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1240 lines
No EOL
55 KiB
Python
1240 lines
No EOL
55 KiB
Python
"""
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Detection Pipeline Module.
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Main detection pipeline orchestration that coordinates detection flow and execution.
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"""
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import asyncio
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import logging
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import time
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import uuid
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from datetime import datetime
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from typing import Dict, List, Optional, Any
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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from ..models.inference import YOLOWrapper
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from ..models.pipeline import PipelineParser
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from .branches import BranchProcessor
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from ..storage.redis import RedisManager
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from ..storage.database import DatabaseManager
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from ..storage.license_plate import LicensePlateManager
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logger = logging.getLogger(__name__)
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class DetectionPipeline:
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"""
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Main detection pipeline that orchestrates the complete detection flow.
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Handles detection execution, branch coordination, and result aggregation.
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"""
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def __init__(self, pipeline_parser: PipelineParser, model_manager: Any, model_id: int, message_sender=None):
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"""
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Initialize detection pipeline.
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Args:
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pipeline_parser: Pipeline parser with loaded configuration
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model_manager: Model manager for loading models
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model_id: The model ID to use for loading models
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message_sender: Optional callback function for sending WebSocket messages
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"""
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self.pipeline_parser = pipeline_parser
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self.model_manager = model_manager
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self.model_id = model_id
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self.message_sender = message_sender
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# Initialize components
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self.branch_processor = BranchProcessor(model_manager, model_id)
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self.redis_manager = None
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self.db_manager = None
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self.license_plate_manager = None
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# Main detection model
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self.detection_model: Optional[YOLOWrapper] = None
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self.detection_model_id = None
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# Thread pool for parallel processing
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self.executor = ThreadPoolExecutor(max_workers=4)
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# Pipeline configuration
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self.pipeline_config = pipeline_parser.pipeline_config
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# SessionId to subscriptionIdentifier mapping (ISOLATED per session process)
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self.session_to_subscription = {}
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# SessionId to processing results mapping (ISOLATED per session process)
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self.session_processing_results = {}
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# Statistics
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self.stats = {
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'detections_processed': 0,
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'branches_executed': 0,
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'actions_executed': 0,
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'total_processing_time': 0.0
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}
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logger.info(f"DetectionPipeline initialized for model {model_id} with ISOLATED state (no shared mappings or cache)")
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logger.info(f"Pipeline instance ID: {id(self)} - unique per session process")
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async def initialize(self) -> bool:
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"""
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Initialize all pipeline components including models, Redis, and database.
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Returns:
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True if successful, False otherwise
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"""
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try:
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# Initialize Redis connection
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if self.pipeline_parser.redis_config:
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self.redis_manager = RedisManager(self.pipeline_parser.redis_config.__dict__)
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if not await self.redis_manager.initialize():
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logger.error("Failed to initialize Redis connection")
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return False
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logger.info("Redis connection initialized")
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# Initialize database connection
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if self.pipeline_parser.postgresql_config:
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self.db_manager = DatabaseManager(self.pipeline_parser.postgresql_config.__dict__)
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if not self.db_manager.connect():
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logger.error("Failed to initialize database connection")
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return False
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# Create required tables
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if not self.db_manager.create_car_frontal_info_table():
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logger.warning("Failed to create car_frontal_info table")
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logger.info("Database connection initialized")
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# Initialize license plate manager (using same Redis config as main Redis manager)
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if self.pipeline_parser.redis_config:
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self.license_plate_manager = LicensePlateManager(self.pipeline_parser.redis_config.__dict__)
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if not await self.license_plate_manager.initialize(self._on_license_plate_result):
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logger.error("Failed to initialize license plate manager")
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return False
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logger.info("License plate manager initialized")
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# Initialize main detection model
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if not await self._initialize_detection_model():
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logger.error("Failed to initialize detection model")
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return False
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# Initialize branch processor
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if not await self.branch_processor.initialize(
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self.pipeline_config,
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self.redis_manager,
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self.db_manager
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):
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logger.error("Failed to initialize branch processor")
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return False
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logger.info("Detection pipeline initialization completed successfully")
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return True
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except Exception as e:
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logger.error(f"Error initializing detection pipeline: {e}", exc_info=True)
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return False
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async def _initialize_detection_model(self) -> bool:
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"""
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Load and initialize the main detection model from pipeline.json configuration.
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Returns:
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True if successful, False otherwise
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"""
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try:
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if not self.pipeline_config:
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logger.error("No pipeline configuration found - cannot initialize detection model")
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return False
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model_file = getattr(self.pipeline_config, 'model_file', None)
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model_id = getattr(self.pipeline_config, 'model_id', None)
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min_confidence = getattr(self.pipeline_config, 'min_confidence', 0.6)
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trigger_classes = getattr(self.pipeline_config, 'trigger_classes', [])
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crop = getattr(self.pipeline_config, 'crop', False)
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if not model_file:
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logger.error("No detection model file specified in pipeline configuration")
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return False
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# Log complete pipeline configuration for main detection model
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logger.info(f"[MAIN MODEL CONFIG] Initializing from pipeline.json:")
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logger.info(f"[MAIN MODEL CONFIG] modelId: {model_id}")
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logger.info(f"[MAIN MODEL CONFIG] modelFile: {model_file}")
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logger.info(f"[MAIN MODEL CONFIG] minConfidence: {min_confidence}")
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logger.info(f"[MAIN MODEL CONFIG] triggerClasses: {trigger_classes}")
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logger.info(f"[MAIN MODEL CONFIG] crop: {crop}")
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# Load detection model using model manager
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logger.info(f"[MAIN MODEL LOADING] Loading {model_file} from model directory {self.model_id}")
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self.detection_model = self.model_manager.get_yolo_model(self.model_id, model_file)
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if not self.detection_model:
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logger.error(f"[MAIN MODEL ERROR] Failed to load detection model {model_file} from model {self.model_id}")
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return False
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self.detection_model_id = model_id
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logger.info(f"[MAIN MODEL SUCCESS] Detection model {model_id} ({model_file}) loaded successfully")
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return True
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except Exception as e:
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logger.error(f"Error initializing detection model: {e}", exc_info=True)
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return False
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async def _on_license_plate_result(self, session_id: str, license_data: Dict[str, Any]):
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"""
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Callback for handling license plate results from LPR service.
