Implement multi-model detection in HVideoTool: updated the detection system to support multiple YOLO models simultaneously, enhancing detection capabilities. Reflected changes in the UI with a new model selection menu and updated documentation in README and CLAUDE.md to guide users on model management and configuration.

hvideotool/core/detection/yolo.py

@@ -34,8 +34,11 @@ def _name_to_type(name: str) -> CensorType:
 
 
 class YoloDetector(Detector):
-    def __init__(self, model_path: str, config: DetectionConfig | None = None) -> None:
+    def __init__(
+        self, model_path: str, config: DetectionConfig | None = None, label: str = ""
+    ) -> None:
         self.cfg = config or DetectionConfig()
+        self._label = label  # category (models/yolo/<label>) tagged onto every detection
         if not os.path.isfile(model_path):
             raise FileNotFoundError(
                 f"Файл весов не найден: {model_path}\n"
@@ -71,7 +74,8 @@ class YoloDetector(Detector):
 
     @property
     def name(self) -> str:
-        return f"YoloDetector(device={self._device})"
+        tag = f", {self._label}" if self._label else ""
+        return f"YoloDetector(device={self._device}{tag})"
 
     def detect(self, frame: Frame) -> list[Detection]:
         results = self._model.predict(
@@ -103,5 +107,7 @@ class YoloDetector(Detector):
             if polygons is not None and i < len(polygons):
                 poly = [(int(px), int(py)) for px, py in polygons[i]]
             ctype = _name_to_type(names.get(int(classes[i]), ""))
-            out.append(Detection(type=ctype, score=float(confs[i]), bbox=bbox, polygon=poly))
+            out.append(Detection(
+                type=ctype, score=float(confs[i]), bbox=bbox, polygon=poly, label=self._label
+            ))
         return out