Kapitel 13/Tutorial.md aktualisiert

Kapitel 13/Tutorial.md

@@ -1021,47 +1021,69 @@ frame_files = sorted([
     if f.endswith(".bmp")
 ])
 
-movement_threshold = 12000
-min_duration = 5
-candidates = []
+# 3 Sekunden pro Frame fix
+SECONDS_PER_FRAME = 3
+MIN_CLIP_DURATION = 15  # Sekunden
+PRE_POST_BUFFER = 2    # Sekunden
 
-prev_frame = None
-active_start = None
+movement_scores = []
+frames = []
 
+# Scores sammeln
 for idx, frame_path in enumerate(frame_files):
     frame = cv2.imread(frame_path, cv2.IMREAD_GRAYSCALE)
     if frame is None:
         continue
 
-    if prev_frame is not None:
-        diff = cv2.absdiff(prev_frame, frame)
-        score = int(np.sum(diff))
-        print(f"[DEBUG] Frame {idx}: Bewegungsscore = {score}")
+    if idx == 0:
+        prev_frame = frame
+        continue
 
-        t = idx * 3
-
-        if score > movement_threshold:
-            if active_start is None:
-                active_start = t
-        else:
-            if active_start is not None and (t - active_start) >= min_duration:
-                # Pre/Post-Puffer hinzufügen
-                start_time = max(0, active_start - 2)
-                end_time = min(t + 2, len(frame_files) * 3)
-                candidates.append({
-                    "start": round(start_time, 2),
-                    "end": round(end_time, 2)
-                })
-                active_start = None
+    diff = cv2.absdiff(prev_frame, frame)
+    score = int(np.sum(diff))
+    movement_scores.append(score)
+    frames.append((idx, score))
 
+    print(f"[DEBUG] Frame {idx}: Bewegungsscore = {score}")
     prev_frame = frame
 
-# Letzter offener Clip am Ende
+if not movement_scores:
+    print("[WARN] Keine Bewegungsscores ermittelt.")
+    with open(output_json, 'w') as f:
+        json.dump([], f)
+    exit(0)
+
+# Schwelle dynamisch bestimmen
+cutoff = np.quantile(movement_scores, 0.99)
+print(f"[INFO] Dynamische Bewegungsschwelle (99%): {int(cutoff)}")
+print(f"[INFO] Min: {min(movement_scores)}, Max: {max(movement_scores)}, Median: {int(np.median(movement_scores))}")
+
+# Kandidaten suchen
+candidates = []
+active_start = None
+
+for idx, score in frames:
+    t = idx * SECONDS_PER_FRAME
+
+    if score > cutoff:
+        if active_start is None:
+            active_start = t
+    else:
+        if active_start is not None and (t - active_start) >= MIN_CLIP_DURATION:
+            start_time = max(0, active_start - PRE_POST_BUFFER)
+            end_time = min(t + PRE_POST_BUFFER, len(frame_files) * SECONDS_PER_FRAME)
+            candidates.append({
+                "start": round(start_time, 2),
+                "end": round(end_time, 2)
+            })
+            active_start = None
+
+# Offener Clip am Ende
 if active_start is not None:
-    t = len(frame_files) * 3
-    if t - active_start >= min_duration:
-        start_time = max(0, active_start - 2)
-        end_time = min(t + 2, len(frame_files) * 3)
+    t = len(frame_files) * SECONDS_PER_FRAME
+    if t - active_start >= MIN_CLIP_DURATION:
+        start_time = max(0, active_start - PRE_POST_BUFFER)
+        end_time = min(t + PRE_POST_BUFFER, len(frame_files) * SECONDS_PER_FRAME)
         candidates.append({
             "start": round(start_time, 2),
             "end": round(end_time, 2)
@@ -1073,7 +1095,6 @@ with open(output_json, "w") as f:
 print(f"[DONE] Bewegungserkennung abgeschlossen → {len(candidates)} Clip(s) geschrieben in {output_json}")
 EOF
 
-
 echo "[INFO] Vergleiche Audio- und visuelle Kandidaten..."
 
 TMP_JSON_FINAL="$TMP_DIR/candidates.final.json"
@@ -1145,6 +1166,7 @@ EOF
 echo "[INFO] Ersetze ursprüngliches JSON mit finaler Version..."
 mv "$TMP_JSON_FINAL" "$TMP_JSON"
 
+
    ```
    1. **SSH Node – Analyze VOD** (Node-Name: Analyze VOD)
       - Node-Typ: SSH