#pragma once
#include <set>
#include <cmath>
#include <json.hpp>
#include "ANSODEngine.h"
#include "ANSLicense.h"   // ANS_DBG macro
#include "ANSYOLOOD.h"
#include "ANSTENSORRTOD.h"
#include "ANSTENSORRTCL.h"
#include "ANSOPENVINOCL.h"
#include "ANSOPENVINOOD.h"
#include "ANSYOLOV10RTOD.h"
#include "ANSYOLOV10OVOD.h"
#include "ANSCUSTOMDetector.h"
#include "ANSFD.h"
#include "ANSANOMALIB.h"
#include "ANSPOSE.h"
#include "ANSSAM.h"
#include <pipelines/metadata.h>
#include <models/input_data.h>
#include "utils/visualizer.hpp"
#include <turbojpeg.h>
#include <vector>
#include <map>
#include <string>
static bool ansodLicenceValid = false;
// Global once_flag to protect license checking
static std::once_flag ansodLicenseOnceFlag;
namespace ANSCENTER
{
	//--------------------------------------------------------------------------------------------------------------------------------
	// Base class
	//--------------------------------------------------------------------------------------------------------------------------------
	
	static void VerifyGlobalANSODLicense(const std::string& licenseKey) {
		try {
			ansodLicenceValid = ANSCENTER::ANSLicenseHelper::LicenseVerification(licenseKey, 1002, "ODHUB-LV");//Default productId=1002 (ODHUB)
			if (!ansodLicenceValid) { // we also support ANSTS license
				ansodLicenceValid = ANSCENTER::ANSLicenseHelper::LicenseVerification(licenseKey, 1003, "ANSVIS");//Default productId=1003 (ANSVIS)
			}
			if (!ansodLicenceValid) { // we also support ANSTS license
				ansodLicenceValid = ANSCENTER::ANSLicenseHelper::LicenseVerification(licenseKey, 1008, "ANSTS");//Default productId=1008 (ANSTS)
			}
		}
		catch (std::exception& e) {
			ansodLicenceValid = false;
		}
	}
	void ANSODBase::CheckLicense() {
		try {
			// Check once globally
			std::call_once(ansodLicenseOnceFlag, [this]() {
				VerifyGlobalANSODLicense(_licenseKey);
				});

			// Update this instance's local license flag
			_licenseValid = ansodLicenceValid;
		}
		catch (const std::exception& e) {
			this->_logger.LogFatal("ANSODBase::CheckLicense. Error:", e.what(), __FILE__, __LINE__);
		}
	}
	bool ANSODBase::LoadModel(const std::string& modelZipFilePath, const std::string& modelZipPassword)
	{
		try {
			ANSCENTER::ANSLibsLoader::Initialize();
			_modelFolder = "";
			_modelConfigFile = "";
			_modelFolder.clear();
			_modelConfigFile.clear();
			// 0. Check if the modelZipFilePath exist?
			if (!FileExist(modelZipFilePath)) {
				if (modelZipFilePath.empty()) {// Support motion detector that does not need model file
					_isInitialized = true;
					// Auto-enable tracker + stabilization by default (BYTETRACK)
					SetTracker(TrackerType::BYTETRACK, true);
					return true;
				}
				else {
					this->_logger.LogFatal("ANSODBase::LoadModel", "Model zip file is not exist", __FILE__, __LINE__);
					return false;
				}
			}
			// 1. Unzip model zip file to a special location with folder name as model file (and version)
			std::string outputFolder;
			std::vector<std::string> passwordArray;
			if (!modelZipPassword.empty()) passwordArray.push_back(modelZipPassword);
			passwordArray.push_back("Sh7O7nUe7vJ/417W0gWX+dSdfcP9hUqtf/fEqJGqxYL3PedvHubJag==");
			passwordArray.push_back("3LHxGrjQ7kKDJBD9MX86H96mtKLJaZcTYXrYRdQgW8BKGt7enZHYMg==");
			passwordArray.push_back("AnsCustomModels20@$");
			passwordArray.push_back("AnsDemoModels20@!");
			std::string modelName = GetFileNameWithoutExtension(modelZipFilePath);
			//this->_logger.LogDebug("ANSODBase::LoadModel. Model name", modelName, __FILE__, __LINE__);
			size_t vectorSize = passwordArray.size();
			for (size_t i = 0; i < vectorSize; i++) {
				if (ExtractPasswordProtectedZip(modelZipFilePath, passwordArray[i], modelName, _modelFolder, false))
					break; // Break the loop when the condition is met. 
			}
			// 2. Check if the outputFolder exist
			if (!std::filesystem::exists(_modelFolder)) {
				this->_logger.LogError("ANSODBase::LoadModel. Output model folder is not exist", _modelFolder, __FILE__, __LINE__);
				return false; // That means the model file is not exist or the password is not correct
			}
			// 3. Check if the model has the configuration file
			std::string modelConfigName = "model_config.json";
			_modelConfigFile = CreateFilePath(_modelFolder, modelConfigName);

			// Auto-enable tracker + stabilization by default (BYTETRACK)
			SetTracker(TrackerType::BYTETRACK, true);

			return true;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::LoadModel", e.what(), __FILE__, __LINE__);
			return false;
		}
	}
	bool ANSODBase::Initialize(std::string licenseKey, ModelConfig modelConfig, const std::string& modelZipFilePath, const std::string& modelZipPassword, std::string& labelMap) {
		try {
			ANSCENTER::ANSLibsLoader::Initialize();
			_licenseKey = licenseKey;
			_licenseValid = false;
			_modelFolder = "";
			_modelConfigFile = "";
			_modelFolder.clear();
			_modelConfigFile.clear();
			_modelConfig = modelConfig;
			CheckLicense();
			if (!_licenseValid) {
				this->_logger.LogError("ANSODBase::Initialize", "Invalid License", __FILE__, __LINE__);
				return false;
			}
			// 0. Check if the modelZipFilePath exist?
			if (!FileExist(modelZipFilePath)) {
				if (modelZipFilePath.empty()) {// Support motion detector that does not need model file
					_isInitialized = true;
					// Auto-enable tracker + stabilization by default (BYTETRACK)
					SetTracker(TrackerType::BYTETRACK, true);
					return true;
				}
				else {
					this->_logger.LogFatal("ANSODBase::Initialize", "Model zip file is not exist", __FILE__, __LINE__);
					return false;
				}

			}
			// 1. Unzip model zip file to a special location with folder name as model file (and version)
			std::string outputFolder;
			std::vector<std::string> passwordArray;
			if (!modelZipPassword.empty()) passwordArray.push_back(modelZipPassword);
			passwordArray.push_back("Sh7O7nUe7vJ/417W0gWX+dSdfcP9hUqtf/fEqJGqxYL3PedvHubJag==");
			passwordArray.push_back("3LHxGrjQ7kKDJBD9MX86H96mtKLJaZcTYXrYRdQgW8BKGt7enZHYMg==");
			passwordArray.push_back("AnsCustomModels20@$");
			passwordArray.push_back("AnsDemoModels20@!");
			std::string modelName = GetFileNameWithoutExtension(modelZipFilePath);
			//this->_logger.LogDebug("ANSODBase::Initialize. Model name", modelName, __FILE__, __LINE__);
			size_t vectorSize = passwordArray.size();
			for (size_t i = 0; i < vectorSize; i++) {
				if (ExtractPasswordProtectedZip(modelZipFilePath, passwordArray[i], modelName, _modelFolder, false))
					break; // Break the loop when the condition is met. 
			}
			// 2. Check if the outputFolder exist
			if (!std::filesystem::exists(_modelFolder)) {
				this->_logger.LogError("ANSODBase::Initialize. Output model folder is not exist", _modelFolder, __FILE__, __LINE__);
				return false; // That means the model file is not exist or the password is not correct
			}
			// 3. Check if the model has the configuration file
			std::string modelConfigName = "model_config.json";
			_modelConfigFile = CreateFilePath(_modelFolder, modelConfigName);
			_isInitialized = true;

			// Auto-enable tracker + stabilization by default (BYTETRACK)
			SetTracker(TrackerType::BYTETRACK, true);

			return true;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::Initialize", e.what(), __FILE__, __LINE__);
			return false;
		}

	}
	bool ANSODBase::OptimizeModel(bool fp16, std::string& optimizedModelFolder) {
		try {
			// Call the library loader's optimization function
			ANSCENTER::ANSLibsLoader::Initialize();
			return true;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::OptimizeModel", e.what(), __FILE__, __LINE__);
			return false;
		}
	}

	void ANSODBase::LoadClassesFromString() {
		try {
			std::string regex_str = ";";
			auto tokens = Split(_classFilePath, regex_str);
			_classes.clear();
			for (auto& item : tokens) {
				_classes.push_back(item);
			}
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::LoadClassesFromString()", e.what(), __FILE__, __LINE__);
		}

	}
	void ANSODBase::LoadClassesFromFile() {
		try {
			std::ifstream inputFile(_classFilePath);
			if (inputFile.is_open())
			{
				_classes.clear();
				std::string classLine;
				while (std::getline(inputFile, classLine))
					_classes.push_back(classLine);
				inputFile.close();
			}
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::LoadClassesFromFile()", e.what(), __FILE__, __LINE__);
		}
	}
	bool ANSODBase::LoadModelFromFolder(std::string licenseKey, ModelConfig modelConfig, std::string modelName, std::string className, const std::string& modelFolder, std::string& labelMap) {
		ANSCENTER::ANSLibsLoader::Initialize();
		_licenseKey = licenseKey;
		_licenseValid = false;
		_modelFolder = modelFolder;
		_modelConfigFile = "";
		_modelConfigFile.clear();
		_modelConfig = modelConfig;
		// 0. Check license
		CheckLicense();
		if (!_licenseValid) {
			this->_logger.LogError("LoadModelFromFolder::Initialize", "Invalid License", __FILE__, __LINE__);
			return false;
		}
		// 1. Check if the modelFolder exist?
		if (!FolderExist(modelFolder)) {

			if (modelFolder.empty()) {// Support motion detector that does not need model file
				_isInitialized = true;
				// Auto-enable tracker + stabilization by default (BYTETRACK)
				SetTracker(TrackerType::BYTETRACK, true);
				return true;
			}
			else {
				this->_logger.LogFatal("LoadModelFromFolder::Initialize", "Model zip file is not exist", __FILE__, __LINE__);
				return false;
			}
		}
		// 3. Check if the model has the configuration file
		std::string modelConfigName = "model_config.json";
		_modelConfigFile = CreateFilePath(_modelFolder, modelConfigName);
		_isInitialized = true;

		// Auto-enable tracker + stabilization by default (BYTETRACK)
		SetTracker(TrackerType::BYTETRACK, true);

		return true;
	}
	bool ANSODBase::isSimilarObject(const Object& obj1, const Object& obj2) {
		try {
			// Compare based on classId and className
			if (obj1.classId != obj2.classId ||
				obj1.className != obj2.className ||
				obj1.cameraId != obj2.cameraId) {
				return false;
			}
			// Define thresholds for similarity
			const double positionThreshold = 50.0;
			const double confidenceThreshold = 0.2;
			const double sizeThreshold = 0.2;  // Allows a 20% size difference

