#include "ANSTENSORRTSEG.h"
#include "Utility.h"
#include <opencv2/cudaimgproc.hpp>
#include <future>
namespace ANSCENTER
{
    bool TENSORRTSEG::OptimizeModel(bool fp16, std::string& optimizedModelFolder) {
        std::lock_guard<std::recursive_mutex> lock(_mutex);
        if (!ANSODBase::OptimizeModel(fp16, optimizedModelFolder)) {
            return false;
        }
        if (!FileExist(_modelFilePath)) {
            this->_logger.LogFatal("TENSORRTSEG::OptimizeModel", "Raw model file path does not exist", __FILE__, __LINE__);
            return false;
        }
        try {
            _fp16 = fp16;
            optimizedModelFolder = GetParentFolder(_modelFilePath);
            // Check if the engine already exists to avoid reinitializing
            if (!m_trtEngine) {
                // Fixed batch size of 1 for this model
                m_options.optBatchSize = _modelConfig.gpuOptBatchSize;
                m_options.maxBatchSize = _modelConfig.gpuMaxBatchSize;
                m_options.deviceIndex = _modelConfig.gpuDeviceIndex;
                m_options.maxInputHeight = _modelConfig.maxInputHeight;
                m_options.minInputHeight = _modelConfig.minInputHeight;
                m_options.optInputHeight = _modelConfig.optInputHeight;
                m_options.maxInputWidth = _modelConfig.maxInputWidth;
                m_options.minInputWidth = _modelConfig.minInputWidth;
                m_options.optInputWidth = _modelConfig.optInputWidth;
                m_options.engineFileDir = optimizedModelFolder;
                m_options.precision = (_fp16 ? Precision::FP16 : Precision::FP32);
                // Create the TensorRT inference engine
                m_trtEngine = std::make_unique<Engine<float>>(m_options);
            }

            // Build the TensorRT engine
            auto succ = m_trtEngine->buildWithRetry(_modelFilePath, SUB_VALS, DIV_VALS, NORMALIZE);
            if (!succ) {
                const std::string errMsg =
                    "Error: Unable to build the TensorRT engine. "
                    "Try increasing TensorRT log severity to kVERBOSE.";
                this->_logger.LogError("TENSORRTSEG::OptimizeModel", errMsg, __FILE__, __LINE__);
                _modelLoadValid = false;
                return false;
            }
            _modelLoadValid = true;
            return true;
        }
        catch (const std::exception& e) {
            this->_logger.LogFatal("TENSORRTSEG::OptimizeModel", e.what(), __FILE__, __LINE__);
            optimizedModelFolder.clear();
            return false;
        }
    }
    bool TENSORRTSEG::LoadModel(const std::string& modelZipFilePath, const std::string& modelZipPassword) {
        std::lock_guard<std::recursive_mutex> lock(_mutex);
        ModelLoadingGuard mlg(_modelLoading);
        try {
            bool result = ANSODBase::LoadModel(modelZipFilePath, modelZipPassword);
            if (!result) return false;
            _modelConfig.detectionType = ANSCENTER::DetectionType::SEGMENTATION;
            _modelConfig.modelType = ModelType::RTSEG;
            _modelConfig.inpHeight = 640;
            _modelConfig.inpWidth = 640;
            if (_modelConfig.modelMNSThreshold < 0.2)
                _modelConfig.modelMNSThreshold = 0.5;
            if (_modelConfig.modelConfThreshold < 0.2)
                _modelConfig.modelConfThreshold = 0.5;
            if (_modelConfig.numKPS <= 0 || _modelConfig.numKPS > 133)  // 133 = COCO wholebody max
                _modelConfig.numKPS = 17;
            if (_modelConfig.kpsThreshold == 0)_modelConfig.kpsThreshold = 0.5; // If not define
            _fp16 = true; // Load Model from Here
            // Load Model from Here
            TOP_K = 100;
            SEG_CHANNELS = 32;
            PROBABILITY_THRESHOLD = _modelConfig.detectionScoreThreshold;
            NMS_THRESHOLD = _modelConfig.modelMNSThreshold;
            SEGMENTATION_THRESHOLD = 0.5f;
            SEG_H = 160;
            SEG_W = 160;
            NUM_KPS = _modelConfig.numKPS;
            KPS_THRESHOLD = _modelConfig.kpsThreshold;
            SEG_CHANNELS = 32;      // For segmentation 