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Args:
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session_id: Session identifier
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license_data: License plate data including text and confidence
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"""
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try:
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license_text = license_data.get('license_plate_text', '')
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confidence = license_data.get('confidence', 0.0)
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logger.info(f"[LICENSE PLATE CALLBACK] Session {session_id}: "
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f"text='{license_text}', confidence={confidence:.3f}")
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# Find matching subscriptionIdentifier for this sessionId
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subscription_id = self.session_to_subscription.get(session_id)
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if not subscription_id:
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logger.warning(f"[LICENSE PLATE] No subscription found for sessionId '{session_id}' (type: {type(session_id)}), cannot send imageDetection")
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logger.warning(f"[LICENSE PLATE DEBUG] Current session mappings: {dict(self.session_to_subscription)}")
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# Try to find by type conversion in case of type mismatch
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# Try as integer if session_id is string
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if isinstance(session_id, str) and session_id.isdigit():
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session_id_int = int(session_id)
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subscription_id = self.session_to_subscription.get(session_id_int)
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if subscription_id:
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logger.info(f"[LICENSE PLATE] Found subscription using int conversion: '{session_id}' -> {session_id_int} -> '{subscription_id}'")
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else:
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logger.error(f"[LICENSE PLATE] Failed to find subscription with int conversion")
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return
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# Try as string if session_id is integer
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elif isinstance(session_id, int):
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session_id_str = str(session_id)
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subscription_id = self.session_to_subscription.get(session_id_str)
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if subscription_id:
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logger.info(f"[LICENSE PLATE] Found subscription using string conversion: {session_id} -> '{session_id_str}' -> '{subscription_id}'")
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else:
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logger.error(f"[LICENSE PLATE] Failed to find subscription with string conversion")
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return
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else:
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logger.error(f"[LICENSE PLATE] Failed to find subscription with any type conversion")
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return
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# Send imageDetection message with license plate data combined with processing results
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await self._send_license_plate_message(subscription_id, license_text, confidence, session_id)
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# Update database with license plate information if database manager is available
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if self.db_manager and license_text:
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success = self.db_manager.execute_update(
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table='car_frontal_info',
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key_field='session_id',
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key_value=session_id,
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fields={
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'license_character': license_text,
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'license_type': 'LPR_detected' # Mark as detected by LPR service
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}
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)
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if success:
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logger.info(f"[LICENSE PLATE] Updated database for session {session_id}")
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else:
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logger.warning(f"[LICENSE PLATE] Failed to update database for session {session_id}")
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except Exception as e:
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logger.error(f"Error in license plate result callback: {e}", exc_info=True)
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async def _send_license_plate_message(self, subscription_id: str, license_text: str, confidence: float, session_id: str = None):
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"""
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Send imageDetection message with license plate data plus any available processing results.
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Args:
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subscription_id: Subscription identifier to send message to
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license_text: License plate text
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confidence: License plate confidence score
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session_id: Session identifier for looking up processing results
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"""
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try:
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if not self.message_sender:
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logger.warning("No message sender configured, cannot send imageDetection")
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return
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# Import here to avoid circular imports
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from ..communication.models import ImageDetectionMessage, DetectionData
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# Get processing results for this session from stored results
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car_brand = None
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body_type = None
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# Find session_id from session mappings (we need session_id as key)
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session_id_for_lookup = None
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# Try direct lookup first (if session_id is already the right type)
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if session_id in self.session_processing_results:
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session_id_for_lookup = session_id
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else:
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# Try to find by type conversion
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for stored_session_id in self.session_processing_results.keys():
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if str(stored_session_id) == str(session_id):
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session_id_for_lookup = stored_session_id
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break
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if session_id_for_lookup and session_id_for_lookup in self.session_processing_results:
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branch_results = self.session_processing_results[session_id_for_lookup]
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logger.info(f"[LICENSE PLATE] Retrieved processing results for session {session_id_for_lookup}")
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if 'car_brand_cls_v2' in branch_results:
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brand_result = branch_results['car_brand_cls_v2'].get('result', {})
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car_brand = brand_result.get('brand')
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if 'car_bodytype_cls_v1' in branch_results:
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bodytype_result = branch_results['car_bodytype_cls_v1'].get('result', {})
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body_type = bodytype_result.get('body_type')
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# Clean up stored results after use
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del self.session_processing_results[session_id_for_lookup]
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logger.debug(f"[LICENSE PLATE] Cleaned up stored results for session {session_id_for_lookup}")
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else:
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logger.warning(f"[LICENSE PLATE] No processing results found for session {session_id}")
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# Create detection data with combined information
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detection_data_obj = DetectionData(
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detection={
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"carBrand": car_brand,
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"carModel": None,
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"bodyType": body_type,
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"licensePlateText": license_text,
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"licensePlateConfidence": confidence
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},
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modelId=self.model_id,
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modelName=self.pipeline_parser.pipeline_config.model_id if self.pipeline_parser.pipeline_config else "detection_model"
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)
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# Create imageDetection message
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detection_message = ImageDetectionMessage(
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subscriptionIdentifier=subscription_id,
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data=detection_data_obj
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)
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# Send message
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await self.message_sender(detection_message)
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logger.info(f"[COMBINED MESSAGE] Sent imageDetection with brand='{car_brand}', bodyType='{body_type}', license='{license_text}' to '{subscription_id}'")
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except Exception as e:
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logger.error(f"Error sending license plate imageDetection message: {e}", exc_info=True)
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async def _send_initial_detection_message(self, subscription_id: str):
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"""
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Send initial imageDetection message when vehicle is first detected.
