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https://github.com/PaddlePaddle/FastDeploy.git
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c7dc7d5eee
* 更正代码格式 * 更正代码格式 * 修复语法错误 * fix rk error * update * update * update * update * update * update * update Co-authored-by: Jason <jiangjiajun@baidu.com>
65 lines
2.3 KiB
C++
65 lines
2.3 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. //NOLINT
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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/contrib/rknpu2/postprocessor.h"
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#include "fastdeploy/vision/detection/contrib/rknpu2/preprocessor.h"
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namespace fastdeploy {
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namespace vision {
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namespace detection {
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class FASTDEPLOY_DECL RKYOLO : public FastDeployModel {
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public:
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RKYOLO(const std::string& model_file,
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::RKNN);
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std::string ModelName() const { return "RKYOLO"; }
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/** \brief Predict the 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(), is a 3-D array with layout HWC, BGR format
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* \param[in] result The output 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(const cv::Mat& img, DetectionResult* result);
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/** \brief Predict the detection 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 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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/// Get preprocessor reference of YOLOv5
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RKYOLOPreprocessor& GetPreprocessor() { return preprocessor_; }
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/// Get postprocessor reference of YOLOv5
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RKYOLOPostprocessor& GetPostprocessor() { return postprocessor_; }
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protected:
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bool Initialize();
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RKYOLOPreprocessor preprocessor_;
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RKYOLOPostprocessor 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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