From f7c464be21a6f298fcb4f1235ad3352a99acf3e0 Mon Sep 17 00:00:00 2001 From: ziesorx Date: Mon, 22 Sep 2025 17:16:51 +0700 Subject: [PATCH] refactor: remove old code --- siwatsystem/database.py | 211 ----------- siwatsystem/pympta.py | 798 ---------------------------------------- 2 files changed, 1009 deletions(-) delete mode 100644 siwatsystem/database.py delete mode 100644 siwatsystem/pympta.py diff --git a/siwatsystem/database.py b/siwatsystem/database.py deleted file mode 100644 index 6340986..0000000 --- a/siwatsystem/database.py +++ /dev/null @@ -1,211 +0,0 @@ -import psycopg2 -import psycopg2.extras -from typing import Optional, Dict, Any -import logging -import uuid - -logger = logging.getLogger(__name__) - -class DatabaseManager: - def __init__(self, config: Dict[str, Any]): - self.config = config - self.connection: Optional[psycopg2.extensions.connection] = None - - def connect(self) -> bool: - try: - self.connection = psycopg2.connect( - host=self.config['host'], - port=self.config['port'], - database=self.config['database'], - user=self.config['username'], - password=self.config['password'] - ) - logger.info("PostgreSQL connection established successfully") - return True - except Exception as e: - logger.error(f"Failed to connect to PostgreSQL: {e}") - return False - - def disconnect(self): - if self.connection: - self.connection.close() - self.connection = None - logger.info("PostgreSQL connection closed") - - def is_connected(self) -> bool: - try: - if self.connection and not self.connection.closed: - cur = self.connection.cursor() - cur.execute("SELECT 1") - cur.fetchone() - cur.close() - return True - except: - pass - return False - - def update_car_info(self, session_id: str, brand: str, model: str, body_type: str) -> bool: - if not self.is_connected(): - if not self.connect(): - return False - - try: - cur = self.connection.cursor() - query = """ - INSERT INTO car_frontal_info (session_id, car_brand, car_model, car_body_type, updated_at) - VALUES (%s, %s, %s, %s, NOW()) - ON CONFLICT (session_id) - DO UPDATE SET - car_brand = EXCLUDED.car_brand, - car_model = EXCLUDED.car_model, - car_body_type = EXCLUDED.car_body_type, - updated_at = NOW() - """ - cur.execute(query, (session_id, brand, model, body_type)) - self.connection.commit() - cur.close() - logger.info(f"Updated car info for session {session_id}: {brand} {model} ({body_type})") - return True - except Exception as e: - logger.error(f"Failed to update car info: {e}") - if self.connection: - self.connection.rollback() - return False - - def execute_update(self, table: str, key_field: str, key_value: str, fields: Dict[str, str]) -> bool: - if not self.is_connected(): - if not self.connect(): - return False - - try: - cur = self.connection.cursor() - - # Build the UPDATE query dynamically - set_clauses = [] - values = [] - - for field, value in fields.items(): - if value == "NOW()": - set_clauses.append(f"{field} = NOW()") - else: - set_clauses.append(f"{field} = %s") - values.append(value) - - # Add schema prefix if table doesn't already have it - full_table_name = table if '.' in table else f"gas_station_1.{table}" - - query = f""" - INSERT INTO {full_table_name} ({key_field}, {', '.join(fields.keys())}) - VALUES (%s, {', '.join(['%s'] * len(fields))}) - ON CONFLICT ({key_field}) - DO UPDATE SET {', '.join(set_clauses)} - """ - - # Add key_value to the beginning of values list - all_values = [key_value] + list(fields.values()) + values - - cur.execute(query, all_values) - self.connection.commit() - cur.close() - logger.info(f"Updated {table} for {key_field}={key_value}") - return True - except Exception as e: - logger.error(f"Failed to execute update on {table}: {e}") - if self.connection: - self.connection.rollback() - return False - - def create_car_frontal_info_table(self) -> bool: - """Create the car_frontal_info table in gas_station_1 schema if it doesn't exist.""" - if not self.is_connected(): - if not self.connect(): - return False - - try: - cur = self.connection.cursor() - - # Create schema if it doesn't exist - cur.execute("CREATE SCHEMA IF NOT EXISTS gas_station_1") - - # Create table if it doesn't exist - create_table_query = """ - CREATE TABLE IF NOT EXISTS gas_station_1.car_frontal_info ( - display_id VARCHAR(255), - captured_timestamp VARCHAR(255), - session_id VARCHAR(255) PRIMARY KEY, - license_character VARCHAR(255) DEFAULT NULL, - license_type VARCHAR(255) DEFAULT 'No model available', - car_brand VARCHAR(255) DEFAULT NULL, - car_model VARCHAR(255) DEFAULT NULL, - car_body_type VARCHAR(255) DEFAULT NULL, - updated_at TIMESTAMP DEFAULT NOW() - ) - """ - - cur.execute(create_table_query) - - # Add