#include "ANSYOLOOD.h"
#include "Utility.h"
#include "EPLoader.h"
#include "ANSGpuFrameRegistry.h"
#include "NV12PreprocessHelper.h"   // tl_currentGpuFrame()
#ifdef USEONNXOV
//#include <openvino_provider_factory.h>
#endif

namespace ANSCENTER
{
	void NMSBoxes(const std::vector<BoundingBox>& boundingBoxes,const std::vector<float>& scores,float scoreThreshold,float nmsThreshold,std::vector<int>& indices)
	{
		indices.clear();
		const size_t numBoxes = boundingBoxes.size();
		if (numBoxes == 0) {
			return;
		}

		// Step 1: Filter out boxes with scores below the threshold
		// and create a list of indices sorted by descending scores
		std::vector<int> sortedIndices;
		sortedIndices.reserve(numBoxes);
		for (size_t i = 0; i < numBoxes; ++i) {
			if (scores[i] >= scoreThreshold) {
				sortedIndices.push_back(static_cast<int>(i));
			}
		}

		// If no boxes remain after thresholding
		if (sortedIndices.empty()) {
			return;
		}

		// Sort the indices based on scores in descending order
		std::sort(sortedIndices.begin(), sortedIndices.end(),
			[&scores](int idx1, int idx2) {
				return scores[idx1] > scores[idx2];
			});

		// Step 2: Precompute the areas of all boxes
		std::vector<float> areas(numBoxes, 0.0f);
		for (size_t i = 0; i < numBoxes; ++i) {
			areas[i] = boundingBoxes[i].width * boundingBoxes[i].height;
		}

		// Step 3: Suppression mask to mark boxes that are suppressed
		std::vector<bool> suppressed(numBoxes, false);

		// Step 4: Iterate through the sorted list and suppress boxes with high IoU
		for (size_t i = 0; i < sortedIndices.size(); ++i) {
			int currentIdx = sortedIndices[i];
			if (suppressed[currentIdx]) {
				continue;
			}

			// Select the current box as a valid detection
			indices.push_back(currentIdx);

			const BoundingBox& currentBox = boundingBoxes[currentIdx];
			const float x1_max = currentBox.x;
			const float y1_max = currentBox.y;
			const float x2_max = currentBox.x + currentBox.width;
			const float y2_max = currentBox.y + currentBox.height;
			const float area_current = areas[currentIdx];

			// Compare IoU of the current box with the rest
			for (size_t j = i + 1; j < sortedIndices.size(); ++j) {
				int compareIdx = sortedIndices[j];
				if (suppressed[compareIdx]) {
					continue;
				}

				const BoundingBox& compareBox = boundingBoxes[compareIdx];
				const float x1 = std::max(x1_max, static_cast<float>(compareBox.x));
				const float y1 = std::max(y1_max, static_cast<float>(compareBox.y));
				const float x2 = std::min(x2_max, static_cast<float>(compareBox.x + compareBox.width));
				const float y2 = std::min(y2_max, static_cast<float>(compareBox.y + compareBox.height));

				const float interWidth = x2 - x1;
				const float interHeight = y2 - y1;

				if (interWidth <= 0 || interHeight <= 0) {
					continue;
				}

				const float intersection = interWidth * interHeight;
				const float unionArea = area_current + areas[compareIdx] - intersection;
				const float iou = (unionArea > 0.0f) ? (intersection / unionArea) : 0.0f;

				if (iou > nmsThreshold) {
					suppressed[compareIdx] = true;
				}
			}
		}
	}

	// End of YOLO V8
	// Utility functions
	void YOLOOD::getBestClassInfo(std::vector<float>::iterator it, const int& numClasses, float& bestConf, int& bestClassId)
	{
		try {
			// first 5 element are box and obj confidence
			bestClassId = 5;
			bestConf = 0;

			for (int i = 5; i < numClasses + 5; i++)
			{
				if (it[i] > bestConf)
				{
					bestConf = it[i];
					bestClassId = i - 5;
				}
			}
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("YOLOOD::getBestClassInfo", e.what(), __FILE__, __LINE__);
		}
	}
	size_t YOLOOD::vectorProduct(const std::vector<int64_t>& vector)
	{
		try {
			if (vector.empty())
				return 0;
			size_t product = 1;
			for (const auto& element : vector)
				product *= element;

			return product;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("YOLOOD::vectorProduct", e.what(), __FILE__, __LINE__);
			return 0;
		}
	}
	void YOLOOD::letterbox(const cv::Mat& image, cv::Mat& outImage, const cv::Size& newShape,
		const cv::Scalar& color, bool auto_, bool scaleFill, bool scaleUp, int stride)
	{
		try {
			cv::Size shape = image.size();
			float r = std::min((float)newShape.height / (float)shape.height,
				(float)newShape.width / (float)shape.width);
			if (!scaleUp)
				r = std::min(r, 1.0f);

			int newUnpad[2]{ (int)std::round((float)shape.width * r),
							 (int)std::round((float)shape.height * r) };

			auto dw = (float)(newShape.width - newUnpad[0]);
			auto dh = (float)(newShape.height - newUnpad[1]);

			if (auto_) {
				dw = (float)((int)dw % stride);
				dh = (float)((int)dh % stride);
			}
			else if (scaleFill) {
				dw = 0.0f;
				dh = 0.0f;
				newUnpad[0] = newShape.width;
				newUnpad[1] = newShape.height;
			}

			dw /= 2.0f;
			dh /= 2.0f;

			// Fix: Use OR instead of AND, and handle the else case
			if (shape.width != newUnpad[0] || shape.height != newUnpad[1]) {
				cv::resize(image, outImage, cv::Size(newUnpad[0], newUnpad[1]));
			}
			else {
				outImage = image.clone();
			}

			// Fix: Better padding calculation
			int top = (int)dh;
			int bottom = newShape.height - newUnpad[1] - top;
			int left = (int)dw;
			int right = newShape.width - newUnpad[0] - left;

			cv::copyMakeBorder(outImage, outImage, top, bottom, left, right, cv::BORDER_CONSTANT, color);

		}
		catch (std::exception& e) {
			this->_logger.LogFatal("YOLOOD::letterbox", e.what(), __FILE__, __LINE__);
		}
	}
	void YOLOOD::scaleCoords(const cv::Size& imageShape, cv::Rect& coords, const cv::Size& imageOriginalShape)
	{
		try {
			float gain = std::min((float)imageShape.height / (float)imageOriginalShape.height,
				(float)imageShape.width / (float)imageOriginalShape.width);

			int pad[2] = { (int)(((float)imageShape.width - (float)imageOriginalShape.width * gain) / 2.0f),
						  (int)(((float)imageShape.height - (float)imageOriginalShape.height * gain) / 2.0f) };

			coords.x = (int)std::round(((float)(coords.x - pad[0]) / gain));
			coords.y = (int)std::round(((float)(coords.y - pad[1]) / gain));

			coords.width = (int)std::round(((float)coords.width / gain));
			coords.height = (int)std::round(((float)coords.height / gain));
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("YOLOOD::scaleCoords", e.what(), __FILE__, __LINE__);
		}
	}
	bool YOLOOD::loadYoloModel(const std::string& modelPath, const bool& isGPU, const cv::Size& inputSize)
	{
		try {
			const auto& ep = ANSCENTER::EPLoader::Current();
			if (Ort::Global<void>::api_ == nullptr)
				Ort::InitApi(static_cast<const OrtApi*>(EPLoader::GetOrtApiRaw()));
			std::cout << "[YOLOOD] EP ready: "
				<< ANSCENTER::EPLoader::EngineTypeName(ep.type) << std::endl;

			env = Ort::Env(OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING, "ONNX_DETECTION");
			sessionOptions = Ort::SessionOptions();
			sessionOptions.SetIntraOpNumThreads(
				std::min(6, static_cast<int>(std::thread::hardware_concurrency())));
			sessionOptions.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);

