|
---
|
|
comments: true
|
|
description: Security Alarm System Project Using Ultralytics YOLOv8. Learn How to implement a Security Alarm System Using ultralytics YOLOv8
|
|
keywords: Object Detection, Security Alarm, Object Tracking, YOLOv8, Computer Vision Projects
|
|
---
|
|
|
|
# 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
|
|
|
|
#### Import Libraries
|
|
|
|
```python
|
|
import torch
|
|
import numpy as np
|
|
import cv2
|
|
from time import time
|
|
from ultralytics import YOLO
|
|
from ultralytics.utils.plotting import Annotator, colors
|
|
import smtplib
|
|
from email.mime.multipart import MIMEMultipart
|
|
from email.mime.text import MIMEText
|
|
```
|
|
|
|
#### 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):
|
|
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):
|
|
# default parameters
|
|
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):
|
|
results = self.model(im0)
|
|
return results
|
|
|
|
def display_fps(self, im0):
|
|
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):
|
|
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):
|
|
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">
|
|
|