            if (!m_trtEngine) {
                // Fixed batch size of 1 for this model
                m_options.optBatchSize = _modelConfig.gpuOptBatchSize;
                m_options.maxBatchSize = _modelConfig.gpuMaxBatchSize;
                m_options.deviceIndex = _modelConfig.gpuDeviceIndex;
                m_options.maxInputHeight = _modelConfig.maxInputHeight;
                m_options.minInputHeight = _modelConfig.minInputHeight;
                m_options.optInputHeight = _modelConfig.optInputHeight;
                m_options.maxInputWidth = _modelConfig.maxInputWidth;
                m_options.minInputWidth = _modelConfig.minInputWidth;
                m_options.optInputWidth = _modelConfig.optInputWidth;
                m_options.engineFileDir = _modelFolder;
                // Use FP16 or FP32 precision based on the input flag
                m_options.precision = (_fp16 ? Precision::FP16 : Precision::FP32);
                // Create the TensorRT inference engine
                m_trtEngine = std::make_unique<Engine<float>>(m_options);
            }
            // 0. Check if the configuration file exist
            if (FileExist(_modelConfigFile)) {
                ModelType modelType;
                std::vector<int> inputShape;
                _classes = ANSUtilityHelper::GetConfigFileContent(_modelConfigFile, modelType, inputShape);
                if (inputShape.size() == 2) {
                    if (inputShape[0] > 0)_modelConfig.inpHeight = inputShape[0];
                    if (inputShape[1] > 0)_modelConfig.inpWidth = inputShape[1];
                }
            }
            else {// This is old version of model zip file
                _modelFilePath = CreateFilePath(_modelFolder, "train_last.onnx");
                _classFilePath = CreateFilePath(_modelFolder, "classes.names");
                std::ifstream isValidFileName(_classFilePath);
                if (!isValidFileName)
                {
                    this->_logger.LogDebug("TENSORRTSEG::Initialize.  Load classes from string", _classFilePath, __FILE__, __LINE__);
                    LoadClassesFromString();
                }
                else {
                    this->_logger.LogDebug("TENSORRTSEG::Initialize.  Load classes from file", _classFilePath, __FILE__, __LINE__);
                    LoadClassesFromFile();
                }
            }
            // Load the TensorRT engine file
            if (this->_loadEngineOnCreation) {
                auto succ = m_trtEngine->buildLoadNetwork(_modelFilePath, SUB_VALS, DIV_VALS, NORMALIZE, m_maxSlotsPerGpu);
                if (!succ) {
                    const std::string errMsg = "Error: Unable to load TensorRT engine weights into memory. " + _modelFilePath;
                    this->_logger.LogError("TENSORRTSEG::Initialize", errMsg, __FILE__, __LINE__);
                    _modelLoadValid = false;
                    return false;
                }
            }
            _modelLoadValid = true;
            _isInitialized = true;
            return true;
        }
        catch (std::exception& e) {
            this->_logger.LogFatal("TENSORRTSEG::LoadModel", e.what(), __FILE__, __LINE__);
            return false;
        }
    }
    bool TENSORRTSEG::LoadModelFromFolder(std::string licenseKey, ModelConfig modelConfig, std::string modelName, std::string className, const std::string& modelFolder, std::string& labelMap) {
        std::lock_guard<std::recursive_mutex> lock(_mutex);
        ModelLoadingGuard mlg(_modelLoading);
        try
        {
            bool result = ANSODBase::LoadModelFromFolder(licenseKey, modelConfig, modelName, className, modelFolder, labelMap);
            if (!result) return false;
            _modelConfig = modelConfig;
            _modelConfig.detectionType = ANSCENTER::DetectionType::SEGMENTATION;
            _modelConfig.modelType = ModelType::RTSEG;
            _modelConfig.inpHeight = 640;
            _modelConfig.precisionType = PrecisionType::FP32; // Default to FP16 for TensorRT models
            if (_modelConfig.numKPS <= 0 || _modelConfig.numKPS > 133)  // 133 = COCO wholebody max
                _modelConfig.numKPS = 17;
            _modelConfig.inpWidth = 640;
            if (_modelConfig.modelMNSThreshold < 0.2)
                _modelConfig.modelMNSThreshold = 0.5;
            if (_modelConfig.modelConfThreshold < 0.2)
                _modelConfig.modelConfThreshold = 0.5;
            if (_modelConfig.kpsThreshold <= 0)_modelConfig.kpsThreshold = 0.5; // If not define
            _fp16 = true; // Load Model from Here
            // Load Model from Here
            TOP_K = 100;
            SEG_CHANNELS = 32;
            PROBABILITY_THRESHOLD = _modelConfig.detectionScoreThreshold;
            NMS_THRESHOLD = _modelConfig.modelMNSThreshold;
            SEGMENTATION_THRESHOLD = 0.5f;
            SEG_H = 160;
            SEG_W = 160;
            NUM_KPS = _modelConfig.numKPS;
            KPS_THRESHOLD = _modelConfig.kpsThreshold;
            SEG_CHANNELS = 32;      // For segmentation 
            std::string _modelName = modelName;
            if (_modelName.empty()) {
                _modelName = "train_last";
            }
            std::string modelFullName = _modelName + ".onnx";
            if (!m_trtEngine) {
                // Fixed batch size of 1 for this model
                m_options.optBatchSize = _modelConfig.gpuOptBatchSize;
                m_options.maxBatchSize = _modelConfig.gpuMaxBatchSize;
                m_options.deviceIndex = _modelConfig.gpuDeviceIndex;
                m_options.maxInputHeight = _modelConfig.maxInputHeight;
                m_options.minInputHeight = _modelConfig.minInputHeight;
                m_options.optInputHeight = _modelConfig.optInputHeight;
                m_options.maxInputWidth = _modelConfig.maxInputWidth;
                m_options.minInputWidth = _modelConfig.minInputWidth;
                m_options.optInputWidth = _modelConfig.optInputWidth;
                m_options.engineFileDir = _modelFolder;
                // Use FP16 or FP32 precision based on the input flag
                m_options.precision = (_fp16 ? Precision::FP16 : Precision::FP32);
                // Create the TensorRT inference engine
                m_trtEngine = std::make_unique<Engine<float>>(m_options);
            }
            // 0. Check if the configuration file exist
            if (FileExist(_modelConfigFile)) {
                ModelType modelType;
                std::vector<int> inputShape;
                _classes = ANSUtilityHelper::GetConfigFileContent(_modelConfigFile, modelType, inputShape);
                if (inputShape.size() == 2) {
                    if (inputShape[0] > 0)_modelConfig.inpHeight = inputShape[0];
                    if (inputShape[1] > 0)_modelConfig.inpWidth = inputShape[1];
                }
            }
            else {// This is old version of model zip file
                _modelFilePath = CreateFilePath(_modelFolder, modelFullName);
                _classFilePath = CreateFilePath(_modelFolder, className);
                std::ifstream isValidFileName(_classFilePath);
                if (!isValidFileName)
                {
                    this->_logger.LogDebug("TENSORRTSEG::Initialize.  Load classes from string", _classFilePath, __FILE__, __LINE__);
                    LoadClassesFromString();
                }
                else {
                    this->_logger.LogDebug("TENSORRTSEG::Initialize.  Load classes from file", _classFilePath, __FILE__, __LINE__);
                    LoadClassesFromFile();
                }
            }
            // 1. Load labelMap and engine
            labelMap.clear();
            if (!_classes.empty())
                labelMap = VectorToCommaSeparatedString(_classes);

