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Optimization: - Cache mean phase per frame in ring buffer for O(1) Doppler access - Sliding window (last 64 frames) instead of full history traversal - Doppler FFT: 253.9us -> 44.9us per frame (5.7x faster) - Full pipeline: 719.2us -> 254.2us per frame (2.8x faster) Trust kill switch: - ./verify: one-command proof replay with SHA-256 hash verification - Enhanced verify.py with source provenance, feature inspection, --audit - Makefile with verify/verify-verbose/verify-audit targets - New hash: 0b82bd45e836e5a99db0494cda7795832dda0bb0a88dac65a2bab0e949950ee0 Benchmark fix: - NN inference_bench.rs uses MockBackend instead of calling forward() which now correctly errors when no weights are loaded https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
WiFi-DensePose v1 (Python Implementation)
This directory contains the original Python implementation of WiFi-DensePose.
Structure
v1/
├── src/ # Python source code
│ ├── api/ # REST API endpoints
│ ├── config/ # Configuration management
│ ├── core/ # Core processing logic
│ ├── database/ # Database models and migrations
│ ├── hardware/ # Hardware interfaces
│ ├── middleware/ # API middleware
│ ├── models/ # Neural network models
│ ├── services/ # Business logic services
│ └── tasks/ # Background tasks
├── tests/ # Test suite
├── docs/ # Documentation
├── scripts/ # Utility scripts
├── data/ # Data files
├── setup.py # Package setup
├── test_application.py # Application tests
└── test_auth_rate_limit.py # Auth/rate limit tests
Requirements
- Python 3.10+
- PyTorch 2.0+
- FastAPI
- PostgreSQL/SQLite
Installation
cd v1
pip install -e .
Usage
# Start API server
python -m src.main
# Run tests
pytest tests/
Note
This is the legacy Python implementation. For the new Rust implementation with improved performance, see /rust-port/wifi-densepose-rs/.