bump version from 0.19.0rc1 to 0.19.0rc2

Merge pull request #855 from roboflow/pre-commit-ci-update-config
chore(pre_commit): ⬆ pre_commit autoupdate
2024-02-05 23:49:09 +01:00 · 2024-02-05 21:11:04 +03:00 · 2024-02-05 17:37:15 +00:00 · 2024-02-05 15:30:44 +03:00 · 2024-02-05 00:47:50 +00:00 · 2024-02-02 20:59:36 +03:00
27 changed files with 879 additions and 190 deletions
@@ -47,7 +47,7 @@ repos:


  -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.1.14
+      rev: v0.2.0
      hooks:
      -   id: ruff
          args: [--fix, --exit-non-zero-on-fix]
@@ -43,7 +43,7 @@ Read more about desktop, headless, and local installation in our [guide](https:/

 ### models

-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.
+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/latest/detection/core/#detections) for the most popular libraries like Ultralytics, Transformers, or MMDetection.

 ```python
 import cv2
@@ -85,7 +85,7 @@ len(detections)

 ### annotators

-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.
+Supervision offers a wide range of highly customizable [annotators](https://supervision.roboflow.com/latest/annotators/), allowing you to compose the perfect visualization for your use case.

 ```python
 import cv2
@@ -105,7 +105,7 @@ https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-

 ### datasets

-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.
+Supervision provides a set of [utils](https://supervision.roboflow.com/latest/datasets/) that allow you to load, split, merge, and save datasets in one of the supported formats.

 ```python
 import supervision as sv
@@ -381,115 +381,115 @@ status: new
    </div>

 <div class="md-typeset">
-  <h2>BoundingBoxAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.BoundingBoxAnnotator">BoundingBoxAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.BoundingBoxAnnotator

 <div class="md-typeset">
-  <h2>RoundBoxAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.RoundBoxAnnotator">RoundBoxAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.RoundBoxAnnotator

 <div class="md-typeset">
-  <h2>BoxCornerAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.BoxCornerAnnotator">BoxCornerAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.BoxCornerAnnotator

 <div class="md-typeset">
-  <h2>OrientedBoxAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.OrientedBoxAnnotator">OrientedBoxAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.OrientedBoxAnnotator

 <div class="md-typeset">
-  <h2>ColorAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.ColorAnnotator">ColorAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.ColorAnnotator

 <div class="md-typeset">
-  <h2>CircleAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.CircleAnnotator">CircleAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.CircleAnnotator

 <div class="md-typeset">
-  <h2>DotAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.DotAnnotator">DotAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.DotAnnotator

 <div class="md-typeset">
-  <h2>TriangleAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.TriangleAnnotator">TriangleAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.TriangleAnnotator

 <div class="md-typeset">
-  <h2>EllipseAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.EllipseAnnotator">EllipseAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.EllipseAnnotator

 <div class="md-typeset">
-  <h2>HaloAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.HaloAnnotator">HaloAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.HaloAnnotator

 <div class="md-typeset">
-  <h2>PercentageBarAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.PercentageBarAnnotator">PercentageBarAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.PercentageBarAnnotator

 <div class="md-typeset">
-  <h2>HeatMapAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.HeatMapAnnotator">HeatMapAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.HeatMapAnnotator

 <div class="md-typeset">
-  <h2>MaskAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.MaskAnnotator">MaskAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.MaskAnnotator

 <div class="md-typeset">
-  <h2>PolygonAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.PolygonAnnotator">PolygonAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.PolygonAnnotator

 <div class="md-typeset">
-  <h2>LabelAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.LabelAnnotator">LabelAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.LabelAnnotator

 <div class="md-typeset">
-  <h2>BlurAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.BlurAnnotator">BlurAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.BlurAnnotator

 <div class="md-typeset">
-  <h2>PixelateAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.PixelateAnnotator">PixelateAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.PixelateAnnotator

 <div class="md-typeset">
-  <h2>TraceAnnotator</h2>
+    <h2><a href="#supervision.annotators.core.TraceAnnotator">TraceAnnotator</a></h2>
 </div>

 :::supervision.annotators.core.TraceAnnotator

 <div class="md-typeset">
-  <h2>ColorLookup</h2>
+    <h2><a href="#supervision.annotators.core.ColorLookup">ColorLookup</a></h2>
 </div>

 :::supervision.annotators.utils.ColorLookup
@@ -1,6 +1,6 @@
 ### 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.
+- Added [#633](https://github.com/roboflow/supervision/pull/720): [`sv.PercentageBarAnnotator`](0.18.0/annotators/#percentagebarannotator) allowing to annotate images and videos with percentage values representing confidence or other custom property.

 ```python
 >>> import supervision as sv
@@ -15,9 +15,9 @@
 ... )
 ```

- 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 [#702](https://github.com/roboflow/supervision/pull/702): [`sv.RoundBoxAnnotator`](/0.18.0/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).
+- Added [#770](https://github.com/roboflow/supervision/pull/770): [`sv.OrientedBoxAnnotator`](/0.18.0/annotators/#orientedboxannotator) allowing to annotate images and videos with OBB (Oriented Bounding Boxes).

 ```python
 import cv2
@@ -37,9 +37,9 @@ annotated_frame = oriented_box_annotator.annotate(
 )
 ```

- 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 [#696](https://github.com/roboflow/supervision/pull/696): [`sv.DetectionsSmoother`](/0.18.0/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.
+- Added [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.from_matplotlib`](/0.18.0/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
@@ -48,35 +48,35 @@ annotated_frame = oriented_box_annotator.annotate(
 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 [#770](https://github.com/roboflow/supervision/pull/770): [`sv.Detections.from_ultralytics`](/0.18.0/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 [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](/0.18.0/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`).
+- Changed [#756](https://github.com/roboflow/supervision/pull/756): [`sv.Color`](/0.18.0/draw/color/#color)'s and [`sv.ColorPalette`](/0.18.0/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`).

 !!! failure "Deprecated"

    `sv.ColorPalette.default()` is deprecated and will be removed in `supervision-0.22.0`. 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 [#769](https://github.com/roboflow/supervision/pull/769): [`sv.ColorPalette.DEFAULT`](/0.18.0/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).
+- Changed [#677](https://github.com/roboflow/supervision/pull/677): `sv.Detections.from_roboflow` to [`sv.Detections.from_inference`](/0.18.0/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).

 !!! failure "Deprecated"

    `Detections.from_roboflow()` is deprecated and will be removed in `supervision-0.22.0`. 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.
+- Fixed [#735](https://github.com/roboflow/supervision/pull/735): [`sv.LineZone`](/0.18.0/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.
+- Added [#633](https://github.com/roboflow/supervision/pull/633): [`sv.PixelateAnnotator`](/0.17.0/annotators/#supervision.annotators.core.PixelateAnnotator) allowing to pixelate objects on images and videos.

- Added [#652](https://github.com/roboflow/supervision/pull/652): [`sv.TriangleAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.TriangleAnnotator) allowing to annotate images and videos with triangle markers.
+- Added [#652](https://github.com/roboflow/supervision/pull/652): [`sv.TriangleAnnotator`](/0.17.0/annotators/#supervision.annotators.core.TriangleAnnotator) allowing to annotate images and videos with triangle markers.

- Added [#602](https://github.com/roboflow/supervision/pull/602): [`sv.PolygonAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.PolygonAnnotator) allowing to annotate images and videos with segmentation mask outline.
+- Added [#602](https://github.com/roboflow/supervision/pull/602): [`sv.PolygonAnnotator`](/0.17.0/annotators/#supervision.annotators.core.PolygonAnnotator) allowing to annotate images and videos with segmentation mask outline.

 ```python
 >>> import supervision as sv
@@ -91,7 +91,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 ... )
 ```

- Added [#476](https://github.com/roboflow/supervision/pull/476): [`sv.assets`](https://supervision.roboflow.com/assets/) allowing download of video files that you can use in your demos.
+- Added [#476](https://github.com/roboflow/supervision/pull/476): [`sv.assets`](/0.18.0/assets/) allowing download of video files that you can use in your demos.