			// ── Log available providers ─────────────────────────────────────────
			std::vector<std::string> availableProviders = Ort::GetAvailableProviders();
			std::cout << "Available Execution Providers:" << std::endl;
			for (const auto& p : availableProviders)
				std::cout << " - " << p << std::endl;
			// ── Attach EP based on runtime-detected hardware ────────────────────
			// No #ifdef, no isGPU flag — EPLoader owns the decision
			if (isGPU) {
				bool attached = false;

				switch (ep.type) {

				case ANSCENTER::EngineType::NVIDIA_GPU: {
					auto cuda_it = std::find(availableProviders.begin(),
						availableProviders.end(),
						"CUDAExecutionProvider");
					if (cuda_it == availableProviders.end()) {
						this->_logger.LogError("YOLOOD::loadYoloModel. CUDA EP failed:", "CUDAExecutionProvider not in DLL — "
							"check ep/cuda/ has the CUDA ORT build.", __FILE__, __LINE__);
						break;
					}
					try {
						OrtCUDAProviderOptionsV2* cuda_options = nullptr;
						Ort::GetApi().CreateCUDAProviderOptions(&cuda_options);

						const char* keys[] = { "device_id" };
						const char* values[] = { "0" };
						Ort::GetApi().UpdateCUDAProviderOptions(cuda_options, keys, values, 1);

						sessionOptions.AppendExecutionProvider_CUDA_V2(*cuda_options);

						Ort::GetApi().ReleaseCUDAProviderOptions(cuda_options);

						std::cout << "[YOLOOD] CUDA EP attached." << std::endl;
						attached = true;
					}
					catch (const Ort::Exception& e) {
						this->_logger.LogError("YOLOOD::loadYoloModel. CUDA EP failed:", e.what(), __FILE__, __LINE__);
					}
					break;
				}
				case ANSCENTER::EngineType::AMD_GPU: {
					auto it = std::find(availableProviders.begin(),
						availableProviders.end(),
						"DmlExecutionProvider");
					if (it == availableProviders.end()) {
						this->_logger.LogError("YOLOOD::loadYoloModel. DirectML EP failed:", "DmlExecutionProvider not in DLL — "
							"check ep/directml/ has the DirectML ORT build.", __FILE__, __LINE__);
						break;
					}
					try {
						std::unordered_map<std::string, std::string> opts = {
							{ "device_id", "0" }
						};
						sessionOptions.AppendExecutionProvider("DML", opts);
						std::cout << "[YOLOOD] DirectML EP attached." << std::endl;
						attached = true;
					}
					catch (const Ort::Exception& e) {
						this->_logger.LogError("YOLOOD::loadYoloModel. DirectML EP failed:", e.what(), __FILE__, __LINE__);
					}
					break;
				}

				case ANSCENTER::EngineType::OPENVINO_GPU: {
					auto it = std::find(availableProviders.begin(),
						availableProviders.end(),
						"OpenVINOExecutionProvider");
					if (it == availableProviders.end()) {
						this->_logger.LogError("YOLOOD::loadYoloModel", "OpenVINOExecutionProvider not in DLL — "
							"check ep/openvino/ has the OpenVINO ORT build.", __FILE__, __LINE__);
						break;
					}

					// Try device configs in priority order
					const std::string precision = "FP16";
					const std::string numberOfThreads = "8";
					const std::string numberOfStreams = "8";

					std::vector<std::unordered_map<std::string, std::string>> try_configs = {
						{ {"device_type","AUTO:NPU,GPU"}, {"precision",precision},
						  {"num_of_threads",numberOfThreads}, {"num_streams",numberOfStreams},
						  {"enable_opencl_throttling","False"}, {"enable_qdq_optimizer","True"} },
						{ {"device_type","GPU.0"}, {"precision",precision},
						  {"num_of_threads",numberOfThreads}, {"num_streams",numberOfStreams},
						  {"enable_opencl_throttling","False"}, {"enable_qdq_optimizer","True"} },
						{ {"device_type","GPU.1"}, {"precision",precision},
						  {"num_of_threads",numberOfThreads}, {"num_streams",numberOfStreams},
						  {"enable_opencl_throttling","False"}, {"enable_qdq_optimizer","True"} },
						{ {"device_type","AUTO:GPU,CPU"}, {"precision",precision},
						  {"num_of_threads",numberOfThreads}, {"num_streams",numberOfStreams},
						  {"enable_opencl_throttling","False"}, {"enable_qdq_optimizer","True"} }
					};

					for (const auto& config : try_configs) {
						try {
							sessionOptions.AppendExecutionProvider_OpenVINO_V2(config);
							std::cout << "[YOLOOD] OpenVINO EP attached ("
								<< config.at("device_type") << ")." << std::endl;
							attached = true;
							break;
						}
						catch (const Ort::Exception& e) {
							// try next config
							this->_logger.LogError("YOLOOD::loadYoloModel", e.what(), __FILE__, __LINE__);
						}
					}

					if (!attached)
						std::cerr << "[YOLOOD] OpenVINO EP: all device configs failed." << std::endl;
					break;
				}

				default:
					break;
				}

				if (!attached) {
					std::cerr << "[YOLOOD] No GPU EP attached — running on CPU." << std::endl;
					this->_logger.LogFatal("YOLOOD::loadYoloModel","GPU EP not attached. Running on CPU.", __FILE__, __LINE__);
				}
			}
			else {
				std::cout << "[YOLOOD] Inference device: CPU (isGPU=false)" << std::endl;
			}

			// ── Load model ──────────────────────────────────────────────────────
			std::wstring w_modelPath = String2WString(modelPath.c_str());
			session = Ort::Session(env, w_modelPath.c_str(), sessionOptions);
			Ort::AllocatorWithDefaultOptions allocator;

			// ── Input shape ─────────────────────────────────────────────────────
			Ort::TypeInfo inputTypeInfo = session.GetInputTypeInfo(0);
			std::vector<int64_t> inputTensorShape =
				inputTypeInfo.GetTensorTypeAndShapeInfo().GetShape();
			this->isDynamicInputShape =
				(inputTensorShape.size() >= 4 &&
					inputTensorShape[2] == -1 &&
					inputTensorShape[3] == -1);

			if (this->isDynamicInputShape)
				std::cout << "Dynamic input shape detected." << std::endl;
			for (auto shape : inputTensorShape)
				std::cout << "Input shape: " << shape << std::endl;

			// ── Node names ──────────────────────────────────────────────────────
			size_t inputNodesNum = session.GetInputCount();
			for (size_t i = 0; i < inputNodesNum; i++) {
				Ort::AllocatedStringPtr name = session.GetInputNameAllocated(i, allocator);
				char* buf = new char[50];
				strcpy(buf, name.get());
				inputNodeNames.push_back(buf);
			}
			size_t outputNodesNum = session.GetOutputCount();
			for (size_t i = 0; i < outputNodesNum; i++) {
				Ort::AllocatedStringPtr name = session.GetOutputNameAllocated(i, allocator);
				char* buf = new char[50];
				strcpy(buf, name.get());
				outputNodeNames.push_back(buf);
			}

			this->inputImageShape = cv::Size2f(inputSize);
			std::cout << "Input image shape: " << inputImageShape << std::endl;
			return true;
		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::loadYoloModel", e.what(), __FILE__, __LINE__);
			return false;
		}
	}
	void YOLOOD::preprocessing(const cv::Mat& image,std::vector<float>& blob,std::vector<int64_t>& inputTensorShape)
	{
		m_imgWidth = image.cols;
		m_imgHeight = image.rows;
		try {
			// Convert grayscale to BGR if needed
			cv::Mat processedImage;
			if (image.channels() == 1) {
				cv::cvtColor(image, processedImage, cv::COLOR_GRAY2BGR);
			}
			else {
				processedImage = image;  // Shallow copy - safe
			}

			// Convert BGR to RGB
			cv::Mat rgbImage;
			cv::cvtColor(processedImage, rgbImage, cv::COLOR_BGR2RGB);

			// Resize with letterbox (SEPARATE output buffer!)
			cv::Mat resizedImage;
			letterbox(rgbImage, resizedImage, inputImageShape,
				cv::Scalar(114, 114, 114), isDynamicInputShape,
				false, true, 32);

			if (resizedImage.empty()) {
				_logger.LogError("YOLOOD::preprocessing",
					"Letterbox operation failed",
					__FILE__, __LINE__);
				return;
			}

			// Update tensor shape
			inputTensorShape = { 1, 3, resizedImage.rows, resizedImage.cols };

			// Normalize to [0, 1] (FIX: Use 1.0f for float division)
			cv::Mat floatImage;
			resizedImage.convertTo(floatImage, CV_32FC3, 1.0f / 255.0f);

			// Convert HWC to CHW
			const size_t channelSize = floatImage.rows * floatImage.cols;
			blob.resize(channelSize * 3);

			std::vector<cv::Mat> channels(3);
			cv::split(floatImage, channels);

			for (int c = 0; c < 3; ++c) {
				std::memcpy(blob.data() + c * channelSize,
					channels[c].data,
					channelSize * sizeof(float));
			}

