Your passport photo, but make it Python

What it does HivisionIDPhotos is a Python pipeline for generating standard ID photos from ordinary portraits. It detects faces, removes backgrounds, swaps in compliant colors, and outputs print-ready layouts at specified dimensions — all without calling home to cloud APIs unless you opt in.

The interesting bit The project treats “boring” bureaucracy as a computer vision problem. It bundles multiple matting models (MODNet, BiRefNet, RMBG) and face detectors (MTCNN, RetinaFace) so you can trade speed for precision. The default MODNet + MTCNN combo runs in ~0.2 seconds on CPU using 410MB RAM — faster than most photo booth queues.

Key highlights

• Pure offline inference; CPU-only by default, with optional GPU acceleration for BiRefNet
• Gradio web UI, FastAPI service, and raw Python CLI all included
• Outputs standard ID photos, transparent PNGs, background-colored variants, and print layout sheets (6-inch, 5-inch, A4, etc.)
• Face alignment, beauty filter, and custom HEX background colors supported
• Active community ports: ComfyUI nodes, WeChat mini-programs, Windows GUI, even a C++ rewrite

Caveats

• “Smart clothing swap” is listed as a planned feature but marked “waiting” — not implemented
• GPU acceleration requires ~16GB VRAM and manual CUDA/cuDNN setup; only BiRefNet benefits
• Higher-precision matting (BiRefNet + RetinaFace) jumps from sub-second to ~7 seconds and 6GB+ memory

Verdict Worth a look if you build photo kiosks, HR onboarding tools, or just hate drugstore photo lines. Skip if you need the clothing-swap feature today or expect one-click GPU setup.