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[Model] Refactor PaddleDetection module (#575)
* 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
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fastdeploy/vision/detection/ppdet/preprocessor.h
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fastdeploy/vision/detection/ppdet/preprocessor.h
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// 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/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 detection {
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/*! @brief Preprocessor object for PaddleDet serials model.
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*/
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class FASTDEPLOY_DECL PaddleDetPreprocessor {
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public:
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PaddleDetPreprocessor() = default;
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/** \brief Create a preprocessor instance for PaddleDet serials model
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*
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* \param[in] config_file Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
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*/
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explicit PaddleDetPreprocessor(const std::string& config_file);
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/** \brief Process the input image and prepare input tensors for runtime
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*
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* \param[in] images The input image data list, all the elements are returned by cv::imread()
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* \param[in] outputs The output tensors which will feed in runtime, include image, scale_factor, im_shape
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* \return true if the preprocess successed, otherwise false
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*/
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bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs);
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private:
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bool BuildPreprocessPipelineFromConfig(const std::string& config_file);
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std::vector<std::shared_ptr<Processor>> processors_;
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bool initialized_ = false;
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};
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} // namespace detection
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} // namespace vision
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} // namespace fastdeploy
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