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Args:
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subscription_id: Subscription identifier to send message to
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"""
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try:
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if not self.message_sender:
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logger.warning("No message sender configured, cannot send imageDetection")
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return
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# Import here to avoid circular imports
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from ..communication.models import ImageDetectionMessage, DetectionData
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# Create detection data with all fields as None (vehicle just detected, no classification yet)
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detection_data_obj = DetectionData(
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detection={
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"carBrand": None,
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"carModel": None,
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"bodyType": None,
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"licensePlateText": None,
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"licensePlateConfidence": None
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},
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modelId=self.model_id,
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modelName=self.pipeline_parser.pipeline_config.model_id if self.pipeline_parser.pipeline_config else "detection_model"
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)
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# Create imageDetection message
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detection_message = ImageDetectionMessage(
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subscriptionIdentifier=subscription_id,
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data=detection_data_obj
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)
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# Send message
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await self.message_sender(detection_message)
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logger.info(f"[INITIAL DETECTION] Sent imageDetection for vehicle detection to '{subscription_id}'")
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except Exception as e:
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logger.error(f"Error sending initial detection imageDetection message: {e}", exc_info=True)
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async def _send_processing_results_message(self, subscription_id: str, branch_results: Dict[str, Any], session_id: Optional[str] = None):
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"""
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Send imageDetection message immediately with processing results, regardless of completeness.
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Sends even if no results, partial results, or complete results are available.
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Args:
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subscription_id: Subscription identifier to send message to
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branch_results: Branch processing results (may be empty or partial)
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session_id: Session identifier for logging
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"""
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try:
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if not self.message_sender:
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logger.warning("No message sender configured, cannot send imageDetection")
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return
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|
|
# Import here to avoid circular imports
|
|
from ..communication.models import ImageDetectionMessage, DetectionData
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|
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# Extract classification results from branch results
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car_brand = None
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body_type = None
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|
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if branch_results:
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# Extract car brand from car_brand_cls_v2 results
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if 'car_brand_cls_v2' in branch_results:
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brand_result = branch_results['car_brand_cls_v2'].get('result', {})
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car_brand = brand_result.get('brand')
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|
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# Extract body type from car_bodytype_cls_v1 results
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if 'car_bodytype_cls_v1' in branch_results:
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bodytype_result = branch_results['car_bodytype_cls_v1'].get('result', {})
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body_type = bodytype_result.get('body_type')
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|
|
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# Create detection data with available results (fields can be None)
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detection_data_obj = DetectionData(
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detection={
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"carBrand": car_brand,
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"carModel": None, # Not implemented yet
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"bodyType": body_type,
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"licensePlateText": None, # Will be updated later if available
|
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"licensePlateConfidence": None
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},
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modelId=self.model_id,