            // Load the TensorRT engine file
            if (this->_loadEngineOnCreation) {
                auto succ = m_trtEngine->buildLoadNetwork(_modelFilePath, SUB_VALS, DIV_VALS, NORMALIZE, m_maxSlotsPerGpu);
                if (!succ) {
                    const std::string errMsg = "Error: Unable to load TensorRT engine weights into memory. " + _modelFilePath;
                    this->_logger.LogError("TENSORRTSEG::Initialize", errMsg, __FILE__, __LINE__);
                    _modelLoadValid = false;
                    return false;
                }
            }
            _modelLoadValid = true;
            _isInitialized = true;
            return true;
        }
        catch (std::exception& e) {
            this->_logger.LogFatal("TENSORRTSEG::LoadModelFromFolder", e.what(), __FILE__, __LINE__);
            return false;
        }
    }
    bool TENSORRTSEG::Initialize(std::string licenseKey, ModelConfig modelConfig, const std::string& modelZipFilePath, const std::string& modelZipPassword, std::string& labelMap) {
        std::lock_guard<std::recursive_mutex> lock(_mutex);
        ModelLoadingGuard mlg(_modelLoading);
        try {
            const bool engineAlreadyLoaded = _modelLoadValid && _isInitialized && m_trtEngine != nullptr;
            _modelLoadValid = false;
            bool result = ANSODBase::Initialize(licenseKey, modelConfig, modelZipFilePath, modelZipPassword, labelMap);
            if (!result) return false;
            // Parsing for YOLO only here
            _modelConfig = modelConfig;
            _modelConfig.detectionType = ANSCENTER::DetectionType::SEGMENTATION;
            _modelConfig.modelType = ModelType::RTSEG;;
            _modelConfig.inpHeight = 640;
            _modelConfig.inpWidth = 640;
            if (_modelConfig.numKPS <= 0 || _modelConfig.numKPS > 133)  // 133 = COCO wholebody max
                _modelConfig.numKPS = 17;
            _modelConfig.precisionType = PrecisionType::FP32; // Default to FP16 for TensorRT models
            if (_modelConfig.modelMNSThreshold < 0.2)
                _modelConfig.modelMNSThreshold = 0.5;
            if (_modelConfig.modelConfThreshold < 0.2)
                _modelConfig.modelConfThreshold = 0.5;
            if (_modelConfig.kpsThreshold == 0)_modelConfig.kpsThreshold = 0.5; // If not define
            _fp16 = true; // Load Model from Here
            // Load Model from Here
            TOP_K = 100;
            SEG_CHANNELS = 32;
            PROBABILITY_THRESHOLD = _modelConfig.detectionScoreThreshold;
            NMS_THRESHOLD = _modelConfig.modelMNSThreshold;
            SEGMENTATION_THRESHOLD = 0.5f;
            SEG_H = 160;
            SEG_W = 160;
            NUM_KPS = _modelConfig.numKPS;
            KPS_THRESHOLD = _modelConfig.kpsThreshold;
            SEG_CHANNELS = 32;      // For segmentation 

            if (!m_trtEngine) {
                // Fixed batch size of 1 for this model
                m_options.optBatchSize = _modelConfig.gpuOptBatchSize;
                m_options.maxBatchSize = _modelConfig.gpuMaxBatchSize;
                m_options.deviceIndex = _modelConfig.gpuDeviceIndex;
                m_options.maxInputHeight = _modelConfig.maxInputHeight;
                m_options.minInputHeight = _modelConfig.minInputHeight;
                m_options.optInputHeight = _modelConfig.optInputHeight;
                m_options.maxInputWidth = _modelConfig.maxInputWidth;
                m_options.minInputWidth = _modelConfig.minInputWidth;
                m_options.optInputWidth = _modelConfig.optInputWidth;
                m_options.engineFileDir = _modelFolder;
                // Use FP16 or FP32 precision based on the input flag
                m_options.precision = (_fp16 ? Precision::FP16 : Precision::FP32);
                // Create the TensorRT inference engine
                m_trtEngine = std::make_unique<Engine<float>>(m_options);
            }
            // 0. Check if the configuration file exist
            if (FileExist(_modelConfigFile)) {
                ModelType modelType;
                std::vector<int> inputShape;
                _classes = ANSUtilityHelper::GetConfigFileContent(_modelConfigFile, modelType, inputShape);
                if (inputShape.size() == 2) {
                    if (inputShape[0] > 0)_modelConfig.inpHeight = inputShape[0];
                    if (inputShape[1] > 0)_modelConfig.inpWidth = inputShape[1];
                }
            }
            else {// This is old version of model zip file
                _modelFilePath = CreateFilePath(_modelFolder, "train_last.onnx");
                _classFilePath = CreateFilePath(_modelFolder, "classes.names");
                std::ifstream isValidFileName(_classFilePath);
                if (!isValidFileName)
                {
                    this->_logger.LogDebug("TENSORRTSEG::Initialize.  Load classes from string", _classFilePath, __FILE__, __LINE__);
                    LoadClassesFromString();
                }
                else {
                    this->_logger.LogDebug("TENSORRTSEG::Initialize.  Load classes from file", _classFilePath, __FILE__, __LINE__);
                    LoadClassesFromFile();
                }
            }
            // 1. Load labelMap and engine
            labelMap.clear();
            if (!_classes.empty())
                labelMap = VectorToCommaSeparatedString(_classes);

            // Load the TensorRT engine file
            if (this->_loadEngineOnCreation && !engineAlreadyLoaded) {
                auto succ = m_trtEngine->buildLoadNetwork(_modelFilePath, SUB_VALS, DIV_VALS, NORMALIZE, m_maxSlotsPerGpu);
                if (!succ) {
                    const std::string errMsg = "Error: Unable to load TensorRT engine weights into memory. " + _modelFilePath;
                    this->_logger.LogError("TENSORRTSEG::Initialize", errMsg, __FILE__, __LINE__);
                    _modelLoadValid = false;
                    return false;
                }
            }
            _modelLoadValid = true;
            _isInitialized = true;
            return true;
        }
        catch (std::exception& e) {
            this->_logger.LogFatal("TENSORRTSEG::Initialize", e.what(), __FILE__, __LINE__);
            return false;
        }
    }
    std::vector<Object> TENSORRTSEG::RunInference(const cv::Mat& inputImgBGR) {
        return RunInference(inputImgBGR, "");
    }  
    std::vector<Object> TENSORRTSEG::RunInference(const cv::Mat& inputImgBGR,const std::string& camera_id)
    {
        if (!PreInferenceCheck("TENSORRTSEG::RunInference")) return {};
        try {
            return DetectObjects(inputImgBGR, camera_id);
        }
        catch (const std::exception& e) {
            _logger.LogFatal("TENSORRTSEG::RunInference", e.what(), __FILE__, __LINE__);
            return {};
        }
    }

    TENSORRTSEG::~TENSORRTSEG() {
        try {
            Destroy();
        }
        catch (std::exception& e) {
            this->_logger.LogError("TENSORRTSEG::~TENSORRTSEG()", e.what(), __FILE__, __LINE__);
        }
    }
    bool TENSORRTSEG::Destroy() {
        try {
            m_trtEngine.reset();  // Releases the current engine and sets m_trtEngine to nullptr.
            return true;
        }
        catch (std::exception& e) {
            this->_logger.LogError("TENSORRTSEG::~TENSORRTSEG()", e.what(), __FILE__, __LINE__);
            return false;
        }
    }
    // private
    std::vector<cv::Point2f> TENSORRTSEG::maskToPolygon(const cv::Mat& binaryMask,const cv::Rect& boundingBox,float simplificationEpsilon,int minContourArea)
    {
        std::vector<cv::Point2f> polygon;

        try {
            // Validate input
            if (binaryMask.empty() || binaryMask.type() != CV_8UC1) {
                return polygon;
            }