		}
		catch (const std::exception& e) {
			_logger.LogFatal("YOLOOD::preprocessing", e.what(), __FILE__, __LINE__);
		}
	}
	cv::Mat YOLOOD::preprocessv11(const cv::Mat& image,std::vector<float>& blob,std::vector<int64_t>& inputTensorShape)
	{
		try {
			// Validation
			if (image.empty()) {
				_logger.LogError("YOLOOD::preprocessv11", "Empty image provided",
					__FILE__, __LINE__);
				return cv::Mat();
			}

			// Convert grayscale to BGR if needed
			cv::Mat processedImage;
			if (image.channels() == 1) {
				cv::cvtColor(image, processedImage, cv::COLOR_GRAY2BGR);
			}
			else {
				processedImage = image;  // Shallow copy - safe!
			}

			// Resize with letterbox
			cv::Mat resizedImage;
			letterbox(processedImage, resizedImage, inputImageShape,
				cv::Scalar(114, 114, 114), isDynamicInputShape,
				false, true, 32);

			if (resizedImage.empty()) {
				_logger.LogError("YOLOOD::preprocessv11",
					"Letterbox operation failed",
					__FILE__, __LINE__);
				return cv::Mat();
			}

			// Convert BGR to RGB
			cv::Mat rgbImage;
			cv::cvtColor(resizedImage, rgbImage, cv::COLOR_BGR2RGB);

			// Normalize to [0, 1]
			cv::Mat floatImage;
			rgbImage.convertTo(floatImage, CV_32FC3, 1.0f / 255.0f);

			// Update input tensor shape
			const int height = floatImage.rows;
			const int width = floatImage.cols;
			const int channels = floatImage.channels();

			inputTensorShape = { 1, channels, height, width };

			// Allocate blob
			const size_t totalSize = height * width * channels;
			blob.resize(totalSize);

			// Convert HWC to CHW efficiently
			std::vector<cv::Mat> rgbChannels(channels);
			cv::split(floatImage, rgbChannels);

			// Copy channels to blob in CHW order
			const size_t channelSize = height * width;
			for (int c = 0; c < channels; ++c) {
				std::memcpy(blob.data() + c * channelSize,
					rgbChannels[c].data,
					channelSize * sizeof(float));
			}

			return floatImage;  // Return for visualization if needed

		}
		catch (const std::exception& e) {
			_logger.LogFatal("YOLOOD::preprocessv11", e.what(), __FILE__, __LINE__);
			return cv::Mat();
		}
	}
	std::vector<Object> YOLOOD::postprocessing(const cv::Size& resizedImageShape,const cv::Size& originalImageShape,std::vector<Ort::Value>& outputTensors,const float& confThreshold,const float& iouThreshold)
	{
		try {
			// Get raw output pointer (NO COPY!)
			const float* rawOutput = outputTensors[0].GetTensorData<float>();
			std::vector<int64_t> outputShape = outputTensors[0].GetTensorTypeAndShapeInfo().GetShape();

			const int numClasses = static_cast<int>(outputShape[2]) - 5;
			const int numDetections = static_cast<int>(outputShape[1]);
			const int stride = static_cast<int>(outputShape[2]);

			// Pre-allocate (assume ~10% detections pass threshold)
			const size_t estimatedDetections = std::min(numDetections / 10, 1000);

			std::vector<cv::Rect> boxes;
			std::vector<float> confs;
			std::vector<int> classIds;

			boxes.reserve(estimatedDetections);
			confs.reserve(estimatedDetections);
			classIds.reserve(estimatedDetections);

			// Parse detections using raw pointer (NO COPY!)
			for (int i = 0; i < numDetections; ++i) {
				const float* detection = rawOutput + i * stride;

				const float objectness = detection[4];
				if (objectness <= confThreshold) {
					continue;
				}

				// Get best class
				float maxClassScore = 0.0f;
				int bestClassId = 0;

				for (int c = 0; c < numClasses; ++c) {
					const float classScore = detection[5 + c];
					if (classScore > maxClassScore) {
						maxClassScore = classScore;
						bestClassId = c;
					}
				}

				const float confidence = objectness * maxClassScore;
				if (confidence <= confThreshold) {
					continue;
				}

				// Parse bounding box (center format -> corner format)
				const int centerX = static_cast<int>(detection[0]);
				const int centerY = static_cast<int>(detection[1]);
				const int width = static_cast<int>(detection[2]);
				const int height = static_cast<int>(detection[3]);
				const int left = centerX - width / 2;
				const int top = centerY - height / 2;

				boxes.emplace_back(left, top, width, height);
				confs.push_back(confidence);
				classIds.push_back(bestClassId);
			}

			// NMS
			std::vector<int> indices;
			cv::dnn::NMSBoxes(boxes, confs, confThreshold, iouThreshold, indices);

			// Build final detections
			std::vector<Object> detections;
			detections.reserve(indices.size());

			const int numClasses_safe = static_cast<int>(_classes.size());

			for (int idx : indices) {
				Object det;

				// Scale coordinates
				det.box = boxes[idx];
				scaleCoords(resizedImageShape, det.box, originalImageShape);

				det.confidence = confs[idx];
				det.classId = classIds[idx];

				// Set class name safely
				if (!_classes.empty()) {
					det.className = (det.classId < numClasses_safe)
						? _classes[det.classId]
						: _classes[numClasses_safe - 1];
				}
				else {
					det.className = "Unknown";
				}

				detections.push_back(std::move(det));
			}

			return detections;

		}
		catch (const std::exception& e) {
			_logger.LogFatal("YOLOOD::postprocessing", e.what(), __FILE__, __LINE__);
			return {};
		}
	}
	BoundingBox YOLOOD::scaleCoordsv11(const cv::Size& imageShape,BoundingBox coords,const cv::Size& imageOriginalShape,bool p_Clip)
	{
		// Calculate scale factor
		const float gain = std::min(
			static_cast<float>(imageShape.width) / imageOriginalShape.width,
			static_cast<float>(imageShape.height) / imageOriginalShape.height
		);

		const float invGain = 1.0f / gain;

		// Calculate padding (once per image, but recalculated here)
		const float padX = (imageShape.width - imageOriginalShape.width * gain) * 0.5f;
		const float padY = (imageShape.height - imageOriginalShape.height * gain) * 0.5f;

		// Scale coordinates
		BoundingBox result;
		result.x = static_cast<int>((coords.x - padX) * invGain + 0.5f);
		result.y = static_cast<int>((coords.y - padY) * invGain + 0.5f);
		result.width = static_cast<int>(coords.width * invGain + 0.5f);
		result.height = static_cast<int>(coords.height * invGain + 0.5f);

		// Clamp to image bounds if requested
		if (p_Clip) {
			result.x = clamp(result.x, 0, imageOriginalShape.width);
			result.y = clamp(result.y, 0, imageOriginalShape.height);
			result.width = clamp(result.width, 0, imageOriginalShape.width - result.x);
			result.height = clamp(result.height, 0, imageOriginalShape.height - result.y);
		}
		return result;
	}
	std::vector<Object> YOLOOD::postprocessv11(const cv::Size& originalImageSize,const cv::Size& resizedImageShape,const std::vector<Ort::Value>& outputTensors,float confThreshold,float iouThreshold)
	{
		try {
			// Get raw output
			const float* rawOutput = outputTensors[0].GetTensorData<float>();
			const std::vector<int64_t> outputShape = outputTensors[0].GetTensorTypeAndShapeInfo().GetShape();

			const size_t numFeatures = outputShape[1];
			const size_t numDetections = outputShape[2];
			const int numClasses = static_cast<int>(numFeatures) - 4;

			// Early exit
			if (numDetections == 0 || numClasses <= 0) {
				return {};
			}

			// Calculate scaling parameters ONCE
			const float gain = std::min(
				static_cast<float>(resizedImageShape.width) / originalImageSize.width,
				static_cast<float>(resizedImageShape.height) / originalImageSize.height
			);
			const float invGain = 1.0f / gain;
			const float padX = (resizedImageShape.width - originalImageSize.width * gain) * 0.5f;
			const float padY = (resizedImageShape.height - originalImageSize.height * gain) * 0.5f;
			const int maxX = originalImageSize.width;
			const int maxY = originalImageSize.height;

			// Pre-allocate (estimate ~10% pass threshold)
			const size_t estimatedCount = std::min(numDetections / 10, size_t(1000));
			std::vector<cv::Rect> boxes;
			std::vector<cv::Rect> nmsBoxes;
			std::vector<float> confs;
			std::vector<int> classIds;

			boxes.reserve(estimatedCount);
			nmsBoxes.reserve(estimatedCount);
			confs.reserve(estimatedCount);
			classIds.reserve(estimatedCount);

