feat: Implement hardware, pose, and stream services for WiFi-DensePose API
- 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.
This commit is contained in:
rUv
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src/services/pose_service.py
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706
src/services/pose_service.py
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"""
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Pose estimation service for WiFi-DensePose API
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"""
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import logging
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import asyncio
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from typing import Dict, List, Optional, Any
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from datetime import datetime, timedelta
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import numpy as np
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import torch
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from src.config.settings import Settings
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from src.config.domains import DomainConfig
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from src.core.csi_processor import CSIProcessor
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from src.core.phase_sanitizer import PhaseSanitizer
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from src.models.densepose_head import DensePoseHead
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from src.models.modality_translation import ModalityTranslationNetwork
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logger = logging.getLogger(__name__)
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class PoseService:
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"""Service for pose estimation operations."""
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def __init__(self, settings: Settings, domain_config: DomainConfig):
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"""Initialize pose service."""
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self.settings = settings
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self.domain_config = domain_config
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self.logger = logging.getLogger(__name__)
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# Initialize components
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self.csi_processor = None
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self.phase_sanitizer = None
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self.densepose_model = None
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self.modality_translator = None
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# Service state
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self.is_initialized = False
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self.is_running = False
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self.last_error = None
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# Processing statistics
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self.stats = {
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"total_processed": 0,
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"successful_detections": 0,
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"failed_detections": 0,
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"average_confidence": 0.0,
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"processing_time_ms": 0.0
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}
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async def initialize(self):
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"""Initialize the pose service."""
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try:
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self.logger.info("Initializing pose service...")
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# Initialize CSI processor
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csi_config = {
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'buffer_size': self.settings.csi_buffer_size,
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'sample_rate': 1000, # Default sampling rate
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'num_subcarriers': 56,
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'num_antennas': 3
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}
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self.csi_processor = CSIProcessor(config=csi_config)
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# Initialize phase sanitizer
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self.phase_sanitizer = PhaseSanitizer()
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# Initialize models if not mocking
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if not self.settings.mock_pose_data:
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await self._initialize_models()
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else:
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self.logger.info("Using mock pose data for development")
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self.is_initialized = True
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self.logger.info("Pose service initialized successfully")
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except Exception as e:
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self.last_error = str(e)
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self.logger.error(f"Failed to initialize pose service: {e}")
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raise
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async def _initialize_models(self):
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"""Initialize neural network models."""
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try:
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# Initialize DensePose model
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if self.settings.pose_model_path:
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self.densepose_model = DensePoseHead()
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# Load model weights if path is provided
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# model_state = torch.load(self.settings.pose_model_path)
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# self.densepose_model.load_state_dict(model_state)
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self.logger.info("DensePose model loaded")
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else:
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self.logger.warning("No pose model path provided, using default model")
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self.densepose_model = DensePoseHead()
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# Initialize modality translation
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config = {
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'input_channels': 64, # CSI data channels
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'hidden_channels': [128, 256, 512],
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'output_channels': 256, # Visual feature channels
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'use_attention': True
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}
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self.modality_translator = ModalityTranslationNetwork(config)
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# Set models to evaluation mode
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self.densepose_model.eval()
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self.modality_translator.eval()
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except Exception as e:
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self.logger.error(f"Failed to initialize models: {e}")
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raise
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async def start(self):
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"""Start the pose service."""
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if not self.is_initialized:
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await self.initialize()
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self.is_running = True
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self.logger.info("Pose service started")
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async def stop(self):
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"""Stop the pose service."""
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self.is_running = False
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self.logger.info("Pose service stopped")
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async def process_csi_data(self, csi_data: np.ndarray, metadata: Dict[str, Any]) -> Dict[str, Any]:
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"""Process CSI data and estimate poses."""
