Merge branch 'feat/visual-emotion-recognition' into demo

pyproject.toml

@@ -7,6 +7,7 @@ requires-python = ">=3.13"
 dependencies = [
   "agentspeak>=0.2.2",
   "colorlog>=6.10.1",
+  "deepface>=0.0.96",
   "fastapi[all]>=0.115.6",
   "mlx-whisper>=0.4.3 ; sys_platform == 'darwin'",
   "numpy>=2.3.3",
@@ -21,6 +22,7 @@ dependencies = [
   "silero-vad>=6.0.0",
   "sphinx>=7.3.7",
   "sphinx-rtd-theme>=3.0.2",
+  "tf-keras>=2.20.1",
   "torch>=2.8.0",
   "uvicorn>=0.37.0",
 ]

src/control_backend/agents/bdi/agentspeak_generator.py

@@ -22,6 +22,7 @@ from control_backend.schemas.program import (
     BaseGoal,
     BasicNorm,
     ConditionalNorm,
+    EmotionBelief,
     GestureAction,
     Goal,
     InferredBelief,
@@ -459,6 +460,10 @@ class AgentSpeakGenerator:
     @_astify.register
     def _(self, sb: SemanticBelief) -> AstExpression:
         return AstLiteral(self.slugify(sb))
+    
+    @_astify.register
+    def _(self, eb: EmotionBelief) -> AstExpression:
+        return AstLiteral("emotion_detected", [AstAtom(eb.emotion)])
 
     @_astify.register
     def _(self, ib: InferredBelief) -> AstExpression:

src/control_backend/agents/communication/ri_communication_agent.py

@@ -8,6 +8,9 @@ from zmq.asyncio import Context
 
 from control_backend.agents import BaseAgent
 from control_backend.agents.actuation.robot_gesture_agent import RobotGestureAgent
+from control_backend.agents.perception.visual_emotion_recognition_agent.visual_emotion_recognition_agent import (  # noqa
+    VisualEmotionRecognitionAgent,
+)
 from control_backend.core.config import settings
 from control_backend.schemas.internal_message import InternalMessage
 from control_backend.schemas.ri_message import PauseCommand
@@ -52,6 +55,7 @@ class RICommunicationAgent(BaseAgent):
         self.connected = False
         self.gesture_agent: RobotGestureAgent | None = None
         self.speech_agent: RobotSpeechAgent | None = None
+        self.visual_emotion_recognition_agent: VisualEmotionRecognitionAgent | None = None
 
     async def setup(self):
         """
@@ -209,6 +213,14 @@ class RICommunicationAgent(BaseAgent):
                 case "audio":
                     vad_agent = VADAgent(audio_in_address=addr, audio_in_bind=bind)
                     await vad_agent.start()
+                case "video":
+                    visual_emotion_agent = VisualEmotionRecognitionAgent(
+                        settings.agent_settings.visual_emotion_recognition_name,
+                        socket_address=addr,
+                        bind=bind,
+                    )
+                    self.visual_emotion_recognition_agent = visual_emotion_agent
+                    await visual_emotion_agent.start()
                 case _:
                     self.logger.warning("Unhandled negotiation id: %s", id)
 
@@ -313,6 +325,9 @@ class RICommunicationAgent(BaseAgent):
 
         if self.speech_agent is not None:
             await self.speech_agent.stop()
+        
+        if self.visual_emotion_recognition_agent is not None:
+            await self.visual_emotion_recognition_agent.stop()
 
         if self.pub_socket is not None:
             self.pub_socket.close()
@@ -322,6 +337,7 @@ class RICommunicationAgent(BaseAgent):
             self.connected = True
 
     async def handle_message(self, msg: InternalMessage):
+        return 
         try:
             pause_command = PauseCommand.model_validate_json(msg.body)
             await self._req_socket.send_json(pause_command.model_dump())

src/control_backend/agents/perception/visual_emotion_recognition_agent/visual_emotion_recognition_agent.py

