diff --git a/.gitignore b/.gitignore index ff8c99d..0f86c21 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,11 @@ +# Do not know how to use +archive/ +Dockerfile + /models app.log *.pt +.venv/ # All pycache directories __pycache__/ diff --git a/CLAUDE.md b/CLAUDE.md deleted file mode 100644 index 3177259..0000000 --- a/CLAUDE.md +++ /dev/null @@ -1,188 +0,0 @@ -# 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. - -## 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 -- **Communication**: WebSocket-based real-time protocol - -## Core Components - -### Main Application (`app.py`) -- **FastAPI WebSocket server** for real-time communication -- **Multi-camera stream management** with shared stream optimization -- **HTTP REST endpoint** for image retrieval (`/camera/{camera_id}/image`) -- **Threading-based frame readers** for RTSP streams and HTTP snapshots -- **Model loading and inference** using MPTA (Machine Learning Pipeline Archive) format -- **Session management** with display identifier mapping -- **Resource monitoring** (CPU, memory, GPU usage via psutil) - -### 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 -- **Dynamic model loading** with GPU optimization -- **Configurable trigger classes and confidence thresholds** - -### Testing & Debugging -- **Protocol test script** (`test_protocol.py`) for WebSocket communication validation -- **Pipeline webcam utility** (`pipeline_webcam.py`) for local testing with visual output -- **RTSP streaming debug tool** (`debug/rtsp_webcam.py`) using GStreamer - -## Code Conventions & Patterns - -### Logging -- **Structured logging** using Python's logging module -- **File + console output** to `detector_worker.log` -- **Debug level separation** for detailed troubleshooting -- **Context-aware messages** with camera IDs and model information - -### Error Handling -- **Graceful failure handling** with retry mechanisms (configurable max_retries) -- **Thread-safe operations** using locks for streams and models -- **WebSocket disconnect handling** with proper cleanup -- **Model loading validation** with detailed error reporting - -### Configuration -- **JSON configuration** (`config.json`) for runtime parameters: - - `poll_interval_ms`: Frame processing interval - - `max_streams`: Concurrent stream limit - - `target_fps`: Target frame rate - - `reconnect_interval_sec`: Stream reconnection delay - - `max_retries`: Maximum retry attempts (-1 for unlimited) - -### Threading Model -- **Frame reader threads** for each camera stream (RTSP/HTTP) -- **Shared stream optimization** - multiple subscriptions can reuse the same camera stream -- **Async WebSocket handling** with concurrent task management -- **Thread-safe data structures** with proper locking mechanisms - -## WebSocket Protocol - -### Message Types -- **subscribe**: Start camera stream with model pipeline -- **unsubscribe**: Stop camera stream processing -- **requestState**: Request current worker status -- **setSessionId**: Associate display with session identifier -- **patchSession**: Update session data -- **stateReport**: Periodic heartbeat with system metrics -- **imageDetection**: Detection results with timestamp and model info - -### Subscription Format -```json -{ - "type": "subscribe", - "payload": { - "subscriptionIdentifier": "display-001;cam-001", - "rtspUrl": "rtsp://...", // OR snapshotUrl - "snapshotUrl": "http://...", - "snapshotInterval": 5000, - "modelUrl": "http://...model.mpta", - "modelId": 101, - "modelName": "Vehicle Detection", - "cropX1": 100, "cropY1": 200, - "cropX2": 300, "cropY2": 400 - } -} -``` - -## Model Pipeline (MPTA) Format - -### Structure -- **ZIP archive** containing models and configuration -- **pipeline.json** - Main configuration file -- **Model files** - YOLO .pt files for detection/classification -- **Redis configuration** - Optional for action execution - -### 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) - -### 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": [...] - }] -} -``` - -## Stream Management - -### Shared Streams -- Multiple subscriptions can share the same camera URL -- Reference counting prevents premature stream termination -- Automatic cleanup when last subscription ends - -### Frame Processing -- **Queue-based buffering** with single frame capacity (latest frame only) -- **Configurable polling interval** based on target FPS -- **Automatic reconnection** with exponential backoff - -## Development & Testing - -### Local Development -```bash -# Install dependencies -pip install -r requirements.txt - -# Run the worker -python app.py - -# Test protocol compliance -python test_protocol.py - -# Test pipeline with webcam -python pipeline_webcam.py --mpta-file path/to/model.mpta --video 0 -``` - -### Docker Deployment -```bash -# Build container -docker build -t detector-worker . - -# Run with volume mounts for models -docker run -p 8000:8000 -v ./models:/app/models detector-worker -``` - -### Testing Commands -- **Protocol testing**: `python test_protocol.py` -- **Pipeline validation**: `python pipeline_webcam.py --mpta-file --video 0` -- **RTSP debugging**: `python debug/rtsp_webcam.py` - -## Dependencies -- **fastapi[standard]**: Web framework with WebSocket support -- **uvicorn**: ASGI server -- **torch, torchvision**: PyTorch for ML inference -- **ultralytics**: YOLO implementation -- **opencv-python**: Computer vision operations -- **websockets**: WebSocket client/server -- **redis**: Redis client for action execution - -## Security Considerations -- Model files are loaded from trusted sources only -- Redis connections use authentication when configured -- WebSocket connections handle disconnects gracefully -- Resource usage is monitored to prevent DoS - -## 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 \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index fd55f68..f7c3612 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,20 +1,20 @@ -# Use the official Python image from the Docker Hub -FROM python:3.13-bookworm +# Use newer, more secure base image +FROM python:3.13-alpine -# Set the working directory in the container -WORKDIR /app +# Update system packages first +RUN apk update && apk upgrade -# Copy the requirements file into the container at /app +# Install minimal dependencies +RUN apk add --no-cache mesa-gl + +# Use specific package versions COPY requirements.txt . +RUN pip install --no-cache-dir --upgrade pip && \ + pip install --no-cache-dir -r requirements.txt -# Update apt, install libgl1, and clear apt cache -RUN apt update && apt install -y libgl1 && rm -rf /var/lib/apt/lists/* +# Run as non-root user +RUN adduser -D -s /bin/sh appuser +USER appuser -# Install any dependencies specified in requirements.txt -RUN pip install --no-cache-dir -r requirements.txt - -# Copy the rest of the application code into the container at /app COPY . . - -# Run the application CMD ["python3", "-m", "fastapi", "run", "--host", "0.0.0.0", "--port", "8000"] \ No newline at end of file diff --git a/app.py b/app.py index 60beb27..5386496 100644 --- a/app.py +++ b/app.py @@ -29,12 +29,6 @@ app = FastAPI() # "models" now holds a nested dict: { camera_id: { modelId: model_tree } } models: Dict[str, Dict[str, Any]] = {} streams: Dict[str, Dict[str, Any]] = {} -# Store session IDs per display -session_ids: Dict[str, int] = {} -# Track shared camera streams by camera URL -camera_streams: Dict[str, Dict[str, Any]] = {} -# Map subscriptions to their camera URL -subscription_to_camera: Dict[str, str] = {} with open("config.json", "r") as f: config = json.load(f) @@ -128,15 +122,6 @@ def fetch_snapshot(url: str): logger.error(f"Exception fetching snapshot from {url}: {str(e)}") return None -# Helper to get crop coordinates from stream -def get_crop_coords(stream): - return { - "cropX1": stream.get("cropX1"), - "cropY1": stream.get("cropY1"), - "cropX2": stream.get("cropX2"), - "cropY2": stream.get("cropY2") - } - #################################################### # REST API endpoint for image retrieval #################################################### @@ -148,24 +133,20 @@ async def get_camera_image(camera_id: str): try: with streams_lock: if camera_id not in streams: - logger.warning(f"Camera ID '{camera_id}' not found in streams. Current streams: {list(streams.keys())}") raise HTTPException(status_code=404, detail=f"Camera {camera_id} not found or not active") stream = streams[camera_id] buffer = stream["buffer"] - logger.debug(f"Camera '{camera_id}' buffer size: {buffer.qsize()}, buffer empty: {buffer.empty()}") - logger.debug(f"Buffer queue contents: {getattr(buffer, 'queue', None)}") if buffer.empty(): - logger.warning(f"No frame available for camera '{camera_id}'. Buffer is empty.") raise HTTPException(status_code=404, detail=f"No frame available for camera {camera_id}") # Get the latest frame (non-blocking) try: frame = buffer.queue[-1] # Get the most recent frame without removing it except IndexError: - logger.warning(f"Buffer queue is empty for camera '{camera_id}' when trying to access last frame.") raise HTTPException(status_code=404, detail=f"No frame available for camera {camera_id}") + # Encode frame as JPEG success, buffer_img = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 85]) if not success: @@ -190,16 +171,9 @@ async def detect(websocket: WebSocket): async def handle_detection(camera_id, stream, frame, websocket, model_tree, persistent_data): try: - # Apply crop if specified - cropped_frame = frame - if all(coord is not None for coord in [stream.get("cropX1"), stream.get("cropY1"), stream.get("cropX2"), stream.get("cropY2")]): - cropX1, cropY1, cropX2, cropY2 = stream["cropX1"], stream["cropY1"], stream["cropX2"], stream["cropY2"] - cropped_frame = frame[cropY1:cropY2, cropX1:cropX2] - logger.debug(f"Applied crop coordinates ({cropX1}, {cropY1}, {cropX2}, {cropY2}) to frame for camera {camera_id}") - logger.debug(f"Processing frame for camera {camera_id} with model {stream['modelId']}") start_time = time.time() - detection_result = run_pipeline(cropped_frame, model_tree) + detection_result = run_pipeline(frame, model_tree) process_time = (time.time() - start_time) * 1000 logger.debug(f"Detection for camera {camera_id} completed in {process_time:.2f}ms") @@ -248,48 +222,22 @@ async def detect(websocket: WebSocket): "box": [0, 0, 0, 0] } - # Convert detection format to match protocol - flatten detection attributes - detection_dict = {} - - # Handle different detection result formats - if isinstance(highest_confidence_detection, dict): - # Copy all fields from the detection result - for key, value in highest_confidence_detection.items(): - if key not in ["box", "id"]: # Skip internal fields - detection_dict[key] = value - - # Extract display identifier for session ID lookup - subscription_parts = stream["subscriptionIdentifier"].split(';') - display_identifier = subscription_parts[0] if subscription_parts else None - session_id = session_ids.get(display_identifier) if display_identifier else None - detection_data = { "type": "imageDetection", - "subscriptionIdentifier": stream["subscriptionIdentifier"], - "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S.%fZ", time.gmtime()), + "cameraIdentifier": camera_id, + "timestamp": time.time(), "data": { - "detection": detection_dict, + "detection": highest_confidence_detection, # Send only the highest confidence detection "modelId": stream["modelId"], "modelName": stream["modelName"] } } - # Add session ID if available - if session_id is not None: - detection_data["sessionId"] = session_id - if highest_confidence_detection["class"] != "none": logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {highest_confidence_detection['confidence']:.2f} using model {stream['modelName']}") - - # Log session ID if available - subscription_parts = stream["subscriptionIdentifier"].split(';') - display_identifier = subscription_parts[0] if subscription_parts else None - session_id = session_ids.get(display_identifier) if display_identifier else None - if session_id: - logger.debug(f"Detection associated with session ID: {session_id}") await websocket.send_json(detection_data) - logger.debug(f"Sent detection data to client for camera {camera_id}") + logger.debug(f"Sent detection data to client for camera {camera_id}:\n{json.dumps(detection_data, indent=2)}") return persistent_data except Exception as e: logger.error(f"Error in handle_detection for camera {camera_id}: {str(e)}", exc_info=True) @@ -356,11 +304,12 @@ async def detect(websocket: WebSocket): if not buffer.empty(): try: buffer.get_nowait() - logger.debug(f"[frame_reader] Removed old frame from buffer for camera {camera_id}") + logger.debug(f"Removed old frame from buffer for camera {camera_id}") except queue.Empty: pass + buffer.put(frame) - logger.debug(f"[frame_reader] Added new frame to buffer for camera {camera_id}. Buffer size: {buffer.qsize()}") + logger.debug(f"Added new frame to buffer for camera {camera_id}") # Short sleep to avoid CPU overuse time.sleep(0.01) @@ -431,11 +380,12 @@ async def detect(websocket: WebSocket): if not buffer.empty(): try: buffer.get_nowait() - logger.debug(f"[snapshot_reader] Removed old snapshot from buffer for camera {camera_id}") + logger.debug(f"Removed old snapshot from buffer for camera {camera_id}") except queue.Empty: pass + buffer.put(frame) - logger.debug(f"[snapshot_reader] Added new snapshot to buffer for camera {camera_id}. Buffer size: {buffer.qsize()}") + logger.debug(f"Added new snapshot to buffer for camera {camera_id}") # Wait for the specified interval elapsed = time.time() - start_time @@ -506,19 +456,18 @@ async def detect(websocket: WebSocket): cpu_usage = psutil.cpu_percent() memory_usage = psutil.virtual_memory().percent if torch.cuda.is_available(): - gpu_usage = torch.cuda.utilization() if