 ```python
 >>> from supervision.assets import download_assets, VideoAssets
@@ -99,35 +99,35 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 "vehicles.mp4"
 ```

- Added [#605](https://github.com/roboflow/supervision/pull/605): [`Position.CENTER_OF_MASS`](https://supervision.roboflow.com/geometry/core/#position) allowing to place labels in center of mass of segmentation masks.
+- Added [#605](https://github.com/roboflow/supervision/pull/605): [`Position.CENTER_OF_MASS`](/0.17.0/geometry/core/#position) allowing to place labels in center of mass of segmentation masks.

- Added [#651](https://github.com/roboflow/supervision/pull/651): [`sv.scale_boxes`](https://supervision.roboflow.com/detection/utils/#supervision.detection.utils.scale_boxes) allowing to scale [`sv.Detections.xyxy`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections) values.
+- Added [#651](https://github.com/roboflow/supervision/pull/651): [`sv.scale_boxes`](/0.17.0/detection/utils/#supervision.detection.utils.scale_boxes) allowing to scale [`sv.Detections.xyxy`](/0.17.0/detection/core/#supervision.detection.core.Detections) values.

- Added [#637](https://github.com/roboflow/supervision/pull/637): [`sv.calculate_dynamic_text_scale`](https://supervision.roboflow.com/draw/utils/#supervision.draw.utils.calculate_dynamic_text_scale) and [`sv.calculate_dynamic_line_thickness`](https://supervision.roboflow.com/draw/utils/#supervision.draw.utils.calculate_dynamic_line_thickness) allowing text scale and line thickness to match image resolution.
+- Added [#637](https://github.com/roboflow/supervision/pull/637): [`sv.calculate_dynamic_text_scale`](/0.17.0/draw/utils/#supervision.draw.utils.calculate_dynamic_text_scale) and [`sv.calculate_dynamic_line_thickness`](/0.17.0/draw/utils/#supervision.draw.utils.calculate_dynamic_line_thickness) allowing text scale and line thickness to match image resolution.

- Added [#620](https://github.com/roboflow/supervision/pull/620): [`sv.Color.as_hex`](https://supervision.roboflow.com/draw/color/#supervision.draw.color.Color.as_hex) allowing to extract color value in HEX format.
+- Added [#620](https://github.com/roboflow/supervision/pull/620): [`sv.Color.as_hex`](/0.17.0/draw/color/#supervision.draw.color.Color.as_hex) allowing to extract color value in HEX format.

- Added [#572](https://github.com/roboflow/supervision/pull/572): [`sv.Classifications.from_timm`](https://supervision.roboflow.com/classification/core/#supervision.classification.core.Classifications.from_timm) allowing to load classification result from [timm](https://huggingface.co/docs/hub/timm) models.
+- Added [#572](https://github.com/roboflow/supervision/pull/572): [`sv.Classifications.from_timm`](/0.17.0/classification/core/#supervision.classification.core.Classifications.from_timm) allowing to load classification result from [timm](https://huggingface.co/docs/hub/timm) models.

- Added [#478](https://github.com/roboflow/supervision/pull/478): [`sv.Classifications.from_clip`](https://supervision.roboflow.com/classification/core/#supervision.classification.core.Classifications.from_clip) allowing to load classification result from [clip](https://github.com/openai/clip) model.
+- Added [#478](https://github.com/roboflow/supervision/pull/478): [`sv.Classifications.from_clip`](/0.17.0/classification/core/#supervision.classification.core.Classifications.from_clip) allowing to load classification result from [clip](https://github.com/openai/clip) model.

- Added [#571](https://github.com/roboflow/supervision/pull/571): [`sv.Detections.from_azure_analyze_image`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_azure_analyze_image) allowing to load detection results from [Azure Image Analysis](https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-object-detection-40).
+- Added [#571](https://github.com/roboflow/supervision/pull/571): [`sv.Detections.from_azure_analyze_image`](/0.17.0/detection/core/#supervision.detection.core.Detections.from_azure_analyze_image) allowing to load detection results from [Azure Image Analysis](https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-object-detection-40).

- Changed [#646](https://github.com/roboflow/supervision/pull/646): `sv.BoxMaskAnnotator` renaming it to [`sv.ColorAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.ColorAnnotator).
+- Changed [#646](https://github.com/roboflow/supervision/pull/646): `sv.BoxMaskAnnotator` renaming it to [`sv.ColorAnnotator`](/0.17.0/annotators/#supervision.annotators.core.ColorAnnotator).

- Changed [#606](https://github.com/roboflow/supervision/pull/606): [`sv.MaskAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.MaskAnnotator) to make it **5x faster**.
+- Changed [#606](https://github.com/roboflow/supervision/pull/606): [`sv.MaskAnnotator`](/0.17.0/annotators/#supervision.annotators.core.MaskAnnotator) to make it **5x faster**.

- Fixed [#584](https://github.com/roboflow/supervision/pull/584): [`sv.DetectionDataset.from_yolo`](https://supervision.roboflow.com/datasets/#supervision.dataset.core.DetectionDataset.from_yolo) to ignore empty lines in annotation files.
+- Fixed [#584](https://github.com/roboflow/supervision/pull/584): [`sv.DetectionDataset.from_yolo`](/0.17.0/datasets/#supervision.dataset.core.DetectionDataset.from_yolo) to ignore empty lines in annotation files.

- Fixed [#555](https://github.com/roboflow/supervision/pull/555): [`sv.BlurAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BlurAnnotator) to trim negative coordinates before bluring detections.
+- Fixed [#555](https://github.com/roboflow/supervision/pull/555): [`sv.BlurAnnotator`](/0.17.0/annotators/#supervision.annotators.core.BlurAnnotator) to trim negative coordinates before bluring detections.

- Fixed [#511](https://github.com/roboflow/supervision/pull/511): [`sv.TraceAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.TraceAnnotator) to respect trace position.
+- Fixed [#511](https://github.com/roboflow/supervision/pull/511): [`sv.TraceAnnotator`](/0.17.0/annotators/#supervision.annotators.core.TraceAnnotator) to respect trace position.

 ### 0.16.0 <small>October 19, 2023</small>

- Added [#422](https://github.com/roboflow/supervision/pull/422): [`sv.BoxMaskAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BoxMaskAnnotator) allowing to annotate images and videos with mox masks.
+- Added [#422](https://github.com/roboflow/supervision/pull/422): [`sv.BoxMaskAnnotator`](/0.16.0/annotators/#supervision.annotators.core.BoxMaskAnnotator) allowing to annotate images and videos with mox masks.

- Added [#433](https://github.com/roboflow/supervision/pull/433): [`sv.HaloAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.HaloAnnotator) allowing to annotate images and videos with halo effect.
+- Added [#433](https://github.com/roboflow/supervision/pull/433): [`sv.HaloAnnotator`](/0.16.0/annotators/#supervision.annotators.core.HaloAnnotator) allowing to annotate images and videos with halo effect.

 ```python
 >>> import supervision as sv
@@ -142,46 +142,46 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 ... )
 ```

- Added [#466](https://github.com/roboflow/supervision/pull/466): [`sv.HeatMapAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.HeatMapAnnotator) allowing to annotate videos with heat maps.
+- Added [#466](https://github.com/roboflow/supervision/pull/466): [`sv.HeatMapAnnotator`](/0.16.0/annotators/#supervision.annotators.core.HeatMapAnnotator) allowing to annotate videos with heat maps.

- Added [#492](https://github.com/roboflow/supervision/pull/492): [`sv.DotAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.DotAnnotator) allowing to annotate images and videos with dots.
+- Added [#492](https://github.com/roboflow/supervision/pull/492): [`sv.DotAnnotator`](/0.16.0/annotators/#supervision.annotators.core.DotAnnotator) allowing to annotate images and videos with dots.

- Added [#449](https://github.com/roboflow/supervision/pull/449): [`sv.draw_image`](https://supervision.roboflow.com/draw/utils/#supervision.draw.utils.draw_image) allowing to draw an image onto a given scene with specified opacity and dimensions.
+- Added [#449](https://github.com/roboflow/supervision/pull/449): [`sv.draw_image`](/0.16.0/draw/utils/#supervision.draw.utils.draw_image) allowing to draw an image onto a given scene with specified opacity and dimensions.

- Added [#280](https://github.com/roboflow/supervision/pull/280): [`sv.FPSMonitor`](https://supervision.roboflow.com/utils/video/#supervision.utils.video.FPSMonitor) for monitoring frames per second (FPS) to benchmark latency.
+- Added [#280](https://github.com/roboflow/supervision/pull/280): [`sv.FPSMonitor`](/0.16.0/utils/video/#supervision.utils.video.FPSMonitor) for monitoring frames per second (FPS) to benchmark latency.

 - Added [#454](https://github.com/roboflow/supervision/pull/454): 🤗 Hugging Face Annotators [space](https://huggingface.co/spaces/Roboflow/Annotators).

- Changed [#482](https://github.com/roboflow/supervision/pull/482): [`sv.LineZone.trigger`](https://supervision.roboflow.com/detection/tools/line_zone/#supervision.detection.line_counter.LineZone.trigger) now return `Tuple[np.ndarray, np.ndarray]`. The first array indicates which detections have crossed the line from outside to inside. The second array indicates which detections have crossed the line from inside to outside.
+- Changed [#482](https://github.com/roboflow/supervision/pull/482): [`sv.LineZone.trigger`](/0.16.0/detection/tools/line_zone/#supervision.detection.line_counter.LineZone.trigger) now return `Tuple[np.ndarray, np.ndarray]`. The first array indicates which detections have crossed the line from outside to inside. The second array indicates which detections have crossed the line from inside to outside.

 - Changed [#465](https://github.com/roboflow/supervision/pull/465): Annotator argument name from `color_map: str` to `color_lookup: ColorLookup` enum to increase type safety.

- Changed [#426](https://github.com/roboflow/supervision/pull/426): [`sv.MaskAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.MaskAnnotator) allowing 2x faster annotation.
+- Changed [#426](https://github.com/roboflow/supervision/pull/426): [`sv.MaskAnnotator`](/0.16.0/annotators/#supervision.annotators.core.MaskAnnotator) allowing 2x faster annotation.

 - Fixed [#477](https://github.com/roboflow/supervision/pull/477): Poetry env definition allowing proper local installation.

- Fixed [#430](https://github.com/roboflow/supervision/pull/430):  [`sv.ByteTrack`](https://supervision.roboflow.com/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) to return `np.array([], dtype=int)` when `svDetections` is empty.
+- Fixed [#430](https://github.com/roboflow/supervision/pull/430):  [`sv.ByteTrack`](/0.16.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack) to return `np.array([], dtype=int)` when `svDetections` is empty.

 !!! failure "Deprecated"

-    `sv.Detections.from_yolov8` and `sv.Classifications.from_yolov8` as those are now replaced by [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_ultralytics) and [`sv.Classifications.from_ultralytics`](https://supervision.roboflow.com/classification/core/#supervision.classification.core.Classifications.from_ultralytics).
+    `sv.Detections.from_yolov8` and `sv.Classifications.from_yolov8` as those are now replaced by [`sv.Detections.from_ultralytics`](/0.16.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) and [`sv.Classifications.from_ultralytics`](/0.16.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics).


 ### 0.15.0 <small>October 5, 2023</small>

- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoundingBoxAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BoundingBoxAnnotator) allowing to annotate images and videos with bounding boxes.
+- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoundingBoxAnnotator`](/0.15.0/annotators/#supervision.annotators.core.BoundingBoxAnnotator) allowing to annotate images and videos with bounding boxes.

- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoxCornerAnnotator `](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BoxCornerAnnotator) allowing to annotate images and videos with just bounding box corners.
+- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.BoxCornerAnnotator `](/0.15.0/annotators/#supervision.annotators.core.BoxCornerAnnotator) allowing to annotate images and videos with just bounding box corners.

- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.MaskAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.MaskAnnotator) allowing to annotate images and videos with segmentation masks.
+- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.MaskAnnotator`](/0.15.0/annotators/#supervision.annotators.core.MaskAnnotator) allowing to annotate images and videos with segmentation masks.

- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.EllipseAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.EllipseAnnotator) allowing to annotate images and videos with ellipses (sports game style).
+- Added [#170](https://github.com/roboflow/supervision/pull/170): [`sv.EllipseAnnotator`](/0.15.0/annotators/#supervision.annotators.core.EllipseAnnotator) allowing to annotate images and videos with ellipses (sports game style).

- Added [#386](https://github.com/roboflow/supervision/pull/386): [`sv.CircleAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.CircleAnnotator) allowing to annotate images and videos with circles.
+- Added [#386](https://github.com/roboflow/supervision/pull/386): [`sv.CircleAnnotator`](/0.15.0/annotators/#supervision.annotators.core.CircleAnnotator) allowing to annotate images and videos with circles.

- Added [#354](https://github.com/roboflow/supervision/pull/354): [`sv.TraceAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.TraceAnnotator) allowing to draw path of moving objects on videos.
+- Added [#354](https://github.com/roboflow/supervision/pull/354): [`sv.TraceAnnotator`](/0.15.0/annotators/#supervision.annotators.core.TraceAnnotator) allowing to draw path of moving objects on videos.

- Added [#405](https://github.com/roboflow/supervision/pull/405): [`sv.BlurAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BlurAnnotator) allowing to blur objects on images and videos.
+- Added [#405](https://github.com/roboflow/supervision/pull/405): [`sv.BlurAnnotator`](/0.15.0/annotators/#supervision.annotators.core.BlurAnnotator) allowing to blur objects on images and videos.

 ```python
 >>> import supervision as sv
@@ -198,17 +198,17 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])

 - Added [#354](https://github.com/roboflow/supervision/pull/354): Supervision usage [example](https://github.com/roboflow/supervision/tree/develop/examples/traffic_analysis). You can now learn how to perform traffic flow analysis with Supervision.

- Changed [#399](https://github.com/roboflow/supervision/pull/399): [`sv.Detections.from_roboflow`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_roboflow) now does not require `class_list` to be specified. The `class_id` value can be extracted directly from the [inference](https://github.com/roboflow/inference) response.
+- Changed [#399](https://github.com/roboflow/supervision/pull/399): [`sv.Detections.from_roboflow`](/0.15.0/detection/core/#supervision.detection.core.Detections.from_roboflow) now does not require `class_list` to be specified. The `class_id` value can be extracted directly from the [inference](https://github.com/roboflow/inference) response.

- Changed [#381](https://github.com/roboflow/supervision/pull/381): [`sv.VideoSink`](https://supervision.roboflow.com/utils/video/#videosink) now allows to customize the output codec.
+- Changed [#381](https://github.com/roboflow/supervision/pull/381): [`sv.VideoSink`](/0.15.0/utils/video/#videosink) now allows to customize the output codec.

- Changed [#361](https://github.com/roboflow/supervision/pull/361): [`sv.InferenceSlicer`](https://supervision.roboflow.com/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can now operate in multithreading mode.
+- Changed [#361](https://github.com/roboflow/supervision/pull/361): [`sv.InferenceSlicer`](/0.15.0/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can now operate in multithreading mode.

- Fixed [#348](https://github.com/roboflow/supervision/pull/348): [`sv.Detections.from_deepsparse`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_deepsparse) to allow processing empty [deepsparse](https://github.com/neuralmagic/deepsparse) result object.
+- Fixed [#348](https://github.com/roboflow/supervision/pull/348): [`sv.Detections.from_deepsparse`](/0.15.0/detection/core/#supervision.detection.core.Detections.from_deepsparse) to allow processing empty [deepsparse](https://github.com/neuralmagic/deepsparse) result object.

 ### 0.14.0 <small>August 31, 2023</small>

- Added [#282](https://github.com/roboflow/supervision/pull/282): support for SAHI inference technique with [`sv.InferenceSlicer`](https://supervision.roboflow.com/detection/tools/inference_slicer).
+- Added [#282](https://github.com/roboflow/supervision/pull/282): support for SAHI inference technique with [`sv.InferenceSlicer`](/0.14.0/detection/tools/inference_slicer).

 ```python
 >>> import cv2
@@ -227,23 +227,23 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 >>> detections = slicer(image)
 ```

- Added [#297](https://github.com/roboflow/supervision/pull/297): [`Detections.from_deepsparse`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_deepsparse) to enable seamless integration with [DeepSparse](https://github.com/neuralmagic/deepsparse) framework.
+- Added [#297](https://github.com/roboflow/supervision/pull/297): [`Detections.from_deepsparse`](/0.14.0/detection/core/#supervision.detection.core.Detections.from_deepsparse) to enable seamless integration with [DeepSparse](https://github.com/neuralmagic/deepsparse) framework.

- Added [#281](https://github.com/roboflow/supervision/pull/281): [`sv.Classifications.from_ultralytics`](https://supervision.roboflow.com/classification/core/#supervision.classification.core.Classifications.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use supervision with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
+- Added [#281](https://github.com/roboflow/supervision/pull/281): [`sv.Classifications.from_ultralytics`](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use supervision with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.

 !!! failure "Deprecated"

-    [sv.Detections.from_yolov8](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](https://supervision.roboflow.com/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.
+    [sv.Detections.from_yolov8](/0.14.0/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](/0.14.0/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with `supervision-0.16.0` release.

 - Added [#341](https://github.com/roboflow/supervision/pull/341): First supervision usage example script showing how to detect and track objects on video using YOLOv8 + Supervision.

- Changed [#296](https://github.com/roboflow/supervision/pull/296): [`sv.ClassificationDataset`](https://supervision.roboflow.com/dataset/core/#supervision.dataset.core.ClassificationDataset) and [`sv.DetectionDataset`](https://supervision.roboflow.com/dataset/core/#supervision.dataset.core.DetectionDataset) now use image path (not image name) as dataset keys.
+- Changed [#296](https://github.com/roboflow/supervision/pull/296): [`sv.ClassificationDataset`](/0.14.0/dataset/core/#supervision.dataset.core.ClassificationDataset) and [`sv.DetectionDataset`](/0.14.0/dataset/core/#supervision.dataset.core.DetectionDataset) now use image path (not image name) as dataset keys.

- Fixed [#300](https://github.com/roboflow/supervision/pull/300): [`Detections.from_roboflow`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_roboflow) to filter out polygons with less than 3 points.
+- Fixed [#300](https://github.com/roboflow/supervision/pull/300): [`Detections.from_roboflow`](/0.14.0/detection/core/#supervision.detection.core.Detections.from_roboflow) to filter out polygons with less than 3 points.

 ### 0.13.0 <small>August 8, 2023</small>

- Added [#236](https://github.com/roboflow/supervision/pull/236): support for mean average precision (mAP) for object detection models with [`sv.MeanAveragePrecision`](https://roboflow.github.io/supervision/metrics/detection/#meanaverageprecision).
+- Added [#236](https://github.com/roboflow/supervision/pull/236): support for mean average precision (mAP) for object detection models with [`sv.MeanAveragePrecision`](/0.13.0/metrics/detection/#meanaverageprecision).

 ```python
 >>> import supervision as sv
@@ -265,17 +265,17 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])
 0.433
 ```

- Added [#256](https://github.com/roboflow/supervision/pull/256): support for ByteTrack for object tracking with [`sv.ByteTrack`](https://roboflow.github.io/supervision/tracker/core/#bytetrack).
+- Added [#256](https://github.com/roboflow/supervision/pull/256): support for ByteTrack for object tracking with [`sv.ByteTrack`](/0.13.0/tracker/core/#bytetrack).

- Added [#222](https://github.com/roboflow/supervision/pull/222): [`sv.Detections.from_ultralytics`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use `supervision` with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
+- Added [#222](https://github.com/roboflow/supervision/pull/222): [`sv.Detections.from_ultralytics`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use `supervision` with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.

 !!! failure "Deprecated"

-    [`sv.Detections.from_yolov8`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.
+    [`sv.Detections.from_yolov8`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_yolov8) is now deprecated and will be removed with `supervision-0.15.0` release.

- Added [#191](https://github.com/roboflow/supervision/pull/191): [`sv.Detections.from_paddledet`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_paddledet) to enable seamless integration with [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) framework.
+- Added [#191](https://github.com/roboflow/supervision/pull/191): [`sv.Detections.from_paddledet`](/0.13.0/detection/core/#supervision.detection.core.Detections.from_paddledet) to enable seamless integration with [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) framework.

- Added [#245](https://github.com/roboflow/supervision/pull/245): support for loading PASCAL VOC segmentation datasets with [`sv.DetectionDataset.`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.DetectionDataset.from_pascal_voc).
+- Added [#245](https://github.com/roboflow/supervision/pull/245): support for loading PASCAL VOC segmentation datasets with [`sv.DetectionDataset.`](/0.13.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_pascal_voc).