            // Extract region of interest from mask
            cv::Rect roi = boundingBox & cv::Rect(0, 0, binaryMask.cols, binaryMask.rows);
            if (roi.area() <= 0) {
                return polygon;
            }

            cv::Mat maskROI = binaryMask(roi);

            // Find contours in the mask
            std::vector<std::vector<cv::Point>> contours;
            std::vector<cv::Vec4i> hierarchy;
            cv::findContours(maskROI.clone(), contours, hierarchy,
                cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);

            if (contours.empty()) {
                return polygon;
            }

            // Find the largest contour (main object)
            int largestIdx = 0;
            double largestArea = 0.0;

            for (size_t i = 0; i < contours.size(); ++i) {
                double area = cv::contourArea(contours[i]);
                if (area > largestArea && area >= minContourArea) {
                    largestArea = area;
                    largestIdx = static_cast<int>(i);
                }
            }

            if (largestArea < minContourArea) {
                return polygon;
            }

            // Simplify the contour to reduce number of points
            std::vector<cv::Point> simplifiedContour;
            cv::approxPolyDP(contours[largestIdx], simplifiedContour,
                simplificationEpsilon, true);

            // Convert to Point2f and offset by ROI position
            polygon.reserve(simplifiedContour.size());
            for (const auto& pt : simplifiedContour) {
                polygon.emplace_back(
                    static_cast<float>(pt.x + roi.x),
                    static_cast<float>(pt.y + roi.y)
                );
            }

            return polygon;
        }
        catch (const cv::Exception& e) {
            // Log error if logger available
            polygon.clear();
            return polygon;
        }
    }
    std::vector<Object> TENSORRTSEG::DetectObjects(const cv::Mat& inputImage, const std::string& camera_id) {
        try {
            // --- 1. Set GPU device context ---
            if (m_trtEngine) {
                m_trtEngine->setDeviceContext();
            }

            // --- 1b. CUDA context health check ---
            if (!m_nv12Helper.isCudaContextHealthy(_logger, "TENSORRTSEG")) {
                return {};
            }

            // --- 2. Preprocess under lock ---
            ImageMetadata meta;
            std::vector<std::vector<cv::cuda::GpuMat>> input;
            bool usedNV12 = false;
            float bgrFullResScaleX = 1.0f, bgrFullResScaleY = 1.0f;
            {
                std::lock_guard<std::recursive_mutex> lock(_mutex);
                const int inferenceGpu = m_trtEngine ? m_trtEngine->getPreferredDeviceIndex() : 0;
                const auto& inputDims = m_trtEngine->getInputDims();
                const int inputW = inputDims[0].d[2];
                const int inputH = inputDims[0].d[1];

                auto nv12 = m_nv12Helper.tryNV12(inputImage, inferenceGpu, inputW, inputH,
                                                  NV12PreprocessHelper::defaultYOLOLauncher(),
                                                  _logger, "TENSORRTSEG");
                if (nv12.succeeded) {
                    meta.imgWidth  = nv12.metaWidth;
                    meta.imgHeight = nv12.metaHeight;
                    meta.ratio     = nv12.ratio;
                    input = {{ std::move(nv12.gpuRGB) }};
                    usedNV12 = true;
                }
                else if (nv12.useBgrFullRes) {
                    input = Preprocess(nv12.bgrFullResImg, meta);
                    usedNV12 = !input.empty();
                    bgrFullResScaleX = nv12.bgrFullResScaleX;
                    bgrFullResScaleY = nv12.bgrFullResScaleY;
                }

                if (input.empty()) {
                    input = Preprocess(inputImage, meta);
                }
                m_nv12Helper.tickInference();
            }

            if (input.empty()) return {};

            // Phase 2: Inference — mutex released; pool dispatches to idle GPU slot
            std::vector<std::vector<std::vector<float>>> featureVectors;
            auto succ = m_trtEngine->runInference(input, featureVectors);
            if (!succ) {
                this->_logger.LogError("TENSORRTSEG::DetectObjects", "Error running inference", __FILE__, __LINE__);
                return {};
            }

            // Phase 3: Postprocess under lock
            std::lock_guard<std::recursive_mutex> lock(_mutex);
            std::vector<std::vector<float>> featureVector;
            Engine<float>::transformOutput(featureVectors, featureVector);
            auto ret = PostProcessSegmentation(featureVector, camera_id, meta);

            // --- 4b. Rescale coords from full-res to display-res ---
            if (bgrFullResScaleX != 1.0f || bgrFullResScaleY != 1.0f) {
                for (auto& obj : ret) {
                    obj.box.x      = static_cast<int>(obj.box.x      * bgrFullResScaleX);
                    obj.box.y      = static_cast<int>(obj.box.y      * bgrFullResScaleY);
                    obj.box.width  = static_cast<int>(obj.box.width  * bgrFullResScaleX);
                    obj.box.height = static_cast<int>(obj.box.height * bgrFullResScaleY);
                    for (auto& pt : obj.polygon) {
                        pt.x *= bgrFullResScaleX;
                        pt.y *= bgrFullResScaleY;
                    }
                }
            }

            if (_trackerEnabled) {
                ret = ApplyTracking(ret, camera_id);
                if (_stabilizationEnabled) ret = StabilizeDetections(ret, camera_id);
            }
            return ret;
        }
        catch (std::exception& e) {
            this->_logger.LogFatal("TENSORRTSEG::DetectObjects", e.what(), __FILE__, __LINE__);
            return {};
        }
    }
    std::vector<std::vector<cv::cuda::GpuMat>> TENSORRTSEG::Preprocess(const cv::Mat& inputImage, ImageMetadata& outMeta) {
        try {
            if (!_licenseValid) {
                this->_logger.LogFatal("TENSORRTSEG::Preprocess", "Invalid license", __FILE__, __LINE__);
                return {};
            }

            // Get model input dimensions
            const auto& inputDims = m_trtEngine->getInputDims();
            const int inputH = inputDims[0].d[1];
            const int inputW = inputDims[0].d[2];
            // --- CPU preprocessing: resize + BGR->RGB before GPU upload ---
            cv::Mat srcImg = inputImage;
            if (srcImg.channels() == 1) {
                cv::cvtColor(srcImg, srcImg, cv::COLOR_GRAY2BGR);
            }

            // Set image size parameters from ORIGINAL image
            outMeta.imgHeight = srcImg.rows;
            outMeta.imgWidth = srcImg.cols;
            if (outMeta.imgHeight > 0 && outMeta.imgWidth > 0) {
                outMeta.ratio = 1.f / std::min(inputDims[0].d[2] / static_cast<float>(srcImg.cols),
                    inputDims[0].d[1] / static_cast<float>(srcImg.rows));

                const auto& outputDims = m_trtEngine->getOutputDims();
                const bool isClassification = !outputDims.empty() && outputDims[0].nbDims <= 2;

