diff --git a/examples/04_security_camera_example/README.md b/examples/04_security_camera_example/README.md new file mode 100644 index 00000000..da90b901 --- /dev/null +++ b/examples/04_security_camera_example/README.md @@ -0,0 +1,206 @@ +# Security Camera Demo + +A real-time security camera demo with face recognition that identifies unique visitors, tracks their activity, and provides an AI assistant to answer questions about security. + +## Features + +- ๐ŸŽฅ **Real-time Face Detection**: Uses face_recognition library for accurate face detection +- ๐Ÿง  **Face Recognition**: Identifies unique individuals and prevents duplicate entries +- ๐Ÿ‘ฅ **Visitor Tracking**: Maintains a 30-minute sliding window of unique visitors +- ๐Ÿ“Š **Visual Overlay**: Displays unique visitor count and face thumbnails in a grid +- ๐Ÿ”ข **Detection Counter**: Shows how many times each visitor has been seen +- ๐Ÿค– **AI Integration**: Ask the AI assistant detailed questions about visitor activity +- โฐ **Timestamp Display**: Shows current date/time on the video feed + +## How It Works + +### Architecture + +The demo uses a custom `SecurityCameraProcessor` that: + +1. **Subscribes to Video Stream**: Uses `VideoForwarder` to receive frames from the camera +2. **Detects Faces**: Runs face_recognition on frames at configurable intervals +3. **Generates Face Encodings**: Creates unique 128-dimensional encodings for each face +4. **Matches Faces**: Compares new detections against known faces to identify individuals +5. **Tracks Visitors**: Stores unique visitors with first/last seen timestamps and detection counts +6. **Cleans Old Data**: Automatically removes visitors not seen within the time window +7. **Creates Overlay**: Composites face thumbnails with detection counts onto the video +8. **Publishes Output**: Sends the annotated video to participants via `QueuedVideoTrack` + +### Video Overlay + +The right side of the video shows: +- **Header**: "SECURITY CAMERA" +- **Visitor Count**: Number of unique visitors in last 30 minutes +- **Face Grid**: Thumbnails of unique visitors (up to 12 most recent) +- **Detection Badges**: Small counters showing how many times each person was seen (e.g., "3x") +- **Timestamp**: Current date and time at bottom of frame + +### LLM Integration + +The AI assistant has access to: +- `get_visitor_count()`: Get count of unique visitors and total detections +- `get_visitor_details()`: Get detailed info on each visitor (first seen, last seen, detection count) +- Real-time processor state with timing information +- Ability to answer detailed questions about security activity and specific visitors + +## Setup + +### Prerequisites + +- Python 3.13+ +- Webcam/camera access +- GetStream account for video transport +- API keys for Gemini, Deepgram, and ElevenLabs + +### Installation + +1. Navigate to this directory: +```bash +cd examples/04_security_camera_example +``` + +2. Install dependencies using uv: +```bash +uv sync +``` + +3. Set up environment variables in `.env`: +```bash +# Stream API credentials +STREAM_API_KEY=your_stream_api_key +STREAM_API_SECRET=your_stream_api_secret + +# LLM API key +GOOGLE_API_KEY=your_gemini_api_key + +# STT API key +DEEPGRAM_API_KEY=your_deepgram_api_key + +# TTS API key +ELEVENLABS_API_KEY=your_elevenlabs_api_key +``` + +## Usage + +### Running the Demo + +```bash +uv run python security_camera_example.py +``` + +The agent will join a call and start monitoring the video feed for faces. + +### Interacting with the AI + +Once connected, you can ask questions like: +- "How many unique people have visited in the last 30 minutes?" +- "How many times has each person been seen?" +- "When was the last person detected?" +- "Who visited most recently?" +- "Give me details on all visitors" +- "Is the same person still there?" + +### Configuration + +You can adjust the processor parameters in `security_camera_example.py`: + +```python +security_processor = SecurityCameraProcessor( + fps=5, # Frames per second to process + time_window=1800, # Time window in seconds (30 min) + thumbnail_size=80, # Size of face thumbnails in pixels + detection_interval=2.0, # Seconds between face detections + face_match_tolerance=0.6, # Face matching tolerance (lower = stricter) +) +``` + +## Implementation Details + +### Face Detection & Recognition + +Uses the `face_recognition` library (built on dlib) which: +- Provides state-of-the-art face detection accuracy +- Generates 128-dimensional face encodings for recognition +- Can identify the same person across different angles and lighting +- Uses HOG (Histogram of Oriented Gradients) for detection +- Automatically downloads pre-trained models on first use +- More accurate than Haar Cascades, though slightly slower + +### Performance Optimization + +- **Threading**: CPU-intensive operations run in ThreadPoolExecutor +- **Configurable FPS**: Process only N frames per second (default: 5) +- **Detection Throttling**: Only detect faces every N seconds (default: 2s) +- **Automatic Cleanup**: Old faces are removed efficiently + +### Memory Management + +- Face thumbnails are resized to 80x80 pixels to save memory +- Sliding window automatically removes old detections +- Maximum of 12 thumbnails displayed at once + +## Extending the Demo + +### Add Named Face Recognition + +To identify specific people by name (the system currently recognizes but doesn't name individuals): +1. Create a database of known faces with names +2. Generate and store encodings for each known person +3. Compare new detections against known encodings +4. Label faces with names in the overlay instead of showing generic IDs + +### Add Motion Detection + +To detect movement before face detection: +1. Use OpenCV's background subtraction +2. Only run face detection when motion is detected +3. Reduce false positives and improve performance + +### Add Recording + +To save clips when faces are detected: +1. Use OpenCV's VideoWriter +2. Buffer frames around detection events +3. Save short clips to disk with timestamps + +### Add Alerts + +To notify when faces are detected: +1. Check visitor count thresholds +2. Send notifications via email/SMS/webhook +3. Trigger alerts for after-hours detections + +## Troubleshooting + +### No faces detected +- Ensure good lighting conditions +- Position camera to show frontal faces clearly +- The face_recognition library works best with direct face views +- Try lowering `detection_interval` +- Check that the camera is working properly + +### Performance issues +- Reduce `fps` parameter (e.g., from 5 to 2-3) +- Increase `detection_interval` (e.g., from 2 to 3-5 seconds) +- Lower video resolution +- Use fewer `max_workers` +- Face recognition is more CPU-intensive than simple detection + +### Too many duplicate faces +- Decrease `face_match_tolerance` (e.g., 0.5 instead of 0.6) +- Increase `detection_interval` to give more time between checks + +### Same person not being recognized +- Increase `face_match_tolerance` (e.g., 0.7 instead of 0.6) +- Ensure consistent lighting and camera angle +- Face encodings work better with clear, frontal face views + +### Video lag +- Check network bandwidth +- Reduce frame processing rate +- Ensure camera has good connection + +## License + +See the main repository LICENSE file for details. diff --git a/examples/04_security_camera_example/__init__.py b/examples/04_security_camera_example/__init__.py new file mode 100644 index 00000000..d86b783f --- /dev/null +++ b/examples/04_security_camera_example/__init__.py @@ -0,0 +1 @@ +# Security Camera Example diff --git a/examples/04_security_camera_example/instructions.md b/examples/04_security_camera_example/instructions.md new file mode 100644 index 00000000..70fc38e1 --- /dev/null +++ b/examples/04_security_camera_example/instructions.md @@ -0,0 +1,17 @@ +You are a home security assistant. You are passive and concise. +Only speak when spoken to or when you answer a query. + +## Capabilities + +You have access to: +- **Activity Log**: A history of events (people arriving, packages detected). Use `get_activity_log` to answer questions like "what happened?" or "did anyone come by?" +- **Visitor Tracking**: Track unique visitors with `get_visitor_count` and `get_visitor_details` +- **Package Detection**: Track packages with `get_package_count` and `get_package_details` +- **Face Memory**: You can remember people's names! When someone says "remember me as [name]" or "my name is [name]", use `remember_my_face` to save their face. Use `get_known_faces` to see who you know. + +## Behavior + +- Use the activity log to answer questions about past events +- When you recognize a known person, greet them by name +- If someone asks you to remember them, use the remember_my_face function +- Keep responses brief and natural diff --git a/examples/04_security_camera_example/pyproject.toml b/examples/04_security_camera_example/pyproject.toml new file mode 100644 index 00000000..d0e16fe1 --- /dev/null +++ b/examples/04_security_camera_example/pyproject.toml @@ -0,0 +1,26 @@ +[project] +name = "security-camera-example" +version = "0.0.0" +requires-python = ">=3.13" + +dependencies = [ + "python-dotenv>=1.0", + "vision-agents-plugins-deepgram", + "vision-agents-plugins-elevenlabs", + "vision-agents-plugins-getstream", + "vision-agents-plugins-gemini", + "vision-agents-plugins-moondream", + "vision-agents", + "opencv-python>=4.8.0", + "google-genai>=1.33.0", + "face-recognition>=1.3.0", + "ultralytics>=8.0.0", +] + +[tool.uv.sources] +"vision-agents-plugins-deepgram" = {path = "../../plugins/deepgram", editable=true} +"vision-agents-plugins-elevenlabs" = {path = "../../plugins/elevenlabs", editable=true} +"vision-agents-plugins-getstream" = {path = "../../plugins/getstream", editable=true} +"vision-agents-plugins-gemini" = {path = "../../plugins/gemini", editable=true} +"vision-agents-plugins-moondream" = {path = "../../plugins/moondream", editable=true} +"vision-agents" = {path = "../../agents-core", editable=true} diff --git a/examples/04_security_camera_example/security_camera_example.py b/examples/04_security_camera_example/security_camera_example.py new file mode 100644 index 00000000..2d41989c --- /dev/null +++ b/examples/04_security_camera_example/security_camera_example.py @@ -0,0 +1,167 @@ +import logging +from typing import Dict, Any, Optional + +from dotenv import load_dotenv + +from vision_agents.core import User, Agent, cli +from vision_agents.core.agents import AgentLauncher +from vision_agents.plugins import deepgram, getstream, gemini, elevenlabs + +from security_camera_processor import ( + SecurityCameraProcessor, + PersonDetectedEvent, + PackageDetectedEvent, +) + +logger = logging.getLogger(__name__) + +load_dotenv() + +""" +Security Camera Demo with Face Detection + +This example demonstrates: +- Real-time face detection from camera feed +- 30-minute sliding window of detected faces +- Video overlay with visitor count and face thumbnails +- LLM integration to answer questions about security activity + +The processor detects faces, stores thumbnails, and displays them in a grid +on the right side of the video with a visitor count. The AI agent can answer +questions about how many people have visited. +""" + + +async def create_agent(**kwargs) -> Agent: + llm = gemini.LLM("gemini-2.5-flash-lite") + + # Create security camera processor + security_processor = SecurityCameraProcessor( + fps=30, # Process 5 frames per second + time_window=1800, # 30 minutes in seconds + thumbnail_size=80, # Size of face thumbnails + detection_interval=2.0, # Detect faces every 2 seconds + ) + + agent = Agent( + edge=getstream.Edge(), + agent_user=User(name="Security AI", id="agent"), + instructions="Read @instructions.md", + processors=[security_processor], # Add the security camera processor + llm=llm, + tts=elevenlabs.TTS(), + stt=deepgram.STT(eager_turn_detection=True), + ) + + # Merge processor events with agent events so subscriptions work + agent.events.merge(security_processor.events) + + # Register function for getting visitor count + @llm.register_function( + description="Get the number of unique visitors detected in the last 30 minutes. Always pass include_details=false for a quick count." + ) + async def get_visitor_count(include_details: Optional[bool] = False) -> Dict[str, Any]: + count = security_processor.get_visitor_count() + state = security_processor.state() + return { + "unique_visitors": count, + "total_detections": state["total_face_detections"], + "time_window": f"{state['time_window_minutes']} minutes", + "last_detection": state["last_face_detection_time"], + } + + # Register function for getting detailed visitor information + @llm.register_function( + description="Get detailed information about all visitors including when they were first and last seen. Always pass include_timestamps=true." + ) + async def get_visitor_details(include_timestamps: Optional[bool] = True) -> Dict[str, Any]: + details = security_processor.get_visitor_details() + return { + "visitors": details, + "total_unique_visitors": len(details), + } + + # Register function for getting package count + @llm.register_function( + description="Get the number of unique packages detected in the last 30 minutes. Always pass include_details=false for a quick count." + ) + async def get_package_count(include_details: Optional[bool] = False) -> Dict[str, Any]: + count = security_processor.get_package_count() + state = security_processor.state() + return { + "unique_packages": count, + "total_package_detections": state["total_package_detections"], + "time_window": f"{state['time_window_minutes']} minutes", + "last_detection": state["last_package_detection_time"], + } + + # Register function for getting detailed package information + @llm.register_function( + description="Get detailed information about all packages including when they were first and last seen, and confidence scores. Always pass include_confidence=true." + ) + async def get_package_details(include_confidence: Optional[bool] = True) -> Dict[str, Any]: + details = security_processor.get_package_details() + return { + "packages": details, + "total_unique_packages": len(details), + } + + # Register function for getting activity log + @llm.register_function( + description="Get the recent activity log showing what happened (people arriving, packages detected, etc.). Use this to answer questions like 'what happened?' or 'did anyone come by?'. Pass limit to control how many entries to return." + ) + async def get_activity_log(limit: Optional[int] = 20) -> Dict[str, Any]: + log = security_processor.get_activity_log(limit=limit or 20) + return { + "activity_log": log, + "total_entries": len(log), + } + + # Register function for remembering a face + @llm.register_function( + description="Register the current person's face with a name so they can be recognized in the future. Use when user says things like 'remember me as [name]' or 'my name is [name]'. Pass the name to remember." + ) + async def remember_my_face(name: str) -> Dict[str, Any]: + result = security_processor.register_current_face_as(name) + return result + + # Register function for listing known faces + @llm.register_function( + description="Get a list of all registered/known faces that can be recognized by name. Pass include_timestamps=true to see when each face was registered." + ) + async def get_known_faces(include_timestamps: Optional[bool] = True) -> Dict[str, Any]: + faces = security_processor.get_known_faces() + return { + "known_faces": faces, + "total_known": len(faces), + } + + # Subscribe to detection events via the agent's merged event system + @agent.events.subscribe + async def on_person_detected(event: PersonDetectedEvent): + if event.is_new: + agent.logger.info(f"๐Ÿšจ NEW PERSON ALERT: {event.face_id} detected!") + else: + agent.logger.info(f"๐Ÿ‘ค Returning visitor: {event.face_id} (seen {event.detection_count}x)") + + @agent.events.subscribe + async def on_package_detected(event: PackageDetectedEvent): + if event.is_new: + agent.logger.info(f"๐Ÿ“ฆ NEW PACKAGE ALERT: {event.package_id} detected! (confidence: {event.confidence:.2f})") + else: + agent.logger.info(f"๐Ÿ“ฆ Package still there: {event.package_id} (seen {event.detection_count}x)") + + return agent + + +async def join_call(agent: Agent, call_type: str, call_id: str, **kwargs) -> None: + call = await agent.create_call(call_type, call_id) + + # Have the agent join the call/room + with await agent.join(call): + # Greet the user + await agent.finish() + + +if __name__ == "__main__": + cli(AgentLauncher(create_agent=create_agent, join_call=join_call)) diff --git a/examples/04_security_camera_example/security_camera_processor.py b/examples/04_security_camera_example/security_camera_processor.py new file mode 100644 index 00000000..22c4ce08 --- /dev/null +++ b/examples/04_security_camera_example/security_camera_processor.py @@ -0,0 +1,1302 @@ +import asyncio +import logging +import time +import uuid +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional + +import aiortc +import av +import cv2 +import face_recognition +import numpy as np + +from pathlib import Path + +from vision_agents.core.events.base import PluginBaseEvent +from vision_agents.core.events.manager import EventManager +from vision_agents.core.processors.base_processor import ( + AudioVideoProcessor, + VideoProcessorMixin, + VideoPublisherMixin, +) +from vision_agents.core.utils.video_forwarder import VideoForwarder +from vision_agents.core.utils.video_track import QueuedVideoTrack + +logger = logging.getLogger(__name__) + + +@dataclass +class PersonDetectedEvent(PluginBaseEvent): + """Event emitted when a person/face is detected.""" + + type: str = field(default="security.person_detected", init=False) + face_id: str = "" + is_new: bool = False + detection_count: int = 1 + first_seen: Optional[str] = None + last_seen: Optional[str] = None + + +@dataclass +class PackageDetectedEvent(PluginBaseEvent): + """Event emitted when a package is detected.""" + + type: str = field(default="security.package_detected", init=False) + package_id: str = "" + is_new: bool = False + detection_count: int = 1 + confidence: float = 0.0 + first_seen: Optional[str] = None + last_seen: Optional[str] = None + + +@dataclass +class FaceDetection: + """Represents a detected face with metadata.""" + + face_id: str + face_image: np.ndarray + face_encoding: np.ndarray + first_seen: float + last_seen: float + bbox: tuple + detection_count: int = 1 + name: Optional[str] = None # Name if this is a known face + + +@dataclass +class PackageDetection: + """Represents a detected package with metadata.""" + + package_id: str + package_image: np.ndarray + first_seen: float + last_seen: float + bbox: tuple + confidence: float + detection_count: int = 1 + + +@dataclass +class KnownFace: + """Represents a known/registered face.""" + + name: str + face_encoding: np.ndarray + registered_at: float + + +@dataclass +class ActivityLogEntry: + """Represents an entry in the activity log.""" + + timestamp: float + event_type: str # "person_detected", "package_detected", "person_left", etc. + description: str + details: Dict[str, Any] = field(default_factory=dict) + + +class