, e.g ./retinaface.onnx
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* \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
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* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
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* \param[in] model_format Model format of the loaded model, default is ONNX format
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*/
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RetinaFace(const std::string& model_file, const std::string& params_file = "",
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::ONNX);
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std::string ModelName() const { return "Pytorch_Retinaface"; }
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/** \brief Predict the face detection result for an input image
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*
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result The output face detection result will be writen to this structure
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* \param[in] conf_threshold confidence threashold for postprocessing, default is 0.25
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* \param[in] nms_iou_threshold iou threashold for NMS, default is 0.4
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* \return true if the prediction successed, otherwise false
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*/
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virtual bool Predict(cv::Mat* im, FaceDetectionResult* result,
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float conf_threshold = 0.25f,
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float nms_iou_threshold = 0.4f);
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/*! @brief
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Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default (640, 640)
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*/
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std::vector<int> size;
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/*! @brief
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Argument for image postprocessing step, variance in RetinaFace's prior-box(anchor) generate process, default (0.1, 0.2)
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*/
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std::vector<float> variance;
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/*! @brief
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Argument for image postprocessing step, downsample strides (namely, steps) for RetinaFace to generate anchors, will take (8,16,32) as default values
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*/
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std::vector<int> downsample_strides;
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/*! @brief
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Argument for image postprocessing step, min sizes, width and height for each anchor, default min_sizes = {{16, 32}, {64, 128}, {256, 512}}
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*/
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std::vector<std::vector<int>> min_sizes;
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/*! @brief
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Argument for image postprocessing step, landmarks_per_face, default 5 in RetinaFace
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*/
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int landmarks_per_face;
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private:
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bool Initialize();
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bool Preprocess(Mat* mat, FDTensor* output,
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std::map<std::string, std::array<float, 2>>* im_info);
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bool Postprocess(std::vector<FDTensor>& infer_result,
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FaceDetectionResult* result,
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const std::map<std::string, std::array<float, 2>>& im_info,
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float conf_threshold, float nms_iou_threshold);
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bool IsDynamicInput() const { return is_dynamic_input_; }
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bool is_dynamic_input_;
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};
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} // namespace facedet
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} // namespace vision
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} // namespace fastdeploy
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