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if not self.is_running:
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raise RuntimeError("Pose service is not running")
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start_time = datetime.now()
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try:
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# Process CSI data
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processed_csi = await self._process_csi(csi_data, metadata)
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# Estimate poses
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poses = await self._estimate_poses(processed_csi, metadata)
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# Update statistics
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processing_time = (datetime.now() - start_time).total_seconds() * 1000
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self._update_stats(poses, processing_time)
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return {
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"timestamp": start_time.isoformat(),
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"poses": poses,
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"metadata": metadata,
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"processing_time_ms": processing_time,
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"confidence_scores": [pose.get("confidence", 0.0) for pose in poses]
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}
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except Exception as e:
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self.last_error = str(e)
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self.stats["failed_detections"] += 1
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self.logger.error(f"Error processing CSI data: {e}")
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raise
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async def _process_csi(self, csi_data: np.ndarray, metadata: Dict[str, Any]) -> np.ndarray:
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"""Process raw CSI data."""
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# Add CSI data to processor
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self.csi_processor.add_data(csi_data, metadata.get("timestamp", datetime.now()))
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# Get processed data
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processed_data = self.csi_processor.get_processed_data()
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# Apply phase sanitization
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if processed_data is not None:
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sanitized_data = self.phase_sanitizer.sanitize(processed_data)
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return sanitized_data
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return csi_data
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async def _estimate_poses(self, csi_data: np.ndarray, metadata: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Estimate poses from processed CSI data."""
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if self.settings.mock_pose_data:
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return self._generate_mock_poses()
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try:
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# Convert CSI data to tensor
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csi_tensor = torch.from_numpy(csi_data).float()
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# Add batch dimension if needed
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if len(csi_tensor.shape) == 2:
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csi_tensor = csi_tensor.unsqueeze(0)
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# Translate modality (CSI to visual-like features)
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with torch.no_grad():
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visual_features = self.modality_translator(csi_tensor)
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# Estimate poses using DensePose
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pose_outputs = self.densepose_model(visual_features)
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# Convert outputs to pose detections
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poses = self._parse_pose_outputs(pose_outputs)
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# Filter by confidence threshold
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filtered_poses = [
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pose for pose in poses
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if pose.get("confidence", 0.0) >= self.settings.pose_confidence_threshold
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]
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# Limit number of persons
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if len(filtered_poses) > self.settings.pose_max_persons:
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filtered_poses = sorted(
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filtered_poses,
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key=lambda x: x.get("confidence", 0.0),
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reverse=True
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)[:self.settings.pose_max_persons]
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return filtered_poses
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except Exception as e:
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self.logger.error(f"Error in pose estimation: {e}")
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return []
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def _parse_pose_outputs(self, outputs: torch.Tensor) -> List[Dict[str, Any]]:
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"""Parse neural network outputs into pose detections."""
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poses = []
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# This is a simplified parsing - in reality, this would depend on the model architecture
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# For now, generate mock poses based on the output shape
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batch_size = outputs.shape[0]
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for i in range(batch_size):
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# Extract pose information (mock implementation)
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confidence = float(torch.sigmoid(outputs[i, 0]).item()) if outputs.shape[1] > 0 else 0.5
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pose = {
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"person_id": i,
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"confidence": confidence,
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"keypoints": self._generate_keypoints(),
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"bounding_box": self._generate_bounding_box(),
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"activity": self._classify_activity(outputs[i] if len(outputs.shape) > 1 else outputs),
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"timestamp": datetime.now().isoformat()
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}
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poses.append(pose)
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return poses
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def _generate_mock_poses(self) -> List[Dict[str, Any]]:
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"""Generate mock pose data for development."""
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import random
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num_persons = random.randint(1, min(3, self.settings.pose_max_persons))
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poses = []
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for i in range(num_persons):
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confidence = random.uniform(0.3, 0.95)
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pose = {
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"person_id": i,
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"confidence": confidence,
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"keypoints": self._generate_keypoints(),
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"bounding_box": self._generate_bounding_box(),
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"activity": random.choice(["standing", "sitting", "walking", "lying"]),
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"timestamp": datetime.now().isoformat()
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}
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poses.append(pose)
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return poses
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def _generate_keypoints(self) -> List[Dict[str, Any]]:
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"""Generate keypoints for a person."""
