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P1 fixes (this sprint): - P1-6: Extract sensing-server modules (cli, types, csi, pose) from main.rs - P1-7: DDA ray march for tomography — O(max(n)) replaces O(n^3) voxel scan - P1-8: Batch neural inference — Tensor::stack/split for single GPU call - P1-10: Eliminate 112KB/frame alloc — islice replaces deque→list copy P2 fixes (this quarter): - P2-11: Python unit tests for 8 modules (rate_limit, auth, error_handler, pose_service, stream_service, hardware_service, health_check, metrics) - P2-13: MAT simulated data safety guard — blocking overlay + pulsing banner - P2-14: Wire token blacklist into auth verification + logout endpoint - P2-15: Frame budget benchmark — confirms pipeline well under 50ms budget Addresses 8 of 10 remaining issues from QE analysis (ADR-080). Co-Authored-By: claude-flow <ruv@ruv.net> |
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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/.