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modelName=self.pipeline_parser.pipeline_config.model_id if self.pipeline_parser.pipeline_config else "detection_model"
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)
|
|
|
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# Create imageDetection message
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detection_message = ImageDetectionMessage(
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subscriptionIdentifier=subscription_id,
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data=detection_data_obj
|
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)
|
|
|
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# Send message
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await self.message_sender(detection_message)
|
|
|
|
# Log what was sent
|
|
result_summary = []
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if car_brand:
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result_summary.append(f"brand='{car_brand}'")
|
|
if body_type:
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|
result_summary.append(f"bodyType='{body_type}'")
|
|
if not result_summary:
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result_summary.append("no classification results")
|
|
|
|
logger.info(f"[PROCESSING COMPLETE] Sent imageDetection with {', '.join(result_summary)} to '{subscription_id}'"
|
|
f"{f' (session {session_id})' if session_id else ''}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error sending processing results imageDetection message: {e}", exc_info=True)
|
|
|
|
async def execute_detection_phase(self,
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|
frame: np.ndarray,
|
|
display_id: str,
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|
subscription_id: str) -> Dict[str, Any]:
|
|
"""
|
|
Execute only the detection phase - run main detection and send imageDetection message.
|
|
This is the first phase that runs when a vehicle is validated.
|
|
|
|
Args:
|
|
frame: Input frame to process
|
|
display_id: Display identifier
|
|
subscription_id: Subscription identifier
|
|
|
|
Returns:
|
|
Dictionary with detection phase results
|
|
"""
|
|
start_time = time.time()
|
|
result = {
|
|
'status': 'success',
|
|
'detections': [],
|
|
'message_sent': False,
|
|
'processing_time': 0.0,
|
|
'timestamp': datetime.now().isoformat()
|
|
}
|
|
|
|
try:
|
|
# Run main detection model
|
|
if not self.detection_model:
|
|
result['status'] = 'error'
|
|
result['message'] = 'Detection model not available'
|
|
return result
|
|
|
|
# Create detection context
|
|
detection_context = {
|
|
'display_id': display_id,
|
|
'subscription_id': subscription_id,
|
|
'timestamp': datetime.now().strftime("%Y-%m-%dT%H-%M-%S"),
|
|
'timestamp_ms': int(time.time() * 1000)
|
|
}
|
|
|
|
# Run inference using direct model call (like ML engineer's approach)
|
|
# Use minConfidence from pipeline.json configuration
|
|
model_confidence = getattr(self.pipeline_config, 'min_confidence', 0.6)
|
|
logger.info(f"[DETECTION PHASE] Running {self.pipeline_config.model_id} with conf={model_confidence} (from pipeline.json)")
|
|
detection_results = self.detection_model.model(
|
|
frame,
|
|
conf=model_confidence,
|
|
verbose=False
|
|
)
|
|
|
|
# Process detection results using clean logic
|
|
valid_detections = []
|
|
detected_regions = {}
|
|
|
|
if detection_results and len(detection_results) > 0:
|
|
result_obj = detection_results[0]
|
|
trigger_classes = getattr(self.pipeline_config, 'trigger_classes', [])
|
|
|
|
# Handle direct model call results which have .boxes for detection models
|
|
if hasattr(result_obj, 'boxes') and result_obj.boxes is not None:
|
|
logger.info(f"[DETECTION PHASE] Found {len(result_obj.boxes)} raw detections from {getattr(self.pipeline_config, 'model_id', 'unknown')}")
|
|
|
|
for i, box in enumerate(result_obj.boxes):
|
|
class_id = int(box.cls[0])
|
|
confidence = float(box.conf[0])
|
|
bbox = box.xyxy[0].cpu().numpy().tolist() # [x1, y1, x2, y2]
|
|
class_name = self.detection_model.model.names[class_id]
|
|
|
|
logger.info(f"[DETECTION PHASE {i+1}] {class_name}: bbox={bbox}, conf={confidence:.3f}")
|
|
|
|
# Check if detection matches trigger classes
|
|
if trigger_classes and class_name not in trigger_classes:
|
|
logger.debug(f"[DETECTION PHASE] Filtered '{class_name}' - not in trigger_classes {trigger_classes}")
|
|
continue
|
|
|
|
logger.info(f"[DETECTION PHASE] Accepted '{class_name}' - matches trigger_classes")
|
|
|
|
# Store detection info
|
|
detection_info = {
|
|
'class_name': class_name,
|
|
'confidence': confidence,
|
|
'bbox': bbox
|
|
}
|
|
valid_detections.append(detection_info)
|
|
|
|
# Store region for processing phase
|
|
detected_regions[class_name] = {
|
|
'bbox': bbox,
|
|
'confidence': confidence
|
|
}
|
|
else:
|
|
logger.warning("[DETECTION PHASE] No boxes found in detection results")
|
|
|
|
# Store detected_regions in result for processing phase
|
|
result['detected_regions'] = detected_regions
|
|
|
|
result['detections'] = valid_detections
|
|
|
|
# If we have valid detections, create session and send initial imageDetection
|
|
if valid_detections:
|
|
logger.info(f"Found {len(valid_detections)} valid detections, storing session mapping")
|
|
|
|
# Store mapping from display_id to subscriptionIdentifier (for detection phase)
|
|
# Note: We'll store session_id mapping later in processing phase
|
|
self.session_to_subscription[display_id] = subscription_id
|
|
logger.info(f"[SESSION MAPPING] Stored mapping: displayId '{display_id}' -> subscriptionIdentifier '{subscription_id}'")
|
|
|
|
# Send initial imageDetection message with empty detection data
|
|
await self._send_initial_detection_message(subscription_id)
|
|
|
|
logger.info(f"Detection phase completed - {len(valid_detections)} detections found for {display_id}")
|
|
result['message_sent'] = True
|
|
else:
|
|
logger.debug("No valid detections found in detection phase")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in detection phase: {e}", exc_info=True)
|
|
result['status'] = 'error'
|
|
result['message'] = str(e)
|
|
|
|
result['processing_time'] = time.time() - start_time
|
|
return result
|
|
|
|
async def execute_processing_phase(self,
|
|
frame: np.ndarray,
|
|
display_id: str,
|
|
session_id: str,
|
|
subscription_id: str,
|
|
detected_regions: Dict[str, Any] = None) -> Dict[str, Any]:
|
|
"""
|
|
Execute the processing phase - run branches and database operations after receiving sessionId.
|
|
This is the second phase that runs after backend sends setSessionId.
