`import sqlite3
import datetime
from Status import Status
import numpy as np

class Database:
    """
    A class to handle interactions with the SQLite database for object tracking.

    Attributes:
        conn (sqlite3.Connection): The database connection.
        cursor (sqlite3.Cursor): The database cursor.
    """
    def __init__(self, db_path='object_tracking.db'):
        """
        Initializes the Database class with a connection to the specified SQLite database.

        Args:
            db_path (str): The path to the SQLite database file.
        """
        self.conn = sqlite3.connect(db_path)
        self.cursor = self.conn.cursor()
        self.create_table()
        self.add_person_name_column()
        self.create_known_faces_table()
        self.create_known_face_object_link_table()

    def create_table(self):
        """
        Creates the 'tracked_objects' table if it does not already exist.
        """
        self.cursor.execute('''
        CREATE TABLE IF NOT EXISTS tracked_objects (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            class_name TEXT,
            x1 INTEGER,
            y1 INTEGER,
            width INTEGER,
            height INTEGER,
            last_seen DATETIME,
            status TEXT,
            detection_count INTEGER DEFAULT 0
        )
    ''')
        self.conn.commit()
        
    def create_known_face_object_link_table(self):
        """
        Creates the 'known_face_object_link' table if it does not already exist.
        """
        self.cursor.execute('''
        CREATE TABLE IF NOT EXISTS known_face_object_link (
            known_face_name INTEGER,
            object_id INTEGER,
            FOREIGN KEY (known_face_name) REFERENCES known_faces(name),
            FOREIGN KEY (object_id) REFERENCES tracked_objects(id),
            PRIMARY KEY (known_face_name, object_id)
        )
    ''')
        self.conn.commit()
        
    def create_known_faces_table(self):
        """
        Creates the 'known_faces' table if it does not already exist.
        """
        self.cursor.execute('''
            CREATE TABLE IF NOT EXISTS known_faces (
                name TEXT NOT NULL PRIMARY KEY,
                embedding BLOB NOT NULL
            )
        ''')
        self.conn.commit()

    def fetch_objects(self, class_name=None):
        """
        Fetches objects from the database, optionally filtered by class name.

        Args:
            class_name (str, optional): The class name to filter objects.

        Returns:
            list: A list of tuples representing the objects.
        """
        if class_name:
            self.cursor.execute('SELECT * FROM tracked_objects WHERE class_name=?', (class_name,))
        else:
            self.cursor.execute('SELECT * FROM tracked_objects')
        return self.cursor.fetchall()

    def fetch_all_objects(self):
        """
        Fetches all objects from the database.

        Returns:
            list: A list of tuples representing all objects.
        """
        self.cursor.execute('SELECT * FROM tracked_objects')
        return self.cursor.fetchall()

    def fetch_object_by_id(self, obj_id):
        """
        Fetches a single object by its ID.

        Args:
            obj_id (int): The ID of the object.

        Returns:
            tuple: The object data as a tuple.
        """
        self.cursor.execute('SELECT * FROM tracked_objects WHERE id=?', (obj_id,))
        return self.cursor.fetchone()

    def update_object(self, obj_id, x1, y1, width, height):
        """
        Updates the position and size of an existing object in the database, and increments detection_count.

        Args:
            obj_id (int): The ID of the object to update.
            x1 (int): The new x-coordinate of the object's top-left corner.
            y1 (int): The new y-coordinate of the object's top-left corner.
            width (int): The new width of the object.
            height (int): The new height of the object.

        Returns:
            int: The ID of the updated object.
        """
        last_seen = datetime.datetime.now().isoformat()
        status = Status.IN_FRAME.value  # Set status to 'In Frame'
        self.cursor.execute('''
            UPDATE tracked_objects 
            SET x1=?, y1=?, width=?, height=?, last_seen=?, status=?, detection_count=detection_count+1 
            WHERE id=?
        ''', (x1, y1, width, height, last_seen, status, obj_id))
        self.conn.commit()
        return obj_id

    def insert_object(self, class_name, x1, y1, width, height, person_name=None):
        """
        Inserts a new object into the database, and returns the new object's ID.

