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1c115bb237
* cpp example run success * add landmarks * fix reviewed problem * add pybind * add readme in examples * fix reviewed problem * new file: tests/models/test_centerface.py * fix reviewed problem 230202
82 lines
3.2 KiB
C++
82 lines
3.2 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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#include "fastdeploy/vision/facedet/contrib/centerface/preprocessor.h"
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#include "fastdeploy/vision/facedet/contrib/centerface/postprocessor.h"
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namespace fastdeploy {
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namespace vision {
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namespace facedet {
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/*! @brief CenterFace model object used when to load a CenterFace model exported by CenterFace.
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*/
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class FASTDEPLOY_DECL CenterFace: 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 ./centerface.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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CenterFace(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() {return "centerface";}
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/** \brief Predict the detection result for an input image
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*
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* \param[in] img 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 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(const cv::Mat& im, FaceDetectionResult* result);
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/** \brief Predict the detection results for a batch of input images
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*
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* \param[in] imgs, The input image list, each element comes from cv::imread()
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* \param[in] results The output detection result list
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* \return true if the prediction successed, otherwise false
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*/
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virtual bool BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<FaceDetectionResult>* results);
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/// Get preprocessor reference of CenterFace
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virtual CenterFacePreprocessor& GetPreprocessor() {
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return preprocessor_;
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}
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/// Get postprocessor reference of CenterFace
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virtual CenterFacePostprocessor& GetPostprocessor() {
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return postprocessor_;
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}
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protected:
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bool Initialize();
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CenterFacePreprocessor preprocessor_;
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CenterFacePostprocessor postprocessor_;
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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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