                // CPU resize to model input size
                cv::Mat cpuResized;
                if (srcImg.rows != inputH || srcImg.cols != inputW) {
                    if (isClassification) {
                        cv::resize(srcImg, cpuResized, cv::Size(inputW, inputH), 0, 0, cv::INTER_LINEAR);
                    } else {
                        cpuResized = Engine<float>::cpuResizeKeepAspectRatioPadRightBottom(srcImg, inputH, inputW);
                    }
                } else {
                    cpuResized = srcImg;
                }

                // CPU BGR -> RGB
                cv::Mat cpuRGB;
                cv::cvtColor(cpuResized, cpuRGB, cv::COLOR_BGR2RGB);

                // Upload small image to GPU
                cv::cuda::Stream stream;
                cv::cuda::GpuMat gpuResized;
                gpuResized.upload(cpuRGB, stream);
                stream.waitForCompletion();

                // Convert to format expected by our inference engine
                std::vector<cv::cuda::GpuMat> input{ std::move(gpuResized) };
                std::vector<std::vector<cv::cuda::GpuMat>> inputs{ std::move(input) };
                return inputs;
            }
            else {
                this->_logger.LogFatal("TENSORRTCL::Preprocess",
                    "Image height or width is zero after processing (Width: " + std::to_string(outMeta.imgWidth) +
                    ", Height: " + std::to_string(outMeta.imgHeight) + ")",
                    __FILE__, __LINE__);
                return {};
            }
        }
        catch (const std::exception& e) {
            this->_logger.LogFatal("TENSORRTSEG::Preprocess", e.what(), __FILE__, __LINE__);
            return {};
        }
    }
    std::vector<Object> TENSORRTSEG::PostProcessSegmentation(std::vector<std::vector<float>>& featureVectors, const std::string& camera_id, const ImageMetadata& meta) {
        try {
            if (!_licenseValid) {
                this->_logger.LogFatal("TENSORRTSEG::PostProcessSegmentation", "Invalid license", __FILE__, __LINE__);
                std::vector<Object> result;
                result.clear();
                return result;
            }
            const auto& outputDims = m_trtEngine->getOutputDims();

            int numChannels = outputDims[0].d[1];
            int numAnchors = outputDims[0].d[2];

            const auto numClasses = numChannels - SEG_CHANNELS - 4;

            // Ensure the output lengths are correct
            if (featureVectors[0].size() == static_cast<size_t>(numChannels) * numAnchors &&
                featureVectors[1].size() == static_cast<size_t>(SEG_CHANNELS) * SEG_H * SEG_W) {

                cv::Mat output = cv::Mat(numChannels, numAnchors, CV_32F, featureVectors[0].data());
                output = output.t();

                cv::Mat protos = cv::Mat(SEG_CHANNELS, SEG_H * SEG_W, CV_32F, featureVectors[1].data());

                std::vector<int> labels;
                std::vector<float> scores;
                std::vector<cv::Rect> bboxes;
                std::vector<cv::Mat> maskConfs;
                std::vector<int> indices;

                // Object the bounding boxes and class labels
                for (int i = 0; i < numAnchors; i++) {
                    auto rowPtr = output.row(i).ptr<float>();
                    auto bboxesPtr = rowPtr;
                    auto scoresPtr = rowPtr + 4;
                    auto maskConfsPtr = rowPtr + 4 + numClasses;
                    auto maxSPtr = std::max_element(scoresPtr, scoresPtr + numClasses);
                    float score = *maxSPtr;
                    if (score > this->_modelConfig.detectionScoreThreshold)
                    {
                        float x = *bboxesPtr++;
                        float y = *bboxesPtr++;
                        float w = *bboxesPtr++;
                        float h = *bboxesPtr;

                        float x0 = std::clamp((x - 0.5f * w) * meta.ratio, 0.f, meta.imgWidth);
                        float y0 = std::clamp((y - 0.5f * h) * meta.ratio, 0.f, meta.imgHeight);
                        float x1 = std::clamp((x + 0.5f * w) * meta.ratio, 0.f, meta.imgWidth);
                        float y1 = std::clamp((y + 0.5f * h) * meta.ratio, 0.f, meta.imgHeight);

                        int label = maxSPtr - scoresPtr;
                        cv::Rect_<float> bbox;
                        bbox.x = x0;
                        bbox.y = y0;
                        bbox.width = x1 - x0;
                        bbox.height = y1 - y0;
                        bbox.x = std::max(0.f, bbox.x);
                        bbox.y = std::max(0.f, bbox.y);
                        bbox.width = std::min(meta.imgWidth - bbox.x, bbox.width);
                        bbox.height = std::min(meta.imgHeight - bbox.y, bbox.height);
                        cv::Mat maskConf = cv::Mat(1, SEG_CHANNELS, CV_32F, maskConfsPtr);
                        bboxes.push_back(bbox);
                        labels.push_back(label);
                        scores.push_back(score);
                        maskConfs.push_back(maskConf);
                    }
                }
                cv::dnn::NMSBoxesBatched(bboxes, scores, labels, PROBABILITY_THRESHOLD, NMS_THRESHOLD, indices);
                cv::Mat masks;
                int classNameSize = static_cast<int>(_classes.size());
                std::vector<Object> objs;
                for (auto& i : indices) {
                    if (scores[i] > _modelConfig.detectionScoreThreshold) {
                        cv::Rect tmp = bboxes[i];
                        Object obj;
                        obj.classId = labels[i];
                        if (!_classes.empty()) {
                            if (obj.classId < classNameSize) {
                                obj.className = _classes[obj.classId];
                            }
                            else {
                                obj.className = _classes[classNameSize - 1]; // Use last valid class name if out of range
                            }
                        }
                        else {
                            obj.className = "Unknown"; // Fallback if _classes is empty
                        }
                        obj.box = tmp;
                        obj.confidence = scores[i];
                        obj.className = _classes[labels[i]];
                        masks.push_back(maskConfs[i]);
                        objs.push_back(obj);
                    }
                }
                if (!masks.empty()) {
                    cv::Mat matmulRes = (masks * protos).t();
                    cv::Mat maskMat = matmulRes.reshape(indices.size(), { _modelConfig.inpWidth, _modelConfig.inpHeight });
                    std::vector<cv::Mat> maskChannels;
                    cv::split(maskMat, maskChannels);
                    const auto inputDims = m_trtEngine->getInputDims();