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import random
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keypoint_names = [
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"nose", "left_eye", "right_eye", "left_ear", "right_ear",
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"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
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"left_wrist", "right_wrist", "left_hip", "right_hip",
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"left_knee", "right_knee", "left_ankle", "right_ankle"
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]
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keypoints = []
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for name in keypoint_names:
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keypoints.append({
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"name": name,
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"x": random.uniform(0.1, 0.9),
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"y": random.uniform(0.1, 0.9),
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"confidence": random.uniform(0.5, 0.95)
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})
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return keypoints
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def _generate_bounding_box(self) -> Dict[str, float]:
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"""Generate bounding box for a person."""
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import random
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x = random.uniform(0.1, 0.6)
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y = random.uniform(0.1, 0.6)
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width = random.uniform(0.2, 0.4)
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height = random.uniform(0.3, 0.5)
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return {
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"x": x,
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"y": y,
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"width": width,
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"height": height
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}
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def _classify_activity(self, features: torch.Tensor) -> str:
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"""Classify activity from features."""
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# Simple mock classification
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import random
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activities = ["standing", "sitting", "walking", "lying", "unknown"]
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return random.choice(activities)
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def _update_stats(self, poses: List[Dict[str, Any]], processing_time: float):
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"""Update processing statistics."""
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self.stats["total_processed"] += 1
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if poses:
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self.stats["successful_detections"] += 1
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confidences = [pose.get("confidence", 0.0) for pose in poses]
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avg_confidence = sum(confidences) / len(confidences)
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# Update running average
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total = self.stats["successful_detections"]
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current_avg = self.stats["average_confidence"]
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self.stats["average_confidence"] = (current_avg * (total - 1) + avg_confidence) / total
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else:
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self.stats["failed_detections"] += 1
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# Update processing time (running average)
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total = self.stats["total_processed"]
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current_avg = self.stats["processing_time_ms"]
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self.stats["processing_time_ms"] = (current_avg * (total - 1) + processing_time) / total
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async def get_status(self) -> Dict[str, Any]:
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"""Get service status."""
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return {
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"status": "healthy" if self.is_running and not self.last_error else "unhealthy",
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"initialized": self.is_initialized,
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"running": self.is_running,
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"last_error": self.last_error,
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"statistics": self.stats.copy(),
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"configuration": {
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"mock_data": self.settings.mock_pose_data,
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"confidence_threshold": self.settings.pose_confidence_threshold,
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"max_persons": self.settings.pose_max_persons,
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"batch_size": self.settings.pose_processing_batch_size
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}
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}
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async def get_metrics(self) -> Dict[str, Any]:
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"""Get service metrics."""
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return {
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"pose_service": {
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"total_processed": self.stats["total_processed"],
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"successful_detections": self.stats["successful_detections"],
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"failed_detections": self.stats["failed_detections"],
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"success_rate": (
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self.stats["successful_detections"] / max(1, self.stats["total_processed"])
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),
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"average_confidence": self.stats["average_confidence"],
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"average_processing_time_ms": self.stats["processing_time_ms"]
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}
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}
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async def reset(self):
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"""Reset service state."""
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self.stats = {
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"total_processed": 0,
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"successful_detections": 0,
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"failed_detections": 0,
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"average_confidence": 0.0,
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"processing_time_ms": 0.0
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}
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self.last_error = None
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self.logger.info("Pose service reset")
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# API endpoint methods
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async def estimate_poses(self, zone_ids=None, confidence_threshold=None, max_persons=None,
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include_keypoints=True, include_segmentation=False):
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"""Estimate poses with API parameters."""
