High-performance, cross-platform real-time ASCII video rendering engine

ASCILINE, a high-performance cross-platform real-time ASCII video rendering engine, transforms video into text-based representations on HTML5 Canvas, enabling ultra-low bandwidth streaming, AI integration, and bypassing browser media restrictions. The engine uses a custom adaptive codec that reduces wire data by up to 375x for static content and 63% for high-motion frames, with optional lossy temporal delta for further savings.

ASCILINE is a high-performance, cross-platform real-time ASCII video rendering engine. Our core objective is to transform the web into a highly dynamic and interactive typographic canvas. By mapping pixels to text-based representations, we unlock new possibilities for web media delivery. Pure Typographic Manipulation : The visual stream is not a standard media file—it's raw HTML/Canvas text. This makes the impossible possible: you can apply real-time CSS filters neon glows, text shadows, animations to video content. Local AI & LLM Ready : By reducing complex pixel streams into structured logical strings, ASCILINE acts as a perfect bridge for AI. Instead of feeding heavy computer vision models, lightweight LLMs can process semantic video summaries. Ultra-Low Bandwidth & IoT Compatibility valid for ASCII MOD : Standard codecs H.264/VP9 choke microcontrollers and weak networks. ASCILINE processes the heavy lifting once on the backend, streaming only a few kilobytes per frame. Bypassing Browser Constraints : Modern browsers aggressively throttle autoplay videos, and ad-blockers restrict traditional media frames. To the browser, ASCILINE is simply "JavaScript updating a canvas"—completely invisible to media restrictions. Cross-Platform : Runs seamlessly on Windows, macOS, and Linux. Real-Time ASCII Streaming : Low-latency video-to-ASCII conversion. Real-Time Pixel Streaming : Replaces characters with colored blocks, approaching 360p video quality. High Performance : Uses HTML5 Canvas for rendering, optimized for cinematic 24-30 FPS playback. High-FPS sources are automatically decimated for stability. Master Clock Sync : The audio track acts as the absolute master clock, guaranteeing perfect A/V synchronization.- Low-Overhead Binary Protocol : Frames are streamed as raw binary Uint8Array directly to the canvas, saving bandwidth and CPU. Multiple Color Modes : Supports everything from classic B&W to 16M color ultra-fidelity. Flexible Video Management : Supports JSON playlists per-video mode & volume , folder-based auto-queuing filesystem order , single-file mode, and infinite loop playback — all controlled via CLI arguments. Backend Python/FastAPI : Decodes video using OpenCV, maps pixels to ASCII characters via NumPy, and streams binary data. Frontend Vanilla JS : Receives binary frames via WebSockets, manages a jitter buffer, and renders to a Canvas grid. Communication : Optimized WebSocket protocol with a custom INIT handshake for dynamic resolution/FPS adjustment. The original binary protocol re-sends the full grid every frame. An opt-in adaptive codec picks the smallest of three encodings per frame and tags it in a 1-byte header — without changing the rendered output : | tag | encoding | best for | |---|---|---| 0 RAW | framebuffer as-is legacy | incompressible frames | 1 ZLIB | zlib framebuffer | general motion | 2 DELTA | only the cells that changed since the last frame | static / low-motion | Clients opt in with /ws?codec=adaptive ; omit it and you get the original protocol byte-for-byte , so existing clients are unaffected. A keyframe is forced periodically so dropped packets / late joiners resync. The decoder codec.js is shared by the browser and the test suite, so the shipped path is the tested one. Measured wire savings mode 5, 200×80 grid : | content | vs. legacy | |---|---| | static screen / slideshow | 0.3% ≈375× | | pixel mode | 11.6% ≈8.6× | | high-motion / full-frame change | 63% never worse than legacy | An optional --quality {lossless,high,balanced,low} enables lossy temporal delta : a colour cell is only re-sent once it drifts past a tolerance from what the viewer already sees the character plane stays exact , cutting the hard cases a further ~15–30% at imperceptible quality. Default is lossless bit-exact . Monitor Bandwidth in Real-Time: You can append the --debug flag when launching the server to see live bandwidth comparisons RAW vs WIRE bytes and the exact compression ratio in your terminal. This is highly useful for measuring the real-time savings of the adaptive codec on your specific video sources. Verified two independent ways, both bit-exact: Python-encoded vectors decoded by codec.js in Node experiments/gen vectors.py → experiments/check vectors.js , and a live adaptive -vs- legacy WebSocket diff experiments/test e2e.js . Generate the test clips with experiments/make test clips.sh . A fuller mutation-test + Autobahn git clone