        Args:
            class_name (str): The class name of the object.
            x1 (int): The x-coordinate of the object's top-left corner.
            y1 (int): The y-coordinate of the object's top-left corner.
            width (int): The width of the object.
            height (int): The height of the object.
            person_name (str, optional): The name of the person associated with the object.

        Returns:
            int: The ID of the newly inserted object.
        """
        last_seen = datetime.datetime.now().isoformat()
        status = Status.IN_FRAME.value
        detection_count = 1
        self.cursor.execute('''
            INSERT INTO tracked_objects (class_name, x1, y1, width, height, last_seen, status, detection_count, person_name) 
            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
        ''', (class_name, x1, y1, width, height, last_seen, status, detection_count, person_name))
        self.conn.commit()
        return self.cursor.lastrowid

    def update_status(self, obj_id, status):
        """
        Updates the status of an object in the database.

        Args:
            obj_id (int): The ID of the object to update.
            status (Status): The new status of the object.
        """
        self.cursor.execute('''
            UPDATE tracked_objects 
            SET status=? 
            WHERE id=?
        ''', (status.value, obj_id))
        self.conn.commit()

    def delete_object(self, obj_id):
        """
        Deletes an object from the database.

        Args:
            obj_id (int): The ID of the object to delete.
        """
        self.cursor.execute('''
            DELETE FROM tracked_objects WHERE id=?
        ''', (obj_id,))
        self.conn.commit()

    def add_person_name_column(self):
        """
        Adds the 'person_name' column to 'tracked_objects' table if it doesn't exist.
        """
        self.cursor.execute("PRAGMA table_info(tracked_objects)")
        columns = [column[1] for column in self.cursor.fetchall()]
        if 'person_name' not in columns:
            self.cursor.execute('ALTER TABLE tracked_objects ADD COLUMN person_name TEXT')
            self.conn.commit()
            
    def insert_known_face(self, name, embedding):
        """
        Inserts a new known face into the database.

        Args:
            name (str): The name of the person.
            embedding (np.ndarray): The face embedding.
        """
        embedding_blob = embedding.astype(np.float32).tobytes()
        self.cursor.execute('''
            INSERT INTO known_faces (name, embedding) VALUES (?, ?)
        ''', (name, embedding_blob))
        self.conn.commit()

    def fetch_known_faces(self):
        """
        Fetches all known faces from the database.

        Returns:
            list: A list of tuples representing known faces.
        """
        self.cursor.execute('SELECT * FROM known_faces')
        known_faces = []
        for row in self.cursor.fetchall():
            name = row[0]
            embedding_blob = row[1]
            embedding = np.frombuffer(embedding_blob, dtype=np.float32)
            known_faces.append((name, embedding))
        return known_faces
    
    def get_known_face_by_name(self, name):
        """
        Fetches a known face by name from the database.

        Args:
            name (str): The name of the person.

        Returns:
            tuple: A tuple representing the known face.
        """
        self.cursor.execute('SELECT * FROM known_faces WHERE name=?', (name,))
        row = self.cursor.fetchone()
        if row:
            name = row[0]
            embedding_blob = row[1]
            embedding = np.frombuffer(embedding_blob, dtype=np.float32)
            return (name, embedding)
        return None

    def update_known_face_object_link(self, obj_id, person_name):
        self.cursor.execute('''
            SELECT object_id FROM known_face_object_link WHERE known_face_name=?
        ''', (person_name,))
        obj_ids = self.cursor.fetchall()
        for obj in obj_ids:
            if obj[0] != obj_id:
                self.cursor.execute('''
                    DELETE FROM tracked_objects WHERE id=?
                ''', (obj[0],))
        
        # Delete any existing records with the same known_face_name
        self.cursor.execute('''
            DELETE FROM known_face_object_link 
            WHERE known_face_name=?
            OR object_id=?
        ''', (person_name,obj_id,))
        
        # Insert the new record
        self.cursor.execute('''
            INSERT INTO known_face_object_link (known_face_name, object_id) 
            VALUES (?, ?)
        ''', (person_name, obj_id))
        
        self.conn.commit()`,`import cv2
import numpy as np
from insightface.app import FaceAnalysis

class FaceInterface:
    """
    A class for interfacing with the face recognition module.
    """
    def __init__(self, db) -> None:
        """
        Initializes the FaceInterface with the given database.

