Ultralytics Code Refactor https://ultralytics.com/actions (#16493)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/guides/data-collection-and-annotation.md

@@ -88,7 +88,7 @@ Let's say you are ready to annotate now. There are several open-source tools ava
 
 - **[Label Studio](https://github.com/HumanSignal/label-studio)**: A flexible tool that supports a wide range of annotation tasks and includes features for managing projects and quality control.
 - **[CVAT](https://github.com/cvat-ai/cvat)**: A powerful tool that supports various annotation formats and customizable workflows, making it suitable for complex projects.
-- **[Labelme](https://github.com/labelmeai/labelme)**: A simple and easy-to-use tool that allows for quick annotation of images with polygons, making it ideal for straightforward tasks.
+- **[Labelme](https://github.com/wkentaro/labelme)**: A simple and easy-to-use tool that allows for quick annotation of images with polygons, making it ideal for straightforward tasks.
 
 <p align="center">
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/labelme-instance-segmentation-annotation.avif" alt="LabelMe Overview">
@@ -167,7 +167,7 @@ Several popular open-source tools can streamline the data annotation process:
 
 - **[Label Studio](https://github.com/HumanSignal/label-studio)**: A flexible tool supporting various annotation tasks, project management, and quality control features.
 - **[CVAT](https://www.cvat.ai/)**: Offers multiple annotation formats and customizable workflows, making it suitable for complex projects.
-- **[Labelme](https://github.com/labelmeai/labelme)**: Ideal for quick and straightforward image annotation with polygons.
+- **[Labelme](https://github.com/wkentaro/labelme)**: Ideal for quick and straightforward image annotation with polygons.
 
 These tools can help enhance the efficiency and accuracy of your annotation workflows. For extensive feature lists and guides, refer to our [data annotation tools documentation](../datasets/index.md).
 

docs/en/guides/steps-of-a-cv-project.md

@@ -100,7 +100,7 @@ However, if you choose to collect images or take your own pictures, you'll need
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/different-types-of-image-annotation.avif" alt="Different Types of Image Annotation">
 </p>
 
-[Data collection and annotation](./data-collection-and-annotation.md) can be a time-consuming manual effort. Annotation tools can help make this process easier. Here are some useful open annotation tools: [LabeI Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/labelmeai/labelme).
+[Data collection and annotation](./data-collection-and-annotation.md) can be a time-consuming manual effort. Annotation tools can help make this process easier. Here are some useful open annotation tools: [LabeI Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/wkentaro/labelme).
 
 ## Step 3: [Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation) and Splitting Your Dataset
 
@@ -215,7 +215,7 @@ Data annotation is vital for teaching your model to recognize patterns. The type
 - **Object Detection**: Bounding boxes drawn around objects.
 - **Image Segmentation**: Each pixel labeled according to the object it belongs to.
 
-Tools like [Label Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/labelmeai/labelme) can assist in this process. For more details, refer to our [data collection and annotation guide](./data-collection-and-annotation.md).
+Tools like [Label Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/wkentaro/labelme) can assist in this process. For more details, refer to our [data collection and annotation guide](./data-collection-and-annotation.md).
 
 ### What steps should I follow to augment and split my dataset effectively?
 

docs/en/integrations/weights-biases.md

@@ -148,7 +148,7 @@ After running the usage code snippet, you can access the Weights & Biases (W&B)
 
 - **Model Artifacts Management**: Access and share model checkpoints, facilitating easy deployment and collaboration.
 
-- **Viewing Inference Results with Image Overlay**: Visualize the prediction results on images using interactive overlays in Weights & Biases, providing a clear and detailed view of model performance on real-world data. For more detailed information on Weights & Biases' image overlay capabilities, check out this [link](https://docs.wandb.ai/guides/track/log/media#image-overlays). [See how Weights & Biases' image overlays helps visualize model inferences](https://imgur.com/a/UTSiufs).
+- **Viewing Inference Results with Image Overlay**: Visualize the prediction results on images using interactive overlays in Weights & Biases, providing a clear and detailed view of model performance on real-world data. For more detailed information on Weights & Biases' image overlay capabilities, check out this [link](https://docs.wandb.ai/guides/track/log/media/#image-overlays). [See how Weights & Biases' image overlays helps visualize model inferences](https://imgur.com/a/UTSiufs).
 
