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https://github.com/PaddlePaddle/FastDeploy.git
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9437dec9f5
* support face alignment PFLD * add PFLD demo * fixed FaceAlignmentResult * fixed bugs * fixed img size * fixed readme * deal with comments * fixed readme * add pfld testcase * update infer.py * add gflags for example * update c++ readme * add gflags in example * fixed for ci * fixed gflags.cmake * deal with comments * update infer demo Co-authored-by: Jason <jiangjiajun@baidu.com>
65 lines
2.6 KiB
C++
65 lines
2.6 KiB
C++
// 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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namespace facealign {
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/*! @brief PFLD model object used when to load a PFLD model exported by PFLD.
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*/
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class FASTDEPLOY_DECL PFLD : 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 ./pfld.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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PFLD(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 "PFLD"; }
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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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* \return true if the prediction successed, otherwise false
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*/
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virtual bool Predict(cv::Mat* im, FaceAlignmentResult* result);
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/// tuple of (width, height), default (112, 112)
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std::vector<int> size;
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private:
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bool Initialize();
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bool Preprocess(Mat* mat, FDTensor* outputs,
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std::map<std::string, std::array<int, 2>>* im_info);
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bool Postprocess(FDTensor& infer_result, FaceAlignmentResult* result,
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const std::map<std::string, std::array<int, 2>>& im_info);
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};
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} // namespace facealign
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} // namespace vision
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} // namespace fastdeploy
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