@@ -0,0 +1,169 @@
+import json
+import time
+from collections import Counter, defaultdict
+
+import cv2
+import numpy as np
+import zmq
+import zmq.asyncio as azmq
+from pydantic_core import ValidationError
+import struct
+
+from control_backend.agents import BaseAgent
+from control_backend.agents.perception.visual_emotion_recognition_agent.visual_emotion_recognizer import (  # noqa
+    DeepFaceEmotionRecognizer,
+)
+from control_backend.core.agent_system import InternalMessage
+from control_backend.core.config import settings
+from control_backend.schemas.belief_message import Belief
+
+
+class VisualEmotionRecognitionAgent(BaseAgent):
+    def __init__(
+        self,
+        name: str,
+        socket_address: str,
+        bind: bool = False,
+        timeout_ms: int = 1000,
+        window_duration: int = settings.behaviour_settings.visual_emotion_recognition_window_duration_s,  # noqa
+        min_frames_required: int = settings.behaviour_settings.visual_emotion_recognition_min_frames_per_face,  # noqa
+    ):
+        """
+        Initialize the Visual Emotion Recognition Agent.
+
+        :param name: Name of the agent
+        :param socket_address: Address of the socket to connect or bind to
+        :param bind: Whether to bind to the socket address (True) or connect (False)
+        :param timeout_ms: Timeout for socket receive operations in milliseconds
+        :param window_duration: Duration in seconds over which to aggregate emotions
+        :param min_frames_required: Minimum number of frames per face required to consider a face
+        valid
+        """
+        super().__init__(name)
+        self.socket_address = socket_address
+        self.socket_bind = bind
+        self.timeout_ms = timeout_ms
+        self.window_duration = window_duration
+        self.min_frames_required = min_frames_required
+
+    async def setup(self):
+        """
+        Initialize the agent resources.
+        1. Initializes the :class:`VisualEmotionRecognizer`.
+        2. Connects to the video input ZMQ socket.
+        3. Starts the background emotion recognition loop.
+        """
+        self.logger.info("Setting up %s.", self.name)
+
+        self.emotion_recognizer = DeepFaceEmotionRecognizer()
+
+        self.video_in_socket = azmq.Context.instance().socket(zmq.SUB)
+
+        if self.socket_bind:
+            self.video_in_socket.bind(self.socket_address)
+        else:
+            self.video_in_socket.connect(self.socket_address)
+
+        self.video_in_socket.setsockopt_string(zmq.SUBSCRIBE, "")
+        self.video_in_socket.setsockopt(zmq.RCVTIMEO, self.timeout_ms)
+        self.video_in_socket.setsockopt(zmq.CONFLATE, 1)
+
+        self.add_behavior(self.emotion_update_loop())
+
+    async def emotion_update_loop(self):
+        """
+        Background loop to receive video frames, recognize emotions, and update beliefs.
+        1. Receives video frames from the ZMQ socket.
+        2. Uses the :class:`VisualEmotionRecognizer` to detect emotions.
+        3. Aggregates emotions over a time window.
+        4. Sends updates to the BDI Core Agent about detected emotions.
+        """
+        # Next time to process the window and update emotions
+        next_window_time = time.time() + self.window_duration
+
+        # Tracks counts of detected emotions per face index
+        face_stats = defaultdict(Counter)
+
+        prev_dominant_emotions = set()
+
+        while self._running:
+            try:
+                frame_bytes = await self.video_in_socket.recv()
+    
+                # Convert bytes to a numpy buffer
+                nparr = np.frombuffer(frame_bytes, np.uint8)
+
+                # Decode image into the generic Numpy Array DeepFace expects
+                frame_image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+                if frame_image is None:
+                    # Could not decode image, skip this frame
+                    self.logger.warning("Received invalid video frame, skipping.")
+                    continue
+
+                # Get the dominant emotion from each face
+                current_emotions = self.emotion_recognizer.sorted_dominant_emotions(frame_image)
+                # Update emotion counts for each detected face
+                for i, emotion in enumerate(current_emotions):
+                    face_stats[i][emotion] += 1
+
+                # If window duration has passed, process the collected stats
+                if time.time() >= next_window_time:
+                    window_dominant_emotions = set()
+                    # Determine dominant emotion for each face in the window
+                    for _, counter in face_stats.items():
+                        total_detections = sum(counter.values())
+
+                        if total_detections >= self.min_frames_required:
+                            dominant_emotion = counter.most_common(1)[0][0]
+                            window_dominant_emotions.add(dominant_emotion)
+
+                    await self.update_emotions(prev_dominant_emotions, window_dominant_emotions)
+                    prev_dominant_emotions = window_dominant_emotions
+                    face_stats.clear()
+                    next_window_time = time.time() + self.window_duration
+
+            except zmq.Again:
+                self.logger.warning("No video frame received within timeout.")
+
+    
+    async def update_emotions(self, prev_emotions: set[str], emotions: set[str]):
+        """
+        Compare emotions from previous window and current emotions,
+        send updates to BDI Core Agent.
+        """
+        emotions_to_remove = prev_emotions - emotions
+        emotions_to_add = emotions - prev_emotions
+
+        if not emotions_to_add and not emotions_to_remove:
+            return
+
+        emotion_beliefs_remove = []
+        for emotion in emotions_to_remove:
+            self.logger.info(f"Emotion '{emotion}' has disappeared.")
+            try:
+                emotion_beliefs_remove.append(
+                    Belief(name="emotion_detected", arguments=[emotion], remove=True)
+                )
+            except ValidationError:
+                self.logger.warning("Invalid belief for emotion removal: %s", emotion)
+
+        emotion_beliefs_add = []
+        for emotion in emotions_to_add:
+            self.logger.info(f"New emotion detected: '{emotion}'")
+            try:
+                emotion_beliefs_add.append(Belief(name="emotion_detected", arguments=[emotion]))
+            except ValidationError:
+                self.logger.warning("Invalid belief for new emotion: %s", emotion)
+
+        beliefs_list_add = [b.model_dump() for b in emotion_beliefs_add]
+        beliefs_list_remove = [b.model_dump() for b in emotion_beliefs_remove]
+        payload = {"create": beliefs_list_add, "delete": beliefs_list_remove}
+
+        message = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            sender=self.name,
+            body=json.dumps(payload),
+            thread="beliefs",
+        )
+        await self.send(message)

src/control_backend/agents/perception/visual_emotion_recognition_agent/visual_emotion_recognizer.py

