Add security alarm system as ultralytics solution (#18281)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar
GitHub
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8 changed files with 216 additions and 128 deletions
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@ -27,149 +27,54 @@ The Security Alarm System Project utilizing Ultralytics YOLO11 integrates advanc
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### Code
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#### Set up the parameters of the message
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???+ note
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App Password Generation is necessary
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- 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.
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- 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 in the code below.
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```python
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password = ""
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from_email = "" # must match the email used to generate the password
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to_email = "" # receiver email
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```
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!!! example "Security Alarm System using YOLO11 Example"
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#### Server creation and authentication
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=== "Python"
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```python
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import smtplib
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```python
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import cv2
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server = smtplib.SMTP("smtp.gmail.com: 587")
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server.starttls()
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server.login(from_email, password)
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```
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from ultralytics import solutions
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#### Email Send Function
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cap = cv2.VideoCapture("Path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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```python
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from email.mime.multipart import MIMEMultipart
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from email.mime.text import MIMEText
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# Video writer
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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video_writer = cv2.VideoWriter("security_alarm_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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from_email = "abc@gmail.com" # The sender email address
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password = "---- ---- ---- ----" # 16-digits password generated via: https://myaccount.google.com/apppasswords
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to_email = "xyz@gmail.com" # The receiver email address
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def send_email(to_email, from_email, object_detected=1):
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"""Sends an email notification indicating the number of objects detected; defaults to 1 object."""
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message = MIMEMultipart()
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message["From"] = from_email
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message["To"] = to_email
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message["Subject"] = "Security Alert"
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# Add in the message body
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message_body = f"ALERT - {object_detected} objects has been detected!!"
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# Init SecurityAlarm
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security = solutions.SecurityAlarm(
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show=True, # Display the output
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model="yolo11n.pt", # i.e. YOLO11s.pt
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records=1, # Total detections count to send an email about security
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)
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message.attach(MIMEText(message_body, "plain"))
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server.sendmail(from_email, to_email, message.as_string())
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```
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security.authenticate(from_email, password, to_email) # Authenticate the email server
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#### Object Detection and Alert Sender
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# Process video
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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print("Video frame is empty or video processing has been successfully completed.")
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break
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im0 = security.monitor(im0)
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video_writer.write(im0)
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```python
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from time import time
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import cv2
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import torch
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from ultralytics import YOLO
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from ultralytics.utils.plotting import Annotator, colors
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class ObjectDetection:
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def __init__(self, capture_index):
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"""Initializes an ObjectDetection instance with a given camera index."""
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self.capture_index = capture_index
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self.email_sent = False
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# model information
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self.model = YOLO("yolo11n.pt")
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# visual information
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self.annotator = None
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self.start_time = 0
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self.end_time = 0
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# device information
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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def predict(self, im0):
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"""Run prediction using a YOLO model for the input image `im0`."""
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results = self.model(im0)
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return results
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def display_fps(self, im0):
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"""Displays the FPS on an image `im0` by calculating and overlaying as white text on a black rectangle."""
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self.end_time = time()
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fps = 1 / round(self.end_time - self.start_time, 2)
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text = f"FPS: {int(fps)}"
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text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1.0, 2)[0]
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gap = 10
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cv2.rectangle(
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im0,
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(20 - gap, 70 - text_size[1] - gap),
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(20 + text_size[0] + gap, 70 + gap),
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(255, 255, 255),
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-1,
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)
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cv2.putText(im0, text, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 2)
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def plot_bboxes(self, results, im0):
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"""Plots bounding boxes on an image given detection results; returns annotated image and class IDs."""
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class_ids = []
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self.annotator = Annotator(im0, 3, results[0].names)
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boxes = results[0].boxes.xyxy.cpu()
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clss = results[0].boxes.cls.cpu().tolist()
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names = results[0].names
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for box, cls in zip(boxes, clss):
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class_ids.append(cls)
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self.annotator.box_label(box, label=names[int(cls)], color=colors(int(cls), True))
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return im0, class_ids
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def __call__(self):
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"""Run object detection on video frames from a camera stream, plotting and showing the results."""
