This project uses a live video feed from an Axis M1013 network camera to generate cryptographically secure random numbers. The camera is pointed at a high-contrast scene (e.g., ceiling and lamps) to ensure ever-changing pixel data, which is used as a source of entropy.

How It Works

Capture Image Frames The script fetches a single frame from the camera's MJPEG stream using OpenCV or manual HTTP requests.
Extract Entropy The pixel data from the frame is hashed using SHA-256 to generate a high-quality entropy pool.
Generate Random Numbers The entropy is used to seed Python's secrets module, which generates cryptographically secure random numbers of specified bit lengths (e.g., 128-bit, 256-bit).

Requirements

Python 3.x
OpenCV (opencv-python)
Pillow (Pillow)
requests library

Install dependencies:

pip install opencv-python Pillow requests

Usage

1. OpenCV Method (Recommended)

Run the OpenCV script to fetch a frame and generate random numbers:

python3 entropy-rng-opencv.py

Example Output:

128-bit random number: 0x67dbaf3c5eba5a0d41429d2173a21f2f
256-bit random number: 0xe3a707c10f70f975e074633ad22ea0eaaa8b482e87c6418e3ae2380b2e647a4

2. Manual HTTP Method

Run the manual script (alternative to OpenCV):

python3 entropy-rng-cam.py

Example Output:

128-bit random number: 0x494d97b11a4b6008e4597cfc29108354
256-bit random number: 0xd59f7f4d22ebf7507b702fb60e0f2a6ccbf2ad4b5e3ab969f9152222da8e9443

Output Format

The generated random numbers are in hexadecimal format (e.g., 0x67dbaf3c5eba5a0d41429d2173a21f2f).

128-bit numbers: 32 hex digits
256-bit numbers: 64 hex digits

Why This Approach?

True Entropy Source: The camera feed provides a dynamic, unpredictable source of entropy.
Cryptographically Secure: Uses SHA-256 hashing and Python's secrets module.
No Additional Hardware: Leverages existing network cameras.

Notes

Ensure the camera feed is dynamic (e.g., pointing at changing light patterns).
For production use, consider adding error handling and logging.