 ### 0.12.0 <small>July 24, 2023</small>

@@ -283,7 +283,7 @@ ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])

    With the `supervision-0.12.0` release, we are terminating official support for Python 3.7.

- Added [#177](https://github.com/roboflow/supervision/pull/177): initial support for object detection model benchmarking with [`sv.ConfusionMatrix`](https://roboflow.github.io/supervision/metrics/detection/#confusionmatrix).
+- Added [#177](https://github.com/roboflow/supervision/pull/177): initial support for object detection model benchmarking with [`sv.ConfusionMatrix`](/0.12.0/metrics/detection/#confusionmatrix).

 ```python
 >>> import supervision as sv
@@ -310,23 +310,23 @@ array([
 ])
 ```

- Added [#173](https://github.com/roboflow/supervision/pull/173): [`Detections.from_mmdetection`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_mmdetection) to enable seamless integration with [MMDetection](https://github.com/open-mmlab/mmdetection) framework.
+- Added [#173](https://github.com/roboflow/supervision/pull/173): [`Detections.from_mmdetection`](/0.12.0/detection/core/#supervision.detection.core.Detections.from_mmdetection) to enable seamless integration with [MMDetection](https://github.com/open-mmlab/mmdetection) framework.

- Added [#130](https://github.com/roboflow/supervision/issues/130): ability to [install](https://roboflow.github.io/supervision/) package in `headless` or `desktop` mode.
+- Added [#130](https://github.com/roboflow/supervision/issues/130): ability to [install](https://supervision.roboflow.com/) package in `headless` or `desktop` mode.

 - Changed [#180](https://github.com/roboflow/supervision/pull/180): packing method from `setup.py` to `pyproject.toml`.

- Fixed [#188](https://github.com/roboflow/supervision/issues/188): [`sv.DetectionDataset.from_cooc`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.DetectionDataset.from_coco) can't be loaded when there are images without annotations.
+- Fixed [#188](https://github.com/roboflow/supervision/issues/188): [`sv.DetectionDataset.from_cooc`](/0.12.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_coco) can't be loaded when there are images without annotations.

- Fixed [#226](https://github.com/roboflow/supervision/issues/226): [`sv.DetectionDataset.from_yolo`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.DetectionDataset.from_yolo) can't load background instances.
+- Fixed [#226](https://github.com/roboflow/supervision/issues/226): [`sv.DetectionDataset.from_yolo`](/0.12.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_yolo) can't load background instances.

 ### 0.11.1 <small>June 29, 2023</small>

- Fix [#165](https://github.com/roboflow/supervision/pull/165): [`as_folder_structure`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.ClassificationDataset.as_folder_structure) fails to save [`sv.ClassificationDataset`](https://roboflow.github.io/supervision/dataset/core/#classificationdataset) when it is result of inference.
+- Fix [#165](https://github.com/roboflow/supervision/pull/165): [`as_folder_structure`](/0.11.1/dataset/core/#supervision.dataset.core.ClassificationDataset.as_folder_structure) fails to save [`sv.ClassificationDataset`](/0.11.1/dataset/core/#classificationdataset) when it is result of inference.

 ### 0.11.0 <small>June 28, 2023</small>

- Added [#150](https://github.com/roboflow/supervision/pull/150): ability to load and save [`sv.DetectionDataset`](https://roboflow.github.io/supervision/dataset/core/#detectiondataset) in COCO format using [`as_coco`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.DetectionDataset.as_coco) and [`from_coco`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.DetectionDataset.from_coco) methods.
+- Added [#150](https://github.com/roboflow/supervision/pull/150): ability to load and save [`sv.DetectionDataset`](/0.11.0/dataset/core/#detectiondataset) in COCO format using [`as_coco`](/0.11.0/dataset/core/#supervision.dataset.core.DetectionDataset.as_coco) and [`from_coco`](/0.11.0/dataset/core/#supervision.dataset.core.DetectionDataset.from_coco) methods.

 ```python
 >>> import supervision as sv
@@ -342,7 +342,7 @@ array([
 ... )
 ```

- Added [#158](https://github.com/roboflow/supervision/pull/158): ability to merge multiple [`sv.DetectionDataset`](https://roboflow.github.io/supervision/dataset/core/#detectiondataset) together using [`merge`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.DetectionDataset.merge) method.
+- Added [#158](https://github.com/roboflow/supervision/pull/158): ability to merge multiple [`sv.DetectionDataset`](/0.11.0/dataset/core/#detectiondataset) together using [`merge`](/0.11.0/dataset/core/#supervision.dataset.core.DetectionDataset.merge) method.

 ```python
 >>> import supervision as sv
@@ -366,13 +366,13 @@ array([
 ['cat', 'dog', 'person']
 ```

- Added [#162](https://github.com/roboflow/supervision/pull/162): additional `start` and `end` arguments to [`sv.get_video_frames_generator`](https://roboflow.github.io/supervision/utils/video/#get_video_frames_generator) allowing to generate frames only for a selected part of the video.
+- Added [#162](https://github.com/roboflow/supervision/pull/162): additional `start` and `end` arguments to [`sv.get_video_frames_generator`](/0.11.0/utils/video/#get_video_frames_generator) allowing to generate frames only for a selected part of the video.

 - Fix [#157](https://github.com/roboflow/supervision/pull/157): incorrect loading of YOLO dataset class names from `data.yaml`.

 ### 0.10.0 <small>June 14, 2023</small>

- Added [#125](https://github.com/roboflow/supervision/pull/125): ability to load and save [`sv.ClassificationDataset`](https://roboflow.github.io/supervision/dataset/core/#classificationdataset) in a folder structure format.
+- Added [#125](https://github.com/roboflow/supervision/pull/125): ability to load and save [`sv.ClassificationDataset`](/0.10.0/dataset/core/#classificationdataset) in a folder structure format.

 ```python
 >>> import supervision as sv
@@ -386,9 +386,9 @@ array([
 ... )
 ```

- Added [#125](https://github.com/roboflow/supervision/pull/125): support for [`sv.ClassificationDataset.split`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.ClassificationDataset.split) allowing to divide `sv.ClassificationDataset` into two parts.
+- Added [#125](https://github.com/roboflow/supervision/pull/125): support for [`sv.ClassificationDataset.split`](/0.10.0/dataset/core/#supervision.dataset.core.ClassificationDataset.split) allowing to divide `sv.ClassificationDataset` into two parts.

- Added [#110](https://github.com/roboflow/supervision/pull/110): ability to extract masks from Roboflow API results using [`sv.Detections.from_roboflow`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_roboflow).
+- Added [#110](https://github.com/roboflow/supervision/pull/110): ability to extract masks from Roboflow API results using [`sv.Detections.from_roboflow`](/0.10.0/detection/core/#supervision.detection.core.Detections.from_roboflow).

 - Added [commit hash](https://github.com/roboflow/supervision/commit/d000292eb2f2342544e0947b65528082e60fb8d6): Supervision Quickstart [notebook](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb) where you can learn more about Detection, Dataset and Video APIs.

@@ -396,7 +396,7 @@ array([

 ### 0.9.0 <small>June 7, 2023</small>

- Added [#118](https://github.com/roboflow/supervision/pull/118): ability to select [`sv.Detections`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.__getitem__) by index, list of indexes or slice. Here is an example illustrating the new selection methods.
+- Added [#118](https://github.com/roboflow/supervision/pull/118): ability to select [`sv.Detections`](/0.9.0/detection/core/#supervision.detection.core.Detections.__getitem__) by index, list of indexes or slice. Here is an example illustrating the new selection methods.

 ```python
 >>> import supervision as sv
@@ -410,11 +410,11 @@ array([
 2
 ```

- Added [#101](https://github.com/roboflow/supervision/pull/101): ability to extract masks from YOLOv8 result using [`sv.Detections.from_yolov8`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_yolov8). Here is an example illustrating how to extract boolean masks from the result of the YOLOv8 model inference.
+- Added [#101](https://github.com/roboflow/supervision/pull/101): ability to extract masks from YOLOv8 result using [`sv.Detections.from_yolov8`](/0.8.0/detection/core/#supervision.detection.core.Detections.from_yolov8). Here is an example illustrating how to extract boolean masks from the result of the YOLOv8 model inference.

- Added [#122](https://github.com/roboflow/supervision/pull/122): ability to crop image using [`sv.crop`](https://roboflow.github.io/supervision/utils/image/#crop). Here is an example showing how to get a separate crop for each detection in `sv.Detections`.
+- Added [#122](https://github.com/roboflow/supervision/pull/122): ability to crop image using [`sv.crop`](/latest/utils/image/#crop). Here is an example showing how to get a separate crop for each detection in `sv.Detections`.

- Added [#120](https://github.com/roboflow/supervision/pull/120): ability to conveniently save multiple images into directory using [`sv.ImageSink`](https://roboflow.github.io/supervision/utils/image/#imagesink). Here is an example showing how to save every tenth video frame as a separate image.
+- Added [#120](https://github.com/roboflow/supervision/pull/120): ability to conveniently save multiple images into directory using [`sv.ImageSink`](/0.9.0/utils/image/#imagesink). Here is an example showing how to save every tenth video frame as a separate image.

 ```python
 >>> import supervision as sv
@@ -424,12 +424,12 @@ array([
 ...         sink.save_image(image=image)
 ```

- Fixed [#106](https://github.com/roboflow/supervision/issues/106): inconvenient handling of [`sv.PolygonZone`](https://roboflow.github.io/supervision/detection/tools/polygon_zone/#polygonzone) coordinates. Now `sv.PolygonZone` accepts coordinates in the form of `[[x1, y1], [x2, y2], ...]` that can be both integers and floats.
+- Fixed [#106](https://github.com/roboflow/supervision/issues/106): inconvenient handling of [`sv.PolygonZone`](/0.8.0/supervision/detection/tools/polygon_zone/#polygonzone) coordinates. Now `sv.PolygonZone` accepts coordinates in the form of `[[x1, y1], [x2, y2], ...]` that can be both integers and floats.