			// Check if centers are within positionThreshold
			cv::Point2f center1 = (obj1.box.tl() + obj1.box.br()) * 0.5;
			cv::Point2f center2 = (obj2.box.tl() + obj2.box.br()) * 0.5;
			if (cv::norm(center1 - center2) >= positionThreshold) {
				return false;
			}
			// Check if bounding box sizes are similar within the size threshold
			if (obj2.box.width == 0 || obj2.box.height == 0) return false;
			double widthRatio = static_cast<double>(obj1.box.width) / obj2.box.width;
			double heightRatio = static_cast<double>(obj1.box.height) / obj2.box.height;
			if (std::abs(1.0 - widthRatio) > sizeThreshold || std::abs(1.0 - heightRatio) > sizeThreshold) {
				return false;
			}
			// Check if confidence scores are within an acceptable range
			if (std::abs(obj1.confidence - obj2.confidence) >= confidenceThreshold) {
				return false;
			}
			// If all checks pass, the objects are considered similar
			return true;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::isSimilarObject()", e.what(), __FILE__, __LINE__);
			return false;
		}
	}
	bool ANSODBase::isOverlayObject(const Object& obj1, const Object& obj2) {
		try {
			// Compare based on classId and className
			if (obj1.classId != obj2.classId ||
				obj1.className != obj2.className ||
				obj1.cameraId != obj2.cameraId) {
				return false;
			}

			//// Define thresholds for similarity
			//const double positionThreshold = 50.0;

			//// Check if centers are within positionThreshold
			//cv::Point2f center1 = (obj1.box.tl() + obj1.box.br()) * 0.5;
			//cv::Point2f center2 = (obj2.box.tl() + obj2.box.br()) * 0.5;
			//if (cv::norm(center1 - center2) >= positionThreshold) {
			//	return false;
			//}

			// Check if bounding boxes overlap
			if ((obj1.box & obj2.box).area() == 0) {
				return false;  // No overlap between the bounding boxes
			}

			// If all checks pass, the objects are considered similar
			return true;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::isOverlayObject()", e.what(), __FILE__, __LINE__);
			return false;
		}
	}
	void ANSODBase::EnqueueDetection(const std::vector<Object>& detectedObjects, const std::string& cameraId) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		try {
			CameraData& camera = GetCameraData(cameraId);// Retrieve camera data
			if (camera._detectionQueue.size() == QUEUE_SIZE) {
				camera._detectionQueue.pop_front();  // Remove the oldest element if the queue is full
			}

			camera._detectionQueue.push_back(detectedObjects);  // Add the new detection, even if empty
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::EnqueueDetection()", e.what(), __FILE__, __LINE__);
		}
	}
	std::deque<std::vector<Object>>ANSODBase::DequeueDetection(const std::string& cameraId) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		CameraData& camera = GetCameraData(cameraId);// Retrieve camera data
		return camera._detectionQueue;
	}
	std::string ANSODBase::GetOpenVINODevice(ov::Core& core) {
		try {
			std::vector<std::string> available_devices = core.get_available_devices();
			bool device_found = false;
			std::string deviceName = "CPU";
			// Search for NPU
			auto it = std::find(available_devices.begin(), available_devices.end(), "NPU");
			if (it != available_devices.end()) {
				core.set_property("NPU", ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY));
				core.set_property("GPU", ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY));
				deviceName = "AUTO:NPU,GPU";
				device_found = true;
				return deviceName;
			}
			// If NPU not found, search for GPU
			if (!device_found) {
				it = std::find(available_devices.begin(), available_devices.end(), "GPU");
				if (it != available_devices.end()) {
					core.set_property("GPU", ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY));
					deviceName = "GPU";
					device_found = true;
					return deviceName;
				}
			}

			// If GPU not found, search for GPU.0
			if (!device_found) {
				it = std::find(available_devices.begin(), available_devices.end(), "GPU.0");
				if (it != available_devices.end()) {
					core.set_property("GPU", ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY));
					deviceName = "GPU";
					device_found = true;
					return deviceName;
				}
			}

			// If neither NPU nor GPU found, default to CPU
			if (!device_found) {
				core.set_property("CPU", ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY));
				deviceName = "CPU";
				return deviceName;
			}
			return deviceName;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::GetOpenVINODevice()", e.what(), __FILE__, __LINE__);
			core.set_property("CPU", ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY));
			return "CPU";
		}
	}
	// ── Multi-object tracker (MOT) ──────────────────────────────────────
	bool ANSODBase::SetTracker(TrackerType trackerType, bool enabled) {
		try {
			std::lock_guard<std::recursive_mutex> lock(_mutex);

			// If type changed, release all per-camera trackers so they'll be
			// lazily recreated with the new type on next ApplyTracking call.
			if (_trackerType != trackerType) {
				for (auto& [key, cam] : _cameras) {
					if (cam._tracker) {
						ReleaseANSMOTHandle(&cam._tracker);
						cam._tracker = nullptr;
					}
				}
			}

			_trackerEnabled = enabled;
			_trackerType = trackerType;

			if (!enabled) return true;

			// Store config so per-camera trackers can be created lazily
			_trackerMotType = 1; // default BYTETRACK
			_trackerParams = R"({"parameters":{"frame_rate":"10","track_buffer":"300","track_threshold":"0.500000","high_threshold":"0.600000","match_thresold":"0.980000"}})";

			switch (trackerType) {
				case TrackerType::BYTETRACK: {
					_trackerMotType = 1;
					break;
				}
				case TrackerType::UCMC: {
					_trackerMotType = 3;
					break;
				}
				case TrackerType::OCSORT: {
					_trackerMotType = 4;
					_trackerParams = R"({"parameters":{"det_thresh":"0.000000","max_age" : "30","min_hits" : "3","iou_threshold" : "0.300000","delta_t" : "3","asso_func" : "0","inertia":"3.000000","use_byte":"0"}})";
					break;
				}
				default: {
					_trackerMotType = 1;
					break;
				}
			}

			// Auto-enable stabilization with defaults when tracker is enabled
			if (!_stabilizationEnabled) {
				_stabilizationEnabled = true;
			}

			return true;
		}
		catch (std::exception& e) {
			_logger.LogFatal("ANSODBase::SetTracker", e.what(), __FILE__, __LINE__);
			return false;
		}
	}

	bool ANSODBase::SetStabilization(bool enabled, int historySize, int maxMisses) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		try {
			if (enabled) {
				// Auto-enable tracker if not already enabled
				if (!_trackerEnabled) {
					if (!SetTracker(_trackerType, true)) {
						_logger.LogError("ANSODBase::SetStabilization",
							"Failed to auto-enable tracker for stabilization", __FILE__, __LINE__);
						return false;
					}
				}
				_stabilizationEnabled = true;
				_stabilizationQueueSize = static_cast<size_t>(std::max(historySize, 5));
				_stabilizationMaxConsecutiveMisses = std::max(maxMisses, 1);
				QUEUE_SIZE = _stabilizationQueueSize;
			}
			else {
				_stabilizationEnabled = false;
			}
			return true;
		}
		catch (std::exception& e) {
			_logger.LogFatal("ANSODBase::SetStabilization", e.what(), __FILE__, __LINE__);
			return false;
		}
	}

	bool ANSODBase::SetStabilizationParameters(const std::string& jsonParams) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		try {
			auto j = nlohmann::json::parse(jsonParams);

			if (j.contains("hysteresis_enter"))
				_hysteresisEnterThreshold = j["hysteresis_enter"].get<float>();
			if (j.contains("hysteresis_keep"))
				_hysteresisKeepThreshold = j["hysteresis_keep"].get<float>();
			if (j.contains("ema_alpha"))
				_emaAlpha = std::clamp(j["ema_alpha"].get<float>(), 0.01f, 1.0f);
			if (j.contains("track_boost_min_frames"))
				_trackBoostMinFrames = std::max(j["track_boost_min_frames"].get<int>(), 1);
			if (j.contains("track_boost_amount"))
				_trackBoostAmount = std::clamp(j["track_boost_amount"].get<float>(), 0.0f, 0.5f);
			if (j.contains("class_consistency_frames"))
				_classConsistencyMinFrames = std::max(j["class_consistency_frames"].get<int>(), 1);
			if (j.contains("confidence_decay"))
				_stabilizationConfidenceDecay = std::clamp(j["confidence_decay"].get<float>(), 0.1f, 1.0f);
			if (j.contains("min_confidence"))
				_stabilizationMinConfidence = std::clamp(j["min_confidence"].get<float>(), 0.0f, 1.0f);

			return true;
		}
		catch (std::exception& e) {
			_logger.LogFatal("ANSODBase::SetStabilizationParameters", e.what(), __FILE__, __LINE__);
			return false;
		}
	}

	bool ANSODBase::SetTrackerParameters(const std::string& jsonParams) {
		try {
			std::lock_guard<std::recursive_mutex> lock(_mutex);
			if (!_trackerEnabled) return false;

			// Store params for future per-camera tracker creation
			_trackerParams = jsonParams;

			// Apply to all existing per-camera trackers
			for (auto& [key, cam] : _cameras) {
				if (cam._tracker) {
					cam._tracker->UpdateParameters(jsonParams);
				}
			}
			return true;
		}
		catch (std::exception& e) {
			_logger.LogFatal("ANSODBase::SetTrackerParameters", e.what(), __FILE__, __LINE__);
			return false;
		}
	}

	std::vector<Object> ANSODBase::ApplyTracking(std::vector<Object>& detections, const std::string& camera_id) {
		if (!_trackerEnabled) return detections;
		if (detections.empty() && !_stabilizationEnabled) return detections;
		// Lock: protects lazy-creation and same-camera concurrent calls.
		// Different cameras use separate tracker instances, so contention is
		// limited to same-camera threads (which must be serialized anyway).
		std::lock_guard<std::recursive_mutex> trackLock(_mutex);
		try {
			// Get or lazily create per-camera tracker
			CameraData& camera = GetCameraData(camera_id);
			if (!camera._tracker) {
				int result = CreateANSMOTHandle(&camera._tracker, _trackerMotType);
				if (result == 0 || !camera._tracker) {
					_logger.LogError("ANSODBase::ApplyTracking",
						"Failed to create per-camera tracker for " + camera_id, __FILE__, __LINE__);
					return detections;
				}
				if (!_trackerParams.empty()) {
					camera._tracker->UpdateParameters(_trackerParams);
				}
			}

			// Step 1: Convert Object[] → TrackerObject[]
			std::vector<TrackerObject> trackerInput;
			trackerInput.reserve(detections.size());
			for (const auto& obj : detections) {
				TrackerObject tobj{};
				tobj.track_id  = 0;
				tobj.class_id  = obj.classId;
				tobj.prob      = obj.confidence;
				tobj.x         = static_cast<float>(obj.box.x);
				tobj.y         = static_cast<float>(obj.box.y);
				tobj.width     = static_cast<float>(obj.box.width);
				tobj.height    = static_cast<float>(obj.box.height);
				tobj.left      = tobj.x;
				tobj.top       = tobj.y;
				tobj.right     = tobj.x + tobj.width;
				tobj.bottom    = tobj.y + tobj.height;
				tobj.object_id = camera_id;
				trackerInput.push_back(tobj);
			}

