refactor: make the matcher and detector safe for concurrent clients

The recognition server will classify crops from several clients at once. Two
things blocked that:

- OpenCV Feature2D objects were shared across calls, so parallel
  classify_crop() calls could corrupt each other. Each thread now gets its own
  extractor set; the template cache is untouched.
- score_crop() is extracted from classify_crop() so callers that need every SKU
  score (live track aggregation) do not have to re-derive it from the top-5
  list. classify_crop() output is byte-identical to before (verified against the
  pre-change module on 82 real crops x 4 methods).
- ShelfProductDetector accepts device/imgsz so the server can pin inference.

Dependencies: pin ultralytics to the version that wrote the checkpoint and that
the session tracker targets (8.4.153), add lap so ultralytics never
pip-installs it mid-request, and add websockets for the streaming endpoint.

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>

recognization/code/detector.py

@@ -99,8 +99,17 @@ from ultralytics import YOLO
 class ShelfProductDetector:
     """Wraps the YOLO detector for supermarket shelf products."""
 
-    def __init__(self, weights_path: str = "runs/detect/train/weights/best.pt"):
+    def __init__(
+        self,
+        weights_path: str = "runs/detect/train/weights/best.pt",
+        device: Optional[str] = None,
+        imgsz: int = 640,
+        half: bool = False
+    ):
         self.weights_path = weights_path
+        self.device = device
+        self.imgsz = imgsz
+        self.half = half
         if not os.path.exists(weights_path):
             raise FileNotFoundError(f"YOLO weights not found at: {weights_path}")
 
@@ -129,7 +138,14 @@ class ShelfProductDetector:
             return {"status": "error", "message": "Failed to read image", "boxes": []}
 
         h, w = image_bgr.shape[:2]
-        results = self.model(image_bgr, conf=conf_threshold, iou=iou_threshold, verbose=False)
+        predict_kwargs: Dict[str, Any] = dict(
+            conf=conf_threshold, iou=iou_threshold, imgsz=self.imgsz, verbose=False
+        )
+        if self.device:
+            predict_kwargs["device"] = self.device
+        if self.half:
+            predict_kwargs["half"] = True
+        results = self.model(image_bgr, **predict_kwargs)
 
         boxes_list = []
         if results and len(results) > 0: