mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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107 lines
4.5 KiB
C++
107 lines
4.5 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/keypointdet/pptinypose/pptinypose_utils.h"
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namespace fastdeploy {
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namespace vision {
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/** \brief All keypoint detection model APIs are defined inside this namespace
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*
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*/
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namespace keypointdetection {
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/*! @brief PPTinyPose model object used when to load a PPTinyPose model exported by PaddleDetection
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*/
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class FASTDEPLOY_DECL PPTinyPose : public FastDeployModel {
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public:
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/** \brief Set path of model file and configuration file, and the configuration of runtime
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*
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* \param[in] model_file Path of model file, e.g pptinypose/model.pdmodel
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* \param[in] params_file Path of parameter file, e.g pptinypose/model.pdiparams, if the model format is ONNX, this parameter will be ignored
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* \param[in] config_file Path of configuration file for deployment, e.g pptinypose/infer_cfg.yml
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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 Paddle format
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*/
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PPTinyPose(const std::string& model_file, const std::string& params_file,
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const std::string& config_file,
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE);
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/// Get model's name
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std::string ModelName() const { return "PaddleDetection/PPTinyPose"; }
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/** \brief Predict the keypoint 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()
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* \param[in] result The output keypoint detection result will be writen to this structure
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* \return true if the keypoint prediction successed, otherwise false
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*/
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bool Predict(cv::Mat* im, KeyPointDetectionResult* result);
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/** \brief Predict the keypoint detection result with given 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()
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* \param[in] result The output keypoint detection result will be writen to this structure
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* \param[in] detection_result The structure strores pedestrian detection result, which is used to crop image for multi-persons keypoint detection
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* \return true if the keypoint prediction successed, otherwise false
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*/
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bool Predict(cv::Mat* im, KeyPointDetectionResult* result,
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const DetectionResult& detection_result);
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/** \brief Whether using Distribution-Aware Coordinate Representation for Human Pose Estimation(DARK for short) in postprocess, default is true
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*/
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bool use_dark = true;
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/// This function will disable normalize in preprocessing step.
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void DisableNormalize() {
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disable_normalize_ = true;
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BuildPreprocessPipelineFromConfig();
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}
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/// This function will disable hwc2chw in preprocessing step.
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void DisablePermute() {
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disable_permute_ = true;
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BuildPreprocessPipelineFromConfig();
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}
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protected:
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bool Initialize();
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/// Build the preprocess pipeline from the loaded model
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bool BuildPreprocessPipelineFromConfig();
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/// Preprocess an input image, and set the preprocessed results to `outputs`
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bool Preprocess(Mat* mat, std::vector<FDTensor>* outputs);
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/// Postprocess the inferenced results, and set the final result to `result`
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bool Postprocess(std::vector<FDTensor>& infer_result,
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KeyPointDetectionResult* result,
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const std::vector<float>& center,
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const std::vector<float>& scale);
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private:
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std::vector<std::shared_ptr<Processor>> processors_;
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std::string config_file_;
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// for recording the switch of hwc2chw
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bool disable_permute_ = false;
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// for recording the switch of normalize
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bool disable_normalize_ = false;
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};
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} // namespace keypointdetection
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} // namespace vision
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} // namespace fastdeploy
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