			// Step 2: Run per-camera tracker
			auto trackerOutput = camera._tracker->UpdateTracker(0, trackerInput);

			// Step 3: IoU-match tracked objects back to detections, assign trackId
			for (const auto& tobj : trackerOutput) {
				cv::Rect trkBox(static_cast<int>(tobj.x), static_cast<int>(tobj.y),
				                static_cast<int>(tobj.width), static_cast<int>(tobj.height));
				float bestIoU = 0.f;
				int bestIdx = -1;
				for (int i = 0; i < static_cast<int>(detections.size()); ++i) {
					cv::Rect inter = trkBox & detections[i].box;
					float interArea = static_cast<float>(inter.area());
					float unionArea = static_cast<float>(trkBox.area() + detections[i].box.area()) - interArea;
					float iou = (unionArea > 0.f) ? interArea / unionArea : 0.f;
					if (iou > bestIoU) {
						bestIoU = iou;
						bestIdx = i;
					}
				}
				if (bestIdx >= 0 && bestIoU > 0.3f) {
					detections[bestIdx].trackId = tobj.track_id;
				}
			}

			// Step 4: If stabilization enabled, inject ghost objects for unmatched tracker predictions
			if (_stabilizationEnabled) {
				// Collect trackIds already assigned to real detections
				std::set<int> matchedTrackIds;
				for (const auto& det : detections) {
					if (det.trackId > 0) matchedTrackIds.insert(det.trackId);
				}

				// Tracker-predicted objects not matched to any detection are "ghosts"
				for (const auto& tobj : trackerOutput) {
					if (tobj.track_id <= 0) continue;
					if (matchedTrackIds.count(tobj.track_id) > 0) continue;

					Object ghost;
					ghost.trackId = tobj.track_id;
					ghost.classId = tobj.class_id;
					ghost.confidence = tobj.prob;
					ghost.box = cv::Rect(
						static_cast<int>(tobj.x), static_cast<int>(tobj.y),
						static_cast<int>(tobj.width), static_cast<int>(tobj.height));
					ghost.cameraId = camera_id;
					TagStabilized(ghost.extraInfo);
					detections.push_back(ghost);
				}
			}

			return detections;
		}
		catch (std::exception& e) {
			_logger.LogFatal("ANSODBase::ApplyTracking", e.what(), __FILE__, __LINE__);
			return detections;
		}
	}

	// ── extraInfo stabilization tag helpers ─────────────────────────────────
	void ANSODBase::TagStabilized(std::string& extraInfo) {
		if (IsTaggedStabilized(extraInfo)) return;
		if (extraInfo.empty())
			extraInfo = "stabilized";
		else
			extraInfo += ";stabilized";
	}

	void ANSODBase::UntagStabilized(std::string& extraInfo) {
		// Remove ";stabilized" or standalone "stabilized"
		std::string::size_type pos = extraInfo.find(";stabilized");
		if (pos != std::string::npos) {
			extraInfo.erase(pos, 11); // remove ";stabilized"
			return;
		}
		pos = extraInfo.find("stabilized;");
		if (pos != std::string::npos) {
			extraInfo.erase(pos, 11); // remove "stabilized;"
			return;
		}
		if (extraInfo == "stabilized") {
			extraInfo.clear();
		}
	}

	bool ANSODBase::IsTaggedStabilized(const std::string& extraInfo) {
		return extraInfo.find("stabilized") != std::string::npos;
	}

	// ── Detection stabilization ─────────────────────────────────────────────
	// Implements:
	//   #1  Confidence hysteresis   — enter vs keep thresholds
	//   #2  EMA confidence smoothing — temporal averaging per track
	//   #5  Track-aware boost       — established tracks get confidence bonus
	//   Ghost interpolation         — keeps objects alive through brief misses
	std::vector<Object> ANSODBase::StabilizeDetections(
		std::vector<Object>& detections, const std::string& camera_id)
	{
		// _cameras map and per-camera state are not thread-safe.
		std::lock_guard<std::recursive_mutex> stabLock(_mutex);
		try {
			CameraData& camera = GetCameraData(camera_id);
			camera._stabilizationFrameCounter++;

			auto& tracks = camera._trackHistories;
			std::set<int> seenTrackIds;

			// ── Resolve hysteresis thresholds (auto from model config if 0) ──
			const float enterThreshold = (_hysteresisEnterThreshold > 0.f)
				? _hysteresisEnterThreshold
				: _modelConfig.detectionScoreThreshold;
			const float keepThreshold = (_hysteresisKeepThreshold > 0.f)
				? _hysteresisKeepThreshold
				: enterThreshold * 0.65f;

			// Phase 1: Update histories, apply EMA and boost for real detections
			for (auto& obj : detections) {
				if (obj.trackId <= 0) continue;
				seenTrackIds.insert(obj.trackId);

				bool isGhost = IsTaggedStabilized(obj.extraInfo);
				auto it = tracks.find(obj.trackId);

				if (it == tracks.end()) {
					// ── New track ──
					CameraData::TrackedObjectHistory hist;
					hist.classId = obj.classId;
					hist.className = obj.className;
					hist.extraInfo = isGhost ? "" : obj.extraInfo;
					hist.lastBox = obj.box;
					hist.lastConfidence = obj.confidence;
					hist.smoothedConfidence = obj.confidence;  // #2 EMA init
					hist.consecutiveMisses = isGhost ? 1 : 0;
					hist.totalDetections = isGhost ? 0 : 1;
					hist.frameFirstSeen = camera._stabilizationFrameCounter;
					hist.isEstablished = false;
					tracks[obj.trackId] = hist;
				}
				else {
					auto& hist = it->second;
					if (isGhost) {
						// Ghost: increment misses, apply confidence decay from smoothed
						hist.consecutiveMisses++;
						obj.confidence = hist.smoothedConfidence *
							std::pow(_stabilizationConfidenceDecay,
								static_cast<float>(hist.consecutiveMisses));
						obj.className = hist.className;
						obj.extraInfo = hist.extraInfo;
						TagStabilized(obj.extraInfo);
						hist.lastBox = obj.box;
					}
					else {
						// ── Real detection ──
						hist.consecutiveMisses = 0;
						hist.totalDetections++;
						hist.lastBox = obj.box;
						hist.extraInfo = obj.extraInfo;
						UntagStabilized(obj.extraInfo);

						// #7 Class consistency: resist sudden class switches
						//    on established tracks. Require N consecutive frames
						//    of the new class before accepting the change.
						if (hist.isEstablished && obj.classId != hist.classId) {
							// Class differs from established track
							if (obj.classId == hist.pendingClassId) {
								hist.pendingClassStreak++;
							}
							else {
								// Different candidate — reset streak
								hist.pendingClassId = obj.classId;
								hist.pendingClassName = obj.className;
								hist.pendingClassStreak = 1;
							}

							if (hist.pendingClassStreak >= _classConsistencyMinFrames) {
								// Confirmed class change: accept it
								hist.classId = obj.classId;
								hist.className = obj.className;
								hist.pendingClassId = -1;
								hist.pendingClassStreak = 0;
							}
							else {
								// Not confirmed yet: override output with established class
								obj.classId = hist.classId;
								obj.className = hist.className;
							}
						}
						else {
							// Same class as history or track not yet established: accept directly
							hist.classId = obj.classId;
							hist.className = obj.className;
							hist.pendingClassId = -1;
							hist.pendingClassStreak = 0;
						}

						// #2 EMA confidence smoothing
						hist.smoothedConfidence = _emaAlpha * obj.confidence
							+ (1.0f - _emaAlpha) * hist.smoothedConfidence;
						hist.lastConfidence = obj.confidence;

						// #5 Track-aware boost for established tracks
						if (hist.totalDetections >= _trackBoostMinFrames) {
							hist.isEstablished = true;
						}

						// Publish the EMA-smoothed confidence as the output
						// (smoothed is more stable than raw single-frame value)
						obj.confidence = hist.smoothedConfidence;

						// Apply boost on top of smoothed confidence for established tracks
						if (hist.isEstablished) {
							obj.confidence = std::min(1.0f,
								obj.confidence + _trackBoostAmount);
						}
					}
				}
			}

			// Phase 2: Hysteresis + ghost pruning
			//   - New objects must exceed enterThreshold to appear
			//   - Established tracked objects use the lower keepThreshold
			//   - Ghosts are pruned by max misses, min confidence, and detection count
			detections.erase(
				std::remove_if(detections.begin(), detections.end(),
					[&](const Object& obj) {
						if (obj.trackId <= 0) return false;

						bool isGhostObj = IsTaggedStabilized(obj.extraInfo);
						auto it = tracks.find(obj.trackId);

						if (isGhostObj) {
							// Ghost pruning
							if (it == tracks.end()) return true;
							if (it->second.consecutiveMisses > _stabilizationMaxConsecutiveMisses) return true;
							if (obj.confidence < _stabilizationMinConfidence) return true;
							if (it->second.totalDetections < 3) return true;
							return false;
						}
						else {
							// #1 Hysteresis: apply different thresholds
							if (it != tracks.end() && it->second.isEstablished) {
								// Established track: use lower keep threshold
								return obj.confidence < keepThreshold;
							}
							else {
								// New/young track: must meet enter threshold
								return obj.confidence < enterThreshold;
							}
						}
					}),
				detections.end());

			// Phase 3: Prune stale track histories (not seen for 2x maxMisses)
			for (auto it = tracks.begin(); it != tracks.end(); ) {
				if (seenTrackIds.count(it->first) == 0) {
					it->second.consecutiveMisses++;
				}
				if (it->second.consecutiveMisses > _stabilizationMaxConsecutiveMisses * 2) {
					it = tracks.erase(it);
				}
				else {
					++it;
				}
			}

			// Phase 4: Enqueue stabilized results for history
			if (camera._detectionQueue.size() >= _stabilizationQueueSize) {
				camera._detectionQueue.pop_front();
			}
			camera._detectionQueue.push_back(detections);

			return detections;
		}
		catch (std::exception& e) {
			_logger.LogFatal("ANSODBase::StabilizeDetections", e.what(), __FILE__, __LINE__);
			return detections;
		}
	}

	ANSODBase::~ANSODBase() {
		try {
			// Per-camera trackers are released by CameraData::clear()
			if (!_cameras.empty())
			{
				for (auto& [key, cameraData] : _cameras)
				{
					cameraData.clear(); // Releases per-camera tracker + clears state
				}
				_cameras.clear();
			}
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::~ANSODBase()", e.what(), __FILE__, __LINE__);
		}
	}

