Merge branch 'main' into sam3-imgsz

docs/en/guides/parking-management.md

@@ -109,6 +109,15 @@ Parking management with [Ultralytics YOLO26](https://github.com/ultralytics/ultr
         cv2.destroyAllWindows()  # destroy all opened windows
         ```
 
+    === "CLI"
+
+        ```bash
+        yolo solutions parking source="path/to/video.mp4" json_file="bounding_boxes.json" show=True
+        ```
+
+        !!! note
+            Create parking zone annotations first using `ParkingPtsSelection()` in Python (Step 2 above), then pass the JSON file to the CLI command.
+
 ### `ParkingManagement` Arguments
 
 Here's a table with the `ParkingManagement` arguments:

docs/en/guides/region-counting.md

@@ -87,6 +87,12 @@ keywords: object counting, regions, YOLO26, computer vision, Ultralytics, effici
          cv2.destroyAllWindows()  # destroy all opened windows
          ```
 
+    === "CLI"
+
+         ```bash
+         yolo solutions region source="path/to/video.mp4" show=True region="[(20, 400), (1080, 400), (1080, 360), (20, 360)]"
+         ```
+
 !!! tip "Ultralytics Example Code"
 
       The Ultralytics region counting module is available in our [examples section](https://github.com/ultralytics/ultralytics/blob/main/examples/YOLOv8-Region-Counter/yolov8_region_counter.py). You can explore this example for code customization and modify it to suit your specific use case.

docs/en/guides/security-alarm-system.md

@@ -79,6 +79,15 @@ The Security Alarm System Project utilizing Ultralytics YOLO26 integrates advanc
         cv2.destroyAllWindows()  # destroy all opened windows
         ```
 
+    === "CLI"
+
+        ```bash
+        yolo solutions security source="path/to/video.mp4" show=True
+        ```
+
+        !!! note
+            Email alerts require the Python API to call `.authenticate()`. The CLI provides detection and visualization only.
+
 When you run the code, you will receive a single email notification if any object is detected. The notification is sent immediately, not repeatedly. You can customize the code to suit your project requirements.
 
 #### Email Received Sample

docs/en/integrations/sony-imx500.md

@@ -44,6 +44,10 @@ Currently, you can only export models that include the following tasks to IMX500
 - [Classification](https://docs.ultralytics.com/tasks/classify/)
 - [Instance segmentation](https://docs.ultralytics.com/tasks/segment/)
 
+!!! note "Supported model variants"
+
+    IMX export is designed and benchmarked for **YOLOv8n** and **YOLO11n** (nano). Other architectures and model scales are not supported.
+
 ## Usage Examples
 
 Export an Ultralytics YOLO11 model to IMX500 format and run inference with the exported model.

docs/en/usage/cli.md

@@ -365,6 +365,34 @@ Ultralytics provides ready-to-use solutions for common computer vision applicati
         yolo solutions trackzone region="[(150, 150), (1130, 150), (1130, 570), (150, 570)]" # configure zone coordinates
         ```
 
+    === "Region"
+
+        Count objects inside specific polygonal regions:
+
+        ```bash
+        yolo solutions region show=True
+        yolo solutions region source="path/to/video.mp4"                                # specify video file path
+        yolo solutions region region="[(20, 400), (1080, 400), (1080, 360), (20, 360)]" # configure region coordinates
+        ```
+
+    === "Security"
+
+        Run security alarm monitoring with object detection:
+
+        ```bash
+        yolo solutions security show=True
+        yolo solutions security source="path/to/video.mp4" # specify video file path
+        ```
+
+    === "Parking"
+
+        Monitor parking lot occupancy using pre-defined zones:
+
+        ```bash
+        yolo solutions parking source="path/to/video.mp4" json_file="bounding_boxes.json" # requires pre-built JSON
+        yolo solutions parking source="path/to/video.mp4" json_file="bounding_boxes.json" model="yolo26n.pt"
+        ```
+
     === "Help"
 
         View available solutions and their options:

ultralytics/cfg/__init__.py

@@ -48,6 +48,9 @@ SOLUTION_MAP = {
     "analytics": "Analytics",
     "inference": "Inference",
     "trackzone": "TrackZone",
+    "region": "RegionCounter",
+    "security": "SecurityAlarm",
+    "parking": "ParkingManagement",
     "help": None,
 }
 
@@ -111,7 +114,16 @@ SOLUTIONS_HELP_MSG = f"""
     6. Track objects within specific zones
         yolo solutions trackzone source="path/to/video.mp4" region="[(150, 150), (1130, 150), (1130, 570), (150, 570)]"
 
-    7. Streamlit real-time webcam inference GUI
+    7. Count objects inside specific regions
+        yolo solutions region source="path/to/video.mp4" region="[(20, 400), (1080, 400), (1080, 360), (20, 360)]"
+
+    8. Run security alarm monitoring (email alerts require Python API)
+        yolo solutions security source="path/to/video.mp4"
+
+    9. Monitor parking occupancy (create JSON annotations first via Python ParkingPtsSelection)
+        yolo solutions parking source="path/to/video.mp4" json_file="bounding_boxes.json"
+
+    10. Streamlit real-time webcam inference GUI
         yolo streamlit-predict
     """
 CLI_HELP_MSG = f"""

ultralytics/solutions/security_alarm.py

@@ -140,7 +140,7 @@ class SecurityAlarm(BaseSolution):
             annotator.box_label(box, label=self.names[cls], color=colors(cls, True))
 
         total_det = len(self.clss)
-        if total_det >= self.records and not self.email_sent:  # Only send email if not sent before
+        if total_det >= self.records and not self.email_sent and self.server:
             self.send_email(im0, total_det)
             self.email_sent = True
 

ultralytics/utils/tal.py

@@ -373,15 +373,16 @@ class RotatedTaskAlignedAssigner(TaskAlignedAssigner):
         Returns:
             (torch.Tensor): Boolean mask of positive anchors with shape (b, n_boxes, h*w).
         """
-        wh_mask = gt_bboxes[..., 2:4] < self.stride[0]
-        gt_bboxes[..., 2:4] = torch.where(
+        gt_bboxes_clone = gt_bboxes.clone()
+        wh_mask = gt_bboxes_clone[..., 2:4] < self.stride[0]
+        gt_bboxes_clone[..., 2:4] = torch.where(
             (wh_mask * mask_gt).bool(),
-            torch.tensor(self.stride_val, dtype=gt_bboxes.dtype, device=gt_bboxes.device),
-            gt_bboxes[..., 2:4],
+            torch.tensor(self.stride_val, dtype=gt_bboxes_clone.dtype, device=gt_bboxes_clone.device),
+            gt_bboxes_clone[..., 2:4],
         )
 
         # (b, n_boxes, 5) --> (b, n_boxes, 4, 2)
-        corners = xywhr2xyxyxyxy(gt_bboxes)
+        corners = xywhr2xyxyxyxy(gt_bboxes_clone)
         # (b, n_boxes, 1, 2)
         a, b, _, d = corners.split(1, dim=-2)
         ab = b - a