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2025-08-10 20:51:16 +07:00 · 2025-08-10 20:51:16 +07:00 · cfc7503a14
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--- a/CLAUDE.md
+++ b/CLAUDE.md
@ -1,13 +1,23 @@
 # Python Detector Worker - CLAUDE.md

 ## Project Overview
-This is a FastAPI-based computer vision detection worker that processes video streams from RTSP/HTTP sources and runs YOLO-based machine learning pipelines for object detection and classification. The system is designed to work within a larger CMS (Content Management System) architecture.
+This is a FastAPI-based computer vision detection worker that processes video streams from RTSP/HTTP sources and runs advanced YOLO-based machine learning pipelines for multi-class object detection and parallel classification. The system features comprehensive database integration, Redis support, and hierarchical pipeline execution designed to work within a larger CMS (Content Management System) architecture.
+
+### Key Features
+- **Multi-Class Detection**: Simultaneous detection of multiple object classes (e.g., Car + Frontal)
+- **Parallel Processing**: Concurrent execution of classification branches using ThreadPoolExecutor
+- **Database Integration**: Automatic PostgreSQL schema management and record updates
+- **Redis Actions**: Image storage with region cropping and pub/sub messaging
+- **Pipeline Synchronization**: Branch coordination with `waitForBranches` functionality
+- **Dynamic Field Mapping**: Template-based field resolution for database operations

 ## Architecture & Technology Stack
 - **Framework**: FastAPI with WebSocket support
 - **ML/CV**: PyTorch, Ultralytics YOLO, OpenCV
 - **Containerization**: Docker (Python 3.13-bookworm base)
- **Data Storage**: Redis integration for action handling
+- **Data Storage**: Redis integration for action handling + PostgreSQL for persistent storage
+- **Database**: Automatic schema management with gas_station_1 database
+- **Parallel Processing**: ThreadPoolExecutor for concurrent classification
 - **Communication**: WebSocket-based real-time protocol

 ## Core Components
@ -24,9 +34,20 @@ This is a FastAPI-based computer vision detection worker that processes video st
 ### Pipeline System (`siwatsystem/pympta.py`)
 - **MPTA file handling** - ZIP archives containing model configurations
 - **Hierarchical pipeline execution** with detection → classification branching
- **Redis action system** for image saving and message publishing
+- **Multi-class detection** - Simultaneous detection of multiple classes (Car + Frontal)
+- **Parallel processing** - Concurrent classification branches with ThreadPoolExecutor
+- **Redis action system** - Image saving with region cropping and message publishing
+- **PostgreSQL integration** - Automatic table creation and combined updates
 - **Dynamic model loading** with GPU optimization
 - **Configurable trigger classes and confidence thresholds**
+- **Branch synchronization** - waitForBranches coordination for database updates
+
+### Database System (`siwatsystem/database.py`)
+- **DatabaseManager class** for PostgreSQL operations
+- **Automatic table creation** with gas_station_1.car_frontal_info schema
+- **Combined update operations** with field mapping from branch results
+- **Session management** with UUID generation
+- **Error handling** and connection management

 ### Testing & Debugging
 - **Protocol test script** (`test_protocol.py`) for WebSocket communication validation
@ -92,33 +113,61 @@ This is a FastAPI-based computer vision detection worker that processes video st

 ## Model Pipeline (MPTA) Format

-### Structure
+### Enhanced Structure
 - **ZIP archive** containing models and configuration
- **pipeline.json** - Main configuration file
+- **pipeline.json** - Main configuration file with Redis + PostgreSQL settings
 - **Model files** - YOLO .pt files for detection/classification
- **Redis configuration** - Optional for action execution
+- **Multi-model support** - Detection + multiple classification models

-### Pipeline Flow
-1. **Detection stage** - YOLO object detection with bounding boxes
-2. **Trigger evaluation** - Check if detected class matches trigger conditions
-3. **Classification stage** - Crop detected region and run classification model
-4. **Action execution** - Redis operations (image saving, message publishing)
+### Advanced Pipeline Flow
+1. **Multi-class detection stage** - YOLO detection of Car + Frontal simultaneously
+2. **Validation stage** - Check for expected classes (flexible matching)
+3. **Database initialization** - Create initial record with session_id
+4. **Redis actions** - Save cropped frontal images with expiration
+5. **Parallel classification** - Concurrent brand and body type classification
+6. **Branch synchronization** - Wait for all classification branches to complete
+7. **Database update** - Combined update with all classification results

