feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>

algorithms/dms_yolo/code/ultralytics/models/nas/predict.py

@@ -0,0 +1,56 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import torch
+
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import ops
+
+
+class NASPredictor(DetectionPredictor):
+    """Ultralytics YOLO NAS Predictor for object detection.
+
+    This class extends the DetectionPredictor from Ultralytics engine and is responsible for post-processing the raw
+    predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and scaling the
+    bounding boxes to fit the original image dimensions.
+
+    Attributes:
+        args (Namespace): Namespace containing various configurations for post-processing including confidence
+            threshold, IoU threshold, agnostic NMS flag, maximum detections, and class filtering options.
+        model (torch.nn.Module): The YOLO NAS model used for inference.
+        batch (list): Batch of inputs for processing.
+
+    Examples:
+        >>> from ultralytics import NAS
+        >>> model = NAS("yolo_nas_s")
+        >>> predictor = model.predictor
+
+        Assume that raw_preds, img, orig_imgs are available
+        >>> results = predictor.postprocess(raw_preds, img, orig_imgs)
+
+    Notes:
+        Typically, this class is not instantiated directly. It is used internally within the NAS class.
+    """
+
+    def postprocess(self, preds_in, img, orig_imgs):
+        """Postprocess NAS model predictions to generate final detection results.
+
+        This method takes raw predictions from a YOLO NAS model, converts bounding box formats, and applies
+        post-processing operations to generate the final detection results compatible with Ultralytics result
+        visualization and analysis tools.
+
+        Args:
+            preds_in (list): Raw predictions from the NAS model, typically containing bounding boxes and class scores.
+            img (torch.Tensor): Input image tensor that was fed to the model, with shape (B, C, H, W).
+            orig_imgs (list | torch.Tensor | np.ndarray): Original images before preprocessing, used for scaling
+                coordinates back to original dimensions.
+
+        Returns:
+            (list): List of Results objects containing the processed predictions for each image in the batch.
+
+        Examples:
+            >>> predictor = NAS("yolo_nas_s").predictor
+            >>> results = predictor.postprocess(raw_preds, img, orig_imgs)
+        """
+        boxes = ops.xyxy2xywh(preds_in[0][0])  # Convert bounding boxes from xyxy to xywh format
+        preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)  # Concatenate boxes with class scores
+        return super().postprocess(preds, img, orig_imgs)