|
|
|
|
Args:
|
|
frame: Input frame to process
|
|
display_id: Display identifier
|
|
session_id: Session ID from backend
|
|
subscription_id: Subscription identifier
|
|
detected_regions: Pre-detected regions from detection phase
|
|
|
|
Returns:
|
|
Dictionary with processing phase results
|
|
"""
|
|
start_time = time.time()
|
|
result = {
|
|
'status': 'success',
|
|
'branch_results': {},
|
|
'actions_executed': [],
|
|
'session_id': session_id,
|
|
'processing_time': 0.0,
|
|
'timestamp': datetime.now().isoformat()
|
|
}
|
|
|
|
try:
|
|
# Create enhanced detection context with session_id
|
|
detection_context = {
|
|
'display_id': display_id,
|
|
'session_id': session_id,
|
|
'subscription_id': subscription_id,
|
|
'timestamp': datetime.now().strftime("%Y-%m-%dT%H-%M-%S"),
|
|
'timestamp_ms': int(time.time() * 1000),
|
|
'uuid': str(uuid.uuid4()),
|
|
'filename': f"{uuid.uuid4()}.jpg"
|
|
}
|
|
|
|
# If no detected_regions provided, re-run detection to get them
|
|
if not detected_regions:
|
|
# Use direct model call for detection (like ML engineer's approach)
|
|
# Use minConfidence from pipeline.json configuration
|
|
model_confidence = getattr(self.pipeline_config, 'min_confidence', 0.6)
|
|
logger.info(f"[PROCESSING PHASE] Re-running {self.pipeline_config.model_id} with conf={model_confidence} (from pipeline.json)")
|
|
detection_results = self.detection_model.model(
|
|
frame,
|
|
conf=model_confidence,
|
|
verbose=False
|
|
)
|
|
|
|
detected_regions = {}
|
|
if detection_results and len(detection_results) > 0:
|
|
result_obj = detection_results[0]
|
|
if hasattr(result_obj, 'boxes') and result_obj.boxes is not None:
|
|
for box in result_obj.boxes:
|
|
class_id = int(box.cls[0])
|
|
confidence = float(box.conf[0])
|
|
bbox = box.xyxy[0].cpu().numpy().tolist() # [x1, y1, x2, y2]
|
|
class_name = self.detection_model.model.names[class_id]
|
|
|
|
detected_regions[class_name] = {
|
|
'bbox': bbox,
|
|
'confidence': confidence
|
|
}
|
|
|
|
# Store session mapping for license plate callback
|
|
if session_id:
|
|
self.session_to_subscription[session_id] = subscription_id
|
|
logger.info(f"[SESSION MAPPING] Stored mapping: sessionId '{session_id}' -> subscriptionIdentifier '{subscription_id}'")
|
|
|
|
# Initialize database record with session_id
|
|
if session_id and self.db_manager:
|
|
success = self.db_manager.insert_initial_detection(
|
|
display_id=display_id,
|
|
captured_timestamp=detection_context['timestamp'],
|
|
session_id=session_id
|
|
)
|
|
if success:
|
|
logger.info(f"Created initial database record with session {session_id}")
|
|
else:
|
|
logger.warning(f"Failed to create initial database record for session {session_id}")
|
|
|
|
# Execute branches in parallel
|
|
if hasattr(self.pipeline_config, 'branches') and self.pipeline_config.branches:
|
|
branch_results = await self.branch_processor.execute_branches(
|
|
frame=frame,
|
|
branches=self.pipeline_config.branches,
|
|
detected_regions=detected_regions,
|
|
detection_context=detection_context
|
|
)
|
|
result['branch_results'] = branch_results
|
|
logger.info(f"Executed {len(branch_results)} branches for session {session_id}")
|
|
|
|
# Execute immediate actions (non-parallel)
|
|
immediate_actions = getattr(self.pipeline_config, 'actions', [])
|
|
if immediate_actions:
|
|
executed_actions = await self._execute_immediate_actions(
|
|
actions=immediate_actions,
|
|
frame=frame,
|
|
detected_regions=detected_regions,
|
|
detection_context=detection_context
|
|
)
|
|
result['actions_executed'].extend(executed_actions)
|
|
|
|
# Execute parallel actions (after all branches complete)
|
|
parallel_actions = getattr(self.pipeline_config, 'parallel_actions', [])
|
|
if parallel_actions:
|
|
# Add branch results to context
|
|
enhanced_context = {**detection_context}
|
|
if result['branch_results']:
|
|
enhanced_context['branch_results'] = result['branch_results']
|
|
|
|
executed_parallel_actions = await self._execute_parallel_actions(
|
|
actions=parallel_actions,
|
|
frame=frame,
|
|
detected_regions=detected_regions,
|
|
context=enhanced_context
|
|
)
|
|
result['actions_executed'].extend(executed_parallel_actions)
|
|
|
|
# Send imageDetection message immediately with available results
|
|
await self._send_processing_results_message(subscription_id, result['branch_results'], session_id)
|
|
|
|
# Store processing results for later combination with license plate data if needed
|
|
if result['branch_results'] and session_id:
|
|
self.session_processing_results[session_id] = result['branch_results']
|
|
logger.info(f"[PROCESSING RESULTS] Stored results for session {session_id} for potential license plate combination")
|
|
|
|
logger.info(f"Processing phase completed for session {session_id}: "
|
|
f"status={result.get('status', 'unknown')}, "
|
|
f"branches={len(result['branch_results'])}, "
|
|
f"actions={len(result['actions_executed'])}, "
|
|
f"processing_time={result.get('processing_time', 0):.3f}s")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in processing phase: {e}", exc_info=True)
|
|
result['status'] = 'error'
|
|
result['message'] = str(e)
|
|
|
|
# Even if there was an error, send imageDetection message with whatever results we have
|
|
try:
|
|
await self._send_processing_results_message(subscription_id, result['branch_results'], session_id)
|
|
except Exception as send_error:
|
|
logger.error(f"Failed to send imageDetection message after processing error: {send_error}")
|
|
|
|
result['processing_time'] = time.time() - start_time
|
|
return result
|
|
|
|
|
|
async def execute_detection(self,
|
|
frame: np.ndarray,
|
|
display_id: str,
|
|
session_id: Optional[str] = None,
|
|
subscription_id: Optional[str] = None) -> Dict[str, Any]:
|
|
"""
|
|
Execute the main detection pipeline on a frame.
|
|
|
|
Args:
|
|
frame: Input frame to process
|
|
display_id: Display identifier
|
|
session_id: Optional session ID
|
|
subscription_id: Optional subscription identifier
|
|
|
|
Returns:
|
|
Dictionary with detection results
|
|
"""
|
|
start_time = time.time()
|
|
result = {
|
|
'status': 'success',
|
|
'detections': [],
|
|
'branch_results': {},
|
|
'actions_executed': [],
|
|
'session_id': session_id,
|
|
'processing_time': 0.0,
|
|
'timestamp': datetime.now().isoformat()
|
|
}
|
|
|
|
try:
|
|
# Update stats
|
|
self.stats['detections_processed'] += 1
|
|
|
|
# Run main detection model
|
|
if not self.detection_model:
|
|
result['status'] = 'error'
|
|
result['message'] = 'Detection model not available'
|
|