-### Branch Configuration
+### Enhanced Branch Configuration
 ```json
 {
-  "modelId": "detector-v1",
-  "modelFile": "detector.pt",
-  "triggerClasses": ["car", "truck"],
-  "minConfidence": 0.5,
-  "branches": [{
-    "modelId": "classifier-v1", 
-    "modelFile": "classifier.pt",
-    "crop": true,
-    "triggerClasses": ["car"],
-    "minConfidence": 0.3,
-    "actions": [...]
-  }]
+  "modelId": "car_frontal_detection_v1",
+  "modelFile": "car_frontal_detection_v1.pt",
+  "multiClass": true,
+  "expectedClasses": ["Car", "Frontal"],
+  "triggerClasses": ["Car", "Frontal"],
+  "minConfidence": 0.8,
+  "actions": [
+    {
+      "type": "redis_save_image",
+      "region": "Frontal",
+      "key": "inference:{display_id}:{timestamp}:{session_id}:{filename}",
+      "expire_seconds": 600
+    }
+  ],
+  "branches": [
+    {
+      "modelId": "car_brand_cls_v1",
+      "modelFile": "car_brand_cls_v1.pt",
+      "parallel": true,
+      "crop": true,
+      "cropClass": "Frontal",
+      "triggerClasses": ["Frontal"],
+      "minConfidence": 0.85
+    }
+  ],
+  "parallelActions": [
+    {
+      "type": "postgresql_update_combined",
+      "table": "car_frontal_info",
+      "key_field": "session_id",
+      "waitForBranches": ["car_brand_cls_v1", "car_bodytype_cls_v1"],
+      "fields": {
+        "car_brand": "{car_brand_cls_v1.brand}",
+        "car_body_type": "{car_bodytype_cls_v1.body_type}"
+      }
+    }
+  ]
 }
 ```

@ -173,6 +222,9 @@ docker run -p 8000:8000 -v ./models:/app/models detector-worker
 - **opencv-python**: Computer vision operations
 - **websockets**: WebSocket client/server
 - **redis**: Redis client for action execution
+- **psycopg2-binary**: PostgreSQL database adapter
+- **scipy**: Scientific computing for advanced algorithms
+- **filterpy**: Kalman filtering and state estimation

 ## Security Considerations
 - Model files are loaded from trusted sources only
@ -180,9 +232,46 @@ docker run -p 8000:8000 -v ./models:/app/models detector-worker
 - WebSocket connections handle disconnects gracefully
 - Resource usage is monitored to prevent DoS

+## Database Integration
+
+### Schema Management
+The system automatically creates and manages PostgreSQL tables:
+
+```sql
+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,
+    created_at TIMESTAMP DEFAULT NOW(),
+    updated_at TIMESTAMP DEFAULT NOW()
+);
+```
+
+### Workflow
+1. **Detection**: When both "Car" and "Frontal" are detected, create initial database record with UUID session_id
+2. **Redis Storage**: Save cropped frontal image to Redis with session_id in key
+3. **Parallel Processing**: Run brand and body type classification concurrently
+4. **Synchronization**: Wait for all branches to complete using `waitForBranches`
+5. **Database Update**: Update record with combined classification results using field mapping
+
+### Field Mapping
+Templates like `{car_brand_cls_v1.brand}` are resolved to actual classification results:
+- `car_brand_cls_v1.brand` → "Honda"
+- `car_bodytype_cls_v1.body_type` → "Sedan"
+
 ## Performance Optimizations
 - GPU acceleration when CUDA is available
 - Shared camera streams reduce resource usage
 - Frame queue optimization (single latest frame)
 - Model caching across subscriptions
- Trigger class filtering for faster inference
+- Trigger class filtering for faster inference
+- Parallel processing with ThreadPoolExecutor for classification branches
+- Multi-class detection reduces inference passes
+- Region-based cropping minimizes processing overhead
+- Database connection pooling and prepared statements
+- Redis image storage with automatic expiration
--- a/pympta.md
+++ b/pympta.md
@ -32,14 +32,15 @@ This modular structure allows for creating complex and efficient inference logic

 ## `pipeline.json` Specification

-This file defines the entire pipeline logic. The root object contains a `pipeline` key for the pipeline definition and an optional `redis` key for Redis configuration.
+This file defines the entire pipeline logic. The root object contains a `pipeline` key for the pipeline definition, optional `redis` key for Redis configuration, and optional `postgresql` key for database integration.