 ### 0.8.0 <small>May 17, 2023</small>

- Added [#100](https://github.com/roboflow/supervision/pull/100): support for dataset inheritance. The current `Dataset` got renamed to `DetectionDataset`. Now [`DetectionDataset`](https://roboflow.github.io/supervision/dataset/core/#detectiondataset) inherits from `BaseDataset`. This change was made to enforce the future consistency of APIs of different types of computer vision datasets.
- Added [#100](https://github.com/roboflow/supervision/pull/100): ability to save datasets in YOLO format using [`DetectionDataset.as_yolo`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.DetectionDataset.as_yolo).
+- Added [#100](https://github.com/roboflow/supervision/pull/100): support for dataset inheritance. The current `Dataset` got renamed to `DetectionDataset`. Now [`DetectionDataset`](/0.8.0/supervision/dataset/core/#detectiondataset) inherits from `BaseDataset`. This change was made to enforce the future consistency of APIs of different types of computer vision datasets.
+- Added [#100](https://github.com/roboflow/supervision/pull/100): ability to save datasets in YOLO format using [`DetectionDataset.as_yolo`](/0.8.0/dataset/core/#supervision.dataset.core.DetectionDataset.as_yolo).

 ```python
 >>> import roboflow
@@ -453,7 +453,7 @@ array([
 ['dog', 'person']
 ```

- Added [#102](https://github.com/roboflow/supervision/pull/103): support for [`DetectionDataset.split`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.DetectionDataset.split) allowing to divide `DetectionDataset` into two parts.
+- Added [#102](https://github.com/roboflow/supervision/pull/103): support for [`DetectionDataset.split`](/0.8.0/dataset/core/#supervision.dataset.core.DetectionDataset.split) allowing to divide `DetectionDataset` into two parts.

 ```python
 >>> import supervision as sv
@@ -0,0 +1,5 @@
+# Code of conduct
+
+```
+--8<-- "CODE_OF_CONDUCT.md"
+```
@@ -0,0 +1 @@
+--8<-- "CONTRIBUTING.md"
@@ -0,0 +1,12 @@
+---
+comments: true
+status: new
+---
+
+# Save Detections
+
+<div class="md-typeset">
+  <h2>CSV Sink</h2>
+</div>
+
+:::supervision.detection.tools.csv_sink.CSVSink
@@ -44,7 +44,7 @@ Now that we have predictions from a model, we can load them into Supervision.

 === "Ultralytics"

-    We can do so using the [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
+    We can do so using the [`sv.Detections.from_ultralytics`](/latest/detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.

    ```python
    import cv2
@@ -59,7 +59,7 @@ Now that we have predictions from a model, we can load them into Supervision.

 === "Inference"

-    We can do so using the [`sv.Detections.from_inference`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
+    We can do so using the [`sv.Detections.from_inference`](/latest/detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.

    ```python
    import cv2
@@ -74,17 +74,17 @@ Now that we have predictions from a model, we can load them into Supervision.

 You can conveniently load predictions from other computer vision frameworks and libraries using:

- [`from_deepsparse`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
- [`from_detectron2`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
- [`from_mmdetection`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
- [`from_inference`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_inference) ([Roboflow Inference](https://github.com/roboflow/inference))
- [`from_sam`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
- [`from_transformers`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_transformers) ([HuggingFace Transformers](https://github.com/huggingface/transformers))
- [`from_yolo_nas`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))
+- [`from_deepsparse`](/latest/detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
+- [`from_detectron2`](/latest/detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
+- [`from_mmdetection`](/latest/detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
+- [`from_inference`](/latest/detection/core/#supervision.detection.core.Detections.from_inference) ([Roboflow Inference](https://github.com/roboflow/inference))
+- [`from_sam`](/latest/detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
+- [`from_transformers`](/latest/detection/core/#supervision.detection.core.Detections.from_transformers) ([HuggingFace Transformers](https://github.com/huggingface/transformers))
+- [`from_yolo_nas`](/latest/detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))

 ## Annotate Image

-Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.LabelAnnotator) classes. If you are running the segmentation model [`sv.MaskAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.MaskAnnotator) is a drop-in replacement for [`sv.BoundingBoxAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BoundingBoxAnnotator) that will allow you to draw masks instead of boxes.
+Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](/latest/annotators/#supervision.annotators.core.LabelAnnotator) classes. If you are running the segmentation model [`sv.MaskAnnotator`](/latest/annotators/#supervision.annotators.core.MaskAnnotator) is a drop-in replacement for [`sv.BoundingBoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoundingBoxAnnotator) that will allow you to draw masks instead of boxes.

 === "Ultralytics"

@@ -138,7 +138,7 @@ Finally, we can annotate the image with the predictions. Since we are working wi

 ## Display Annotated Image

-To display the annotated image in Jupyter Notebook or Google Colab, use the [`sv.plot_image`](https://supervision.roboflow.com/utils/notebook/#supervision.utils.notebook.plot_image) function.
+To display the annotated image in Jupyter Notebook or Google Colab, use the [`sv.plot_image`](/latest/utils/notebook/#supervision.utils.notebook.plot_image) function.

 ```python
 sv.plot_image(annotated_image)
@@ -5,7 +5,7 @@ comments: true
 # Track Objects

 Leverage Supervision's advanced capabilities for enhancing your video analysis by
-seamlessly [tracking](https://supervision.roboflow.com/trackers/) objects recognized by
+seamlessly [tracking](/latest/trackers/) objects recognized by
 a multitude of object detection and segmentation models. This comprehensive guide will
 take you through the steps to perform inference using the YOLOv8 model via either the
 [Inference](https://github.com/roboflow/inference) or
@@ -15,7 +15,7 @@ for a deeper analysis.

