- added implementation to create 50sec video files out of camera lifestream for every 5 seconds

- added action unit recognition to the camera stream

dataset_creation/AU_creation/AU_creation_service.py

@@ -3,11 +3,11 @@ from feat.utils.io import get_test_data_path
 from moviepy.video.io.VideoFileClip import VideoFileClip
 import os
 
-def extract_aus(path, model):
+def extract_aus(path, model, skip_frames):
   detector = Detector(au_model=model)
 
   video_prediction = detector.detect(
-      path, data_type="video", skip_frames=24*5, face_detection_threshold=0.95 # alle 5 Sekunden einbeziehen - 24 Frames pro Sekunde
+      path, data_type="video", skip_frames=skip_frames, face_detection_threshold=0.95 # alle 5 Sekunden einbeziehen - 24 Frames pro Sekunde
   )
 
   return video_prediction.aus.sum()
@@ -38,13 +38,13 @@ def split_video(path, chunk_length=120):
 
     return output_path
 
-def start(path):
-    results = []
-    clips = split_video(path)
+# def start(path):
+#     results = []
+#     clips = split_video(path)
 
-    for clip in clips:
-      results.append(extract_aus(clip, 'svm'))
-    return results
+#     for clip in clips:
+#       results.append(extract_aus(clip, 'svm', 25*5))
+#     return results
 
 if __name__ == "__main__":
   results = []
@@ -53,6 +53,6 @@ if __name__ == "__main__":
   clips = split_video(test_video_path)
 
   for clippath in clips:
-    results.append(extract_aus(clippath, 'svm'))
+    results.append(extract_aus(clippath, 'svm', 25*5))
 
   print(results)

dataset_creation/camera_handling/camera_stream.py

@@ -0,0 +1,132 @@
+import cv2
+import time
+import os
+import threading
+from datetime import datetime
+from feat import Detector
+import torch
+
+# Konfiguration
+CAMERA_INDEX = 0
+OUTPUT_DIR = "recordings"
+VIDEO_DURATION = 50  # Sekunden
+START_INTERVAL = 5   # Sekunden bis zum nächsten Start
+FPS = 25.0           # Feste FPS
+
+if not os.path.exists(OUTPUT_DIR):
+    os.makedirs(OUTPUT_DIR)
+
+# Globaler Detector, um ihn nicht bei jedem Video neu laden zu müssen (spart massiv Zeit/Speicher)
+print("Initialisiere AU-Detector (bitte warten)...")
+detector = Detector(au_model="xgb") 
+
+def extract_aus(path, skip_frames):
+
+    # torch.no_grad() deaktiviert die Gradientenberechnung.
+    # Das löst den "Can't call numpy() on Tensor that requires grad" Fehler.
+    with torch.no_grad():
+        video_prediction = detector.detect_video(
+            path, 
+            skip_frames=skip_frames, 
+            face_detection_threshold=0.95
+        )
+        
+        # Falls video_prediction oder .aus noch Tensoren sind, 
+        # stellen wir sicher, dass sie korrekt summiert werden.
+        try:
+            # Wir nehmen die Summe der Action Units über alle detektierten Frames
+            res = video_prediction.aus.sum()
+            return res
+        except Exception as e:
+            print(f"Fehler bei der Summenbildung: {e}")
+            return 0
+
+def startAU_creation(video_path):
+    """Diese Funktion läuft nun in einem eigenen Thread."""
+    try:
+        print(f"\n[THREAD START] Analyse läuft für: {video_path}")
+        # skip_frames berechnen (z.B. alle 5 Sekunden bei 25 FPS = 125)
+        output = extract_aus(video_path, skip_frames=int(FPS*5))
+        
+        print(f"\n--- Ergebnis für {os.path.basename(video_path)} ---")
+        print(output)
+        print("--------------------------------------------------\n")
+    except Exception as e:
+        print(f"Fehler bei der Analyse von {video_path}: {e}")
+
+class VideoRecorder:
+    def __init__(self, filename, width, height):
+        self.filename = filename
+        fourcc = cv2.VideoWriter_fourcc(*'XVID')
+        self.out = cv2.VideoWriter(filename, fourcc, FPS, (width, height))
+        self.frames_to_record = int(VIDEO_DURATION * FPS)
+        self.frames_count = 0
+        self.is_finished = False
+
+    def write_frame(self, frame):
+        if self.frames_count < self.frames_to_record:
+            self.out.write(frame)
+            self.frames_count += 1
+        else:
+            self.finish()
+
+    def finish(self):
+        if not self.is_finished:
+            self.out.release()
+            self.is_finished = True
+            abs_path = os.path.abspath(self.filename)
+            print(f"Video fertig gespeichert: {self.filename}")
+
+            # --- MULTITHREADING HIER ---
+            # Wir starten die Analyse in einem neuen Thread, damit main() sofort weiter frames lesen kann
+            analysis_thread = threading.Thread(target=startAU_creation, args=(abs_path,))
+            analysis_thread.daemon = True  # Beendet sich, wenn das Hauptprogramm schließt
+            analysis_thread.start()
+
+def main():
+    cap = cv2.VideoCapture(CAMERA_INDEX)
+    if not cap.isOpened():
+        print("Fehler: Kamera konnte nicht geöffnet werden.")
+        return
+
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    
+    active_recorders = []
+    last_start_time = 0
+    
+    print("Aufnahme läuft. Drücke 'q' zum Beenden.")
+
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            current_time = time.time()
+
+            if current_time - last_start_time >= START_INTERVAL:
+                timestamp = datetime.now().strftime("%H%M%S")
+                filename = os.path.join(OUTPUT_DIR, f"rec_{timestamp}.avi")
+                new_recorder = VideoRecorder(filename, width, height)
+                active_recorders.append(new_recorder)
+                last_start_time = current_time
+
+            for rec in active_recorders[:]:
+                rec.write_frame(frame)
+                if rec.is_finished:
+                    active_recorders.remove(rec)
+
+            cv2.imshow('Kamera Livestream', frame)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+            time.sleep(1/FPS)
+
+    finally:
+        cap.release()
+        cv2.destroyAllWindows()
+        print("Programm beendet. Warte ggf. auf laufende Analysen...")
+
+if __name__ == "__main__":
+    main()