I have threaded the analysis and integrated a mqtt messaging for future usage.

Overall improved the performance but still some parts are buggy. We have to put more time into fixing it.

.gitignore

+/.venv/
+/.idea/
\ No newline at end of file

.idea/Mood Meter.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv/python_stuff" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.8" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.9 (mood-meter)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>
\ No newline at end of file

.idea/misc.xml

 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
+  <component name="Black">
+    <option name="sdkName" value="Python 3.9 (mood-meter)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (mood-meter)" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file

Mosquitto_Authorized/Dockerfile

+FROM eclipse-mosquitto
+
+COPY mosquitto.conf ./mosquitto/config/
+COPY mosquitto.passwd ./mosquitto/config/mosquitto.passwd

Mosquitto_Authorized/docker-compose.yaml

+version: "3.7"
+services:
+  mosquitto:    
+    build: ./
+    image: uwebreitenbuecher/mosquitto
+    ports:
+      - "1883:1883" 
+      - "9001:9001"     
\ No newline at end of file

Mosquitto_Authorized/mosquitto.conf

+allow_anonymous false
+listener 1883
+listener 9001
+protocol websockets
+persistence false
+persistence_file mosquitto.db
+persistence_location /mosquitto/data/
+log_dest stdout
+password_file /mosquitto/config/mosquitto.passwd
\ No newline at end of file

Mosquitto_Authorized/mosquitto.passwd

+standardUser:$7$101$qj8csF6pkG5K5UgI$5L3+LNq5NAzXf5Cb9GXLDuhU1agDFnSYFKc+hBUy0bV9wZeo4QND7YqTaK0m92pDnj2tHjA4BztW4CHHf5q/uQ==

main.py

@@ -5,6 +5,8 @@ warnings.filterwarnings("ignore")
 import time
 from deepface import DeepFace
 import threading
+import paho.mqtt.client as mqtt
+import json
 
 # Initialisiere Text-to-Speech
 engine = pyttsx3.init('sapi5')
@@ -14,9 +16,42 @@ video_capture = cv2.VideoCapture(0)
 face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
 
 class Main:
+    currentclient = None
+    unacked_publish = set()
+    def on_connect(self,client, userdata, flags, reason_code, properties):
+        print(f"Connected with result code {reason_code}\n")
+        client.subscribe("Topic1")
+    def on_publish(self,client, userdata, mid, reason_code, properties):
+        try:
+            userdata.remove(mid)
+        except KeyError:
+           "Something went wrong. Please check if you have removed the mid from the userdata"
+    def connectwithmqtt(self, adress:str, targetport:int):
+        mqttc = mqtt.Client(mqtt.CallbackAPIVersion.VERSION2, userdata=None)
+        mqttc.on_connect = self.on_connect
+        mqttc.on_publish = self.on_publish
+        mqttc.user_data_set(self.unacked_publish)
+        mqttc.username_pw_set(username="standardUser", password="GreatHHZ4Ever!")
+        mqttc.connect(host=adress, port=targetport)
+        while not mqttc.is_connected():
+            mqttc.loop()
+            print("Waiting for connection...")
+        return mqttc
+    def sendMessage(self, mqttc, topic, message):
+        mqttc.loop_start()
+        msg = mqttc.publish(topic, message, qos=1)
+        self.unacked_publish.add(msg.mid)
+        while len(self.unacked_publish):
+            time.sleep(0.1)
+        msg.wait_for_publish()
     def __init__(self):
+        self.analysis_results = None
+        self.analysis_thread = None
+        self.faceframe = None
+        self.last_analysis_time = 0
         self.speaking_thread = None  # Verfolgt den aktuellen Sprech-Thread
         self.last_speak_time = 0  # Speichert den Zeitpunkt der letzten Sprachausgabe
+        self.main_time = 0
 
     def speak(self, text):
         engine.say(text)
@@ -27,29 +62,40 @@ class Main:
         if (self.speaking_thread is None or not self.speaking_thread.is_alive()) and (current_time - self.last_speak_time > 8):
             self.last_speak_time = current_time  # Aktualisiere den Zeitpunkt der letzten Sprachausgabe
             self.speaking_thread = threading.Thread(target=self.speak, args=(text,))
+            self.speaking_thread.daemon = True
             self.speaking_thread.start()
+    def facial_analysis_thread(self):
+        current_time = time.time()
+        if (self.analysis_thread is None or not self.analysis_thread.is_alive()) and (current_time - self.last_analysis_time > 8):
+            self.last_analysis_time = current_time
+            self.analysis_thread = threading.Thread(target=self.facial_analysis)
+            self.analysis_thread.daemon = True
+            self.analysis_thread.start()
+    def facial_analysis(self):
+        self.analysis_results = DeepFace.analyze(self.faceframe, actions=["emotion", "age", "gender"], enforce_detection=False)
+
 
     # Welcome Message
     def welcome_message(self):
         self.speak_in_thread('Welcome to the face recognition and prediction tool of the herman hollerith center')
 
-    def face_recognition(self):
+    def face_recognition(self, mqttc):
         last_analysis_time = time.time()
-        analysis_interval = 8
-        analyze = None
-
         while True:
             ret, frame = video_capture.read()
+            self.faceframe = frame
             gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
             faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
-
             current_time = time.time()
-
-            if current_time - last_analysis_time > analysis_interval:
             try:
-                    analysis_results = DeepFace.analyze(frame, actions=["emotion", "age", "gender"], enforce_detection=False)
-                    analyze = analysis_results[0] if analysis_results else None
-                    last_analysis_time = current_time
+                if current_time - self.main_time > 10:
+                    self.main_time = current_time
+                    self.facial_analysis_thread()
+                    analyze = self.analysis_results[0] if self.analysis_results else None
+                    analysis_results_dict = {"age": analyze.get("age", "N/A"), "gender": analyze.get("dominant_gender", "N/A"), "emotion": analyze.get("dominant_emotion", "N/A")}
+                    self.sendMessage(mqttc=mqttc, topic="Topic1", message=json.dumps(analysis_results_dict))
+
+
             except Exception as e:
                 print("Error in DeepFace Analysis:", e)
                 analyze = None
@@ -68,6 +114,9 @@ class Main:
             cv2.imshow("video_capture", frame)
 
             if cv2.waitKey(1) & 0xFF == ord('q'):
+                print("Goodbye!")
+                mqttc.disconnect()
+                mqttc.loop_stop()
                 break
 
         video_capture.release()
@@ -76,4 +125,5 @@ class Main:
 if __name__ == '__main__':
     main = Main()
     main.welcome_message()
-    main.face_recognition()
+    main.currentclient =main.connectwithmqtt(adress="localhost", targetport=1883)
+    main.face_recognition(mqttc=main.currentclient)