hasattr(torch.cuda, 'utilization') else None - gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2) + gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2) # MB + gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2) # MB else: gpu_usage = None gpu_memory_usage = None camera_connections = [ { - "subscriptionIdentifier": stream["subscriptionIdentifier"], + "cameraIdentifier": camera_id, "modelId": stream["modelId"], "modelName": stream["modelName"], - "online": True, - **{k: v for k, v in get_crop_coords(stream).items() if v is not None} + "online": True } for camera_id, stream in streams.items() ] @@ -548,70 +497,56 @@ async def detect(websocket: WebSocket): if msg_type == "subscribe": payload = data.get("payload", {}) - subscriptionIdentifier = payload.get("subscriptionIdentifier") + camera_id = payload.get("cameraIdentifier") rtsp_url = payload.get("rtspUrl") snapshot_url = payload.get("snapshotUrl") - snapshot_interval = payload.get("snapshotInterval") - model_url = payload.get("modelUrl") + snapshot_interval = payload.get("snapshotInterval") # in milliseconds + model_url = payload.get("modelUrl") # may be remote or local modelId = payload.get("modelId") modelName = payload.get("modelName") - cropX1 = payload.get("cropX1") - cropY1 = payload.get("cropY1") - cropX2 = payload.get("cropX2") - cropY2 = payload.get("cropY2") - - # Extract camera_id from subscriptionIdentifier (format: displayIdentifier;cameraIdentifier) - parts = subscriptionIdentifier.split(';') - if len(parts) != 2: - logger.error(f"Invalid subscriptionIdentifier format: {subscriptionIdentifier}") - continue - - display_identifier, camera_identifier = parts - camera_id = subscriptionIdentifier # Use full subscriptionIdentifier as camera_id for mapping if model_url: with models_lock: if (camera_id not in models) or (modelId not in models[camera_id]): logger.info(f"Loading model from {model_url} for camera {camera_id}, modelId {modelId}") - extraction_dir = os.path.join("models", camera_identifier, str(modelId)) + extraction_dir = os.path.join("models", camera_id, str(modelId)) os.makedirs(extraction_dir, exist_ok=True) # If model_url is remote, download it first. parsed = urlparse(model_url) if parsed.scheme in ("http", "https"): - logger.info(f"Downloading remote .mpta file from {model_url}") - filename = os.path.basename(parsed.path) or f"model_{modelId}.mpta" - local_mpta = os.path.join(extraction_dir, filename) + logger.info(f"Downloading remote model from {model_url}") + local_mpta = os.path.join(extraction_dir, os.path.basename(parsed.path)) logger.debug(f"Download destination: {local_mpta}") local_path = download_mpta(model_url, local_mpta) if not local_path: - logger.error(f"Failed to download the remote .mpta file from {model_url}") + logger.error(f"Failed to download the remote mpta file from {model_url}") error_response = { "type": "error", - "subscriptionIdentifier": subscriptionIdentifier, + "cameraIdentifier": camera_id, "error": f"Failed to download model from {model_url}" } await websocket.send_json(error_response) continue model_tree = load_pipeline_from_zip(local_path, extraction_dir) else: - logger.info(f"Loading local .mpta file from {model_url}") + logger.info(f"Loading local model from {model_url}") # Check if file exists before attempting to load if not os.path.exists(model_url): - logger.error(f"Local .mpta file not found: {model_url}") + logger.error(f"Local model file not found: {model_url}") logger.debug(f"Current working directory: {os.getcwd()}") error_response = { "type": "error", - "subscriptionIdentifier": subscriptionIdentifier, + "cameraIdentifier": camera_id, "error": f"Model file not found: {model_url}" } await websocket.send_json(error_response) continue model_tree = load_pipeline_from_zip(model_url, extraction_dir) if model_tree is None: - logger.error(f"Failed to load model {modelId} from .mpta file for camera {camera_id}") + logger.error(f"Failed to load model {modelId} from mpta file for camera {camera_id}") error_response = { "type": "error", - "subscriptionIdentifier": subscriptionIdentifier, + "cameraIdentifier": camera_id, "error": f"Failed to load model {modelId}" } await websocket.send_json(error_response) @@ -620,137 +555,95 @@ async def detect(websocket: WebSocket): models[camera_id] = {} models[camera_id][modelId] = model_tree logger.info(f"Successfully loaded model {modelId} for camera {camera_id}") - logger.debug(f"Model extraction directory: {extraction_dir}") + success_response = { + "type": "modelLoaded", + "cameraIdentifier": camera_id, + "modelId": modelId + } + await websocket.send_json(success_response) if camera_id and (rtsp_url or snapshot_url): with streams_lock: - # Determine camera URL for shared stream management - camera_url = snapshot_url if snapshot_url else rtsp_url - if camera_id not in streams and len(streams) < max_streams: - # Check if we already have a stream for this camera URL - shared_stream = camera_streams.get(camera_url) + buffer = queue.Queue(maxsize=1) + stop_event = threading.Event() - if shared_stream: - # Reuse existing stream - logger.info(f"Reusing existing stream for camera URL: {camera_url}") - buffer = shared_stream["buffer"] - stop_event = shared_stream["stop_event"] - thread = shared_stream["thread"] - mode = shared_stream["mode"] - - # Increment reference count - shared_stream["ref_count"] = shared_stream.get("ref_count", 0) + 1 - else: - # Create new stream - buffer = queue.Queue(maxsize=1) - stop_event = threading.Event() - - if snapshot_url and snapshot_interval: - logger.info(f"Creating new snapshot stream for camera {camera_id}: {snapshot_url}") - thread = threading.Thread(target=snapshot_reader, args=(camera_identifier, snapshot_url, snapshot_interval, buffer, stop_event)) - thread.daemon = True - thread.start() - mode = "snapshot" - - # Store shared stream info - shared_stream = { - "buffer": buffer, - "thread": thread, - "stop_event": stop_event, - "mode": mode, - "url": snapshot_url, - "snapshot_interval": snapshot_interval, - "ref_count": 1 - } - camera_streams[camera_url] = shared_stream - - elif rtsp_url: - logger.info(f"Creating new RTSP stream for camera {camera_id}: {rtsp_url}") - cap = cv2.VideoCapture(rtsp_url) - if not cap.isOpened(): - logger.error(f"Failed to open RTSP stream for camera {camera_id}") - continue - thread = threading.Thread(target=frame_reader, args=(camera_identifier, cap, buffer, stop_event)) - thread.daemon = True - thread.start() - mode = "rtsp" - - # Store shared stream info - shared_stream = { - "buffer": buffer, - "thread": thread, - "stop_event": stop_event, - "mode": mode, - "url": rtsp_url, - "cap": cap, - "ref_count": 1 - } - camera_streams[camera_url] = shared_stream - else: - logger.error(f"No valid URL provided for camera {camera_id}") + # Choose between snapshot and RTSP based on availability + if snapshot_url and snapshot_interval: + logger.info(f"Using