mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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312e1b097d
* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs Co-authored-by: Jason <jiangjiajun@baidu.com>
91 lines
3.8 KiB
C++
91 lines
3.8 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/segmentation/ppseg/preprocessor.h"
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#include "fastdeploy/vision/segmentation/ppseg/postprocessor.h"
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namespace fastdeploy {
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namespace vision {
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/** \brief All segmentation model APIs are defined inside this namespace
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*
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*/
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namespace segmentation {
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/*! @brief PaddleSeg serials model object used when to load a PaddleSeg model exported by PaddleSeg repository
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*/
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class FASTDEPLOY_DECL PaddleSegModel : 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 unet/model.pdmodel
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* \param[in] params_file Path of parameter file, e.g unet/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 unet/deploy.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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PaddleSegModel(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 "PaddleSeg"; }
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/** \brief DEPRECATED Predict the segmentation 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 segmentation result will be writen to this structure
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* \return true if the segmentation prediction successed, otherwise false
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*/
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virtual bool Predict(cv::Mat* im, SegmentationResult* result);
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/** \brief Predict the segmentation 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 segmentation result will be writen to this structure
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* \return true if the segmentation prediction successed, otherwise false
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*/
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virtual bool Predict(const cv::Mat& im, SegmentationResult* result);
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/** \brief Predict the segmentation results for a batch of input images
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*
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* \param[in] imgs, The input image list, each element comes from cv::imread()
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* \param[in] results The output segmentation 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<SegmentationResult>* results);
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/// Get preprocessor reference of PaddleSegModel
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virtual PaddleSegPreprocessor& GetPreprocessor() {
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return preprocessor_;
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}
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/// Get postprocessor reference of PaddleSegModel
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virtual PaddleSegPostprocessor& GetPostprocessor() {
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return postprocessor_;
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}
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protected:
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bool Initialize();
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PaddleSegPreprocessor preprocessor_;
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PaddleSegPostprocessor postprocessor_;
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};
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} // namespace segmentation
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} // namespace vision
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} // namespace fastdeploy
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