@@ -0,0 +1,55 @@
+import abc
+
+import numpy as np
+from deepface import DeepFace
+
+
+class VisualEmotionRecognizer(abc.ABC):
+    @abc.abstractmethod
+    def load_model(self):
+        """Load the visual emotion recognition model into memory."""
+        pass
+
+    @abc.abstractmethod
+    def sorted_dominant_emotions(self, image) -> list[str]:
+        """
+        Recognize dominant emotions from faces in the given image.
+        Emotions can be one of ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'].
+        To minimize false positives, consider filtering faces with low confidence.
+
+        :param image: The input image for emotion recognition.
+        :return: List of dominant emotion detected for each face in the image, 
+        sorted per face.
+        """
+        pass
+
+class DeepFaceEmotionRecognizer(VisualEmotionRecognizer):
+    """
+    DeepFace-based implementation of VisualEmotionRecognizer.
+    DeepFape has proven to be quite a pessimistic model, so expect sad, fear and neutral 
+    emotions to be over-represented.
+    """
+    def __init__(self):
+        self.load_model()
+    
+    def load_model(self):
+        print("Loading Deepface Emotion Model...")
+        dummy_img = np.zeros((224, 224, 3), dtype=np.uint8)
+        # analyze does not take a model as an argument, calling it once on a dummy image to load 
+        # the model
+        DeepFace.analyze(dummy_img, actions=['emotion'], enforce_detection=False)
+        print("Deepface Emotion Model loaded.")
+    
+    def sorted_dominant_emotions(self, image) -> list[str]:
+        analysis = DeepFace.analyze(image, 
+                                    actions=['emotion'], 
+                                    enforce_detection=False
+                                    )
+        
+        # Sort faces by x coordinate to maintain left-to-right order
+        analysis.sort(key=lambda face: face['region']['x'])
+
+        analysis = [face for face in analysis if face['face_confidence'] >= 0.90]
+        
+        dominant_emotions = [face['dominant_emotion'] for face in analysis]
+        return dominant_emotions

src/control_backend/core/config.py

@@ -50,6 +50,7 @@ class AgentSettings(BaseModel):
     # agent names
     bdi_core_name: str = "bdi_core_agent"
     bdi_program_manager_name: str = "bdi_program_manager_agent"
+    visual_emotion_recognition_name: str = "visual_emotion_recognition_agent"
     text_belief_extractor_name: str = "text_belief_extractor_agent"
     vad_name: str = "vad_agent"
     llm_name: str = "llm_agent"
@@ -77,6 +78,10 @@ class BehaviourSettings(BaseModel):
     :ivar transcription_words_per_token: Estimated words per token for transcription timing.
     :ivar transcription_token_buffer: Buffer for transcription tokens.
     :ivar conversation_history_length_limit: The maximum amount of messages to extract beliefs from.
+    :ivar visual_emotion_recognition_window_duration_s: Duration in seconds over which to aggregate 
+    emotions and update emotion beliefs.
+    :ivar visual_emotion_recognition_min_frames_per_face: Minimum number of frames per face required
+    to consider a face valid.
     """
 
     # ATTENTION: When adding/removing settings, make sure to update the .env.example file
@@ -100,6 +105,9 @@ class BehaviourSettings(BaseModel):
     # Text belief extractor settings
     conversation_history_length_limit: int = 10
 
+    # Visual Emotion Recognition settings
+    visual_emotion_recognition_window_duration_s: int = 5
+    visual_emotion_recognition_min_frames_per_face: int = 3
 
 class LLMSettings(BaseModel):
     """

src/control_backend/schemas/program.py

@@ -28,8 +28,8 @@ class LogicalOperator(Enum):
     OR = "OR"
 
 
-type Belief = KeywordBelief | SemanticBelief | InferredBelief
+type Belief = KeywordBelief | SemanticBelief | InferredBelief | EmotionBelief
-type BasicBelief = KeywordBelief | SemanticBelief
+type BasicBelief = KeywordBelief | SemanticBelief | EmotionBelief
 
 
 class KeywordBelief(ProgramElement):
@@ -69,6 +69,15 @@ class InferredBelief(ProgramElement):
     left: Belief
     right: Belief
 
+class EmotionBelief(ProgramElement):
+    """
+    Represents a belief that is set when a certain emotion is detected.
+
+    :ivar emotion: The emotion on which this belief gets set.
+    """
+
+    name: str = ""
+    emotion: str
 
 class Norm(ProgramElement):
     """
@@ -226,3 +235,9 @@ class Program(BaseModel):
     """
 
     phases: list[Phase]
+
+
+if __name__ == "__main__":
+    input = input("Enter program JSON: ")
+    program = Program.model_validate_json(input)
+    print(program)