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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
91 lines
3.7 KiB
C++
91 lines
3.7 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/detection/ppdet/preprocessor.h"
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#include "fastdeploy/vision/detection/ppdet/postprocessor.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/utils/utils.h"
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namespace fastdeploy {
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namespace vision {
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/** \brief All object detection model APIs are defined inside this namespace
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*
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*/
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namespace detection {
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/*! @brief Base model object used when to load a model exported by PaddleDetection
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*/
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class FASTDEPLOY_DECL PPDetBase : 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 ppyoloe/model.pdmodel
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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] config_file Path of configuration file for deployment, e.g ppyoloe/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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PPDetBase(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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virtual std::string ModelName() const { return "PaddleDetection/BaseModel"; }
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/** \brief DEPRECATED Predict the 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 detection result
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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, DetectionResult* result);
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/** \brief Predict the detection result for an input image
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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 detection result
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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, DetectionResult* result);
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/** \brief Predict the detection result for an input image list
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* \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
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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>& imgs,
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std::vector<DetectionResult>* results);
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PaddleDetPreprocessor& GetPreprocessor() {
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return preprocessor_;
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}
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PaddleDetPostprocessor& GetPostprocessor() {
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return postprocessor_;
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}
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protected:
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virtual bool Initialize();
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PaddleDetPreprocessor preprocessor_;
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PaddleDetPostprocessor postprocessor_;
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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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