 To make it easier for you to follow our tutorial download the video we will use as an
 example. You can do this using
-[`supervision[assets]`](https://supervision.roboflow.com/assets/) extension.
+[`supervision[assets]`](/latest/assets/) extension.

 ```python
 from supervision.assets import download_assets, VideoAssets
@@ -32,7 +32,7 @@ download_assets(VideoAssets.PEOPLE_WALKING)
 First, you'll need to obtain predictions from your object detection or segmentation
 model. In this tutorial, we are using the YOLOv8 model as an example. However,
 Supervision is versatile and compatible with various models. Check this
-[link](https://supervision.roboflow.com/how_to/detect_and_annotate/#load-predictions-into-supervision)
+[link](latest/how_to/detect_and_annotate/#load-predictions-into-supervision)
 for guidance on how to plug in other models.

 We will define a `callback` function, which will process each frame of the video
@@ -92,7 +92,7 @@ it will be modified to include tracking, labeling, and trace annotations.

 After running inference and obtaining predictions, the next step is to track the
 detected objects throughout the video. Utilizing Supervision’s
-[`sv.ByteTrack`](https://supervision.roboflow.com/trackers/#supervision.tracker.byte_tracker.core.ByteTrack)
+[`sv.ByteTrack`](/latest/trackers/#supervision.tracker.byte_tracker.core.ByteTrack)
 functionality, each detected object is assigned a unique tracker ID,
 enabling the continuous following of the object's motion path across different frames.

@@ -148,7 +148,7 @@ enabling the continuous following of the object's motion path across different f

 Annotating the video with tracking IDs helps in distinguishing and following each object
 distinctly. With the
-[`sv.LabelAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.LabelAnnotator)
+[`sv.LabelAnnotator`](/latest/annotators.md/#supervision.annotators.core.LabelAnnotator)
 in Supervision, we can overlay the tracker IDs and class labels on the detected objects,
 offering a clear visual representation of each object's class and unique identifier.

@@ -230,7 +230,7 @@ offering a clear visual representation of each object's class and unique identif

 Adding traces to the video involves overlaying the historical paths of the detected
 objects. This feature, powered by the
-[`sv.TraceAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.TraceAnnotator),
+[`sv.TraceAnnotator`](/latest/annotators/#supervision.annotators.core.TraceAnnotator),
 allows for visualizing the trajectories of objects, helping in understanding the
 movement patterns and interactions between objects in the video.

@@ -24,40 +24,6 @@ We write your reusable computer vision tools. Whether you need to load your data
    >
 </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.md)
-
-   __Track Objects__
-
-    ---
-
-    Discover how to enhance video analysis by implementing seamless object tracking
-
-    [:octicons-arrow-right-24: Tutorial](how_to/track_objects.md)
-
-   > __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
@@ -117,3 +83,38 @@ You can install `supervision` with pip in a
        # desktop install
        poetry install --extras "desktop"
        ```
+
+
+## 🚀 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.md)
+
+-   __Track Objects__
+
+    ---
+
+    Discover how to enhance video analysis by implementing seamless object tracking
+
+    [:octicons-arrow-right-24: Tutorial](how_to/track_objects.md)
+
+-   > __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>
@@ -0,0 +1,5 @@
+# License
+
+```
+--8<-- "LICENSE.md"
+```
@@ -2,15 +2,21 @@
 comments: true
 ---

+#  Detection Metrics
+
 !!! warning

    Evaluation API is still fluid and may change. If you use Evaluation API in your project until further notice, freeze the
    `supervision` version in your `requirements.txt` or `setup.py`.

-## ConfusionMatrix
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.detection.ConfusionMatrix">ConfusionMatrix</a></h2>
+</div>

 :::supervision.metrics.detection.ConfusionMatrix

-## MeanAveragePrecision
+<div class="md-typeset">
+    <h2><a href="#supervision.annotators.core.MeanAveragePrecision">MeanAveragePrecision</a></h2>
+</div>

 :::supervision.metrics.detection.MeanAveragePrecision
@@ -1,4 +1,4 @@
-## examples
+# Examples

 This repository is packed with real-world use-cases, provided through Python scripts or
 interactive notebooks. Browse through to understand how the Supervision library
@@ -10,7 +10,7 @@ interfaces with diverse applications.
 - [speed estimation](./speed_estimation) by [@SkalskiP](https://github.com/SkalskiP)
 - [heatmap and track](./heatmap_and_track/) by [@HinePo](https://github.com/HinePo)

-## contributing
+## Contributing

 We welcome contributions from the community in the form of examples, applications, and
 guides. To contribute, please follow these steps:
@@ -30,6 +30,9 @@ class ViewTransformer:
        self.m = cv2.getPerspectiveTransform(source, target)

    def transform_points(self, points: np.ndarray) -> np.ndarray:
+        if points.size == 0:
+            return points
+
        reshaped_points = points.reshape(-1, 1, 2).astype(np.float32)
        transformed_points = cv2.perspectiveTransform(reshaped_points, self.m)
        return transformed_points.reshape(-1, 2)
@@ -30,6 +30,9 @@ class ViewTransformer:
        self.m = cv2.getPerspectiveTransform(source, target)

    def transform_points(self, points: np.ndarray) -> np.ndarray:
+        if points.size == 0:
+            return points
+
        reshaped_points = points.reshape(-1, 1, 2).astype(np.float32)
        transformed_points = cv2.perspectiveTransform(reshaped_points, self.m)
        return transformed_points.reshape(-1, 2)
@@ -11,12 +11,18 @@ extra:
  social:
    - icon: fontawesome/brands/github
      link: https://github.com/roboflow
+    - icon: fontawesome/brands/python
+      link: https://pypi.org/project/supervision
+    - icon: fontawesome/brands/docker
+      link: https://hub.docker.com/u/roboflow
    - icon: fontawesome/brands/youtube
      link: https://www.youtube.com/roboflow
    - icon: fontawesome/brands/linkedin
-      link: https://www.linkedin.com/company/roboflow-ai/mycompany/
+      link: https://www.linkedin.com/company/roboflow-ai/
    - icon: fontawesome/brands/x-twitter
      link: https://twitter.com/roboflow
+    - icon: fontawesome/brands/discord
+      link: https://discord.gg/GbfgXGJ8Bk
  analytics:
    provider: google
    property: G-P7ZG0Y19G5
@@ -43,6 +49,7 @@ nav:
        - Polygon Zone: detection/tools/polygon_zone.md
        - Inference Slicer: detection/tools/inference_slicer.md
        - Detection Smoother: detection/tools/smoother.md
+        - Save Detections: detection/tools/save_detections.md
    - Annotators: annotators.md
    - Trackers: trackers.md
    - Datasets: datasets.md
@@ -51,15 +58,22 @@ nav:
    - Draw:
        - Color: draw/color.md
        - Utils: draw/utils.md
+    - Geometry:
+        - Position: geometry/core.md
    - Utils:
        - Video: utils/video.md
        - Image: utils/image.md
        - Notebook: utils/notebook.md
        - File: utils/file.md
  - Assets: assets.md
+  - Development:
+    - Contributing: contributing.md
+    - Code of Conduct: code_of_conduct.md
  - Changelog:
        - Changelog: changelog.md
        - Deprecated: deprecated.md
+  - License: license.md
+

 theme:
  name: 'material'
@@ -88,8 +102,25 @@ theme:
    - content.code.annotate

 plugins:
-  - mkdocstrings
  - search
+  - mkdocstrings:
+      default_handler: python
+      handlers:
+        python:
+          options:
+            parameter_headings: true
+            paths: [supervision]
+            load_external_modules: true
+            allow_inspection: true
+            show_bases: true
+            group_by_category: true
+            docstring_style: google
+            show_symbol_type_heading: true
+            show_symbol_type_toc: true
+            show_category_heading: true
+          import:
+          - https://docs.python-requests.org/en/master/objects.inv
+          domains: [std, py]

 markdown_extensions:
  - admonition
@@ -104,6 +135,8 @@ markdown_extensions:
  - pymdownx.emoji:
      emoji_index: !!python/name:material.extensions.emoji.twemoji
      emoji_generator: !!python/name:material.extensions.emoji.to_svg
+  - pymdownx.snippets:
+      check_paths: true

 extra_javascript:
  - "https://widget.kapa.ai/kapa-widget.bundle.js"
@@ -1954,13 +1954,13 @@ mkdocs = ">=1.1"

 [[package]]
 name = "mkdocs-material"
-version = "9.5.6"
+version = "9.5.7"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.6-py3-none-any.whl", hash = "sha256:e115b90fccf5cd7f5d15b0c2f8e6246b21041628b8f590630e7fca66ed7fcf6c"},
-    {file = "mkdocs_material-9.5.6.tar.gz", hash = "sha256:5b24df36d8ac6cecd611241ce6f6423ccde3e1ad89f8360c3f76d5565fc2d82a"},
+    {file = "mkdocs_material-9.5.7-py3-none-any.whl", hash = "sha256:0be8ce8bcfebb52bae9b00cf9b851df45b8a92d629afcfd7f2c09b2dfa155ea3"},
+    {file = "mkdocs_material-9.5.7.tar.gz", hash = "sha256:16110292575d88a338d2961f3cb665cf12943ff8829e551a9b364f24019e46af"},
 ]

 [package.dependencies]
@@ -3416,28 +3416,28 @@ files = [

 [[package]]
 name = "ruff"
-version = "0.1.15"
+version = "0.2.0"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.1.15-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:5fe8d54df166ecc24106db7dd6a68d44852d14eb0729ea4672bb4d96c320b7df"},
-    {file = "ruff-0.1.15-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:6f0bfbb53c4b4de117ac4d6ddfd33aa5fc31beeaa21d23c45c6dd249faf9126f"},
-    {file = "ruff-0.1.15-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e0d432aec35bfc0d800d4f70eba26e23a352386be3a6cf157083d18f6f5881c8"},
-    {file = "ruff-0.1.15-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9405fa9ac0e97f35aaddf185a1be194a589424b8713e3b97b762336ec79ff807"},
-    {file = "ruff-0.1.15-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c66ec24fe36841636e814b8f90f572a8c0cb0e54d8b5c2d0e300d28a0d7bffec"},
-    {file = "ruff-0.1.15-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:6f8ad828f01e8dd32cc58bc28375150171d198491fc901f6f98d2a39ba8e3ff5"},
-    {file = "ruff-0.1.15-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86811954eec63e9ea162af0ffa9f8d09088bab51b7438e8b6488b9401863c25e"},
-    {file = "ruff-0.1.15-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fd4025ac5e87d9b80e1f300207eb2fd099ff8200fa2320d7dc066a3f4622dc6b"},
-    {file = "ruff-0.1.15-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b17b93c02cdb6aeb696effecea1095ac93f3884a49a554a9afa76bb125c114c1"},
-    {file = "ruff-0.1.15-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:ddb87643be40f034e97e97f5bc2ef7ce39de20e34608f3f829db727a93fb82c5"},
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-    {file = "ruff-0.1.15-py3-none-musllinux_1_2_i686.whl", hash = "sha256:6c629cf64bacfd136c07c78ac10a54578ec9d1bd2a9d395efbee0935868bf852"},
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+    {file = "ruff-0.2.0-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:638ea3294f800d18bae84a492cb5a245c8d29c90d19a91d8e338937a4c27fca0"},
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+    {file = "ruff-0.2.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bf9faafbdcf4f53917019f2c230766da437d4fd5caecd12ddb68bb6a17d74399"},
+    {file = "ruff-0.2.0-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:8153a3e4128ed770871c47545f1ae7b055023e0c222ff72a759f5a341ee06483"},