			// Parse detections
			for (size_t d = 0; d < numDetections; ++d) {
				// Extract coordinates
				const float centerX = rawOutput[0 * numDetections + d];
				const float centerY = rawOutput[1 * numDetections + d];
				const float width = rawOutput[2 * numDetections + d];
				const float height = rawOutput[3 * numDetections + d];

				// Find best class
				int classId = 0;
				float maxScore = rawOutput[4 * numDetections + d];

				for (int c = 1; c < numClasses; ++c) {
					const float score = rawOutput[(4 + c) * numDetections + d];
					if (score > maxScore) {
						maxScore = score;
						classId = c;
					}
				}

				// Threshold check
				if (maxScore <= confThreshold) {
					continue;
				}

				// Convert to corner format and scale inline
				const float left = (centerX - width * 0.5f - padX) * invGain;
				const float top = (centerY - height * 0.5f - padY) * invGain;
				const float scaledWidth = width * invGain;
				const float scaledHeight = height * invGain;

				// Round and clamp to original image bounds
				const int x = clamp(static_cast<int>(left + 0.5f), 0, maxX);
				const int y = clamp(static_cast<int>(top + 0.5f), 0, maxY);
				const int w = clamp(static_cast<int>(scaledWidth + 0.5f), 0, maxX - x);
				const int h = clamp(static_cast<int>(scaledHeight + 0.5f), 0, maxY - y);

				// Store scaled box
				boxes.emplace_back(x, y, w, h);

				// Create NMS box with class offset (class-specific NMS)
				const int nmsOffset = classId * 7680;
				nmsBoxes.emplace_back(x + nmsOffset, y + nmsOffset, w, h);

				confs.push_back(maxScore);
				classIds.push_back(classId);
			}

			// Apply class-specific NMS
			std::vector<int> indices;
			cv::dnn::NMSBoxes(nmsBoxes, confs, confThreshold, iouThreshold, indices);

			// Build final detections
			std::vector<Object> detections;
			detections.reserve(indices.size());

			const int numClassNames = static_cast<int>(_classes.size());

			for (int idx : indices) {
				Object det;

				// Use original scaled box (not NMS box)
				det.box.x = boxes[idx].x;
				det.box.y = boxes[idx].y;
				det.box.width = boxes[idx].width;
				det.box.height = boxes[idx].height;

				det.confidence = confs[idx];
				det.classId = classIds[idx];

				// Set class name
				if (!_classes.empty() && det.classId < numClassNames) {
					det.className = _classes[det.classId];
				}
				else {
					det.className = _classes.empty() ? "Unknown" : _classes[numClassNames - 1];
				}

				// Generate normalized polygon
				det.polygon = ANSUtilityHelper::RectToNormalizedPolygon(
					det.box, m_imgWidth, m_imgHeight
				);

				detections.emplace_back(std::move(det));
			}

			return detections;

		}
		catch (const std::exception& e) {
			_logger.LogFatal("YOLOOD::postprocessv11", e.what(), __FILE__, __LINE__);
			return {};
		}
	}
	std::vector<Object> YOLOOD::detect(cv::Mat& image, const float& confThreshold, const float& iouThreshold) {
		if (image.empty()) {
			this->_logger.LogFatal("YOLOOD::detect", "Error: Empty image provided to detector", __FILE__, __LINE__);
			return {};
		}
		try {
			// Step 1: Preprocessing - reuse member buffer to avoid repeated allocations
			std::vector<int64_t> inputTensorShape;
			inputTensorShape.reserve(4);

			this->_blob.clear();  // Clear but keep capacity
			this->preprocessing(image, this->_blob, inputTensorShape);

			// Step 2: Validate input tensor shape
			if (inputTensorShape.size() != 4 || inputTensorShape[2] <= 0 || inputTensorShape[3] <= 0) {
				this->_logger.LogFatal("YOLOOD::detect", "Invalid input tensor shape!", __FILE__, __LINE__);
				return {};
			}

			const size_t inputTensorSize = vectorProduct(inputTensorShape);
			if (this->_blob.size() != inputTensorSize) {
				this->_logger.LogFatal("YOLOOD::detect", "Mismatch in input tensor size!", __FILE__, __LINE__);
				return {};
			}

			// Step 3: Create ONNX Tensor - static MemoryInfo avoids repeated creation
			static const Ort::MemoryInfo memoryInfo = Ort::MemoryInfo::CreateCpu(
				OrtAllocatorType::OrtArenaAllocator,
				OrtMemType::OrtMemTypeDefault
			);

			std::array<Ort::Value, 1> inputTensors = {
				Ort::Value::CreateTensor<float>(
					memoryInfo,
					this->_blob.data(),
					this->_blob.size(),
					inputTensorShape.data(),
					inputTensorShape.size()
				)
			};

			// Step 4: Run ONNX Inference
			std::vector<Ort::Value> outputTensors = this->session.Run(
				Ort::RunOptions{ nullptr },
				inputNodeNames.data(),
				inputTensors.data(),
				inputNodeNames.size(),
				outputNodeNames.data(),
				outputNodeNames.size()
			);

			// Step 5: Postprocessing - return directly for RVO
			const cv::Size resizedShape(
				static_cast<int>(inputTensorShape[3]),
				static_cast<int>(inputTensorShape[2])
			);
			return this->postprocessing(resizedShape, image.size(), outputTensors, confThreshold, iouThreshold);

		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::detect", e.what(), __FILE__, __LINE__);
			return {};
		}
	}
	std::vector<Object> YOLOOD::detectv11(const cv::Mat& image, float confThreshold, float iouThreshold) {
		if (image.empty()) {
			this->_logger.LogFatal("YOLOOD::detectv11", "Error: Empty image provided to detector", __FILE__, __LINE__);
			return {};
		}

		try {
			// Define input tensor shape (batch, channels, height, width)
			std::vector<int64_t> inputTensorShape = {
				1, 3, inputImageShape.height, inputImageShape.width
			};

			// Preprocess using reusable member buffer
			this->_blob.clear();
			cv::Mat preprocessedImage = preprocessv11(image, this->_blob, inputTensorShape);

			// Validate blob size
			const size_t inputTensorSize = vectorProduct(inputTensorShape);
			if (this->_blob.size() != inputTensorSize) {
				this->_logger.LogFatal("YOLOOD::detectv11", "Error: Blob size mismatch with expected input tensor size", __FILE__, __LINE__);
				return {};
			}

			// Static memory info - created once, reused across calls
			static const Ort::MemoryInfo memoryInfo = Ort::MemoryInfo::CreateCpu(
				OrtArenaAllocator,
				OrtMemTypeDefault
			);

			// Create input tensor
			Ort::Value inputTensor = Ort::Value::CreateTensor<float>(
				memoryInfo,
				this->_blob.data(),
				inputTensorSize,
				inputTensorShape.data(),
				inputTensorShape.size()
			);

			// Run inference
			std::vector<Ort::Value> outputTensors = this->session.Run(
				Ort::RunOptions{ nullptr },
				inputNodeNames.data(),
				&inputTensor,
				inputNodeNames.size(),
				outputNodeNames.data(),
				outputNodeNames.size()
			);

			// Postprocess and return directly for RVO
			const cv::Size resizedImageShape(
				static_cast<int>(inputTensorShape[3]),
				static_cast<int>(inputTensorShape[2])
			);
			return postprocessv11(image.size(), resizedImageShape, outputTensors, confThreshold, iouThreshold);

		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::detectv11", e.what(), __FILE__, __LINE__);
			return {};
		}
	}
	
	// YOLOOD
	bool YOLOOD::OptimizeModel(bool fp16, std::string& optimizedModelFolder) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		try {
			if (!ANSODBase::OptimizeModel(fp16, optimizedModelFolder)) {
				return false;
			}
			if (FileExist(_modelFilePath)) {
				optimizedModelFolder = GetParentFolder(_modelFilePath);
				this->_logger.LogDebug("YOLOOD::OptimizeModel", "This model is optimized. No need other optimization.", __FILE__, __LINE__);
				return true;
			}
			else {
				optimizedModelFolder = "";
				this->_logger.LogFatal("YOLOOD::OptimizeModel", "This model is not exist. Please check the model path again.", __FILE__, __LINE__);
				return false;
			}
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("YOLOOD::OptimizeModel", e.what(), __FILE__, __LINE__);
			return false;
		}