snapshot mode for camera {camera_id}: {snapshot_url}") + thread = threading.Thread(target=snapshot_reader, args=(camera_id, snapshot_url, snapshot_interval, buffer, stop_event)) + thread.daemon = True + thread.start() + streams[camera_id] = { + "buffer": buffer, + "thread": thread, + "snapshot_url": snapshot_url, + "snapshot_interval": snapshot_interval, + "stop_event": stop_event, + "modelId": modelId, + "modelName": modelName, + "mode": "snapshot" + } + logger.info(f"Subscribed to camera {camera_id} (snapshot mode) with modelId {modelId}, modelName {modelName}, URL {snapshot_url}, interval {snapshot_interval}ms") + elif rtsp_url: + logger.info(f"Using RTSP mode for camera {camera_id}: {rtsp_url}") + cap = cv2.VideoCapture(rtsp_url) + if not cap.isOpened(): + logger.error(f"Failed to open RTSP stream for camera {camera_id}") continue - - # Create stream info for this subscription - stream_info = { - "buffer": buffer, - "thread": thread, - "stop_event": stop_event, - "modelId": modelId, - "modelName": modelName, - "subscriptionIdentifier": subscriptionIdentifier, - "cropX1": cropX1, - "cropY1": cropY1, - "cropX2": cropX2, - "cropY2": cropY2, - "mode": mode, - "camera_url": camera_url - } - - if mode == "snapshot": - stream_info["snapshot_url"] = snapshot_url - stream_info["snapshot_interval"] = snapshot_interval - elif mode == "rtsp": - stream_info["rtsp_url"] = rtsp_url - stream_info["cap"] = shared_stream["cap"] - - streams[camera_id] = stream_info - subscription_to_camera[camera_id] = camera_url - + thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event)) + thread.daemon = True + thread.start() + streams[camera_id] = { + "cap": cap, + "buffer": buffer, + "thread": thread, + "rtsp_url": rtsp_url, + "stop_event": stop_event, + "modelId": modelId, + "modelName": modelName, + "mode": "rtsp" + } + logger.info(f"Subscribed to camera {camera_id} (RTSP mode) with modelId {modelId}, modelName {modelName}, URL {rtsp_url}") + else: + logger.error(f"No valid URL provided for camera {camera_id}") + continue elif camera_id and camera_id in streams: # If already subscribed, unsubscribe first - logger.info(f"Resubscribing to camera {camera_id}") - # Note: Keep models in memory for reuse across subscriptions + stream = streams.pop(camera_id) + stream["stop_event"].set() + stream["thread"].join() + if "cap" in stream: + stream["cap"].release() + logger.info(f"Unsubscribed from camera {camera_id} for resubscription") + with models_lock: + if camera_id in models and modelId in models[camera_id]: + del models[camera_id][modelId] + if not models[camera_id]: + del models[camera_id] elif msg_type == "unsubscribe": payload = data.get("payload", {}) - subscriptionIdentifier = payload.get("subscriptionIdentifier") - camera_id = subscriptionIdentifier + camera_id = payload.get("cameraIdentifier") + logger.debug(f"Unsubscribing from camera {camera_id}") with streams_lock: if camera_id and camera_id in streams: stream = streams.pop(camera_id) - camera_url = subscription_to_camera.pop(camera_id, None) - - if camera_url and camera_url in camera_streams: - shared_stream = camera_streams[camera_url] - shared_stream["ref_count"] -= 1 - - # If no more references, stop the shared stream - if shared_stream["ref_count"] <= 0: - logger.info(f"Stopping shared stream for camera URL: {camera_url}") - shared_stream["stop_event"].set() - shared_stream["thread"].join() - if "cap" in shared_stream: - shared_stream["cap"].release() - del camera_streams[camera_url] - else: - logger.info(f"Shared stream for {camera_url} still has {shared_stream['ref_count']} references") - + stream["stop_event"].set() + stream["thread"].join() + # Only release cap if it exists (RTSP mode) + if "cap" in stream: + stream["cap"].release() + logger.info(f"Released RTSP capture for camera {camera_id}") + else: + logger.info(f"Released snapshot reader for camera {camera_id}") logger.info(f"Unsubscribed from camera {camera_id}") - # Note: Keep models in memory for potential reuse + with models_lock: + if camera_id in models: + del models[camera_id] elif msg_type == "requestState": cpu_usage = psutil.cpu_percent() memory_usage = psutil.virtual_memory().percent if torch.cuda.is_available(): - gpu_usage = torch.cuda.utilization() if hasattr(torch.cuda, 'utilization') else None + gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2) gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2) else: gpu_usage = None @@ -758,11 +651,10 @@ async def detect(websocket: WebSocket): camera_connections = [ { - "subscriptionIdentifier": stream["subscriptionIdentifier"], + "cameraIdentifier": camera_id, "modelId": stream["modelId"], "modelName": stream["modelName"], - "online": True, - **{k: v for k, v in get_crop_coords(stream).items() if v is not None} + "online": True } for camera_id, stream in streams.items() ] @@ -776,37 +668,6 @@ async def detect(websocket: WebSocket): "cameraConnections": camera_connections } await websocket.send_text(json.dumps(state_report)) - - elif msg_type == "setSessionId": - payload = data.get("payload", {}) - display_identifier = payload.get("displayIdentifier") - session_id = payload.get("sessionId") - - if display_identifier: - # Store session ID for this display - if session_id is None: - session_ids.pop(display_identifier, None) - logger.info(f"Cleared session ID for display {display_identifier}") - else: - session_ids[display_identifier] = session_id - logger.info(f"Set session ID {session_id} for display {display_identifier}") - - elif msg_type == "patchSession": - session_id = data.get("sessionId") - patch_data = data.get("data", {}) - - # For now, just acknowledge the patch - actual implementation depends on backend requirements - response = { - "type": "patchSessionResult", - "payload": { - "sessionId": session_id, - "success": True, - "message": "Session patch acknowledged" - } - } - await websocket.send_json(response) - logger.info(f"Acknowledged patch for session {session_id}") - else: logger.error(f"Unknown message type: {msg_type}") except json.JSONDecodeError: @@ -817,6 +678,7 @@ async def detect(websocket: WebSocket): except Exception as e: logger.error(f"Error handling message: {e}") break + try: await websocket.accept() stream_task = asyncio.create_task(process_streams()) @@ -829,23 +691,19 @@ async def detect(websocket: WebSocket): stream_task.cancel() await stream_task with streams_lock: - # Clean up shared camera streams - for camera_url, shared_stream in camera_streams.items(): - shared_stream["stop_event"].set() - shared_stream["thread"].join() - if "cap" in shared_stream: - shared_stream["cap"].release() - while not shared_stream["buffer"].empty(): + for camera_id, stream in streams.items(): + stream["stop_event"].set() + stream["thread"].join() + # Only release cap if it exists (RTSP mode) + if "cap" in stream: + stream["cap"].release() + while not stream["buffer"].empty(): try: - shared_stream["buffer"].get_nowait() + stream["buffer"].get_nowait() except queue.Empty: pass - logger.info(f"Released shared camera stream for {camera_url}") - + logger.info(f"Released camera {camera_id} and cleaned up resources") streams.clear() - camera_streams.clear() - subscription_to_camera.clear() with models_lock: models.clear() - session_ids.clear() logger.info("WebSocket connection closed") diff --git a/pympta.md b/pympta.md deleted file mode 100644 index ac61f4a..0000000 --- a/pympta.md +++ /dev/null @@ -1,204 +0,0 @@ -# pympta: Modular Pipeline Task Executor - -`pympta` is a Python module designed to load and execute modular, multi-stage AI pipelines defined in a special package format (`.mpta`). It is primarily used within the detector worker to run complex computer vision tasks where the output of one model can trigger a subsequent model on a specific region of interest. - -## Core Concepts - -### 1. MPTA Package (`.mpta`) - -An `.mpta` file is a standard `.zip` archive with a different extension. It bundles all the necessary components for a pipeline to run. - -A typical `.mpta` file has the following structure: - -``` -my_pipeline.mpta/ -├── pipeline.json -├── model1.pt -├── model2.pt -└── ... -``` - -- **`pipeline.json`**: (Required) The manifest file that defines the structure of the pipeline, the models to use, and the logic connecting them. -- **Model Files (`.pt`, etc.)**: The actual pre-trained model files (e.g., PyTorch, ONNX). The pipeline currently uses `ultralytics.YOLO` models. - -### 2. Pipeline Structure - -A pipeline is a tree-like structure of "nodes," defined in `pipeline.json`. - -- **Root Node**: The entry point of the pipeline. It processes the initial, full-frame image. -- **Branch Nodes**: Child nodes that are triggered by specific detection results from their parent. For example, a root node might detect a "vehicle," which then triggers a branch node to detect a "license plate" within the vehicle's bounding box. - -This modular structure allows for creating complex and efficient inference logic, avoiding the need to run every model on every frame. - -## `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. - -### 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. | - -### Redis Configuration (`redis`) - -| Key | Type | Required | Description | -| ---------- | ------ | -------- | ------------------------------------------------------- | -| `host` | String | Yes | The hostname or IP address of the Redis server. | -| `port` | Number | Yes | The port number of the Redis server. | -| `password` | String | No | The password for Redis authentication. | -| `db` | Number | No | The Redis database number to use. Defaults to `0`. | - -### Node Object Structure - -| Key | Type | Required | Description | -| ------------------- | ------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------- | -| `modelId` | String | Yes | A unique identifier for this model node (e.g., "vehicle-detector"). | -| `modelFile` | String | Yes | The path to the model file within the `.mpta` archive (e.g., "yolov8n.pt"). | -| `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 | 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`. | -| `branches` | Array | No | A list of child node objects that can be triggered by this node's detections. | -| `actions` | Array | No | A list of actions to execute upon a successful detection in this node. | - -### Action Object Structure - -Actions allow the pipeline to interact with Redis. They are executed sequentially for a given detection. - -#### Action Context & Dynamic Keys - -All actions have access to a dynamic context for formatting keys and messages. The context is created for each detection event and includes: - -- All key-value pairs from the detection result (e.g., `class`, `confidence`, `id`). -- `{timestamp_ms}`: The current Unix timestamp in milliseconds. -- `{uuid}`: A unique identifier (UUID4) for the detection event. -- `{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` - -Saves the current image frame (or cropped sub-image) to a Redis key. - -| Key | Type | Required | Description | -| ---------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- | -| `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. | -| `expire_seconds` | Number | No | If provided, sets an expiration time (in seconds) for the Redis key. | - -#### `redis_publish` - -Publishes a message to a Redis channel. - -| Key | Type | Required | Description | -| --------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- | -| `type` | String | Yes | Must be `"redis_publish"`. | -| `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 - -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. - -```json -{ - "redis": { - "host": "redis.local", - "port": 6379, - "password": "your-super-secret-password" - }, - "pipeline": { - "modelId": "vehicle-detector", - "modelFile": "vehicle_model.pt", - "minConfidence": 0.6, - "triggerClasses": ["car", "truck"], - "actions": [ - { - "type": "redis_save_image", - "key": "detections:{class}:{timestamp_ms}:{uuid}", - "expire_seconds": 3600 - }, - { - "type": "redis_publish", - "channel": "vehicle_events", - "message": "{\"event\":\"new_detection\",\"class\":\"{class}\",\"confidence\":{confidence},\"image_key\":\"{image_key}\"}" - } - ], - "branches": [] - } -} -``` - -## API Reference - -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`. - -- **Parameters:** - - `zip_source` (str): The file path to the local `.mpta` zip archive. - - `target_dir` (str): A directory path where the archive's contents will be extracted. -- **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)` - -Executes the inference pipeline on a single image frame. - -- **Parameters:** - - `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`. -- **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`). - -## 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. - -```python -import cv2 -from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline - -# 1. Define paths -MPTA_FILE = "path/to/your/pipeline.mpta" -CACHE_DIR = ".mptacache" - -# 2. Load the pipeline from the .mpta file -# This reads pipeline.json and loads the YOLO models into memory. -model_tree = load_pipeline_from_zip(MPTA_FILE, CACHE_DIR) - -if not model_tree: - print("Failed to load pipeline.") - exit() - -# 3. Open a video source -cap = cv2.VideoCapture(0) - -while True: - ret, frame = cap.read() - 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) - - # 5. Display the results - if detection_result: - print(f"Detected: {detection_result['class']} with confidence {detection_result['confidence']:.2f}") - if bounding_box: - x1, y1, x2, y2 = bounding_box - cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) - cv2.putText(frame, detection_result['class'], (x1, y1 - 10), - cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2) - - cv2.imshow("Pipeline Output", frame) - - if