return result
|
|
|
|
# Create detection context
|
|
detection_context = {
|
|
'display_id': display_id,
|
|
'session_id': session_id,
|
|
'subscription_id': subscription_id,
|
|
'timestamp': datetime.now().strftime("%Y-%m-%dT%H-%M-%S"),
|
|
'timestamp_ms': int(time.time() * 1000),
|
|
'uuid': str(uuid.uuid4()),
|
|
'filename': f"{uuid.uuid4()}.jpg"
|
|
}
|
|
|
|
|
|
# Run inference using direct model call (like ML engineer's approach)
|
|
# Use minConfidence from pipeline.json configuration
|
|
model_confidence = getattr(self.pipeline_config, 'min_confidence', 0.6)
|
|
logger.info(f"[PIPELINE EXECUTE] Running {self.pipeline_config.model_id} with conf={model_confidence} (from pipeline.json)")
|
|
detection_results = self.detection_model.model(
|
|
frame,
|
|
conf=model_confidence,
|
|
verbose=False
|
|
)
|
|
|
|
# Process detection results
|
|
detected_regions = {}
|
|
valid_detections = []
|
|
|
|
if detection_results and len(detection_results) > 0:
|
|
result_obj = detection_results[0]
|
|
trigger_classes = getattr(self.pipeline_config, 'trigger_classes', [])
|
|
|
|
# Handle direct model call results which have .boxes for detection models
|
|
if hasattr(result_obj, 'boxes') and result_obj.boxes is not None:
|
|
logger.info(f"[PIPELINE RAW] Found {len(result_obj.boxes)} raw detections from {getattr(self.pipeline_config, 'model_id', 'unknown')}")
|
|
|
|
for i, box in enumerate(result_obj.boxes):
|
|
class_id = int(box.cls[0])
|
|
confidence = float(box.conf[0])
|
|
bbox = box.xyxy[0].cpu().numpy().tolist() # [x1, y1, x2, y2]
|
|
class_name = self.detection_model.model.names[class_id]
|
|
|
|
logger.info(f"[PIPELINE RAW {i+1}] {class_name}: bbox={bbox}, conf={confidence:.3f}")
|
|
|
|
# Check if detection matches trigger classes
|
|
if trigger_classes and class_name not in trigger_classes:
|
|
continue
|
|
|
|
# Store detection info
|
|
detection_info = {
|
|
'class_name': class_name,
|
|
'confidence': confidence,
|
|
'bbox': bbox
|
|
}
|
|
valid_detections.append(detection_info)
|
|
|
|
# Store region for cropping
|
|
detected_regions[class_name] = {
|
|
'bbox': bbox,
|
|
'confidence': confidence
|
|
}
|
|
logger.info(f"[PIPELINE DETECTION] {class_name}: bbox={bbox}, conf={confidence:.3f}")
|
|
|
|
result['detections'] = valid_detections
|
|
|
|
# If we have valid detections, proceed with branches and actions
|
|
if valid_detections:
|
|
logger.info(f"Found {len(valid_detections)} valid detections for pipeline processing")
|
|
|
|
# Initialize database record if session_id is provided
|
|
if session_id and self.db_manager:
|
|
success = self.db_manager.insert_initial_detection(
|
|
display_id=display_id,
|
|
captured_timestamp=detection_context['timestamp'],
|
|
session_id=session_id
|
|
)
|
|
if not success:
|
|
logger.warning(f"Failed to create initial database record for session {session_id}")
|
|
|
|
# Execute branches in parallel
|
|
if hasattr(self.pipeline_config, 'branches') and self.pipeline_config.branches:
|
|
branch_results = await self.branch_processor.execute_branches(
|
|
frame=frame,
|
|
branches=self.pipeline_config.branches,
|
|
detected_regions=detected_regions,
|
|
detection_context=detection_context
|
|
)
|
|
result['branch_results'] = branch_results
|
|
self.stats['branches_executed'] += len(branch_results)
|
|
|
|
# Execute immediate actions (non-parallel)
|
|
immediate_actions = getattr(self.pipeline_config, 'actions', [])
|
|
if immediate_actions:
|
|
executed_actions = await self._execute_immediate_actions(
|
|
actions=immediate_actions,
|
|
frame=frame,
|
|
detected_regions=detected_regions,
|
|
detection_context=detection_context
|
|
)
|
|
result['actions_executed'].extend(executed_actions)
|
|
|
|
# Execute parallel actions (after all branches complete)
|
|
parallel_actions = getattr(self.pipeline_config, 'parallel_actions', [])
|
|
if parallel_actions:
|
|
# Add branch results to context
|
|
enhanced_context = {**detection_context}
|
|
if result['branch_results']:
|
|
enhanced_context['branch_results'] = result['branch_results']
|
|
|
|
executed_parallel_actions = await self._execute_parallel_actions(
|
|
actions=parallel_actions,
|
|
frame=frame,
|
|
detected_regions=detected_regions,
|
|
context=enhanced_context
|
|
)
|
|
result['actions_executed'].extend(executed_parallel_actions)
|
|
|
|
self.stats['actions_executed'] += len(result['actions_executed'])
|
|
else:
|
|
logger.debug("No valid detections found for pipeline processing")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in detection pipeline execution: {e}", exc_info=True)
|
|
result['status'] = 'error'
|
|
result['message'] = str(e)
|
|
|
|
# Update timing
|
|
processing_time = time.time() - start_time
|
|
result['processing_time'] = processing_time
|
|
self.stats['total_processing_time'] += processing_time
|
|
|
|
return result
|
|
|
|
async def _execute_immediate_actions(self,
|
|
actions: List[Dict],
|
|
frame: np.ndarray,
|
|
detected_regions: Dict[str, Any],
|
|
detection_context: Dict[str, Any]) -> List[Dict]:
|
|
"""
|
|
Execute immediate actions (non-parallel).
|
|
|
|
Args:
|
|
actions: List of action configurations
|
|
frame: Input frame
|
|
detected_regions: Dictionary of detected regions
|
|
detection_context: Detection context data
|
|
|
|
Returns:
|
|
List of executed action results
|
|
"""
|
|
executed_actions = []
|
|
|
|
for action in actions:
|
|
try:
|
|
action_type = action.type.value
|
|
logger.debug(f"Executing immediate action: {action_type}")
|
|
|
|
if action_type == 'redis_save_image':
|
|
result = await self._execute_redis_save_image(
|
|
action, frame, detected_regions, detection_context
|
|
)
|
|
elif action_type == 'redis_publish':
|
|
result = await self._execute_redis_publish(
|
|
action, detection_context
|
|
)
|
|
else:
|
|
logger.warning(f"Unknown immediate action type: {action_type}")
|
|
result = {'status': 'error', 'message': f'Unknown action type: {action_type}'}
|
|
|
|
executed_actions.append({
|
|
'action_type': action_type,
|
|
'result': result
|
|
})
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error executing immediate action {action_type}: {e}", exc_info=True)
|
|
executed_actions.append({
|
|
'action_type': action.type.value,
|
|
'result': {'status': 'error', 'message': str(e)}
|
|
})
|
|
|
|
return executed_actions
|
|
|
|
async def _execute_parallel_actions(self,
|
|
actions: List[Dict],
|
|
frame: np.ndarray,
|
|
detected_regions: Dict[str, Any],
|
|
context: Dict[str, Any]) -> List[Dict]:
|
|
"""
|
|
Execute parallel actions (after branches complete).