        Args:
            db (Database): The database instance.
        """
        self.db = db
        # Initialize FaceAnalysis for face recognition
        self.face_recognition = FaceAnalysis(allowed_modules=['detection', 'recognition'])
        self.face_recognition.prepare(ctx_id=0, det_size=(320, 320))

    def add_known_face(self, image_path: str, name: str) -> bool:
        """
        Adds a known face to the database.

        Args:
            image_path (str): Path to the image file containing the face.
            name (str): Name of the person.

        Returns:
            bool: True if the face was successfully added, False otherwise.
        """
        # Read the image from the given path
        img = cv2.imread(image_path)
        if img is None:
            print(f"Failed to read image at {image_path}")
            return False

        # Detect faces and get embeddings
        faces = self.face_recognition.get(img)
        if not faces:
            print(f"No face detected in image {image_path}")
            return False

        # Use the first detected face
        face = faces[0]
        embedding = face.embedding
        if embedding is None:
            print(f"Failed to get embedding for face in image {image_path}")
            return False

        # Normalize the embedding
        embedding = embedding / np.linalg.norm(embedding)
        
        if self.db.get_known_face_by_name(name):
            print(f"Face with name {name} already exists in the database")
            return True

        # Insert the name and embedding into the database
        self.db.insert_known_face(name, embedding)
        return True`,`# Import necessary libraries
from Database import Database
from ObjectDetection import ObjectDetection
from ObjectTracker import ObjectTracker
from FaceInterface import FaceInterface
import cv2
import argparse
import time

def add_known_faces(file_path: str, face_interface: FaceInterface) -> None:
    """
    Add known faces from the CSV file to the face recognition module.

    Args:
        file_path (str): Path to the CSV file containing image paths and names.
        face_interface (FaceInterface): The face recognition interface.
        
    Raises:
        ValueError: If a known face could not be added.
    """
    if file_path:
        with open(file_path, 'r') as file:
            for line in file:
                image_path, name = line.strip().split(',')
                if not face_interface.add_known_face(image_path, name): 
                    raise ValueError(f"Failed to add {name} from {image_path}")

def main():
    """
    The main function to initialize object detection and tracking,
    and process the video stream from the webcam.
    """
    # Set up argument parser
    parser = argparse.ArgumentParser(description="CSV file of image_path, name. For face recognition.")
    parser.add_argument('--csv', type=str, help='Path to the CSV file for face recognition.')
    parser.add_argument('--model', type=str, default='yolov8n.pt', help='The YOLO model weights name. Default is yolov8n.pt.')
    parser.add_argument('--show', action='store_true', help='Display the video stream.')
    parser.add_argument('--fps', action='store_true', help='Whether to display the FPS.')
    args = parser.parse_args()
    
    # Initialize the database, detection model, and tracker
    db = Database()
    detection_model = ObjectDetection(model=args.model)
    tracker = ObjectTracker(db, detection_model)   
    face_interface = FaceInterface(db)
    
    # Add known faces from the CSV file
    add_known_faces(args.csv, face_interface)

    # Set up video capture from the default webcam
    cap = cv2.VideoCapture(0)
    cap.set(3, 640)  # Set width
    cap.set(4, 480)  # Set height

    # Initialize FPS calculation variables
    frame_count = 0
    start_time = time.time()

    while True:
        success, img = cap.read()
        if not success:
            break

        # Perform object tracking on the current frame
        img = tracker.track_objects(img)

        if args.fps:
            # Increment frame count
            frame_count += 1

            # Calculate FPS
            elapsed_time = time.time() - start_time
            fps = frame_count / elapsed_time
            
            # Show the FPS on the frame
            cv2.putText(img, f'FPS: {fps:.2f}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

        if args.show:
            cv2.imshow('Webcam', img)

        # Exit if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # Release resources
    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()`,`from ultralytics import YOLO
import os

class ObjectDetection:
    """
    A class for object detection using a YOLO model.

    Attributes:
        model (YOLO): The YOLO model used for detection.
        classNames (list): List of class names that the model can detect.
        allowed_classes (set): Set of class indices that are allowed for tracking.
    """
    def __init__(self, model="yolo11n.pt"):
        """
        Initializes the ObjectDetection class with the specified YOLO model.

