Merge pull request #764 from roboflow/develop

`supervision-0.18.0` release
Merge pull request #775 from roboflow/feature/supervision-0-18-0-changelog
2024-01-25 10:33:16 +01:00 · 2024-01-25 10:31:17 +01:00 · 2024-01-25 09:29:43 +00:00 · 2024-01-25 10:27:22 +01:00 · 2024-01-24 18:10:56 +01:00 · 2024-01-24 17:50:41 +01:00
33 changed files with 1578 additions and 1166 deletions
@@ -38,7 +38,6 @@ jobs:
              numpy==1.21.2 \
              opencv-python==4.5.5.64 \
              packaging==23.2 \
-              Pillow==10.1.0 \
              pluggy==1.3.0 \
              pyparsing==3.1.1 \
              pytest==7.2.0 \
@@ -13,6 +13,7 @@ repos:
      - id: trailing-whitespace
        exclude: test/.*\.py
      - id: check-yaml
+        exclude: mkdocs.yml
      - id: check-docstring-first
      - id: check-executables-have-shebangs
      - id: check-toml
@@ -52,7 +52,7 @@ git push -u origin main

 ### Pre-commit tool

-This project utilizes the [pre-commit](https://pre-commit.com/) tool to maintain code quality and consistency. Before submitting a pull request or making any commits, it is important to run the pre-commit tool to ensure that your changes meet the project's guidelines.
+This project uses the [pre-commit](https://pre-commit.com/) tool to maintain code quality and consistency. Before submitting a pull request or making any commits, it is important to run the pre-commit tool to ensure that your changes meet the project's guidelines.

 Furthermore, we have integrated a pre-commit GitHub Action into our workflow. This means that with every pull request opened, the pre-commit checks will be automatically enforced, streamlining the code review process and ensuring that all contributions adhere to our quality standards.

@@ -46,17 +46,17 @@ Read more about desktop, headless, and local installation in our [guide](https:/
 Supervision was designed to be model agnostic. Just plug in any classification, detection, or segmentation model. For your convenience, we have created [connectors](https://supervision.roboflow.com/detection/core/#detections) for the most popular libraries like Ultralytics, Transformers, or MMDetection.

 ```python
->>> import cv2
->>> import supervision as sv
->>> from ultralytics import YOLO
+import cv2
+import supervision as sv
+from ultralytics import YOLO

->>> image = cv2.imread(...)
->>> model = YOLO('yolov8s.pt')
->>> result = model(image)[0]
->>> detections = sv.Detections.from_ultralytics(result)
+image = cv2.imread(...)
+model = YOLO('yolov8s.pt')
+result = model(image)[0]
+detections = sv.Detections.from_ultralytics(result)

->>> len(detections)
-5
+len(detections)
+# 5
 ```

 <details>
@@ -67,17 +67,17 @@ Supervision was designed to be model agnostic. Just plug in any classification,
    Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key).

    ```python
-    >>> import cv2
-    >>> import supervision as sv
-    >>> from inference.models.utils import get_roboflow_model
+    import cv2
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model

-    >>> image = cv2.imread(...)
-    >>> model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
-    >>> result = model.infer(image)[0]
-    >>> detections = sv.Detections.from_inference(result)
+    image = cv2.imread(...)
+    model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
+    result = model.infer(image)[0]
+    detections = sv.Detections.from_inference(result)

-    >>> len(detections)
-    >>> 5
+    len(detections)
+    # 5

    ```

@@ -88,17 +88,17 @@ Supervision was designed to be model agnostic. Just plug in any classification,
 Supervision offers a wide range of highly customizable [annotators](https://supervision.roboflow.com/annotators/), allowing you to compose the perfect visualization for your use case.

 ```python
->>> import cv2
->>> import supervision as sv
+import cv2
+import supervision as sv

->>> image = cv2.imread(...)
->>> detections = sv.Detections(...)
+image = cv2.imread(...)
+detections = sv.Detections(...)

->>> bounding_box_annotator = sv.BoundingBoxAnnotator()
->>> annotated_frame = bounding_box_annotator.annotate(
-...     scene=image.copy(),
-...     detections=detections
-... )
+bounding_box_annotator = sv.BoundingBoxAnnotator()
+annotated_frame = bounding_box_annotator.annotate(
+    scene=image.copy(),
+    detections=detections
+)
 ```

 https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce
@@ -108,19 +108,19 @@ https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-
 Supervision provides a set of [utils](https://supervision.roboflow.com/datasets/) that allow you to load, split, merge, and save datasets in one of the supported formats.

 ```python
->>> import supervision as sv
+import supervision as sv

->>> dataset = sv.DetectionDataset.from_yolo(
-...     images_directory_path=...,
-...     annotations_directory_path=...,
-...     data_yaml_path=...
-... )
+dataset = sv.DetectionDataset.from_yolo(
+    images_directory_path=...,
+    annotations_directory_path=...,
+    data_yaml_path=...
+)

->>> dataset.classes
+dataset.classes
 ['dog', 'person']

->>> len(dataset)
-1000
+len(dataset)
+# 1000
 ```

 <details close>
@@ -129,86 +129,86 @@ Supervision provides a set of [utils](https://supervision.roboflow.com/datasets/
 - load

  ```python
-  >>> dataset = sv.DetectionDataset.from_yolo(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...,
-  ...     data_yaml_path=...
-  ... )
+  dataset = sv.DetectionDataset.from_yolo(
+      images_directory_path=...,
+      annotations_directory_path=...,
+      data_yaml_path=...
+  )

-  >>> dataset = sv.DetectionDataset.from_pascal_voc(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...
-  ... )
+  dataset = sv.DetectionDataset.from_pascal_voc(
+      images_directory_path=...,
+      annotations_directory_path=...
+  )

-  >>> dataset = sv.DetectionDataset.from_coco(
-  ...     images_directory_path=...,
-  ...     annotations_path=...
-  ... )
+  dataset = sv.DetectionDataset.from_coco(
+      images_directory_path=...,
+      annotations_path=...
+  )
  ```

 - split

  ```python
-  >>> train_dataset, test_dataset = dataset.split(split_ratio=0.7)
-  >>> test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
+  train_dataset, test_dataset = dataset.split(split_ratio=0.7)
+  test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)

-  >>> len(train_dataset), len(test_dataset), len(valid_dataset)
-  (700, 150, 150)
+  len(train_dataset), len(test_dataset), len(valid_dataset)
+  # (700, 150, 150)
  ```

 - merge

  ```python
-  >>> ds_1 = sv.DetectionDataset(...)
-  >>> len(ds_1)
-  100
-  >>> ds_1.classes
-  ['dog', 'person']
+  ds_1 = sv.DetectionDataset(...)
+  len(ds_1)
+  # 100
+  ds_1.classes
+  # ['dog', 'person']

-  >>> ds_2 = sv.DetectionDataset(...)
-  >>> len(ds_2)
-  200
-  >>> ds_2.classes
-  ['cat']
+  ds_2 = sv.DetectionDataset(...)
+  len(ds_2)
+  # 200
+  ds_2.classes
+  # ['cat']

-  >>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
-  >>> len(ds_merged)
-  300
-  >>> ds_merged.classes
-  ['cat', 'dog', 'person']
+  ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
+  len(ds_merged)
+  # 300
+  ds_merged.classes
+  # ['cat', 'dog', 'person']
  ```

 - save

  ```python
-  >>> dataset.as_yolo(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...,
-  ...     data_yaml_path=...
-  ... )
+  dataset.as_yolo(
+      images_directory_path=...,
+      annotations_directory_path=...,
+      data_yaml_path=...
+  )

-  >>> dataset.as_pascal_voc(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...
-  ... )
+  dataset.as_pascal_voc(
+      images_directory_path=...,
+      annotations_directory_path=...
+  )

-  >>> dataset.as_coco(
-  ...     images_directory_path=...,
-  ...     annotations_path=...
-  ... )
+  dataset.as_coco(
+      images_directory_path=...,
+      annotations_path=...
+  )
  ```

 - convert

  ```python
-  >>> sv.DetectionDataset.from_yolo(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...,
-  ...     data_yaml_path=...
-  ... ).as_pascal_voc(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...
-  ... )
+  sv.DetectionDataset.from_yolo(
+      images_directory_path=...,
+      annotations_directory_path=...,
+      data_yaml_path=...
+  ).as_pascal_voc(
+      images_directory_path=...,
+      annotations_directory_path=...
+  )
  ```

 </details>
@@ -1,20 +1,21 @@
 ---
 comments: true
+status: new
 ---

 === "BoundingBox"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> bounding_box_annotator = sv.BoundingBoxAnnotator()
-    >>> annotated_frame = bounding_box_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    annotated_frame = bounding_box_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -26,16 +27,16 @@ comments: true
 === "RoundBox"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> round_box_annotator = sv.RoundBoxAnnotator()
-    >>> annotated_frame = round_box_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    round_box_annotator = sv.RoundBoxAnnotator()
+    annotated_frame = round_box_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -47,16 +48,16 @@ comments: true
 === "BoxCorner"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> corner_annotator = sv.BoxCornerAnnotator()
-    >>> annotated_frame = corner_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    corner_annotator = sv.BoxCornerAnnotator()
+    annotated_frame = corner_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -68,16 +69,16 @@ comments: true
 === "Color"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> color_annotator = sv.ColorAnnotator()
-    >>> annotated_frame = color_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    color_annotator = sv.ColorAnnotator()
+    annotated_frame = color_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -89,16 +90,16 @@ comments: true
 === "Circle"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> circle_annotator = sv.CircleAnnotator()
-    >>> annotated_frame = circle_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    circle_annotator = sv.CircleAnnotator()
+    annotated_frame = circle_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -110,16 +111,16 @@ comments: true
 === "Dot"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> dot_annotator = sv.DotAnnotator()
-    >>> annotated_frame = dot_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    dot_annotator = sv.DotAnnotator()
+    annotated_frame = dot_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -131,16 +132,16 @@ comments: true
 === "Triangle"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> triangle_annotator = sv.TriangleAnnotator()
-    >>> annotated_frame = triangle_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    triangle_annotator = sv.TriangleAnnotator()
+    annotated_frame = triangle_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -152,16 +153,16 @@ comments: true
 === "Ellipse"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> ellipse_annotator = sv.EllipseAnnotator()
-    >>> annotated_frame = ellipse_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    ellipse_annotator = sv.EllipseAnnotator()
+    annotated_frame = ellipse_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -173,16 +174,16 @@ comments: true
 === "Halo"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> halo_annotator = sv.HaloAnnotator()
-    >>> annotated_frame = halo_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    halo_annotator = sv.HaloAnnotator()
+    annotated_frame = halo_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -194,16 +195,16 @@ comments: true
 === "PercentageBar"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> percentage_bar_annotator = sv.PercentageBarAnnotator()
-    >>> annotated_frame = percentage_bar_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    percentage_bar_annotator = sv.PercentageBarAnnotator()
+    annotated_frame = percentage_bar_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -215,16 +216,16 @@ comments: true
 === "Mask"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> mask_annotator = sv.MaskAnnotator()
-    >>> annotated_frame = mask_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    mask_annotator = sv.MaskAnnotator()
+    annotated_frame = mask_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -236,16 +237,16 @@ comments: true
 === "Polygon"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> polygon_annotator = sv.PolygonAnnotator()
-    >>> annotated_frame = polygon_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    polygon_annotator = sv.PolygonAnnotator()
+    annotated_frame = polygon_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -257,16 +258,16 @@ comments: true
 === "Label"

    ```python
-    >>> import supervision as sv
+     import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
-    >>> annotated_frame = label_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
+    annotated_frame = label_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -278,16 +279,16 @@ comments: true
 === "Blur"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> blur_annotator = sv.BlurAnnotator()
-    >>> annotated_frame = blur_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    blur_annotator = sv.BlurAnnotator()
+    annotated_frame = blur_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -299,16 +300,16 @@ comments: true
 === "Pixelate"

    ```python
-    >>> import supervision as sv
+    import supervision as sv

-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)

-    >>> pixelate_annotator = sv.PixelateAnnotator()
-    >>> annotated_frame = pixelate_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    pixelate_annotator = sv.PixelateAnnotator()
+    annotated_frame = pixelate_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
    ```

    <div class="result" markdown>
@@ -320,26 +321,26 @@ comments: true
 === "Trace"

    ```python
-    >>> import supervision as sv
-    >>> from ultralytics import YOLO
+    import supervision as sv
+    from ultralytics import YOLO

-    >>> model = YOLO('yolov8x.pt')
+    model = YOLO('yolov8x.pt')

-    >>> trace_annotator = sv.TraceAnnotator()
+    trace_annotator = sv.TraceAnnotator()

-    >>> video_info = sv.VideoInfo.from_video_path(video_path='...')
-    >>> frames_generator = get_video_frames_generator(source_path='...')
-    >>> tracker = sv.ByteTrack()
+    video_info = sv.VideoInfo.from_video_path(video_path='...')
+    frames_generator = get_video_frames_generator(source_path='...')
+    tracker = sv.ByteTrack()

-    >>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
-    ...    for frame in frames_generator:
-    ...        result = model(frame)[0]
-    ...        detections = sv.Detections.from_ultralytics(result)
-    ...        detections = tracker.update_with_detections(detections)
-    ...        annotated_frame = trace_annotator.annotate(
-    ...            scene=frame.copy(),
-    ...            detections=detections)
-    ...        sink.write_frame(frame=annotated_frame)
+    with sv.VideoSink(target_path='...', video_info=video_info) as sink:
+        for frame in frames_generator:
+            result = model(frame)[0]
+            detections = sv.Detections.from_ultralytics(result)
+            detections = tracker.update_with_detections(detections)
+            annotated_frame = trace_annotator.annotate(
+                scene=frame.copy(),
+                detections=detections)
+            sink.write_frame(frame=annotated_frame)
    ```

    <div class="result" markdown>
@@ -351,24 +352,24 @@ comments: true
 === "HeatMap"

    ```python
-    >>> import supervision as sv
-    >>> from ultralytics import YOLO
+    import supervision as sv
+    from ultralytics import YOLO

-    >>> model = YOLO('yolov8x.pt')
+    model = YOLO('yolov8x.pt')

-    >>> heat_map_annotator = sv.HeatMapAnnotator()
+    heat_map_annotator = sv.HeatMapAnnotator()

-    >>> video_info = sv.VideoInfo.from_video_path(video_path='...')
-    >>> frames_generator = get_video_frames_generator(source_path='...')
+    video_info = sv.VideoInfo.from_video_path(video_path='...')
+    frames_generator = get_video_frames_generator(source_path='...')