cv2.waitKey(1) & 0xFF == ord('q'): - break - -cap.release() -cv2.destroyAllWindows() -``` \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 49ca601..84f45cc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,5 +5,4 @@ torchvision ultralytics opencv-python websockets -fastapi[standard] -redis \ No newline at end of file +fastapi[standard] \ No newline at end of file diff --git a/siwatsystem/pympta.py b/siwatsystem/pympta.py index f151b55..5e32596 100644 --- a/siwatsystem/pympta.py +++ b/siwatsystem/pympta.py @@ -7,16 +7,13 @@ import requests import zipfile import shutil import traceback -import redis -import time -import uuid from ultralytics import YOLO from urllib.parse import urlparse # Create a logger specifically for this module logger = logging.getLogger("detector_worker.pympta") -def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client) -> dict: +def load_pipeline_node(node_config: dict, mpta_dir: str) -> 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): @@ -47,15 +44,13 @@ def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client) -> dict: "triggerClassIndices": trigger_class_indices, "crop": node_config.get("crop", False), "minConfidence": node_config.get("minConfidence", None), - "actions": node_config.get("actions", []), "model": model, - "branches": [], - "redis_client": redis_client + "branches": [] } 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)) + node["branches"].append(load_pipeline_node(child, mpta_dir)) return node def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict: @@ -163,26 +158,7 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict: 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"] - 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 - - return load_pipeline_node(pipeline_config["pipeline"], mpta_dir, redis_client) + return load_pipeline_node(pipeline_config["pipeline"], mpta_dir) except json.JSONDecodeError as e: logger.error(f"Error parsing pipeline.json: {str(e)}", exc_info=True) return None @@ -193,39 +169,6 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict: logger.error(f"Error loading pipeline.json: {str(e)}", exc_info=True) return None -def execute_actions(node, frame, detection_result): - if not node["redis_client"] or not node["actions"]: - return - - # Create a dynamic context for this detection event - action_context = { - **detection_result, - "timestamp_ms": int(time.time() * 1000), - "uuid": str(uuid.uuid4()), - } - - for action in node["actions"]: - try: - if action["type"] == "redis_save_image": - key = action["key"].format(**action_context) - _, buffer = cv2.imencode('.jpg', frame) - 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}") - # Add the generated key to the context for subsequent actions - action_context["image_key"] = key - elif action["type"] == "redis_publish": - channel = action["channel"] - message = action["message"].format(**action_context) - node["redis_client"].publish(channel, message) - logger.info(f"Published message to Redis channel '{channel}': {message}") - except Exception as e: - logger.error(f"Error executing action {action['type']}: {e}") - def run_pipeline(frame, node: dict, return_bbox: bool=False): """ - For detection nodes (task != 'classify'): @@ -263,7 +206,6 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False): "confidence": top1_conf, "id": None } - execute_actions(node, frame, det) return (det, None) if return_bbox else det @@ -312,11 +254,9 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False): det2, _ = run_pipeline(sub, br, return_bbox=True) if det2: # return classification result + original bbox - execute_actions(br, sub, det2) return (det2, best_box) if return_bbox else det2 # ─── No branch matched → return this detection ───────────── - execute_actions(node, frame, best_det) return (best_det, best_box) if return_bbox else best_det except Exception as e: diff --git a/test_protocol.py b/test_protocol.py deleted file mode 100644 index 74af7d8..0000000 --- a/test_protocol.py +++ /dev/null @@ -1,125 +0,0 @@ -#!/usr/bin/env python3 -""" -Test script to verify the worker implementation follows the protocol -""" -import json -import asyncio -import websockets -import time - -async def test_protocol(): - """Test the worker protocol implementation""" - uri = "ws://localhost:8000" - - try: - async with websockets.connect(uri) as websocket: - print("✓ Connected to worker") - - # Test 1: Check if we receive heartbeat (stateReport) - print("\n1. Testing heartbeat...") - try: - message = await asyncio.wait_for(websocket.recv(), timeout=5) - data = json.loads(message) - if data.get("type") == "stateReport": - print("✓ Received stateReport heartbeat") - print(f" - CPU Usage: {data.get('cpuUsage', 'N/A')}%") - print(f" - Memory Usage: {data.get('memoryUsage', 'N/A')}%") - print(f" - Camera Connections: {len(data.get('cameraConnections', []))}") - else: - print(f"✗ Expected stateReport, got {data.get('type')}") - except asyncio.TimeoutError: - print("✗ No heartbeat received within 5 seconds") - - # Test 2: Request state - print("\n2. Testing requestState...") - await websocket.send(json.dumps({"type": "requestState"})) - try: - message = await asyncio.wait_for(websocket.recv(), timeout=5) - data = json.loads(message) - if data.get("type") == "stateReport": - print("✓ Received stateReport response") - else: - print(f"✗ Expected stateReport, got {data.get('type')}") - except asyncio.TimeoutError: - print("✗ No response to requestState within 5 seconds") - - # Test 3: Set session ID - print("\n3. Testing setSessionId...") - session_message = { - "type": "setSessionId", - "payload": { - "displayIdentifier": "display-001", - "sessionId": 12345 - } - } - await websocket.send(json.dumps(session_message)) - print("✓ Sent setSessionId message") - - # Test 4: Test patchSession - print("\n4. Testing patchSession...") - patch_message = { - "type": "patchSession", - "sessionId": 12345, - "data": { - "currentCar": { - "carModel": "Civic", - "carBrand": "Honda" - } - } - } - await websocket.send(json.dumps(patch_message)) - - # Wait for patchSessionResult - try: - message = await asyncio.wait_for(websocket.recv(), timeout=5) - data = json.loads(message) - if data.get("type") == "patchSessionResult": - print("✓ Received patchSessionResult") - print(f" - Success: {data.get('payload', {}).get('success')}") - print(f" - Message: {data.get('payload', {}).get('message')}") - else: - print(f"✗ Expected patchSessionResult, got {data.get('type')}") - except asyncio.TimeoutError: - print("✗ No patchSessionResult received within 5 seconds") - - # Test 5: Test subscribe message format (without actual camera) - print("\n5. Testing subscribe message format...") - subscribe_message = { - "type": "subscribe", - "payload": { - "subscriptionIdentifier": "display-001;cam-001", - "snapshotUrl": "http://example.com/snapshot.jpg", - "snapshotInterval": 