	// Very slow and not recommended for real-time applications
	std::vector<Object> ANSODBase::RunInferences(const cv::Mat& input, int tiledWidth, int tiledHeight, double overLap, const std::string& camera_id) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		std::vector<Object> allResults;
		allResults.clear();
		try {
			ANS_DBG("ODEngine", "SAHI START: %dx%d tile=%dx%d overlap=%.1f cam=%s",
					input.cols, input.rows, tiledWidth, tiledHeight, overLap, camera_id.c_str());
			auto _sahiStart = std::chrono::steady_clock::now();
			cv::Mat image = input.clone();
			if (image.empty() || !image.data || !image.u) {
				return allResults;
			}
			if ((image.cols < 5) || (image.rows < 5)) return allResults;
			//1. Get patches (slices) from the image
			std::vector<cv::Rect> patches = ANSUtilityHelper::GetPatches(image, tiledWidth, tiledHeight, overLap);

			//2. Extract and resize each patch, then run inference
			for (const auto& region : patches) {
				// Ensure the region is within the image boundaries
				cv::Rect boundedRegion(
					std::max(region.x, 0),
					std::max(region.y, 0),
					std::min(region.width, image.cols - std::max(region.x, 0)),
					std::min(region.height, image.rows - std::max(region.y, 0))
				);

				// Extract and clone the patch from the image
				cv::Mat patch = image(boundedRegion).clone();

				// Perform inference on the extracted patch
				std::vector<Object> patchResults = RunInference(patch);

				// Adjust patch bounding boxes to global image coordinates
				for (auto& result : patchResults) {
					result.box.x += boundedRegion.x;
					result.box.y += boundedRegion.y;
				}

				// Append results to the overall results container
				allResults.insert(allResults.end(), patchResults.begin(), patchResults.end());
				patch.release();
			}
			//3. Optionally run full-image inference
			std::vector<Object> fullImageResults = RunInference(image, camera_id);
			allResults.insert(allResults.end(), fullImageResults.begin(), fullImageResults.end());

			//4. Apply Non-Maximum Suppression (NMS) to merge overlapping results
			float iouThreshold = 0.1;
			std::vector<Object> finalResults = ANSUtilityHelper::ApplyNMS(allResults, iouThreshold);
			{
				double _sahiMs = std::chrono::duration<double, std::milli>(
					std::chrono::steady_clock::now() - _sahiStart).count();
				ANS_DBG("ODEngine", "SAHI DONE: %.1fms patches=%zu results=%zu cam=%s",
						_sahiMs, patches.size() + 1, finalResults.size(), camera_id.c_str());
				if (_sahiMs > 2000.0) {
					ANS_DBG("ODEngine", "SAHI SLOW: %.1fms — %zu patches held _mutex entire time!",
							_sahiMs, patches.size() + 1);
				}
			}
			image.release();
			return finalResults;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::RunInferences", e.what(), __FILE__, __LINE__);
			return allResults;
		}
	}
	std::vector<Object> ANSODBase::RunInferenceFromJpegString(const char* jpegData, unsigned long jpegSize, const std::string& camera_id) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		int width, height, jpegSubsamp, jpegColorspace;
		std::vector<Object> detectionObjects;
		detectionObjects.clear();
		tjhandle _jpegDecompressor = nullptr;
		try {
			_jpegDecompressor = tjInitDecompress();
			if (_jpegDecompressor) {
				int result = tjDecompressHeader3(_jpegDecompressor, reinterpret_cast<const unsigned char*>(jpegData), jpegSize, &width, &height, &jpegSubsamp, &jpegColorspace);
				if (result >= 0) {
					cv::Mat img(height, width, CV_8UC3);
					int pixelFormat = TJPF_BGR; // OpenCV uses BGR format
					result = tjDecompress2(_jpegDecompressor, reinterpret_cast<const unsigned char*>(jpegData), jpegSize, img.data, width, 0, height, pixelFormat, TJFLAG_FASTDCT);
					if (result >= 0) {
						detectionObjects = RunInference(img, camera_id);
					}
					else {
						this->_logger.LogError("ANSODBase::RunInferenceFromJpegString", "Failed to decompress JPEG string", __FILE__, __LINE__);
					}
				}
				tjDestroy(_jpegDecompressor);
				_jpegDecompressor = nullptr;
			}
			return detectionObjects;
		}
		catch (std::exception& e) {
			if (_jpegDecompressor) tjDestroy(_jpegDecompressor);
			this->_logger.LogFatal("ANSODBase::RunInferenceFromJpegString", e.what(), __FILE__, __LINE__);
			return detectionObjects;
		}
	}
	ModelConfig ANSODBase::GetModelConfig()
	{
		return _modelConfig;
	}
	std::vector<Object> ANSODBase::DetectMovement(const cv::Mat& input, const std::string& camera_id) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		std::vector<Object> objects;
		objects.clear();
		try {
			if (!_licenseValid) return objects;
			if (input.empty() || !input.data || !input.u) {
				return objects;
			}
			if ((input.cols < 5) || (input.rows < 5)) return objects;
			cv::Mat frame = input.clone();
			objects = this->_handler.MovementDetect(camera_id, frame);
			frame.release();
			return objects;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::DetectMovement", e.what(), __FILE__, __LINE__);
			return objects;
		}
	}
	std::vector<Object> ANSODBase::RunTiledInferenceFromJpegString(const char* jpegData, unsigned long jpegSize, int tiledWith, int tiledHeight, double overLap, const std::string& camera_id) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		int width, height, jpegSubsamp, jpegColorspace;
		std::vector<Object> detectionObjects;
		detectionObjects.clear();
		tjhandle _jpegDecompressor = nullptr;
		try {
			_jpegDecompressor = tjInitDecompress();
			if (_jpegDecompressor) {
				int result = tjDecompressHeader3(_jpegDecompressor, reinterpret_cast<const unsigned char*>(jpegData), jpegSize, &width, &height, &jpegSubsamp, &jpegColorspace);
				if (result >= 0) {
					cv::Mat img(height, width, CV_8UC3);
					int pixelFormat = TJPF_BGR; // OpenCV uses BGR format
					result = tjDecompress2(_jpegDecompressor, reinterpret_cast<const unsigned char*>(jpegData), jpegSize, img.data, width, 0, height, pixelFormat, TJFLAG_FASTDCT);
					if (result >= 0) {
						detectionObjects = RunInferences(img, tiledWith, tiledHeight, overLap, camera_id);
					}
					else {
						this->_logger.LogError("ANSODBase::RunTiledInferenceFromJpegString", "Failed to decompress JPEG string", __FILE__, __LINE__);
					}
				}
				tjDestroy(_jpegDecompressor);
				_jpegDecompressor = nullptr;
			}
			return detectionObjects;
		}
		catch (std::exception& e) {
			if (_jpegDecompressor) tjDestroy(_jpegDecompressor);
			this->_logger.LogFatal("ANSODBase::RunTiledInferenceFromJpegString", e.what(), __FILE__, __LINE__);
			return detectionObjects;
		}
	}
	std::vector<Object> ANSODBase::RunInference(const cv::Mat& input, std::vector<cv::Rect> Bbox, const std::string& camera_id) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		std::vector<ANSCENTER::Object> detectionObjects;
		detectionObjects.clear();
		if (!_licenseValid) {
			this->_logger.LogError("ANSODBase::RunInference", "Invalid License", __FILE__, __LINE__);
			return detectionObjects;
		}
		if (!_isInitialized) {
			this->_logger.LogError("ANSODBase::RunInference", "Model is not initialized", __FILE__, __LINE__);
			return detectionObjects;
		}
		try {
			if (input.empty()) return detectionObjects;
			if ((input.cols <= 5) || (input.rows <= 5)) return detectionObjects;
			if (Bbox.size() > 0) {
				cv::Mat frame = input;
				int fWidth = frame.cols;
				int fHeight = frame.rows;
				for (const auto& bbox : Bbox) {
					cv::Rect objectPos(
						std::max(bbox.x, 0),
						std::max(bbox.y, 0),
						std::min(bbox.width, frame.cols - std::max(bbox.x, 0)),
						std::min(bbox.height, frame.rows - std::max(bbox.y, 0))
					);
					// Crop the object from the frame
					cv::Mat croppedObject = frame(objectPos).clone();
					// Run inference on the cropped object
					auto tempObjects = RunInference(croppedObject, camera_id);
					// If any objects are detected, adjust their coordinates and add to results
					for (const auto& tempObject : tempObjects) {
						ANSCENTER::Object detectionObject = tempObject;
						// Adjust bounding box position to global coordinates
						detectionObject.box.x += objectPos.x;
						detectionObject.box.y += objectPos.y;
						// Add the adjusted object to the results
						detectionObjects.push_back(detectionObject);
					}
					croppedObject.release();
				}
				frame.release();
			}
			else {
				detectionObjects = RunInference(input, camera_id);
			}
			return detectionObjects;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::RunInference", e.what(), __FILE__, __LINE__);
			return detectionObjects;
		}
	}
	std::vector<Object> ANSODBase::RunInference(const cv::Mat& input, std::vector<cv::Point> Polygon, const std::string& camera_id) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		std::vector<ANSCENTER::Object> detectionObjects;
		detectionObjects.clear();
		if (!_licenseValid) {
			this->_logger.LogError("ANSODBase::RunInference", "Invalid License", __FILE__, __LINE__);
			return detectionObjects;
		}
		if (!_isInitialized) {
			this->_logger.LogError("ANSODBase::RunInference", "Model is not initialized", __FILE__, __LINE__);
			return detectionObjects;
		}
		try {
			if (input.empty()) return detectionObjects;
			if ((input.cols <= 5) || (input.rows <= 5)) return detectionObjects;
			if (Polygon.size() > 0) {
				cv::Mat frame = input.clone();
				cv::Rect rect = ANSCENTER::ANSUtilityHelper::GetBoundingBoxFromPolygon(Polygon);
				rect.x = std::max(rect.x, 0);
				rect.y = std::max(rect.y, 0);
				rect.width = std::min(rect.width, frame.cols - std::max(rect.x, 0));
				rect.height = std::min(rect.height, frame.rows - std::max(rect.y, 0));
				cv::Mat croppedImage = frame(rect).clone();
				// Run inference on the cropped object
				auto tempObjects = RunInference(croppedImage, camera_id);
				// If any objects are detected, adjust their coordinates and add to results
				for (const auto& tempObject : tempObjects) {
					ANSCENTER::Object detectionObject = tempObject;
					// Adjust bounding box position to global coordinates
					detectionObject.box.x += rect.x;
					detectionObject.box.y += rect.y;
					// Add the adjusted object to the results
					detectionObjects.push_back(detectionObject);
				}
				croppedImage.release();
				frame.release();
			}
			else {
				detectionObjects = RunInference(input, camera_id);
			}
			return detectionObjects;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::RunInference", e.what(), __FILE__, __LINE__);
			return detectionObjects;
		}
	}
	cv::Rect ANSODBase::computeCandidateROI(const cv::Rect& unionBox, int fixedWidth, int fixedHeight, int imageWidth, int imageHeight) {
		// Compute center of the union.
		float centerX = unionBox.x + unionBox.width / 2.0f;
		float centerY = unionBox.y + unionBox.height / 2.0f;

		// Compute the required half-size in each dimension to cover the unionBox.
		float reqHalfWidth = std::max(centerX - static_cast<float>(unionBox.x), static_cast<float>(unionBox.x + unionBox.width) - centerX);
		float reqHalfHeight = std::max(centerY - static_cast<float>(unionBox.y), static_cast<float>(unionBox.y + unionBox.height) - centerY);

		// Desired full dimensions: at least fixed size, but as much as needed to cover unionBox.
		int desiredWidth = static_cast<int>(std::ceil(2 * reqHalfWidth));
		int desiredHeight = static_cast<int>(std::ceil(2 * reqHalfHeight));

		// Allow expansion up to 200% of fixed dimensions.
		int minWidth = fixedWidth;
		int minHeight = fixedHeight;
		int maxWidth = fixedWidth * 2;
		int maxHeight = fixedHeight * 2;

		int candidateWidth = std::min(std::max(desiredWidth, minWidth), maxWidth);
		int candidateHeight = std::min(std::max(desiredHeight, minHeight), maxHeight);

		// Compute top-left so that ROI is centered at (centerX, centerY).
		int roiX = static_cast<int>(std::round(centerX - candidateWidth / 2.0f));
		int roiY = static_cast<int>(std::round(centerY - candidateHeight / 2.0f));

		// Clamp the ROI to image boundaries.
		if (roiX < 0) roiX = 0;
		if (roiY < 0) roiY = 0;
		if (roiX + candidateWidth > imageWidth) roiX = imageWidth - candidateWidth;
		if (roiY + candidateHeight > imageHeight) roiY = imageHeight - candidateHeight;

		return cv::Rect(roiX, roiY, candidateWidth, candidateHeight);
	}
	std::vector<cv::Rect>   ANSODBase::GenerateFixedROIs(const std::vector<Object>& movementObjects, int fixedWidth, int fixedHeight, int imageWidth, int imageHeight) {
		try {
			// Early check: if the image is smaller than the fixed ROI size, return one ROI covering the full image.
			if (imageWidth < fixedWidth || imageHeight < fixedHeight) {
				return { cv::Rect(0, 0, imageWidth, imageHeight) };
			}
			int maxImageSize = std::max(imageWidth, imageHeight);
			if (maxImageSize <= 1920) {
				int maxFixedSize = std::max(fixedWidth, fixedHeight);
				std::vector<cv::Rect> fixedROIs;
				cv::Rect   singleFixedROI= GenerateMinimumSquareBoundingBox(movementObjects, maxFixedSize);
				singleFixedROI.x = std::max(singleFixedROI.x, 0);
				singleFixedROI.y = std::max(singleFixedROI.y, 0);
				singleFixedROI.width = std::min(singleFixedROI.width, imageWidth - singleFixedROI.x);
				singleFixedROI.height = std::min(singleFixedROI.height, imageHeight - singleFixedROI.y);
				fixedROIs.push_back(singleFixedROI);
				return fixedROIs;
			}

			// --- Step 1: Group objects by proximity.
			// Use a threshold (fixedWidth/2) so that objects whose centers are within that distance belong to the same group.
			float groupingThreshold = fixedWidth / 2.0f;
			std::vector<Group> groups;

			for (const auto& obj : movementObjects) {
				cv::Point2f center(obj.box.x + obj.box.width / 2.0f, obj.box.y + obj.box.height / 2.0f);
				bool added = false;
				for (auto& grp : groups) {
					// Use the current group's center (from its unionBox).
					cv::Point2f groupCenter(grp.unionBox.x + grp.unionBox.width / 2.0f,
						grp.unionBox.y + grp.unionBox.height / 2.0f);
					if (distance(center, groupCenter) < groupingThreshold) {
						grp.objects.push_back(obj);
						grp.unionBox = unionRect(grp.unionBox, obj.box);
						added = true;
						break;
					}
				}
				if (!added) {
					Group newGroup;
					newGroup.objects.push_back(obj);
					newGroup.unionBox = obj.box;
					groups.push_back(newGroup);
				}
			}

			// --- Step 2: For each group, compute a candidate ROI that may expand up to 50% larger than fixed size.
			// Then merge groups whose candidate ROIs overlap.
			bool merged = true;
			while (merged) {
				merged = false;
				for (size_t i = 0; i < groups.size(); ++i) {
					cv::Rect roi_i = computeCandidateROI(groups[i].unionBox, fixedWidth, fixedHeight, imageWidth, imageHeight);
					for (size_t j = i + 1; j < groups.size(); ++j) {
						cv::Rect roi_j = computeCandidateROI(groups[j].unionBox, fixedWidth, fixedHeight, imageWidth, imageHeight);
						if (isOverlap(roi_i, roi_j)) {
							// Merge group j into group i.
							for (const auto& obj : groups[j].objects) {
								groups[i].objects.push_back(obj);
							}
							groups[i].unionBox = unionRect(groups[i].unionBox, groups[j].unionBox);
							groups.erase(groups.begin() + j);
							merged = true;
							break; // Break inner loop to restart merging.
						}
					}
					if (merged) break;
				}
			}

			// --- Step 3: Compute final candidate ROIs for each (merged) group.
			std::vector<cv::Rect> fixedROIs;
			for (const auto& grp : groups) {
				cv::Rect candidate = computeCandidateROI(grp.unionBox, fixedWidth, fixedHeight, imageWidth, imageHeight);
				fixedROIs.push_back(candidate);
			}

			// (Optional) sort the final ROIs.
			std::sort(fixedROIs.begin(), fixedROIs.end(), [](const cv::Rect& a, const cv::Rect& b) {
				return (a.y == b.y) ? (a.x < b.x) : (a.y < b.y);
				});

			return fixedROIs;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSFDBase::GenerateFixedROIs", e.what(), __FILE__, __LINE__);
			return { cv::Rect(0, 0, imageWidth, imageHeight) };
		}

	}
	cv::Rect ANSODBase::GenerateMinimumSquareBoundingBox(const std::vector<ANSCENTER::Object>& detectedObjects, int minSize) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		try {
			// Ensure there are rectangles to process
			int adMinSize = minSize - 20;
			if (adMinSize < 0) adMinSize = 0;
			if (detectedObjects.empty()) return cv::Rect(0, 0, minSize, minSize);

			// Initialize min and max coordinates
			int minX = detectedObjects[0].box.x;
			int minY = detectedObjects[0].box.y;
			int maxX = detectedObjects[0].box.x + detectedObjects[0].box.width;
			int maxY = detectedObjects[0].box.y + detectedObjects[0].box.height;

			// Calculate bounding box that includes all rectangles
			for (const auto& rect : detectedObjects) {
				minX = std::min(minX, rect.box.x);
				minY = std::min(minY, rect.box.y);
				maxX = std::max(maxX, rect.box.x + rect.box.width);
				maxY = std::max(maxY, rect.box.y + rect.box.height);
			}

			// Calculate width and height of the bounding box
			int width = maxX - minX;
			int height = maxY - minY;

			// Determine the size of the square
			int squareSize = std::max({ width, height, adMinSize });

			// Center the square around the bounding box
			int centerX = minX + width / 2;
			int centerY = minY + height / 2;
			int squareX = centerX - squareSize / 2;
			int squareY = centerY - squareSize / 2;

			return cv::Rect(squareX - 10, squareY - 10, squareSize + 20, squareSize + 20); // add 10 pixels to the square
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSFDBase::GenerateMinimumSquareBoundingBox", e.what(), __FILE__, __LINE__);
			return cv::Rect(0, 0, minSize, minSize);
		}
	}
	void   ANSODBase::UpdateAndFilterDetectionObjects(std::vector<Object>& detectionObjects, int threshold) {
		detectionObjects.erase(
			std::remove_if(detectionObjects.begin(), detectionObjects.end(),
				[&](Object& obj) {
					if (!obj.extraInfo.empty()) {
						try {
							int value = std::stoi(obj.extraInfo); // Convert extraInfo to an integer
							if (value >= threshold) {
								return true; // Remove object if value exceeds threshold
							}
							obj.extraInfo = std::to_string(value + 1); // Increment extraInfo
						}
						catch (const std::exception&) {
							obj.extraInfo = "1"; // Reset to "1" if conversion fails
						}
					}
					return false; // Keep object
				}),
			detectionObjects.end()); // Remove flagged objects
	}
	bool   ANSODBase::ContainsIntersectingObject(const std::vector<Object>& movementObjects, const Object& result) {
		for (const auto& obj : movementObjects) {
			// Compute the intersection of the two bounding boxes.
			cv::Rect intersection = obj.box & result.box;
			if (intersection.area() > 0) {
				return true; // Found an intersecting object.
			}
		}
		return false; // No intersections found.
	}
	std::vector<Object> ANSODBase::RunDynamicInference(const cv::Mat& input, cv::Rect Bbox, const std::string& camera_id) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		std::vector<Object> output;
		output.clear();
		try {
			// 1. Check if the input image is valid
			if (input.empty() || !input.data || !input.u) {
				return output;
			}
			if ((input.cols < 5) || (input.rows < 5)) return output;
			// Check ROI
			int orginalHeight = input.rows;
			int orginalWidth = input.cols;
			cv::Rect boundingBox;
			// Ensure the bounding box is within the image boundaries
			boundingBox.x = std::max(0, Bbox.x);
			boundingBox.y = std::max(0, Bbox.y);
			boundingBox.width = MIN(orginalWidth, Bbox.width);
			boundingBox.height = MIN(orginalHeight, Bbox.height);
			if (boundingBox.width == 0)boundingBox.width = orginalWidth;
			if (boundingBox.height == 0)boundingBox.height = orginalHeight;