-    >>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
-    ...    for frame in frames_generator:
-    ...        result = model(frame)[0]
-    ...        detections = sv.Detections.from_ultralytics(result)
-    ...        annotated_frame = heat_map_annotator.annotate(
-    ...            scene=frame.copy(),
-    ...            detections=detections)
-    ...        sink.write_frame(frame=annotated_frame)
+    with sv.VideoSink(target_path='...', video_info=video_info) as sink:
+        for frame in frames_generator:
+            result = model(frame)[0]
+            detections = sv.Detections.from_ultralytics(result)
+            annotated_frame = heat_map_annotator.annotate(
+                scene=frame.copy(),
+                detections=detections)
+            sink.write_frame(frame=annotated_frame)
    ```

    <div class="result" markdown>
@@ -389,6 +390,10 @@ comments: true

 :::supervision.annotators.core.BoxCornerAnnotator

+## OrientedBoxAnnotator
+
+:::supervision.annotators.core.OrientedBoxAnnotator
+
 ## ColorAnnotator

 :::supervision.annotators.core.ColorAnnotator
@@ -1,3 +1,75 @@
+### 0.18.0 <small>January 25, 2024</small>
+
+- Added [#633](https://github.com/roboflow/supervision/pull/720): [`sv.PercentageBarAnnotator`](https://supervision.roboflow.com/annotators/#percentagebarannotator) allowing to annotate images and videos with percentage values representing confidence or other custom property.
+
+```python
+>>> import supervision as sv
+
+>>> image = ...
+>>> detections = sv.Detections(...)
+
+>>> percentage_bar_annotator = sv.PercentageBarAnnotator()
+>>> annotated_frame = percentage_bar_annotator.annotate(
+...     scene=image.copy(),
+...     detections=detections
+... )
+```
+
+- Added [#702](https://github.com/roboflow/supervision/pull/702): [`sv.RoundBoxAnnotator`](https://supervision.roboflow.com/annotators/#roundboxannotator) allowing to annotate images and videos with rounded corners bounding boxes.
+
+- Added [#770](https://github.com/roboflow/supervision/pull/770): [`sv.OrientedBoxAnnotator`](https://supervision.roboflow.com/annotators/#orientedboxannotator) allowing to annotate images and videos with OBB (Oriented Bounding Boxes).
+
+```python
+import cv2
+import supervision as sv
+from ultralytics import YOLO
+
+image = cv2.imread(<SOURCE_IMAGE_PATH>)
+model = YOLO("yolov8n-obb.pt")
+
+result = model(image)[0]
+detections = sv.Detections.from_ultralytics(result)
+
+oriented_box_annotator = sv.OrientedBoxAnnotator()
+annotated_frame = oriented_box_annotator.annotate(
+    scene=image.copy(),
+    detections=detections
+)
+```
+
+- Added [#696](https://github.com/roboflow/supervision/pull/696): [`sv.DetectionsSmoother`](https://supervision.roboflow.com/detection/tools/smoother/#detection-smoother) allowing for smoothing detections over multiple frames in video tracking.
+
+- Added [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.from_matplotlib`](https://supervision.roboflow.com/draw/color/#supervision.draw.color.ColorPalette.from_matplotlib) allowing users to create a `sv.ColorPalette` instance from a Matplotlib color palette.
+
+```python
+>>> import supervision as sv
+
+>>> sv.ColorPalette.from_matplotlib('viridis', 5)
+ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
+```
+
+- Changed [#770](https://github.com/roboflow/supervision/pull/770): [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_ultralytics) adding support for OBB (Oriented Bounding Boxes).
+
+- Changed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](https://supervision.roboflow.com/detection/tools/line_zone/#linezone) to now accept a list of specific box anchors that must cross the line for a detection to be counted. This update marks a significant improvement from the previous requirement, where all four box corners were necessary. Users can now specify a single anchor, such as `sv.Position.BOTTOM_CENTER`, or any other combination of anchors defined as `List[sv.Position]`.
+
+- Changed [#756](https://github.com/roboflow/supervision/pull/756): [`sv.Color`](https://supervision.roboflow.com/draw/color/#color)'s and [`sv.ColorPalette`](https://supervision.roboflow.com/draw/color/#colorpalette)'s method of accessing predefined colors, transitioning from a function-based approach (`sv.Color.red()`) to a more intuitive and conventional property-based method (`sv.Color.RED`).
+
+!!! warning
+
+    `sv.ColorPalette.default()` is deprecated and will be removed in `supervision-0.21.0`. Use `sv.ColorPalette.DEFAULT` instead.
+
+
+- Changed [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.DEFAULT`](https://supervision.roboflow.com/draw/color/#colorpalette) value, giving users a more extensive set of annotation colors.
+
+- Changed [#677](https://github.com/roboflow/supervision/pull/677): `sv.Detections.from_roboflow` to [`sv.Detections.from_inference`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_inference) streamlining its functionality to be compatible with both the both [inference](https://github.com/roboflow/inference) pip package and the Robloflow [hosted API](https://docs.roboflow.com/deploy/hosted-api).
+
+!!! warning
+
+    `Detections.from_roboflow()` is deprecated and will be removed in `supervision-0.21.0`. Use `Detections.from_inference` instead.
+
+
+- Fixed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](https://supervision.roboflow.com/detection/tools/line_zone/#linezone) functionality to accurately update the counter when an object crosses a line from any direction, including from the side. This enhancement enables more precise tracking and analytics, such as calculating individual in/out counts for each lane on the road.
+
 ### 0.17.0 <small>December 06, 2023</small>

 - Added [#633](https://github.com/roboflow/supervision/pull/633): [`sv.PixelateAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.PixelateAnnotator) allowing to pixelate objects on images and videos.
@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---

 ## Detections
@@ -0,0 +1,8 @@
+---
+comments: true
+status: new
+---
+
+## Detection Smoother
+
+:::supervision.detection.tools.smoother.DetectionsSmoother
@@ -1,5 +0,0 @@
---
-comments: true
---
-
-🚧 Page under construction.
@@ -1,5 +0,0 @@
---
-comments: true
---
-
-🚧 Page under construction.
@@ -7,7 +7,7 @@ comments: true
    <a align="center" href="" target="_blank">
      <img
        width="850"
-        src="https://media.roboflow.com/open-source/supervision/roboflow-supervision-banner.png?ik-sdk-version=javascript-1.4.3&updatedAt=1674062891088"
+        src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529"
      >
    </a>
  </p>
@@ -17,6 +17,47 @@ comments: true

 We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us!

+<video controls>
+    <source
+        src="https://media.roboflow.com/traffic_analysis_result.mp4"
+        type="video/mp4"
+    >
+</video>
+
+## 🚀 Quickstart
+
+<div class="grid cards" markdown>
+
+-   __Detect and Annotate__
+
+    ---
+
+    Annotate predictions from a range of object detection and segmentation models
+
+    [:octicons-arrow-right-24: Tutorial](how_to/detect_and_annotate)
+
+-   __Track Objects__
+
+    ---
+
+    Discover how to enhance video analysis by implementing seamless object tracking
+
+    [:octicons-arrow-right-24: Tutorial](how_to/track_objects)
+
+-   > __Count Objects Crossing Line__
+
+    ---
+
+    Explore methods to accurately count and analyze objects crossing a predefined line
+
+-   > __Filter Objects in Zone__
+
+    ---
+
+    Master the techniques to selectively filter and focus on objects within a specific zone
+
+</div>
+
 ## 💻 Install

 You can install `supervision` with pip in a
@@ -28,10 +28,8 @@ nav:
  - Home: index.md
  - How to:
      - Detect and Annotate: how_to/detect_and_annotate.md
-      - Process Video: how_to/process_video.md
      - Track Objects: how_to/track_objects.md
      - Filter Detections: how_to/filter_detections.md
-      - Evaluate Model: how_to/evaluate_model.md
  - API:
    - Classifications:
        - Core: classification/core.md
@@ -42,6 +40,7 @@ nav:
        - Line Zone: detection/tools/line_zone.md
        - Polygon Zone: detection/tools/polygon_zone.md
        - Inference Slicer: detection/tools/inference_slicer.md
+        - Detection Smoother: detection/tools/smoother.md
    - Annotators: annotators.md
    - Trackers: trackers.md
    - Datasets: datasets.md
@@ -82,6 +81,7 @@ theme:
    code: Roboto Mono
  features:
    - content.code.copy
+    - content.code.annotate

 plugins:
  - mkdocstrings
@@ -97,6 +97,9 @@ markdown_extensions:
      alternate_style: true
  - toc:
      permalink: true
+  - pymdownx.emoji:
+      emoji_index: !!python/name:material.extensions.emoji.twemoji
+      emoji_generator: !!python/name:material.extensions.emoji.to_svg

 extra_javascript:
  - "https://widget.kapa.ai/kapa-widget.bundle.js"
@@ -672,47 +672,56 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]

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+version = "42.0.0"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
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 ]

 [package.dependencies]
-cffi = ">=1.12"
+cffi = {version = ">=1.12", markers = "platform_python_implementation != \"PyPy\""}

 [package.extras]
 docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"]
-docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
+docstest = ["pyenchant (>=1.6.11)", "readme-renderer", "sphinxcontrib-spelling (>=4.0.1)"]
 nox = ["nox"]
-pep8test = ["black", "check-sdist", "mypy", "ruff"]
+pep8test = ["check-sdist", "click", "mypy", "ruff"]
 sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]

 [[package]]
@@ -1254,13 +1263,13 @@ files = [

 [[package]]
 name = "jsonschema"
-version = "4.21.0"
+version = "4.21.1"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]

 [[package]]
@@ -1907,13 +1926,13 @@ mkdocs = ">=1.1"

 [[package]]
 name = "mkdocs-material"
-version = "9.5.4"
+version = "9.5.5"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]

 [package.dependencies]
@@ -1925,7 +1944,7 @@ markdown = ">=3.2,<4.0"
 mkdocs = ">=1.5.3,<1.6.0"
 mkdocs-material-extensions = ">=1.3,<2.0"
 paginate = ">=0.5,<1.0"
-pillow = {version = ">=9.4,<10.0", optional = true, markers = "extra == \"imaging\""}
+pillow = {version = ">=10.2,<11.0", optional = true, markers = "extra == \"imaging\""}
 pygments = ">=2.16,<3.0"
 pymdown-extensions = ">=10.2,<11.0"
 regex = ">=2022.4"
@@ -1933,7 +1952,7 @@ requests = ">=2.26,<3.0"

 [package.extras]
 git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2,<2.0)"]
-imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=9.4,<10.0)"]
+imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=10.2,<11.0)"]
 recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"]

 [[package]]
@@ -2143,13 +2162,13 @@ test = ["pep440", "pre-commit", "pytest", "testpath"]

 [[package]]
 name = "nest-asyncio"
-version = "1.5.9"
+version = "1.6.0"
 description = "Patch asyncio to allow nested event loops"
 optional = false
 python-versions = ">=3.5"
 files = [
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 ]

 [[package]]
@@ -2284,12 +2303,31 @@ files = [
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 ]

+[package.dependencies]
+numpy = {version = ">=1.23.5", markers = "python_version >= \"3.11\""}
+
+[[package]]
+name = "opencv-python"
+version = "4.9.0.80"
+description = "Wrapper package for OpenCV python bindings."
+optional = true
+python-versions = ">=3.6"
+files = [
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+    {file = "opencv_python-4.9.0.80-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:7e5f7aa4486651a6ebfa8ed4b594b65bd2d2f41beeb4241a3e4b1b85acbbbadb"},
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+    {file = "opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7b34a52e9da36dda8c151c6394aed602e4b17fa041df0b9f5b93ae10b0fcca2a"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4088cab82b66a3b37ffc452976b14a3c599269c247895ae9ceb4066d8188a57"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-win32.whl", hash = "sha256:dcf000c36dd1651118a2462257e3a9e76db789a78432e1f303c7bac54f63ef6c"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-win_amd64.whl", hash = "sha256:3f16f08e02b2a2da44259c7cc712e779eff1dd8b55fdb0323e8cab09548086c0"},
+]
+
 [package.dependencies]
 numpy = [
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-    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
    {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
    {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
@@ -2310,25 +2348,44 @@ files = [
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 ]