 By using these features, you can effectively track, analyze, and optimize your YOLOv8 model's training, ensuring the best possible performance and efficiency.
 
@@ -156,7 +156,7 @@ By using these features, you can effectively track, analyze, and optimize your Y
 
 This guide helped you explore Ultralytics' YOLOv8 integration with Weights & Biases. It illustrates the ability of this integration to efficiently track and visualize model training and prediction results.
 
-For further details on usage, visit [Weights & Biases' official documentation](https://docs.wandb.ai/guides/integrations/ultralytics).
+For further details on usage, visit [Weights & Biases' official documentation](https://docs.wandb.ai/guides/integrations/ultralytics/).
 
 Also, be sure to check out the [Ultralytics integration guide page](../integrations/index.md), to learn more about different exciting integrations.
 

ultralytics/models/sam/modules/sam.py

@@ -645,9 +645,7 @@ class SAM2Model(torch.nn.Module):
         # The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images.
         # In this case, we skip the fusion with any memory.
         if self.num_maskmem == 0:  # Disable memory and skip fusion
-            pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
+            return current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
-            return pix_feat
-
         num_obj_ptr_tokens = 0
         # Step 1: condition the visual features of the current frame on previous memories
         if not is_init_cond_frame:

ultralytics/solutions/object_counter.py

@@ -176,22 +176,24 @@ class ObjectCounter:
 
                 # Count objects using line
                 elif len(self.reg_pts) == 2:
-                    if prev_position is not None and track_id not in self.count_ids:
+                    if (
-                        # Check if the object's movement segment intersects the counting line
+                        prev_position is not None
-                        if LineString([(prev_position[0], prev_position[1]), (box[0], box[1])]).intersects(
+                        and track_id not in self.count_ids
+                        and LineString([(prev_position[0], prev_position[1]), (box[0], box[1])]).intersects(
                             self.counting_line_segment
-                        ):
+                        )
-                            self.count_ids.append(track_id)
+                    ):
+                        self.count_ids.append(track_id)
 
-                            # Determine the direction of movement (IN or OUT)
+                        # Determine the direction of movement (IN or OUT)
-                            dx = (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0])
+                        dx = (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0])
-                            dy = (box[1] - prev_position[1]) * (self.counting_region.centroid.y - prev_position[1])
+                        dy = (box[1] - prev_position[1]) * (self.counting_region.centroid.y - prev_position[1])
-                            if dx > 0 and dy > 0:
+                        if dx > 0 and dy > 0:
-                                self.in_counts += 1
+                            self.in_counts += 1
-                                self.class_wise_count[self.names[cls]]["IN"] += 1
+                            self.class_wise_count[self.names[cls]]["IN"] += 1
-                            else:
+                        else:
-                                self.out_counts += 1
+                            self.out_counts += 1
-                                self.class_wise_count[self.names[cls]]["OUT"] += 1
+                            self.class_wise_count[self.names[cls]]["OUT"] += 1
 
         labels_dict = {}
 

ultralytics/solutions/parking_management.py

@@ -128,14 +128,13 @@ class ParkingPtsSelection:
 
         rg_data = []  # regions data
         for box in self.rg_data:
-            rs_box = []  # rescaled box list
+            rs_box = [
-            for x, y in box:
+                (
-                rs_box.append(
+                    int(x * self.imgw / self.canvas.winfo_width()),  # width scaling
-                    (
+                    int(y * self.imgh / self.canvas.winfo_height()),  # height scaling
-                        int(x * self.imgw / self.canvas.winfo_width()),  # width scaling
+                )
-                        int(y * self.imgh / self.canvas.winfo_height()),
+                for x, y in box
-                    )
+            ]
-                )  # height scaling
             rg_data.append({"points": rs_box})
         with open("bounding_boxes.json", "w") as f:
             json.dump(rg_data, f, indent=4)