columns if they don't exist (for existing tables) - alter_queries = [ - "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS car_brand VARCHAR(255) DEFAULT NULL", - "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS car_model VARCHAR(255) DEFAULT NULL", - "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS car_body_type VARCHAR(255) DEFAULT NULL", - "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS updated_at TIMESTAMP DEFAULT NOW()" - ] - - for alter_query in alter_queries: - try: - cur.execute(alter_query) - logger.debug(f"Executed: {alter_query}") - except Exception as e: - # Ignore errors if column already exists (for older PostgreSQL versions) - if "already exists" in str(e).lower(): - logger.debug(f"Column already exists, skipping: {alter_query}") - else: - logger.warning(f"Error in ALTER TABLE: {e}") - - self.connection.commit() - cur.close() - logger.info("Successfully created/verified car_frontal_info table with all required columns") - return True - - except Exception as e: - logger.error(f"Failed to create car_frontal_info table: {e}") - if self.connection: - self.connection.rollback() - return False - - def insert_initial_detection(self, display_id: str, captured_timestamp: str, session_id: str = None) -> str: - """Insert initial detection record and return the session_id.""" - if not self.is_connected(): - if not self.connect(): - return None - - # Generate session_id if not provided - if not session_id: - session_id = str(uuid.uuid4()) - - try: - # Ensure table exists - if not self.create_car_frontal_info_table(): - logger.error("Failed to create/verify table before insertion") - return None - - cur = self.connection.cursor() - insert_query = """ - INSERT INTO gas_station_1.car_frontal_info - (display_id, captured_timestamp, session_id, license_character, license_type, car_brand, car_model, car_body_type) - VALUES (%s, %s, %s, NULL, 'No model available', NULL, NULL, NULL) - ON CONFLICT (session_id) DO NOTHING - """ - - cur.execute(insert_query, (display_id, captured_timestamp, session_id)) - self.connection.commit() - cur.close() - logger.info(f"Inserted initial detection record with session_id: {session_id}") - return session_id - - except Exception as e: - logger.error(f"Failed to insert initial detection record: {e}") - if self.connection: - self.connection.rollback() - return None \ No newline at end of file diff --git a/siwatsystem/pympta.py b/siwatsystem/pympta.py deleted file mode 100644 index d21232d..0000000 --- a/siwatsystem/pympta.py +++ /dev/null @@ -1,798 +0,0 @@ -import os -import json -import logging -import torch -import cv2 -import zipfile -import shutil -import traceback -import redis -import time -import uuid -import concurrent.futures -from ultralytics import YOLO -from urllib.parse import urlparse -from .database import DatabaseManager - -# Create a logger specifically for this module -logger = logging.getLogger("detector_worker.pympta") - -def validate_redis_config(redis_config: dict) -> bool: - """Validate Redis configuration parameters.""" - required_fields = ["host", "port"] - for field in required_fields: - if field not in redis_config: - logger.error(f"Missing required Redis config field: {field}") - return False - - if not isinstance(redis_config["port"], int) or redis_config["port"] <= 0: - logger.error(f"Invalid Redis port: {redis_config['port']}") - return False - - return True - -def validate_postgresql_config(pg_config: dict) -> bool: - """Validate PostgreSQL configuration parameters.""" - required_fields = ["host", "port", "database", "username", "password"] - for field in required_fields: - if field not in pg_config: - logger.error(f"Missing required PostgreSQL config field: {field}") - return False - - if not isinstance(pg_config["port"], int) or pg_config["port"] <= 0: - logger.error(f"Invalid PostgreSQL port: {pg_config['port']}") - return False - - return True - -def crop_region_by_class(frame, regions_dict, class_name): - """Crop a specific region from frame based on detected class.""" - if class_name not in regions_dict: - logger.warning(f"Class '{class_name}' not found in detected regions") - return None - - bbox = regions_dict[class_name]['bbox'] - x1, y1, x2, y2 = bbox - cropped = frame[y1:y2, x1:x2] - - if cropped.size == 0: - logger.warning(f"Empty crop for class '{class_name}' with bbox {bbox}") - return None - - return cropped - -def format_action_context(base_context, additional_context=None): - """Format action context with dynamic values.""" - context = {**base_context} - if additional_context: - context.update(additional_context) - return context - -def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client, db_manager=None) -> dict: - # Recursively load a model node from configuration. - model_path = os.path.join(mpta_dir, node_config["modelFile"]) - if not os.path.exists(model_path): - logger.error(f"Model file {model_path} not found. Current directory: {os.getcwd()}") - logger.error(f"Directory content: {os.listdir(os.path.dirname(model_path))}") - raise FileNotFoundError(f"Model file {model_path} not found.") - logger.info(f"Loading model for node {node_config['modelId']} from {model_path}") - model = YOLO(model_path) - if torch.cuda.is_available(): - logger.info(f"CUDA available. Moving model {node_config['modelId']} to GPU") - model.to("cuda") - else: - logger.info(f"CUDA not available. Using CPU for model {node_config['modelId']}") - - # Prepare trigger class indices for optimization - trigger_classes = node_config.get("triggerClasses", []) - trigger_class_indices = None - if trigger_classes and hasattr(model, "names"): - # Convert class names to indices for the model - trigger_class_indices = [i for i, name in model.names.items() - if name in trigger_classes] - logger.debug(f"Converted trigger classes to indices: {trigger_class_indices}") - - node = { - "modelId": node_config["modelId"], - "modelFile": node_config["modelFile"], - "triggerClasses": trigger_classes, - "triggerClassIndices": trigger_class_indices, - "crop": node_config.get("crop", False), - "cropClass": node_config.get("cropClass"), - "minConfidence": node_config.get("minConfidence", None), - "multiClass": node_config.get("multiClass", False), - "expectedClasses": node_config.get("expectedClasses", []), - "parallel": node_config.get("parallel", False), - "actions": node_config.get("actions", []), - "parallelActions": node_config.get("parallelActions", []), - "model": model, - "branches": [], - "redis_client": redis_client, - "db_manager": db_manager - } - logger.debug(f"Configured node {node_config['modelId']} with trigger classes: {node['triggerClasses']}") - for child in node_config.get("branches", []): - logger.debug(f"Loading branch for parent node {node_config['modelId']}") - node["branches"].append(load_pipeline_node(child, mpta_dir, redis_client, db_manager)) - return node - -def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict: - logger.info(f"Attempting to load pipeline from {zip_source} to {target_dir}") - os.makedirs(target_dir, exist_ok=True) - zip_path = os.path.join(target_dir, "pipeline.mpta") - - # Parse the source; only local files are supported here. - parsed = urlparse(zip_source) - if parsed.scheme in ("", "file"): - local_path = parsed.path if parsed.scheme == "file" else zip_source - logger.debug(f"Checking if local file exists: {local_path}") - if os.path.exists(local_path): - try: - shutil.copy(local_path, zip_path) - logger.info(f"Copied local .mpta file from {local_path} to {zip_path}") - except Exception as e: - logger.error(f"Failed to copy local .mpta file from {local_path}: {str(e)}", exc_info=True) - return None - else: - logger.error(f"Local file {local_path} does not exist. Current directory: {os.getcwd()}") - # List all subdirectories of models directory to help debugging - if os.path.exists("models"): - logger.error(f"Content of models directory: {os.listdir('models')}") - for root, dirs, files in os.walk("models"): - logger.error(f"Directory {root} contains subdirs: {dirs} and files: {files}") - else: - logger.error("The models directory doesn't exist") - return None - else: - logger.error(f"HTTP download functionality has been moved. Use a local file path here. Received: {zip_source}") - return None - - try: - if not os.path.exists(zip_path): - logger.error(f"Zip file not found at expected location: {zip_path}") - return None - - logger.debug(f"Extracting .mpta file from {zip_path} to {target_dir}") - # Extract contents and track the directories created - extracted_dirs = [] - with zipfile.ZipFile(zip_path, "r") as zip_ref: - file_list = zip_ref.namelist() - logger.debug(f"Files in .mpta archive: {file_list}") - - # Extract and track the top-level directories - for file_path in file_list: - parts = file_path.split('/') - if len(parts) > 1: - top_dir = parts[0] - if top_dir and top_dir not in extracted_dirs: - extracted_dirs.append(top_dir) - - # Now extract the files - zip_ref.extractall(target_dir) - - logger.info(f"Successfully extracted .mpta file to {target_dir}") - logger.debug(f"Extracted directories: {extracted_dirs}") - - # Check what was actually created after extraction - actual_dirs = [d for d in os.listdir(target_dir) if os.path.isdir(os.path.join(target_dir, d))] - logger.debug(f"Actual directories created: {actual_dirs}") - except zipfile.BadZipFile as e: - logger.error(f"Bad zip file {zip_path}: {str(e)}", exc_info=True) - return None - except Exception as e: - logger.error(f"Failed to extract .mpta file {zip_path}: {str(e)}", exc_info=True) - return None - finally: - if os.path.exists(zip_path): - os.remove(zip_path) - logger.debug(f"Removed temporary zip file: {zip_path}") - - # Use the first extracted directory if it exists, otherwise use the expected name - pipeline_name = os.path.basename(zip_source) - pipeline_name = os.path.splitext(pipeline_name)[0] - - # Find the directory with pipeline.json - mpta_dir = None - # First try the expected directory name - expected_dir = os.path.join(target_dir, pipeline_name) - if os.path.exists(expected_dir) and os.path.exists(os.path.join(expected_dir, "pipeline.json")): - mpta_dir = expected_dir - logger.debug(f"Found pipeline.json in the expected directory: {mpta_dir}") - else: - # Look through all subdirectories for pipeline.json - for subdir in actual_dirs: - potential_dir = os.path.join(target_dir, subdir) - if os.path.exists(os.path.join(potential_dir, "pipeline.json")): - mpta_dir = potential_dir - logger.info(f"Found pipeline.json in directory: {mpta_dir} (different from expected: {expected_dir})") - break - - if not mpta_dir: - logger.error(f"Could not find pipeline.json in any extracted directory. Directory content: {os.listdir(target_dir)}") - return None - - pipeline_json_path = os.path.join(mpta_dir, "pipeline.json") - if not os.path.exists(pipeline_json_path): - logger.error(f"pipeline.json not found in the .mpta file. Files in directory: {os.listdir(mpta_dir)}") - return None - - try: - with open(pipeline_json_path, "r") as f: - pipeline_config = json.load(f) - logger.info(f"Successfully loaded pipeline configuration from {pipeline_json_path}") - logger.debug(f"Pipeline config: {json.dumps(pipeline_config, indent=2)}") - - # Establish Redis connection if configured - redis_client = None - if "redis" in pipeline_config: - redis_config = pipeline_config["redis"] - if not validate_redis_config(redis_config): - logger.error("Invalid Redis configuration, skipping Redis connection") - else: - try: - redis_client = redis.Redis( - host=redis_config["host"], - port=redis_config["port"], - password=redis_config.get("password"), - db=redis_config.get("db", 0), - decode_responses=True - ) - redis_client.ping() - logger.info(f"Successfully connected to Redis at {redis_config['host']}:{redis_config['port']}") - except redis.exceptions.ConnectionError as e: - logger.error(f"Failed to connect to Redis: {e}") - redis_client = None - - # Establish PostgreSQL connection if configured - db_manager = None - if "postgresql" in pipeline_config: - pg_config = pipeline_config["postgresql"] - if not validate_postgresql_config(pg_config): - logger.error("Invalid PostgreSQL configuration, skipping database connection") - else: - try: - db_manager = DatabaseManager(pg_config) - if db_manager.connect(): - logger.info(f"Successfully connected to PostgreSQL at {pg_config['host']}:{pg_config['port']}") - else: - logger.error("Failed to connect to PostgreSQL") - db_manager = None - except Exception as e: - logger.error(f"Error initializing PostgreSQL connection: {e}") - db_manager = None - - return load_pipeline_node(pipeline_config["pipeline"], mpta_dir, redis_client, db_manager) - except json.JSONDecodeError as e: - logger.error(f"Error parsing pipeline.json: {str(e)}", exc_info=True) - return None - except KeyError as e: - logger.error(f"Missing key in pipeline.json: {str(e)}", exc_info=True) - return None - except Exception as e: - logger.error(f"Error loading pipeline.json: {str(e)}", exc_info=True) - return None - -def execute_actions(node, frame, detection_result, regions_dict=None): - if not node["redis_client"] or not node["actions"]: - return - - # Create a dynamic context for this detection event - from datetime import datetime - action_context = { - **detection_result, - "timestamp_ms": int(time.time() * 1000), - "uuid": str(uuid.uuid4()), - "timestamp": datetime.now().strftime("%Y-%m-%dT%H-%M-%S"), - "filename": f"{uuid.uuid4()}.jpg" - } - - for action in node["actions"]: - try: - if action["type"] == "redis_save_image": - key = action["key"].format(**action_context) - - # Check if we need to crop a specific region - region_name = action.get("region") - image_to_save = frame - - if region_name and regions_dict: - cropped_image = crop_region_by_class(frame, regions_dict, region_name) - if cropped_image is not None: - image_to_save = cropped_image - logger.debug(f"Cropped region '{region_name}' for redis_save_image") - else: - logger.warning(f"Could