			cv::Mat im = input(boundingBox).clone(); // We can get crop from bounding box in here
			if ((im.cols <= 5) || (im.rows <= 5))return output;
			// 2. Define active ROI
			std::vector<cv::Rect> activeROIs;
			std::vector<Object> movementObjects;
			activeROIs.clear();
			movementObjects.clear();
			int imageSize = std::max(im.cols, im.rows);
			if (imageSize <= 640) {
				activeROIs.push_back(cv::Rect(0, 0, im.cols, im.rows));
			}
			else {
				std::vector<Object> movementResults = DetectMovement(im, camera_id);
				if ((!movementResults.empty()) && ((movementResults.size() < 12)))
				{
					movementObjects.insert(movementObjects.end(), movementResults.begin(), movementResults.end());
					if (!_detectedObjects.empty())movementObjects.insert(movementObjects.end(), _detectedObjects.begin(), _detectedObjects.end());
				}
				else {
					if (!_detectedObjects.empty())movementObjects.insert(movementObjects.end(), _detectedObjects.begin(), _detectedObjects.end());
				}
				if (!movementObjects.empty()) {
					cv::Rect   singleFixedROI = GenerateMinimumSquareBoundingBox(movementObjects, 640);
					singleFixedROI.x = std::max(singleFixedROI.x, 0);
					singleFixedROI.y = std::max(singleFixedROI.y, 0);
					singleFixedROI.width = MIN(singleFixedROI.width, im.cols - singleFixedROI.x);
					singleFixedROI.height =MIN(singleFixedROI.height, im.rows - singleFixedROI.y);
					activeROIs.push_back(singleFixedROI);	
				}
				else {
					activeROIs.push_back(cv::Rect(0, 0, im.cols, im.rows));// Use the orginal image
				}
				if ((activeROIs.size() <= 0) ||
					(activeROIs.empty()))
				{
					return output;
				}
				UpdateAndFilterDetectionObjects(_detectedObjects, 80);
			}
			// 3. Run inference on each active ROI
#ifdef DEBUGENGINE
			cv::Mat draw = im.clone();
			if (movementObjects.size() > 0) {
				for (int i = 0; i < movementObjects.size(); i++) {
					cv::rectangle(draw, movementObjects[i].box, cv::Scalar(0, 255, 255), 2); // RED for detectedArea
				}
			}
			for (int i = 0; i < activeROIs.size(); i++) {
				cv::rectangle(draw, activeROIs[i], cv::Scalar(0, 0, 255), 2); // RED for detectedArea
			}
#endif
			for (int j = 0; j < activeROIs.size(); j++) {
				cv::Rect activeROI;
				activeROI.x = std::max(0, activeROIs[j].x);
				activeROI.y = std::max(0, activeROIs[j].y);
				activeROI.width = MIN(im.cols, activeROIs[j].width);
				activeROI.height = MIN(im.rows, activeROIs[j].height);
				cv::Mat frame = im(activeROI).clone();
				std::vector<Object> detectedObjects = RunInference(frame, camera_id);
				// 5. Return the detected objects
				for (int i = 0; i < detectedObjects.size(); i++)
				{
					if (detectedObjects[i].confidence > _modelConfig.detectionScoreThreshold) {

						Object result;
						// 0. Get the face bounding box
						int x_min = detectedObjects[i].box.x + activeROI.x;
						int y_min = detectedObjects[i].box.y + activeROI.y ;
						int width = detectedObjects[i].box.width ;
						int height = detectedObjects[i].box.height;
						// 1. Update the bounding box coordinates
						result.classId = detectedObjects[i].classId;
						result.className = detectedObjects[i].className;
						result.confidence = detectedObjects[i].confidence;
						result.box.x = x_min+ boundingBox.x;
						result.box.y = y_min+ boundingBox.y;
						result.box.width = width;
						result.box.height = height;
						result.kps = detectedObjects[i].kps; // landmarks as array of x,y,x,y...
						result.cameraId = camera_id;
						output.push_back(result);
						// Update result 
						result.extraInfo = "0";
						result.box.x = x_min;
						result.box.y = y_min;
#ifdef DEBUGENGINE
						cv::rectangle(draw, result.box, cv::Scalar(225, 255, 0), 2); // 
#endif
						// Find if obj already exists in detectionObjects using ContainsIntersectingObject
						auto it = std::find_if(_detectedObjects.begin(), _detectedObjects.end(),
							[&](Object& existingObj) {
								return ContainsIntersectingObject(_detectedObjects, result);
							});

						if (it != _detectedObjects.end()) {
							*it = result;  // Replace existing object with the new one
						}
						else {
							// If not found, add the new object to the list
							_detectedObjects.push_back(result);
						}

					}
				}
				frame.release();
			}
#ifdef DEBUGENGINE
			cv::imshow("Combined Detected Areas", draw);// Debugging: Diplsay the frame with the combined detected areas
			cv::waitKey(1);// Debugging: Diplsay the frame with the combined detected areas
			draw.release();// Debugging: Diplsay the frame with the combined detected areas
#endif
			im.release();
			return output;
		
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::RunDynamicInference", e.what(), __FILE__, __LINE__);
			return {};
		}
	}
	std::vector<Object> ANSODBase::RunStaticInference(const cv::Mat& input, cv::Rect Bbox, const std::string& camera_id) {
		// No coarse _mutex — only uses local variables and virtual RunInference() which has its own engine lock
		std::vector<Object> output;
		output.clear();
		try {
			// 1. Check if the input image is valid
			if (input.empty() || !input.data || !input.u) {
				return output;
			}
			if ((input.cols < 5) || (input.rows < 5)) return output;
			int orginalHeight = input.rows;
			int orginalWidth = input.cols;
			cv::Rect boundingBox;
			boundingBox.x = std::max(0, Bbox.x);
			boundingBox.y = std::max(0, Bbox.y);
			boundingBox.width = MIN(orginalWidth, Bbox.width);
			boundingBox.height = MIN(orginalHeight, Bbox.height);
			if (boundingBox.width == 0)boundingBox.width = orginalWidth;
			if (boundingBox.height == 0)boundingBox.height = orginalHeight;

			cv::Mat im = input(boundingBox).clone(); // We can get crop from bounding box in here
			if ((im.cols <= 5) || (im.rows <= 5))return output;
			// 2. Define active ROI
			std::vector<cv::Rect> activeROIs;
			std::vector<Object> movementObjects;
			activeROIs.clear();
			movementObjects.clear();
			int imageSize = std::max(im.cols, im.rows);			
			activeROIs.push_back(cv::Rect(0, 0, im.cols, im.rows));
			// 3. Run inference on each active ROI
			for (int j = 0; j < activeROIs.size(); j++) {
				cv::Rect activeROI;
				activeROI.x = std::max(0, activeROIs[j].x);
				activeROI.y = std::max(0, activeROIs[j].y);
				activeROI.width = MIN(im.cols, activeROIs[j].width);
				activeROI.height = MIN(im.rows, activeROIs[j].height);
				cv::Mat frame = im(activeROI).clone();
				std::vector<Object> detectedObjects = RunInference(frame, camera_id);
				for (int i = 0; i < detectedObjects.size(); i++)
				{
					if (detectedObjects[i].confidence > _modelConfig.detectionScoreThreshold) {
						Object result;
						int x_min = detectedObjects[i].box.x + activeROI.x;
						int y_min = detectedObjects[i].box.y + activeROI.y;
						int width = detectedObjects[i].box.width;
						int height = detectedObjects[i].box.height;
						result.classId = detectedObjects[i].classId;
						result.className = detectedObjects[i].className;
						result.confidence = detectedObjects[i].confidence;
						result.box.x = x_min + boundingBox.x;
						result.box.y = y_min + boundingBox.y;
						result.box.width = width;
						result.box.height = height;
						result.kps = detectedObjects[i].kps; // landmarks as array of x,y,x,y...
						result.cameraId = camera_id;
						output.push_back(result);					
					}
				}
				frame.release();
			}
			im.release();
			return output;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::RunDynamicInference", e.what(), __FILE__, __LINE__);
			return {};
		}
	}

	bool ANSODBase::RunInference(const cv::Mat& input, const std::string& camera_id, std::string& detectionResult) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		if (!_licenseValid) {
			this->_logger.LogError("ANSODBase::RunInference", "Invalid License", __FILE__, __LINE__);
			return false;
		}
		if (!_isInitialized) {
			this->_logger.LogError("ANSODBase::RunInference", "Model is not initialized", __FILE__, __LINE__);
			return false;
		}
		try {
			std::vector<Object> detectionObjects;
			detectionObjects.clear();
			if (input.empty() || !input.data || !input.u) {
				return false;
			}
			if ((input.cols < 5) || (input.rows < 5)) return false;
			if (input.cols > 0 && input.rows > 0) {
				cv::Mat frame = input.clone();
				detectionObjects = RunInference(frame, camera_id);
				// Convert detectionObjects to JSON string
				detectionResult=ANSCENTER::ANSUtilityHelper::VectorDetectionToJsonString(detectionObjects);
				frame.release();
				return true;
			}
			else {
				this->_logger.LogError("ANSODBase::RunInference", "Invalid image size", __FILE__, __LINE__);
				return false;
			}
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::RunInference", e.what(), __FILE__, __LINE__);
			return false;
		}
	}
	
	std::vector<std::vector<Object>> ANSODBase::RunInferencesBatch(const std::vector<cv::Mat>& inputs,const std::string& camera_id) 
	{

		std::lock_guard<std::recursive_mutex> lock(_mutex);
		std::vector<std::vector<Object>> batchDetections;

		// Early validation
		if (!_licenseValid) {
			_logger.LogError("ANSODBase::RunInferencesBatch",
				"Invalid License", __FILE__, __LINE__);
			return batchDetections;
		}

		if (!_isInitialized) {
			_logger.LogError("ANSODBase::RunInferencesBatch",
				"Model is not initialized", __FILE__, __LINE__);
			return batchDetections;
		}

		if (inputs.empty()) {
			_logger.LogWarn("ANSODBase::RunInferencesBatch",
				"Empty input batch", __FILE__, __LINE__);
			return batchDetections;
		}

		try {
			batchDetections.reserve(inputs.size()); // Pre-allocate
			for (size_t i = 0; i < inputs.size(); ++i) {
				auto detections = RunInference(inputs[i], camera_id);
				//if (detections.empty()) {
				//	_logger.LogWarn("ANSODBase::RunInferencesBatch",
				//		"No detections for frame " + std::to_string(i),
				//		__FILE__, __LINE__);
				//}
				batchDetections.push_back(std::move(detections));
			}
			return batchDetections;
		}
		catch (const std::exception& e) {
			_logger.LogFatal("ANSODBase::RunInferencesBatch",
				e.what(), __FILE__, __LINE__);
			return std::vector<std::vector<Object>>(); // Return fresh empty vector
		}
	}



	// Functions for screen size division
	double ANSODBase::calculateDistanceToCenter(const cv::Point& center, const cv::Rect& rect) {
		cv::Point rectCenter(rect.x + rect.width / 2, rect.y + rect.height / 2);
		return std::sqrt(std::pow(rectCenter.x - center.x, 2) + std::pow(rectCenter.y - center.y, 2));
	}
	std::vector<ANSODBase::ImageSection> ANSODBase::divideImage(const cv::Mat& image) {
		if (image.empty()) {
			std::cerr << "Error: Empty image!" << std::endl;
			return cachedSections;
		}
		cv::Size currentSize(image.cols, image.rows);

		// Check if the image size has changed
		if (currentSize == previousImageSize) {
			return cachedSections; // Return cached sections if size is the same
		}

		// Update previous size
		previousImageSize = currentSize;
		cachedSections.clear();

		int width = image.cols;
		int height = image.rows;
		int maxDimension = std::max(width, height);
		int numSections = 10;// std::max(1, numSections); // Ensure at least 1 section
		if (maxDimension <= 2560)numSections = 8;
		if (maxDimension <= 1280)numSections = 6;
		if (maxDimension <= 960)numSections = 4;
		if (maxDimension <= 640)numSections = 2;
		if (maxDimension <= 320)numSections = 1;

		int gridRows = std::sqrt(numSections);
		int gridCols = (numSections + gridRows - 1) / gridRows; // Ensure all sections are covered

		int sectionWidth = width / gridCols;
		int sectionHeight = height / gridRows;

		cv::Point imageCenter(width / 2, height / 2);
		std::vector<std::pair<double, ImageSection>> distancePriorityList;

