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https://github.com/PaddlePaddle/FastDeploy.git
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beaa0fd190
* Add namespace for functions * Refactor PaddleDetection module * finish all the single image test * Update preprocessor.cc * fix some litte detail * add python api * Update postprocessor.cc
68 lines
2.5 KiB
C++
68 lines
2.5 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/result.h"
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#include "fastdeploy/vision/detection/ppdet/model.h"
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#include "fastdeploy/vision/keypointdet/pptinypose/pptinypose.h"
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namespace fastdeploy {
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/** \brief All pipeline model APIs are defined inside this namespace
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*
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*/
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namespace pipeline {
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/*! @brief PPTinyPose Pipeline object used when to load a detection model + pptinypose model
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*/
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class FASTDEPLOY_DECL PPTinyPose {
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public:
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/** \brief Set initialized detection model object and pptinypose model object
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*
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* \param[in] det_model Initialized detection model object
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* \param[in] pptinypose_model Initialized pptinypose model object
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*/
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PPTinyPose(
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fastdeploy::vision::detection::PicoDet* det_model,
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fastdeploy::vision::keypointdetection::PPTinyPose* pptinypose_model);
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/** \brief Predict the keypoint 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()
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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 prediction successed, otherwise false
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*/
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virtual bool Predict(cv::Mat* img,
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fastdeploy::vision::KeyPointDetectionResult* result);
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/* \brief The score threshold for detectin model to filter bbox before inputting pptinypose model
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*/
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float detection_model_score_threshold = 0;
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protected:
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fastdeploy::vision::detection::PicoDet* detector_ = nullptr;
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fastdeploy::vision::keypointdetection::PPTinyPose* pptinypose_model_ =
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nullptr;
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virtual bool Detect(cv::Mat* img,
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fastdeploy::vision::DetectionResult* result);
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virtual bool KeypointDetect(
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cv::Mat* img, fastdeploy::vision::KeyPointDetectionResult* result,
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fastdeploy::vision::DetectionResult& detection_result);
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};
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} // namespace pipeline
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} // namespace fastdeploy
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