not crop region '{region_name}', saving full frame instead") - - # Encode image with specified format and quality (default to JPEG) - img_format = action.get("format", "jpeg").lower() - quality = action.get("quality", 90) - - if img_format == "jpeg": - encode_params = [cv2.IMWRITE_JPEG_QUALITY, quality] - success, buffer = cv2.imencode('.jpg', image_to_save, encode_params) - elif img_format == "png": - success, buffer = cv2.imencode('.png', image_to_save) - else: - success, buffer = cv2.imencode('.jpg', image_to_save, [cv2.IMWRITE_JPEG_QUALITY, quality]) - - if not success: - logger.error(f"Failed to encode image for redis_save_image") - continue - - expire_seconds = action.get("expire_seconds") - if expire_seconds: - node["redis_client"].setex(key, expire_seconds, buffer.tobytes()) - logger.info(f"Saved image to Redis with key: {key} (expires in {expire_seconds}s)") - else: - node["redis_client"].set(key, buffer.tobytes()) - logger.info(f"Saved image to Redis with key: {key}") - action_context["image_key"] = key - elif action["type"] == "redis_publish": - channel = action["channel"] - try: - # Handle JSON message format by creating it programmatically - message_template = action["message"] - - # Check if the message is JSON-like (starts and ends with braces) - if message_template.strip().startswith('{') and message_template.strip().endswith('}'): - # Create JSON data programmatically to avoid formatting issues - json_data = {} - - # Add common fields - json_data["event"] = "frontal_detected" - json_data["display_id"] = action_context.get("display_id", "unknown") - json_data["session_id"] = action_context.get("session_id") - json_data["timestamp"] = action_context.get("timestamp", "") - json_data["image_key"] = action_context.get("image_key", "") - - # Convert to JSON string - message = json.dumps(json_data) - else: - # Use regular string formatting for non-JSON messages - message = message_template.format(**action_context) - - # Publish to Redis - if not node["redis_client"]: - logger.error("Redis client is None, cannot publish message") - continue - - # Test Redis connection - try: - node["redis_client"].ping() - logger.debug("Redis connection is active") - except Exception as ping_error: - logger.error(f"Redis connection test failed: {ping_error}") - continue - - result = node["redis_client"].publish(channel, message) - logger.info(f"Published message to Redis channel '{channel}': {message}") - logger.info(f"Redis publish result (subscribers count): {result}") - - # Additional debug info - if result == 0: - logger.warning(f"No subscribers listening to channel '{channel}'") - else: - logger.info(f"Message delivered to {result} subscriber(s)") - - except KeyError as e: - logger.error(f"Missing key in redis_publish message template: {e}") - logger.debug(f"Available context keys: {list(action_context.keys())}") - except Exception as e: - logger.error(f"Error in redis_publish action: {e}") - logger.debug(f"Message template: {action['message']}") - logger.debug(f"Available context keys: {list(action_context.keys())}") - import traceback - logger.debug(f"Full traceback: {traceback.format_exc()}") - except Exception as e: - logger.error(f"Error executing action {action['type']}: {e}") - -def execute_parallel_actions(node, frame, detection_result, regions_dict): - """Execute parallel actions after all required branches have completed.""" - if not node.get("parallelActions"): - return - - logger.debug("Executing parallel actions...") - branch_results = detection_result.get("branch_results", {}) - - for action in node["parallelActions"]: - try: - action_type = action.get("type") - logger.debug(f"Processing parallel action: {action_type}") - - if action_type == "postgresql_update_combined": - # Check if all required branches have completed - wait_for_branches = action.get("waitForBranches", []) - missing_branches = [branch for branch in wait_for_branches if branch not in branch_results] - - if missing_branches: - logger.warning(f"Cannot execute postgresql_update_combined: missing branch results for {missing_branches}") - continue - - logger.info(f"All required branches completed: {wait_for_branches}") - - # Execute the database update - execute_postgresql_update_combined(node, action, detection_result, branch_results) - else: - logger.warning(f"Unknown parallel action type: {action_type}") - - except Exception as e: - logger.error(f"Error executing parallel action {action.get('type', 'unknown')}: {e}") - import traceback - logger.debug(f"Full traceback: {traceback.format_exc()}") - -def execute_postgresql_update_combined(node, action, detection_result, branch_results): - """Execute