+    {file = "ruff-0.2.0-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e8a75a98ae989a27090e9c51f763990ad5bbc92d20626d54e9701c7fe597f399"},
+    {file = "ruff-0.2.0-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:87057dd2fdde297130ff99553be8549ca38a2965871462a97394c22ed2dfc19d"},
+    {file = "ruff-0.2.0-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6d232f99d3ab00094ebaf88e0fb7a8ccacaa54cc7fa3b8993d9627a11e6aed7a"},
+    {file = "ruff-0.2.0-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3d3c641f95f435fc6754b05591774a17df41648f0daf3de0d75ad3d9f099ab92"},
+    {file = "ruff-0.2.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3826fb34c144ef1e171b323ed6ae9146ab76d109960addca730756dc19dc7b22"},
+    {file = "ruff-0.2.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:eceab7d85d09321b4de18b62d38710cf296cb49e98979960a59c6b9307c18cfe"},
+    {file = "ruff-0.2.0-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:30ad74687e1f4a9ff8e513b20b82ccadb6bd796fe5697f1e417189c5cde6be3e"},
+    {file = "ruff-0.2.0-py3-none-musllinux_1_2_i686.whl", hash = "sha256:a7e3818698f8460bd0f8d4322bbe99db8327e9bc2c93c789d3159f5b335f47da"},
+    {file = "ruff-0.2.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:edf23041242c48b0d8295214783ef543847ef29e8226d9f69bf96592dba82a83"},
+    {file = "ruff-0.2.0-py3-none-win32.whl", hash = "sha256:e155147199c2714ff52385b760fe242bb99ea64b240a9ffbd6a5918eb1268843"},
+    {file = "ruff-0.2.0-py3-none-win_amd64.whl", hash = "sha256:ba918e01cdd21e81b07555564f40d307b0caafa9a7a65742e98ff244f5035c59"},
+    {file = "ruff-0.2.0-py3-none-win_arm64.whl", hash = "sha256:3fbaff1ba9564a2c5943f8f38bc221f04bac687cc7485e45237579fee7ccda79"},
+    {file = "ruff-0.2.0.tar.gz", hash = "sha256:63856b91837606c673537d2889989733d7dffde553828d3b0f0bacfa6def54be"},
 ]

 [[package]]
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.19.0rc1"
+version = "0.19.0rc2"
 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>"]
@@ -36,6 +36,7 @@ from supervision.dataset.core import (
 from supervision.detection.annotate import BoxAnnotator
 from supervision.detection.core import Detections
 from supervision.detection.line_counter import LineZone, LineZoneAnnotator
+from supervision.detection.tools.csv_sink import CSVSink
 from supervision.detection.tools.inference_slicer import InferenceSlicer
 from supervision.detection.tools.polygon_zone import PolygonZone, PolygonZoneAnnotator
 from supervision.detection.tools.smoother import DetectionsSmoother
@@ -99,7 +99,7 @@ class OrientedBoxAnnotator(BaseAnnotator):

    def __init__(
        self,
-        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        thickness: int = 2,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
@@ -1101,7 +1101,7 @@ class TraceAnnotator:
    !!! warning

        This annotator uses the `sv.Detections.tracker_id`. Read
-        [here](https://supervision.roboflow.com/trackers/) to learn how to plug
+        [here](/latest/trackers/) to learn how to plug
        tracking into your inference pipeline.
    """

@@ -118,7 +118,7 @@ class Classifications:
        [timm](https://huggingface.co/docs/hub/timm) inference result.

        Args:
-            timm_results: The inference result from timm model.
+            timm_results (torch.Tensor): The inference result from timm model.

        Returns:
            Classifications: A new Classifications object.
@@ -17,7 +17,7 @@ class LineZone:
    !!! warning

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

    Attributes:
@@ -137,7 +137,7 @@ class LineZone:
                continue

            triggers = [
-                self.vector.cross_product(point=anchor) > 0 for anchor in box_anchors
+                self.vector.cross_product(point=anchor) < 0 for anchor in box_anchors
            ]

            if len(set(triggers)) == 2:
@@ -0,0 +1,181 @@
+from __future__ import annotations
+
+import csv
+import os
+from typing import Any, Dict, List, Optional
+
+from supervision.detection.core import Detections
+
+BASE_HEADER = [
+    "x_min",
+    "y_min",
+    "x_max",
+    "y_max",
+    "class_id",
+    "confidence",
+    "tracker_id",
+]
+
+
+class CSVSink:
+    """
+    A utility class for saving detection data to a CSV file. This class is designed to
+    efficiently serialize detection objects into a CSV format, allowing for the
+    inclusion of bounding box coordinates and additional attributes like `confidence`,
+    `class_id`, and `tracker_id`.
+
+    !!! tip
+
+        CSVSink allow to pass custom data alongside the detection fields, providing
+        flexibility for logging various types of information.
+
+    Args:
+        file_name (str): The name of the CSV file where the detections will be stored.
+            Defaults to 'output.csv'.
+
+    Example:
+        ```python
+        import supervision as sv
+        from ultralytics import YOLO
+
+        model = YOLO(<SOURCE_MODEL_PATH>)
+        csv_sink = sv.CSVSink(<RESULT_CSV_FILE_PATH>)
+        frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+        with csv_sink:
+            for frame in frames_generator:
+                result = model(frame)[0]
+                detections = sv.Detections.from_ultralytics(result)
+                sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
+        ```
+    """  # noqa: E501 // docs
+
+    def __init__(self, file_name: str = "output.csv") -> None:
+        """
+        Initialize the CSVSink instance.
+
+        Args:
+            file_name (str): The name of the CSV file.
+
+        Returns:
+            None
+        """
+        self.file_name = file_name
+        self.file: Optional[open] = None
+        self.writer: Optional[csv.writer] = None
+        self.header_written = False
+        self.field_names = []
+
+    def __enter__(self) -> CSVSink:
+        self.open()
+        return self
+
+    def __exit__(
+        self,
+        exc_type: Optional[type],
+        exc_val: Optional[Exception],
+        exc_tb: Optional[Any],
+    ) -> None:
+        self.close()
+
+    def open(self) -> None:
+        """
+        Open the CSV file for writing.
+
+        Returns:
+            None
+        """
+        parent_directory = os.path.dirname(self.file_name)
+        if parent_directory and not os.path.exists(parent_directory):
+            os.makedirs(parent_directory)
+
+        self.file = open(self.file_name, "w", newline="")
+        self.writer = csv.writer(self.file)
+
+    def close(self) -> None:
+        """
+        Close the CSV file.
+
+        Returns:
+            None
+        """
+        if self.file:
+            self.file.close()
+
+    @staticmethod
+    def parse_detection_data(
+        detections: Detections, custom_data: Dict[str, Any] = None
+    ) -> List[Dict[str, Any]]:
+        parsed_rows = []
+        for i in range(len(detections.xyxy)):
+            row = {
+                "x_min": detections.xyxy[i][0],
+                "y_min": detections.xyxy[i][1],
+                "x_max": detections.xyxy[i][2],
+                "y_max": detections.xyxy[i][3],
+                "class_id": ""
+                if detections.class_id is None
+                else str(detections.class_id[i]),
+                "confidence": ""
+                if detections.confidence is None
+                else str(detections.confidence[i]),
+                "tracker_id": ""
+                if detections.tracker_id is None
+                else str(detections.tracker_id[i]),
+            }
+
+            if hasattr(detections, "data"):
+                for key, value in detections.data.items():
+                    if value.ndim == 0:
+                        row[key] = value
+                    else:
+                        row[key] = value[i]
+
+            if custom_data:
+                row.update(custom_data)
+            parsed_rows.append(row)
+        return parsed_rows
+
+    def append(
+        self, detections: Detections, custom_data: Dict[str, Any] = None
+    ) -> None:
+        """
+        Append detection data to the CSV file.
+
+        Args:
+            detections (Detections): The detection data.
+            custom_data (Dict[str, Any]): Custom data to include.
+
+        Returns:
+            None
+        """
+        if not self.writer:
+            raise Exception(
+                f"Cannot append to CSV: The file '{self.file_name}' is not open."
+            )
+        field_names = CSVSink.parse_field_names(detections, custom_data)
+        if not self.header_written:
+            self.field_names = field_names
+            self.writer.writerow(field_names)
+            self.header_written = True
+
+        if field_names != self.field_names:
+            print(
+                f"Field names do not match the header. "
+                f"Expected: {self.field_names}, given: {field_names}"
+            )
+
+        parsed_rows = CSVSink.parse_detection_data(detections, custom_data)
+        for row in parsed_rows:
+            self.writer.writerow(
+                [row.get(field_name, "") for field_name in self.field_names]
+            )
+
+    @staticmethod
+    def parse_field_names(
+        detections: Detections, custom_data: Dict[str, Any]
+    ) -> List[str]:
+        dynamic_header = sorted(
+            set(custom_data.keys()) | set(getattr(detections, "data", {}).keys())
+        )
+        return BASE_HEADER + dynamic_header
@@ -22,7 +22,7 @@ class DetectionsSmoother:
    !!! warning

        - `DetectionsSmoother` requires the `tracker_id` for each detection. Refer to
-          [Roboflow Trackers](https://supervision.roboflow.com/trackers/) for
+          [Roboflow Trackers](/latest/trackers/) for
          information on integrating tracking into your inference pipeline.
        - This class is not compatible with segmentation models.

@@ -72,7 +72,7 @@ class DetectionsSmoother:
        if detections.tracker_id is None:
            print(
                "Smoothing skipped. DetectionsSmoother requires tracker_id. Refer to "
-                "https://supervision.roboflow.com/trackers for more information."
+                "https://supervision.roboflow.com/latest/trackers for more information."
            )
            return detections

@@ -39,6 +39,14 @@ class BaseTrack:
        BaseTrack._count += 1
        return BaseTrack._count

+    @staticmethod
+    def reset_counter():
+        BaseTrack._count = 0
+        BaseTrack.track_id = 0
+        BaseTrack.start_frame = 0
+        BaseTrack.frame_id = 0
+        BaseTrack.time_since_update = 0
+
    def activate(self, *args):
        raise NotImplementedError

@@ -210,7 +210,6 @@ class ByteTrack:
            ```python
            import supervision as sv
            from ultralytics import YOLO
-            import numpy as np

            model = YOLO(<MODEL_PATH>)
            tracker = sv.ByteTrack()
@@ -261,6 +260,21 @@ class ByteTrack:

        return detections

+    def reset(self):
+        """
+        Resets the internal state of the ByteTrack tracker.
+
+        This method clears the tracking data, including tracked, lost,
+        and removed tracks, as well as resetting the frame counter. It's
+        particularly useful when processing multiple videos sequentially,
+        ensuring the tracker starts with a clean state for each new video.
+        """
+        self.frame_id = 0
+        self.tracked_tracks: List[STrack] = []
+        self.lost_tracks: List[STrack] = []
+        self.removed_tracks: List[STrack] = []
+        BaseTrack.reset_counter()