        Args:
            model (str): The path to the YOLO model weights.
        """
        try:
            self.model = YOLO(self.get_ncnn_model(model), task='detect')
        except Exception as e:
            raise Exception(f"Error loading YOLO model: {e}")
        
        self.classNames = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat",
                           "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat",
                           "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
                           "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat",
                           "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup",
                           "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
                           "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
                           "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
                           "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
                           "teddy bear", "hair drier", "toothbrush"]
        # Only allow certain classes for tracking
        self.allowed_classes = {0, 24, 25, 26, 31, 36, 37, 39, 40, 41, 53, 63, 65, 67}
        
    def get_ncnn_model(self, model: str) -> str:
        """
        Converts the given YOLO model to NCNN format.

        Args:
            model (str): The name of the YOLO model file.

        Raises:
            ValueError: If the model conversion fails.

        Returns:
            str: The path to the converted NCNN model.
        """
        ncnn_model_path = model.split('.')[0] + '_ncnn_model'
        if os.path.exists(ncnn_model_path):
            return ncnn_model_path
        try:
            model_obj = YOLO(model)
            export_result = model_obj.export(format='ncnn')
            # Assuming export returns the path to the exported model
            return export_result
        except Exception as e:
            raise ValueError(f"Failed to convert model to NCNN format: {e}")

    def detect_objects(self, img):
        """
        Detects objects in the given image with dynamic confidence threshold based on object size.

        Args:
            img (numpy.ndarray): The image in which to detect objects.

        Returns:
            list: A list of detections, each containing class name, bounding box coordinates, width, height, and confidence.
        """
        results = self.model(img, stream=True)
        detections = []
        img_height, img_width = img.shape[:2]
        image_area = img_width * img_height

        for r in results:
            for box in r.boxes:
                # Get bounding box coordinates
                x1, y1, x2, y2 = map(int, box.xyxy[0])
                width, height = x2 - x1, y2 - y1
                box_area = width * height
                relative_size = box_area / image_area

                # Define dynamic threshold based on relative_size
                # Example: Linear interpolation between min_threshold and base_threshold
                min_threshold = 0.3
                base_threshold = 0.55
                max_size = 0.25  # Relative area where base_threshold is applied
                min_size = 0.01  # Relative area where min_threshold is applied

                if relative_size >= max_size:
                    confidence_threshold = base_threshold
                elif relative_size <= min_size:
                    confidence_threshold = min_threshold
                else:
                    # Linear interpolation
                    confidence_threshold = min_threshold + (base_threshold - min_threshold) * (
                        (relative_size - min_size) / (max_size - min_size)
                    )
                # Get the confidence score
                confidence = box.conf[0].item()
                if confidence < confidence_threshold:
                    continue  # Skip detections below dynamic threshold

                # Get the class index
                cls = int(box.cls[0])
                if cls not in self.allowed_classes:
                    continue  # Skip classes that are not allowed

                class_name = self.classNames[cls]
                detections.append((class_name, x1, y1, width, height, confidence))

        return detections`,`import cv2
from datetime import datetime, timedelta
from Status import Status
from insightface.app import FaceAnalysis
import numpy as np
import concurrent.futures
import math


class ObjectTracker:
    """
    A class for tracking objects and updating their status in the database.

    Attributes:
        db (Database): The database instance for storing object data.
        detection_model (ObjectDetection): The object detection model.
        object_positions (dict): Dictionary to store the positions of each object.
    """
    def __init__(self, db, detection_model):
        """
        Initializes the ObjectTracker class with the given database and detection model.

        Args:
            db (Database): The database instance.
            detection_model (ObjectDetection): The object detection model.
        """
        self.db = db
        self.detection_model = detection_model
        self.object_positions = {}  # To store positions of each object
        # Initialize ArcFace for face recognition
        self.face_recognizer = FaceAnalysis(allowed_modules=['detection', 'recognition'])
        self.face_recognizer.prepare(ctx_id=0, det_size=(320, 320))
        self.face_recognition_cache = {}  # Stores obj_id: (person_name, last_recognition_frame)
        self.face_recognition_interval = 10  # Number of frames to wait before re-running face recognition
        self.frame_count = 0  # Initialize frame counter
        self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=2)

    def is_near(self, existing_obj, new_obj, size_similarity_threshold=0.2, location_similarity_threshold=30):
        """
        Determines if a new object is similar to an existing object based on size and location.