https://github.com/YusufB5/ASCILINE.git cd ASCILINE pip install fastapi uvicorn opencv-python numpy websockets To enable server-side audio processing Volume 1-5 , you must have FFmpeg installed. Option 1: Package Manager Recommended Windows: winget install ffmpeg macOS: brew install ffmpeg Linux: sudo apt install ffmpeg Option 2: Manual Installation Windows If you get a FileNotFoundError or don't want to modify system variables: - Download FFmpeg ZIP https://github.com/BtbN/FFmpeg-Builds/releases/latest . - Extract ffmpeg.exe from the bin folder. - Drop it directly into your ASCILINE project folder alongside stream server.py . Single video: python stream server.py video.mp4 --cols 240 Folder mode — drop your videos into videos/ and run: python stream server.py --folder videos --cols 200 python stream server.py --folder videos --cols 230 --loop infinite loop python stream server.py --folder videos --mode 5 --pixel --cols 320 --vol 2 all videos same settings Videos play in filesystem order top to bottom as they appear in the folder, not alphabetically . Just add/remove files from the videos/ folder to control the queue. JSON Playlist — full control per video: python stream server.py --playlist playlist.json --cols 220 python stream server.py --playlist playlist.json --cols 220 --loop Use playlist.json when you need different --mode or --vol settings for each video. 💡 Windows Users:You can use the included serve.bat shortcut for quicker typing: .\serve video.mp4 --cols 240 Open http://localhost:8000 in your browser. If you prefer to bypass the web interface, you can render the video directly inside an ANSI-supported terminal zero-flicker, true color : python ascii video player2.py video.mp4 --cols 100 --quality 0 💡 Windows Users:Use the shortcut .\play video.mp4 -c 100 -q 0 ⚠️ Note:Do not resize your terminal window during playback, as dynamic text wrapping will corrupt the ASCII layout. You can easily customize the look and feel of the engine: Edit style.css to change the accent colors and typography using CSS variables: :root { --accent-color: 00ff41; / Classic Matrix Green / --bg-color: 050505; } The engine supports different fidelity levels via the --mode flag: 1 : Black & White DOM mode 2 : 512 Colors 3 : 32K Colors 4 : 262K Colors 5 : 16M Colors Ultra python stream server.py --mode 5 --cols 240 --rows 100 By default, you only need to specify the width --cols . ASCILINE will automatically calculate the correct --rows based on the source video's aspect ratio to prevent stretching. ASCII Mode Recommended: --cols 200 to --cols 240 Best balance of text detail and cinematic 30 FPS performance . Pixel Mode Recommended: --cols 600 to --cols 900 Provides near-HD visual quality. Performance heavily depends on your machine's CPU/VRAM .- Smart Defaults: If you do not specify a --cols value, ASCILINE automatically defaults to 450 when Pixel Mode is enabled, and 200 for standard ASCII text mode. - ⚠️ Hardware Limits & A/V Sync: If you push the --cols too high for your specific hardware e.g., 1350 on a laptop vs a gaming desktop , the Python backend won't be able to encode and send the massive frames fast enough. When the video stream lags behind the audio, you will experience A/V desync audio finishing early . If this happens, simply lower your --cols value python stream server.py video.mp4 --mode 5 --cols 240 Terminal will show: AUTO 1920x1080 → grid 240x67 Volume is controlled at the server level via the --vol flag scale 0–5 . When set to 0 , the audio engine FFmpeg never runs , saving CPU and bandwidth. --vol | FFmpeg Multiplier | Description | |---|---|---| 0 | — | Muted no processing | 1 | 1.0× | Normal default | 3 | 1.5× | Loud | 5 | 2.0× | Double volume | python stream server.py video.mp4 --pixel --cols 560 --vol 0 Silent python stream server.py video.mp4 --cols 220 --vol 3 Loud Each entry can override the global --mode , --pixel , --vol , and --cols defaults: { "video": "intro.mp4", "mode": 1, "vol": 1 }, { "video": "main.mp4", "mode": 5, "pixel": true, "vol": 3, "cols": 520 }, { "video": "outro.mp4", "mode": 3, "vol": 2, "cols": 240 } Video paths are resolved automatically — the engine checks the project root and the videos/ subfolder, so you can write just the filename. ASCILINE is distributed under the MIT License, but with a strict ethical guardrail. Because this engine bypasses standard browser constraints and ad-blockers by rendering pure text instead of video , we strictly prohibit its use by ad-networks to serve unblockable advertisements. See the LICENSE /YusufB5/ASCILINE/blob/main/LICENSE file for the full text, which includes the ANTI-ADVERTISEMENT RESTRICTION clause. If you find this project helpful, you can support me by donating crypto: Solana SOL / USDC : H1wSQAhjgsu7AxenF4e5ZBYiBjkhDLVzkKaZuVPcrE14 Ethereum ETH / USDT : 0x85B2f970045c0F7c282089Ab6CF897C20230e086 Bitcoin BTC : bc1qvtcl55v54gkzwnp2zxn70usea3gf5ncncqa0fv MIT License with Anti-Ad Restriction