+[package.dependencies]
+numpy = {version = ">=1.23.5", markers = "python_version >= \"3.11\""}
+
+[[package]]
+name = "opencv-python-headless"
+version = "4.9.0.80"
+description = "Wrapper package for OpenCV python bindings."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "opencv-python-headless-4.9.0.80.tar.gz", hash = "sha256:71a4cd8cf7c37122901d8e81295db7fb188730e33a0e40039a4e59c1030b0958"},
+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:2ea8a2edc4db87841991b2fbab55fc07b97ecb602e0f47d5d485bd75cee17c1a"},
+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:e0ee54e27be493e8f7850847edae3128e18b540dac1d7b2e4001b8944e11e1c6"},
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+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:976656362d68d9f40a5c66f83901430538002465f7db59142784f3893918f3df"},
+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-win32.whl", hash = "sha256:11e3849d83e6651d4e7699aadda9ec7ed7c38957cbbcb99db074f2a2d2de9670"},
+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-win_amd64.whl", hash = "sha256:a8056c2cb37cd65dfcdf4153ca16f7362afcf3a50d600d6bb69c660fc61ee29c"},
+]
+
 [package.dependencies]
 numpy = [
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-    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
    {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
    {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
 ]

 [[package]]
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-version = "7.4.0"
+version = "7.6.0"
 description = "A decorator to automatically detect mismatch when overriding a method."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]

 [[package]]
@@ -2416,82 +2473,88 @@ files = [

 [[package]]
 name = "pillow"
-version = "9.5.0"
+version = "10.2.0"
 description = "Python Imaging Library (Fork)"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "scipy-1.12.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8b8066bce124ee5531d12a74b617d9ac0ea59245246410e19bca549656d9a40a"},
+    {file = "scipy-1.12.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:8bee4993817e204d761dba10dbab0774ba5a8612e57e81319ea04d84945375ba"},
+    {file = "scipy-1.12.0-cp311-cp311-win_amd64.whl", hash = "sha256:a24024d45ce9a675c1fb8494e8e5244efea1c7a09c60beb1eeb80373d0fecc70"},
+    {file = "scipy-1.12.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e7e76cc48638228212c747ada851ef355c2bb5e7f939e10952bc504c11f4e372"},
+    {file = "scipy-1.12.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:f7ce148dffcd64ade37b2df9315541f9adad6efcaa86866ee7dd5db0c8f041c3"},
+    {file = "scipy-1.12.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c39f92041f490422924dfdb782527a4abddf4707616e07b021de33467f917bc"},
+    {file = "scipy-1.12.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a7ebda398f86e56178c2fa94cad15bf457a218a54a35c2a7b4490b9f9cb2676c"},
+    {file = "scipy-1.12.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:95e5c750d55cf518c398a8240571b0e0782c2d5a703250872f36eaf737751338"},
+    {file = "scipy-1.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:e646d8571804a304e1da01040d21577685ce8e2db08ac58e543eaca063453e1c"},
+    {file = "scipy-1.12.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:913d6e7956c3a671de3b05ccb66b11bc293f56bfdef040583a7221d9e22a2e35"},
+    {file = "scipy-1.12.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:bba1b0c7256ad75401c73e4b3cf09d1f176e9bd4248f0d3112170fb2ec4db067"},
+    {file = "scipy-1.12.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:730badef9b827b368f351eacae2e82da414e13cf8bd5051b4bdfd720271a5371"},
+    {file = "scipy-1.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6546dc2c11a9df6926afcbdd8a3edec28566e4e785b915e849348c6dd9f3f490"},
+    {file = "scipy-1.12.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:196ebad3a4882081f62a5bf4aeb7326aa34b110e533aab23e4374fcccb0890dc"},
+    {file = "scipy-1.12.0-cp39-cp39-win_amd64.whl", hash = "sha256:b360f1b6b2f742781299514e99ff560d1fe9bd1bff2712894b52abe528d1fd1e"},
+    {file = "scipy-1.12.0.tar.gz", hash = "sha256:4bf5abab8a36d20193c698b0f1fc282c1d083c94723902c447e5d2f1780936a3"},
 ]

 [package.dependencies]
-numpy = ">=1.21.6,<1.28.0"
+numpy = ">=1.22.4,<1.29.0"

 [package.extras]
 dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
 doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
-test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]

 [[package]]
 name = "secretstorage"
@@ -3909,4 +3972,4 @@ desktop = ["opencv-python"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "3d59673b11422c0c463ec7b7e20b8a295155d71a6c42ad97f819e9798643b0ef"
+content-hash = "5e5a6f753fc524c096453bc89bb70514d1a7b4ffda3a9c8ed4938b549d194b3f"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.18.0rc3"
+version = "0.18.0"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
@@ -37,9 +37,8 @@ scipy = [
    { version = "1.10.0", python = "<3.9" },
    { version = "^1.10.0", python = ">=3.9" }
 ]
-matplotlib = ">=3.5.0"
+matplotlib = ">=3.6.0"
 pyyaml = ">=5.3"
-pillow = ">=9.4"
 opencv-python = { version = ">=4.5.5.64", optional = true }
 opencv-python-headless = ">=4.5.5.64"
 requests = { version = ">=2.26.0,<=2.31.0", optional = true }
@@ -47,6 +46,7 @@ tqdm = { version = ">=4.62.3,<=4.66.1", optional = true }
 defusedxml = "^0.7.1"


+
 [tool.poetry.extras]
 desktop = ["opencv-python"]
 assets = ["requests","tqdm"]
@@ -65,7 +65,7 @@ tox = "^4.11.4"


 [tool.poetry.group.docs.dependencies]
-mkdocs-material = {extras = ["imaging"], version = "^9.5.4"}
+mkdocs-material = {extras = ["imaging"], version = "^9.5.5"}
 mkdocstrings = {extras = ["python"], version = ">=0.20,<0.25"}


@@ -18,6 +18,7 @@ from supervision.annotators.core import (
    HeatMapAnnotator,
    LabelAnnotator,
    MaskAnnotator,
+    OrientedBoxAnnotator,
    PercentageBarAnnotator,
    PixelateAnnotator,
    PolygonAnnotator,
@@ -37,6 +38,7 @@ from supervision.detection.core import Detections
 from supervision.detection.line_counter import LineZone, LineZoneAnnotator
 from supervision.detection.tools.inference_slicer import InferenceSlicer
 from supervision.detection.tools.polygon_zone import PolygonZone, PolygonZoneAnnotator
+from supervision.detection.tools.smoother import DetectionsSmoother
 from supervision.detection.utils import (
    box_iou_batch,
    calculate_masks_centroids,
@@ -57,16 +57,16 @@ class BoundingBoxAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> bounding_box_annotator = sv.BoundingBoxAnnotator()
-            >>> annotated_frame = bounding_box_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            bounding_box_annotator = sv.BoundingBoxAnnotator()
+            annotated_frame = bounding_box_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![bounding-box-annotator-example](https://media.roboflow.com/
@@ -92,13 +92,93 @@ class BoundingBoxAnnotator(BaseAnnotator):
        return scene


+class OrientedBoxAnnotator(BaseAnnotator):
+    """
+    A class for drawing oriented bounding boxes on an image using provided detections.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        thickness: int = 2,
+        color_lookup: ColorLookup = ColorLookup.CLASS,
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating detections.
+            thickness (int): Thickness of the bounding box lines.
+            color_lookup (str): Strategy for mapping colors to annotations.
+                Options are `INDEX`, `CLASS`, `TRACK`.
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.thickness: int = thickness
+        self.color_lookup: ColorLookup = color_lookup
+
+    def annotate(
+        self,
+        scene: np.ndarray,
+        detections: Detections,
+        custom_color_lookup: Optional[np.ndarray] = None,
+    ) -> np.ndarray:
+        """
+        Annotates the given scene with oriented bounding boxes based on the provided detections.
+
+        Args:
+            scene (np.ndarray): The image where bounding boxes will be drawn.
+            detections (Detections): Object detections to annotate.
+            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
+                Allows to override the default color mapping strategy.
+
+        Returns:
+            The annotated image.
+
+        Example:
+            ```python
+            import cv2
+            import supervision as sv
+            from ultralytics import YOLO
+
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            model = YOLO("yolov8n-obb.pt")
+
+            result = model(image)[0]
+            detections = sv.Detections.from_ultralytics(result)
+
+            oriented_box_annotator = sv.OrientedBoxAnnotator()
+            annotated_frame = oriented_box_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
+            ```
+        """  # noqa E501 // docs
+
+        if detections.data is None or "xyxyxyxy" not in detections.data:
+            return scene
+
+        for detection_idx in range(len(detections)):
+            bbox = np.int0(detections.data.get("xyxyxyxy")[detection_idx])
+            color = resolve_color(
+                color=self.color,
+                detections=detections,
+                detection_idx=detection_idx,
+                color_lookup=self.color_lookup
+                if custom_color_lookup is None
+                else custom_color_lookup,
+            )
+
+            cv2.drawContours(scene, [bbox], 0, color.as_bgr(), self.thickness)
+
+        return scene
+
+
 class MaskAnnotator(BaseAnnotator):
    """
    A class for drawing masks on an image using provided detections.

    !!! warning

-        This annotator utilizes the `sv.Detections.mask`.
+        This annotator uses `sv.Detections.mask`.
    """

    def __init__(
@@ -139,16 +219,16 @@ class MaskAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> mask_annotator = sv.MaskAnnotator()
-            >>> annotated_frame = mask_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            mask_annotator = sv.MaskAnnotator()
+            annotated_frame = mask_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![mask-annotator-example](https://media.roboflow.com/
@@ -181,7 +261,7 @@ class PolygonAnnotator(BaseAnnotator):

    !!! warning

-        This annotator utilizes the `sv.Detections.mask`.
+        This annotator uses `sv.Detections.mask`.
    """

    def __init__(
@@ -222,16 +302,16 @@ class PolygonAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> polygon_annotator = sv.PolygonAnnotator()
-            >>> annotated_frame = polygon_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            polygon_annotator = sv.PolygonAnnotator()
+            annotated_frame = polygon_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![polygon-annotator-example](https://media.roboflow.com/
@@ -304,16 +384,16 @@ class ColorAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> color_annotator = sv.ColorAnnotator()
-            >>> annotated_frame = color_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            color_annotator = sv.ColorAnnotator()
+            annotated_frame = color_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![box-mask-annotator-example](https://media.roboflow.com/
@@ -349,7 +429,7 @@ class HaloAnnotator(BaseAnnotator):

    !!! warning

-        This annotator utilizes the `sv.Detections.mask`.
+        This annotator uses `sv.Detections.mask`.
    """

    def __init__(
@@ -394,16 +474,16 @@ class HaloAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> halo_annotator = sv.HaloAnnotator()
-            >>> annotated_frame = halo_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            halo_annotator = sv.HaloAnnotator()
+            annotated_frame = halo_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![halo-annotator-example](https://media.roboflow.com/
@@ -488,16 +568,16 @@ class EllipseAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> ellipse_annotator = sv.EllipseAnnotator()
-            >>> annotated_frame = ellipse_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            ellipse_annotator = sv.EllipseAnnotator()
+            annotated_frame = ellipse_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![ellipse-annotator-example](https://media.roboflow.com/
@@ -575,16 +655,16 @@ class BoxCornerAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> corner_annotator = sv.BoxCornerAnnotator()
-            >>> annotated_frame = corner_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            corner_annotator = sv.BoxCornerAnnotator()
+            annotated_frame = corner_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![box-corner-annotator-example](https://media.roboflow.com/
@@ -659,16 +739,16 @@ class CircleAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> circle_annotator = sv.CircleAnnotator()
-            >>> annotated_frame = circle_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            circle_annotator = sv.CircleAnnotator()
+            annotated_frame = circle_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```


@@ -745,16 +825,16 @@ class DotAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> dot_annotator = sv.DotAnnotator()
-            >>> annotated_frame = dot_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            dot_annotator = sv.DotAnnotator()
+            annotated_frame = dot_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![dot-annotator-example](https://media.roboflow.com/
@@ -872,16 +952,16 @@ class LabelAnnotator:

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
-            >>> annotated_frame = label_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
+            annotated_frame = label_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![label-annotator-example](https://media.roboflow.com/
@@ -986,16 +1066,16 @@ class BlurAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> blur_annotator = sv.BlurAnnotator()
-            >>> annotated_frame = circle_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            blur_annotator = sv.BlurAnnotator()
+            annotated_frame = circle_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![blur-annotator-example](https://media.roboflow.com/
@@ -1020,7 +1100,7 @@ class TraceAnnotator:

    !!! warning

-        This annotator utilizes the `sv.Detections.tracker_id`. Read
+        This annotator uses the `sv.Detections.tracker_id`. Read
        [here](https://supervision.roboflow.com/trackers/) to learn how to plug
        tracking into your inference pipeline.
    """
@@ -1071,26 +1151,25 @@ class TraceAnnotator:

        Example:
            ```python
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import supervision as sv
+            from ultralytics import YOLO

-            >>> model = YOLO('yolov8x.pt')
+            model = YOLO('yolov8x.pt')
+            trace_annotator = sv.TraceAnnotator()

-            >>> trace_annotator = sv.TraceAnnotator()
+            video_info = sv.VideoInfo.from_video_path(video_path='...')
+            frames_generator = sv.get_video_frames_generator(source_path='...')
+            tracker = sv.ByteTrack()

-            >>> video_info = sv.VideoInfo.from_video_path(video_path='...')
-            >>> frames_generator = sv.get_video_frames_generator(source_path='...')
-            >>> tracker = sv.ByteTrack()
-
-            >>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
-            ...    for frame in frames_generator:
-            ...        result = model(frame)[0]
-            ...        detections = sv.Detections.from_ultralytics(result)
-            ...        detections = tracker.update_with_detections(detections)
-            ...        annotated_frame = trace_annotator.annotate(
-            ...            scene=frame.copy(),
-            ...            detections=detections)
-            ...        sink.write_frame(frame=annotated_frame)
+            with sv.VideoSink(target_path='...', video_info=video_info) as sink:
+               for frame in frames_generator:
+                   result = model(frame)[0]
+                   detections = sv.Detections.from_ultralytics(result)
+                   detections = tracker.update_with_detections(detections)
+                   annotated_frame = trace_annotator.annotate(
+                       scene=frame.copy(),
+                       detections=detections)
+                   sink.write_frame(frame=annotated_frame)
            ```

        ![trace-annotator-example](https://media.roboflow.com/
@@ -1167,24 +1246,24 @@ class HeatMapAnnotator:

        Example:
            ```python
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import supervision as sv
+            from ultralytics import YOLO

-            >>> model = YOLO('yolov8x.pt')
+            model = YOLO('yolov8x.pt')

-            >>> heat_map_annotator = sv.HeatMapAnnotator()
+            heat_map_annotator = sv.HeatMapAnnotator()

-            >>> video_info = sv.VideoInfo.from_video_path(video_path='...')
-            >>> frames_generator = get_video_frames_generator(source_path='...')
+            video_info = sv.VideoInfo.from_video_path(video_path='...')
+            frames_generator = get_video_frames_generator(source_path='...')