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cap = cv2.VideoCapture(self.capture_index)
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assert cap.isOpened()
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
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frame_count = 0
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while True:
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self.start_time = time()
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ret, im0 = cap.read()
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assert ret
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results = self.predict(im0)
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im0, class_ids = self.plot_bboxes(results, im0)
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if len(class_ids) > 0: # Only send email If not sent before
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if not self.email_sent:
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send_email(to_email, from_email, len(class_ids))
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self.email_sent = True
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else:
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self.email_sent = False
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self.display_fps(im0)
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cv2.imshow("YOLO11 Detection", im0)
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frame_count += 1
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if cv2.waitKey(5) & 0xFF == 27:
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break
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cap.release()
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cv2.destroyAllWindows()
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server.quit()
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```
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#### Call the Object Detection class and Run the Inference
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```python
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detector = ObjectDetection(capture_index=0)
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detector()
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```
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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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.
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@ -177,6 +82,21 @@ That's it! When you execute the code, you'll receive a single notification on yo
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<img width="256" src="https://github.com/ultralytics/docs/releases/download/0/email-received-sample.avif" alt="Email Received Sample">
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### Arguments `SecurityAlarm`
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Here's a table with the `SecurityAlarm` arguments:
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| Name | Type | Default | Description |
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| ------------ | ------ | ------- | ------------------------------------------------------- |
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| `model` | `str` | `None` | Path to Ultralytics YOLO Model File |
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| `line_width` | `int` | `2` | Line thickness for bounding boxes. |
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| `show` | `bool` | `False` | Flag to control whether to display the video stream. |
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| `records` | `int` | `5` | Total detections count to send an email about security. |
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### Arguments `model.track`
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{% include "macros/track-args.md" %}
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## FAQ
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### How does Ultralytics YOLO11 improve the accuracy of a security alarm system?
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@ -9,3 +9,4 @@
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| `kpts` | `list` | `[6, 8, 10]` | List of keypoints used for monitoring workouts. These keypoints correspond to body joints or parts, such as shoulders, elbows, and wrists, for exercises like push-ups, pull-ups, squats, ab-workouts. |
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| `analytics_type` | `str` | `line` | Specifies the type of analytics visualization to generate. Options include `"line"`, `"pie"`, `"bar"`, or `"area"`. The default is `"line"` for trend visualization. |
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| `json_file` | `str` | `None` | Path to the JSON file defining regions for parking systems or similar applications. Enables flexible configuration of analysis areas. |
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| `records` | `int` | `5` | Total detections count that triggers an automated email notification about unusual activity. |
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16
docs/en/reference/solutions/security_alarm.md
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docs/en/reference/solutions/security_alarm.md
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@ -0,0 +1,16 @@
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---
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description: Discover how Ultralytics' Security Alarm System enhances real-time surveillance with intelligent object detection and tracking. Learn about setup, monitoring, and threat detection.
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keywords: Ultralytics, Security Alarm System, Real-time Surveillance, Object Detection, Video Monitoring, Python, Threat Detection
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---
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# Reference for `ultralytics/solutions/security_alarm.py`
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!!! note
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This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/security_alarm.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/security_alarm.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/solutions/security_alarm.py) 🛠️. Thank you 🙏!
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<br>
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## ::: ultralytics.solutions.security_alarm.SecurityAlarm
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<br><br>
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@ -49,6 +49,10 @@ Here's our curated list of Ultralytics solutions that can be used to create awes
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yolo solutions source="path/to/video/file.mp4" # specify video file path
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```
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## Arguments
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{% include "macros/solutions-args.md" %}
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## Contribute to Our Solutions
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We welcome contributions from the community! If you've mastered a particular aspect of Ultralytics YOLO that's not yet covered in our solutions, we encourage you to share your expertise. Writing a guide is a great way to give back to the community and help us make our documentation more comprehensive and user-friendly.
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