#ifndef ANSONNXPOSE_H
#define ANSONNXPOSE_H
#pragma once
#include "ANSEngineCommon.h"
#include "engine.h"

namespace ANSCENTER {
    class ANSENGINE_API ANSONNXPOSE :public ANSODBase {
    public:
        virtual bool Initialize(std::string licenseKey, ModelConfig modelConfig, const std::string& modelZipFilePath, const std::string& modelZipPassword, std::string& labelMap) override;
        virtual bool LoadModel(const std::string& modelZipFilePath, const std::string& modelZipPassword)override;
        virtual bool LoadModelFromFolder(std::string licenseKey, ModelConfig modelConfig, std::string modelName, std::string className, const std::string& modelFolder, std::string& labelMap)override;
        virtual bool OptimizeModel(bool fp16, std::string& optimizedModelFolder);
        std::vector<Object> RunInference(const cv::Mat& input);
        std::vector<Object> RunInference(const cv::Mat& input, const std::string& camera_id);
        bool Destroy();
        ~ANSONNXPOSE();
        int getInstanceId() const { return instanceId_; }

    private:
        std::string     _modelFilePath;
        bool            _modelLoadValid;
        bool            _fp16{ false };
        size_t vectorProduct(const std::vector<int64_t>& vector);
        void letterBox(const cv::Mat& image, cv::Mat& outImage,
            const cv::Size& newShape,
            const cv::Scalar& color = cv::Scalar(114, 114, 114),
            bool auto_ = true,
            bool scaleFill = false,
            bool scaleUp = true,
            int stride = 32);
        void NMSBoxes(const std::vector<BoundingBox>& boundingBoxes,
            const std::vector<float>& scores,
            float scoreThreshold,
            float nmsThreshold,
            std::vector<int>& indices);
        void drawPoseEstimation(cv::Mat& image,
            const std::vector<Object>& detections,
            float confidenceThreshold = 0.5,
            float kptThreshold = 0.5);
        void warmupModel();
        bool Init(const std::string& modelPath, bool useGPU=true, int deviceId = 0);
        cv::Mat preprocess(const cv::Mat& image, float*& blob, std::vector<int64_t>& inputTensorShape);
        std::vector<Object> postprocess(const cv::Size& originalImageSize, const cv::Size& resizedImageShape,
                                        const std::vector<Ort::Value>& outputTensors, const std::string& camera_id);
        std::vector<Object> detect(const cv::Mat& image, const std::string& camera_id);
    private:
        static std::atomic<int> instanceCounter_;  // Thread-safe counter
        int instanceId_;
        int deviceId_ = 0;
        Ort::Env env{ nullptr };                         // ONNX Runtime environment
        Ort::SessionOptions sessionOptions{ nullptr };   // Session options for ONNX Runtime
        Ort::Session session{ nullptr };                 // ONNX Runtime session for running inference
        bool isDynamicInputShape{};                     // Flag indicating if input shape is dynamic
        cv::Size inputImageShape;                       // Expected input image shape for the model

        // Vectors to hold allocated input and output node names
        std::vector<Ort::AllocatedStringPtr> inputNodeNameAllocatedStrings;
        std::vector<const char*> inputNames;
        std::vector<Ort::AllocatedStringPtr> outputNodeNameAllocatedStrings;
        std::vector<const char*> outputNames;

        size_t numInputNodes, numOutputNodes;          // Number of input and output nodes in the model

        const std::vector<std::pair<int, int>> POSE_SKELETON = {
            // Face connections
            {0,1}, {0,2}, {1,3}, {2,4},
            // Head-to-shoulder connections
            {3,5}, {4,6},
            // Arms
            {5,7}, {7,9}, {6,8}, {8,10},
            // Body
            {5,6}, {5,11}, {6,12}, {11,12},
            // Legs
            {11,13}, {13,15}, {12,14}, {14,16}
        };
    };
}
#endif
#pragma once