5000, - "modelUrl": "http://example.com/model.mpta", - "modelName": "Test Model", - "modelId": 101, - "cropX1": 100, - "cropY1": 200, - "cropX2": 300, - "cropY2": 400 - } - } - await websocket.send(json.dumps(subscribe_message)) - print("✓ Sent subscribe message (will fail without actual camera/model)") - - # Listen for a few more messages to catch any errors - print("\n6. Listening for additional messages...") - for i in range(3): - try: - message = await asyncio.wait_for(websocket.recv(), timeout=2) - data = json.loads(message) - msg_type = data.get("type") - print(f" - Received {msg_type}") - if msg_type == "error": - print(f" Error: {data.get('error')}") - except asyncio.TimeoutError: - break - - print("\n✓ Protocol test completed successfully!") - - except Exception as e: - print(f"✗ Connection failed: {e}") - print("Make sure the worker is running on localhost:8000") - -if __name__ == "__main__": - asyncio.run(test_protocol()) \ No newline at end of file diff --git a/worker.md b/worker.md deleted file mode 100644 index c50bae5..0000000 --- a/worker.md +++ /dev/null @@ -1,483 +0,0 @@ -# Worker Communication Protocol - -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. - -## 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. - -Upon a successful connection from the backend, you should begin sending `stateReport` messages as heartbeats. - -## 2. Communication Overview - -Communication is bidirectional and asynchronous. All messages are JSON objects with a `type` field that indicates the message's purpose, and an optional `payload` field containing the data. - -- **Worker -> Backend:** You will send messages to the backend to report status, forward detection events, or request changes to session data. -- **Backend -> Worker:** The backend will send commands to you to manage camera subscriptions. - -## 3. Dynamic Configuration via MPTA File - -To enable modularity and dynamic configuration, the backend will send you a URL to a `.mpta` file when it issues a `subscribe` command. This file is a renamed `.zip` archive that contains everything your worker needs to perform its task. - -**Your worker is responsible for:** - -1. Fetching this file from the provided 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: - -- 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. - -Essentially, the `.mpta` file is a self-contained package that tells your worker *how* to process the video stream for a given subscription. - -## 4. Messages from Worker to Backend - -These are the messages your worker is expected to send to the backend. - -### 4.1. State Report (Heartbeat) - -This message is crucial for the backend to monitor your worker's health and status, including GPU usage. - -- **Type:** `stateReport` -- **When to Send:** Periodically (e.g., every 2 seconds) after a connection is established. - -**Payload:** - -```json -{ - "type": "stateReport", - "cpuUsage": 75.5, - "memoryUsage": 40.2, - "gpuUsage": 60.0, - "gpuMemoryUsage": 25.1, - "cameraConnections": [ - { - "subscriptionIdentifier": "display-001;cam-001", - "modelId": 101, - "modelName": "General Object Detection", - "online": true, - "cropX1": 100, - "cropY1": 200, - "cropX2": 300, - "cropY2": 400 - } - ] -} -``` - -> **Note:** -> -> - `cropX1`, `cropY1`, `cropX2`, `cropY2` (optional, integer) should be included in each camera connection to indicate the crop coordinates for that subscription. - -### 4.2. Image Detection - -Sent when the worker detects a relevant object. The `detection` object should be flat and contain key-value pairs corresponding to the detected attributes. - -- **Type:** `imageDetection` - -**Payload Example:** - -```json -{ - "type": "imageDetection", - "subscriptionIdentifier": "display-001;cam-001", - "timestamp": "2025-07-14T12:34:56.789Z", - "data": { - "detection": { - "carModel": "Civic", - "carBrand": "Honda", - "carYear": 2023, - "bodyType": "Sedan", - "licensePlateText": "ABCD1234", - "licensePlateConfidence": 0.95 - }, - "modelId": 101, - "modelName": "US-LPR-and-Vehicle-ID" - } -} -``` - -### 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. - -Allows the worker to request a modification to an active session's data. The `data` payload must be a partial object of the `DisplayPersistentData` structure. - -- **Type:** `patchSession` - -**Payload Example:** - -```json -{ - "type": "patchSession", - "sessionId": 12345, - "data": { - "currentCar": { - "carModel": "Civic", - "carBrand": "Honda", - "licensePlateText": "ABCD1234" - } - } -} -``` - -The backend will respond with a `patchSessionResult` command. - -#### `DisplayPersistentData` Structure - -The `data` object in the `patchSession` message is merged with the existing `DisplayPersistentData` on the backend. Here is its structure: - -```typescript -interface DisplayPersistentData { - progressionStage: "welcome" | "car_fueling" | "car_waitpayment" | "car_postpayment" | null; - qrCode: string | null; - adsPlayback: { - playlistSlotOrder: number; // The 'order' of the current slot - adsId: number | null; - adsUrl: string | null; - } | null; - currentCar: { - carModel?: string; - carBrand?: string; - carYear?: number; - bodyType?: string; - licensePlateText?: string; - licensePlateType?: string; - } | null; - fuelPump: { /* FuelPumpData structure */ } | null; - weatherData: { /* WeatherResponse structure */ } | null; - sessionId: number | null; -} -``` - -#### Patching Behavior - -- The patch is a **deep merge**. -- **`undefined`** values are ignored. -- **`null`** values will set the corresponding field to `null`. -- Nested objects are merged recursively. - -## 5. Commands from Backend to Worker - -These are the commands your worker will receive from the backend. - -### 5.1. Subscribe to Camera - -Instructs the worker to process a camera's RTSP stream using the configuration from the specified `.mpta` file. - -- **Type:** `subscribe` - -**Payload:** - -```json -{ - "type": "subscribe", - "payload": { - "subscriptionIdentifier": "display-001;cam-002", - "rtspUrl": "rtsp://user:pass@host:port/stream", - "snapshotUrl": "http://go2rtc/snapshot/1", - "snapshotInterval": 5000, - "modelUrl": "http://storage/models/us-lpr.mpta", - "modelName": "US-LPR-and-Vehicle-ID", - "modelId": 102, - "cropX1": 100, - "cropY1": 200, - "cropX2": 300, - "cropY2": 400 - } -} -``` - -> **Note:** -> -> - `cropX1`, `cropY1`, `cropX2`, `cropY2` (optional, integer) specify the crop coordinates for the camera stream. These values are configured per display and passed in the subscription payload. If not provided, the worker should process the full frame. -> -> **Important:** -> If multiple displays are bound to the same camera, your worker must ensure that only **one stream** is opened per camera. When you receive multiple subscriptions for the same camera (with different `subscriptionIdentifier` values), you should: -> -> - Open the RTSP stream **once** for that camera if using