 ### Top-Level Object Structure

-| Key        | Type   | Required | Description                                             |
-| ---------- | ------ | -------- | ------------------------------------------------------- |
-| `pipeline` | Object | Yes      | The root node object of the pipeline.                   |
-| `redis`    | Object | No       | Configuration for connecting to a Redis server.         |
+| Key          | Type   | Required | Description                                             |
+| ------------ | ------ | -------- | ------------------------------------------------------- |
+| `pipeline`   | Object | Yes      | The root node object of the pipeline.                   |
+| `redis`      | Object | No       | Configuration for connecting to a Redis server.         |
+| `postgresql` | Object | No       | Configuration for connecting to a PostgreSQL database.  |

 ### Redis Configuration (`redis`)

@ -50,6 +51,16 @@ This file defines the entire pipeline logic. The root object contains a `pipelin
 | `password` | String | No       | The password for Redis authentication.                  |
 | `db`       | Number | No       | The Redis database number to use. Defaults to `0`.      |

+### PostgreSQL Configuration (`postgresql`)
+
+| Key        | Type   | Required | Description                                             |
+| ---------- | ------ | -------- | ------------------------------------------------------- |
+| `host`     | String | Yes      | The hostname or IP address of the PostgreSQL server.    |
+| `port`     | Number | Yes      | The port number of the PostgreSQL server.              |
+| `database` | String | Yes      | The database name to connect to.                       |
+| `username` | String | Yes      | The username for database authentication.              |
+| `password` | String | Yes      | The password for database authentication.              |
+
 ### Node Object Structure

 | Key                 | Type          | Required | Description                                                                                                                            |
@ -59,12 +70,17 @@ This file defines the entire pipeline logic. The root object contains a `pipelin
 | `minConfidence`     | Float         | Yes      | The minimum confidence score (0.0 to 1.0) required for a detection to be considered valid and potentially trigger a branch.              |
 | `triggerClasses`    | Array<String> | Yes      | A list of class names that, when detected by the parent, can trigger this node. For the root node, this lists all classes of interest. |
 | `crop`              | Boolean       | No       | If `true`, the image is cropped to the parent's detection bounding box before being passed to this node's model. Defaults to `false`.    |
+| `cropClass`         | String        | No       | The specific class to use for cropping (e.g., "Frontal" for frontal view cropping).                                                   |
+| `multiClass`        | Boolean       | No       | If `true`, enables multi-class detection mode where multiple classes can be detected simultaneously.                                   |
+| `expectedClasses`   | Array<String> | No       | When `multiClass` is true, defines which classes are expected. At least one must be detected for processing to continue.              |
+| `parallel`          | Boolean       | No       | If `true`, this branch will be processed in parallel with other parallel branches.                                                    |
 | `branches`          | Array<Node>   | No       | A list of child node objects that can be triggered by this node's detections.                                                          |
 | `actions`           | Array<Action> | No       | A list of actions to execute upon a successful detection in this node.                                                                 |
+| `parallelActions`   | Array<Action> | No       | A list of actions to execute after all specified branches have completed.                                                             |

 ### Action Object Structure

-Actions allow the pipeline to interact with Redis. They are executed sequentially for a given detection.
+Actions allow the pipeline to interact with Redis and PostgreSQL databases. They are executed sequentially for a given detection.

 #### Action Context & Dynamic Keys

@ -72,7 +88,12 @@ All actions have access to a dynamic context for formatting keys and messages. T

 - All key-value pairs from the detection result (e.g., `class`, `confidence`, `id`).
 - `{timestamp_ms}`: The current Unix timestamp in milliseconds.
+- `{timestamp}`: Formatted timestamp string (YYYY-MM-DDTHH-MM-SS).
 - `{uuid}`: A unique identifier (UUID4) for the detection event.
+- `{filename}`: Generated filename with UUID.
+- `{camera_id}`: Full camera subscription identifier.
+- `{display_id}`: Display identifier extracted from subscription.
+- `{session_id}`: Session ID for database operations.
 - `{image_key}`: If a `redis_save_image` action has already been executed for this event, this placeholder will be replaced with the key where the image was stored.