                    cv::Rect roi;
                    if (meta.imgHeight > meta.imgWidth) {
                        roi = cv::Rect(0, 0, _modelConfig.inpWidth * meta.imgWidth / meta.imgHeight, _modelConfig.inpHeight);
                    }
                    else {
                        roi = cv::Rect(0, 0, _modelConfig.inpWidth, _modelConfig.inpHeight * meta.imgHeight / meta.imgWidth);
                    }
                    for (size_t i = 0; i < indices.size(); i++)
                    {
                        cv::Mat dest, mask;
                        cv::exp(-maskChannels[i], dest);
                        dest = 1.0 / (1.0 + dest);
                        dest = dest(roi);
                        objs[i].cameraId = camera_id;
                        cv::resize(
                            dest,
                            mask,
                            cv::Size(static_cast<int>(meta.imgWidth), static_cast<int>(meta.imgHeight)),
                            cv::INTER_LINEAR
                        );
                        objs[i].mask = mask(objs[i].box) > _modelConfig.modelConfThreshold;// Need to check segmentation
                        objs[i].polygon = maskToPolygon(objs[i].mask, objs[i].box, 2.0f, 10);

                        // Alternative: Get multiple polygons if needed
                        // auto polygons = maskToPolygons(finalMask, result.box, 2.0f, 10, 3);
                        // result.polygon = polygons.empty() ? std::vector<cv::Point2f>() : polygons[0];

                        // Validate polygon
                        if (objs[i].polygon.size() < 3) {
                            // Fallback to bounding box if polygon extraction failed
                            objs[i].polygon = {
                                cv::Point2f(objs[i].box.x, objs[i].box.y),
                                cv::Point2f(objs[i].box.x + objs[i].box.width, objs[i].box.y),
                                cv::Point2f(objs[i].box.x + objs[i].box.width, objs[i].box.y + objs[i].box.height),
                                cv::Point2f(objs[i].box.x, objs[i].box.y + objs[i].box.height)
                            };
                        }
                    }
                }
                //EnqueueDetection(objs, camera_id);
                return objs;
            }
            else {
                return std::vector<Object>();
            }
        }
        catch (std::exception& e) {
            this->_logger.LogFatal("TENSORRTSEG::PostProcessSegmentation", e.what(), __FILE__, __LINE__);
            std::vector<Object>result;
            result.clear();
            return result;
        }
    }

    std::vector<std::vector<Object>> TENSORRTSEG::DetectObjectsBatch(const std::vector<cv::Mat>& inputImages, const std::string& camera_id) {
        {
            std::lock_guard<std::recursive_mutex> lock(_mutex);
            if (inputImages.empty()) {
                _logger.LogFatal("TENSORRTSEG::DetectObjectsBatch", "Empty input images vector", __FILE__, __LINE__);
                return {};
            }
        }

        // Auto-split if batch exceeds engine capacity
        const int maxBatch = m_options.maxBatchSize > 0 ? m_options.maxBatchSize : 1;
        if (static_cast<int>(inputImages.size()) > maxBatch) {
            const size_t numImages = inputImages.size();
            std::vector<std::vector<Object>> allResults;
            allResults.reserve(numImages);
            // Process chunks sequentially to avoid GPU contention on the same engine
            for (size_t start = 0; start < numImages; start += static_cast<size_t>(maxBatch)) {
                const size_t end = std::min(start + static_cast<size_t>(maxBatch), numImages);
                std::vector<cv::Mat> chunk(inputImages.begin() + start, inputImages.begin() + end);
                auto chunkResults = DetectObjectsBatch(chunk, camera_id);
                if (chunkResults.size() == chunk.size()) {
                    for (auto& r : chunkResults) allResults.push_back(std::move(r));
                }
                else {
                    _logger.LogError("TENSORRTSEG::DetectObjectsBatch",
                        "Chunk returned " + std::to_string(chunkResults.size()) +
                        " results, expected " + std::to_string(chunk.size()) +
                        ". Padding with empty results.", __FILE__, __LINE__);
                    for (auto& r : chunkResults) allResults.push_back(std::move(r));
                    for (size_t pad = chunkResults.size(); pad < chunk.size(); ++pad) {
                        allResults.push_back({});
                    }
                }
            }
            return allResults;
        }

        _logger.LogDebug("TENSORRTSEG::DetectObjectsBatch",
            "Processing batch of " + std::to_string(inputImages.size()) + " images",
            __FILE__, __LINE__);

        // Phase 1: Preprocess under brief lock
        BatchMetadata metadata;
        std::vector<std::vector<cv::cuda::GpuMat>> inputs;
        {
            std::lock_guard<std::recursive_mutex> lock(_mutex);
            inputs = PreprocessBatch(inputImages, metadata);
        }
        if (inputs.empty() || inputs[0].empty()) {
            _logger.LogFatal("TENSORRTSEG::DetectObjectsBatch", "Preprocessing failed", __FILE__, __LINE__);
            return {};
        }

        // Phase 2: Inference -- mutex released; pool dispatches to idle GPU slot
        std::vector<std::vector<std::vector<float>>> featureVectors;
        auto succ = m_trtEngine->runInference(inputs, featureVectors);
        if (!succ) {
            _logger.LogError("TENSORRTSEG::DetectObjectsBatch", "Error running inference", __FILE__, __LINE__);
            return {};
        }

        // Phase 3: Parallel postprocessing -- each image is independent
        const size_t numBatch = featureVectors.size();
        std::vector<std::vector<Object>> batchDetections(numBatch);
        std::vector<std::future<std::vector<Object>>> postFutures;
        postFutures.reserve(numBatch);
        for (size_t batchIdx = 0; batchIdx < numBatch; ++batchIdx) {
            const auto& batchOutput = featureVectors[batchIdx];
            std::vector<std::vector<float>> fv =
                batchOutput.empty() ? std::vector<std::vector<float>>{} : batchOutput;
            postFutures.push_back(std::async(std::launch::async,
                [this, fv = std::move(fv), cid = camera_id,
                 idx = batchIdx, &metadata]() mutable {
                    return PostProcessSegmentationBatch(fv, cid, idx, metadata);
                }));
        }
        for (size_t i = 0; i < numBatch; ++i)
            batchDetections[i] = postFutures[i].get();

        if (_trackerEnabled) {
            for (auto& dets : batchDetections) {
                if (!dets.empty()) {
                    dets = ApplyTracking(dets, camera_id);
                    if (_stabilizationEnabled) dets = StabilizeDetections(dets, camera_id);
                }
            }
        }