-            >>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
-            ...    for frame in frames_generator:
-            ...        result = model(frame)[0]
-            ...        detections = sv.Detections.from_ultralytics(result)
-            ...        annotated_frame = heat_map_annotator.annotate(
-            ...            scene=frame.copy(),
-            ...            detections=detections)
-            ...        sink.write_frame(frame=annotated_frame)
+            with sv.VideoSink(target_path='...', video_info=video_info) as sink:
+               for frame in frames_generator:
+                   result = model(frame)[0]
+                   detections = sv.Detections.from_ultralytics(result)
+                   annotated_frame = heat_map_annotator.annotate(
+                       scene=frame.copy(),
+                       detections=detections)
+                   sink.write_frame(frame=annotated_frame)
            ```

        ![heatmap-annotator-example](https://media.roboflow.com/
@@ -1244,16 +1323,16 @@ class PixelateAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> pixelate_annotator = sv.PixelateAnnotator()
-            >>> annotated_frame = pixelate_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            pixelate_annotator = sv.PixelateAnnotator()
+            annotated_frame = pixelate_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![pixelate-annotator-example](https://media.roboflow.com/
@@ -1330,16 +1409,16 @@ class TriangleAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> triangle_annotator = sv.TriangleAnnotator()
-            >>> annotated_frame = triangle_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            triangle_annotator = sv.TriangleAnnotator()
+            annotated_frame = triangle_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![triangle-annotator-example](https://media.roboflow.com/
@@ -1423,16 +1502,16 @@ class RoundBoxAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> round_box_annotator = sv.RoundBoxAnnotator()
-            >>> annotated_frame = round_box_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            round_box_annotator = sv.RoundBoxAnnotator()
+            annotated_frame = round_box_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![round-box-annotator-example](https://media.roboflow.com/
@@ -1563,16 +1642,16 @@ class PercentageBarAnnotator(BaseAnnotator):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)

-            >>> percentage_bar_annotator = sv.BoundingBoxAnnotator()
-            >>> annotated_frame = percentage_bar_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            percentage_bar_annotator = sv.BoundingBoxAnnotator()
+            annotated_frame = percentage_bar_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
            ```

        ![percentage-bar-example](https://media.roboflow.com/
@@ -54,9 +54,9 @@ def download_assets(asset_name: Union[VideoAssets, str]) -> str:

    Example:
        ```python
-        >>> from supervision.assets import download_assets, VideoAssets
+        from supervision.assets import download_assets, VideoAssets

-        >>> download_assets(VideoAssets.VEHICLES)
+        download_assets(VideoAssets.VEHICLES)
        "vehicles.mp4"
        ```
    """
@@ -59,18 +59,18 @@ class Classifications:

        Example:
            ```python
-            >>> from PIL import Image
-            >>> import clip
-            >>> import supervision as sv
+            from PIL import Image
+            import clip
+            import supervision as sv

-            >>> model, preprocess = clip.load('ViT-B/32')
+            model, preprocess = clip.load('ViT-B/32')

-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> image = preprocess(image).unsqueeze(0)
+            image = cv2.imread(SOURCE_IMAGE_PATH)
+            image = preprocess(image).unsqueeze(0)

-            >>> text = clip.tokenize(["a diagram", "a dog", "a cat"])
-            >>> output, _ = model(image, text)
-            >>> classifications = sv.Classifications.from_clip(output)
+            text = clip.tokenize(["a diagram", "a dog", "a cat"])
+            output, _ = model(image, text)
+            classifications = sv.Classifications.from_clip(output)
            ```
        """

@@ -97,15 +97,15 @@ class Classifications:

        Example:
            ```python
-            >>> import cv2
-            >>> from ultralytics import YOLO
-            >>> import supervision as sv
+            import cv2
+            from ultralytics import YOLO
+            import supervision as sv

-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> model = YOLO('yolov8n-cls.pt')
+            image = cv2.imread(SOURCE_IMAGE_PATH)
+            model = YOLO('yolov8n-cls.pt')

-            >>> output = model(image)[0]
-            >>> classifications = sv.Classifications.from_ultralytics(output)
+            output = model(image)[0]
+            classifications = sv.Classifications.from_ultralytics(output)
            ```
        """
        confidence = ultralytics_results.probs.data.cpu().numpy()
@@ -125,25 +125,25 @@ class Classifications:

        Example:
            ```python
-            >>> from PIL import Image
-            >>> import timm
-            >>> from timm.data import resolve_data_config, create_transform
-            >>> import supervision as sv
+            from PIL import Image
+            import timm
+            from timm.data import resolve_data_config, create_transform
+            import supervision as sv

-            >>> model = timm.create_model(
-            ...     model_name='hf-hub:nateraw/resnet50-oxford-iiit-pet',
-            ...     pretrained=True
-            ... ).eval()
+            model = timm.create_model(
+                model_name='hf-hub:nateraw/resnet50-oxford-iiit-pet',
+                pretrained=True
+            ).eval()

-            >>> config = resolve_data_config({}, model=model)
-            >>> transform = create_transform(**config)
+            config = resolve_data_config({}, model=model)
+            transform = create_transform(**config)

-            >>> image = Image.open(SOURCE_IMAGE_PATH).convert('RGB')
-            >>> x = transform(image).unsqueeze(0)
+            image = Image.open(SOURCE_IMAGE_PATH).convert('RGB')
+            x = transform(image).unsqueeze(0)

-            >>> output = model(x)
+            output = model(x)

-            >>> classifications = sv.Classifications.from_timm(output)
+            classifications = sv.Classifications.from_timm(output)
            ```
        """
        confidence = timm_results.cpu().detach().numpy()[0]
@@ -168,11 +168,11 @@ class Classifications:

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> classifications = sv.Classifications(...)
+            classifications = sv.Classifications(...)

-            >>> classifications.get_top_k(1)
+            classifications.get_top_k(1)

            (array([1]), array([0.9]))
            ```
@@ -118,13 +118,13 @@ class DetectionDataset(BaseDataset):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> ds = sv.DetectionDataset(...)
-            >>> train_ds, test_ds = ds.split(split_ratio=0.7,
-            ...                              random_state=42, shuffle=True)
-            >>> len(train_ds), len(test_ds)
-            (700, 300)
+            ds = sv.DetectionDataset(...)
+            train_ds, test_ds = ds.split(split_ratio=0.7,
+                                         random_state=42, shuffle=True)
+            len(train_ds), len(test_ds)
+            # (700, 300)
            ```
        """

@@ -231,24 +231,24 @@ class DetectionDataset(BaseDataset):

        Example:
            ```python
-            >>> import roboflow
-            >>> from roboflow import Roboflow
-            >>> import supervision as sv
+            import roboflow
+            from roboflow import Roboflow
+            import supervision as sv

-            >>> roboflow.login()
+            roboflow.login()

-            >>> rf = Roboflow()
+            rf = Roboflow()

-            >>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
-            >>> dataset = project.version(PROJECT_VERSION).download("voc")
+            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
+            dataset = project.version(PROJECT_VERSION).download("voc")

-            >>> ds = sv.DetectionDataset.from_pascal_voc(
-            ...     images_directory_path=f"{dataset.location}/train/images",
-            ...     annotations_directory_path=f"{dataset.location}/train/labels"
-            ... )
+            ds = sv.DetectionDataset.from_pascal_voc(
+                images_directory_path=f"{dataset.location}/train/images",
+                annotations_directory_path=f"{dataset.location}/train/labels"
+            )

-            >>> ds.classes
-            ['dog', 'person']
+            ds.classes
+            # ['dog', 'person']
            ```
        """

@@ -288,25 +288,24 @@ class DetectionDataset(BaseDataset):

        Example:
            ```python
-            >>> import roboflow
-            >>> from roboflow import Roboflow
-            >>> import supervision as sv
+            import roboflow
+            from roboflow import Roboflow
+            import supervision as sv

-            >>> roboflow.login()
+            roboflow.login()
+            rf = Roboflow()

-            >>> rf = Roboflow()
+            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
+            dataset = project.version(PROJECT_VERSION).download("yolov5")

-            >>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
-            >>> dataset = project.version(PROJECT_VERSION).download("yolov5")
+            ds = sv.DetectionDataset.from_yolo(
+                images_directory_path=f"{dataset.location}/train/images",
+                annotations_directory_path=f"{dataset.location}/train/labels",
+                data_yaml_path=f"{dataset.location}/data.yaml"
+            )

-            >>> ds = sv.DetectionDataset.from_yolo(
-            ...     images_directory_path=f"{dataset.location}/train/images",
-            ...     annotations_directory_path=f"{dataset.location}/train/labels",
-            ...     data_yaml_path=f"{dataset.location}/data.yaml"
-            ... )
-
-            >>> ds.classes
-            ['dog', 'person']
+            ds.classes
+            # ['dog', 'person']
            ```
        """
        classes, images, annotations = load_yolo_annotations(
@@ -394,24 +393,23 @@ class DetectionDataset(BaseDataset):

        Example:
            ```python
-            >>> import roboflow
-            >>> from roboflow import Roboflow
-            >>> import supervision as sv
+            import roboflow
+            from roboflow import Roboflow
+            import supervision as sv

-            >>> roboflow.login()
+            roboflow.login()
+            rf = Roboflow()

-            >>> rf = Roboflow()
+            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
+            dataset = project.version(PROJECT_VERSION).download("coco")

-            >>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
-            >>> dataset = project.version(PROJECT_VERSION).download("coco")
+            ds = sv.DetectionDataset.from_coco(
+                images_directory_path=f"{dataset.location}/train",
+                annotations_path=f"{dataset.location}/train/_annotations.coco.json",
+            )

-            >>> ds = sv.DetectionDataset.from_coco(
-            ...     images_directory_path=f"{dataset.location}/train",
-            ...     annotations_path=f"{dataset.location}/train/_annotations.coco.json",
-            ... )
-
-            >>> ds.classes
-            ['dog', 'person']
+            ds.classes
+            # ['dog', 'person']
            ```
        """
        classes, images, annotations = load_coco_annotations(
@@ -486,25 +484,25 @@ class DetectionDataset(BaseDataset):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> ds_1 = sv.DetectionDataset(...)
-            >>> len(ds_1)
-            100
-            >>> ds_1.classes
-            ['dog', 'person']
+            ds_1 = sv.DetectionDataset(...)
+            len(ds_1)
+            # 100
+            ds_1.classes
+            # ['dog', 'person']

-            >>> ds_2 = sv.DetectionDataset(...)
-            >>> len(ds_2)
-            200
-            >>> ds_2.classes
-            ['cat']
+            ds_2 = sv.DetectionDataset(...)
+            len(ds_2)
+            # 200
+            ds_2.classes
+            # ['cat']

-            >>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
-            >>> len(ds_merged)
-            300
-            >>> ds_merged.classes
-            ['cat', 'dog', 'person']
+            ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
+            len(ds_merged)
+            # 300
+            ds_merged.classes
+            # ['cat', 'dog', 'person']
            ```
        """
        merged_images, merged_annotations = {}, {}
@@ -571,13 +569,13 @@ class ClassificationDataset(BaseDataset):

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> cd = sv.ClassificationDataset(...)
-            >>> train_cd,test_cd = cd.split(split_ratio=0.7,
-            ...                             random_state=42,shuffle=True)
-            >>> len(train_cd), len(test_cd)
-            (700, 300)
+            cd = sv.ClassificationDataset(...)
+            train_cd,test_cd = cd.split(split_ratio=0.7,
+                                        random_state=42,shuffle=True)
+            len(train_cd), len(test_cd)
+            # (700, 300)
            ```
        """
        image_names = list(self.images.keys())
@@ -639,20 +637,19 @@ class ClassificationDataset(BaseDataset):

        Example:
            ```python
-            >>> import roboflow
-            >>> from roboflow import Roboflow
-            >>> import supervision as sv
+            import roboflow
+            from roboflow import Roboflow
+            import supervision as sv

-            >>> roboflow.login()
+            roboflow.login()
+            rf = Roboflow()

-            >>> rf = Roboflow()
+            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
+            dataset = project.version(PROJECT_VERSION).download("folder")

-            >>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
-            >>> dataset = project.version(PROJECT_VERSION).download("folder")
-
-            >>> cd = sv.ClassificationDataset.from_folder_structure(
-            ...     root_directory_path=f"{dataset.location}/train"
-            ... )
+            cd = sv.ClassificationDataset.from_folder_structure(
+                root_directory_path=f"{dataset.location}/train"
+            )
            ```
        """
        classes = os.listdir(root_directory_path)
@@ -63,23 +63,22 @@ class BoxAnnotator:

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> classes = ['person', ...]
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            classes = ['person', ...]
+            image = ...
+            detections = sv.Detections(...)

