Show HN: Rendering 18,000 videos in real-time with Python
2 days ago
- #computational-art
- #python-optimization
- #video-rendering
- The project involves creating an image where each pixel is a video, requiring massive computational resources.
- Initial challenges include rendering a 2 gigapixel video needing 2513GB of RAM and taking 11 minutes per frame in Python.
- Technique 1: Mip-mapping reduces detail for distant objects, speeding up rendering by 1024x.
- Technique 2: Culling ignores off-screen elements, saving processing power and enabling higher zoom levels.
- Technique 3: Using Numba and PyGame bypasses Python's limitations, increasing rendering speed to 120 FPS.
- Technique 4: Texture streaming and dynamic allocation optimize RAM usage by loading only visible tiles.
- Advanced optimizations include LRU caching, prefetching, and fine-tuned heuristics to manage RAM efficiently.
- The final system can render videos in real-time with minimal RAM, demonstrating a Parks & Recreation episode where each pixel is a frame from the show.
- The project utilizes multi-threading for decoding, mosaic generation, tile streaming, and rendering, all synchronized in Python.