        _logger.LogDebug("TENSORRTSEG::DetectObjectsBatch",
            "Batch processing complete. Images: " + std::to_string(numBatch),
            __FILE__, __LINE__);
        return batchDetections;
    }
    std::vector<std::vector<cv::cuda::GpuMat>> TENSORRTSEG::PreprocessBatch(const std::vector<cv::Mat>& inputImages, BatchMetadata& outMetadata) {
        try {
            if (!_licenseValid) {
                _logger.LogFatal("TENSORRTSEG::PreprocessBatch", "Invalid license", __FILE__, __LINE__);
                return {};
            }

            const auto& inputDims = m_trtEngine->getInputDims();
            const int inputH = inputDims[0].d[1];
            const int inputW = inputDims[0].d[2];

            // Store original image dimensions for each image in batch
            outMetadata.imgHeights.resize(inputImages.size());
            outMetadata.imgWidths.resize(inputImages.size());
            outMetadata.ratios.resize(inputImages.size());

            std::vector<cv::cuda::GpuMat> batchProcessed;
            batchProcessed.reserve(inputImages.size());

            cv::cuda::Stream stream;

            // Process each image
            for (size_t i = 0; i < inputImages.size(); ++i) {
                const auto& inputImage = inputImages[i];

                if (inputImage.empty()) {
                    _logger.LogFatal("TENSORRTSEG::PreprocessBatch",
                        "Empty input image at index " + std::to_string(i), __FILE__, __LINE__);
                    return {};
                }

                // CPU preprocessing: resize + BGR->RGB before GPU upload
                cv::Mat srcImg = inputImage;
                if (srcImg.channels() == 1) {
                    cv::cvtColor(srcImg, srcImg, cv::COLOR_GRAY2BGR);
                }

                // Store original dimensions
                outMetadata.imgHeights[i] = srcImg.rows;
                outMetadata.imgWidths[i] = srcImg.cols;

                if (outMetadata.imgHeights[i] <= 0 || outMetadata.imgWidths[i] <= 0) {
                    _logger.LogFatal("TENSORRTSEG::PreprocessBatch",
                        "Image " + std::to_string(i) + " has invalid dimensions (Width: " +
                        std::to_string(outMetadata.imgWidths[i]) + ", Height: " +
                        std::to_string(outMetadata.imgHeights[i]) + ")",
                        __FILE__, __LINE__);
                    return {};
                }

                const auto& outputDims = m_trtEngine->getOutputDims();
                const bool isClassification = !outputDims.empty() && outputDims[0].nbDims <= 2;

                // Calculate ratio for this image
                outMetadata.ratios[i] = isClassification ? 1.f : 1.f / std::min(inputW / static_cast<float>(srcImg.cols),
                    inputH / static_cast<float>(srcImg.rows));

                // CPU resize to model input size
                cv::Mat cpuResized;
                if (srcImg.rows != inputH || srcImg.cols != inputW) {
                    if (isClassification) {
                        cv::resize(srcImg, cpuResized, cv::Size(inputW, inputH), 0, 0, cv::INTER_LINEAR);
                    } else {
                        cpuResized = Engine<float>::cpuResizeKeepAspectRatioPadRightBottom(srcImg, inputH, inputW);
                    }
                } else {
                    cpuResized = srcImg;
                }

                cv::Mat cpuRGB;
                cv::cvtColor(cpuResized, cpuRGB, cv::COLOR_BGR2RGB);

                cv::cuda::GpuMat gpuResized;
                gpuResized.upload(cpuRGB, stream);
                batchProcessed.push_back(std::move(gpuResized));
            }

            stream.waitForCompletion();

            // Return as required format
            std::vector<std::vector<cv::cuda::GpuMat>> inputs;
            inputs.push_back(std::move(batchProcessed));

            return inputs;
        }
        catch (const std::exception& e) {
            _logger.LogFatal("TENSORRTSEG::PreprocessBatch", e.what(), __FILE__, __LINE__);
            return {};
        }
    }
    std::vector<Object> TENSORRTSEG::PostProcessSegmentationBatch(std::vector<std::vector<float>>& featureVectors, const std::string& camera_id, size_t batchIdx, const BatchMetadata& metadata) {
        try {
            if (!_licenseValid) {
                _logger.LogFatal("TENSORRTSEG::PostProcessSegmentationBatch", "Invalid license", __FILE__, __LINE__);
                return {};
            }

            const auto& outputDims = m_trtEngine->getOutputDims();

            int numChannels = outputDims[0].d[1];
            int numAnchors = outputDims[0].d[2];

            const auto numClasses = numChannels - SEG_CHANNELS - 4;

            // Get batch-specific dimensions
            float ratio = metadata.ratios[batchIdx];
            float imgWidth = static_cast<float>(metadata.imgWidths[batchIdx]);
            float imgHeight = static_cast<float>(metadata.imgHeights[batchIdx]);

            // Ensure the output lengths are correct
            if (featureVectors.size() < 2) {
                _logger.LogError("TENSORRTSEG::PostProcessSegmentationBatch",
                    "Invalid feature vectors size: " + std::to_string(featureVectors.size()),
                    __FILE__, __LINE__);
                return {};
            }

            if (featureVectors[0].size() != static_cast<size_t>(numChannels) * numAnchors) {
                _logger.LogError("TENSORRTSEG::PostProcessSegmentationBatch",
                    "Detection output size mismatch", __FILE__, __LINE__);
                return {};
            }

            if (featureVectors[1].size() != static_cast<size_t>(SEG_CHANNELS) * SEG_H * SEG_W) {
                _logger.LogError("TENSORRTSEG::PostProcessSegmentationBatch",
                    "Segmentation mask size mismatch", __FILE__, __LINE__);
                return {};
            }

            cv::Mat output = cv::Mat(numChannels, numAnchors, CV_32F, featureVectors[0].data());
            output = output.t();

            cv::Mat protos = cv::Mat(SEG_CHANNELS, SEG_H * SEG_W, CV_32F, featureVectors[1].data());

            std::vector<int> labels;
            std::vector<float> scores;
            std::vector<cv::Rect> bboxes;
            std::vector<cv::Mat> maskConfs;
            std::vector<int> indices;

            // Extract bounding boxes and class labels
            for (int i = 0; i < numAnchors; i++) {
                auto rowPtr = output.row(i).ptr<float>();
                auto bboxesPtr = rowPtr;
                auto scoresPtr = rowPtr + 4;
                auto maskConfsPtr = rowPtr + 4 + numClasses;
                auto maxSPtr = std::max_element(scoresPtr, scoresPtr + numClasses);
                float score = *maxSPtr;

                if (score > _modelConfig.detectionScoreThreshold) {
                    float x = *bboxesPtr++;
                    float y = *bboxesPtr++;
                    float w = *bboxesPtr++;
                    float h = *bboxesPtr;