-            >>> box_annotator = sv.BoxAnnotator()
-            >>> labels = [
-            ...     f"{classes[class_id]} {confidence:0.2f}"
-            ...     for _, _, confidence, class_id, _
-            ...     in detections
-            ... ]
-            >>> annotated_frame = box_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections,
-            ...     labels=labels
-            ... )
+            box_annotator = sv.BoxAnnotator()
+            labels = [
+                f"{classes[class_id]} {confidence:0.2f}"
+                for _, _, confidence, class_id, _ in detections
+            ]
+            annotated_frame = box_annotator.annotate(
+                scene=image.copy(),
+                detections=detections,
+                labels=labels
+            )
            ```
        """
        font = cv2.FONT_HERSHEY_SIMPLEX
@@ -126,14 +126,14 @@ class Detections:

        Example:
            ```python
-            >>> import cv2
-            >>> import torch
-            >>> import supervision as sv
+            import cv2
+            import torch
+            import supervision as sv

-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
-            >>> result = model(image)
-            >>> detections = sv.Detections.from_yolov5(result)
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+            result = model(image)
+            detections = sv.Detections.from_yolov5(result)
            ```
        """
        yolov5_detections_predictions = yolov5_results.pred[0].cpu().cpu().numpy()
@@ -150,6 +150,13 @@ class Detections:
        Creates a Detections instance from a
            [YOLOv8](https://github.com/ultralytics/ultralytics) inference result.

+        !!! Note
+
+            `from_ultralytics` is compatible with
+            [detection](https://docs.ultralytics.com/tasks/detect/),
+            [segmentation](https://docs.ultralytics.com/tasks/segment/), and
+            [OBB](https://docs.ultralytics.com/tasks/obb/) models.
+
        Args:
            ultralytics_results (ultralytics.yolo.engine.results.Results):
                The output Results instance from YOLOv8
@@ -159,16 +166,28 @@ class Detections:

        Example:
            ```python
-            >>> import cv2
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import cv2
+            import supervision as sv
+            from ultralytics import YOLO

-            >>> image = cv2.imread(...)
-            >>> model = YOLO('yolov8s.pt')
-            >>> result = model(image)[0]
-            >>> detections = sv.Detections.from_ultralytics(result)
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            model = YOLO('yolov8s.pt')
+
+            result = model(image)[0]
+            detections = sv.Detections.from_ultralytics(result)
            ```
-        """
+        """  # noqa: E501 // docs
+
+        if ultralytics_results.obb is not None:
+            return cls(
+                xyxy=ultralytics_results.obb.xyxy.cpu().numpy(),
+                data={"xyxyxyxy": ultralytics_results.obb.xyxyxyxy.cpu().numpy()},
+                confidence=ultralytics_results.obb.conf.cpu().numpy(),
+                class_id=ultralytics_results.obb.cls.cpu().numpy().astype(int),
+                tracker_id=ultralytics_results.obb.id.int().cpu().numpy()
+                if ultralytics_results.obb.id is not None
+                else None,
+            )

        return cls(
            xyxy=ultralytics_results.boxes.xyxy.cpu().numpy(),
@@ -198,14 +217,15 @@ class Detections:

        Example:
            ```python
-            >>> import cv2
-            >>> from super_gradients.training import models
-            >>> import supervision as sv
+            import cv2
+            from super_gradients.training import models
+            import supervision as sv

-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> model = models.get('yolo_nas_l', pretrained_weights="coco")
-            >>> result = list(model.predict(image, conf=0.35))[0]
-            >>> detections = sv.Detections.from_yolo_nas(result)
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            model = models.get('yolo_nas_l', pretrained_weights="coco")
+
+            result = list(model.predict(image, conf=0.35))[0]
+            detections = sv.Detections.from_yolo_nas(result)
            ```
        """
        if np.asarray(yolo_nas_results.prediction.bboxes_xyxy).shape[0] == 0:
@@ -235,20 +255,16 @@ class Detections:

        Example:
            ```python
-            >>> import tensorflow as tf
-            >>> import tensorflow_hub as hub
-            >>> import numpy as np
-            >>> import cv2
+            import tensorflow as tf
+            import tensorflow_hub as hub
+            import numpy as np
+            import cv2

-            >>> module_handle = "https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1"
-
-            >>> model = hub.load(module_handle)
-
-            >>> img = np.array(cv2.imread(SOURCE_IMAGE_PATH))
-
-            >>> result = model(img)
-
-            >>> detections = sv.Detections.from_tensorflow(result)
+            module_handle = "https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1"
+            model = hub.load(module_handle)
+            img = np.array(cv2.imread(SOURCE_IMAGE_PATH))
+            result = model(img)
+            detections = sv.Detections.from_tensorflow(result)
            ```
        """  # noqa: E501 // docs

@@ -278,15 +294,15 @@ class Detections:

        Example:
            ```python
-            >>> import supervision as sv
-            >>> from deepsparse import Pipeline
+            import supervision as sv
+            from deepsparse import Pipeline

-            >>> yolo_pipeline = Pipeline.create(
-            ...     task="yolo",
-            ...     model_path = "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned80_quant-none"
-            ... )
-            >>> result = yolo_pipeline(<SOURCE IMAGE PATH>)
-            >>> detections = sv.Detections.from_deepsparse(result)
+            yolo_pipeline = Pipeline.create(
+                task="yolo",
+                model_path = "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned80_quant-none"
+             )
+            result = yolo_pipeline(<SOURCE IMAGE PATH>)
+            detections = sv.Detections.from_deepsparse(result)
            ```
        """  # noqa: E501 // docs

@@ -302,9 +318,9 @@ class Detections:
    @classmethod
    def from_mmdetection(cls, mmdet_results) -> Detections:
        """
-        Creates a Detections instance from
-        a [mmdetection](https://github.com/open-mmlab/mmdetection) inference result.
-        Also supported for [mmyolo](https://github.com/open-mmlab/mmyolo)
+        Creates a Detections instance from a
+        [mmdetection](https://github.com/open-mmlab/mmdetection) and
+        [mmyolo](https://github.com/open-mmlab/mmyolo) inference result.

        Args:
            mmdet_results (mmdet.structures.DetDataSample):
@@ -315,16 +331,17 @@ class Detections:

        Example:
            ```python
-            >>> import cv2
-            >>> import supervision as sv
-            >>> from mmdet.apis import DetInferencer
+            import cv2
+            import supervision as sv
+            from mmdet.apis import init_detector, inference_detector

-            >>> inferencer = DetInferencer(model_name, checkpoint, device)
-            >>> mmdet_result = inferencer(SOURCE_IMAGE_PATH, out_dir='./output',
-            ...                           return_datasamples=True)["predictions"][0]
-            >>> detections = sv.Detections.from_mmdetection(mmdet_result)
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            model = init_detector(<CONFIG_PATH>, <WEIGHTS_PATH>, device=<DEVICE>)
+
+            result = inference_detector(model, image)
+            detections = sv.Detections.from_mmdetection(result)
            ```
-        """
+        """  # noqa: E501 // docs

        return cls(
            xyxy=mmdet_results.pred_instances.bboxes.cpu().numpy(),
@@ -364,18 +381,20 @@ class Detections:

        Example:
            ```python
-            >>> import cv2
-            >>> from detectron2.engine import DefaultPredictor
-            >>> from detectron2.config import get_cfg
-            >>> import supervision as sv
+            import cv2
+            import supervision as sv
+            from detectron2.engine import DefaultPredictor
+            from detectron2.config import get_cfg

-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> cfg = get_cfg()
-            >>> cfg.merge_from_file("path/to/config.yaml")
-            >>> cfg.MODEL.WEIGHTS = "path/to/model_weights.pth"
-            >>> predictor = DefaultPredictor(cfg)
-            >>> result = predictor(image)
-            >>> detections = sv.Detections.from_detectron2(result)
+
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            cfg = get_cfg()
+            cfg.merge_from_file(<CONFIG_PATH>)
+            cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
+            predictor = DefaultPredictor(cfg)
+
+            result = predictor(image)
+            detections = sv.Detections.from_detectron2(result)
            ```
        """

@@ -412,14 +431,15 @@ class Detections:

        Example:
            ```python
-            >>> import cv2
-            >>> import supervision as sv
-            >>> from inference.models.utils import get_roboflow_model
+            import cv2
+            import supervision as sv
+            from inference.models.utils import get_roboflow_model

-            >>> image = cv2.imread(...)
-            >>> model = get_roboflow_model(model_id="yolov8s-640")
-            >>> result = model.infer(image)[0]
-            >>> detections = sv.Detections.from_inference(result)
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            model = get_roboflow_model(model_id="yolov8s-640")
+
+            result = model.infer(image)[0]
+            detections = sv.Detections.from_inference(result)
            ```
        """
        with suppress(AttributeError):
@@ -461,14 +481,15 @@ class Detections:

        Example:
            ```python
-            >>> import cv2
-            >>> import supervision as sv
-            >>> from inference.models.utils import get_roboflow_model
+            import cv2
+            import supervision as sv
+            from inference.models.utils import get_roboflow_model

-            >>> image = cv2.imread(...)
-            >>> model = get_roboflow_model(model_id="yolov8s-640")
-            >>> result = model.infer(image)[0]
-            >>> detections = sv.Detections.from_roboflow(result)
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            model = get_roboflow_model(model_id="yolov8s-640")
+
+            result = model.infer(image)[0]
+            detections = sv.Detections.from_roboflow(result)
            ```
        """
        return cls.from_inference(roboflow_result)
@@ -488,17 +509,17 @@ class Detections:

        Example:
            ```python
-            >>> import supervision as sv
-            >>> from segment_anything import (
-            ...     sam_model_registry,
-            ...     SamAutomaticMaskGenerator
-            ... )
+            import supervision as sv
+            from segment_anything import (
+                sam_model_registry,
+                SamAutomaticMaskGenerator
+             )

-            >>> sam_model_reg = sam_model_registry[MODEL_TYPE]
-            >>> sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
-            >>> mask_generator = SamAutomaticMaskGenerator(sam)
-            >>> sam_result = mask_generator.generate(IMAGE)
-            >>> detections = sv.Detections.from_sam(sam_result=sam_result)
+            sam_model_reg = sam_model_registry[MODEL_TYPE]
+            sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
+            mask_generator = SamAutomaticMaskGenerator(sam)
+            sam_result = mask_generator.generate(IMAGE)
+            detections = sv.Detections.from_sam(sam_result=sam_result)
            ```
        """

@@ -535,25 +556,25 @@ class Detections:

        Example:
            ```python
-            >>> import requests
-            >>> import supervision as sv
+            import requests
+            import supervision as sv

-            >>> image = open(input, "rb").read()
+            image = open(input, "rb").read()

-            >>> endpoint = "https://.cognitiveservices.azure.com/"
-            >>> subscription_key = "..."
+            endpoint = "https://.cognitiveservices.azure.com/"
+            subscription_key = ""

-            >>> headers = {
-            ...    "Content-Type": "application/octet-stream",
-            ...    "Ocp-Apim-Subscription-Key": subscription_key
-            ... }
+            headers = {
+                "Content-Type": "application/octet-stream",
+                "Ocp-Apim-Subscription-Key": subscription_key
+             }

-            >>> response = requests.post(endpoint,
-            ...     headers=self.headers,
-            ...     data=image
-            ... ).json()
+            response = requests.post(endpoint,
+                headers=self.headers,
+                data=image
+             ).json()

-            >>> detections = sv.Detections.from_azure_analyze_image(response)
+            detections = sv.Detections.from_azure_analyze_image(response)
            ```
        """
        if "error" in azure_result:
@@ -617,21 +638,21 @@ class Detections:

        Example:
            ```python
-            >>> import supervision as sv
-            >>> import paddle
-            >>> from ppdet.engine import Trainer
-            >>> from ppdet.core.workspace import load_config
+            import supervision as sv
+            import paddle
+            from ppdet.engine import Trainer
+            from ppdet.core.workspace import load_config

-            >>> weights = (...)
-            >>> config = (...)
+            weights = ()
+            config = ()

-            >>> cfg = load_config(config)
-            >>> trainer = Trainer(cfg, mode='test')
-            >>> trainer.load_weights(weights)
+            cfg = load_config(config)
+            trainer = Trainer(cfg, mode='test')
+            trainer.load_weights(weights)

-            >>> paddledet_result = trainer.predict([images])[0]
+            paddledet_result = trainer.predict([images])[0]

-            >>> detections = sv.Detections.from_paddledet(paddledet_result)
+            detections = sv.Detections.from_paddledet(paddledet_result)
            ```
        """

@@ -655,9 +676,9 @@ class Detections:

        Example:
            ```python
-            >>> from supervision import Detections
+            from supervision import Detections

-            >>> empty_detections = Detections.empty()
+            empty_detections = Detections.empty()
            ```
        """
        return cls(
@@ -689,29 +710,29 @@ class Detections:
            import numpy as np
            import supervision as sv

-            >>> detections_1 = sv.Detections(
-            ...     xyxy=np.array([[15, 15, 100, 100], [200, 200, 300, 300]]),
-            ...     class_id=np.array([1, 2]),
-            ...     data={'feature_vector': np.array([0.1, 0.2)])}
-            ... )
+            detections_1 = sv.Detections(
+                xyxy=np.array([[15, 15, 100, 100], [200, 200, 300, 300]]),
+                class_id=np.array([1, 2]),
+                data={'feature_vector': np.array([0.1, 0.2)])}
+             )