		// Create sections and store their distance from the center
		for (int r = 0; r < gridRows; ++r) {
			for (int c = 0; c < gridCols; ++c) {
				int x = c * sectionWidth;
				int y = r * sectionHeight;
				int w = (c == gridCols - 1) ? width - x : sectionWidth;
				int h = (r == gridRows - 1) ? height - y : sectionHeight;

				ImageSection section(cv::Rect(x, y, w, h));
				double distance = calculateDistanceToCenter(imageCenter, section.region);
				distancePriorityList.emplace_back(distance, section);
			}
		}

		// Sort sections based on distance from center, then top-to-bottom, then left-to-right
		std::sort(distancePriorityList.begin(), distancePriorityList.end(),
			[](const std::pair<double, ImageSection>& a, const std::pair<double, ImageSection>& b) {
				if (std::abs(a.first - b.first) > 1e-5) {
					return a.first < b.first; // Sort by closest distance to center
				}
				// If distance is the same, prioritize top to bottom, then left to right
				return a.second.region.y == b.second.region.y
					? a.second.region.x < b.second.region.x
					: a.second.region.y < b.second.region.y;
			});

		// Assign priority
		int priority = 1;
		for (auto& entry : distancePriorityList) {
			entry.second.priority = priority++;
			cachedSections.push_back(entry.second);
		}

		return cachedSections;
	}
	std::vector<ANSODBase::ImageSection> ANSODBase::createSlideScreens(const cv::Mat& image) {
		if (image.empty()) {
			std::cerr << "Error: Empty image!" << std::endl;
			return cachedSections;
		}

		cv::Size currentSize(image.cols, image.rows);
		if (currentSize == previousImageSize) {
			return cachedSections;
		}
		previousImageSize = currentSize;
		cachedSections.clear();

		int maxSize = std::max(image.cols, image.rows);
		const int minCellSize = 320; // Minimum size for each cell
		int maxSections = 10;
		const float minAspectRatio = 0.8f;
		const float maxAspectRatio = 1.2f;

		if (maxSize <= 640)	maxSections = 1;
		else if (maxSize <= 960) maxSections = 2;
		else if (maxSize <= 1280) maxSections = 4;
		else if (maxSize <= 2560) maxSections = 6;
		else if (maxSize <= 3840) maxSections = 8;
		else maxSections = 10;

		int width = image.cols;
		int height = image.rows;

		int bestRows = 1, bestCols = 1;
		int bestTileSize = std::numeric_limits<int>::max();

		for (int rows = 1; rows <= maxSections; ++rows) {
			for (int cols = 1; cols <= maxSections; ++cols) {
				if (rows * cols > maxSections) continue;

				int tileWidth = (width + cols - 1) / cols;
				int tileHeight = (height + rows - 1) / rows;

				if (tileWidth < minCellSize || tileHeight < minCellSize)
					continue;

				float aspectRatio = static_cast<float>(tileWidth) / static_cast<float>(tileHeight);
				if (aspectRatio < minAspectRatio || aspectRatio > maxAspectRatio)
					continue;

				int maxTileDim = std::max(tileWidth, tileHeight);
				if (maxTileDim < bestTileSize) {
					bestTileSize = maxTileDim;
					bestRows = rows;
					bestCols = cols;
				}
			}
		}

		// Generate tiles using bestRows, bestCols
		int tileWidth = (width + bestCols - 1) / bestCols;
		int tileHeight = (height + bestRows - 1) / bestRows;

		int priority = 1;
		for (int r = bestRows - 1; r >= 0; --r) {
			for (int c = 0; c < bestCols; ++c) {
				int x = c * tileWidth;
				int y = r * tileHeight;

				int w = std::min(tileWidth, width - x);
				int h = std::min(tileHeight, height - y);

				if (w <= 0 || h <= 0) continue;

				cv::Rect region(x, y, w, h);
				ImageSection section(region);
				section.priority = priority++;
				cachedSections.push_back(section);
			}
		}

		return cachedSections;
	}
	int ANSODBase::getHighestPriorityRegion() {
		if (!cachedSections.empty()) {
			return cachedSections.front().priority; // First element has the highest priority
		}
		return 0; // Return empty rect if no sections exist
	}
	int ANSODBase::getLowestPriorityRegion() {
		if (!cachedSections.empty()) {
			return cachedSections.back().priority; // Last element has the lowest priority
		}
		return 0; // Return empty rect if no sections exist
	}
	cv::Rect ANSODBase::getRegionByPriority(int priority) {
		for (const auto& section : cachedSections) {
			if (section.priority == priority) {
				return section.region;
			}
		}
		return cv::Rect(); // Return empty rect if priority not found
	}
	std::vector<Object> ANSODBase::AdjustDetectedBoundingBoxes(
									const std::vector<Object>& detectionsInROI,
									const cv::Rect& roi,
									const cv::Size& fullImageSize,
									float aspectRatio,
									int padding
	) {
		std::vector<Object> adjustedDetections;

		try {
			// Basic input validation
			if (detectionsInROI.empty()) {
				return adjustedDetections;
			}

			if (roi.width <= 0 || roi.height <= 0 ||
				fullImageSize.width <= 0 || fullImageSize.height <= 0) {
				return adjustedDetections;
			}

			for (const auto& detInROI : detectionsInROI) {
				try {
					if (detInROI.box.width <= 0 || detInROI.box.height <= 0)
						continue; // Skip invalid box

					// Convert ROI-relative box to full-image coordinates
					cv::Rect detInFullImg;
					try {
						detInFullImg = detInROI.box + roi.tl();
						detInFullImg &= cv::Rect(0, 0, fullImageSize.width, fullImageSize.height);
					}
					catch (const std::exception& e) {
						std::cerr << "[AdjustBBox] Failed to calculate full image box: " << e.what() << std::endl;
						continue;
					}

					// Check if it touches ROI border
					bool touchesLeft = detInROI.box.x <= 0;
					bool touchesRight = detInROI.box.x + detInROI.box.width >= roi.width - 1;
					bool touchesTop = detInROI.box.y <= 0;
					bool touchesBottom = detInROI.box.y + detInROI.box.height >= roi.height - 1;
					bool touchesBorder = touchesLeft || touchesRight || touchesTop || touchesBottom;

					// Compute target width based on aspect ratio
					int targetWidth = 0, expandWidth = 0;
					try {
						targetWidth = std::max(detInFullImg.width, static_cast<int>(detInFullImg.height * aspectRatio));
						expandWidth = std::max(0, targetWidth - detInFullImg.width);
					}
					catch (const std::exception& e) {
						std::cerr << "[AdjustBBox] Aspect ratio adjustment failed: " << e.what() << std::endl;
						continue;
					}

					int expandLeft = expandWidth / 2;
					int expandRight = expandWidth - expandLeft;
					int padX = touchesBorder ? padding : 0;
					int padY = touchesBorder ? (padding / 2) : 0;

					// Apply padded and expanded box
					int newX = std::max(0, detInFullImg.x - expandLeft - padX);
					int newY = std::max(0, detInFullImg.y - padY);
					int newWidth = detInFullImg.width + expandWidth + 2 * padX;
					int newHeight = detInFullImg.height + 2 * padY;

					// Clamp to image boundaries
					if (newX + newWidth > fullImageSize.width) {
						newWidth = fullImageSize.width - newX;
					}
					if (newY + newHeight > fullImageSize.height) {
						newHeight = fullImageSize.height - newY;
					}

					if (newWidth <= 0 || newHeight <= 0)
						continue;

					// Construct adjusted object
					Object adjustedDet = detInROI;
					adjustedDet.box = cv::Rect(newX, newY, newWidth, newHeight);
					adjustedDetections.push_back(adjustedDet);
				}
				catch (const std::exception& e) {
					std::cerr << "[AdjustBBox] Exception per detection: " << e.what() << std::endl;
					continue;
				}
				catch (...) {
					std::cerr << "[AdjustBBox] Unknown exception per detection." << std::endl;
					continue;
				}
			}
		}
		catch (const std::exception& e) {
			std::cerr << "[AdjustBBox] Fatal error: " << e.what() << std::endl;
		}
		catch (...) {
			std::cerr << "[AdjustBBox] Unknown fatal error occurred." << std::endl;
		}

		return adjustedDetections;
	}

	cv::Rect ANSODBase::GetActiveWindow(const cv::Mat& input) {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		try {
			std::vector<ImageSection> sections = divideImage(input);
			int lowestPriority = getLowestPriorityRegion();
			if ((_currentPriority > lowestPriority) || (_currentPriority == 0))
			{
				_currentPriority = getHighestPriorityRegion();
			}
			else _currentPriority++;
			cv::Rect regionByPriority = getRegionByPriority(_currentPriority);
			return regionByPriority;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::GetActiveWindow", e.what(), __FILE__, __LINE__);
			return cv::Rect(0, 0, input.cols, input.rows);
		}
	}
	void ANSODBase::UpdateNoDetectionCondition() {
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		if (_isObjectDetected) {
			_retainDetectedArea++;
			if (_retainDetectedArea >= RETAINDETECTEDFRAMES) {// Reset detected area after 80 frames
				_detectedArea.width = 0;
				_detectedArea.height = 0;
				_retainDetectedArea = 0;
				_isObjectDetected = false;
			}
		}
		else {
			_detectedArea.width = 0;
			_detectedArea.height = 0;
			_retainDetectedArea = 0;
		}
	}
	void ANSODBase::UpdateActiveROI(const cv::Mat& frame, ANSCENTER::Object detectedObj) {
		int cropSize = 640; // Only use 640x640 for the detected area
		int imagegSize = std::max(frame.cols, frame.rows);
		if (imagegSize > 1920) cropSize = 640;
		else if (imagegSize > 1280) cropSize = 480;
		else if (imagegSize > 640) cropSize = 320;
		else cropSize = 320;
		int x1 = detectedObj.box.x;
		int y1 = detectedObj.box.y;
		int xc = x1 + detectedObj.box.width / 2;
		int yc = y1 + detectedObj.box.height / 2;
		int  x1_new = std::max(xc - cropSize / 2, 0);
		int  y1_new = std::max(yc - cropSize / 2, 0);
		x1_new = std::min(x1_new, frame.cols - cropSize);
		y1_new = std::min(y1_new, frame.rows - cropSize);
		_detectedArea.x = std::max(x1_new, 0);
		_detectedArea.y = std::max(y1_new, 0);
		_detectedArea.width = std::min(cropSize, frame.cols - _detectedArea.x);
		_detectedArea.height = std::min(cropSize, frame.rows - _detectedArea.y);
	}
	bool ANSODBase::IsValidObject(const Object& obj, std::vector<int> objectIds) {
		bool validObject = false;
		for (const auto& id : objectIds) {
			if (obj.classId == id) {
				validObject = true;
				break;
			}
		}
		return validObject;
	}
	std::vector<Object>  ANSODBase::RunInferenceInScanningMode(const cv::Mat& input, const std::string& camera_id) {
		std::vector<Object> detectionOutputs;
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		try {
			//1. Use divideImage to divide the image into sections
			std::vector<ImageSection> sections = createSlideScreens(input);
			int lowestPriority = getLowestPriorityRegion();
			if ((_currentPriority > lowestPriority) || (_currentPriority == 0))
			{
				_currentPriority = getHighestPriorityRegion();
			}
			else _currentPriority++;
			cv::Rect regionByPriority = getRegionByPriority(_currentPriority);
			_detectedArea = regionByPriority;

