Radek
1b99573b65
api.py runs the camera producer in a background thread and serves the
latest entropy blob over HTTP. Endpoints: GET / (info), GET /healthz
(liveness, unauthenticated), GET /entropy (returns {hex, bits, ts}).
Auth is a single shared secret via X-API-Key header, controlled by the
ENTROPY_API_KEY env var; empty means no auth, so it is easy to start
open and lock down later. All config is via env vars for containers.
Dockerfile uses python:3.12-slim, installs OpenCV runtime libs, pins
deps from requirements.txt, exposes 8000, and has a healthcheck against
/healthz. Run with: docker build -t entropy-rng . && docker run -p 8000:8000 \
-e ENTROPY_CAMERA_URL=http://192.168.0.200/mjpg/video.mjpg entropy-rng
Generated by Mistral Vibe.
Co-Authored-By: Mistral Vibe <vibe@mistral.ai>
Entropy-RNG: Camera-Based Random Number Generator
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
secretsmodule, 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) requestslibrary
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
secretsmodule. - 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.