feat: 完善 AI 分割与工作区标注闭环

功能增加:

- 将视频导入和生成帧拆成两个明确动作，项目库生成帧时选择 FPS，工作区不再自动触发拆帧。

- 为工作区新增调整多边形工具，支持选中 mask、拖动顶点、边中点插点、双击边界按位置插点，并保留多 polygon 子区域编辑。

- 打通 AI 页 SAM2/SAM3 结果到工作区的联动，生成 mask 后自动选中，可在右侧分类树换标签，并推送到工作区继续编辑。

- 增强 Dashboard WebSocket 连接状态与心跳，使用真实 onopen/onclose/onerror 状态驱动前端显示。

- 完善 SAM3 external worker 适配，支持 box prompt、semantic 请求级阈值和 video tracker 路径。

bugfix:

- 修复 SAM2 文本语义误走自动分割的问题，改为提示使用点提示或切换 SAM3。

- 修复 SAM2 多候选重叠显示的问题，点提示和 auto fallback 默认只采用最高分候选。

- 修复 SAM2 反向点看起来无效的问题，带负点时启用背景过滤，过滤为空时移除旧候选。

- 修复 SAM3 单个 2D mask 结果无法转 polygon、低阈值 semantic 返回被默认阈值吞掉的问题。

- 修复 AI 页 mask 未选中导致分类树无法修改 SAM2 结果标签的问题。

测试和文档:

- 补充 CanvasArea、AISegmentation、ProjectLibrary、VideoWorkspace、Dashboard、websocket 和 SAM engine/API 测试。

- 新增 backend/tests/test_sam2_engine.py，覆盖 SAM2 单候选请求和 auto fallback 行为。

- 更新 README、AGENTS 和 doc 需求/设计/接口/测试矩阵，按当前实现冻结功能状态。

backend/services/sam2_engine.py

@@ -207,7 +207,7 @@ class SAM2Engine:
                 masks, scores, _ = self._predictor.predict(
                     point_coords=pts,
                     point_labels=lbls,
-                    multimask_output=True,
+                    multimask_output=False,
                 )
 
             polygons = []
@@ -335,16 +335,16 @@ class SAM2Engine:
                 masks, scores, _ = self._predictor.predict(
                     point_coords=pts,
                     point_labels=lbls,
-                    multimask_output=True,
+                    multimask_output=False,
                 )
 
             polygons = []
-            for m in masks[:3]:  # Limit to top 3 masks
+            for m in masks[:1]:
                 poly = self._mask_to_polygon(m)
                 if poly:
                     polygons.append(poly)
 
-            return polygons, scores[:3].tolist()
+            return polygons, scores[:1].tolist()
         except Exception as exc:  # noqa: BLE001
             logger.error("SAM2 auto prediction failed: %s", exc)
             return self._dummy_polygons(image.shape[1], image.shape[0]), [0.5]

backend/services/sam3_engine.py

@@ -260,6 +260,7 @@ class SAM3Engine:
         *,
         text: str = "",
         box: list[float] | None = None,
+        confidence_threshold: float | None = None,
     ) -> tuple[list[list[list[float]]], list[float]]:
         status = self._external_status(force=True)
         if not status.get("available"):
@@ -279,7 +280,11 @@ class SAM3Engine:
                         "box": box,
                         "model_version": settings.sam3_model_version,
                         "checkpoint_path": self._checkpoint_path(),
-                        "confidence_threshold": settings.sam3_confidence_threshold,
+                        "confidence_threshold": (
+                            confidence_threshold
+                            if confidence_threshold is not None
+                            else settings.sam3_confidence_threshold
+                        ),
                     },
                     ensure_ascii=False,
                 ),
@@ -312,8 +317,18 @@ class SAM3Engine:
             raise RuntimeError(str(payload["error"]))
         return payload.get("polygons", []), payload.get("scores", [])
 
-    def _predict_semantic_external(self, image: np.ndarray, text: str) -> tuple[list[list[list[float]]], list[float]]:
-        return self._predict_external(image, "semantic", text=text)
+    def _predict_semantic_external(
+        self,
+        image: np.ndarray,
+        text: str,
+        confidence_threshold: float | None = None,
+    ) -> tuple[list[list[list[float]]], list[float]]:
+        return self._predict_external(
+            image,
+            "semantic",
+            text=text,
+            confidence_threshold=confidence_threshold,
+        )
 
     def _predict_box_external(self, image: np.ndarray, box: list[float]) -> tuple[list[list[list[float]]], list[float]]:
         return self._predict_external(image, "box", box=box)
@@ -378,11 +393,16 @@ class SAM3Engine:
             raise RuntimeError(str(payload["error"]))
         return payload.get("frames", [])
 
-    def predict_semantic(self, image: np.ndarray, text: str) -> tuple[list[list[list[float]]], list[float]]:
+    def predict_semantic(
+        self,
+        image: np.ndarray,
+        text: str,
+        confidence_threshold: float | None = None,
+    ) -> tuple[list[list[list[float]]], list[float]]:
         if not text.strip():
             raise ValueError("SAM 3 semantic prompt requires non-empty text.")
         if not self._can_load() and self._external_status().get("available"):
-            return self._predict_semantic_external(image, text)
+            return self._predict_semantic_external(image, text, confidence_threshold=confidence_threshold)
         if not self._ensure_ready():
             raise RuntimeError(self.status()["message"])
 

backend/services/sam3_external_worker.py

@@ -190,7 +190,9 @@ def _video_outputs_to_response(outputs: dict[str, Any]) -> dict[str, Any]:
 def _prediction_to_response(output: dict[str, Any]) -> dict[str, Any]:
     masks = _to_numpy(output.get("masks", []))
     scores = _to_numpy(output.get("scores", []))
-    if masks.ndim == 4:
+    if masks.ndim == 2:
+        masks = masks[None, :, :]
+    elif masks.ndim == 4:
         masks = masks[:, 0]
     elif masks.ndim == 3 and masks.shape[0] == 1:
         masks = masks[None, 0]

backend/services/sam_registry.py

@@ -83,10 +83,20 @@ class SAMRegistry:
     def predict_auto(self, model_id: str | None, image: Any):
         return self._ensure_available(model_id).predict_auto(image)
 
-    def predict_semantic(self, model_id: str | None, image: Any, text: str):
+    def predict_semantic(
+        self,
+        model_id: str | None,
+        image: Any,
+        text: str,
+        confidence_threshold: float | None = None,
+    ):
         model = self.normalize_model_id(model_id)
         if model == "sam3":
-            return self._ensure_available(model).predict_semantic(image, text)
+            return self._ensure_available(model).predict_semantic(
+                image,
+                text,
+                confidence_threshold=confidence_threshold,
+            )
         return self._ensure_available(model).predict_auto(image)
 
     def propagate_video(