			//2. Run inference on the active region
			if ((_detectedArea.width > 50) && (_detectedArea.height > 50)) {
				cv::Mat activeFrame = input(_detectedArea).clone();
				std::vector<Object> detectedObjects = RunInference(activeFrame, camera_id);
				//3. Adjust the detected bounding boxes
				detectionOutputs = AdjustDetectedBoundingBoxes(detectedObjects, _detectedArea, input.size(), 1.0f, 0);
				//4. Apply Non-Maximum Suppression (NMS) to merge overlapping results
				float iouThreshold = 0.1;
				std::vector<Object> finalResults = ANSUtilityHelper::ApplyNMS(detectionOutputs, iouThreshold);
				return finalResults;
			}
			return detectionOutputs;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::RunInferenceInScanningMode", e.what(), __FILE__, __LINE__);
			return detectionOutputs;
		}
		catch (...) {
			this->_logger.LogFatal("ANSODBase::RunInferenceInScanningMode", "Unknown error", __FILE__, __LINE__);
			return detectionOutputs;
		}
		
	}
	std::vector<Object>  ANSODBase::RunInferenceInTrackingMode(const cv::Mat& input, const std::string& camera_id, std::vector<int> trackingObjectIds) {
		std::vector<Object> detectionOutputs;
		std::lock_guard<std::recursive_mutex> lock(_mutex);
		try {
			//1. If the detected area valid then we use this.
			if ((_detectedArea.width > 50) && (_detectedArea.height > 50)){
				cv::Mat activeROI = input(_detectedArea).clone();
				std::vector<ANSCENTER::Object> detectedObjects = RunInference(activeROI,camera_id);
				if (!detectedObjects.empty()) { // We have some detections here
					for (auto& detectedObj : detectedObjects) {
						if (IsValidObject(detectedObj, trackingObjectIds)) {
							detectedObj.box.x += _detectedArea.x;
							detectedObj.box.y += _detectedArea.y;
							detectedObj.cameraId = camera_id;
							// Filter out the detected objects with low confidence
							detectionOutputs.push_back(detectedObj);
							UpdateActiveROI(input, detectedObj);
							_isObjectDetected = true;// variable to retain the detected area after 40 frames tracking of detected area
							_retainDetectedArea = 0;// Reset the retain detected area	
						}
					}
				}
				else {
					UpdateNoDetectionCondition();
				}
				activeROI.release();
				float iouThreshold = 0.1;
				std::vector<Object> finalResults = ANSUtilityHelper::ApplyNMS(detectionOutputs, iouThreshold);
				return finalResults;
			}
			//2. Othersise we need to find it.
			else {
				// Reset the detected area before running the inference
				//2.1. Apply scanning technique first to get correct detected area
				std::vector<ImageSection> sections = divideImage(input);
				int lowestPriority = getLowestPriorityRegion();
				if ((_currentPriority > lowestPriority) || (_currentPriority == 0))
				{
					_currentPriority = getHighestPriorityRegion();
				}
				else _currentPriority++;
				cv::Rect regionByPriority = getRegionByPriority(_currentPriority);
				_detectedArea = regionByPriority;
				//2.2. Run inference on the active region to redjust it.
				if ((_detectedArea.width > 50) && (_detectedArea.height > 50)) {
					cv::Mat detectedROI = input(_detectedArea).clone();  // Get sub-image of the detected area
					std::vector<ANSCENTER::Object> detectedObjects = RunInference(detectedROI,camera_id);
					float maxScore = 0.0;
					// Sorting to find the detection that has the highest score
					if (detectedObjects.size() > 0) {
						for (auto& detectedObj : detectedObjects) {
							detectedObj.box.x += _detectedArea.x;
							detectedObj.box.y += _detectedArea.y;
							detectedObj.cameraId = camera_id;
							if ((detectedObj.confidence > 0.35)&&(IsValidObject(detectedObj, trackingObjectIds))) {
								cv::Rect box = detectedObj.box;
								cv::Mat crop = input(box).clone();
								if (maxScore < detectedObj.confidence)
								{
									maxScore = detectedObj.confidence;
									int cropSize = 640;
									int imagegSize = std::max(input.cols, input.rows);
									if (imagegSize > 1920) cropSize = 640;
									else if (imagegSize > 1280) cropSize = 480;
									else if (imagegSize > 640) cropSize = 320;
									else cropSize = 224;
									maxScore = detectedObj.confidence;
									int x1 = detectedObj.box.x;
									int y1 = detectedObj.box.y;
									int xc = x1 + detectedObj.box.width / 2;
									int yc = y1 + detectedObj.box.height / 2;
									int  x1_new = std::max(xc - cropSize / 2, 0);
									int  y1_new = std::max(yc - cropSize / 2, 0);
									x1_new = std::min(x1_new, input.cols - cropSize);
									y1_new = std::min(y1_new, input.rows - cropSize);
									_detectedArea.x = std::max(x1_new, 0);
									_detectedArea.y = std::max(y1_new, 0);
									_detectedArea.width = std::min(cropSize, input.cols - _detectedArea.x);
									_detectedArea.height = std::min(cropSize, input.rows - _detectedArea.y);
								}
								detectionOutputs.push_back(detectedObj);
								_isObjectDetected = true;// variable to retain the detected area after 40 frames tracking of detected area
								_retainDetectedArea = 0;// Reset the retain detected area
								break;
							}
						}
					}
					else {
						// Reset the detected area before running the inference
						_detectedArea.width = 0;
						_detectedArea.height = 0;
					}
					detectedROI.release();
				}
				if (detectionOutputs.size() == 0) {
					_detectedArea.width = 0;
					_detectedArea.height = 0;
				}
				//else detectionOutputs.clear(); // Discard the detected objects in the scanning mode
				//2.3. Apply Non-Maximum Suppression (NMS) to merge overlapping results
				float iouThreshold = 0.1;
				std::vector<Object> finalResults = ANSUtilityHelper::ApplyNMS(detectionOutputs, iouThreshold);
				return finalResults;
			}
		
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::RunInferenceInTrackingMode", e.what(), __FILE__, __LINE__);
			return detectionOutputs;
		}
		catch (...) {
			this->_logger.LogFatal("ANSODBase::RunInferenceInTrackingMode", "Unknown error", __FILE__, __LINE__);
			return detectionOutputs;
		}
	}
	std::vector<Object>   ANSODBase::RunInferenceWithOption(const cv::Mat& input, const std::string& camera_id, const std::string activeROIMode) {
		// No coarse _mutex — sub-components (engines, trackers) have their own locks.
		// LabVIEW semaphore controls concurrency at the caller level.
		try {
			ANS_DBG("ODEngine", "RunInferenceWithOption: cam=%s %dx%d mode=%s",
					camera_id.c_str(), input.cols, input.rows, activeROIMode.c_str());
			int mode = 0;
			double confidenceThreshold = 0.35;
			std::vector<int> trackingObjectIds;
			int cropSize = 640; // Default crop size
			int imagegSize = std::max(input.cols, input.rows);
			if (imagegSize > 1920) cropSize = 640;
			else if (imagegSize > 1280) cropSize = 480;
			else if (imagegSize > 640) cropSize = 320;
			else cropSize = 224;
			// Parsing
			ANSCENTER::ANSUtilityHelper::ParseActiveROIMode(activeROIMode, mode, confidenceThreshold, trackingObjectIds);
			if (confidenceThreshold <= 0) confidenceThreshold = 0;
			if (confidenceThreshold > 1) confidenceThreshold = 1;

			// Update model configuration with the new parameters (brief lock for config)
			if (confidenceThreshold > 0) {
				auto cfgLock = TryLockWithTimeout("ANSODBase::RunInferenceWithOption", 2000);
				if (cfgLock.owns_lock())
					_modelConfig.detectionScoreThreshold = confidenceThreshold;
			}
			switch (mode) {
			case 0: // Normal mode
				return RunInference(input, camera_id); //RunInference
			case 1: // Scanning  mode
				return RunInferenceInScanningMode(input, camera_id);
			case 2: // Tracking  mode
				return RunInferenceInTrackingMode(input, camera_id, trackingObjectIds);
			case 3: // SAHI mode
				return RunInferences(input, cropSize, cropSize, 0.2, camera_id);
			default:
				return RunInference(input, camera_id);
			}
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::RunInferenceWithOption", e.what(), __FILE__, __LINE__);
			return {};
		}
		catch (...) {
			this->_logger.LogFatal("ANSODBase::RunInferenceWithOption", "Unknown error", __FILE__, __LINE__);
			return {};
		}
	}
	bool ANSODBase::UpdateDetectionThreshold(float detectionScore) {
		try {
			if (detectionScore < 0 || detectionScore > 1) {
				this->_logger.LogFatal("ANSODBase::UpdateDetectionThreshold", "Invalid detection score", __FILE__, __LINE__);
				return false;
			}
			_modelConfig.detectionScoreThreshold = detectionScore;
			return true;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::UpdateDetectionThreshold", e.what(), __FILE__, __LINE__);
			return false;
		}
	}
	bool ANSODBase::SetParameters(const Params& param) {
		try {
			this->_params.ROI_Config.clear();
			for (auto& cf : param.ROI_Config) {
				this->_params.ROI_Config.push_back(cf);
			}
			this->_params.ROI_Options.clear();
			for (auto& op : param.ROI_Options) {
				this->_params.ROI_Options.push_back(op);
			}
			this->_params.Parameters.clear();
			for (auto& par : param.Parameters) {
				this->_params.Parameters.push_back(par);
			}
			this->_params.ROI_Values.clear();
			for (auto& roi : param.ROI_Values) {
				this->_params.ROI_Values.push_back(roi);
			}
			return true;
		}
		catch (std::exception& e) {
			this->_logger.LogFatal("ANSODBase::SetParamaters", e.what(), __FILE__, __LINE__);
			return false;
		}
	}
	bool ANSODBase::ConfigureParameters(Params& param) {
		try {
			// loop throught this->_params and set the values
			param.ROI_Config.clear();
			for (auto& cf : this->_params.ROI_Config) {
				param.ROI_Config.push_back(cf);
			}
			param.ROI_Options.clear();
			for (auto& op : this->_params.ROI_Options) {
				param.ROI_Options.push_back(op);
			}
			param.Parameters.clear();
			for (auto& par : this->_params.Parameters) {
				param.Parameters.push_back(par);
			}
			param.ROI_Values.clear();
			for (auto& roi : this->_params.ROI_Values) {
				param.ROI_Values.push_back(roi);
			}
			return true;
		}
		catch (std::exception& e) {
			return false;
		}		
	}
	//------------------------------------------------------------------------------------------------------------------------//
}