        Args:
            existing_obj (tuple): The existing object's coordinates and size (x, y, w, h).
            new_obj (tuple): The new object's coordinates and size (x, y, w, h).
            size_similarity_threshold (float, optional): Maximum relative difference in size to consider similar
                                                        (default is 0.2 for 20%).
            location_similarity_threshold (int, optional): Maximum distance between centers to consider similar
                                                            (default is 30 pixels).

        Returns:
            bool: True if the objects are similar in size and location, False otherwise.
        """
        ex, ey, ew, eh = existing_obj
        nx, ny, nw, nh = new_obj

        # Calculate centers of existing and new objects
        existing_center_x = ex + ew / 2
        existing_center_y = ey + eh / 2
        new_center_x = nx + nw / 2
        new_center_y = ny + nh / 2

        # Calculate Euclidean distance between centers
        center_distance = math.sqrt((existing_center_x - new_center_x) ** 2 + (existing_center_y - new_center_y) ** 2)

        # Check location similarity
        if center_distance > location_similarity_threshold:
            return False  # Objects are too far apart in location

        # Calculate relative size differences
        width_difference = abs(ew - nw) / max(ew, nw)
        height_difference = abs(eh - nh) / max(eh, nh)

        # Check size similarity
        if width_difference > size_similarity_threshold or height_difference > size_similarity_threshold:
            return False  # Objects differ too much in size

        return True  # Objects are similar in both size and location

    def is_within_frame(self, obj_id, frame_width, frame_height, border_threshold=20, size_threshold=60):
        """
        Checks if an object is within the frame or near the borders. If the object is near a border and its size is
        small (less than size_threshold), it is considered out of bounds.

        Args:
            obj_id (int): The ID of the object.
            frame_width (int): The width of the frame.
            frame_height (int): The height of the frame.
            border_threshold (int, optional): The distance from borders to consider as 'near the border'.
            size_threshold (int, optional): The size threshold to consider the object as out of bounds if near border.

        Returns:
            bool: True if the object is within the frame, False if it's out of bounds.
        """
        positions = self.object_positions.get(obj_id, [])
        if not positions:
            return False  # No position data available

        x, y, w, h = positions[-1]  # Get the most recent position

        # Check distances from borders
        distance_left = x
        distance_right = frame_width - (x + w)
        distance_top = y
        distance_bottom = frame_height - (y + h)

        # If object is near left border
        if distance_left <= border_threshold and w <= size_threshold:
            return False  # Object is out of bounds
        # If object is near right border
        if distance_right <= border_threshold and w <= size_threshold:
            return False
        # If object is near top border
        if distance_top <= border_threshold and h <= size_threshold:
            return False
        # If object is near bottom border
        if distance_bottom <= border_threshold and h <= size_threshold:
            return False

        # If none of the above conditions met, object is within frame
        return True

    def check_for_false_positives(self, frame_width, frame_height):
        """
        Checks the database for objects that haven't been seen for more than 500 ms.
        If an object hasn't been seen for more than 500 ms and did not leave frame,
        removes it from the database.

        Args:
            frame_width (int): The width of the frame.
            frame_height (int): The height of the frame.
        """
        all_objects = self.db.fetch_all_objects()
        now = datetime.now()
        detection_timeout = now - timedelta(milliseconds=500)

        for obj in all_objects:
            obj_id = obj[0]
            class_name = obj[1]
            x1 = obj[2]
            y1 = obj[3]
            width = obj[4]
            height = obj[5]
            last_seen_str = obj[6]
            status = obj[7]
            detection_count = obj[8]

            last_seen = datetime.fromisoformat(last_seen_str)

            # If the object hasn't been seen for more than 500 ms
            if last_seen < detection_timeout:
                if status != Status.LEFT_FRAME.value:
                    # Delete the object from the database
                    self.db.delete_object(obj_id)
                    # Remove from position tracking
                    if obj_id in self.object_positions:
                        del self.object_positions[obj_id]
    
    def track_objects(self, img):
        self.frame_count += 1  # Update frame count
        frame_height, frame_width = img.shape[:2]
        detections = self.detection_model.detect_objects(img)
        # Fetch all existing objects once per frame
        existing_objs = self.db.fetch_all_objects()
        existing_objs_dict = {}  # Map class_name to list of objects
        for obj in existing_objs:
            class_name = obj[1]
            if class_name not in existing_objs_dict:
                existing_objs_dict[class_name] = []
            existing_objs_dict[class_name].append(obj)