a PostgreSQL update with combined branch results.""" - if not node.get("db_manager"): - logger.error("No database manager available for postgresql_update_combined action") - return - - try: - table = action["table"] - key_field = action["key_field"] - key_value_template = action["key_value"] - fields = action["fields"] - - # Create context for key value formatting - action_context = {**detection_result} - key_value = key_value_template.format(**action_context) - - logger.info(f"Executing database update: table={table}, {key_field}={key_value}") - - # Process field mappings - mapped_fields = {} - for db_field, value_template in fields.items(): - try: - mapped_value = resolve_field_mapping(value_template, branch_results, action_context) - if mapped_value is not None: - mapped_fields[db_field] = mapped_value - logger.debug(f"Mapped field: {db_field} = {mapped_value}") - else: - logger.warning(f"Could not resolve field mapping for {db_field}: {value_template}") - except Exception as e: - logger.error(f"Error mapping field {db_field} with template '{value_template}': {e}") - - if not mapped_fields: - logger.warning("No fields mapped successfully, skipping database update") - return - - # Execute the database update - success = node["db_manager"].execute_update(table, key_field, key_value, mapped_fields) - - if success: - logger.info(f"Successfully updated database: {table} with {len(mapped_fields)} fields") - else: - logger.error(f"Failed to update database: {table}") - - except KeyError as e: - logger.error(f"Missing required field in postgresql_update_combined action: {e}") - except Exception as e: - logger.error(f"Error in postgresql_update_combined action: {e}") - import traceback - logger.debug(f"Full traceback: {traceback.format_exc()}") - -def resolve_field_mapping(value_template, branch_results, action_context): - """Resolve field mapping templates like {car_brand_cls_v1.brand}.""" - try: - # Handle simple context variables first (non-branch references) - if not '.' in value_template: - return value_template.format(**action_context) - - # Handle branch result references like {model_id.field} - import re - branch_refs = re.findall(r'\{([^}]+\.[^}]+)\}', value_template) - - resolved_template = value_template - for ref in branch_refs: - try: - model_id, field_name = ref.split('.', 1) - - if model_id in branch_results: - branch_data = branch_results[model_id] - if field_name in branch_data: - field_value = branch_data[field_name] - resolved_template = resolved_template.replace(f'{{{ref}}}', str(field_value)) - logger.debug(f"Resolved {ref} to {field_value}") - else: - logger.warning(f"Field '{field_name}' not found in branch '{model_id}' results. Available fields: {list(branch_data.keys())}") - return None - else: - logger.warning(f"Branch '{model_id}' not found in results. Available branches: {list(branch_results.keys())}") - return None - except ValueError as e: - logger.error(f"Invalid branch reference format: {ref}") - return None - - # Format any remaining simple variables - try: - final_value = resolved_template.format(**action_context) - return final_value - except KeyError as e: - logger.warning(f"Could not resolve context variable in template: {e}") - return resolved_template - - except Exception as e: - logger.error(f"Error resolving field mapping '{value_template}': {e}") - return None - -def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None): - """ - Enhanced pipeline that supports: - - Multi-class detection (detecting multiple classes simultaneously) - - Parallel branch processing - - Region-based actions and cropping - - Context passing for session/camera information - """ - try: - task = getattr(node["model"], "task", None) - - # ─── Classification stage ─────────────────────────────────── - if task == "classify": - results = node["model"].predict(frame, stream=False) - if not results: - return (None, None) if return_bbox else None - - r = results[0] - probs = r.probs - if probs is None: - return (None, None) if return_bbox else None - - top1_idx = int(probs.top1) - top1_conf = float(probs.top1conf) - class_name = node["model"].names[top1_idx] - - det = { - "class": class_name, - "confidence": top1_conf, - "id": None, - class_name: class_name # Add class name as key for backward compatibility - } - - # Add specific field mappings for database operations based on model type - model_id = node.get("modelId", "").lower() - if "brand" in model_id or "brand_cls" in model_id: - det["brand"] = class_name - elif "bodytype" in model_id or "body" in model_id: - det["body_type"] = class_name - elif "color" in model_id: - det["color"] = class_name - - execute_actions(node, frame, det) - return (det, None) if return_bbox else det - - # ─── Detection stage - Multi-class support ────────────────── - tk = node["triggerClassIndices"] - logger.debug(f"Running detection for node {node['modelId']} with trigger classes: {node.get('triggerClasses', [])} (indices: {tk})") - logger.debug(f"Node configuration: minConfidence={node['minConfidence']}, multiClass={node.get('multiClass', False)}") - - res = node["model"].track( - frame, - stream=False, - persist=True, - **({"classes": tk} if tk else {}) - )[0] - - # Collect all detections above confidence threshold - all_detections = [] - all_boxes = [] - regions_dict = {} - - logger.debug(f"Raw detection results from model: {len(res.boxes) if res.boxes is not None else 0} detections") - - for i, box in enumerate(res.boxes): - conf = float(box.cpu().conf[0]) - cid = int(box.cpu().cls[0]) - name = node["model"].names[cid] - - logger.debug(f"Detection {i}: class='{name}' (id={cid}), confidence={conf:.3f}, threshold={node['minConfidence']}") - - if conf < node["minConfidence"]: - logger.debug(f" -> REJECTED: confidence {conf:.3f} < threshold {node['minConfidence']}") - continue - - xy = box.cpu().xyxy[0] - x1, y1, x2, y2 = map(int, xy) - bbox = (x1, y1, x2, y2) - - detection = { - "class": name, - "confidence": conf, - "id": box.id.item() if hasattr(box, "id") else None, - "bbox": bbox - } - - all_detections.append(detection) - all_boxes.append(bbox) - - logger.debug(f" -> ACCEPTED: {name} with confidence {conf:.3f}, bbox={bbox}") - - # Store highest confidence detection for each class - if name not in regions_dict or conf > regions_dict[name]["confidence"]: - regions_dict[name] = { - "bbox": bbox, - "confidence": conf, - "detection": detection - } - logger.debug(f" -> Updated regions_dict['{name}'] with confidence {conf:.3f}") - - logger.info(f"Detection summary: {len(all_detections)} accepted detections from {len(res.boxes) if res.boxes is not None else 0} total") - logger.info(f"Detected classes: {list(regions_dict.keys())}") - - if not all_detections: - logger.warning("No detections above confidence threshold - returning null") - return (None, None) if return_bbox else None - - # ─── Multi-class validation ───────────────────────────────── - if node.get("multiClass", False) and node.get("expectedClasses"): - expected_classes = node["expectedClasses"] - detected_classes = list(regions_dict.keys()) - - logger.info(f"Multi-class validation: expected={expected_classes}, detected={detected_classes}") - - # Check if at least one expected class is detected (flexible mode) - matching_classes = [cls for cls in expected_classes if cls in detected_classes] - missing_classes = [cls for cls in expected_classes if cls not in detected_classes] - - logger.debug(f"Matching classes: {matching_classes}, Missing classes: {missing_classes}") - - if not matching_classes: - # No expected classes found at all - logger.warning(f"PIPELINE REJECTED: No expected classes detected. Expected: {expected_classes}, Detected: {detected_classes}") - return (None, None) if return_bbox else None - - if missing_classes: - logger.info(f"Partial multi-class detection: {matching_classes} found, {missing_classes} missing") - else: - logger.info(f"Complete multi-class detection success: {detected_classes}") - else: - logger.debug("No multi-class validation - proceeding with all detections") - - # ─── Execute actions with region information ──────────────── - detection_result = { - "detections": all_detections, - "regions": regions_dict, - **(context or {}) - } - - # ─── Create initial database record when Car+Frontal detected ──── - if node.get("db_manager") and node.get("multiClass", False): - # Only create database record if we have both Car and Frontal - has_car = "Car" in regions_dict - has_frontal = "Frontal" in regions_dict - - if has_car and has_frontal: - # Generate UUID session_id since client session is None for now - import uuid as uuid_lib - from datetime import datetime - generated_session_id = str(uuid_lib.uuid4()) - - # Insert initial detection record - display_id = detection_result.get("display_id", "unknown") - timestamp = datetime.now().strftime("%Y-%m-%dT%H-%M-%S") - - inserted_session_id = node["db_manager"].insert_initial_detection( - display_id=display_id, - captured_timestamp=timestamp, - session_id=generated_session_id - ) - - if inserted_session_id: - # Update detection_result with the generated session_id for actions and branches - detection_result["session_id"] = inserted_session_id - detection_result["timestamp"] = timestamp # Update with proper timestamp - logger.info(f"Created initial database record with