feat: (hopefully) face detection

Simplified implementation, relying on the already-present VED Agent.

ref: N25B-395

src/control_backend/agents/bdi/agentspeak_generator.py

@@ -30,6 +30,7 @@ from control_backend.schemas.program import (
     BasicNorm,
     ConditionalNorm,
     EmotionBelief,
+    FaceBelief,
     GestureAction,
     Goal,
     InferredBelief,
@@ -687,6 +688,10 @@ class AgentSpeakGenerator:
     def _(self, eb: EmotionBelief) -> AstExpression:
         return AstLiteral("emotion_detected", [AstAtom(eb.emotion)])
 
+    @_astify.register
+    def _(self, fb: FaceBelief) -> AstExpression:
+        return AstLiteral("face_present")
+
     @_astify.register
     def _(self, ib: InferredBelief) -> AstExpression:
         """

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

@@ -14,7 +14,7 @@ from control_backend.agents.perception.visual_emotion_recognition_agent.visual_e
 )
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-from control_backend.schemas.belief_message import Belief
+from control_backend.schemas.belief_message import Belief, BeliefMessage
 
 
 class VisualEmotionRecognitionAgent(BaseAgent):
@@ -44,6 +44,7 @@ class VisualEmotionRecognitionAgent(BaseAgent):
         self.timeout_ms = timeout_ms
         self.window_duration = window_duration
         self.min_frames_required = min_frames_required
+        self._face_detected = False
 
         # Pause functionality
         # NOTE: flag is set when running, cleared when paused
@@ -97,8 +98,8 @@ class VisualEmotionRecognitionAgent(BaseAgent):
 
                 width, height, image_bytes = await self.video_in_socket.recv_multipart()
 
-                width = int.from_bytes(width, 'little')
+                width = int.from_bytes(width, "little")
-                height = int.from_bytes(height, 'little')
+                height = int.from_bytes(height, "little")
 
                 # Convert bytes to a numpy buffer
                 image_array = np.frombuffer(image_bytes, np.uint8)
@@ -107,6 +108,15 @@ class VisualEmotionRecognitionAgent(BaseAgent):
 
                 # Get the dominant emotion from each face
                 current_emotions = self.emotion_recognizer.sorted_dominant_emotions(frame)
+
+                # Form (or unform) face_detected belief
+                if len(current_emotions) == 0 and self._face_detected:
+                    self._face_detected = False
+                    await self._inform_face_detected()
+                elif len(current_emotions) > 0 and not self._face_detected:
+                    self._face_detected = True
+                    await self._inform_face_detected()
+
                 # Update emotion counts for each detected face
                 for i, emotion in enumerate(current_emotions):
                     face_stats[i][emotion] += 1
@@ -133,7 +143,6 @@ class VisualEmotionRecognitionAgent(BaseAgent):
             except Exception as e:
                 self.logger.error(f"Error in emotion recognition loop: {e}")
 
-    
     async def update_emotions(self, prev_emotions: set[str], emotions: set[str]):
         """
         Compare emotions from previous window and current emotions,
@@ -149,9 +158,7 @@ class VisualEmotionRecognitionAgent(BaseAgent):
         for emotion in emotions_to_remove:
             self.logger.info(f"Emotion '{emotion}' has disappeared.")
             try:
-                emotion_beliefs_remove.append(
+                emotion_beliefs_remove.append(Belief(name="emotion_detected", arguments=[emotion]))
-                    Belief(name="emotion_detected", arguments=[emotion], remove=True)
-                )
             except ValidationError:
                 self.logger.warning("Invalid belief for emotion removal: %s", emotion)
 
@@ -175,6 +182,20 @@ class VisualEmotionRecognitionAgent(BaseAgent):
         )
         await self.send(message)
 
+    async def _inform_face_detected(self):
+        if self._face_detected:
+            belief_message = BeliefMessage(create=[Belief(name="face_present")])
+        else:
+            belief_message = BeliefMessage(delete=[Belief(name="face_present")])
+
+        msg = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            thread="beliefs",
+            body=belief_message.model_dump_json(),
+        )
+
+        await self.send(msg)
+
     async def handle_message(self, msg: InternalMessage):
         """
         Handle incoming messages.
@@ -204,4 +225,3 @@ class VisualEmotionRecognitionAgent(BaseAgent):
         """
         self.video_in_socket.close()
         await super().stop()
-        

src/control_backend/schemas/program.py

@@ -41,8 +41,8 @@ class LogicalOperator(Enum):
     OR = "OR"
 
 
-type Belief = KeywordBelief | SemanticBelief | InferredBelief | EmotionBelief
+type Belief = KeywordBelief | SemanticBelief | InferredBelief | EmotionBelief | FaceBelief
-type BasicBelief = KeywordBelief | SemanticBelief | EmotionBelief
+type BasicBelief = KeywordBelief | SemanticBelief | EmotionBelief | FaceBelief
 
 
 class KeywordBelief(ProgramElement):
@@ -105,6 +105,7 @@ class InferredBelief(ProgramElement):
     left: Belief
     right: Belief
 
+
 class EmotionBelief(ProgramElement):
     """
     Represents a belief that is set when a certain emotion is detected.
@@ -115,6 +116,16 @@ class EmotionBelief(ProgramElement):
     name: str = ""
     emotion: str
 
+
+class FaceBelief(ProgramElement):
+    """
+    Represents the belief that at least one face is currently in view.
+    """
+
+    name: str = ""
+    face_present: bool
+
+
 class Norm(ProgramElement):
     """
     Base class for behavioral norms that guide the robot's interactions.