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feat: visual emotion recognition agent

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This commit is contained in:
Luijkx,S.O.H. (Storm)
2026-01-30 16:53:15 +00:00
committed by Kasper Marinus
parent 68f445c8bc
commit 45b8597f15
12 changed files with 1533 additions and 112 deletions
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5
src/control_backend/agents/bdi/agentspeak_generator.py
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@@ -29,6 +29,7 @@ from control_backend.schemas.program import (
BaseGoal,
BasicNorm,
ConditionalNorm,
EmotionBelief,
GestureAction,
Goal,
InferredBelief,
@@ -681,6 +682,10 @@ class AgentSpeakGenerator:
:return: An AstLiteral representing the semantic belief.
"""
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:

27
src/control_backend/agents/communication/ri_communication_agent.py
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@@ -9,14 +9,14 @@ import json
import zmq
import zmq.asyncio as azmq
from pydantic import ValidationError
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
from ..actuation.robot_speech_agent import RobotSpeechAgent
from ..perception import VADAgent
@@ -58,6 +58,7 @@ 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):
"""
@@ -215,6 +216,14 @@ 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)
@@ -319,6 +328,9 @@ 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()
@@ -326,11 +338,4 @@ class RICommunicationAgent(BaseAgent):
self.logger.debug("Restarting communication negotiation.")
if await self._negotiate_connection(max_retries=2):
self.connected = True
async def handle_message(self, msg: InternalMessage):
try:
pause_command = PauseCommand.model_validate_json(msg.body)
await self._req_socket.send_json(pause_command.model_dump())
self.logger.debug(await self._req_socket.recv_json())
except ValidationError:
self.logger.warning("Incorrect message format for PauseCommand.")

207
src/control_backend/agents/perception/visual_emotion_recognition_agent/visual_emotion_recognition_agent.py Normal file
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@@ -0,0 +1,207 @@
import asyncio
import json
import time
from collections import Counter, defaultdict
import numpy as np
import zmq
import zmq.asyncio as azmq
from pydantic_core import ValidationError
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
# Pause functionality
# NOTE: flag is set when running, cleared when paused
self._paused = asyncio.Event()
self._paused.set()
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)
self.video_in_socket.setsockopt(zmq.RCVHWM, 3)
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.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:
await self._paused.wait()
width, height, image_bytes = await self.video_in_socket.recv_multipart()
width = int.from_bytes(width, 'little')
height = int.from_bytes(height, 'little')
# Convert bytes to a numpy buffer
image_array = np.frombuffer(image_bytes, np.uint8)
frame = image_array.reshape((height, width, 3))
# Get the dominant emotion from each face
current_emotions = self.emotion_recognizer.sorted_dominant_emotions(frame)
# 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.")
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,
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)
async def handle_message(self, msg: InternalMessage):
"""
Handle incoming messages.
Expects messages to pause or resume the Visual Emotion Recognition
processing from User Interrupt Agent.
:param msg: The received internal message.
"""
sender = msg.sender
if sender == settings.agent_settings.user_interrupt_name:
if msg.body == "PAUSE":
self.logger.info("Pausing Visual Emotion Recognition processing.")
self._paused.clear()
elif msg.body == "RESUME":
self.logger.info("Resuming Visual Emotion Recognition processing.")
self._paused.set()
else:
self.logger.warning(f"Unknown command from User Interrupt Agent: {msg.body}")
else:
self.logger.debug(f"Ignoring message from unknown sender: {sender}")
async def stop(self):
"""
Clean up resources used by the agent.
"""
self.video_in_socket.close()
await super().stop()

53
src/control_backend/agents/perception/visual_emotion_recognition_agent/visual_emotion_recognizer.py Normal file
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@@ -0,0 +1,53 @@
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):
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)
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

30
src/control_backend/agents/user_interrupt/user_interrupt_agent.py
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@@ -18,7 +18,6 @@ from control_backend.schemas.belief_message import Belief, BeliefMessage
from control_backend.schemas.program import ConditionalNorm, Goal, Program
from control_backend.schemas.ri_message import (
GestureCommand,
PauseCommand,
RIEndpoint,
SpeechCommand,
)
@@ -398,34 +397,29 @@ class UserInterruptAgent(BaseAgent):
self.logger.debug("Sending experiment control '%s' to BDI Core.", thread)
await self.send(out_msg)
async def _send_pause_command(self, pause):
async def _send_pause_command(self, pause: str):
"""
Send a pause command to the Robot Interface via the RI Communication Agent.
Send a pause command to the other internal agents; for now just VAD agent.
Send a pause command to the other internal agents; for now just VAD and VED agent.
"""
cmd = PauseCommand(data=pause)
message = InternalMessage(
to=settings.agent_settings.ri_communication_name,
sender=self.name,
body=cmd.model_dump_json(),
)
await self.send(message)
if pause == "true":
# Send pause to VAD agent
# Send pause to VAD and VED agent
vad_message = InternalMessage(
to=settings.agent_settings.vad_name,
to=[settings.agent_settings.vad_name,
settings.agent_settings.visual_emotion_recognition_name],
sender=self.name,
body="PAUSE",
)
await self.send(vad_message)
self.logger.info("Sent pause command to VAD Agent and RI Communication Agent.")
# Voice Activity Detection and Visual Emotion Recognition agents
self.logger.info("Sent pause command to VAD and VED agents.")
else:
# Send resume to VAD agent
# Send resume to VAD and VED agents
vad_message = InternalMessage(
to=settings.agent_settings.vad_name,
to=[settings.agent_settings.vad_name,
settings.agent_settings.visual_emotion_recognition_name],
sender=self.name,
body="RESUME",
)
await self.send(vad_message)
self.logger.info("Sent resume command to VAD Agent and RI Communication Agent.")
# Voice Activity Detection and Visual Emotion Recognition agents
self.logger.info("Sent resume command to VAD and VED agents.")

8
src/control_backend/core/config.py
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@@ -54,6 +54,7 @@ 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"
@@ -81,6 +82,10 @@ 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.
:ivar trigger_time_to_wait: Amount of milliseconds to wait before informing the UI about trigger
completion.
"""
@@ -106,6 +111,9 @@ 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
# AgentSpeak related settings
trigger_time_to_wait: int = 2000
agentspeak_file: str = "src/control_backend/agents/bdi/agentspeak.asl"

19
src/control_backend/schemas/program.py
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@@ -41,8 +41,8 @@ class LogicalOperator(Enum):
OR = "OR"
type Belief = KeywordBelief | SemanticBelief | InferredBelief
type BasicBelief = KeywordBelief | SemanticBelief
type Belief = KeywordBelief | SemanticBelief | InferredBelief | EmotionBelief
type BasicBelief = KeywordBelief | SemanticBelief | EmotionBelief
class KeywordBelief(ProgramElement):
@@ -105,6 +105,15 @@ 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):
"""
@@ -315,3 +324,9 @@ class Program(BaseModel):
"""
phases: list[Phase]
if __name__ == "__main__":
input = input("Enter program JSON: ")
program = Program.model_validate_json(input)
print(program)