---
comments: true
description: Enhance your security with real-time object detection using Ultralytics YOLOv8. Reduce false positives and integrate seamlessly with existing systems.
keywords: YOLOv8, Security Alarm System, real-time object detection, Ultralytics, computer vision, integration, false positives
---

# Security Alarm System Project Using Ultralytics YOLOv8

<img src="https://github.com/RizwanMunawar/ultralytics/assets/62513924/f4e4a613-fb25-4bd0-9ec5-78352ddb62bd" alt="Security Alarm System">

The Security Alarm System Project utilizing Ultralytics YOLOv8 integrates advanced computer vision capabilities to enhance security measures. YOLOv8, developed by Ultralytics, provides real-time object detection, allowing the system to identify and respond to potential security threats promptly. This project offers several advantages:

- **Real-time Detection:** YOLOv8's efficiency enables the Security Alarm System to detect and respond to security incidents in real-time, minimizing response time.
- **Accuracy:** YOLOv8 is known for its accuracy in object detection, reducing false positives and enhancing the reliability of the security alarm system.
- **Integration Capabilities:** The project can be seamlessly integrated with existing security infrastructure, providing an upgraded layer of intelligent surveillance.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/_1CmwUzoxY4"
    title="YouTube video player" frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
    allowfullscreen>
  </iframe>
  <br>
  <strong>Watch:</strong> Security Alarm System Project with Ultralytics YOLOv8 Object Detection
</p>

### Code

#### Set up the parameters of the message

???+ tip "Note"

    App Password Generation is necessary

- Navigate to [App Password Generator](https://myaccount.google.com/apppasswords), designate an app name such as "security project," and obtain a 16-digit password. Copy this password and paste it into the designated password field as instructed.

```python
password = ""
from_email = ""  # must match the email used to generate the password
to_email = ""  # receiver email
```

#### Server creation and authentication

```python
import smtplib

server = smtplib.SMTP("smtp.gmail.com: 587")
server.starttls()
server.login(from_email, password)
```

#### Email Send Function

```python
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText


def send_email(to_email, from_email, object_detected=1):
    """Sends an email notification indicating the number of objects detected; defaults to 1 object."""
    message = MIMEMultipart()
    message["From"] = from_email
    message["To"] = to_email
    message["Subject"] = "Security Alert"
    # Add in the message body
    message_body = f"ALERT - {object_detected} objects has been detected!!"

    message.attach(MIMEText(message_body, "plain"))
    server.sendmail(from_email, to_email, message.as_string())
```

#### Object Detection and Alert Sender

```python
from time import time

import cv2
import torch

from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors


class ObjectDetection:
    def __init__(self, capture_index):
        """Initializes an ObjectDetection instance with a given camera index."""
        self.capture_index = capture_index
        self.email_sent = False

        # model information
        self.model = YOLO("yolov8n.pt")

        # visual information
        self.annotator = None
        self.start_time = 0
        self.end_time = 0

        # device information
        self.device = "cuda" if torch.cuda.is_available() else "cpu"

    def predict(self, im0):
        """Run prediction using a YOLO model for the input image `im0`."""
        results = self.model(im0)
        return results

    def display_fps(self, im0):
        """Displays the FPS on an image `im0` by calculating and overlaying as white text on a black rectangle."""
        self.end_time = time()
        fps = 1 / round(self.end_time - self.start_time, 2)
        text = f"FPS: {int(fps)}"
        text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1.0, 2)[0]
        gap = 10
        cv2.rectangle(
            im0,
            (20 - gap, 70 - text_size[1] - gap),
            (20 + text_size[0] + gap, 70 + gap),
            (255, 255, 255),
            -1,
        )
        cv2.putText(im0, text, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 2)

    def plot_bboxes(self, results, im0):
        """Plots bounding boxes on an image given detection results; returns annotated image and class IDs."""
        class_ids = []
        self.annotator = Annotator(im0, 3, results[0].names)
        boxes = results[0].boxes.xyxy.cpu()
        clss = results[0].boxes.cls.cpu().tolist()
        names = results[0].names
        for box, cls in zip(boxes, clss):
            class_ids.append(cls)
            self.annotator.box_label(box, label=names[int(cls)], color=colors(int(cls), True))
        return im0, class_ids

    def __call__(self):
        """Run object detection on video frames from a camera stream, plotting and showing the results."""
        cap = cv2.VideoCapture(self.capture_index)
        assert cap.isOpened()
        cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
        frame_count = 0
        while True:
            self.start_time = time()
            ret, im0 = cap.read()
            assert ret
            results = self.predict(im0)
            im0, class_ids = self.plot_bboxes(results, im0)

            if len(class_ids) > 0:  # Only send email If not sent before
                if not self.email_sent:
                    send_email(to_email, from_email, len(class_ids))
                    self.email_sent = True
            else:
                self.email_sent = False

            self.display_fps(im0)
            cv2.imshow("YOLOv8 Detection", im0)
            frame_count += 1
            if cv2.waitKey(5) & 0xFF == 27:
                break
        cap.release()
        cv2.destroyAllWindows()
        server.quit()
```

#### Call the Object Detection class and Run the Inference

```python
detector = ObjectDetection(capture_index=0)
detector()
```

That's it! When you execute the code, you'll receive a single notification on your email if any object is detected. The notification is sent immediately, not repeatedly. However, feel free to customize the code to suit your project requirements.

#### Email Received Sample

<img width="256" src="https://github.com/RizwanMunawar/ultralytics/assets/62513924/db79ccc6-aabd-4566-a825-b34e679c90f9" alt="Email Received Sample">
