64 lines
4.1 KiB
C
64 lines
4.1 KiB
C
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#ifndef ANSYOLOOD_H
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#define ANSYOLOOD_H
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#pragma once
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#include "ANSEngineCommon.h"
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#include <onnxruntime_cxx_api.h>
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//#define USEOPENCVDNN
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namespace ANSCENTER {
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class ANSENGINE_API YOLOOD :public ANSODBase {
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public:
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virtual bool Initialize(std::string licenseKey, ModelConfig modelConfig, const std::string& modelZipFilePath, const std::string& modelZipPassword, std::string& labelMap) override;
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virtual bool LoadModel(const std::string& modelZipFilePath, const std::string& modelZipPassword)override;
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virtual bool LoadModelFromFolder(std::string licenseKey, ModelConfig modelConfig, std::string modelName, std::string className, const std::string& modelFolder, std::string& labelMap)override;
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virtual bool OptimizeModel(bool fp16, std::string& optimizedModelFolder);
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std::vector<Object> RunInference(const cv::Mat& input);
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std::vector<Object> RunInference(const cv::Mat& input, const std::string& camera_id);
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bool Destroy();
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~YOLOOD();
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private:
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std::shared_ptr<cv::dnn::Net> _net;
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std::string _modelFilePath;
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bool _letterBoxForSquare{ true };
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bool _isDarkNet{false};
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std::vector<float> _blob; // Reusable buffer
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cv::Mat _frameBuffer; // Reusable buffer for color conversion
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private:
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std::vector<Object> RunInferenceFromONNX(const cv::Mat& input, const std::string& camera_id);
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std::vector<Object> RunInferenceFromYoloV5(const cv::Mat& input, const std::string& camera_id);
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std::vector<Object> RunInferenceFromYoloV11(const cv::Mat& input, const std::string& camera_id);
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std::vector<Object> RunInferenceFromDarkNet(const cv::Mat& input, const std::string& camera_id);
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std::vector<Object> RunInferenceFromYoloV8(const cv::Mat& input, const std::string& camera_id);// OpenCV Style
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void LoadOnnxNetwork();
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void LoadDarknetNetwork();
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void SetEngineType();
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private:
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Ort::Env env{ nullptr };
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Ort::SessionOptions sessionOptions{ nullptr };
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Ort::Session session{ nullptr };
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bool loadYoloModel(const std::string& modelPath,const bool& isGPU = true,const cv::Size& inputSize = cv::Size(640, 640));
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std::vector<Object> detect(cv::Mat& image, const float& confThreshold, const float& iouThreshold);
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void preprocessing(const cv::Mat& image,std::vector<float>& blob,std::vector<int64_t>& inputTensorShape);
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//cv::Mat preprocessv11(const cv::Mat& image, float*& blob, std::vector<int64_t>& inputTensorShape);
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cv::Mat preprocessv11(const cv::Mat& image, std::vector<float>& blob, std::vector<int64_t>& inputTensorShape);
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std::vector<Object> postprocessing(const cv::Size& resizedImageShape,const cv::Size& originalImageShape,std::vector<Ort::Value>& outputTensors,
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const float& confThreshold, const float& iouThreshold);
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std::vector<Object> postprocessv11(const cv::Size& originalImageSize,const cv::Size& resizedImageShape,const std::vector<Ort::Value>& outputTensors,float confThreshold,float iouThreshold);
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BoundingBox scaleCoordsv11(const cv::Size& imageShape, BoundingBox coords,const cv::Size& imageOriginalShape, bool p_Clip);
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std::vector<const char*> inputNodeNames;
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std::vector<const char*> outputNodeNames;
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bool isDynamicInputShape{};
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cv::Size inputImageShape; // Expected input image shape for the model
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float resizeScales;//letterbox scale
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float m_imgWidth = 0;
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float m_imgHeight = 0;
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std::vector<Object> detectv11(const cv::Mat& image, float confThreshold, float iouThreshold);
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//ultility functions
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void getBestClassInfo(std::vector<float>::iterator it, const int& numClasses,float& bestConf, int& bestClassId);
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void letterbox(const cv::Mat& image, cv::Mat& outImage,const cv::Size& newShape,const cv::Scalar& color,bool auto_,bool scaleFill,bool scaleUp,int stride);
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void scaleCoords(const cv::Size& imageShape, cv::Rect& box, const cv::Size& imageOriginalShape);
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size_t vectorProduct(const std::vector<int64_t>& vector);
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};
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}
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#endif
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