 #### `redis_save_image`
@ -83,6 +104,9 @@ Saves the current image frame (or cropped sub-image) to a Redis key.
 | ---------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- |
 | `type`           | String | Yes      | Must be `"redis_save_image"`.                                                                           |
 | `key`            | String | Yes      | The Redis key to save the image to. Can contain any of the dynamic placeholders.                        |
+| `region`         | String | No       | Specific detected region to crop and save (e.g., "Frontal").                                           |
+| `format`         | String | No       | Image format: "jpeg" or "png". Defaults to "jpeg".                                                     |
+| `quality`        | Number | No       | JPEG quality (1-100). Defaults to 90.                                                                  |
 | `expire_seconds` | Number | No       | If provided, sets an expiration time (in seconds) for the Redis key.                                    |

 #### `redis_publish`
@ -95,35 +119,98 @@ Publishes a message to a Redis channel.
 | `channel` | String | Yes      | The Redis channel to publish the message to.                                                            |
 | `message` | String | Yes      | The message to publish. Can contain any of the dynamic placeholders, including `{image_key}`.           |

-### Example `pipeline.json` with Redis
+#### `postgresql_update_combined`

-This example demonstrates a pipeline that detects vehicles, saves a uniquely named image of each detection that expires in one hour, and then publishes a notification with the image key.
+Updates PostgreSQL database with results from multiple branches after they complete.
+
+| Key                | Type          | Required | Description                                                                                             |
+| ------------------ | ------------- | -------- | ------------------------------------------------------------------------------------------------------- |
+| `type`             | String        | Yes      | Must be `"postgresql_update_combined"`.                                                                 |
+| `table`            | String        | Yes      | The database table name (will be prefixed with `gas_station_1.` schema).                              |
+| `key_field`        | String        | Yes      | The field to use as the update key (typically "session_id").                                           |
+| `key_value`        | String        | Yes      | Template for the key value (e.g., "{session_id}").                                                     |
+| `waitForBranches`  | Array<String> | Yes      | List of branch model IDs to wait for completion before executing update.                               |
+| `fields`           | Object        | Yes      | Field mapping object where keys are database columns and values are templates (e.g., "{branch.field}").|
+
+### Complete Example `pipeline.json`
+
+This example demonstrates a comprehensive pipeline for vehicle detection with parallel classification and database integration:

 ```json
 {
  "redis": {
-    "host": "redis.local",
+    "host": "10.100.1.3",
    "port": 6379,
-    "password": "your-super-secret-password"
+    "password": "your-redis-password",
+    "db": 0
+  },
+  "postgresql": {
+    "host": "10.100.1.3",
+    "port": 5432,
+    "database": "inference",
+    "username": "root",
+    "password": "your-db-password"
  },
  "pipeline": {
-    "modelId": "vehicle-detector",
-    "modelFile": "vehicle_model.pt",
-    "minConfidence": 0.6,
-    "triggerClasses": ["car", "truck"],
+    "modelId": "car_frontal_detection_v1",
+    "modelFile": "car_frontal_detection_v1.pt",
+    "crop": false,
+    "triggerClasses": ["Car", "Frontal"],
+    "minConfidence": 0.8,
+    "multiClass": true,
+    "expectedClasses": ["Car", "Frontal"],
    "actions": [
      {
        "type": "redis_save_image",
-        "key": "detections:{class}:{timestamp_ms}:{uuid}",
-        "expire_seconds": 3600
+        "region": "Frontal",
+        "key": "inference:{display_id}:{timestamp}:{session_id}:{filename}",
+        "expire_seconds": 600,
+        "format": "jpeg",
+        "quality": 90
      },
      {
        "type": "redis_publish",
-        "channel": "vehicle_events",
-        "message": "{\"event\":\"new_detection\",\"class\":\"{class}\",\"confidence\":{confidence},\"image_key\":\"{image_key}\"}"
+        "channel": "car_detections",
+        "message": "{\"event\":\"frontal_detected\"}"
      }
    ],
-    "branches": []
+    "branches": [
+      {
+        "modelId": "car_brand_cls_v1",
+        "modelFile": "car_brand_cls_v1.pt",
+        "crop": true,
+        "cropClass": "Frontal",
+        "resizeTarget": [224, 224],
+        "triggerClasses": ["Frontal"],
+        "minConfidence": 0.85,
+        "parallel": true,
+        "branches": []
+      },
+      {
+        "modelId": "car_bodytype_cls_v1",
+        "modelFile": "car_bodytype_cls_v1.pt",
+        "crop": true,
+        "cropClass": "Car",
+        "resizeTarget": [224, 224],
+        "triggerClasses": ["Car"],
+        "minConfidence": 0.85,
+        "parallel": true,
+        "branches": []
+      }
+    ],
+    "parallelActions": [
+      {
+        "type": "postgresql_update_combined",
+        "table": "car_frontal_info",
+        "key_field": "session_id",
+        "key_value": "{session_id}",
+        "waitForBranches": ["car_brand_cls_v1", "car_bodytype_cls_v1"],
+        "fields": {
+          "car_brand": "{car_brand_cls_v1.brand}",
+          "car_body_type": "{car_bodytype_cls_v1.body_type}"
+        }
+      }
+    ]
  }
 }
 ```
@ -134,7 +221,7 @@ The `pympta` module exposes two main functions.