-            >>> detections_2 = sv.Detections(
-            ...     xyxy=np.array([[30, 30, 120, 120]]),
-            ...     class_id=np.array([1]),
-            ...     data={'feature_vector': [np.array([0.3])]}
-            ... )
+            detections_2 = sv.Detections(
+                xyxy=np.array([[30, 30, 120, 120]]),
+                class_id=np.array([1]),
+                data={'feature_vector': [np.array([0.3])]}
+             )

-            >>> merged_detections = Detections.merge([detections_1, detections_2])
+            merged_detections = Detections.merge([detections_1, detections_2])

-            >>> merged_detections.xyxy
+            merged_detections.xyxy
            array([[ 15,  15, 100, 100],
                   [200, 200, 300, 300],
                   [ 30,  30, 120, 120]])

-            >>> merged_detections.class_id
+            merged_detections.class_id
            array([1, 2, 1])

-            >>> merged_detections.data['feature_vector']
+            merged_detections.data['feature_vector']
            array([0.1, 0.2, 0.3])
            ```
        """
@@ -844,17 +865,17 @@ class Detections:

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> detections = sv.Detections(...)
+            detections = sv.Detections()

-            >>> first_detection = detections[0]
-            >>> first_10_detections = detections[0:10]
-            >>> some_detections = detections[[0, 2, 4]]
-            >>> class_0_detections = detections[detections.class_id == 0]
-            >>> high_confidence_detections = detections[detections.confidence > 0.5]
+            first_detection = detections[0]
+            first_10_detections = detections[0:10]
+            some_detections = detections[[0, 2, 4]]
+            class_0_detections = detections[detections.class_id == 0]
+            high_confidence_detections = detections[detections.confidence > 0.5]

-            >>> feature_vector = detections['feature_vector']
+            feature_vector = detections['feature_vector']
            ```
        """
        if isinstance(index, str):
@@ -880,22 +901,21 @@ class Detections:

        Example:
            ```python
-            >>> import cv2
-            >>> from ultralytics import YOLO
-            >>> import supervision as sv
+            import cv2
+            import supervision as sv
+            from ultralytics import YOLO

-            >>> model = YOLO('yolov8s.pt')
+            image = cv2.imread(<SOURCE_IMAGE_PATH>)
+            model = YOLO('yolov8s.pt')

-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
+            result = model(image)[0]
+            detections = sv.Detections.from_ultralytics(result)

-            >>> result = model(image)[0]
-            >>> detections = sv.Detections.from_ultralytics(result)
-
-            >>> detections['names'] = [
-            ...     model.model.names[class_id]
-            ...     for class_id
-            ...     in detections.class_id
-            ... ]
+            detections['names'] = [
+                 model.model.names[class_id]
+                 for class_id
+                 in detections.class_id
+             ]
            ```
        """
        if not isinstance(value, (np.ndarray, list)):
@@ -915,7 +935,7 @@ class Detections:

        Returns:
          np.ndarray: An array of floats containing the area of each detection
-            in the format of `(area_1, area_2, ..., area_n)`,
+            in the format of `(area_1, area_2, , area_n)`,
            where n is the number of detections.
        """
        if self.mask is not None:
@@ -930,7 +950,7 @@ class Detections:

        Returns:
            np.ndarray: An array of floats containing the area of each bounding
-                box in the format of `(area_1, area_2, ..., area_n)`,
+                box in the format of `(area_1, area_2, , area_n)`,
                where n is the number of detections.
        """
        return (self.xyxy[:, 3] - self.xyxy[:, 1]) * (self.xyxy[:, 2] - self.xyxy[:, 0])
@@ -943,7 +963,8 @@ class Detections:

        Args:
            threshold (float, optional): The intersection-over-union threshold
-                to use for non-maximum suppression. Defaults to 0.5.
+                to use for non-maximum suppression. I'm the lower the value the more
+                restrictive the NMS becomes. Defaults to 0.5.
            class_agnostic (bool, optional): Whether to perform class-agnostic
                non-maximum suppression. If True, the class_id of each detection
                will be ignored. Defaults to False.
@@ -15,7 +15,7 @@ class LineZone:

    !!! warning

-        LineZone utilizes the `tracker_id`. Read
+        LineZone uses the `tracker_id`. Read
        [here](https://supervision.roboflow.com/trackers/) to learn how to plug
        tracking into your inference pipeline.

@@ -77,20 +77,20 @@ class InferenceSlicer:

        Example:
            ```python
-            >>> import cv2
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import cv2
+            import supervision as sv
+            from ultralytics import YOLO

-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> model = YOLO(...)
+            image = cv2.imread(SOURCE_IMAGE_PATH)
+            model = YOLO(...)

-            >>> def callback(image_slice: np.ndarray) -> sv.Detections:
-            ...     result = model(image_slice)[0]
-            ...     return sv.Detections.from_ultralytics(result)
+            def callback(image_slice: np.ndarray) -> sv.Detections:
+                result = model(image_slice)[0]
+                return sv.Detections.from_ultralytics(result)

-            >>> slicer = sv.InferenceSlicer(callback = callback)
+            slicer = sv.InferenceSlicer(callback = callback)

-            >>> detections = slicer(image)
+            detections = slicer(image)
            ```
        """
        detections_list = []
@@ -0,0 +1,118 @@
+from collections import defaultdict, deque
+from copy import deepcopy
+from typing import Optional
+
+import numpy as np
+
+from supervision.detection.core import Detections
+
+
+class DetectionsSmoother:
+    """
+    A utility class for smoothing detections over multiple frames in video tracking.
+    It maintains a history of detections for each track and provides smoothed
+    predictions based on these histories.
+
+    <video controls>
+        <source
+            src="https://media.roboflow.com/supervision-detection-smoothing.mp4"
+            type="video/mp4">
+    </video>
+
+    !!! warning
+
+        - `DetectionsSmoother` requires the `tracker_id` for each detection. Refer to
+          [Roboflow Trackers](https://supervision.roboflow.com/trackers/) for
+          information on integrating tracking into your inference pipeline.
+        - This class is not compatible with segmentation models.
+
+    Example:
+        ```python
+        import supervision as sv
+
+        from ultralytics import YOLO
+
+        video_info = sv.VideoInfo.from_video_path(video_path=<SOURCE_FILE_PATH>)
+        frame_generator = sv.get_video_frames_generator(source_path=<SOURCE_FILE_PATH>)
+
+        model = YOLO(<MODEL_PATH>)
+        tracker = sv.ByteTrack(frame_rate=video_info.fps)
+        smoother = sv.DetectionsSmoother()
+
+        annotator = sv.BoundingBoxAnnotator()
+
+        with sv.VideoSink(<TARGET_FILE_PATH>, video_info=video_info) as sink:
+            for frame in frame_generator:
+                result = model(frame)[0]
+                detections = sv.Detections.from_ultralytics(result)
+                detections = tracker.update_with_detections(detections)
+                detections = smoother.update_with_detections(detections)
+
+                annotated_frame = bounding_box_annotator.annotate(frame.copy(), detections)
+                sink.write_frame(annotated_frame)
+        ```
+    """  # noqa: E501 // docs
+
+    def __init__(self, length: int = 5) -> None:
+        """
+        Args:
+            length (int): The maximum number of frames to consider for smoothing
+                detections. Defaults to 5.
+        """
+        self.tracks = defaultdict(lambda: deque(maxlen=length))
+
+    def update_with_detections(self, detections: Detections) -> Detections:
+        """
+        Updates the smoother with a new set of detections from a frame.
+
+        Args:
+            detections (Detections): The detections to add to the smoother.
+        """
+
+        if detections.tracker_id is None:
+            print(
+                "Smoothing skipped. DetectionsSmoother requires tracker_id. Refer to "
+                "https://supervision.roboflow.com/trackers for more information."
+            )
+            return detections
+
+        for detection_idx in range(len(detections)):
+            tracker_id = detections.tracker_id[detection_idx]
+            if tracker_id is None:
+                continue
+
+            self.tracks[tracker_id].append(detections[detection_idx])
+
+        for track_id in self.tracks.keys():
+            if track_id not in detections.tracker_id:
+                self.tracks[track_id].append(None)
+
+        for track_id in list(self.tracks.keys()):
+            if all([d is None for d in self.tracks[track_id]]):
+                del self.tracks[track_id]
+
+        return self.get_smoothed_detections()
+
+    def get_track(self, track_id: int) -> Optional[Detections]:
+        track = self.tracks.get(track_id, None)
+        if track is None:
+            return None
+
+        track = [d for d in track if d is not None]
+        if len(track) == 0:
+            return None
+
+        ret = deepcopy(track[0])
+        ret.xyxy = np.mean([d.xyxy for d in track], axis=0)
+        ret.confidence = np.mean([d.confidence for d in track], axis=0)
+
+        return ret
+
+    def get_smoothed_detections(self) -> Detections:
+        tracked_detections = []
+        for track_id in self.tracks:
+            track = self.get_track(track_id)
+            if track is not None:
+                tracked_detections.append(track)
+
+        return Detections.merge(tracked_detections)
@@ -415,16 +415,17 @@ def move_boxes(xyxy: np.ndarray, offset: np.ndarray) -> np.ndarray:

    Example:
        ```python
-        >>> import numpy as np
-        >>> import supervision as sv
+        import numpy as np
+        import supervision as sv

-        >>> boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
-        >>> offset = np.array([5, 5])
-        >>> sv.move_boxes(boxes, offset)
-        ... array([
-        ...     [15, 15, 25, 25],
-        ...     [35, 35, 45, 45]
-        ... ])
+        boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
+        offset = np.array([5, 5])
+        moved_box = sv.move_boxes(boxes, offset)
+        print(moved_box)
+        # np.array([
+        #    [15, 15, 25, 25],
+        #     [35, 35, 45, 45]
+        # ])
        ```
    """
    return xyxy + np.hstack([offset, offset])
@@ -446,16 +447,17 @@ def scale_boxes(xyxy: np.ndarray, factor: float) -> np.ndarray:

    Example:
        ```python
-        >>> import numpy as np
-        >>> import supervision as sv
+        import numpy as np
+        import supervision as sv

-        >>> boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
-        >>> factor = 1.5
-        >>> sv.scale_boxes(boxes, factor)
-        ... array([
-        ...     [ 7.5,  7.5, 22.5, 22.5],
-        ...     [27.5, 27.5, 42.5, 42.5]
-        ... ])
+        boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
+        factor = 1.5
+        scaled_bb = sv.scale_boxes(boxes, factor)
+        print(scaled_bb)
+        # np.array([
+        #    [ 7.5,  7.5, 22.5, 22.5],
+        #    [27.5, 27.5, 42.5, 42.5]
+        # ])
        ```
    """
    centers = (xyxy[:, :2] + xyxy[:, 2:]) / 2
@@ -135,9 +135,11 @@ def draw_text(

    Examples:
        ```python
-        >>> scene = np.zeros((100, 100, 3), dtype=np.uint8)
-        >>> text_anchor = Point(x=50, y=50)
-        >>> scene = draw_text(scene=scene, text="Hello, world!",text_anchor=text_anchor)
+        import numpy as np
+
+        scene = np.zeros((100, 100, 3), dtype=np.uint8)
+        text_anchor = Point(x=50, y=50)
+        scene = draw_text(scene=scene, text="Hello, world!",text_anchor=text_anchor)
        ```
    """
    text_width, text_height = cv2.getTextSize(
@@ -22,10 +22,11 @@ def get_polygon_center(polygon: np.ndarray) -> Point:

    Examples:
        ```python
-        >>> from supervision.geometry.utils import get_polygon_center
+        from supervision.geometry.utils import get_polygon_center
+        import numpy as np

-        >>> vertices = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
-        >>> get_center(vertices)
+        vertices = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
+        get_center(vertices)
        Point(x=0.5, y=0.5)
        ```
    """
@@ -116,31 +116,31 @@ class ConfusionMatrix:

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> targets = [
-            ...     sv.Detections(...),
-            ...     sv.Detections(...)
-            ... ]
+            targets = [
+                sv.Detections(...),
+                sv.Detections(...)
+            ]

-            >>> predictions = [
-            ...     sv.Detections(...),
-            ...     sv.Detections(...)
-            ... ]
+            predictions = [
+                sv.Detections(...),
+                sv.Detections(...)
+            ]

-            >>> confusion_matrix = sv.ConfusionMatrix.from_detections(
-            ...     predictions=predictions,
-            ...     targets=target,
-            ...     classes=['person', ...]
-            ... )
+            confusion_matrix = sv.ConfusionMatrix.from_detections(
+                predictions=predictions,
+                targets=target,
+                classes=['person', ...]
+            )

-            >>> confusion_matrix.matrix
-            array([
-                [0., 0., 0., 0.],
-                [0., 1., 0., 1.],
-                [0., 1., 1., 0.],
-                [1., 1., 0., 0.]
-            ])
+            print(confusion_matrix.matrix)
+            # np.array([
+            #    [0., 0., 0., 0.],
+            #    [0., 1., 0., 1.],
+            #    [0., 1., 1., 0.],
+            #    [1., 1., 0., 0.]
+            # ])
            ```
        """

@@ -191,46 +191,47 @@ class ConfusionMatrix:

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv
+            import numpy as np

