separate pipeline

pipeline/run_pipeline.py

@@ -14,6 +14,7 @@ Funktionales Äquivalent zu run/run_pipeline.bat:
 Aufruf:
     python pipeline/run_pipeline.py data/simulation/Scene8
     python pipeline/run_pipeline.py data/simulation/Scene8 --robot data/robot/robot.json
+    python pipeline/run_pipeline.py data/simulation/Scene8 --cameras a,c,f
 
 Im Container (Pipeline-Image) ist das der Standard-Einstiegspunkt.
 """
@@ -51,6 +52,8 @@ def main() -> None:
     ap.add_argument("--robot", default=os.environ.get("ROBOT_JSON", str(ROOT / "data" / "robot" / "robot.json")))
     ap.add_argument("--evalDir", default=None, help="Ausgabeordner (Default: data/evaluations/<SceneName>)")
     ap.add_argument("--lambdaWeight", default="100.0")
+    ap.add_argument("--cameras", default=None,
+                    help="Kommagetrennte Kamera-IDs, z.B. a,b,d  (Standard: alle)")
     args = ap.parse_args()
 
     scene = Path(args.scene).resolve()
@@ -59,10 +62,14 @@ def main() -> None:
     robot = args.robot
 
     imgs = sorted(glob.glob(str(scene / "render_*.png")))
+    if args.cameras:
+        wanted = set(args.cameras.split(","))
+        imgs = [img for img in imgs if cam_id_of(img) in wanted]
     if not imgs:
-        print(f"[ERROR] keine render_*.png in {scene}")
+        print(f"[ERROR] keine passenden render_*.png in {scene}")
         sys.exit(1)
-    print(f"[INFO] Scene={scene.name}  Bilder={len(imgs)}  ->  {eval_dir}")
+    cam_ids = [cam_id_of(i) for i in imgs]
+    print(f"[INFO] Scene={scene.name}  Kameras={cam_ids}  ->  {eval_dir}")
 
     # ── Schritt 1: ArUco-Detektion (pro Bild seine EIGENE npz) ──
     print("\n[STEP 1] ArUco-Detektion")