 ### `load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict`

-Loads, extracts, and parses an `.mpta` file to build a pipeline tree in memory. It also establishes a Redis connection if configured in `pipeline.json`.
+Loads, extracts, and parses an `.mpta` file to build a pipeline tree in memory. It also establishes Redis and PostgreSQL connections if configured in `pipeline.json`.

 - **Parameters:**
  - `zip_source` (str): The file path to the local `.mpta` zip archive.
@ -142,7 +229,7 @@ Loads, extracts, and parses an `.mpta` file to build a pipeline tree in memory.
 - **Returns:**
  - A dictionary representing the root node of the pipeline, ready to be used with `run_pipeline`. Returns `None` if loading fails.

-### `run_pipeline(frame, node: dict, return_bbox: bool = False)`
+### `run_pipeline(frame, node: dict, return_bbox: bool = False, context: dict = None)`

 Executes the inference pipeline on a single image frame.

@ -150,12 +237,43 @@ Executes the inference pipeline on a single image frame.
  - `frame`: The input image frame (e.g., a NumPy array from OpenCV).
  - `node` (dict): The pipeline node to execute (typically the root node returned by `load_pipeline_from_zip`).
  - `return_bbox` (bool): If `True`, the function returns a tuple `(detection, bounding_box)`. Otherwise, it returns only the `detection`.
+  - `context` (dict): Optional context dictionary containing camera_id, display_id, session_id for action formatting.
 - **Returns:**
  - The final detection result from the last executed node in the chain. A detection is a dictionary like `{'class': 'car', 'confidence': 0.95, 'id': 1}`. If no detection meets the criteria, it returns `None` (or `(None, None)` if `return_bbox` is `True`).

+## Database Integration
+
+The pipeline system includes automatic PostgreSQL database management:
+
+### Table Schema (`gas_station_1.car_frontal_info`)
+
+The system automatically creates and manages the following table structure:
+
+```sql
+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,
+    created_at TIMESTAMP DEFAULT NOW(),
+    updated_at TIMESTAMP DEFAULT NOW()
+);
+```
+
+### Workflow
+
+1. **Initial Record Creation**: When both "Car" and "Frontal" are detected, an initial database record is created with a UUID session_id.
+2. **Redis Storage**: Vehicle images are stored in Redis with keys containing the session_id.
+3. **Parallel Classification**: Brand and body type classification run concurrently.
+4. **Database Update**: After all branches complete, the database record is updated with classification results.
+
 ## Usage Example