-            >>> targets = (
-            ...     [
-            ...         array(
-            ...             [
-            ...                 [0.0, 0.0, 3.0, 3.0, 1],
-            ...                 [2.0, 2.0, 5.0, 5.0, 1],
-            ...                 [6.0, 1.0, 8.0, 3.0, 2],
-            ...             ]
-            ...         ),
-            ...         array([1.0, 1.0, 2.0, 2.0, 2]),
-            ...     ]
-            ... )
+            targets = (
+                [
+                    np.array(
+                        [
+                            [0.0, 0.0, 3.0, 3.0, 1],
+                            [2.0, 2.0, 5.0, 5.0, 1],
+                            [6.0, 1.0, 8.0, 3.0, 2],
+                        ]
+                    ),
+                    np.array([1.0, 1.0, 2.0, 2.0, 2]),
+                ]
+            )

-            >>> predictions = [
-            ...     array(
-            ...         [
-            ...             [0.0, 0.0, 3.0, 3.0, 1, 0.9],
-            ...             [0.1, 0.1, 3.0, 3.0, 0, 0.9],
-            ...             [6.0, 1.0, 8.0, 3.0, 1, 0.8],
-            ...             [1.0, 6.0, 2.0, 7.0, 1, 0.8],
-            ...         ]
-            ...     ),
-            ...     array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
-            ... ]
+            predictions = [
+                np.array(
+                    [
+                        [0.0, 0.0, 3.0, 3.0, 1, 0.9],
+                        [0.1, 0.1, 3.0, 3.0, 0, 0.9],
+                        [6.0, 1.0, 8.0, 3.0, 1, 0.8],
+                        [1.0, 6.0, 2.0, 7.0, 1, 0.8],
+                    ]
+                ),
+                np.array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
+            ]

-            >>> confusion_matrix = sv.ConfusionMatrix.from_tensors(
-            ...     predictions=predictions,
-            ...     targets=targets,
-            ...     classes=['person', ...]
-            ... )
+            confusion_matrix = sv.ConfusionMatrix.from_tensors(
+                predictions=predictions,
+                targets=targets,
+                classes=['person', ...]
+            )

-            >>> confusion_matrix.matrix
-            array([
-                [0., 0., 0., 0.],
-                [0., 1., 0., 1.],
-                [0., 1., 1., 0.],
-                [1., 1., 0., 0.]
-            ])
+            print(confusion_matrix.matrix)
+            # np.array([
+            #     [0., 0., 0., 0.],
+            #     [0., 1., 0., 1.],
+            #     [0., 1., 1., 0.],
+            #     [1., 1., 0., 0.]
+            # ])
            ```
        """
        validate_input_tensors(predictions, targets)
@@ -365,28 +366,28 @@ class ConfusionMatrix:

        Example:
            ```python
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import supervision as sv
+            from ultralytics import YOLO

-            >>> dataset = sv.DetectionDataset.from_yolo(...)
+            dataset = sv.DetectionDataset.from_yolo(...)

-            >>> model = YOLO(...)
-            >>> def callback(image: np.ndarray) -> sv.Detections:
-            ...     result = model(image)[0]
-            ...     return sv.Detections.from_ultralytics(result)
+            model = YOLO(...)
+            def callback(image: np.ndarray) -> sv.Detections:
+                result = model(image)[0]
+                return sv.Detections.from_ultralytics(result)

-            >>> confusion_matrix = sv.ConfusionMatrix.benchmark(
-            ...     dataset = dataset,
-            ...     callback = callback
-            ... )
+            confusion_matrix = sv.ConfusionMatrix.benchmark(
+                dataset = dataset,
+                callback = callback
+            )

-            >>> confusion_matrix.matrix
-            array([
-                [0., 0., 0., 0.],
-                [0., 1., 0., 1.],
-                [0., 1., 1., 0.],
-                [1., 1., 0., 0.]
-            ])
+            print(confusion_matrix.matrix)
+            # np.array([
+            #     [0., 0., 0., 0.],
+            #     [0., 1., 0., 1.],
+            #     [0., 1., 1., 0.],
+            #     [1., 1., 0., 0.]
+            # ])
            ```
        """
        predictions, targets = [], []
@@ -532,25 +533,25 @@ class MeanAveragePrecision:

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> targets = [
-            ...     sv.Detections(...),
-            ...     sv.Detections(...)
-            ... ]
+            targets = [
+                sv.Detections(...),
+                sv.Detections(...)
+            ]

-            >>> predictions = [
-            ...     sv.Detections(...),
-            ...     sv.Detections(...)
-            ... ]
+            predictions = [
+                sv.Detections(...),
+                sv.Detections(...)
+            ]

-            >>> mean_average_precision = sv.MeanAveragePrecision.from_detections(
-            ...     predictions=predictions,
-            ...     targets=target,
-            ... )
+            mean_average_precision = sv.MeanAveragePrecision.from_detections(
+                predictions=predictions,
+                targets=target,
+            )

-            >>> mean_average_precison.map50_95
-            0.2899
+            print(mean_average_precison.map50_95)
+            # 0.2899
            ```
        """
        prediction_tensors = []
@@ -583,23 +584,23 @@ class MeanAveragePrecision:

        Example:
            ```python
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import supervision as sv
+            from ultralytics import YOLO

-            >>> dataset = sv.DetectionDataset.from_yolo(...)
+            dataset = sv.DetectionDataset.from_yolo(...)

-            >>> model = YOLO(...)
-            >>> def callback(image: np.ndarray) -> sv.Detections:
-            ...     result = model(image)[0]
-            ...     return sv.Detections.from_ultralytics(result)
+            model = YOLO(...)
+            def callback(image: np.ndarray) -> sv.Detections:
+                result = model(image)[0]
+                return sv.Detections.from_ultralytics(result)

-            >>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
-            ...     dataset = dataset,
-            ...     callback = callback
-            ... )
+            mean_average_precision = sv.MeanAveragePrecision.benchmark(
+                dataset = dataset,
+                callback = callback
+            )

-            >>> mean_average_precision.map50_95
-            0.433
+            print(mean_average_precision.map50_95)
+            # 0.433
            ```
        """
        predictions, targets = [], []
@@ -637,40 +638,41 @@ class MeanAveragePrecision:

        Example:
            ```python
-            >>> import supervision as sv
+            import supervision as sv
+            import numpy as np

-            >>> targets = (
-            ...     [
-            ...         array(
-            ...             [
-            ...                 [0.0, 0.0, 3.0, 3.0, 1],
-            ...                 [2.0, 2.0, 5.0, 5.0, 1],
-            ...                 [6.0, 1.0, 8.0, 3.0, 2],
-            ...             ]
-            ...         ),
-            ...         array([1.0, 1.0, 2.0, 2.0, 2]),
-            ...     ]
-            ... )
+            targets = (
+                [
+                    np.array(
+                        [
+                            [0.0, 0.0, 3.0, 3.0, 1],
+                            [2.0, 2.0, 5.0, 5.0, 1],
+                            [6.0, 1.0, 8.0, 3.0, 2],
+                        ]
+                    ),
+                    np.array([[1.0, 1.0, 2.0, 2.0, 2]]),
+                ]
+            )

-            >>> predictions = [
-            ...     array(
-            ...         [
-            ...             [0.0, 0.0, 3.0, 3.0, 1, 0.9],
-            ...             [0.1, 0.1, 3.0, 3.0, 0, 0.9],
-            ...             [6.0, 1.0, 8.0, 3.0, 1, 0.8],
-            ...             [1.0, 6.0, 2.0, 7.0, 1, 0.8],
-            ...         ]
-            ...     ),
-            ...     array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
-            ... ]
+            predictions = [
+                np.array(
+                    [
+                        [0.0, 0.0, 3.0, 3.0, 1, 0.9],
+                        [0.1, 0.1, 3.0, 3.0, 0, 0.9],
+                        [6.0, 1.0, 8.0, 3.0, 1, 0.8],
+                        [1.0, 6.0, 2.0, 7.0, 1, 0.8],
+                    ]
+                ),
+                np.array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
+            ]

-            >>> mean_average_precison = sv.MeanAveragePrecision.from_tensors(
-            ...     predictions=predictions,
-            ...     targets=targets,
-            ... )
+            mean_average_precison = sv.MeanAveragePrecision.from_tensors(
+                predictions=predictions,
+                targets=targets,
+            )

-            >>> mean_average_precison.map50_95
-            0.2899
+            print(mean_average_precison.map50_95)
+            # 0.6649
            ```
        """
        validate_input_tensors(predictions, targets)
@@ -167,13 +167,17 @@ class ByteTrack:
    """
    Initialize the ByteTrack object.

+    <video controls>
+        <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/annotate-video-with-traces.mp4" type="video/mp4">
+    </video>
+
    Parameters:
        track_thresh (float, optional): Detection confidence threshold
            for track activation.
        track_buffer (int, optional): Number of frames to buffer when a track is lost.
        match_thresh (float, optional): Threshold for matching tracks with detections.
        frame_rate (int, optional): The frame rate of the video.
-    """
+    """  # noqa: E501 // docs

    def __init__(
        self,
@@ -196,39 +200,41 @@ class ByteTrack:

    def update_with_detections(self, detections: Detections) -> Detections:
        """
-        Updates the tracker with the provided detections and
-            returns the updated detection results.
+        Updates the tracker with the provided detections and returns the updated
+        detection results.
+
+        Args:
+            detections (Detections): The detections to pass through the tracker.

-        Parameters:
-            detections: The new detections to update with.
-        Returns:
-            Detection: The updated detection results that now include tracking IDs.
        Example:
            ```python
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import supervision as sv
+            from ultralytics import YOLO

-            >>> model = YOLO(...)
-            >>> byte_tracker = sv.ByteTrack()
-            >>> annotator = sv.BoxAnnotator()
+            model = YOLO(<MODEL_PATH>)
+            tracker = sv.ByteTrack()

-            >>> def callback(frame: np.ndarray, index: int) -> np.ndarray:
-            ...     results = model(frame)[0]
-            ...     detections = sv.Detections.from_ultralytics(results)
-            ...     detections = byte_tracker.update_with_detections(detections)
-            ...     labels = [
-            ...         f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}"
-            ...         for _, _, confidence, class_id, tracker_id
-            ...         in detections
-            ...     ]
-            ...     return annotator.annotate(scene=frame.copy(),
-            ...                               detections=detections, labels=labels)
+            bounding_box_annotator = sv.BoundingBoxAnnotator()
+            label_annotator = sv.LabelAnnotator()

-            >>> sv.process_video(
-            ...     source_path='...',
-            ...     target_path='...',
-            ...     callback=callback
-            ... )
+            def callback(frame: np.ndarray, index: int) -> np.ndarray:
+                results = model(frame)[0]
+                detections = sv.Detections.from_ultralytics(results)
+                detections = byte_tracker.update_with_detections(detections)
+
+                labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]
+
+                annotated_frame = bounding_box_annotator.annotate(
+                    scene=frame.copy(), detections=detections)
+                annotated_frame = label_annotator.annotate(
+                    scene=annotated_frame, detections=detections, labels=labels)
+                return annotated_frame
+
+            sv.process_video(
+                source_path=<SOURCE_VIDEO_PATH>,
+                target_path=<TARGET_VIDEO_PATH>,
+                callback=callback
+            )
            ```
        """

@@ -34,14 +34,14 @@ def list_files_with_extensions(

    Examples:
        ```python
-        >>> import supervision as sv
+        import supervision as sv

-        >>> # List all files in the directory
-        >>> files = sv.list_files_with_extensions(directory='my_directory')
+        # List all files in the directory
+        files = sv.list_files_with_extensions(directory='my_directory')

-        >>> # List only files with '.txt' and '.md' extensions
-        >>> files = sv.list_files_with_extensions(
-        ...     directory='my_directory', extensions=['txt', 'md'])
+        # List only files with '.txt' and '.md' extensions
+        files = sv.list_files_with_extensions(
+            directory='my_directory', extensions=['txt', 'md'])
        ```
    """

@@ -20,13 +20,13 @@ def crop_image(image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:

    Examples:
        ```python
-        >>> import supervision as sv
+        import supervision as sv

-        >>> detection = sv.Detections(...)
-        >>> with sv.ImageSink(target_dir_path='target/directory/path') as sink:
-        ...     for xyxy in detection.xyxy:
-        ...         cropped_image = sv.crop_image(image=image, xyxy=xyxy)
-        ...         sink.save_image(image=image)
+        detection = sv.Detections(...)
+        with sv.ImageSink(target_dir_path='target/directory/path') as sink:
+            for xyxy in detection.xyxy:
+                cropped_image = sv.crop_image(image=image, xyxy=xyxy)
+                sink.save_image(image=image)
        ```
    """

@@ -55,13 +55,13 @@ class ImageSink:

        Examples:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> with sv.ImageSink(target_dir_path='target/directory/path',
-            ...                   overwrite=True) as sink:
-            ...     for image in sv.get_video_frames_generator(
-            ...         source_path='source_video.mp4', stride=2):
-            ...         sink.save_image(image=image)
+            with sv.ImageSink(target_dir_path='target/directory/path',
+                              overwrite=True) as sink:
+                for image in sv.get_video_frames_generator(
+                    source_path='source_video.mp4', stride=2):
+                    sink.save_image(image=image)
            ```
        """

@@ -18,13 +18,13 @@ def plot_image(

    Examples:
        ```python
-        >>> import cv2
-        >>> import supervision as sv
+        import cv2
+        import supervision as sv

-        >>> image = cv2.imread("path/to/image.jpg")
+        image = cv2.imread("path/to/image.jpg")