        # Keep track of which object IDs have been updated in this frame
        updated_obj_ids = set()

        for class_name, x1, y1, width, height, confidence in detections:
            new_obj = (x1, y1, width, height)
            cv2.rectangle(img, (x1, y1), (x1 + width, y1 + height), (255, 0, 255), 3)
            cv2.putText(img, f'{class_name} {confidence:.2f}', (x1, y1 - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)

            person_name = None  # Initialize person_name

            objs_in_class = existing_objs_dict.get(class_name, [])
            preexisting_obj = False
            obj_id = None

            for existing_obj in objs_in_class:
                db_x1 = existing_obj[2]
                db_y1 = existing_obj[3]
                db_width = existing_obj[4]
                db_height = existing_obj[5]
                existing_obj_id = existing_obj[0]

                if existing_obj_id in updated_obj_ids:
                    continue  # Skip already updated objects

                if self.is_near((db_x1, db_y1, db_width, db_height), new_obj):
                    preexisting_obj = True
                    obj_id = existing_obj[0]
                    self.db.update_object(obj_id, x1, y1, width, height)
                    self.object_positions[obj_id] = [(x1, y1, width, height)]
                    updated_obj_ids.add(obj_id)
                    break

            if not preexisting_obj:
                obj_id = self.db.insert_object(class_name, x1, y1, width, height)
                self.object_positions[obj_id] = [(x1, y1, width, height)]
                self.db.update_status(obj_id, Status.IN_FRAME)
                updated_obj_ids.add(obj_id)
                # Perform face recognition for new object
                if class_name == 'person':
                    person_name = self.perform_face_recognition(img, x1, y1, width, height)
                    if person_name:
                        self.face_recognition_cache[obj_id] = (person_name, self.frame_count)
            else:
                last_recognition_frame = self.face_recognition_cache.get(obj_id, (None, -self.face_recognition_interval))[1]
                if self.frame_count - last_recognition_frame >= self.face_recognition_interval:
                    if class_name == 'person':
                        person_name = self.perform_face_recognition(img, x1, y1, width, height)
                        if person_name:
                            self.face_recognition_cache[obj_id] = (person_name, self.frame_count)
                else:
                    person_name = self.face_recognition_cache.get(obj_id, (None, None))[0]

            if person_name and person_name != 'Unknown' and class_name == 'person' and person_name != 'No Face Detected':
                self.db.update_known_face_object_link(obj_id, person_name)
                cv2.putText(img, f'Name: {person_name} - {obj_id}', (x1, y1 - 30),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
            else:
                cv2.putText(img, f'ID: {obj_id}', (x1, y1 - 30),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

        # After processing all detections, check for false positives
        self.check_for_false_positives(frame_width, frame_height)
        return img

    def perform_face_recognition(self, img, x1, y1, width, height):
        person_img = img[y1:y1+height, x1:x1+width]
        faces = self.face_recognizer.get(person_img)
        if faces:
            face = faces[0]
            face_embedding = face.embedding / np.linalg.norm(face.embedding)
            known_faces = self.db.fetch_known_faces()
            min_distance = float('inf')
            matched_name = None

            for known_face in known_faces:
                known_name = known_face[0]
                known_embedding = known_face[1] / np.linalg.norm(known_face[1])
                distance = np.linalg.norm(face_embedding - known_embedding)
                if distance < min_distance:
                    min_distance = distance
                    matched_name = known_name

            threshold = 1.0  # Adjust based on your model and data
            if min_distance < threshold:
                return matched_name
            else:
                return 'Unknown'
        else:
            return 'No Face Detected'`,`from enum import Enum

class Status(str, Enum):
    """
    An enumeration to represent the status of tracked objects.
    """
    IN_FRAME = "In Frame"
    LEFT_FRAME = "Left Frame"`. Alter this code so if a detected object is of similar size and position to one stored in database, assume its the same.