-This snippet, inspired by `pipeline_webcam.py`, shows how to use `pympta` to load a pipeline and process an image from a webcam.
+This snippet shows how to use `pympta` with the enhanced features:

 ```python
 import cv2
@ -181,9 +299,14 @@ while True:
    if not ret:
        break

-    # 4. Run the pipeline on the current frame
-    # The function will handle the entire logic tree (e.g., find a car, then find its license plate).
-    detection_result, bounding_box = run_pipeline(frame, model_tree, return_bbox=True)
+    # 4. Run the pipeline on the current frame with context
+    context = {
+        "camera_id": "display-001;cam-001",
+        "display_id": "display-001", 
+        "session_id": None  # Will be generated automatically
+    }
+    
+    detection_result, bounding_box = run_pipeline(frame, model_tree, return_bbox=True, context=context)

    # 5. Display the results
    if detection_result:
--- a/worker.md
+++ b/worker.md
@ -2,6 +2,12 @@

 This document outlines the WebSocket-based communication protocol between the CMS backend and a detector worker. As a worker developer, your primary responsibility is to implement a WebSocket server that adheres to this protocol.

+The current Python Detector Worker implementation supports advanced computer vision pipelines with:
+- Multi-class YOLO detection with parallel processing
+- PostgreSQL database integration with automatic schema management
+- Redis integration for image storage and pub/sub messaging
+- Hierarchical pipeline execution with detection → classification branching
+
 ## 1. Connection

 The worker must run a WebSocket server, preferably on port `8000`. The backend system, which is managed by a container orchestration service, will automatically discover and establish a WebSocket connection to your worker.
@ -25,14 +31,34 @@ To enable modularity and dynamic configuration, the backend will send you a URL
 2. Extracting its contents.
 3. Interpreting the contents to configure its internal pipeline.

-**The contents of the `.mpta` file are entirely up to the user who configures the model in the CMS.** This allows for maximum flexibility. For example, the archive could contain:
+**The current implementation supports comprehensive pipeline configurations including:**

- AI/ML Models: Pre-trained models for libraries like TensorFlow, PyTorch, or ONNX.
- Configuration Files: A `config.json` or `pipeline.yaml` that defines a sequence of operations, specifies model paths, or sets detection thresholds.
- Scripts: Custom Python scripts for pre-processing or post-processing.
- API Integration Details: A JSON file with endpoint information and credentials for interacting with third-party detection services.
+- **AI/ML Models**: YOLO models (.pt files) for detection and classification
+- **Pipeline Configuration**: `pipeline.json` defining hierarchical detection→classification workflows
+- **Multi-class Detection**: Simultaneous detection of multiple object classes (e.g., Car + Frontal)
+- **Parallel Processing**: Concurrent execution of classification branches with ThreadPoolExecutor
+- **Database Integration**: PostgreSQL configuration for automatic table creation and updates
+- **Redis Actions**: Image storage with region cropping and pub/sub messaging
+- **Dynamic Field Mapping**: Template-based field resolution for database operations

-Essentially, the `.mpta` file is a self-contained package that tells your worker *how* to process the video stream for a given subscription.
+**Enhanced MPTA Structure:**
+```
+pipeline.mpta/
+├── pipeline.json          # Main configuration with redis/postgresql settings
+├── car_detection.pt       # Primary YOLO detection model
+├── brand_classifier.pt    # Classification model for car brands
+├── bodytype_classifier.pt # Classification model for body types
+└── ...
+```
+
+The `pipeline.json` now supports advanced features like:
+- Multi-class detection with `expectedClasses` validation
+- Parallel branch processing with `parallel: true`
+- Database actions with `postgresql_update_combined`
+- Redis actions with region-specific image cropping
+- Branch synchronization with `waitForBranches`
+
+Essentially, the `.mpta` file is a self-contained package that tells your worker *how* to process the video stream for a given subscription, including complex multi-stage AI pipelines with database persistence.

 ## 4. Messages from Worker to Backend

@ -79,6 +105,15 @@ Sent when the worker detects a relevant object. The `detection` object should be

 - **Type:** `imageDetection`

+**Enhanced Detection Capabilities:**
+
+The current implementation supports multi-class detection with parallel classification processing. When a vehicle is detected, the system:
+
+1. **Multi-Class Detection**: Simultaneously detects "Car" and "Frontal" classes
+2. **Parallel Processing**: Runs brand and body type classification concurrently
+3. **Database Integration**: Automatically creates and updates PostgreSQL records
+4. **Redis Storage**: Saves cropped frontal images with expiration
+
 **Payload Example:**

 ```json
@ -88,19 +123,38 @@ Sent when the worker detects a relevant object. The `detection` object should be
  "timestamp": "2025-07-14T12:34:56.789Z",
  "data": {
    "detection": {
-      "carModel": "Civic",
+      "class": "Car",
+      "confidence": 0.92,
      "carBrand": "Honda",
-      "carYear": 2023,
+      "carModel": "Civic",
      "bodyType": "Sedan",
-      "licensePlateText": "ABCD1234",
-      "licensePlateConfidence": 0.95
+      "branch_results": {
+        "car_brand_cls_v1": {
+          "class": "Honda",
+          "confidence": 0.89,
+          "brand": "Honda"
+        },
+        "car_bodytype_cls_v1": {
+          "class": "Sedan", 
+          "confidence": 0.85,
+          "body_type": "Sedan"
+        }
+      }
    },
    "modelId": 101,
-    "modelName": "US-LPR-and-Vehicle-ID"
+    "modelName": "Car Frontal Detection V1"
  }
 }
 ```

+**Database Integration:**
+
+Each detection automatically:
+- Creates a record in `gas_station_1.car_frontal_info` table
+- Generates a unique `session_id` for tracking
+- Updates the record with classification results after parallel processing completes
+- Stores cropped frontal images in Redis with the session_id as key
+
 ### 4.3. Patch Session

 > **Note:** Patch messages are only used when the worker can't keep up and needs to retroactively send detections. Normally, detections should be sent in real-time using `imageDetection` messages. Use `patchSession` only to update session data after the fact.