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try:
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# Generate mock CSI data for estimation
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mock_csi = np.random.randn(64, 56, 3) # Mock CSI data
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metadata = {
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"timestamp": datetime.now(),
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"zone_ids": zone_ids or ["zone_1"],
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"confidence_threshold": confidence_threshold or self.settings.pose_confidence_threshold,
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"max_persons": max_persons or self.settings.pose_max_persons
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}
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# Process the data
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result = await self.process_csi_data(mock_csi, metadata)
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# Format for API response
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persons = []
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for i, pose in enumerate(result["poses"]):
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person = {
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"person_id": str(pose["person_id"]),
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"confidence": pose["confidence"],
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"bounding_box": pose["bounding_box"],
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"zone_id": zone_ids[0] if zone_ids else "zone_1",
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"activity": pose["activity"],
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"timestamp": datetime.fromisoformat(pose["timestamp"])
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}
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if include_keypoints:
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person["keypoints"] = pose["keypoints"]
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if include_segmentation:
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person["segmentation"] = {"mask": "mock_segmentation_data"}
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persons.append(person)
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# Zone summary
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zone_summary = {}
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for zone_id in (zone_ids or ["zone_1"]):
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zone_summary[zone_id] = len([p for p in persons if p.get("zone_id") == zone_id])
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return {
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"timestamp": datetime.now(),
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"frame_id": f"frame_{int(datetime.now().timestamp())}",
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"persons": persons,
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"zone_summary": zone_summary,
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"processing_time_ms": result["processing_time_ms"],
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"metadata": {"mock_data": self.settings.mock_pose_data}
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}
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except Exception as e:
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self.logger.error(f"Error in estimate_poses: {e}")
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raise
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async def analyze_with_params(self, zone_ids=None, confidence_threshold=None, max_persons=None,
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include_keypoints=True, include_segmentation=False):
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"""Analyze pose data with custom parameters."""
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return await self.estimate_poses(zone_ids, confidence_threshold, max_persons,
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include_keypoints, include_segmentation)
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async def get_zone_occupancy(self, zone_id: str):
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"""Get current occupancy for a specific zone."""
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try:
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# Mock occupancy data
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import random
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count = random.randint(0, 5)
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persons = []
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for i in range(count):
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persons.append({
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"person_id": f"person_{i}",
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"confidence": random.uniform(0.7, 0.95),
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"activity": random.choice(["standing", "sitting", "walking"])
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})
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return {
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"count": count,
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"max_occupancy": 10,
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"persons": persons,
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"timestamp": datetime.now()
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}
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except Exception as e:
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self.logger.error(f"Error getting zone occupancy: {e}")
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return None
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async def get_zones_summary(self):
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"""Get occupancy summary for all zones."""
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try:
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import random
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zones = ["zone_1", "zone_2", "zone_3", "zone_4"]
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zone_data = {}
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total_persons = 0
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active_zones = 0
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for zone_id in zones:
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count = random.randint(0, 3)
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zone_data[zone_id] = {
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"occupancy": count,
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"max_occupancy": 10,
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"status": "active" if count > 0 else "inactive"
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}
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total_persons += count
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if count > 0:
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active_zones += 1
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return {
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"total_persons": total_persons,
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"zones": zone_data,
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"active_zones": active_zones
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}
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except Exception as e:
|
||||
self.logger.error(f"Error getting zones summary: {e}")
|
||||
raise
|
||||
|
||||
async def get_historical_data(self, start_time, end_time, zone_ids=None,
|
||||
aggregation_interval=300, include_raw_data=False):
|
||||
"""Get historical pose estimation data."""