session_id: {inserted_session_id}") - else: - logger.debug(f"Database record not created - missing required classes. Has Car: {has_car}, Has Frontal: {has_frontal}") - - execute_actions(node, frame, detection_result, regions_dict) - - # ─── Parallel branch processing ───────────────────────────── - if node["branches"]: - branch_results = {} - - # Filter branches that should be triggered - active_branches = [] - for br in node["branches"]: - trigger_classes = br.get("triggerClasses", []) - min_conf = br.get("minConfidence", 0) - - logger.debug(f"Evaluating branch {br['modelId']}: trigger_classes={trigger_classes}, min_conf={min_conf}") - - # Check if any detected class matches branch trigger - branch_triggered = False - for det_class in regions_dict: - det_confidence = regions_dict[det_class]["confidence"] - logger.debug(f" Checking detected class '{det_class}' (confidence={det_confidence:.3f}) against triggers {trigger_classes}") - - if (det_class in trigger_classes and det_confidence >= min_conf): - active_branches.append(br) - branch_triggered = True - logger.info(f"Branch {br['modelId']} activated by class '{det_class}' (conf={det_confidence:.3f} >= {min_conf})") - break - - if not branch_triggered: - logger.debug(f"Branch {br['modelId']} not triggered - no matching classes or insufficient confidence") - - if active_branches: - if node.get("parallel", False) or any(br.get("parallel", False) for br in active_branches): - # Run branches in parallel - with concurrent.futures.ThreadPoolExecutor(max_workers=len(active_branches)) as executor: - futures = {} - - for br in active_branches: - crop_class = br.get("cropClass", br.get("triggerClasses", [])[0] if br.get("triggerClasses") else None) - sub_frame = frame - - logger.info(f"Starting parallel branch: {br['modelId']}, crop_class: {crop_class}") - - if br.get("crop", False) and crop_class: - cropped = crop_region_by_class(frame, regions_dict, crop_class) - if cropped is not None: - sub_frame = cv2.resize(cropped, (224, 224)) - logger.debug(f"Successfully cropped {crop_class} region for {br['modelId']}") - else: - logger.warning(f"Failed to crop {crop_class} region for {br['modelId']}, skipping branch") - continue - - future = executor.submit(run_pipeline, sub_frame, br, True, context) - futures[future] = br - - # Collect results - for future in concurrent.futures.as_completed(futures): - br = futures[future] - try: - result, _ = future.result() - if result: - branch_results[br["modelId"]] = result - logger.info(f"Branch {br['modelId']} completed: {result}") - except Exception as e: - logger.error(f"Branch {br['modelId']} failed: {e}") - else: - # Run branches sequentially - for br in active_branches: - crop_class = br.get("cropClass", br.get("triggerClasses", [])[0] if br.get("triggerClasses") else None) - sub_frame = frame - - logger.info(f"Starting sequential branch: {br['modelId']}, crop_class: {crop_class}") - - if br.get("crop", False) and crop_class: - cropped = crop_region_by_class(frame, regions_dict, crop_class) - if cropped is not None: - sub_frame = cv2.resize(cropped, (224, 224)) - logger.debug(f"Successfully cropped {crop_class} region for {br['modelId']}") - else: - logger.warning(f"Failed to crop {crop_class} region for {br['modelId']}, skipping branch") - continue - - try: - result, _ = run_pipeline(sub_frame, br, True, context) - if result: - branch_results[br["modelId"]] = result - logger.info(f"Branch {br['modelId']} completed: {result}") - else: - logger.warning(f"Branch {br['modelId']} returned no result") - except Exception as e: - logger.error(f"Error in sequential branch {br['modelId']}: {e}") - import traceback - logger.debug(f"Branch error traceback: {traceback.format_exc()}") - - # Store branch results in detection_result for parallel actions - detection_result["branch_results"] = branch_results - - # ─── Execute Parallel Actions ─────────────────────────────── - if node.get("parallelActions") and "branch_results" in detection_result: - execute_parallel_actions(node, frame, detection_result, regions_dict) - - # ─── Return detection result ──────────────────────────────── - primary_detection = max(all_detections, key=lambda x: x["confidence"]) - primary_bbox = primary_detection["bbox"] - - # Add branch results to primary detection for compatibility - if "branch_results" in detection_result: - primary_detection["branch_results"] = detection_result["branch_results"] - - return (primary_detection, primary_bbox) if return_bbox else primary_detection - - except Exception as e: - logger.error(f"Error in node {node.get('modelId')}: {e}") - traceback.print_exc() - return (None, None) if return_bbox else None