	}
	bool YOLOOD::LoadModel(const std::string& modelZipFilePath, const std::string& modelZipPassword) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		try {
			bool result = ANSODBase::LoadModel(modelZipFilePath, modelZipPassword);
			if (!result) return false;
			_modelConfig.detectionType = ANSCENTER::DetectionType::DETECTION;
			_modelConfig.inpHeight = 640;
			_modelConfig.inpWidth = 640;
			if (_modelConfig.modelMNSThreshold < 0.2)
				_modelConfig.modelMNSThreshold = 0.5;
			if (_modelConfig.modelConfThreshold < 0.2)
				_modelConfig.modelConfThreshold = 0.5;
			_letterBoxForSquare = true;
			// 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];
				}
				_modelConfig.modelType = modelType;
				if (_modelConfig.modelType == ModelType::YOLOV4) {
					_isDarkNet = true;
					_modelFilePath = CreateFilePath(_modelFolder, "train_last.weights");
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV4 weight", _modelFilePath, __FILE__, __LINE__);
				}
				else if (_modelConfig.modelType == ModelType::YOLOV5) {
					_isDarkNet = false;
					_modelFilePath = CreateFilePath(_modelFolder, "train_last.onnx");
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV5 weight", _modelFilePath, __FILE__, __LINE__);
				}
				else if (_modelConfig.modelType == ModelType::YOLOV8) {
					_isDarkNet = false;
					_modelFilePath = CreateFilePath(_modelFolder, "train_last.onnx");
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV8 weight", _modelFilePath, __FILE__, __LINE__);
				}
				else {
					this->_logger.LogError("YOLOOD::Initialize.  Model Type is not valid. Please check the model config file", _modelConfigFile, __FILE__, __LINE__);
					return false;
				}
				if (!FileExist(_modelFilePath)) {
					this->_logger.LogError("YOLOOD::Initialize.  Model file is not exist", _modelFilePath, __FILE__, __LINE__);
					return false;
				}
			}
			else {// This is old version of model zip file
				std::string weightfile = CreateFilePath(_modelFolder, "train_last.weights");
				if (FileExist(weightfile)) {
					// This is the darknet Yolov4
					_modelFilePath = weightfile;
					_classFilePath = CreateFilePath(_modelFolder, "classes.names");
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV4 weight", _modelFilePath, __FILE__, __LINE__);
					_modelConfig.modelType = ModelType::YOLOV4;
					_isDarkNet = true;
					_modelConfig.inpHeight = 416;
					_modelConfig.inpWidth = 416;
				}
				else {
					_isDarkNet = false;
					std::string onnxfile = CreateFilePath(_modelFolder, "train_last.onnx");
					if (std::filesystem::exists(onnxfile)) {
						_modelFilePath = onnxfile;
						_classFilePath = CreateFilePath(_modelFolder, "classes.names");
						this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV8 weight", _modelFilePath, __FILE__, __LINE__);
					}
					else {
						this->_logger.LogError("YOLOOD::Initialize.  Model file is not exist", _modelFilePath, __FILE__, __LINE__);
						return false;
					}
				}
				//std::ifstream isValidFileName(_classFilePath);
				if (FileExist(_classFilePath))
				{
					this->_logger.LogDebug("YOLOOD::Initialize.  Load classes from file", _classFilePath, __FILE__, __LINE__);
					LoadClassesFromFile();
				}
				else {
					this->_logger.LogDebug("YOLOOD::Initialize.  Load classes from string", _classFilePath, __FILE__, __LINE__);
					LoadClassesFromString();
				}
			}
			_isInitialized = true;
			return true;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("YOLOOD::LoadModel", e.what(), __FILE__, __LINE__);
			return false;
		}

	}
	bool YOLOOD::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);
		try {
			bool result = ANSODBase::LoadModelFromFolder(licenseKey, modelConfig, modelName, className, modelFolder, labelMap);
			if (!result) return false;
			std::string _modelName = modelName;
			if (_modelName.empty()) {
				_modelName = "train_last";
			}
			std::string weightFileName = _modelName + ".weights";
			_modelConfig = modelConfig;
			_modelConfig.detectionType = ANSCENTER::DetectionType::DETECTION;
			_modelConfig.inpHeight = 640;
			_modelConfig.inpWidth = 640;
			if (_modelConfig.modelMNSThreshold < 0.2)
				_modelConfig.modelMNSThreshold = 0.5;
			if (_modelConfig.modelConfThreshold < 0.2)
				_modelConfig.modelConfThreshold = 0.5;
			_letterBoxForSquare = true;
			// 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];
				}
				_modelConfig.modelType = modelType;
				if (_modelConfig.modelType == ModelType::YOLOV4) {
					_isDarkNet = true;
					_modelFilePath = CreateFilePath(_modelFolder, weightFileName);
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV4 weight", _modelFilePath, __FILE__, __LINE__);
				}
				else if (_modelConfig.modelType == ModelType::YOLOV5) {
					_isDarkNet = false;
					weightFileName = _modelName + ".onnx";
					_modelFilePath = CreateFilePath(_modelFolder, weightFileName);
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV5 weight", _modelFilePath, __FILE__, __LINE__);
				}
				else if (_modelConfig.modelType == ModelType::YOLOV8) {
					_isDarkNet = false;
					weightFileName = _modelName + ".onnx";
					_modelFilePath = CreateFilePath(_modelFolder, weightFileName);
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV8 weight", _modelFilePath, __FILE__, __LINE__);
				}
				else {
					this->_logger.LogError("YOLOOD::Initialize.  Model Type is not valid. Please check the model config file", _modelConfigFile, __FILE__, __LINE__);
					return false;
				}
				if (!FileExist(_modelFilePath)) {
					this->_logger.LogError("YOLOOD::Initialize.  Model file is not exist", _modelFilePath, __FILE__, __LINE__);
					return false;
				}
			}
			else {// This is old version of model zip file
				weightFileName = _modelName + ".weights";
				std::string weightfile = CreateFilePath(_modelFolder, weightFileName);
				if (FileExist(weightfile)) {
					// This is the darknet Yolov4
					_modelFilePath = weightfile;
					_classFilePath = CreateFilePath(_modelFolder, className);
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV4 weight", _modelFilePath, __FILE__, __LINE__);
					_modelConfig.modelType = ModelType::YOLOV4;
					_isDarkNet = true;
					_modelConfig.inpHeight = 416;
					_modelConfig.inpWidth = 416;
				}
				else {
					_isDarkNet = false;
					weightFileName = _modelName + ".onnx";
					std::string onnxfile = CreateFilePath(_modelFolder, weightFileName);
					if (std::filesystem::exists(onnxfile)) {
						// This is the yovoV5 or yolov8 format
						_modelFilePath = onnxfile;
						_classFilePath = CreateFilePath(_modelFolder, className);
						this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV8 weight", _modelFilePath, __FILE__, __LINE__);
					}
					else {
						this->_logger.LogError("YOLOOD::Initialize.  Model file is not exist", _modelFilePath, __FILE__, __LINE__);
						return false;
					}
				}
				std::ifstream isValidFileName(_classFilePath);
				if (!isValidFileName)
				{
					this->_logger.LogDebug("YOLOOD::Initialize.  Load classes from string", _classFilePath, __FILE__, __LINE__);
					LoadClassesFromString();
				}
				else {
					this->_logger.LogDebug("YOLOOD::Initialize.  Load classes from file", _classFilePath, __FILE__, __LINE__);
					LoadClassesFromFile();
				}
			}

			// 1. Load labelMap and engine
			labelMap.clear();
			if (!_classes.empty())
				labelMap = VectorToCommaSeparatedString(_classes);

			if (_isDarkNet) {
				LoadDarknetNetwork();