|
||||
try:
|
||||
# Mock historical data
|
||||
import random
|
||||
from datetime import timedelta
|
||||
|
||||
current_time = start_time
|
||||
aggregated_data = []
|
||||
raw_data = [] if include_raw_data else None
|
||||
|
||||
while current_time < end_time:
|
||||
# Generate aggregated data point
|
||||
data_point = {
|
||||
"timestamp": current_time,
|
||||
"total_persons": random.randint(0, 8),
|
||||
"zones": {}
|
||||
}
|
||||
|
||||
for zone_id in (zone_ids or ["zone_1", "zone_2", "zone_3"]):
|
||||
data_point["zones"][zone_id] = {
|
||||
"occupancy": random.randint(0, 3),
|
||||
"avg_confidence": random.uniform(0.7, 0.95)
|
||||
}
|
||||
|
||||
aggregated_data.append(data_point)
|
||||
|
||||
# Generate raw data if requested
|
||||
if include_raw_data:
|
||||
for _ in range(random.randint(0, 5)):
|
||||
raw_data.append({
|
||||
"timestamp": current_time + timedelta(seconds=random.randint(0, aggregation_interval)),
|
||||
"person_id": f"person_{random.randint(1, 10)}",
|
||||
"zone_id": random.choice(zone_ids or ["zone_1", "zone_2", "zone_3"]),
|
||||
"confidence": random.uniform(0.5, 0.95),
|
||||
"activity": random.choice(["standing", "sitting", "walking"])
|
||||
})
|
||||
|
||||
current_time += timedelta(seconds=aggregation_interval)
|
||||
|
||||
return {
|
||||
"aggregated_data": aggregated_data,
|
||||
"raw_data": raw_data,
|
||||
"total_records": len(aggregated_data)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting historical data: {e}")
|
||||
raise
|
||||
|
||||
async def get_recent_activities(self, zone_id=None, limit=10):
|
||||
"""Get recently detected activities."""
|
||||
try:
|
||||
import random
|
||||
activities = []
|
||||
|
||||
for i in range(limit):
|
||||
activity = {
|
||||
"activity_id": f"activity_{i}",
|
||||
"person_id": f"person_{random.randint(1, 5)}",
|
||||
"zone_id": zone_id or random.choice(["zone_1", "zone_2", "zone_3"]),
|
||||
"activity": random.choice(["standing", "sitting", "walking", "lying"]),
|
||||
"confidence": random.uniform(0.6, 0.95),
|
||||
"timestamp": datetime.now() - timedelta(minutes=random.randint(0, 60)),
|
||||
"duration_seconds": random.randint(10, 300)
|
||||
}
|
||||
activities.append(activity)
|
||||
|
||||
return activities
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting recent activities: {e}")
|
||||
raise
|
||||
|
||||
async def is_calibrating(self):
|
||||
"""Check if calibration is in progress."""
|
||||
return False # Mock implementation
|
||||
|
||||
async def start_calibration(self):
|
||||
"""Start calibration process."""
|
||||
import uuid
|
||||
calibration_id = str(uuid.uuid4())
|
||||
self.logger.info(f"Started calibration: {calibration_id}")
|
||||
return calibration_id
|
||||
|
||||
async def run_calibration(self, calibration_id):
|
||||
"""Run calibration process."""
|
||||
self.logger.info(f"Running calibration: {calibration_id}")
|
||||
# Mock calibration process
|
||||
await asyncio.sleep(5)
|
||||
self.logger.info(f"Calibration completed: {calibration_id}")
|
||||
|
||||
async def get_calibration_status(self):
|
||||
"""Get current calibration status."""
|
||||
return {
|
||||
"is_calibrating": False,
|
||||
"calibration_id": None,
|
||||
"progress_percent": 100,
|
||||
"current_step": "completed",
|
||||
"estimated_remaining_minutes": 0,
|
||||
"last_calibration": datetime.now() - timedelta(hours=1)
|
||||
}
|
||||
|
||||
async def get_statistics(self, start_time, end_time):
|
||||
"""Get pose estimation statistics."""
