If you’ve got an old USB webcam lying around and a Raspberry Pi collecting dust, here’s the perfect weekend project: turn them into a real-time object detection and web livestream system using Python, OpenCV, and the powerful YOLOv8 model!
This project is lightweight, beginner-friendly, and completely open-source. You can find the full code on GitHub here: Webcam-Livestream Repository
What You’ll Need
- Raspberry Pi (any model with a USB port, but Pi 4 is ideal)
- A USB webcam
- MicroSD card with Raspberry Pi OS
- Basic knowledge of Python
What This Project Does
This project allows you to:
- Capture video from a USB webcam in real-time.
- Detect objects in the video feed using YOLOv8 (a fast, accurate deep learning model).
- Serve the processed video stream to a browser using a lightweight web server.
- View it all live from your local network!
What This Project Does
This project allows you to:
- Capture video from a USB webcam in real-time.
- Detect objects in the video feed using YOLOv8 (a fast, accurate deep learning model).
- Serve the processed video stream to a browser using a lightweight web server.
- View it all live from your local network!
Under the Hood
Here’s how it works:
1. Real-Time Video with OpenCV
OpenCV grabs frames from your USB webcam and sends them for processing.
cap = cv2.VideoCapture(0)
ret, frame = cap.read()2. YOLOv8 for Object Detection
I’m using the Ultralytics YOLOv8n model—it’s compact and fast enough for Raspberry Pi:
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
results = model(frame)3. Annotate and Stream
Bounding boxes are drawn on the frame, and the output is streamed using Python’s aiohttp server.
cv2.rectangle(...)
web.Response(body=jpeg_frame, content_type="image/jpeg")For full detailed project: https://github.com/nickdu088/Webcam-Livestream
If you build something with it, let me know or contribute to the repo!