			}
			else {
#ifdef USEOPENCVDNN
				if (_modelConfig.modelType == ModelType::YOLOV8)
					LoadOnnxNetwork();
				else
					loadYoloModel(_modelFilePath, true, cv::Size(_modelConfig.inpWidth, _modelConfig.inpHeight));//Assume that GPU is available
#else
				loadYoloModel(_modelFilePath, true, cv::Size(_modelConfig.inpWidth, _modelConfig.inpHeight));//Assume that GPU is available
#endif
			}
			_isInitialized = true;
			return true;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("YOLOOD::LoadModel", e.what(), __FILE__, __LINE__);
			return false;
		}
	}
	bool YOLOOD::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);
		try {
			bool result = ANSODBase::Initialize(licenseKey, modelConfig, modelZipFilePath, modelZipPassword, labelMap);
			if (!result) return false;
			// Parsing for YOLO only here
			_modelConfig = modelConfig;
			_modelConfig.detectionType = ANSCENTER::DetectionType::DETECTION;
			_modelConfig.inpHeight = 640;
			_modelConfig.inpWidth = 640;
			if (_modelConfig.modelMNSThreshold < 0.2)
				_modelConfig.modelMNSThreshold = 0.5;
			if (_modelConfig.modelConfThreshold < 0.2)
				_modelConfig.modelConfThreshold = 0.5;
			_letterBoxForSquare = true;
			// 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];
				}
				_modelConfig.modelType = modelType;
				if (_modelConfig.modelType == ModelType::YOLOV4) {
					_isDarkNet = true;
					_modelFilePath = CreateFilePath(_modelFolder, "train_last.weights");
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV4 weight", _modelFilePath, __FILE__, __LINE__);
				}
				else if (_modelConfig.modelType == ModelType::YOLOV5) {
					_isDarkNet = false;
					_modelFilePath = CreateFilePath(_modelFolder, "train_last.onnx");
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV5 weight", _modelFilePath, __FILE__, __LINE__);
				}
				else if (_modelConfig.modelType == ModelType::YOLOV8) {
					_isDarkNet = false;
					_modelFilePath = CreateFilePath(_modelFolder, "train_last.onnx");
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV8 weight", _modelFilePath, __FILE__, __LINE__);
				}
				else {
					this->_logger.LogError("YOLOOD::Initialize.  Model Type is not valid. Please check the model config file", _modelConfigFile, __FILE__, __LINE__);
					return false;
				}
				if (!FileExist(_modelFilePath)) {
					this->_logger.LogError("YOLOOD::Initialize.  Model file is not exist", _modelFilePath, __FILE__, __LINE__);
					return false;
				}
			}
			else {// This is old version of model zip file
				std::string weightfile = CreateFilePath(_modelFolder, "train_last.weights");
				if (FileExist(weightfile)) {
					// This is the darknet Yolov4
					_modelFilePath = weightfile;
					_classFilePath = CreateFilePath(_modelFolder, "classes.names");
					this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV4 weight", _modelFilePath, __FILE__, __LINE__);
					_modelConfig.modelType = ModelType::YOLOV4;
					_isDarkNet = true;
					_modelConfig.inpHeight = 416;
					_modelConfig.inpWidth = 416;
				}
				else {
					_isDarkNet = false;
					std::string onnxfile = CreateFilePath(_modelFolder, "train_last.onnx");
					if (std::filesystem::exists(onnxfile)) {
						_modelFilePath = onnxfile;
						_classFilePath = CreateFilePath(_modelFolder, "classes.names");
						this->_logger.LogDebug("YOLOOD::Initialize.  Loading YoloV8/Yolov5 weight", _modelFilePath, __FILE__, __LINE__);
					}
					else {
						this->_logger.LogError("YOLOOD::Initialize.  Model file is not exist", _modelFilePath, __FILE__, __LINE__);
						return false;
					}
				}
				if (FileExist(_classFilePath))
				{
					this->_logger.LogDebug("YOLOOD::Initialize.  Load classes from file", _classFilePath, __FILE__, __LINE__);
					LoadClassesFromFile();
				}
				else {
					this->_logger.LogDebug("YOLOOD::Initialize.  Load classes from string", _classFilePath, __FILE__, __LINE__);
					LoadClassesFromString();
				}
			}

			// 1. Load labelMap and engine
			labelMap.clear();
			if (!_classes.empty())
				labelMap = VectorToCommaSeparatedString(_classes);

			if (_isDarkNet) {
				LoadDarknetNetwork();
			}
			else {
#ifdef USEOPENCVDNN
				if (_modelConfig.modelType == ModelType::YOLOV8)
					LoadOnnxNetwork();
				else
					loadYoloModel(_modelFilePath, true, cv::Size(_modelConfig.inpWidth, _modelConfig.inpHeight));//Assume that GPU is available
#else
				loadYoloModel(_modelFilePath, true, cv::Size(_modelConfig.inpWidth, _modelConfig.inpHeight));//Assume that GPU is available
#endif
			}
			_isInitialized = true;
			return true;

		}
		catch (std::exception& e) {
			this->_logger.LogFatal("YOLOOD::Initialize", e.what(), __FILE__, __LINE__);
			return false;
		}
	}
	std::vector<Object> YOLOOD::RunInference(const cv::Mat& input) {
		return RunInference(input, "CustomCam");
	}
	std::vector<Object> YOLOOD::RunInference(const cv::Mat& input, const std::string& camera_id) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);

		// Early validation - before try block
		if (!_licenseValid) {
			this->_logger.LogError("YOLOOD::RunInference", "Invalid License", __FILE__, __LINE__);
			return {};
		}

		if (!_isInitialized) {
			this->_logger.LogError("YOLOOD::RunInference", "Model is not initialized", __FILE__, __LINE__);
			return {};
		}

		if (input.empty() || input.cols < 10 || input.rows < 10) {
			return {};
		}

		try {
			// --- NV12 fast path: try to get full-res BGR from GPU NV12 frame ---
			cv::Mat inferenceImage = input;
			float bgrScaleX = 1.0f, bgrScaleY = 1.0f;
			{
				auto* gpuData = tl_currentGpuFrame();
				if (gpuData && gpuData->width > 0 && gpuData->height > 0) {
					if (gpuData->cpuYPlane && gpuData->cpuUvPlane &&
					    gpuData->cpuYLinesize >= gpuData->width &&
					    gpuData->cpuUvLinesize >= gpuData->width) {
						const int fw = gpuData->width;
						const int fh = gpuData->height;
						// NV12 requires even dimensions
						if ((fw % 2) == 0 && (fh % 2) == 0) {
							try {
								cv::Mat yPlane(fh, fw, CV_8UC1,
								               gpuData->cpuYPlane, static_cast<size_t>(gpuData->cpuYLinesize));
								cv::Mat uvPlane(fh / 2, fw / 2, CV_8UC2,
								                gpuData->cpuUvPlane, static_cast<size_t>(gpuData->cpuUvLinesize));
								cv::Mat fullResBGR;
								cv::cvtColorTwoPlane(yPlane, uvPlane, fullResBGR, cv::COLOR_YUV2BGR_NV12);
								if (!fullResBGR.empty()) {
									bgrScaleX = static_cast<float>(input.cols) / fullResBGR.cols;
									bgrScaleY = static_cast<float>(input.rows) / fullResBGR.rows;
									inferenceImage = fullResBGR;
								}
							} catch (...) { /* NV12 conversion failed — fall back to input */ }
						}
					}
				}
			}
			std::vector<Object> ret;
			if (_isDarkNet) {
				ret = RunInferenceFromDarkNet(inferenceImage, camera_id);
			}
			else if (_modelConfig.modelType == ModelType::YOLOV5) {
				ret = RunInferenceFromYoloV5(inferenceImage, camera_id);
			}
			else {
#ifdef USEOPENCVDNN
				ret = RunInferenceFromONNX(inferenceImage, camera_id);
#else
				ret = RunInferenceFromYoloV8(inferenceImage, camera_id);
#endif
			}

			// --- Rescale coordinates from full-res back to display-res ---
			if (bgrScaleX != 1.0f || bgrScaleY != 1.0f) {
				for (auto& obj : ret) {
					obj.box.x      = static_cast<int>(obj.box.x      * bgrScaleX);
					obj.box.y      = static_cast<int>(obj.box.y      * bgrScaleY);
					obj.box.width  = static_cast<int>(obj.box.width  * bgrScaleX);
					obj.box.height = static_cast<int>(obj.box.height * bgrScaleY);
					for (size_t k = 0; k < obj.kps.size(); k += 2) {
						obj.kps[k]     *= bgrScaleX;  // x
						if (k + 1 < obj.kps.size())
							obj.kps[k + 1] *= bgrScaleY;  // y
					}
					for (auto& pt : obj.polygon) {
						pt.x *= bgrScaleX;
						pt.y *= bgrScaleY;
					}
				}
			}

			if (_trackerEnabled) {
				ret = ApplyTracking(ret, camera_id);
				if (_stabilizationEnabled) ret = StabilizeDetections(ret, camera_id);
			}
			return ret;
		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::RunInference", e.what(), __FILE__, __LINE__);
			return {};
		}
	}	
	YOLOOD::~YOLOOD() {
		try {
			this->_logger.LogDebug("YOLOOD::~YOLOOD()", "Release YOLOOD ", __FILE__, __LINE__);
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("YOLOOD::~YOLOOD()", e.what(), __FILE__, __LINE__);
		}
	}
	bool YOLOOD::Destroy() {
		try {
			_net.reset();
			return true;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("YOLOOD::Destroy()", e.what(), __FILE__, __LINE__);
			return false;
		}
	}