|
||||
try:
|
||||
import random
|
||||
|
||||
# Mock statistics
|
||||
total_detections = random.randint(100, 1000)
|
||||
successful_detections = int(total_detections * random.uniform(0.8, 0.95))
|
||||
|
||||
return {
|
||||
"total_detections": total_detections,
|
||||
"successful_detections": successful_detections,
|
||||
"failed_detections": total_detections - successful_detections,
|
||||
"success_rate": successful_detections / total_detections,
|
||||
"average_confidence": random.uniform(0.75, 0.90),
|
||||
"average_processing_time_ms": random.uniform(50, 200),
|
||||
"unique_persons": random.randint(5, 20),
|
||||
"most_active_zone": random.choice(["zone_1", "zone_2", "zone_3"]),
|
||||
"activity_distribution": {
|
||||
"standing": random.uniform(0.3, 0.5),
|
||||
"sitting": random.uniform(0.2, 0.4),
|
||||
"walking": random.uniform(0.1, 0.3),
|
||||
"lying": random.uniform(0.0, 0.1)
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting statistics: {e}")
|
||||
raise
|
||||
|
||||
async def process_segmentation_data(self, frame_id):
|
||||
"""Process segmentation data in background."""
|
||||
self.logger.info(f"Processing segmentation data for frame: {frame_id}")
|
||||
# Mock background processing
|
||||
await asyncio.sleep(2)
|
||||
self.logger.info(f"Segmentation processing completed for frame: {frame_id}")
|
||||
|
||||
# WebSocket streaming methods
|
||||
async def get_current_pose_data(self):
|
||||
"""Get current pose data for streaming."""
|
||||
try:
|
||||
# Generate current pose data
|
||||
result = await self.estimate_poses()
|
||||
|
||||
# Format data by zones for WebSocket streaming
|
||||
zone_data = {}
|
||||
|
||||
# Group persons by zone
|
||||
for person in result["persons"]:
|
||||
zone_id = person.get("zone_id", "zone_1")
|
||||
|
||||
if zone_id not in zone_data:
|
||||
zone_data[zone_id] = {
|
||||
"pose": {
|
||||
"persons": [],
|
||||
"count": 0
|
||||
},
|
||||
"confidence": 0.0,
|
||||
"activity": None,
|
||||
"metadata": {
|
||||
"frame_id": result["frame_id"],
|
||||
"processing_time_ms": result["processing_time_ms"]
|
||||
}
|
||||
}
|
||||
|
||||
zone_data[zone_id]["pose"]["persons"].append(person)
|
||||
zone_data[zone_id]["pose"]["count"] += 1
|
||||
|
||||
# Update zone confidence (average)
|
||||
current_confidence = zone_data[zone_id]["confidence"]
|
||||
person_confidence = person.get("confidence", 0.0)
|
||||
zone_data[zone_id]["confidence"] = (current_confidence + person_confidence) / 2
|
||||
|
||||
# Set activity if not already set
|
||||
if not zone_data[zone_id]["activity"] and person.get("activity"):
|
||||
zone_data[zone_id]["activity"] = person["activity"]
|
||||
|
||||
return zone_data
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error getting current pose data: {e}")
|
||||
# Return empty zone data on error
|
||||
return {}
|
||||
|
||||
# Health check methods
|
||||
async def health_check(self):
|
||||
"""Perform health check."""
|
||||
try:
|
||||
status = "healthy" if self.is_running and not self.last_error else "unhealthy"
|
||||
|
||||
return {
|
||||
"status": status,
|
||||
"message": self.last_error if self.last_error else "Service is running normally",
|
||||
"uptime_seconds": 0.0, # TODO: Implement actual uptime tracking
|
||||
"metrics": {
|
||||
"total_processed": self.stats["total_processed"],
|
||||
"success_rate": (
|
||||
self.stats["successful_detections"] / max(1, self.stats["total_processed"])
|
||||
),
|
||||
"average_processing_time_ms": self.stats["processing_time_ms"]
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return {
|
||||
"status": "unhealthy",
|
||||
"message": f"Health check failed: {str(e)}"
|
||||
}
|
||||
|
||||
async def is_ready(self):
|
||||
"""Check if service is ready."""
|
||||
return self.is_initialized and self.is_running
|
||||
Reference in New Issue
Block a user