Implemented TTS

main.py

-#model.h5 is best model
 import cv2
 import warnings
 import pyttsx3
 warnings.filterwarnings("ignore")
 import time
 from deepface import DeepFace
+import threading
 
-# Intiliazing text to speech
+# Initialisiere Text-to-Speech
 engine = pyttsx3.init('sapi5')
-# Defining camera and starting video capture
+# Definiere Kamera und starte Videoaufnahme
 video_capture = cv2.VideoCapture(0)
-# Defining which cascade opencv should use
+# Definiere, welche Cascade OpenCV verwenden soll
 face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
 
-
 class Main:
-    # Welcome Message
-    def welcome_message(self):
-        # Intiliazing text to speech
+    def __init__(self):
+        self.speaking_thread = None  # Verfolgt den aktuellen Sprech-Thread
+        self.last_speak_time = 0  # Speichert den Zeitpunkt der letzten Sprachausgabe
 
-
-        engine.say('Welcome to the face recognition and prediction tool of the herman hollerith center')
+    def speak(self, text):
+        engine.say(text)
         engine.runAndWait()
 
-    def face_recognition(self):
+    def speak_in_thread(self, text):
+        current_time = time.time()
+        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.start()
 
-        # Intiliazing text to speech
-        engine = pyttsx3.init('sapi5')
-        last_analysis_time = time.time()  # Initialisiere die letzte Analysezeit
-        analysis_interval = 4
+    # 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):
+        last_analysis_time = time.time()
+        analysis_interval = 8
         analyze = None
 
         while True:
@@ -40,36 +47,33 @@ class Main:
 
             if current_time - last_analysis_time > analysis_interval:
                 try:
-                    # Annahme: DeepFace.analyze gibt eine Liste zurück. Zugriff auf das erste Element.
                     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
                 except Exception as e:
                     print("Error in DeepFace Analysis:", e)
-                    analyze = None  # Bei Fehlern wird `analyze` zurückgesetzt
+                    analyze = None
+
+            if len(faces) > 0 and analyze:
+                text_to_speak = f'Your age is {analyze.get("age", "N/A")}, your gender is {analyze.get("dominant_gender", "N/A")} and your current emotional state is {analyze.get("dominant_emotion", "N/A")}.'
+                self.speak_in_thread(text_to_speak)
 
             for (x, y, w, h) in faces:
                 cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 1)
                 if analyze:
-                    # Zugriff auf die Analyseergebnisse, wenn `analyze` nicht None ist
                     cv2.putText(frame, f'Approx. Age: {analyze.get("age", "N/A")}', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
                     cv2.putText(frame, f'Approx. Gender: {analyze.get("dominant_gender", "N/A")}', (x, y - 40), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 255), 2)
                     cv2.putText(frame, f'Current emotion: {analyze.get("dominant_emotion", "N/A")}', (x, y - 70), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 0), 2)
 
             cv2.imshow("video_capture", frame)
+
             if cv2.waitKey(1) & 0xFF == ord('q'):
                 break
 
         video_capture.release()
         cv2.destroyAllWindows()
 
-
 if __name__ == '__main__':
     main = Main()
-
-    #main.welcome_message()
+    main.welcome_message()
     main.face_recognition()
-
-
-
-