	std::vector<Object> YOLOOD::RunInferenceFromYoloV5(const cv::Mat& input, const std::string& camera_id) {
		// Early validation - no need for try block
		if (input.empty() || input.data == nullptr) {
			return {};
		}
		if (input.cols < 10 || input.rows < 10) {
			return {};
		}

		try {
			// Color conversion only if needed
			cv::Mat frame;
			if (input.channels() == 1) {
				cv::cvtColor(input, frame, cv::COLOR_GRAY2BGR);
			}
			else {
				frame = input;  // Shallow copy (shares data, no allocation)
			}
			return detect(frame, _modelConfig.detectionScoreThreshold, _modelConfig.modelMNSThreshold);
		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::RunInferenceFromYoloV5", e.what(), __FILE__, __LINE__);
			return {};
		}
	}
	std::vector<Object> YOLOOD::RunInferenceFromYoloV8(const cv::Mat& input, const std::string& camera_id) {
		// Early validation - before try block
		if (input.empty() || input.data == nullptr) {
			return {};
		}
		if (input.cols < 10 || input.rows < 10) {
			return {};
		}

		try {
			// Color conversion using reusable buffer
			cv::Mat* modelInputPtr;
			if (input.channels() == 1) {
				cv::cvtColor(input, this->_frameBuffer, cv::COLOR_GRAY2BGR);
				modelInputPtr = &this->_frameBuffer;
			}
			else {
				modelInputPtr = const_cast<cv::Mat*>(&input);
			}
			cv::Mat& modelInput = *modelInputPtr;

			// Model shape and letterbox
			const cv::Size2f modelShape(_modelConfig.inpWidth, _modelConfig.inpHeight);
			if (_letterBoxForSquare && modelShape.width == modelShape.height) {
				modelInput = ANSUtilityHelper::FormatToSquare(modelInput);
			}

			// Create blob
			cv::Mat blob;
			cv::dnn::blobFromImage(modelInput, blob, 1.0 / 255.0, modelShape, cv::Scalar(), true, false);

			// Create input tensor - static MemoryInfo
			static const Ort::MemoryInfo memoryInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
			const std::array<int64_t, 4> inputShape = { 1, blob.size[1], blob.size[2], blob.size[3] };

			Ort::Value inputTensor = Ort::Value::CreateTensor<float>(
				memoryInfo,
				reinterpret_cast<float*>(blob.data),
				blob.total(),
				inputShape.data(),
				inputShape.size()
			);

			// Run inference
			auto outputTensors = session.Run(
				Ort::RunOptions{ nullptr },
				inputNodeNames.data(), &inputTensor, inputNodeNames.size(),
				outputNodeNames.data(), outputNodeNames.size()
			);

			// Parse output
			const float* rawOutput = outputTensors[0].GetTensorData<float>();
			const std::vector<int64_t> outputShape = outputTensors[0].GetTensorTypeAndShapeInfo().GetShape();

			const int dimensions = static_cast<int>(outputShape[1]);  // 4 + num_classes
			const int rows = static_cast<int>(outputShape[2]);        // 8400
			const int numClasses = dimensions - 4;
			const int maxWidth = modelInput.cols;
			const int maxHeight = modelInput.rows;

			cv::Mat outputMat(dimensions, rows, CV_32F, const_cast<float*>(rawOutput));
			cv::transpose(outputMat, outputMat);

			float* data = reinterpret_cast<float*>(outputMat.data);
			const float x_factor = static_cast<float>(modelInput.cols) / modelShape.width;
			const float y_factor = static_cast<float>(modelInput.rows) / modelShape.height;

			// Pre-allocate vectors
			std::vector<int> class_ids;
			std::vector<float> confidences;
			std::vector<cv::Rect> boxes;
			const size_t estimatedDetections = std::min(static_cast<size_t>(rows / 10), size_t{ 100 });
			class_ids.reserve(estimatedDetections);
			confidences.reserve(estimatedDetections);
			boxes.reserve(estimatedDetections);

			const float scoreThreshold = _modelConfig.detectionScoreThreshold;

			for (int i = 0; i < rows; ++i) {
				float* classes_scores = data + 4;
				cv::Mat scores(1, numClasses, CV_32FC1, classes_scores);
				cv::Point class_id;
				double maxClassScore;
				cv::minMaxLoc(scores, nullptr, &maxClassScore, nullptr, &class_id);

				if (maxClassScore >= scoreThreshold) {
					confidences.push_back(static_cast<float>(maxClassScore));
					class_ids.push_back(class_id.x);

					const float x = data[0];
					const float y = data[1];
					const float w = data[2];
					const float h = data[3];

					int left = static_cast<int>((x - 0.5f * w) * x_factor);
					int top = static_cast<int>((y - 0.5f * h) * y_factor);
					int width = static_cast<int>(w * x_factor);
					int height = static_cast<int>(h * y_factor);

					// Clamp to image bounds
					left = std::max(0, left);
					top = std::max(0, top);
					width = std::min(maxWidth - left - 5, width);
					height = std::min(maxHeight - top - 5, height);

					boxes.emplace_back(left, top, width, height);
				}
				data += dimensions;
			}

			// NMS
			std::vector<int> nms_result;
			cv::dnn::NMSBoxes(boxes, confidences, _modelConfig.modelConfThreshold, _modelConfig.modelMNSThreshold, nms_result);

			// Build final detections
			std::vector<Object> detections;
			detections.reserve(nms_result.size());

			const size_t classNameSize = _classes.size();
			const int inputCols = input.cols;
			const int inputRows = input.rows;

			for (const int idx : nms_result) {
				if (confidences[idx] > scoreThreshold) {
					Object result;
					result.classId = class_ids[idx];
					result.confidence = confidences[idx];

					// Safe class name lookup
					if (classNameSize > 0) {
						const size_t safeIdx = static_cast<size_t>(result.classId) < classNameSize
							? static_cast<size_t>(result.classId)
							: classNameSize - 1;
						result.className = _classes[safeIdx];
					}
					else {
						result.className = "Unknown";
					}

					result.box = boxes[idx];
					result.polygon = ANSUtilityHelper::RectToNormalizedPolygon(result.box, inputCols, inputRows);
					result.cameraId = camera_id;
					detections.push_back(std::move(result));
				}
			}

			return detections;

		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::RunInferenceFromYoloV8", e.what(), __FILE__, __LINE__);
			return {};
		}
	}
	std::vector<Object> YOLOOD::RunInferenceFromYoloV11(const cv::Mat& input, [[maybe_unused]] const std::string& camera_id) {
		// Early validation - before try block
		if (input.empty() || input.data == nullptr) {
			this->_logger.LogError("YOLOOD::RunInferenceFromYoloV11",
				"Input image is empty or null", __FILE__, __LINE__);
			return {};
		}

		if (input.cols < 10 || input.rows < 10) {
			this->_logger.LogError("YOLOOD::RunInferenceFromYoloV11",
				"Input image too small: " + std::to_string(input.cols) + "x" + std::to_string(input.rows),
				__FILE__, __LINE__);
			return {};
		}

		const int channels = input.channels();
		if (channels != 1 && channels != 3) {
			this->_logger.LogError("YOLOOD::RunInferenceFromYoloV11",
				"Unsupported channel count: " + std::to_string(channels),
				__FILE__, __LINE__);
			return {};
		}

		try {
			// Color conversion if needed
			if (channels == 1) {
				cv::cvtColor(input, this->_frameBuffer, cv::COLOR_GRAY2BGR);
				return detectv11(this->_frameBuffer, _modelConfig.detectionScoreThreshold, _modelConfig.modelMNSThreshold);
			}

			return detectv11(input, _modelConfig.detectionScoreThreshold, _modelConfig.modelMNSThreshold);

		}
		catch (const cv::Exception& e) {
			this->_logger.LogFatal("YOLOOD::RunInferenceFromYoloV11",
				"OpenCV error: " + std::string(e.what()), __FILE__, __LINE__);
		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::RunInferenceFromYoloV11",
				"Standard error: " + std::string(e.what()), __FILE__, __LINE__);
		}

		return {};
	}
	std::vector<Object> YOLOOD::RunInferenceFromONNX(const cv::Mat& input, const std::string& camera_id) {
		// Early validation - before try block
		if (input.empty() || input.data == nullptr) {
			return {};
		}
		if (input.cols < 10 || input.rows < 10) {
			return {};
		}

		try {
			// Color conversion using reusable buffer
			cv::Mat* modelInputPtr;
			if (input.channels() == 1) {
				cv::cvtColor(input, this->_frameBuffer, cv::COLOR_GRAY2BGR);
				modelInputPtr = &this->_frameBuffer;
			}
			else {
				modelInputPtr = const_cast<cv::Mat*>(&input);  // Safe: we don't modify it
			}
			cv::Mat& modelInput = *modelInputPtr;

