Enhance HVideoTool's detection and restoration features: added support for tracking the model used in detections, improved temporal coherence by allowing the use of already-restored frames in the restoration process, and updated the UI to reflect these changes with new indicators and configuration options. Documentation in CLAUDE.md has been updated accordingly.

hvideotool/core/detection/types.py

@@ -24,6 +24,7 @@ class Detection:
     bbox: tuple[int, int, int, int]           # x, y, w, h
     polygon: list[tuple[int, int]] = field(default_factory=list)  # contour points
     label: str = ""                           # model category (models/yolo/<label>); drives colour/grouping
+    model: str = ""                           # weights file that produced it (the .pt stem)
 
     @property
     def display(self) -> str:
@@ -37,6 +38,7 @@ class Detection:
             "bbox": list(self.bbox),
             "polygon": [list(p) for p in self.polygon],
             "label": self.label,
+            "model": self.model,
         }
 
     @classmethod
@@ -47,4 +49,5 @@ class Detection:
             bbox=tuple(data["bbox"]),  # type: ignore[arg-type]
             polygon=[tuple(p) for p in data.get("polygon", [])],
             label=data.get("label", ""),
+            model=data.get("model", ""),
         )

hvideotool/core/detection/yolo.py

@@ -35,10 +35,14 @@ def _name_to_type(name: str) -> CensorType:
 
 class YoloDetector(Detector):
     def __init__(
-        self, model_path: str, config: DetectionConfig | None = None, label: str = ""
+        self, model_path: str, config: DetectionConfig | None = None,
+        label: str = "", model_name: str = "",
     ) -> None:
         self.cfg = config or DetectionConfig()
         self._label = label  # category (models/yolo/<label>) tagged onto every detection
+        # weights filename (stem) tagged onto every detection, so the UI can show
+        # which specific model predicted it (a category folder may hold several).
+        self._model_name = model_name or os.path.splitext(os.path.basename(model_path))[0]
         if not os.path.isfile(model_path):
             raise FileNotFoundError(
                 f"Файл весов не найден: {model_path}\n"
@@ -108,6 +112,7 @@ class YoloDetector(Detector):
                 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, label=self._label
+                type=ctype, score=float(confs[i]), bbox=bbox, polygon=poly,
+                label=self._label, model=self._model_name,
             ))
         return out