|
|
|
|
Args:
|
|
actions: List of parallel action configurations
|
|
frame: Input frame
|
|
detected_regions: Dictionary of detected regions
|
|
context: Enhanced context with branch results
|
|
|
|
Returns:
|
|
List of executed action results
|
|
"""
|
|
executed_actions = []
|
|
|
|
for action in actions:
|
|
try:
|
|
action_type = action.type.value
|
|
logger.debug(f"Executing parallel action: {action_type}")
|
|
|
|
if action_type == 'postgresql_update_combined':
|
|
result = await self._execute_postgresql_update_combined(action, context)
|
|
|
|
# Update session state with processing results after database update
|
|
if result.get('status') == 'success':
|
|
await self._update_session_with_processing_results(context)
|
|
else:
|
|
logger.warning(f"Unknown parallel action type: {action_type}")
|
|
result = {'status': 'error', 'message': f'Unknown action type: {action_type}'}
|
|
|
|
executed_actions.append({
|
|
'action_type': action_type,
|
|
'result': result
|
|
})
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error executing parallel action {action_type}: {e}", exc_info=True)
|
|
executed_actions.append({
|
|
'action_type': action.type.value,
|
|
'result': {'status': 'error', 'message': str(e)}
|
|
})
|
|
|
|
return executed_actions
|
|
|
|
async def _execute_redis_save_image(self,
|
|
action: Dict,
|
|
frame: np.ndarray,
|
|
detected_regions: Dict[str, Any],
|
|
context: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Execute redis_save_image action."""
|
|
if not self.redis_manager:
|
|
return {'status': 'error', 'message': 'Redis not available'}
|
|
|
|
try:
|
|
# Get image to save (cropped or full frame)
|
|
image_to_save = frame
|
|
region_name = action.params.get('region')
|
|
|
|
if region_name and region_name in detected_regions:
|
|
# Crop the specified region
|
|
bbox = detected_regions[region_name]['bbox']
|
|
x1, y1, x2, y2 = [int(coord) for coord in bbox]
|
|
cropped = frame[y1:y2, x1:x2]
|
|
if cropped.size > 0:
|
|
image_to_save = cropped
|
|
logger.debug(f"Cropped region '{region_name}' for redis_save_image")
|
|
else:
|
|
logger.warning(f"Empty crop for region '{region_name}', using full frame")
|
|
|
|
# Format key with context
|
|
key = action.params['key'].format(**context)
|
|
|
|
# Save image to Redis
|
|
result = await self.redis_manager.save_image(
|
|
key=key,
|
|
image=image_to_save,
|
|
expire_seconds=action.params.get('expire_seconds'),
|
|
image_format=action.params.get('format', 'jpeg'),
|
|
quality=action.params.get('quality', 90)
|
|
)
|
|
|
|
if result:
|
|
# Add image_key to context for subsequent actions
|
|
context['image_key'] = key
|
|
return {'status': 'success', 'key': key}
|
|
else:
|
|
return {'status': 'error', 'message': 'Failed to save image to Redis'}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in redis_save_image action: {e}", exc_info=True)
|
|
return {'status': 'error', 'message': str(e)}
|
|
|
|
async def _execute_redis_publish(self, action: Dict, context: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Execute redis_publish action."""
|
|
if not self.redis_manager:
|
|
return {'status': 'error', 'message': 'Redis not available'}
|
|
|
|
try:
|
|
channel = action.params['channel']
|
|
message_template = action.params['message']
|
|
|
|
# Format message with context
|
|
message = message_template.format(**context)
|
|
|
|
# Publish message
|
|
result = await self.redis_manager.publish_message(channel, message)
|
|
|
|
if result >= 0: # Redis publish returns number of subscribers
|
|
return {'status': 'success', 'subscribers': result, 'channel': channel}
|
|
else:
|
|
return {'status': 'error', 'message': 'Failed to publish message to Redis'}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in redis_publish action: {e}", exc_info=True)
|
|
return {'status': 'error', 'message': str(e)}
|
|
|
|
async def _execute_postgresql_update_combined(self,
|
|
action: Dict,
|
|
context: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Execute postgresql_update_combined action."""