			// Model shape
			const cv::Size2f modelShape(_modelConfig.inpWidth, _modelConfig.inpHeight);

			if (_letterBoxForSquare && modelShape.width == modelShape.height) {
				modelInput = ANSUtilityHelper::FormatToSquare(modelInput);
			}

			// Blob creation and inference
			cv::Mat blob;
			cv::dnn::blobFromImage(modelInput, blob, 1.0 / 255.0, modelShape, cv::Scalar(), true, false);
			_net->setInput(blob);

			std::vector<cv::Mat> outputs;
			_net->forward(outputs, _net->getUnconnectedOutLayersNames());

			// Parse output dimensions
			const int rows = outputs[0].size[2];
			const int dimensions = outputs[0].size[1];
			const int maxWidth = modelInput.cols;
			const int maxHeight = modelInput.rows;

			outputs[0] = outputs[0].reshape(1, dimensions);
			cv::transpose(outputs[0], outputs[0]);

			float* data = reinterpret_cast<float*>(outputs[0].data);
			const float x_factor = static_cast<float>(modelInput.cols) / modelShape.width;
			const float y_factor = static_cast<float>(modelInput.rows) / modelShape.height;

			// Pre-allocate with estimated capacity
			std::vector<int> class_ids;
			std::vector<float> confidences;
			std::vector<cv::Rect> boxes;
			const size_t estimatedDetections = std::min(static_cast<size_t>(rows / 10), size_t{ 100 });
			class_ids.reserve(estimatedDetections);
			confidences.reserve(estimatedDetections);
			boxes.reserve(estimatedDetections);

			const size_t numClasses = _classes.size();
			const float scoreThreshold = _modelConfig.detectionScoreThreshold;

			for (int i = 0; i < rows; ++i) {
				float* classes_scores = data + 4;
				cv::Mat scores(1, static_cast<int>(numClasses), CV_32FC1, classes_scores);
				cv::Point class_id;
				double maxClassScore;
				cv::minMaxLoc(scores, nullptr, &maxClassScore, nullptr, &class_id);

				if (maxClassScore >= scoreThreshold) {
					confidences.push_back(static_cast<float>(maxClassScore));
					class_ids.push_back(class_id.x);

					const float x = data[0];
					const float y = data[1];
					const float w = data[2];
					const float h = data[3];

					int left = static_cast<int>((x - 0.5f * w) * x_factor);
					int top = static_cast<int>((y - 0.5f * h) * y_factor);
					int width = static_cast<int>(w * x_factor);
					int height = static_cast<int>(h * y_factor);

					// Clamp to image bounds
					left = std::max(0, left);
					top = std::max(0, top);
					width = std::min(maxWidth - left - 5, width);
					height = std::min(maxHeight - top - 5, height);

					boxes.emplace_back(left, top, width, height);
				}
				data += dimensions;
			}

			// NMS
			std::vector<int> nms_result;
			cv::dnn::NMSBoxes(boxes, confidences, _modelConfig.modelConfThreshold, _modelConfig.modelMNSThreshold, nms_result);

			// Build final detections
			std::vector<Object> detections;
			detections.reserve(nms_result.size());

			for (const int idx : nms_result) {
				if (confidences[idx] > scoreThreshold) {
					Object result;
					result.classId = class_ids[idx];
					result.confidence = confidences[idx];
					result.className = _classes[result.classId];
					result.box = boxes[idx];
					result.polygon = ANSUtilityHelper::RectToNormalizedPolygon(result.box, input.cols, input.rows);
					result.cameraId = camera_id;
					detections.push_back(std::move(result));
				}
			}

			return detections;

		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::RunInferenceFromONNX", e.what(), __FILE__, __LINE__);
			return {};
		}
	}
	std::vector<Object> YOLOOD::RunInferenceFromDarkNet(const cv::Mat& input, const std::string& camera_id) {
		// Early validation - before try block
		if (input.empty() || input.data == nullptr) {
			return {};
		}
		if (input.cols < 10 || input.rows < 10) {
			return {};
		}

		try {
			// Color conversion using reusable buffer
			cv::Mat* modelInputPtr;
			if (input.channels() == 1) {
				cv::cvtColor(input, this->_frameBuffer, cv::COLOR_GRAY2BGR);
				modelInputPtr = &this->_frameBuffer;
			}
			else {
				modelInputPtr = const_cast<cv::Mat*>(&input);
			}
			cv::Mat& modelInput = *modelInputPtr;

			// Model shape
			const cv::Size2f modelShape(_modelConfig.inpWidth, _modelConfig.inpHeight);

			if (_letterBoxForSquare && modelShape.width == modelShape.height) {
				modelInput = ANSUtilityHelper::FormatToSquare(modelInput);
			}

			// Setup detection model
			cv::dnn::DetectionModel model(*_net);
			model.setInputParams(1.0 / 255.0,
				cv::Size(static_cast<int>(_modelConfig.inpHeight), static_cast<int>(_modelConfig.inpHeight)),
				cv::Scalar(), true);

			// Run detection
			std::vector<int> classIds;
			std::vector<float> scores;
			std::vector<cv::Rect> boxes;
			model.detect(modelInput, classIds, scores, boxes,
				_modelConfig.detectionScoreThreshold, _modelConfig.modelMNSThreshold);

			// Build results
			std::vector<Object> detections;
			detections.reserve(classIds.size());

			const float scoreThreshold = _modelConfig.detectionScoreThreshold;
			const int maxWidth = modelInput.cols;
			const int maxHeight = modelInput.rows;
			const int inputCols = input.cols;
			const int inputRows = input.rows;

			for (size_t i = 0; i < classIds.size(); ++i) {
				if (scores[i] > scoreThreshold) {
					Object result;
					result.classId = classIds[i];
					result.confidence = scores[i];
					result.className = _classes[result.classId];

					// Clamp box to image bounds
					cv::Rect& box = boxes[i];
					box.x = std::max(0, box.x);
					box.y = std::max(0, box.y);
					box.width = std::min(maxWidth - box.x - 5, box.width);
					box.height = std::min(maxHeight - box.y - 5, box.height);

					result.box = box;
					result.polygon = ANSUtilityHelper::RectToNormalizedPolygon(box, inputCols, inputRows);
					result.cameraId = camera_id;
					detections.push_back(std::move(result));
				}
			}

			return detections;

		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::RunInferenceFromDarkNet", e.what(), __FILE__, __LINE__);
			return {};
		}
	}
	void YOLOOD::SetEngineType() {
		try {
			EngineType engineType = ANSLicenseHelper::CheckHardwareInformation();
			switch (engineType) {
			case EngineType::NVIDIA_GPU: // NVIDIA CUDA
				this->_logger.LogDebug("YOLOOD::SetEngineType", "Running on CUDA GPU", __FILE__, __LINE__);
				_net->setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
				_net->setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
				break;
			default: // Normal CPU
				this->_logger.LogDebug("YOLOOD::SetEngineType", "Running on CPU", __FILE__, __LINE__);
				_net->setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
				_net->setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
				break;
			}
		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::SetEngineType()", e.what(), __FILE__, __LINE__);
		}

	}
	void YOLOOD::LoadOnnxNetwork() {  //For Yolov5 and Yolov8
		try {
			_net = std::make_shared<cv::dnn::Net>(cv::dnn::readNetFromONNX(_modelFilePath));
			_isDarkNet = false;
			_modelConfig.inpHeight = 640;
			_modelConfig.inpWidth = 640;
			SetEngineType();
		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::LoadOnnxNetwork()", e.what(), __FILE__, __LINE__);
		}

	}
	void YOLOOD::LoadDarknetNetwork() {
		try {
			// Retrieve the cfg file
			std::string modelCfgFile = CreateFilePath(_modelFolder, "test.cfg");//It is always test.cfg
			_net = std::make_shared<cv::dnn::Net>(cv::dnn::readNetFromDarknet(modelCfgFile, _modelFilePath));
			_modelConfig.inpHeight = 416;
			_modelConfig.inpWidth = 416;
			_isDarkNet = true;
			SetEngineType();
		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("YOLOOD::LoadDarknetNetwork()", e.what(), __FILE__, __LINE__);
		}


	}
}