RTSP. -> - Capture each snapshot only once per cycle, and reuse it for all display subscriptions sharing that camera. -> - Capture each frame/image only once per cycle. -> - Reuse the same captured image and snapshot for all display subscriptions that share the camera, processing and routing detection results separately for each display as needed. -> This avoids unnecessary load and bandwidth usage, and ensures consistent detection results and snapshots across all displays sharing the same camera. - -### 5.2. Unsubscribe from Camera - -Instructs the worker to stop processing a camera's stream. - -- **Type:** `unsubscribe` - -**Payload:** - -```json -{ - "type": "unsubscribe", - "payload": { - "subscriptionIdentifier": "display-001;cam-002" - } -} -``` - -### 5.3. Request State - -Direct request for the worker's current state. Respond with a `stateReport` message. - -- **Type:** `requestState` - -**Payload:** - -```json -{ - "type": "requestState" -} -``` - -### 5.4. Patch Session Result - -Backend's response to a `patchSession` message. - -- **Type:** `patchSessionResult` - -**Payload:** - -```json -{ - "type": "patchSessionResult", - "payload": { - "sessionId": 12345, - "success": true, - "message": "Session updated successfully." - } -} -``` - -### 5.5. Set Session ID - -Allows the backend to instruct the worker to associate a session ID with a subscription. This is useful for linking detection events to a specific session. The session ID can be `null` to indicate no active session. - -- **Type:** `setSessionId` - -**Payload:** - -```json -{ - "type": "setSessionId", - "payload": { - "displayIdentifier": "display-001", - "sessionId": 12345 - } -} -``` - -Or to clear the session: - -```json -{ - "type": "setSessionId", - "payload": { - "displayIdentifier": "display-001", - "sessionId": null - } -} -``` - -> **Note:** -> -> - The worker should store the session ID for the given subscription and use it in subsequent detection or patch messages as appropriate. If `sessionId` is `null`, the worker should treat the subscription as having no active session. - -## Subscription Identifier Format - -The `subscriptionIdentifier` used in all messages is constructed as: - -``` -displayIdentifier;cameraIdentifier -``` - -This uniquely identifies a camera subscription for a specific display. - -### Session ID Association - -When the backend sends a `setSessionId` command, it will only provide the `displayIdentifier` (not the full `subscriptionIdentifier`). - -**Worker Responsibility:** - -- The worker must match the `displayIdentifier` to all active subscriptions for that display (i.e., all `subscriptionIdentifier` values that start with `displayIdentifier;`). -- The worker should set or clear the session ID for all matching subscriptions. - -## 6. Example Communication Log - -This section shows a typical sequence of messages between the backend and the worker. Patch messages are not included, as they are only used when the worker cannot keep up. - -> **Note:** Unsubscribe is triggered when a user removes a camera or when the node is too heavily loaded and needs rebalancing. - -1. **Connection Established** & **Heartbeat** - * **Worker -> Backend** - ```json - { - "type": "stateReport", - "cpuUsage": 70.2, - "memoryUsage": 38.1, - "gpuUsage": 55.0, - "gpuMemoryUsage": 20.0, - "cameraConnections": [] - } - ``` -2. **Backend Subscribes Camera** - * **Backend -> Worker** - ```json - { - "type": "subscribe", - "payload": { - "subscriptionIdentifier": "display-001;entry-cam-01", - "rtspUrl": "rtsp://192.168.1.100/stream1", - "modelUrl": "http://storage/models/vehicle-id.mpta", - "modelName": "Vehicle Identification", - "modelId": 201 - } - } - ``` -3. **Worker Acknowledges in Heartbeat** - * **Worker -> Backend** - ```json - { - "type": "stateReport", - "cpuUsage": 72.5, - "memoryUsage": 39.0, - "gpuUsage": 57.0, - "gpuMemoryUsage": 21.0, - "cameraConnections": [ - { - "subscriptionIdentifier": "display-001;entry-cam-01", - "modelId": 201, - "modelName": "Vehicle Identification", - "online": true - } - ] - } - ``` -4. **Worker Detects a Car** - * **Worker -> Backend** - ```json - { - "type": "imageDetection", - "subscriptionIdentifier": "display-001;entry-cam-01", - "timestamp": "2025-07-15T10:00:00.000Z", - "data": { - "detection": { - "carBrand": "Honda", - "carModel": "CR-V", - "bodyType": "SUV", - "licensePlateText": "GEMINI-AI", - "licensePlateConfidence": 0.98 - }, - "modelId": 201, - "modelName": "Vehicle Identification" - } - } - ``` - * **Worker -> Backend** - ```json - { - "type": "imageDetection", - "subscriptionIdentifier": "display-001;entry-cam-01", - "timestamp": "2025-07-15T10:00:01.000Z", - "data": { - "detection": { - "carBrand": "Toyota", - "carModel": "Corolla", - "bodyType": "Sedan", - "licensePlateText": "CMS-1234", - "licensePlateConfidence": 0.97 - }, - "modelId": 201, - "modelName": "Vehicle Identification" - } - } - ``` - * **Worker -> Backend** - ```json - { - "type": "imageDetection", - "subscriptionIdentifier": "display-001;entry-cam-01", - "timestamp": "2025-07-15T10:00:02.000Z", - "data": { - "detection": { - "carBrand": "Ford", - "carModel": "Focus", - "bodyType": "Hatchback", - "licensePlateText": "CMS-5678", - "licensePlateConfidence": 0.96 - }, - "modelId": 201, - "modelName": "Vehicle Identification" - } - } - ``` -5. **Backend Unsubscribes Camera** - * **Backend -> Worker** - ```json - { - "type": "unsubscribe", - "payload": { - "subscriptionIdentifier": "display-001;entry-cam-01" - } - } - ``` -6. **Worker Acknowledges Unsubscription** - * **Worker -> Backend** - ```json - { - "type": "stateReport", - "cpuUsage": 68.0, - "memoryUsage": 37.0, - "gpuUsage": 50.0, - "gpuMemoryUsage": 18.0, - "cameraConnections": [] - } - ``` -## 7. HTTP API: Image Retrieval - -In addition to the WebSocket protocol, the worker exposes an HTTP endpoint for retrieving the latest image frame from a camera. - -### Endpoint - -``` -GET /camera/{camera_id}/image -``` - -- **`camera_id`**: The full `subscriptionIdentifier` (e.g., `display-001;cam-001`). - -### Response - -- **Success (200):** Returns the latest JPEG image from the camera stream. - - `Content-Type: image/jpeg` - - Binary JPEG data. - -- **Error (404):** If the camera is not found or no frame is available. - - JSON error response. - -- **Error (500):** Internal server error. - -### Example Request - -``` -GET /camera/display-001;cam-001/image -``` - -### Example Response - -- **Headers:** - ``` - Content-Type: image/jpeg - ``` -- **Body:** Binary JPEG image. - -### Notes - -- The endpoint returns the most recent frame available for the specified camera subscription. -- If multiple displays share the same camera, each subscription has its own buffer; the endpoint uses the buffer for the given `camera_id`. -- This API is useful for debugging, monitoring, or integrating with external systems that require direct image access.