feat: HSAP platform v2 — modular navigation, quality review, audit log, world model simulation

Major changes:
- New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind
- 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理
- Data catalog with charts (DMS/ADAS/Lane 3-tab view)
- Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance
- Audit enhancements: batch operations, rejection categories, Feishu notifications
- Operation audit log (操作日志)
- World model simulation studio (仿真工坊)
- Dataset version management with snapshots and diff
- ADAS 7-class dataset integration (138K images organized + compressed)
- User management with Feishu integration and pagination
- CRUD/search/filter on all pages, card layout redesign
- PIL-optimized image overlay rendering
- Auto-snapshot on build, in_review workflow stage
- Removed embedded algorithm code (now in workspace)

algorithms/dms_yolo/code.embedded.bak/ultralytics/solutions/object_cropper.py

@@ -0,0 +1,87 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from pathlib import Path
+from typing import Any
+
+from ultralytics.solutions.solutions import BaseSolution, SolutionResults
+from ultralytics.utils.plotting import save_one_box
+
+
+class ObjectCropper(BaseSolution):
+    """A class to manage the cropping of detected objects in a real-time video stream or images.
+
+    This class extends the BaseSolution class and provides functionality for cropping objects based on detected bounding
+    boxes. The cropped images are saved to a specified directory for further analysis or usage.
+
+    Attributes:
+        crop_dir (str): Directory where cropped object images are stored.
+        crop_idx (int): Counter for the total number of cropped objects.
+        iou (float): IoU (Intersection over Union) threshold for non-maximum suppression.
+        conf (float): Confidence threshold for filtering detections.
+
+    Methods:
+        process: Crop detected objects from the input image and save them to the output directory.
+
+    Examples:
+        >>> cropper = ObjectCropper()
+        >>> frame = cv2.imread("frame.jpg")
+        >>> processed_results = cropper.process(frame)
+        >>> print(f"Total cropped objects: {cropper.crop_idx}")
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the ObjectCropper class for cropping objects from detected bounding boxes.
+
+        Args:
+            **kwargs (Any): Keyword arguments passed to the parent class and used for configuration including:
+                - crop_dir (str): Path to the directory for saving cropped object images.
+        """
+        super().__init__(**kwargs)
+
+        self.crop_dir = self.CFG["crop_dir"]  # Directory for storing cropped detections
+        Path(self.crop_dir).mkdir(parents=True, exist_ok=True)
+        if self.CFG["show"]:
+            self.LOGGER.warning(f"show=True is not supported for ObjectCropper; saving crops to '{self.crop_dir}'.")
+            self.CFG["show"] = False
+        self.crop_idx = 0  # Initialize counter for total cropped objects
+        self.iou = self.CFG["iou"]
+        self.conf = self.CFG["conf"]
+
+    def process(self, im0) -> SolutionResults:
+        """Crop detected objects from the input image and save them as separate images.
+
+        Args:
+            im0 (np.ndarray): The input image containing detected objects.
+
+        Returns:
+            (SolutionResults): A SolutionResults object containing the total number of cropped objects and processed
+                image.
+
+        Examples:
+            >>> cropper = ObjectCropper()
+            >>> frame = cv2.imread("image.jpg")
+            >>> results = cropper.process(frame)
+            >>> print(f"Total cropped objects: {results.total_crop_objects}")
+        """
+        with self.profilers[0]:
+            results = self.model.predict(
+                im0,
+                classes=self.classes,
+                conf=self.conf,
+                iou=self.iou,
+                device=self.CFG["device"],
+                verbose=False,
+            )[0]
+            self.clss = results.boxes.cls.tolist()  # required for logging only.
+
+        for box in results.boxes:
+            self.crop_idx += 1
+            save_one_box(
+                box.xyxy,
+                im0,
+                file=Path(self.crop_dir) / f"crop_{self.crop_idx}.jpg",
+                BGR=True,
+            )
+
+        # Return SolutionResults
+        return SolutionResults(plot_im=im0, total_crop_objects=self.crop_idx)