|
|
if not self.db_manager:
|
|
return {'status': 'error', 'message': 'Database not available'}
|
|
|
|
try:
|
|
# Wait for required branches if specified
|
|
wait_for_branches = action.params.get('waitForBranches', [])
|
|
branch_results = context.get('branch_results', {})
|
|
|
|
# Log which branches are available vs. expected
|
|
missing_branches = [branch_id for branch_id in wait_for_branches if branch_id not in branch_results]
|
|
available_branches = [branch_id for branch_id in wait_for_branches if branch_id in branch_results]
|
|
|
|
if missing_branches:
|
|
logger.warning(f"Some branches missing for database update - available: {available_branches}, missing: {missing_branches}")
|
|
else:
|
|
logger.info(f"All expected branches available for database update: {available_branches}")
|
|
|
|
# Continue with update using whatever results are available (don't fail on missing branches)
|
|
|
|
# Prepare fields for database update
|
|
table = action.params.get('table', 'car_frontal_info')
|
|
key_field = action.params.get('key_field', 'session_id')
|
|
key_value = action.params.get('key_value', '{session_id}').format(**context)
|
|
field_mappings = action.params.get('fields', {})
|
|
|
|
# Resolve field values using branch results
|
|
resolved_fields = {}
|
|
for field_name, field_template in field_mappings.items():
|
|
try:
|
|
# Replace template variables with actual values from branch results
|
|
resolved_value = self._resolve_field_template(field_template, branch_results, context)
|
|
resolved_fields[field_name] = resolved_value
|
|
except Exception as e:
|
|
logger.warning(f"Failed to resolve field {field_name}: {e}")
|
|
resolved_fields[field_name] = None
|
|
|
|
# Execute database update with available data
|
|
success = self.db_manager.execute_update(
|
|
table=table,
|
|
key_field=key_field,
|
|
key_value=key_value,
|
|
fields=resolved_fields
|
|
)
|
|
|
|
# Log the update result with details about what data was available
|
|
non_null_fields = {k: v for k, v in resolved_fields.items() if v is not None}
|
|
null_fields = [k for k, v in resolved_fields.items() if v is None]
|
|
|
|
if success:
|
|
logger.info(f"[DATABASE UPDATE] Success for session {key_value}: "
|
|
f"updated {len(non_null_fields)} fields {list(non_null_fields.keys())}"
|
|
f"{f', {len(null_fields)} null fields {null_fields}' if null_fields else ''}")
|
|
return {
|
|
'status': 'success',
|
|
'table': table,
|
|
'key': f'{key_field}={key_value}',
|
|
'fields': resolved_fields,
|
|
'updated_fields': non_null_fields,
|
|
'null_fields': null_fields,
|
|
'available_branches': available_branches,
|
|
'missing_branches': missing_branches
|
|
}
|
|
else:
|
|
logger.error(f"[DATABASE UPDATE] Failed for session {key_value}")
|
|
return {'status': 'error', 'message': 'Database update failed'}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in postgresql_update_combined action: {e}", exc_info=True)
|
|
return {'status': 'error', 'message': str(e)}
|
|
|
|
def _resolve_field_template(self, template: str, branch_results: Dict, context: Dict) -> str:
|
|
"""
|
|
Resolve field template using branch results and context.
|
|
|
|
Args:
|
|
template: Template string like "{car_brand_cls_v2.brand}"
|
|
branch_results: Dictionary of branch execution results
|
|
context: Detection context
|
|
|
|
Returns:
|
|
Resolved field value
|
|
"""
|
|
try:
|
|
# Handle simple context variables first
|
|
if template.startswith('{') and template.endswith('}'):
|
|
var_name = template[1:-1]
|
|
|
|
# Check for branch result reference (e.g., "car_brand_cls_v2.brand")
|
|
if '.' in var_name:
|
|
branch_id, field_name = var_name.split('.', 1)
|
|
if branch_id in branch_results:
|
|
branch_data = branch_results[branch_id]
|
|
# Look for the field in branch results
|
|
if isinstance(branch_data, dict) and 'result' in branch_data:
|
|
result_data = branch_data['result']
|
|
if isinstance(result_data, dict) and field_name in result_data:
|
|
return str(result_data[field_name])
|
|
logger.warning(f"Field {field_name} not found in branch {branch_id} results")
|
|
return None
|
|
else:
|
|
logger.warning(f"Branch {branch_id} not found in results")
|
|
return None
|
|
|
|
# Simple context variable
|
|
elif var_name in context:
|
|
return str(context[var_name])
|
|
|
|
logger.warning(f"Template variable {var_name} not found in context or branch results")
|
|
return None
|
|
|
|
# Return template as-is if not a template variable
|
|
return template
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error resolving field template {template}: {e}")
|
|
return None
|
|
|
|
async def _update_session_with_processing_results(self, context: Dict[str, Any]):
|
|
"""
|
|
Update session state with processing results from branch execution.
|
|
|
|
Args:
|
|
context: Detection context containing branch results and session info
|
|
"""
|
|
try:
|
|
branch_results = context.get('branch_results', {})
|
|
session_id = context.get('session_id', '')
|
|
subscription_id = context.get('subscription_id', '')
|
|
|
|
if not session_id:
|
|
logger.warning("No session_id in context for processing results")
|
|
return
|
|
|
|
# Extract car brand from car_brand_cls_v2 results
|
|
car_brand = None
|
|
if 'car_brand_cls_v2' in branch_results:
|
|
brand_result = branch_results['car_brand_cls_v2'].get('result', {})
|
|
car_brand = brand_result.get('brand')
|
|
|
|
# Extract body type from car_bodytype_cls_v1 results
|
|
body_type = None
|
|
if 'car_bodytype_cls_v1' in branch_results:
|
|
bodytype_result = branch_results['car_bodytype_cls_v1'].get('result', {})
|
|
body_type = bodytype_result.get('body_type')
|
|
|
|
logger.info(f"[PROCESSING RESULTS] Completed for session {session_id}: "
|
|
f"brand={car_brand}, bodyType={body_type}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error updating session with processing results: {e}", exc_info=True)
|
|
|
|
def get_statistics(self) -> Dict[str, Any]:
|
|
"""Get detection pipeline statistics."""
|
|
branch_stats = self.branch_processor.get_statistics() if self.branch_processor else {}
|
|
license_stats = self.license_plate_manager.get_statistics() if self.license_plate_manager else {}
|
|
|
|
return {
|
|
'pipeline': self.stats,
|
|
'branches': branch_stats,
|
|
'license_plate': license_stats,
|
|
'redis_available': self.redis_manager is not None,
|
|
'database_available': self.db_manager is not None,
|
|
'detection_model_loaded': self.detection_model is not None
|
|
}
|
|
|
|
def cleanup(self):
|
|
"""Cleanup resources."""
|
|
if self.executor:
|
|
self.executor.shutdown(wait=False)
|
|
|
|
if self.redis_manager:
|
|
self.redis_manager.cleanup()
|
|
|
|
if self.db_manager:
|
|
self.db_manager.disconnect()
|
|
|
|
if self.branch_processor:
|
|
self.branch_processor.cleanup()
|
|
|
|
if self.license_plate_manager:
|
|
# Schedule cleanup task and track it to prevent warnings
|
|
cleanup_task = asyncio.create_task(self.license_plate_manager.close())
|
|
cleanup_task.add_done_callback(lambda _: None) # Suppress "Task exception was never retrieved"
|
|
|
|
logger.info("Detection pipeline cleaned up") |