                    // Use batch-specific ratio and dimensions
                    float x0 = std::clamp((x - 0.5f * w) * ratio, 0.f, imgWidth);
                    float y0 = std::clamp((y - 0.5f * h) * ratio, 0.f, imgHeight);
                    float x1 = std::clamp((x + 0.5f * w) * ratio, 0.f, imgWidth);
                    float y1 = std::clamp((y + 0.5f * h) * ratio, 0.f, imgHeight);

                    int label = maxSPtr - scoresPtr;
                    cv::Rect_<float> bbox;
                    bbox.x = x0;
                    bbox.y = y0;
                    bbox.width = x1 - x0;
                    bbox.height = y1 - y0;

                    // Clamp to image boundaries
                    bbox.x = std::max(0.f, bbox.x);
                    bbox.y = std::max(0.f, bbox.y);
                    bbox.width = std::min(imgWidth - bbox.x, bbox.width);
                    bbox.height = std::min(imgHeight - bbox.y, bbox.height);

                    cv::Mat maskConf = cv::Mat(1, SEG_CHANNELS, CV_32F, maskConfsPtr);
                    bboxes.push_back(bbox);
                    labels.push_back(label);
                    scores.push_back(score);
                    maskConfs.push_back(maskConf);
                }
            }

            // Run NMS
            cv::dnn::NMSBoxesBatched(bboxes, scores, labels, PROBABILITY_THRESHOLD, NMS_THRESHOLD, indices);

            cv::Mat masks;
            int classNameSize = static_cast<int>(_classes.size());
            std::vector<Object> objs;

            for (auto& i : indices) {
                if (scores[i] > _modelConfig.detectionScoreThreshold) {
                    cv::Rect tmp = bboxes[i];
                    Object obj;
                    obj.classId = labels[i];

                    if (!_classes.empty()) {
                        if (obj.classId < classNameSize) {
                            obj.className = _classes[obj.classId];
                        }
                        else {
                            obj.className = _classes[classNameSize - 1];
                        }
                    }
                    else {
                        obj.className = "Unknown";
                    }

                    obj.box = tmp;
                    obj.confidence = scores[i];
                    masks.push_back(maskConfs[i]);
                    objs.push_back(obj);
                }
            }

            // Process segmentation masks
            if (!masks.empty()) {
                cv::Mat matmulRes = (masks * protos).t();
                cv::Mat maskMat = matmulRes.reshape(indices.size(), { _modelConfig.inpWidth, _modelConfig.inpHeight });
                std::vector<cv::Mat> maskChannels;
                cv::split(maskMat, maskChannels);

                cv::Rect roi;
                if (imgHeight > imgWidth) {
                    roi = cv::Rect(0, 0, _modelConfig.inpWidth * imgWidth / imgHeight, _modelConfig.inpHeight);
                }
                else {
                    roi = cv::Rect(0, 0, _modelConfig.inpWidth, _modelConfig.inpHeight * imgHeight / imgWidth);
                }

                for (size_t i = 0; i < indices.size(); i++) {
                    cv::Mat dest, mask;
                    cv::exp(-maskChannels[i], dest);
                    dest = 1.0 / (1.0 + dest);
                    dest = dest(roi);
                    objs[i].cameraId = camera_id;

                    cv::resize(
                        dest,
                        mask,
                        cv::Size(static_cast<int>(imgWidth), static_cast<int>(imgHeight)),
                        cv::INTER_LINEAR
                    );

                    objs[i].mask = mask(objs[i].box) > _modelConfig.modelConfThreshold;
                    objs[i].polygon = maskToPolygon(objs[i].mask, objs[i].box, 2.0f, 10);

                    // Validate polygon
                    if (objs[i].polygon.size() < 3) {
                        // Fallback to bounding box if polygon extraction failed
                        objs[i].polygon = {
                            cv::Point2f(objs[i].box.x, objs[i].box.y),
                            cv::Point2f(objs[i].box.x + objs[i].box.width, objs[i].box.y),
                            cv::Point2f(objs[i].box.x + objs[i].box.width, objs[i].box.y + objs[i].box.height),
                            cv::Point2f(objs[i].box.x, objs[i].box.y + objs[i].box.height)
                        };
                    }
                }
            }

            return objs;
        }
        catch (std::exception& e) {
            _logger.LogFatal("TENSORRTSEG::PostProcessSegmentationBatch", e.what(), __FILE__, __LINE__);
            return {};
        }
    }
    std::vector<std::vector<Object>> TENSORRTSEG::RunInferencesBatch(
        const std::vector<cv::Mat>& inputs, const std::string& camera_id)
    {
        if (!PreInferenceCheck("TENSORRTSEG::RunInferencesBatch")) return {};
        try {
            return DetectObjectsBatch(inputs, camera_id);
        }
        catch (const std::exception& e) {
            _logger.LogFatal("TENSORRTSEG::RunInferencesBatch", e.what(), __FILE__, __LINE__);
            return {};
        }
    }
}

  /*std::vector<Object> TENSORRTSEG::RunInference(const cv::Mat& inputImgBGR, const std::string& camera_id) {
      std::lock_guard<std::recursive_mutex> lock(_mutex);
      if (!_modelLoadValid) {
          this->_logger.LogFatal("TENSORRTSEG::RunInference", "Cannot load the TensorRT model. Please check if it is exist", __FILE__, __LINE__);
          std::vector<Object> result;
          result.clear();
          return result;
      }
      if (!_licenseValid) {
          this->_logger.LogFatal("TENSORRTSEG::RunInference", "Runtime license is not valid or expired. Please contact ANSCENTER", __FILE__, __LINE__);
          std::vector<Object> result;
          result.clear();
          return result;
      }
      if (!_isInitialized) {
          this->_logger.LogFatal("TENSORRTSEG::RunInference", "Model is not initialized", __FILE__, __LINE__);
          std::vector<Object> result;
          result.clear();
          return result;
      }
      try {
          std::vector<Object> result;
          if (inputImgBGR.empty()) return result;
          if ((inputImgBGR.cols < 10) || (inputImgBGR.rows < 10)) return result;
          return DetectObjects(inputImgBGR, camera_id);
      }
      catch (std::exception& e) {
          this->_logger.LogFatal("TENSORRTSEG::RunInference", e.what(), __FILE__, __LINE__);
          std::vector<Object> result;
          result.clear();
          return result;
      }
  }*/