SecurityCameraProcessor( + AudioVideoProcessor, VideoProcessorMixin, VideoPublisherMixin +): + """ + Security camera processor that detects and recognizes faces and packages. + + This processor: + - Detects faces in real-time using OpenCV + - Uses face_recognition library to identify unique individuals + - Detects packages using YOLO object detection model + - Prevents duplicate entries for the same person/package + - Maintains a 30-minute sliding window of unique visitors and packages + - Displays visitor count, package count, and thumbnails in a grid overlay + + Args: + fps: Frame processing rate (default: 5) + max_workers: Number of worker threads (default: 10) + time_window: Time window in seconds to track faces/packages (default: 1800 = 30 minutes) + thumbnail_size: Size of face/package thumbnails in overlay (default: 80) + detection_interval: Minimum seconds between face detections (default: 2) + face_match_tolerance: Face recognition tolerance (default: 0.6, lower = stricter) + model_path: Path to YOLO model file (default: "yolo11n.pt") + device: Device to run YOLO model on (default: "cpu") + package_detection_interval: Minimum seconds between package detections (default: 3) + package_fps: FPS for package detection (default: 1) + package_conf_threshold: Confidence threshold for package detection (default: 0.3) + """ + + name = "security_camera" + + def __init__( + self, + fps: int = 5, + max_workers: int = 10, + time_window: int = 1800, + thumbnail_size: int = 80, + detection_interval: float = 2.0, + face_match_tolerance: float = 0.6, + model_path: str = "weights.pt", + device: str = "cpu", + package_detection_interval: float = 3.0, + package_fps: int = 1, + package_conf_threshold: float = 0.3, + ): + super().__init__(interval=0, receive_audio=False, receive_video=True) + + self.fps = fps + self.max_workers = max_workers + self.time_window = time_window + self.thumbnail_size = thumbnail_size + self.detection_interval = detection_interval + self.face_match_tolerance = face_match_tolerance + self.package_detection_interval = package_detection_interval + self.package_fps = package_fps + self.package_conf_threshold = package_conf_threshold + + # Storage for unique detected faces (keyed by face_id) + self._detected_faces: Dict[str, FaceDetection] = {} + self._last_detection_time = 0.0 + + # Storage for unique detected packages (keyed by package_id) + self._detected_packages: Dict[str, PackageDetection] = {} + self._last_package_detection_time = 0.0 + + # Known faces database for named recognition + self._known_faces: Dict[str, KnownFace] = {} + + # Activity log for event history + self._activity_log: List[ActivityLogEntry] = [] + self._max_activity_log_entries = 100 # Keep last 100 events + + # Thread pool for CPU-intensive operations + self.executor = ThreadPoolExecutor( + max_workers=max_workers, thread_name_prefix="security_camera" + ) + + # Video track for publishing + self._video_track: QueuedVideoTrack = QueuedVideoTrack() + self._video_forwarder: Optional[VideoForwarder] = None + + # Load OpenCV face detector + self._face_cascade = None + + # Initialize YOLO model for package detection + self.model_path = model_path + self.device = device + self.yolo_model: Optional[Any] = None + self.package_detect_classes = [ + "package", + "box", + "parcel", + "suitcase", + "backpack", + ] + + # Event manager for detection events + self.events = EventManager() + self.events.register(PersonDetectedEvent) + self.events.register(PackageDetectedEvent) + + logger.info("๐ŸŽฅ Security Camera Processor initialized") + logger.info(f"๐Ÿ“Š Time window: {time_window}s ({time_window // 60} minutes)") + logger.info(f"๐Ÿ–ผ๏ธ Thumbnail size: {thumbnail_size}x{thumbnail_size}") + logger.info( + f"๐Ÿ“ฆ Package detection: {package_fps} FPS, interval: {package_detection_interval}s" + ) + + async def warmup(self): + """Load OpenCV Haar Cascade face detector and YOLO model.""" + try: + cascade_path = cv2.data.haarcascades + "haarcascade_frontalface_default.xml" + self._face_cascade = cv2.CascadeClassifier(cascade_path) + logger.info("โœ… Face detector loaded") + except Exception as e: + logger.exception(f"โŒ Failed to load face detector: {e}") + raise + + try: + from ultralytics import YOLO + + loop = asyncio.get_event_loop() + + def load_yolo_model(): + if not Path(self.model_path).exists(): + logger.warning( + f"Model file {self.model_path} not found. YOLO will download it automatically." + ) + logger.debug("Loading model file...") + model = YOLO(self.model_path) + model.to(self.device) + return model + + self.yolo_model = await loop.run_in_executor(self.executor, load_yolo_model) + logger.info(f"โœ… YOLO model loaded: {self.model_path} on {self.device}") + except Exception as e: + logger.warning(f"โš ๏ธ Failed to load YOLO model: {e}") + logger.warning("โš ๏ธ Package detection will be disabled") + self.yolo_model = None + + def _cleanup_old_faces(self, current_time: float): + cutoff_time = current_time - self.time_window + + # Remove faces whose last_seen is older than the cutoff + faces_to_remove = [ + face_id + for face_id, face in self._detected_faces.items() + if face.last_seen < cutoff_time + ] + + for face_id in faces_to_remove: + del self._detected_faces[face_id] + + removed = len(faces_to_remove) + if removed > 0: + logger.debug(f"๐Ÿงน Cleaned up {removed} old face(s)") + + def _cleanup_old_packages(self, current_time: float): + cutoff_time = current_time - self.time_window + + # Remove packages whose last_seen is older than the cutoff + packages_to_remove = [ + package_id + for package_id, package in self._detected_packages.items() + if package.last_seen < cutoff_time + ] + + for package_id in packages_to_remove: + del self._detected_packages[package_id] + + removed = len(packages_to_remove) + if removed > 0: + logger.debug(f"๐Ÿงน Cleaned up {removed} old package(s)") + + def _check_for_picked_up_packages(self, current_time: float): + """Check if any packages have disappeared (picked up). + + A package is considered "picked up" if it hasn't been seen for 15 seconds + but was detected within the last 5 minutes. + """ + pickup_threshold = 15.0 # seconds without seeing = picked up + max_age = 300.0 # only consider packages from last 5 minutes + + packages_picked_up = [] + + for package_id, package in list(self._detected_packages.items()): + time_since_seen = current_time - package.last_seen + package_age = current_time - package.first_seen + + # Package disappeared recently (not seen for 15s, but was active recently) + if pickup_threshold < time_since_seen < max_age and package_age < max_age: + # Check if already marked as picked up + if not package_id.startswith("picked_"): + packages_picked_up.append(package) + + for package in packages_picked_up: + # Find who was present when the package disappeared + picker = self._find_person_present_at(package.last_seen) + picker_name = picker.name if picker and picker.name else (picker.face_id[:8] if picker else "unknown person") + + logger.info(f"๐Ÿ“ฆ Package {package.package_id[:8]} was picked up by {picker_name}") + + # Log activity + self._log_activity( + event_type="package_picked_up", + description=f"Package picked up by {picker_name}", + details={ + "package_id": package.package_id[:8], + "picked_up_by": picker_name, + "picker_face_id": picker.face_id[:8] if picker else None, + "picker_is_known": picker.name is not None if picker else False, + }, + ) + + # Remove the package from tracking + del self._detected_packages[package.package_id] + + def _find_person_present_at(self, timestamp: float) -> Optional[FaceDetection]: + """Find who was present around a given timestamp. + + Returns the person who was most recently seen around that time. + """ + window = 10.0 # Look within 10 seconds of the timestamp + + candidates = [] + for face in self._detected_faces.values(): + # Check if person was seen around that time + if abs(face.last_seen - timestamp) < window: + candidates.append(face) + + if not candidates: + return None + + # Return the most recently seen person + return max(candidates, key=lambda f: f.last_seen) + + def _calculate_iou(self, bbox1: tuple, bbox2: tuple) -> float: + """Calculate Intersection over Union (IoU) between two bounding boxes. + + Args: + bbox1: (x, y, w, h) format + bbox2: (x, y, w, h) format + + Returns: + IoU value between 0 and 1 + """ + x1, y1, w1, h1 = bbox1 + x2, y2, w2, h2 = bbox2 + + # Convert to (x_min, y_min, x_max, y_max) format + x1_min, y1_min = x1, y1 + x1_max, y1_max = x1 + w1, y1 + h1 + x2_min, y2_min = x2, y2 + x2_max, y2_max = x2 + w2, y2 + h2 + + # Calculate intersection + inter_x_min = max(x1_min, x2_min) + inter_y_min = max(y1_min, y2_min) + inter_x_max = min(x1_max, x2_max) + inter_y_max = min(y1_max, y2_max) + + if inter_x_max <= inter_x_min or inter_y_max <= inter_y_min: + return 0.0 + + inter_area = (inter_x_max - inter_x_min) * (inter_y_max - inter_y_min) + bbox1_area = w1 * h1 + bbox2_area = w2 * h2 + union_area = bbox1_area + bbox2_area - inter_area + + if union_area == 0: + return 0.0 + + return inter_area / union_area + + def _find_matching_package( + self, bbox: tuple, iou_threshold: float = 0.3 + ) -> Optional[str]: + """Find matching package based on IoU overlap. + + Args: + bbox: (x, y, w, h) format + iou_threshold: Minimum IoU to consider a match (default: 0.3) + + Returns: + package_id if match found, None otherwise + """ + if not self._detected_packages: + return None + + best_match_id = None + best_iou = 0.0 + + for package_id, package in self._detected_packages.items(): + iou = self._calculate_iou(bbox, package.bbox) + if iou > best_iou and iou >= iou_threshold: + best_iou = iou + best_match_id = package_id + + return best_match_id + + def _detect_faces_sync(self, frame_rgb: np.ndarray) -> List[Dict[str, Any]]: + face_locations = face_recognition.face_locations(frame_rgb, model="hog") + + if not face_locations: + return [] + + # Generate face encodings + face_encodings = face_recognition.face_encodings(frame_rgb, face_locations) + + # Convert to list of dicts with bbox in (x, y, w, h) format + results = [] + for (top, right, bottom, left), encoding in zip(face_locations, face_encodings): + # Convert from (top, right, bottom, left) to (x, y, w, h) + x = left + y = top + w = right - left + h = bottom - top + + results.append({"bbox": (x, y, w, h), "encoding": encoding}) + + return results + + def _find_matching_face(self, face_encoding: np.ndarray) -> Optional[str]: + if not self._detected_faces: + return None + + # Get all existing face encodings + known_face_ids = list(self._detected_faces.keys()) + known_encodings = [ + self._detected_faces[face_id].face_encoding for face_id in known_face_ids + ] + + # Compare against all known faces + matches = face_recognition.compare_faces( + known_encodings, face_encoding, tolerance=self.face_match_tolerance + ) + + # If we found a match, return the face_id + for i, is_match in enumerate(matches): + if is_match: + return known_face_ids[i] + + return None + + def _find_known_face_name(self, face_encoding: np.ndarray) -> Optional[str]: + """Check if face matches any known/registered face and return the name.""" + if not self._known_faces: + return None + + known_names = list(self._known_faces.keys()) + known_encodings = [ + self._known_faces[name].face_encoding for name in known_names + ] + + matches = face_recognition.compare_faces( + known_encodings, face_encoding, tolerance=self.face_match_tolerance + ) + + for i, is_match in enumerate(matches): + if is_match: + return known_names[i] + + return None + + def _log_activity( + self, + event_type: str, + description: str, + details: Optional[Dict[str, Any]] = None, + ) -> None: + """Add an entry to the activity log.""" + entry = ActivityLogEntry( + timestamp=time.time(), + event_type=event_type, + description=description, + details=details or {}, + ) + self._activity_log.append(entry) + + # Trim log if too long + if len(self._activity_log) > self._max_activity_log_entries: + self._activity_log = self._activity_log[-self._max_activity_log_entries :] + + async def _detect_and_store_faces( + self, frame_bgr: np.ndarray, current_time: float + ) -> int: + """ + Detect faces in frame and store new unique faces or update existing ones. + + Returns: + Number of new unique faces detected + """ + # Check if enough time has passed since last detection + if current_time - self._last_detection_time < self.detection_interval: + return 0 + + # Convert BGR to RGB for face_recognition library + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + + # Run detection in thread pool + loop = asyncio.get_event_loop() + detected_faces = await loop.run_in_executor( + self.executor, self._detect_faces_sync, frame_rgb + ) + + new_faces = 0 + updated_faces = 0 + + for face_data in detected_faces: + x, y, w, h = face_data["bbox"] + face_encoding = face_data["encoding"] + + # Extract face thumbnail (convert back to BGR for storage) + face_roi = frame_bgr[y : y + h, x : x + w] + face_thumbnail = cv2.resize( + face_roi, (self.thumbnail_size, self.thumbnail_size) + ) + + # Check if this is a known/registered face + known_name = self._find_known_face_name(face_encoding) + + # Check if this face matches any existing face in current session + matching_face_id = self._find_matching_face(face_encoding) + + if matching_face_id: + # Update existing face + face_detection = self._detected_faces[matching_face_id] + face_detection.last_seen = current_time + face_detection.detection_count += 1 + face_detection.bbox = (x, y, w, h) + # Update thumbnail to latest image + face_detection.face_image = face_thumbnail + # Update name if we now recognize them + if known_name and not face_detection.name: + face_detection.name = known_name + updated_faces += 1 + + display_name = face_detection.name or matching_face_id[:8] + logger.debug( + f"๐Ÿ”„ Updated existing face {display_name} " + f"(seen {face_detection.detection_count} times)" + ) + # Emit event for returning visitor + self.events.send( + PersonDetectedEvent( + plugin_name="security_camera", + face_id=display_name, + is_new=False, + detection_count=face_detection.detection_count, + first_seen=time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(face_detection.first_seen) + ), + last_seen=time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(current_time) + ), + ) + ) + else: + # New unique face + face_id = str(uuid.uuid4()) + detection = FaceDetection( + face_id=face_id, + face_image=face_thumbnail, + face_encoding=face_encoding, + first_seen=current_time, + last_seen=current_time, + bbox=(x, y, w, h), + detection_count=1, + name=known_name, # Will be None if not recognized + ) + self._detected_faces[face_id] = detection + new_faces += 1 + + display_name = known_name or face_id[:8] + logger.info(f"๐Ÿ‘ค New unique visitor detected: {display_name}") + + # Log activity + self._log_activity( + event_type="person_arrived", + description=f"New person arrived: {display_name}", + details={ + "face_id": face_id[:8], + "name": known_name, + "is_known": known_name is not None, + }, + ) + + # Emit event for new visitor + self.events.send( + PersonDetectedEvent( + plugin_name="security_camera", + face_id=display_name, + is_new=True, + detection_count=1, + first_seen=time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(current_time) + ), + last_seen=time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(current_time) + ), + ) + ) + + if new_faces > 0 or updated_faces > 0: + self._last_detection_time = current_time + logger.info( + f"๐Ÿ“Š Detection summary - New: {new_faces}, Updated: {updated_faces}, " + f"Total unique visitors: {len(self._detected_faces)}" + ) + + return new_faces + + def _detect_packages_sync(self, frame_rgb: np.ndarray) -> List[Dict[str, Any]]: + """Run YOLO package detection synchronously. + + Args: + frame_rgb: Frame in RGB format + + Returns: + List of detection dicts with bbox and confidence + """ + if not self.yolo_model: + return [] + + height, width = frame_rgb.shape[:2] + all_detections = [] + + try: + results = self.yolo_model( + frame_rgb, + verbose=False, + conf=self.package_conf_threshold, + device=self.device, + ) + + if not results: + return [] + + result = results[0] + + if result.boxes is None or len(result.boxes) == 0: + return [] + + boxes = result.boxes.xyxy.cpu().numpy() + confidences = result.boxes.conf.cpu().numpy() + class_ids = result.boxes.cls.cpu().numpy().astype(int) + class_names = result.names + + for box, conf, cls_id in zip(boxes, confidences, class_ids): + class_name = class_names[cls_id].lower() + + if any( + detect_class in class_name + for detect_class in self.package_detect_classes + ): + x_min, y_min, x_max, y_max = box + + x_min = int(max(0, min(x_min, width - 1))) + y_min = int(max(0, min(y_min, height - 1))) + x_max = int(max(x_min + 1, min(x_max, width))) + y_max = int(max(y_min + 1, min(y_max, height))) + + x = x_min + y = y_min + w = x_max - x_min + h = y_max - y_min + + all_detections.append( + { + "bbox": (x, y, w, h), + "confidence": float(conf), + "label": class_name, + } + ) + + except Exception as e: + logger.warning(f"โš ๏ธ Failed to detect packages with YOLO: {e}") + + return all_detections + + async def _detect_and_store_packages( + self, frame_bgr: np.ndarray, current_time: float + ) -> int: + """ + Detect packages in frame and store new unique packages or update existing ones. + + Returns: + Number of new unique packages detected + """ + if not self.yolo_model: + return 0 + + # Check if enough time has passed since last detection + if ( + current_time - self._last_package_detection_time + < self.package_detection_interval + ): + return 0 + + # Convert BGR to RGB for Moondream + frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) + + # Run detection in thread pool + loop = asyncio.get_event_loop() + detected_packages = await loop.run_in_executor( + self.executor, self._detect_packages_sync, frame_rgb + ) + + new_packages = 0 + updated_packages = 0 + + for package_data in detected_packages: + x, y, w, h = package_data["bbox"] + confidence = package_data["confidence"] + + # Ensure coordinates are integers and within frame bounds + height, width = frame_bgr.shape[:2] + x = int(max(0, min(x, width - 1))) + y = int(max(0, min(y, height - 1))) + w = int(max(1, min(w, width - x))) + h = int(max(1, min(h, height - y))) + + # Extract package thumbnail + package_roi = frame_bgr[y : y + h, x : x + w] + if package_roi.size == 0: + continue + + package_thumbnail = cv2.resize( + package_roi, (self.thumbnail_size, self.thumbnail_size) + ) + + # Check if this package matches any existing package + matching_package_id = self._find_matching_package((x, y, w, h)) + + if matching_package_id: + # Update existing package + package_detection = self._detected_packages[matching_package_id] + package_detection.last_seen = current_time + package_detection.detection_count += 1 + package_detection.bbox = (x, y, w, h) + package_detection.confidence = max( + package_detection.confidence, confidence + ) + package_detection.package_image = package_thumbnail + updated_packages += 1 + logger.debug( + f"๐Ÿ”„ Updated existing package {matching_package_id[:8]} " + f"(seen {package_detection.detection_count} times)" + ) + # Emit event for