PyFaceBlur

An interactive command-line tool that automatically detects, clusters, and blurs faces in videos. It guides you through a simple step-by-step process to extract frames, group people by facial identity, select who you want to blur, and re-encode the video.

Features

• Interactive CLI: Built with rich and questionary for a clean, prompt-based UX including file path auto-completion.
• Accurate Face Recognition: Uses UniFace (RetinaFace detection + ArcFace 512-dim neural embeddings via ONNX Runtime) to accurately re-identify the same person across a video.
• DBSCAN Clustering: Automatically groups identical faces into "clusters" using Cosine similarity.
• Hardware-Accelerated Encoding: Automatically detects and leverages GPU encoders like av1_vaapi, hevc_vaapi, h264_vaapi, h264_nvenc, and more via FFmpeg.
• Visual Face Selection: Extracts one high-quality thumbnail per detected person and opens your system's file explorer so you can easily check boxes for who to blur.
• Multiple Blur Styles: Choose from Gaussian, Pixelate, Blackout, Elliptical, or Median blur methods.
• Smooth Interpolation: Bounding boxes are linearly interpolated between sampled keyframes and held static when faces exit/enter, ensuring smooth blurring without split-second exposures.

Requirements

• Python 3.11+
• uv for fast dependency management
• ffmpeg installed and available in your system $PATH (for frame extraction and re-encoding)

Setup

# Clone the repository and navigate to the project directory
cd py-faceblur

# Sync dependencies using uv
uv sync

Usage

Run the interactive wizard:

uv run pyfaceblur

The Pipeline

1. Input: You provide the path to your video and the frame sampling interval (e.g., sample every 30th frame).
2. Processing: The app uses FFmpeg to extract frames, runs RetinaFace to find all faces, and generates ArcFace embeddings.
3. Clustering: DBSCAN groups the embeddings to identify unique individuals.
4. Selection: The app saves a thumbnail of each person to a temporary folder, opens it, and asks you to select which people to blur using interactive checkboxes.
5. Encoding: The app finds the best available video encoder on your system, applies the chosen blur method to the selected faces, interpolates their movement, and generates a new *_blurred.mp4 video.

Advanced / Legacy CLI

The original proof-of-concept command-line interface is also still available for purely extracting and debugging the clustering outputs into an output folder.

uv run pyfaceblur-legacy detect --video input.mp4 --output ./output --interval 30 --confidence 0.7