        %matplotlib inline
-        >>> sv.plot_image(image=image, size=(16, 16))
+        sv.plot_image(image=image, size=(16, 16))
        ```
    """
    plt.figure(figsize=size)
@@ -63,18 +63,18 @@ def plot_images_grid(

    Examples:
        ```python
-        >>> import cv2
-        >>> import supervision as sv
+        import cv2
+        import supervision as sv

-        >>> image1 = cv2.imread("path/to/image1.jpg")
-        >>> image2 = cv2.imread("path/to/image2.jpg")
-        >>> image3 = cv2.imread("path/to/image3.jpg")
+        image1 = cv2.imread("path/to/image1.jpg")
+        image2 = cv2.imread("path/to/image2.jpg")
+        image3 = cv2.imread("path/to/image3.jpg")

-        >>> images = [image1, image2, image3]
-        >>> titles = ["Image 1", "Image 2", "Image 3"]
+        images = [image1, image2, image3]
+        titles = ["Image 1", "Image 2", "Image 3"]

        %matplotlib inline
-        >>> plot_images_grid(images, grid_size=(2, 2), titles=titles, size=(16, 16))
+        plot_images_grid(images, grid_size=(2, 2), titles=titles, size=(16, 16))
        ```
    """
    nrows, ncols = grid_size
@@ -24,15 +24,15 @@ class VideoInfo:

    Examples:
        ```python
-        >>> import supervision as sv
+        import supervision as sv

-        >>> video_info = sv.VideoInfo.from_video_path(video_path='video.mp4')
+        video_info = sv.VideoInfo.from_video_path(video_path='video.mp4')

-        >>> video_info
-        VideoInfo(width=3840, height=2160, fps=25, total_frames=538)
+        video_info
+        # VideoInfo(width=3840, height=2160, fps=25, total_frames=538)

-        >>> video_info.resolution_wh
-        (3840, 2160)
+        video_info.resolution_wh
+        # (3840, 2160)
        ```
    """

@@ -71,14 +71,14 @@ class VideoSink:

    Example:
        ```python
-        >>> import supervision as sv
+        import supervision as sv

-        >>> video_info = sv.VideoInfo.from_video_path('source.mp4')
-        >>> frames_generator = sv.get_video_frames_generator('source.mp4')
+        video_info = sv.VideoInfo.from_video_path('source.mp4')
+        frames_generator = sv.get_video_frames_generator('source.mp4')

-        >>> with sv.VideoSink(target_path='target.mp4', video_info=video_info) as sink:
-        ...     for frame in frames_generator:
-        ...         sink.write_frame(frame=frame)
+        with sv.VideoSink(target_path='target.mp4', video_info=video_info) as sink:
+            for frame in frames_generator:
+                sink.write_frame(frame=frame)
        ```
    """

@@ -143,10 +143,10 @@ def get_video_frames_generator(

    Examples:
        ```python
-        >>> import supervision as sv
+        import supervision as sv

-        >>> for frame in sv.get_video_frames_generator(source_path='source_video.mp4'):
-        ...     ...
+        for frame in sv.get_video_frames_generator(source_path='source_video.mp4'):
+            ...
        ```
    """
    video, start, end = _validate_and_setup_video(source_path, start, end)
@@ -183,16 +183,16 @@ def process_video(

    Examples:
        ```python
-        >>> import supervision as sv
+        import supervision as sv

-        >>> def callback(scene: np.ndarray, index: int) -> np.ndarray:
-        ...     ...
+        def callback(scene: np.ndarray, index: int) -> np.ndarray:
+            ...

-        >>> process_video(
-        ...     source_path='...',
-        ...     target_path='...',
-        ...     callback=callback
-        ... )
+        process_video(
+            source_path='...',
+            target_path='...',
+            callback=callback
+        )
        ```
    """
    source_video_info = VideoInfo.from_video_path(video_path=source_path)
@@ -217,15 +217,15 @@ class FPSMonitor:

        Examples:
            ```python
-            >>> import supervision as sv
+            import supervision as sv

-            >>> frames_generator = sv.get_video_frames_generator('source.mp4')
-            >>> fps_monitor = sv.FPSMonitor()
+            frames_generator = sv.get_video_frames_generator('source.mp4')
+            fps_monitor = sv.FPSMonitor()

-            >>> for frame in frames_generator:
-            ...     # your processing code here
-            ...     fps_monitor.tick()
-            ...     fps = fps_monitor()
+            for frame in frames_generator:
+                # your processing code here
+                fps_monitor.tick()
+                fps = fps_monitor()
            ```
        """
        self.all_timestamps = deque(maxlen=sample_size)
Author	SHA1	Message	Date
Piotr Skalski	53f4cde18d	Merge pull request #764 from roboflow/develop `supervision-0.18.0` release	2024-01-25 10:33:16 +01:00
Piotr Skalski	181724e2d3	Merge pull request #775 from roboflow/feature/supervision-0-18-0-changelog `supervision-0.18.0` change log update	2024-01-25 10:31:17 +01:00
pre-commit-ci[bot]	205b2d200a	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-01-25 09:29:43 +00:00
SkalskiP	3dcb330822	- version bump from `0.18.0rc4` to `0.18.0` - `supervision-0.18.0` release changelog update - minot docs changes	2024-01-25 10:27:22 +01:00
SkalskiP	6fa4f25250	bump version from `0.18.0rc3` to `0.18.0rc4`	2024-01-24 18:10:56 +01:00
Piotr Skalski	35d5738622	Merge pull request #770 from roboflow/yolov8-obb feat: ✨ yolov8-obb support and OrientedBoxAnnotator added for obb	2024-01-24 17:50:41 +01:00
pre-commit-ci[bot]	9d56d8a9fe	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-01-24 16:47:55 +00:00
SkalskiP	c7d7e8b71e	Merge remote-tracking branch 'origin/yolov8-obb' into yolov8-obb # Conflicts: # docs/annotators.md	2024-01-24 17:47:41 +01:00
SkalskiP	6565fae1c2	final docs updates	2024-01-24 17:47:11 +01:00
Onuralp SEZER	a5bef3b15e	docs: 📝 >>>/... removed from obb docs and code example Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 19:31:46 +03:00
SkalskiP	164b9bedce	Merge branch 'develop' into yolov8-obb	2024-01-24 16:49:00 +01:00
Piotr Skalski	aabe60be6a	Merge pull request #774 from roboflow/docs-emojis-fix fix(docs): 🐞 emoji extension added for show emojis properly	2024-01-24 16:42:19 +01:00
Piotr Skalski	533a8a1aa3	Merge pull request #773 from roboflow/readme-example-docs docs: 📝 readme >>>/... removed	2024-01-24 16:33:43 +01:00
Onuralp SEZER	c7790fdf56	fix(docs): 🐞 emoji extension added for show emojis properly Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 17:53:56 +03:00
Onuralp SEZER	dd53cd06b9	docs: 📝 readme >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 17:43:10 +03:00
Onuralp SEZER	24593b64d5	Removed '>>>' and '...' from docs - Merge PR #761 from RaghavvGupta/improved-code-usability Removed '>>>' and '...' from examples and docs and improved some indentation.	2024-01-24 17:21:00 +03:00
Piotr Skalski	80b7eecfaf	Merge pull request #696 from roboflow/prediction-smoothing Add Prediction Smoothing	2024-01-24 15:13:13 +01:00
SkalskiP	6b063736d9	Merge remote-tracking branch 'origin/prediction-smoothing' into prediction-smoothing	2024-01-24 15:11:50 +01:00
SkalskiP	7b0d5e729e	ready for merge	2024-01-24 15:09:47 +01:00
pre-commit-ci[bot]	7916d6a6a4	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-01-24 14:07:46 +00:00
SkalskiP	79560945f5	ready for merge	2024-01-24 15:07:32 +01:00
Onuralp SEZER	d4e0d8d46d	docs: 📝 supervision/annotators/core.py PercentageBarAnnotator >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 15:41:18 +03:00
Onuralp SEZER	5a89e2170f	Merge remote-tracking branch 'mainland/develop' into improved-code-usability	2024-01-24 15:39:07 +03:00
Onuralp SEZER	ab5a86596f	docs: 📝 supervision downloader classfication draw geometry and utils >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 15:26:43 +03:00
Onuralp SEZER	12b42f80de	docs: 📝 supervision/utils/notebook.py >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 15:17:49 +03:00
Onuralp SEZER	8a5cd1bc3e	docs: 📝 supervision/utils/video.py >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 15:17:29 +03:00
Onuralp SEZER	e29e960c5f	docs: 📝 supervision/metrics/detection.py >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 15:14:18 +03:00
pre-commit-ci[bot]	f6e5b6dc54	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-01-24 12:06:21 +00:00
SkalskiP	e2751368d5	improvements	2024-01-24 13:06:07 +01:00
Onuralp SEZER	955a6d67fb	docs: 📝 supervision/tracker/byte_tracker/core.py >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 13:13:56 +03:00
Onuralp SEZER	833edc95ad	docs: 📝 supervision/detection/utils.py >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 13:10:48 +03:00
Onuralp SEZER	4df21ded6a	docs: 📝 supervision/detection/annotate.py >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 13:03:34 +03:00
Onuralp SEZER	f03be21a9f	docs: 📝 supervision/dataset/core.py >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 13:01:55 +03:00
Onuralp SEZER	2d1aa3b996	docs: 📝 supervision/detection/tools/inference_slicer.py >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 12:52:34 +03:00
Onuralp SEZER	7af914bf56	docs: 📝 supervision/annotators/core.py docs/annotators.md >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 12:50:23 +03:00
Onuralp SEZER	8872151d0c	docs: 📝 core/detection/core.py >>>/... removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 12:28:45 +03:00
raghav	d961aa9eb4	docs: example image = ... reverted and added annotators.md	2024-01-24 12:05:57 +03:00
raghav	845c9cc2c2	docs: '>>>' and '...' are removed and indentation improvements	2024-01-24 12:05:48 +03:00
Piotr Skalski	4667e02d79	Merge pull request #772 from roboflow/mkdocs-matplotlib-updates build(deps): 📦 mkdocs-material and matplotlib update for security	2024-01-24 09:07:43 +01:00
Onuralp SEZER	047672d458	build(deps): 📦 mkdocs-material and matplotlib update for security Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-24 10:51:41 +03:00
Piotr Skalski	2622144660	Merge pull request #771 from roboflow/removal-pillow-reqs build(deps): 📦 package pillow removed	2024-01-23 20:15:31 +01:00
Onuralp SEZER	fb0500c394	build(deps): 📦 package pillow removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-23 21:19:01 +03:00
Onuralp SEZER	10f3b73e79	docs: 📝 OrientedBox Annotator docs added Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-23 21:07:09 +03:00
Onuralp SEZER	564f979977	Merge branch 'develop' into yolov8-obb	2024-01-23 20:52:07 +03:00
Onuralp SEZER	64302f450c	feat: ✨ yolov8-obb support and OrientedBoxAnnotator added for obb Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-23 20:18:47 +03:00
James Gallagher	626636cfa7	update tracking logic	2024-01-23 17:10:02 +00:00
James Gallagher	21791c8522	remove idle trackers	2024-01-23 13:38:15 +00:00
James Gallagher	24ca25ef94	fix formatting	2024-01-23 13:28:00 +00:00
James	09d771c139	Merge branch 'develop' into prediction-smoothing	2024-01-23 12:59:28 +00:00
James Gallagher	6efd51dfb1	respond to feedback	2024-01-23 12:57:36 +00:00
James Gallagher	c299a929c5	respond to feedback	2024-01-23 12:57:31 +00:00
Brad Dwyer	310a09859d	Remove unused tracker_length	2023-12-29 11:42:25 -06:00
pre-commit-ci[bot]	bf427d5061	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-12-28 18:21:48 +00:00
Brad Dwyer	8ab7cb659c	Merge branch 'prediction-smoothing' of https://github.com/roboflow/supervision into prediction-smoothing	2023-12-28 12:21:38 -06:00
Brad Dwyer	a58c8ef0fe	Add set_length and use deque	2023-12-28 12:21:07 -06:00
pre-commit-ci[bot]	3e0a665ef2	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-12-28 18:04:43 +00:00
Brad Dwyer	3a0682db08	Remove NoneDict and add type hints	2023-12-28 12:04:33 -06:00
Brad Dwyer	a36b426dff	Merge branch 'develop' into prediction-smoothing	2023-12-28 11:50:15 -06:00
Brad Dwyer	3f77433f2e	Merge branch 'prediction-smoothing' of https://github.com/roboflow/supervision into prediction-smoothing	2023-12-28 11:50:12 -06:00
Brad Dwyer	5d1e76fbd0	Update copy based on feedback from James & Piotr	2023-12-28 11:50:06 -06:00
pre-commit-ci[bot]	6600d7533a	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-12-28 17:43:55 +00:00
Brad Dwyer	1d4d7dd4ea	Move into tools directory	2023-12-28 11:42:55 -06:00
Brad Dwyer	d7420c545e	Move video to correct spot in the docs	2023-12-27 15:17:28 -06:00
Brad Dwyer	729eb16ea6	Merge branch 'prediction-smoothing' of https://github.com/roboflow/supervision into prediction-smoothing	2023-12-27 15:15:31 -06:00
Brad Dwyer	ca6ba30375	Fix linter errors	2023-12-27 15:15:18 -06:00
pre-commit-ci[bot]	7f4b27a556	fix(pre_commit): 🎨 auto format pre-commit hooks	2023-12-27 21:09:26 +00:00
Brad Dwyer	ffdab06376	Update Docs	2023-12-27 15:02:02 -06:00
Brad Dwyer	b912db7a06	Add Detections Smoother	2023-12-27 14:13:40 -06:00