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Face Scan

face-scan is a small OpenCV toolkit for running face detection across images, live camera feeds, and video files. The project now shares one reusable core instead of separate ad hoc scripts, and it adds structured summaries, audit trails, privacy redaction modes, and better output validation.

What Changed

The project has been overhauled around a reusable face_scan/ package:

  • Shared detector, media, workflow, and observability modules
  • Safer output handling for images, recordings, snapshots, and JSON summaries
  • Dedicated video-file processing via detect_video.py
  • Better CLI parity across image, video, and live capture modes
  • Support for camera indexes or device/file paths in live capture
  • Cleaner error handling when cascades or output targets are invalid
  • Lightweight tests for audit logging, JSON output, and capture-source parsing

Requirements

  • Python 3.x
  • OpenCV

Install dependencies:

/home/wellington/env/bin/python -m pip install -r requirements.txt

Commands

Use /home/wellington/env/bin/python for every command below.

Detect Faces in an Image

/home/wellington/env/bin/python detect.py image.jpg --output annotated.jpg --summary-json reports/image.json --show-metrics --draw-labels --no-show

Useful flags:

  • --privacy blur|pixelate|black
  • --cascade-sha256 <sha256>
  • --audit-log logs/audit.jsonl
  • --log-format json --log-file logs/run.log

Detect Faces from a Webcam or Capture Device

/home/wellington/env/bin/python detectCapture.py --camera 0 --show-metrics --snapshot-dir snapshots --record output/live.mp4 --summary-json reports/live.json

Useful flags:

  • --camera 0 for numeric camera indexes
  • --camera /path/to/device-or-video-source for string sources
  • --timeout 60
  • --reconnect-attempts 3 --reconnect-delay 0.5
  • --privacy blur|pixelate|black

Detect Faces in a Video File

/home/wellington/env/bin/python detect_video.py input.mp4 --output output/annotated.mp4 --summary-json reports/video.json --snapshot-dir snapshots/video --sample-every 2 --show-metrics

By default this opens an annotated playback window while processing. Press q or Esc to stop early. Use --no-display for headless runs.

Useful flags:

  • --sample-every N to trade accuracy for speed
  • --max-frames N to cap long runs
  • --snapshot-dir <dir> to save sampled frames containing faces
  • --privacy blur|pixelate|black

Summary Output

Each workflow can emit a JSON summary with fields such as:

  • mode
  • source
  • frames_processed
  • frames_with_faces
  • total_faces
  • max_faces_in_frame
  • avg_faces_per_processed_frame
  • avg_detection_seconds

Audit Log Verification

If you write an audit log with --audit-log, verify the tamper-evident hash chain with:

/home/wellington/env/bin/python verify_audit.py logs/audit.jsonl

Tests

Run the lightweight regression suite with:

/home/wellington/env/bin/python -m unittest discover -s tests -p 'test_*.py'