- Created comprehensive API reference documentation covering authentication, request/response formats, error handling, and various API endpoints for pose estimation, system management, health checks, and WebSocket interactions.
- Developed a detailed deployment guide outlining prerequisites, Docker and Kubernetes deployment steps, cloud deployment options for AWS, GCP, and Azure, and configuration for production environments.
- Added HardwareService for managing router interfaces, data collection, and monitoring.
- Introduced PoseService for processing CSI data and estimating poses using neural networks.
- Created StreamService for real-time data streaming via WebSocket connections.
- Implemented initialization, start, stop, and status retrieval methods for each service.
- Added data processing, error handling, and statistics tracking across services.
- Integrated mock data generation for development and testing purposes.
- Added CSIExtractor class for extracting CSI data from WiFi routers.
- Implemented RouterInterface class for SSH communication with routers.
- Developed DensePoseHead class for body part segmentation and UV coordinate regression.
- Created unit tests for CSIExtractor and RouterInterface to ensure functionality and error handling.
- Integrated paramiko for SSH connections and command execution.
- Established configuration validation for both extractor and router interface.
- Added context manager support for resource management in both classes.
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads.
- Added a comprehensive training utility for the model, including loss functions and training steps.
- Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
