128 lines
4.9 KiB
C++
128 lines
4.9 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 PIPNet model object used when to load a PIPNet model exported by PIPNet.
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*/
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class FASTDEPLOY_DECL PIPNet : 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 ./pipnet.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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PIPNet(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 "PIPNet"; }
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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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/** \brief Get the number of landmakrs
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*
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* \return Integer type, default num_landmarks = 19
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*/
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int GetNumLandmarks() {return num_landmarks_; }
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/** \brief Get the mean values for normalization
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*
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* \return Vector of float values, default mean_vals = {0.485f, 0.456f, 0.406f}
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*/
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std::vector<float> GetMeanVals() { return mean_vals_; }
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/** \brief Get the std values for normalization
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*
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* \return Vector of float values, default std_vals = {0.229f, 0.224f, 0.225f}
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*/
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std::vector<float> GetStdVals() { return std_vals_; }
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/** \brief Get the input size of image
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*
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* \return Vector of int values, default {256, 256}
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*/
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std::vector<int> GetSize() { return size_; }
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/** \brief Set the number of landmarks
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*
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* \param[in] num_landmarks Integer value which represents number of landmarks
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*/
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void SetNumLandmarks(const int& num_landmarks);
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/** \brief Set the mean values for normalization
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*
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* \param[in] mean_vals Vector of float values whose length is equal to 3
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*/
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void SetMeanVals(const std::vector<float>& mean_vals) {
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mean_vals_ = mean_vals;
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}
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/** \brief Set the std values for normalization
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*
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* \param[in] std_vals Vector of float values whose length is equal to 3
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*/
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void SetStdVals(const std::vector<float>& std_vals) { std_vals_ = std_vals; }
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/** \brief Set the input size of image
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*
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* \param[in] size Vector of int values which represents {width, height} of image
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*/
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void SetSize(const std::vector<int>& size) { size_ = 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(std::vector<FDTensor>& infer_result,
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FaceAlignmentResult* result,
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const std::map<std::string, std::array<int, 2>>& im_info);
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void GenerateLandmarks(std::vector<FDTensor>& infer_result,
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FaceAlignmentResult* result,
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float img_height, float img_width);
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std::map<int, int> num_lms_map_;
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std::map<int, int> max_len_map_;
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std::map<int, std::vector<int>> reverse_index1_map_;
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std::map<int, std::vector<int>> reverse_index2_map_;
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int num_nb_;
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int net_stride_;
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// Now PIPNet support num_landmarks in {19, 29, 68, 98}
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std::vector<int> supported_num_landmarks_;
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// tuple of (width, height), default (256, 256)
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std::vector<int> size_;
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// Mean parameters for normalize, size should be the the same as channels,
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// default mean_vals = {0.485f, 0.456f, 0.406f}
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std::vector<float> mean_vals_;
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// Std parameters for normalize, size should be the the same as channels,
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// default std_vals = {0.229f, 0.224f, 0.225f}
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std::vector<float> std_vals_;
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// number of landmarks
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int num_landmarks_;
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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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