---
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
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.
Watch: Security Alarm System Project with Ultralytics YOLOv8 Object Detection
### Code
#### Import Libraries
```python
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from time import time
import cv2
import numpy as np
import torch
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
```
#### 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
server = smtplib.SMTP("smtp.gmail.com: 587")
server.starttls()
server.login(from_email, password)
```
#### Email Send Function
```python
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
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 / np.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):
"""Executes object detection on video frames from a specified camera index, plotting bounding boxes and returning modified frames."""
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