returning package + self.events.send( + PackageDetectedEvent( + plugin_name="security_camera", + package_id=matching_package_id[:8], + is_new=False, + detection_count=package_detection.detection_count, + confidence=package_detection.confidence, + first_seen=time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(package_detection.first_seen) + ), + last_seen=time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(current_time) + ), + ) + ) + else: + # New unique package + package_id = str(uuid.uuid4()) + detection = PackageDetection( + package_id=package_id, + package_image=package_thumbnail, + first_seen=current_time, + last_seen=current_time, + bbox=(x, y, w, h), + confidence=confidence, + detection_count=1, + ) + self._detected_packages[package_id] = detection + new_packages += 1 + logger.info(f"๐Ÿ“ฆ New unique package detected: {package_id[:8]}") + + # Log activity + self._log_activity( + event_type="package_arrived", + description=f"New package detected (confidence: {confidence:.2f})", + details={ + "package_id": package_id[:8], + "confidence": confidence, + }, + ) + + # Emit event for new package + self.events.send( + PackageDetectedEvent( + plugin_name="security_camera", + package_id=package_id[:8], + is_new=True, + detection_count=1, + confidence=confidence, + first_seen=time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(current_time) + ), + last_seen=time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(current_time) + ), + ) + ) + + if new_packages > 0 or updated_packages > 0: + self._last_package_detection_time = current_time + logger.info( + f"๐Ÿ“ฆ Package detection summary - New: {new_packages}, Updated: {updated_packages}, " + f"Total unique packages: {len(self._detected_packages)}" + ) + + return new_packages + + def _create_overlay( + self, frame_bgr: np.ndarray, face_count: int, package_count: int + ) -> np.ndarray: + """ + Create video overlay with face count, package count, and thumbnail grid. + + Args: + frame_bgr: Original frame in BGR format + face_count: Number of faces in time window + package_count: Number of packages in time window + + Returns: + Frame with overlay applied + """ + height, width = frame_bgr.shape[:2] + overlay_width = 200 # Width of right panel + grid_cols = 2 # Number of columns in thumbnail grid + + # Create a copy to draw on + frame_with_overlay = frame_bgr.copy() + + # Draw package bounding boxes on the frame + for package in self._detected_packages.values(): + x, y, w, h = package.bbox + # Ensure coordinates are integers + x, y, w, h = int(x), int(y), int(w), int(h) + # Ensure coordinates are within bounds + x = max(0, min(x, width - 1)) + y = max(0, min(y, height - 1)) + x2 = min(x + w, width) + y2 = min(y + h, height) + # Draw blue rectangle for packages + cv2.rectangle(frame_with_overlay, (x, y), (x2, y2), (255, 0, 0), 2) + # Draw package label + label_text = f"Package {package.confidence:.2f}" + cv2.putText( + frame_with_overlay, + label_text, + (x, max(10, y - 5)), + cv2.FONT_HERSHEY_SIMPLEX, + 0.4, + (255, 0, 0), + 1, + cv2.LINE_AA, + ) + + # Draw semi-transparent overlay panel on right side + overlay = frame_with_overlay.copy() + cv2.rectangle( + overlay, + (width - overlay_width, 0), + (width, height), + (40, 40, 40), + -1, + ) + cv2.addWeighted(overlay, 0.7, frame_with_overlay, 0.3, 0, frame_with_overlay) + + # Draw header text + header_text = "SECURITY CAMERA" + cv2.putText( + frame_with_overlay, + header_text, + (width - overlay_width + 10, 30), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + (255, 255, 255), + 1, + cv2.LINE_AA, + ) + + # Draw face count + count_text = f"Visitors (30m): {face_count}" + cv2.putText( + frame_with_overlay, + count_text, + (width - overlay_width + 10, 60), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + (0, 255, 0), + 1, + cv2.LINE_AA, + ) + + # Draw package count + package_text = f"Packages (30m): {package_count}" + cv2.putText( + frame_with_overlay, + package_text, + (width - overlay_width + 10, 85), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + (255, 0, 0), + 1, + cv2.LINE_AA, + ) + + # Draw thumbnail grid + if self._detected_faces: + grid_start_y = 80 + grid_padding = 10 + thumb_size = self.thumbnail_size + + # Get most recent 12 faces sorted by last_seen + recent_faces = sorted( + self._detected_faces.values(), key=lambda f: f.last_seen, reverse=True + )[:12] + + for idx, detection in enumerate(recent_faces): + row = idx // grid_cols + col = idx % grid_cols + + x_pos = width - overlay_width + 10 + col * (thumb_size + grid_padding) + y_pos = grid_start_y + row * (thumb_size + grid_padding) + + # Check if we're still within the frame bounds + if y_pos + thumb_size > height: + break + + # Draw thumbnail + try: + frame_with_overlay[ + y_pos : y_pos + thumb_size, x_pos : x_pos + thumb_size + ] = detection.face_image + + # Draw detection count badge + if detection.detection_count > 1: + badge_text = f"{detection.detection_count}x" + badge_size = cv2.getTextSize( + badge_text, cv2.FONT_HERSHEY_SIMPLEX, 0.3, 1 + )[0] + badge_x = x_pos + thumb_size - badge_size[0] - 2 + badge_y = y_pos + thumb_size - 2 + + # Draw badge background + cv2.rectangle( + frame_with_overlay, + (badge_x - 2, badge_y - badge_size[1] - 2), + (x_pos + thumb_size, y_pos + thumb_size), + (0, 0, 0), + -1, + ) + + # Draw badge text + cv2.putText( + frame_with_overlay, + badge_text, + (badge_x, badge_y), + cv2.FONT_HERSHEY_SIMPLEX, + 0.3, + (255, 255, 255), + 1, + cv2.LINE_AA, + ) + except Exception as e: + logger.debug(f"Failed to draw thumbnail: {e}") + continue + + timestamp_text = time.strftime("%Y-%m-%d %H:%M:%S") + cv2.putText( + frame_with_overlay, + timestamp_text, + (10, height - 10), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + (255, 255, 255), + 1, + cv2.LINE_AA, + ) + + return frame_with_overlay + + async def _process_and_add_frame(self, frame: av.VideoFrame): + try: + current_time = time.time() + + # Convert frame to BGR (OpenCV format) + frame_rgb = frame.to_ndarray(format="rgb24") + frame_bgr = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2BGR) + + # Clean up old faces and packages + self._cleanup_old_faces(current_time) + self._cleanup_old_packages(current_time) + + # Check if any packages were picked up + self._check_for_picked_up_packages(current_time) + + # Detect and store new faces + await self._detect_and_store_faces(frame_bgr, current_time) + + # Detect and store new packages + await self._detect_and_store_packages(frame_bgr, current_time) + + # Create overlay with stats and thumbnails + frame_with_overlay = self._create_overlay( + frame_bgr, len(self._detected_faces), len(self._detected_packages) + ) + + # Convert back to RGB and then to av.VideoFrame + frame_rgb_overlay = cv2.cvtColor(frame_with_overlay, cv2.COLOR_BGR2RGB) + processed_frame = av.VideoFrame.from_ndarray( + frame_rgb_overlay, format="rgb24" + ) + + # Publish the processed frame + await self._video_track.add_frame(processed_frame) + + except Exception as e: + logger.exception(f"โŒ Frame processing failed: {e}") + # Pass through original frame on error + await self._video_track.add_frame(frame) + + async def process_video( + self, + incoming_track: aiortc.mediastreams.MediaStreamTrack, + participant: Any, + shared_forwarder=None, + ): + """ + Set up video processing pipeline. + + Args: + incoming_track: The incoming video track to process + participant: Participant information + shared_forwarder: Optional shared VideoForwarder + """ + logger.info("โœ… Security Camera process_video starting") + + if shared_forwarder is not None: + self._video_forwarder = shared_forwarder + logger.info( + f"๐ŸŽฅ Security Camera subscribing to shared VideoForwarder at {self.fps} FPS" + ) + self._video_forwarder.add_frame_handler( + self._process_and_add_frame, fps=float(self.fps), name="security_camera" + ) + else: + self._video_forwarder = VideoForwarder( + incoming_track, + max_buffer=30, + fps=self.fps, + name="security_camera_forwarder", + ) + self._video_forwarder.add_frame_handler(self._process_and_add_frame) + + logger.info("โœ… Security Camera video processing pipeline started") + + def publish_video_track(self): + """Return the video track for publishing.""" + logger.info("๐Ÿ“น publish_video_track called") + return self._video_track + + def state(self) -> Dict[str, Any]: + """ + Return current state for LLM context. + + Returns: + Dictionary with visitor count, package count, and timing info + """ + current_time = time.time() + self._cleanup_old_faces(current_time) + self._cleanup_old_packages(current_time) + + total_face_detections = sum( + face.detection_count for face in self._detected_faces.values() + ) + total_package_detections = sum( + package.detection_count for package in self._detected_packages.values() + ) + + return { + "unique_visitors": len(self._detected_faces), + "total_face_detections": total_face_detections, + "unique_packages": len(self._detected_packages), + "total_package_detections": total_package_detections, + "time_window_minutes": self.time_window // 60, + "last_face_detection_time": ( + time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(self._last_detection_time) + ) + if self._last_detection_time > 0 + else "No detections yet" + ), + "last_package_detection_time": ( + time.strftime( + "%Y-%m-%d %H:%M:%S", + time.localtime(self._last_package_detection_time), + ) + if self._last_package_detection_time > 0 + else "No detections yet" + ), + } + + def get_visitor_count(self) -> int: + """ + Get the current unique visitor count (for function calling). + + Returns: + Number of unique faces detected in the time window + """ + current_time = time.time() + self._cleanup_old_faces(current_time) + return len(self._detected_faces) + + def get_visitor_details(self) -> List[Dict[str, Any]]: + """ + Get detailed information about all visitors. + + Returns: + List of visitor details + """ + current_time = time.time() + self._cleanup_old_faces(current_time) + + visitors = [] + for face in sorted( + self._detected_faces.values(), key=lambda f: f.last_seen, reverse=True + ): + visitors.append( + { + "face_id": face.face_id[:8], # Shortened ID + "name": face.name, # Will be None if unknown + "is_known": face.name is not None, + "first_seen": time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(face.first_seen) + ), + "last_seen": time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(face.last_seen) + ), + "detection_count": face.detection_count, + } + ) + + return visitors + + def get_package_count(self) -> int: + """ + Get the current unique package count (for function calling). + + Returns: + Number of unique packages detected in the time window + """ + current_time = time.time() + self._cleanup_old_packages(current_time) + return len(self._detected_packages) + + def get_package_details(self) -> List[Dict[str, Any]]: + """ + Get detailed information about all packages. + + Returns: + List of package details + """ + current_time = time.time() + self._cleanup_old_packages(current_time) + + packages = [] + for package in sorted( + self._detected_packages.values(), + key=lambda p: p.last_seen, + reverse=True, + ): + packages.append( + { + "package_id": package.package_id[:8], + "first_seen": time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(package.first_seen) + ), + "last_seen": time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(package.last_seen) + ), + "detection_count": package.detection_count, + "confidence": package.confidence, + } + ) + + return packages + + def get_activity_log(self, limit: int = 20) -> List[Dict[str, Any]]: + """ + Get recent activity log entries. + + Args: + limit: Maximum number of entries to return (default: 20) + + Returns: + List of activity log entries, most recent first + """ + entries = [] + for entry in reversed(self._activity_log[-limit:]): + entries.append( + { + "timestamp": time.strftime( + "%Y-%m-%d %H:%M:%S", time.localtime(entry.timestamp) + ), + "event_type": entry.event_type, + "description": entry.description, + "details": entry.details, + } + ) + return entries + + def register_known_face(self, name: str, face_encoding: np.ndarray) -> bool: + """ + Register a face encoding with a name for future recognition. + + Args: + name: Name to associate with the face + face_encoding: 128-dimensional face encoding from face_recognition + + Returns: + True if registered successfully + """ + self._known_faces[name] = KnownFace( + name=name, + face_encoding=face_encoding, + registered_at=time.time(), + ) + logger.info(f"โœ… Registered known face: {name}") + + # Log activity + self._log_activity( + event_type="face_registered", + description=f"Registered new known face: {name}", + details={"name": name}, + ) + + return True + + def register_current_face_as(self, name: str) -> Dict[str, Any]: + """ + Register the most recently detected face with a name. + Useful for "remember me as [name]" functionality. + + Args: + name: Name to associate with the face + + Returns: + Dict with success status and message + """ + if not self._detected_faces: + return { + "success": False, + "message": "No faces currently detected. Please make sure your face is visible.", + } + + # Get the most recently seen face + most_recent_face = max( + self._detected_faces.values(), key=lambda f: f.last_seen + ) + + # Register the face encoding + self.register_known_face(name, most_recent_face.face_encoding) + + # Update the face detection with the name + most_recent_face.name = name + + return { + "success": True, + "message": f"I'll remember you as {name}! 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import load_dotenv + +from vision_agents.core import User, Agent, cli +from vision_agents.core.agents import AgentLauncher +from vision_agents.plugins import getstream, gemini, moondream, openai +from vision_agents.core.events import CallSessionParticipantJoinedEvent + +logger = logging.getLogger(__name__) + +load_dotenv() + +async def create_agent(**kwargs) -> Agent: + moondream_processor = moondream.CloudDetectionProcessor( + detect_objects=["person", "headphones"], + fps=10.0 + ) + + # create an agent to run with Stream's edge, openAI llm + agent = Agent( + edge=getstream.Edge(), + agent_user=User( + name="My happy AI friend", id="agent" + ), + llm=openai.Realtime(fps=1), + processors=[moondream_processor] + ) + return agent + + +async def join_call(agent: Agent, call_type: str, call_id: str, **kwargs) -> None: + # ensure the agent user is created + await agent.create_user() + # Create a call + call = await agent.create_call(call_type, call_id) + + @agent.events.subscribe + async def on_participant_joined(event: CallSessionParticipantJoinedEvent): + if event.participant.user.id != "agent": + await asyncio.sleep(2) + # await agent.simple_response("Describe what you currently see") + + # Have the agent join the call/room + with await agent.join(call): + # run till the call ends + await agent.finish() + + +if __name__ == "__main__": + cli(AgentLauncher(create_agent=create_agent, join_call=join_call)) diff --git a/examples/moondream_example/main-cloud-llm.py b/examples/moondream_example/main-cloud-llm.py new file mode 100644 index 00000000..4cefb755 --- /dev/null +++ b/examples/moondream_example/main-cloud-llm.py @@ -0,0 +1,51 @@ +import asyncio +import logging +from dotenv import load_dotenv + +from vision_agents.core import User, Agent, cli +from vision_agents.core.agents import AgentLauncher +from vision_agents.plugins import deepgram, getstream, elevenlabs, moondream +from vision_agents.core.events import CallSessionParticipantJoinedEvent +import os + +logger = logging.getLogger(__name__) + +load_dotenv() + +async def create_agent(**kwargs) -> Agent: + llm = moondream.CloudVLM( + api_key=os.getenv("MOONDREAM_API_KEY"), + ) + # create an agent to run with Stream's edge, openAI llm + agent = Agent( + edge=getstream.Edge(), # low latency edge. clients for React, iOS, Android, RN, Flutter etc. + agent_user=User( + name="My happy AI friend", id="agent" + ), + llm=llm, + tts=elevenlabs.TTS(), + stt=deepgram.STT(), + ) + return agent + + +async def join_call(agent: Agent, call_type: str, call_id: str, **kwargs) -> None: + # ensure the agent user is created + await agent.create_user() + # Create a call + call = await agent.create_call(call_type, call_id) + + @agent.events.subscribe + async def on_participant_joined(event: CallSessionParticipantJoinedEvent): + if event.participant.user.id != "agent": + await asyncio.sleep(2) + await agent.simple_response("Describe what you currently see") + + # Have the agent join the call/room + with await agent.join(call): + # run till the call ends + await agent.finish() + + +if __name__ == "__main__": + cli(AgentLauncher(create_agent=create_agent, join_call=join_call)) diff --git a/examples/moondream_example/main-local-detection.py b/examples/moondream_example/main-local-detection.py new file mode 100644 index 00000000..2954ba07 --- /dev/null +++ b/examples/moondream_example/main-local-detection.py @@ -0,0 +1,52 @@ +import asyncio +import logging +from dotenv import load_dotenv + +from vision_agents.core import User, Agent, cli +from vision_agents.core.agents import AgentLauncher +from vision_agents.plugins import getstream, gemini, moondream +from vision_agents.core.events import CallSessionParticipantJoinedEvent + +logger = logging.getLogger(__name__) + +load_dotenv() + +async def create_agent(**kwargs) -> Agent: + moondream_processor = moondream.LocalDetectionProcessor( + detect_objects="person", # or ["person", "car", "dog"] for multiple + force_cpu=True, + fps=1 + ) + + # create an agent to run with Stream's edge, openAI llm + agent = Agent( + edge=getstream.Edge(), + agent_user=User( + name="My happy AI friend", id="agent" + ), + llm=gemini.Realtime(fps=1), + processors=[moondream_processor] + ) + return agent + + +async def join_call(agent: Agent, call_type: str, call_id: str, **kwargs) -> None: + # ensure the agent user is created + await agent.create_user() + # Create a call + call = await agent.create_call(call_type, call_id) + + @agent.events.subscribe + async def on_participant_joined(event: CallSessionParticipantJoinedEvent): + if event.participant.user.id != "agent": + await asyncio.sleep(2) + await agent.simple_response("Describe what you currently see") + + # Have the agent join the call/room + with await agent.join(call): + # run till the call ends + await agent.finish() + + +if __name__ == "__main__": + cli(AgentLauncher(create_agent=create_agent, join_call=join_call)) diff --git a/examples/moondream_example/main-local-llm.py b/examples/moondream_example/main-local-llm.py new file mode 100644 index 00000000..2e98351b --- /dev/null +++ b/examples/moondream_example/main-local-llm.py @@ -0,0 +1,51 @@ +import asyncio +import logging +from dotenv import load_dotenv + +from vision_agents.core import User, Agent, cli +from vision_agents.core.agents import AgentLauncher +from vision_agents.plugins import deepgram, getstream, elevenlabs, moondream +from vision_agents.core.events import CallSessionParticipantJoinedEvent + +logger = logging.getLogger(__name__) + +load_dotenv() + +async def create_agent(**kwargs) -> Agent: + llm = moondream.LocalVLM( + mode="vqa", # alternative: "caption" + force_cpu=False + ) + # create an agent to run with Stream's edge, openAI llm + agent = Agent( + edge=getstream.Edge(), # low latency edge. clients for React, iOS, Android, RN, Flutter etc. + agent_user=User( + name="My happy AI friend", id="agent" + ), + llm=llm, + tts=elevenlabs.TTS(), + stt=deepgram.STT(), + ) + return agent + + +async def join_call(agent: Agent, call_type: str, call_id: str, **kwargs) -> None: + # ensure the agent user is created + await agent.create_user() + # Create a call + call = await agent.create_call(call_type, call_id) + + @agent.events.subscribe + async def on_participant_joined(event: CallSessionParticipantJoinedEvent): + if event.participant.user.id != "agent": + await asyncio.sleep(2) + await agent.simple_response("Describe what you currently see") + + # Have the agent join the call/room + with await agent.join(call): + # run till the call ends + await agent.finish() + + +if __name__ == "__main__": + cli(AgentLauncher(create_agent=create_agent, join_call=join_call)) diff --git a/examples/moondream_example/main-tiled-detection.py b/examples/moondream_example/main-tiled-detection.py new file mode 100644 index 00000000..0c2b4222 --- /dev/null +++ b/examples/moondream_example/main-tiled-detection.py @@ -0,0 +1,55 @@ +import asyncio +import logging +from dotenv import load_dotenv + +from vision_agents.core import User, Agent, cli +from vision_agents.core.agents import AgentLauncher +from vision_agents.plugins import getstream, moondream, openai +from vision_agents.core.events import CallSessionParticipantJoinedEvent + +logger = logging.getLogger(__name__) + +load_dotenv() + +async def create_agent(**kwargs) -> Agent: + # Tiled processor: collects 4 frames (2x2 grid), sends one API request, + # maps detections back to each frame. + # + # With 2 RPS API limit and parallel processing: + # - input_fps=8 โ†’ collect 4 frames in 500ms per batch + # - 2 batches can run in parallel โ†’ effectively 4 RPS worth of frames + # - output_fps=8 โ†’ smooth playback matching input rate + tiled_processor = moondream.TiledDetectionProcessor( + detect_objects=["person"], # Single object = 1 API call per batch + tile_grid=(2, 2), # 2x2 = 4 frames per API call + input_fps=8.0, # Capture at 8 FPS (4 frames = 500ms to fill a batch) + output_fps=8.0, # Output at 8 FPS to match input + ) + + agent = Agent( + edge=getstream.Edge(), + agent_user=User( + name="My happy AI friend", id="agent" + ), + llm=openai.Realtime(fps=1), + processors=[tiled_processor] + ) + return agent + + +async def join_call(agent: Agent, call_type: str, call_id: str, **kwargs) -> None: + await agent.create_user() + call = await agent.create_call(call_type, call_id) + + @agent.events.subscribe + async def on_participant_joined(event: CallSessionParticipantJoinedEvent): + if event.participant.user.id != "agent": + await asyncio.sleep(2) + + with await agent.join(call): + await agent.finish() + + +if __name__ == "__main__": + cli(AgentLauncher(create_agent=create_agent, join_call=join_call)) + diff --git a/examples/moondream_example/main.py b/examples/moondream_example/main.py new file mode 100644 index 00000000..4cefb755 --- /dev/null +++ b/examples/moondream_example/main.py @@ -0,0 +1,51 @@ +import asyncio +import logging +from dotenv import load_dotenv + +from vision_agents.core import User, Agent, cli +from vision_agents.core.agents import AgentLauncher +from vision_agents.plugins import deepgram, getstream, elevenlabs, moondream +from vision_agents.core.events import CallSessionParticipantJoinedEvent +import os + +logger = logging.getLogger(__name__) + +load_dotenv() + +async def create_agent(**kwargs) -> Agent: + llm = moondream.CloudVLM( + api_key=os.getenv("MOONDREAM_API_KEY"), + ) + # create an agent to run with Stream's edge, openAI llm + agent = Agent( + edge=getstream.Edge(), # low latency edge. clients for React, iOS, Android, RN, Flutter etc. + agent_user=User( + name="My happy AI friend", id="agent" + ), + llm=llm, + tts=elevenlabs.TTS(), + stt=deepgram.STT(), + ) + return agent + + +async def join_call(agent: Agent, call_type: str, call_id: str, **kwargs) -> None: + # ensure the agent user is created + await agent.create_user() + # Create a call + call = await agent.create_call(call_type, call_id) + + @agent.events.subscribe + async def on_participant_joined(event: CallSessionParticipantJoinedEvent): + if event.participant.user.id != "agent": + await asyncio.sleep(2) + await agent.simple_response("Describe what you currently see") + + # Have the agent join the call/room + with await agent.join(call): + # run till the call ends + await agent.finish() + + +if __name__ == "__main__": + cli(AgentLauncher(create_agent=create_agent, join_call=join_call)) diff --git a/examples/moondream_example/pyproject.toml b/examples/moondream_example/pyproject.toml new file mode 100644 index 00000000..c94759bf --- /dev/null +++ b/examples/moondream_example/pyproject.toml @@ -0,0 +1,24 @@ +[project] +name = "moondream-example" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "vision-agents>=0.1.0", + "vision-agents-plugins-getstream", + "vision-agents-plugins-gemini", + "vision-agents-plugins-openai", + "vision-agents-plugins-moondream", + 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+"""Tiled detection processor that batches multiple frames into a single API call.""" + +import asyncio +import logging +import os +from concurrent.futures import ThreadPoolExecutor +from typing import Any, Dict, List, Optional, Tuple, Union, TYPE_CHECKING + +import aiortc +import av +import moondream as md +import numpy as np +from PIL import Image + +from vision_agents.core.processors.base_processor import VideoProcessorPublisher +from vision_agents.core.utils.video_forwarder import VideoForwarder +from vision_agents.plugins.moondream.detection.moondream_video_track import ( + MoondreamVideoTrack, +) +from vision_agents.plugins.moondream.moondream_utils import ( + annotate_detections, + parse_detection_bbox, +) + +logger = logging.getLogger(__name__) + +DEFAULT_WIDTH = 640 +DEFAULT_HEIGHT = 480 + + +class TiledDetectionProcessor(VideoProcessorPublisher): + """Performs object detection by tiling multiple frames into a single image. + + This processor collects N frames, tiles them into a grid (e.g., 2x2), + sends one API request to Moondream, then maps detections back to each + frame. This allows higher effective FPS while staying within API rate limits. + + Args: + api_key: API key for Moondream Cloud API. + tile_grid: Tuple of (rows, cols) for the tile grid. Default (2, 2) = 4 frames. + conf_threshold: Confidence threshold for detections (default: 0.3) + detect_objects: Object(s) to detect. + input_fps: Frame rate to collect frames at (default: 10) + output_fps: Frame rate to output processed frames at (default: 2) + max_workers: Number of worker threads (default: 10) + """ + + name = "moondream_tiled" + + def __init__( + self, + api_key: Optional[str] = None, + tile_grid: Tuple[int, int] = (2, 2), + conf_threshold: float = 0.3, + detect_objects: Union[str, List[str]] = "person", + input_fps: float = 10.0, + output_fps: float = 2.0, + max_workers: int = 10, + ): + self.api_key = api_key or os.getenv("MOONDREAM_API_KEY") + self.tile_rows, self.tile_cols = tile_grid + self.num_tiles = self.tile_rows * self.tile_cols + self.conf_threshold = conf_threshold + self.input_fps = input_fps + self.output_fps = output_fps + self.max_workers = max_workers + self._shutdown = False + + # Frame buffer to collect frames for tiling + self._frame_buffer: List[av.VideoFrame] = [] + self._buffer_lock = asyncio.Lock() + + # Last detection results to apply to new frames while waiting for API + self._last_detections: List[Dict[str, Any]] = [] + + # Semaphore to limit concurrent API calls (matches Moondream's 2 RPS limit) + self._api_semaphore: Optional[asyncio.Semaphore] = None + + # Queue for outputting frames in order + self._output_queue: asyncio.Queue[Tuple[int, List[Tuple[av.VideoFrame, Dict[str, Any]]]]] = asyncio.Queue() + self._next_output_batch: int = 0 + self._batch_counter: int = 0 + self._pending_batches: Dict[int, List[Tuple[av.VideoFrame, Dict[str, Any]]]] = {} + self._output_task: Optional[asyncio.Task] = None + + # Normalize detect_objects to list + if isinstance(detect_objects, str): + self.detect_objects = [detect_objects] + elif isinstance(detect_objects, list): + if not all(isinstance(obj, str) for obj in detect_objects): + raise ValueError("detect_objects must be str or list of strings") + self.detect_objects = detect_objects + else: + raise ValueError("detect_objects must be str or list of strings") + + self.executor = ThreadPoolExecutor( + max_workers=max_workers, thread_name_prefix="moondream_tiled" + ) + + self._video_track: MoondreamVideoTrack = MoondreamVideoTrack() + self._video_forwarder: Optional[VideoForwarder] = None + + self._load_model() + + logger.info( + f"๐ŸŒ™ Moondream Tiled Processor initialized with {self.tile_rows}x{self.tile_cols} grid" + ) + logger.info(f"๐ŸŽฏ Detection configured for objects: {self.detect_objects}") + + async def process_video( + self, + incoming_track: aiortc.VideoStreamTrack, + participant_id: Optional[str], + shared_forwarder: Optional[VideoForwarder] = None, + ): + """Process incoming video track with tiled detection.""" + logger.info("โœ… Moondream Tiled process_video starting") + + if shared_forwarder is not None: + self._video_forwarder = shared_forwarder + logger.info( + f"๐ŸŽฅ Moondream Tiled subscribing to shared VideoForwarder at {self.input_fps} FPS" + ) + self._video_forwarder.add_frame_handler( + self._on_frame_received, + fps=float(self.input_fps), + name="moondream_tiled", + ) + else: + self._video_forwarder = VideoForwarder( + incoming_track, # type: ignore[arg-type] + max_buffer=30, + fps=self.input_fps, + name="moondream_tiled_forwarder", + ) + self._video_forwarder.add_frame_handler(self._on_frame_received) + + logger.info("โœ… Moondream Tiled video processing pipeline started") + + def publish_video_track(self): + logger.info("๐Ÿ“น publish_video_track called") + return self._video_track + + def _load_model(self): + if not self.api_key: + raise ValueError("api_key is required for Moondream Cloud API") + self.model = md.vl(api_key=self.api_key) + logger.info("โœ… Moondream SDK initialized") + + async def _on_frame_received(self, frame: av.VideoFrame): + """Collect frames and process when we have enough for a tile.""" + # Initialize semaphore on first frame (needs event loop) + if self._api_semaphore is None: + self._api_semaphore = asyncio.Semaphore(2) # Allow 2 concurrent API calls + self._output_task = asyncio.create_task(self._output_frames_in_order()) + + async with self._buffer_lock: + self._frame_buffer.append(frame) + + # When we have enough frames, process them as a batch + if len(self._frame_buffer) >= self.num_tiles: + frames_to_process = self._frame_buffer[:self.num_tiles] + self._frame_buffer = self._frame_buffer[self.num_tiles:] + batch_id = self._batch_counter + self._batch_counter += 1 + # Process in parallel - don't await, let multiple batches run concurrently + asyncio.create_task(self._process_tiled_frames_parallel(frames_to_process, batch_id)) + + async def _process_tiled_frames_parallel(self, frames: List[av.VideoFrame], batch_id: int): + """Process a batch with semaphore to limit concurrent API calls.""" + async with self._api_semaphore: + try: + # Convert frames to numpy arrays + frame_arrays = [f.to_ndarray(format="rgb24") for f in frames] + + # Create tiled image + tiled_image, tile_info = self._create_tiled_image(frame_arrays) + + # Run detection on tiled image + all_detections = await self._run_tiled_inference(tiled_image) + + # Map detections back to individual frames + per_frame_detections = self._map_detections_to_frames( + all_detections, tile_info + ) + + # Cache the detections for reference + self._last_detections = all_detections + + # Prepare frame+detection pairs + frame_results = [ + (frame, {"detections": detections}) + for frame, detections in zip(frames, per_frame_detections, strict=False) + ] + + except Exception as e: + logger.exception(f"โŒ Tiled processing failed for batch {batch_id}: {e}") + # On error, output original frames without detections + frame_results = [(frame, {"detections": []}) for frame in frames] + + # Store results and trigger output if this batch is next in line + async with self._buffer_lock: + self._pending_batches[batch_id] = frame_results + await self._try_output_pending_batches() + + async def _try_output_pending_batches(self): + """Output any pending batches that are ready (in order).""" + while self._next_output_batch in self._pending_batches: + frame_results = self._pending_batches.pop(self._next_output_batch) + await self._output_queue.put((self._next_output_batch, frame_results)) + self._next_output_batch += 1 + + async def _output_frames_in_order(self): + """Background task that outputs frames from the queue at the target FPS.""" + frame_interval = 1.0 / self.output_fps + + while not self._shutdown: + try: + batch_id, frame_results = await asyncio.wait_for( + self._output_queue.get(), timeout=1.0 + ) + + for i, (frame, results) in enumerate(frame_results): + await self._output_annotated_frame(frame, results) + # Add delay between frames (but not after the last one) + if i < len(frame_results) - 1: + await asyncio.sleep(frame_interval) + + except asyncio.TimeoutError: + continue + except Exception as e: + logger.exception(f"โŒ Output task error: {e}") + + def _create_tiled_image( + self, frame_arrays: List[np.ndarray] + ) -> Tuple[np.ndarray, Dict[str, Any]]: + """Create a tiled image from multiple frames. + + Returns the tiled image and metadata for mapping detections back. + """ + if not frame_arrays: + raise ValueError("No frames to tile") + + # Get dimensions from first frame + h, w = frame_arrays[0].shape[:2] + + # Create empty canvas for tiled image + tiled_h = h * self.tile_rows + tiled_w = w * self.tile_cols + tiled = np.zeros((tiled_h, tiled_w, 3), dtype=np.uint8) + + # Place each frame in its tile position + tile_positions = [] + for idx, frame_array in enumerate(frame_arrays): + row = idx // self.tile_cols + col = idx % self.tile_cols + y_start = row * h + x_start = col * w + + # Resize frame if needed to match expected dimensions + if frame_array.shape[:2] != (h, w): + frame_array = np.array( + Image.fromarray(frame_array).resize((w, h), Image.Resampling.LANCZOS) + ) + + tiled[y_start : y_start + h, x_start : x_start + w] = frame_array + tile_positions.append( + { + "idx": idx, + "x_start": x_start, + "y_start": y_start, + "width": w, + "height": h, + } + ) + + tile_info = { + "tile_positions": tile_positions, + "tiled_width": tiled_w, + "tiled_height": tiled_h, + "frame_width": w, + "frame_height": h, + } + + return tiled, tile_info + + async def _run_tiled_inference( + self, tiled_array: np.ndarray + ) -> List[Dict[str, Any]]: + """Run detection on the tiled image.""" + loop = asyncio.get_event_loop() + return await loop.run_in_executor( + self.executor, self._run_detection_sync, tiled_array + ) + + def _run_detection_sync(self, frame_array: np.ndarray) -> List[Dict]: + """Synchronous detection on tiled image.""" + import time + + image = Image.fromarray(frame_array) + + if self._shutdown: + return [] + + all_detections = [] + + # Combine all object types into a single query to minimize API calls + # Moondream's detect() takes a single object string, so we need to call per object + # but we can log timing to understand the bottleneck + for object_type in self.detect_objects: + start = time.time() + logger.info(f"๐Ÿ” Detecting '{object_type}' on tiled image...") + try: + result = self.model.detect(image, object_type) + except Exception as e: + logger.warning(f"โš ๏ธ Failed to detect '{object_type}': {e}") + continue + elapsed = time.time() - start + logger.info(f"โœ… Detection for '{object_type}' took {elapsed:.2f}s") + + for obj in result.get("objects", []): + detection = parse_detection_bbox(obj, object_type, self.conf_threshold) + if detection: + all_detections.append(detection) + + logger.info(f"๐Ÿ” Tiled detection found {len(all_detections)} objects total") + return all_detections + + def _map_detections_to_frames( + self, detections: List[Dict[str, Any]], tile_info: Dict[str, Any] + ) -> List[List[Dict[str, Any]]]: + """Map detections from tiled image back to individual frames. + + Detections have normalized coordinates (0-1) relative to the tiled image. + We need to determine which tile each detection belongs to and convert + coordinates to be relative to that tile. + """ + tile_positions = tile_info["tile_positions"] + tiled_w = tile_info["tiled_width"] + tiled_h = tile_info["tiled_height"] + frame_w = tile_info["frame_width"] + frame_h = tile_info["frame_height"] + + # Initialize per-frame detection lists + per_frame: List[List[Dict[str, Any]]] = [[] for _ in tile_positions] + + for detection in detections: + bbox = detection.get("bbox", []) + if len(bbox) != 4: + continue + + x_min, y_min, x_max, y_max = bbox + + # Convert normalized coords to pixel coords in tiled image + px_x_min = x_min * tiled_w + px_y_min = y_min * tiled_h + px_x_max = x_max * tiled_w + px_y_max = y_max * tiled_h + + # Find center of detection to determine which tile it belongs to + center_x = (px_x_min + px_x_max) / 2 + center_y = (px_y_min + px_y_max) / 2 + + # Determine tile index based on center position + tile_col = int(center_x // frame_w) + tile_row = int(center_y // frame_h) + tile_idx = tile_row * self.tile_cols + tile_col + + if tile_idx < 0 or tile_idx >= len(tile_positions): + continue + + tile = tile_positions[tile_idx] + + # Convert coordinates to be relative to this tile (normalized 0-1) + new_x_min = (px_x_min - tile["x_start"]) / frame_w + new_y_min = (px_y_min - tile["y_start"]) / frame_h + new_x_max = (px_x_max - tile["x_start"]) / frame_w + new_y_max = (px_y_max - tile["y_start"]) / frame_h + + # Clamp to valid range + new_x_min = max(0.0, min(1.0, new_x_min)) + new_y_min = max(0.0, min(1.0, new_y_min)) + new_x_max = max(0.0, min(1.0, new_x_max)) + new_y_max = max(0.0, min(1.0, new_y_max)) + + mapped_detection = { + "label": detection.get("label", "object"), + "bbox": [new_x_min, new_y_min, new_x_max, new_y_max], + "confidence": detection.get("confidence", 1.0), + } + per_frame[tile_idx].append(mapped_detection) + + return per_frame + + async def _output_annotated_frame( + self, frame: av.VideoFrame, results: Dict[str, Any] + ): + """Annotate a frame with detections and output it.""" + try: + frame_array = frame.to_ndarray(format="rgb24") + + if results.get("detections"): + frame_array = annotate_detections(frame_array, results) + + processed_frame = av.VideoFrame.from_ndarray(frame_array, format="rgb24") + await self._video_track.add_frame(processed_frame) + except Exception as e: + logger.exception(f"โŒ Frame annotation failed: {e}") + await self._video_track.add_frame(frame) + + async def close(self): + """Clean up resources.""" + self._shutdown = True + if self._output_task: + self._output_task.cancel() + try: + await self._output_task + except asyncio.CancelledError: + pass + self.executor.shutdown(wait=False) + logger.info("๐Ÿ›‘ Moondream Tiled Processor closed") +