fix: reset counter after each loop

ref: N25B-395

src/control_backend/agents/bdi/agentspeak_generator.py

@@ -4,7 +4,6 @@ University within the Software Project course.
 © Copyright Utrecht University (Department of Information and Computing Sciences)
 """
 
-import logging
 from functools import singledispatchmethod
 
 from slugify import slugify
@@ -31,6 +30,7 @@ from control_backend.schemas.program import (
     BasicNorm,
     ConditionalNorm,
     EmotionBelief,
+    FaceBelief,
     GestureAction,
     Goal,
     InferredBelief,
@@ -67,7 +67,6 @@ class AgentSpeakGenerator:
     """
 
     _asp: AstProgram
-    logger = logging.getLogger(__name__)
 
     def generate(self, program: Program) -> str:
         """
@@ -481,7 +480,6 @@ class AgentSpeakGenerator:
         :param main_goal: Whether this is a main goal (for UI notification purposes).
         """
         context: list[AstExpression] = [self._astify(phase)]
-        if goal.can_fail:
         context.append(~self._astify(goal, achieved=True))
         if previous_goal and previous_goal.can_fail:
             context.append(self._astify(previous_goal, achieved=True))
@@ -506,10 +504,6 @@ class AgentSpeakGenerator:
         if not goal.can_fail and not continues_response:
             body.append(AstStatement(StatementType.ADD_BELIEF, self._astify(goal, achieved=True)))
 
-        if len(body) == 0:
-            self.logger.warning("Goal with no plan detected: %s", goal.name)
-            body.append(AstStatement(StatementType.EMPTY, AstLiteral("true")))
-
         self._asp.plans.append(AstPlan(TriggerType.ADDED_GOAL, self._astify(goal), context, body))
 
         self._asp.plans.append(
@@ -570,10 +564,10 @@ class AgentSpeakGenerator:
             )
         )
         for step in trigger.plan.steps:
-            if isinstance(step, Goal):
-                new_step = step.model_copy(update={"can_fail": False})  # triggers are sequence
-                subgoals.append(new_step)
             body.append(self._step_to_statement(step))
+            if isinstance(step, Goal):
+                step.can_fail = False  # triggers are continuous sequence
+                subgoals.append(step)
 
         # Arbitrary wait for UI to display nicely
         body.append(
@@ -694,6 +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
@@ -89,6 +90,9 @@ class VisualEmotionRecognitionAgent(BaseAgent):
         # Tracks counts of detected emotions per face index
         face_stats = defaultdict(Counter)
 
+        # How many times a face has been detected
+        face_detection_yes_no = [0, 0]
+
         prev_dominant_emotions = set()
 
         while self._running:
@@ -97,8 +101,8 @@ 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 +111,13 @@ class VisualEmotionRecognitionAgent(BaseAgent):
 
                 # Get the dominant emotion from each face
                 current_emotions = self.emotion_recognizer.sorted_dominant_emotions(frame)
+
+                # Update face face_detection_yes_no
+                if len(current_emotions) > 0:
+                    face_detection_yes_no[0] += 1
+                else:
+                    face_detection_yes_no[1] += 1
+
                 # Update emotion counts for each detected face
                 for i, emotion in enumerate(current_emotions):
                     face_stats[i][emotion] += 1
@@ -122,6 +133,20 @@ class VisualEmotionRecognitionAgent(BaseAgent):
                             dominant_emotion = counter.most_common(1)[0][0]
                             window_dominant_emotions.add(dominant_emotion)
 
+                    if (
+                        face_detection_yes_no[0] > face_detection_yes_no[1]
+                        and not self._face_detected
+                    ):
+                        self._face_detected = True
+                        await self._inform_face_detected()
+                    elif (
+                        face_detection_yes_no[0] <= face_detection_yes_no[1] and self._face_detected
+                    ):
+                        self._face_detected = False
+                        await self._inform_face_detected()
+
+                    face_detection_yes_no = [0, 0]
+
                     await self.update_emotions(prev_dominant_emotions, window_dominant_emotions)
                     prev_dominant_emotions = window_dominant_emotions
                     face_stats.clear()
@@ -133,7 +158,6 @@ 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 +173,7 @@ 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 +197,20 @@ 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 +240,3 @@ class VisualEmotionRecognitionAgent(BaseAgent):
         """
         self.video_in_socket.close()
         await super().stop()
-        

src/control_backend/core/config.py

@@ -112,7 +112,7 @@ class BehaviourSettings(BaseModel):
     conversation_history_length_limit: int = 10
 
     # Visual Emotion Recognition settings
-    visual_emotion_recognition_window_duration_s: int = 5
+    visual_emotion_recognition_window_duration_s: int = 3
     visual_emotion_recognition_min_frames_per_face: int = 3
     # AgentSpeak related settings
     trigger_time_to_wait: int = 2000

src/control_backend/schemas/program.py

@@ -7,7 +7,7 @@ University within the Software Project course.
 from enum import Enum
 from typing import Literal
 
-from pydantic import UUID4, BaseModel, field_validator
+from pydantic import UUID4, BaseModel
 
 
 class ProgramElement(BaseModel):
@@ -24,13 +24,6 @@ class ProgramElement(BaseModel):
     # To make program elements hashable
     model_config = {"frozen": True}
 
-    @field_validator("name")
-    @classmethod
-    def name_must_not_start_with_number(cls, v: str) -> str:
-        if v and v[0].isdigit():
-            raise ValueError('Field "name" must not start with a number.')
-        return v
-
 
 class LogicalOperator(Enum):
     """
@@ -48,8 +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):
@@ -124,6 +117,15 @@ class EmotionBelief(ProgramElement):
     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.