+
    def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
        """
        Updates the tracker with the provided tensors and returns the updated tracks.
@@ -306,6 +320,7 @@ class ByteTrack:
        """ Add newly detected tracklets to tracked_stracks"""
        unconfirmed = []
        tracked_stracks = []  # type: list[STrack]
+
        for track in self.tracked_tracks:
            if not track.is_activated:
                unconfirmed.append(track)
@@ -0,0 +1,415 @@
+import csv
+import os
+from test.test_utils import mock_detections
+from typing import Any, Dict, List
+
+import pytest
+
+import supervision as sv
+
+
+@pytest.mark.parametrize(
+    "detections, custom_data, "
+    "second_detections, second_custom_data, "
+    "file_name, expected_result",
+    [
+        (
+            mock_detections(
+                xyxy=[[10, 20, 30, 40], [50, 60, 70, 80]],
+                confidence=[0.7, 0.8],
+                class_id=[0, 0],
+                tracker_id=[0, 1],
+                data={"class_name": ["person", "person"]},
+            ),
+            {"frame_number": 42},
+            mock_detections(
+                xyxy=[[15, 25, 35, 45], [55, 65, 75, 85]],
+                confidence=[0.6, 0.9],
+                class_id=[1, 1],
+                tracker_id=[2, 3],
+                data={"class_name": ["car", "car"]},
+            ),
+            {"frame_number": 43},
+            "test_detections.csv",
+            [
+                [
+                    "x_min",
+                    "y_min",
+                    "x_max",
+                    "y_max",
+                    "class_id",
+                    "confidence",
+                    "tracker_id",
+                    "class_name",
+                    "frame_number",
+                ],
+                ["10.0", "20.0", "30.0", "40.0", "0", "0.7", "0", "person", "42"],
+                ["50.0", "60.0", "70.0", "80.0", "0", "0.8", "1", "person", "42"],
+                ["15.0", "25.0", "35.0", "45.0", "1", "0.6", "2", "car", "43"],
+                ["55.0", "65.0", "75.0", "85.0", "1", "0.9", "3", "car", "43"],
+            ],
+        ),  # multiple detections
+        (
+            mock_detections(
+                xyxy=[[60, 70, 80, 90], [100, 110, 120, 130]],
+                tracker_id=[4, 5],
+                data={"class_name": ["bike", "dog"]},
+            ),
+            {"frame_number": 44},
+            mock_detections(
+                xyxy=[[65, 75, 85, 95], [105, 115, 125, 135]],
+                confidence=[0.5, 0.4],
+                data={"class_name": ["tree", "cat"]},
+            ),
+            {"frame_number": 45},
+            "test_detections_missing_fields.csv",
+            [
+                [
+                    "x_min",
+                    "y_min",
+                    "x_max",
+                    "y_max",
+                    "class_id",
+                    "confidence",
+                    "tracker_id",
+                    "class_name",
+                    "frame_number",
+                ],
+                ["60.0", "70.0", "80.0", "90.0", "", "", "4", "bike", "44"],
+                ["100.0", "110.0", "120.0", "130.0", "", "", "5", "dog", "44"],
+                ["65.0", "75.0", "85.0", "95.0", "", "0.5", "", "tree", "45"],
+                ["105.0", "115.0", "125.0", "135.0", "", "0.4", "", "cat", "45"],
+            ],
+        ),  # missing fields
+        (
+            mock_detections(
+                xyxy=[[10, 11, 12, 13]],
+                confidence=[0.95],
+                data={"class_name": "unknown", "is_detected": True, "score": 1},
+            ),
+            {"frame_number": 46},
+            mock_detections(
+                xyxy=[[14, 15, 16, 17]],
+                data={"class_name": "artifact", "is_detected": False, "score": 0.85},
+            ),
+            {"frame_number": 47},
+            "test_detections_varied_data.csv",
+            [
+                [
+                    "x_min",
+                    "y_min",
+                    "x_max",
+                    "y_max",
+                    "class_id",
+                    "confidence",
+                    "tracker_id",
+                    "class_name",
+                    "frame_number",
+                    "is_detected",
+                    "score",
+                ],
+                [
+                    "10.0",
+                    "11.0",
+                    "12.0",
+                    "13.0",
+                    "",
+                    "0.95",
+                    "",
+                    "unknown",
+                    "46",
+                    "True",
+                    "1",
+                ],
+                [
+                    "14.0",
+                    "15.0",
+                    "16.0",
+                    "17.0",
+                    "",
+                    "",
+                    "",
+                    "artifact",
+                    "47",
+                    "False",
+                    "0.85",
+                ],
+            ],
+        ),  # Inconsistent Data Types
+        (
+            mock_detections(
+                xyxy=[[20, 21, 22, 23]],
+            ),
+            {
+                "metadata": {"sensor_id": 101, "location": "north"},
+                "tags": ["urgent", "review"],
+            },
+            mock_detections(
+                xyxy=[[14, 15, 16, 17]],
+            ),
+            {
+                "metadata": {"sensor_id": 104, "location": "west"},
+                "tags": ["not-urgent", "done"],
+            },
+            "test_detections_complex_data.csv",
+            [
+                [
+                    "x_min",
+                    "y_min",
+                    "x_max",
+                    "y_max",
+                    "class_id",
+                    "confidence",
+                    "tracker_id",
+                    "metadata",
+                    "tags",
+                ],
+                [
+                    "20.0",
+                    "21.0",
+                    "22.0",
+                    "23.0",
+                    "",
+                    "",
+                    "",
+                    "{'sensor_id': 101, 'location': 'north'}",
+                    "['urgent', 'review']",
+                ],
+                [
+                    "14.0",
+                    "15.0",
+                    "16.0",
+                    "17.0",
+                    "",
+                    "",
+                    "",
+                    "{'sensor_id': 104, 'location': 'west'}",
+                    "['not-urgent', 'done']",
+                ],
+            ],
+        ),  # Complex Data
+    ],
+)
+def test_csv_sink(
+    detections: mock_detections,
+    custom_data: Dict[str, Any],
+    second_detections: mock_detections,
+    second_custom_data: Dict[str, Any],
+    file_name: str,
+    expected_result: List[List[Any]],
+) -> None:
+    with sv.CSVSink(file_name) as sink:
+        sink.append(detections, custom_data)
+        sink.append(second_detections, second_custom_data)
+
+    assert_csv_equal(file_name, expected_result)
+
+
+@pytest.mark.parametrize(
+    "detections, custom_data, "
+    "second_detections, second_custom_data, "
+    "file_name, expected_result",
+    [
+        (
+            mock_detections(
+                xyxy=[[10, 20, 30, 40], [50, 60, 70, 80]],
+                confidence=[0.7, 0.8],
+                class_id=[0, 0],
+                tracker_id=[0, 1],
+                data={"class_name": ["person", "person"]},
+            ),
+            {"frame_number": 42},
+            mock_detections(
+                xyxy=[[15, 25, 35, 45], [55, 65, 75, 85]],
+                confidence=[0.6, 0.9],
+                class_id=[1, 1],
+                tracker_id=[2, 3],
+                data={"class_name": ["car", "car"]},
+            ),
+            {"frame_number": 43},
+            "test_detections.csv",
+            [
+                [
+                    "x_min",
+                    "y_min",
+                    "x_max",
+                    "y_max",
+                    "class_id",
+                    "confidence",
+                    "tracker_id",
+                    "class_name",
+                    "frame_number",
+                ],
+                ["10.0", "20.0", "30.0", "40.0", "0", "0.7", "0", "person", "42"],
+                ["50.0", "60.0", "70.0", "80.0", "0", "0.8", "1", "person", "42"],
+                ["15.0", "25.0", "35.0", "45.0", "1", "0.6", "2", "car", "43"],
+                ["55.0", "65.0", "75.0", "85.0", "1", "0.9", "3", "car", "43"],
+            ],
+        ),  # multiple detections
+        (
+            mock_detections(
+                xyxy=[[60, 70, 80, 90], [100, 110, 120, 130]],
+                tracker_id=[4, 5],
+                data={"class_name": ["bike", "dog"]},
+            ),
+            {"frame_number": 44},
+            mock_detections(
+                xyxy=[[65, 75, 85, 95], [105, 115, 125, 135]],
+                confidence=[0.5, 0.4],
+                data={"class_name": ["tree", "cat"]},
+            ),
+            {"frame_number": 45},
+            "test_detections_missing_fields.csv",
+            [
+                [
+                    "x_min",
+                    "y_min",
+                    "x_max",
+                    "y_max",
+                    "class_id",
+                    "confidence",
+                    "tracker_id",
+                    "class_name",
+                    "frame_number",
+                ],
+                ["60.0", "70.0", "80.0", "90.0", "", "", "4", "bike", "44"],
+                ["100.0", "110.0", "120.0", "130.0", "", "", "5", "dog", "44"],
+                ["65.0", "75.0", "85.0", "95.0", "", "0.5", "", "tree", "45"],
+                ["105.0", "115.0", "125.0", "135.0", "", "0.4", "", "cat", "45"],
+            ],
+        ),  # missing fields
+        (
+            mock_detections(
+                xyxy=[[10, 11, 12, 13]],
+                confidence=[0.95],
+                data={"class_name": "unknown", "is_detected": True, "score": 1},
+            ),
+            {"frame_number": 46},
+            mock_detections(
+                xyxy=[[14, 15, 16, 17]],
+                data={"class_name": "artifact", "is_detected": False, "score": 0.85},
+            ),
+            {"frame_number": 47},
+            "test_detections_varied_data.csv",
+            [
+                [
+                    "x_min",
+                    "y_min",
+                    "x_max",
+                    "y_max",
+                    "class_id",
+                    "confidence",
+                    "tracker_id",
+                    "class_name",
+                    "frame_number",
+                    "is_detected",
+                    "score",
+                ],
+                [
+                    "10.0",
+                    "11.0",
+                    "12.0",
+                    "13.0",
+                    "",
+                    "0.95",
+                    "",
+                    "unknown",
+                    "46",
+                    "True",
+                    "1",
+                ],
+                [
+                    "14.0",
+                    "15.0",
+                    "16.0",
+                    "17.0",
+                    "",
+                    "",
+                    "",
+                    "artifact",
+                    "47",
+                    "False",
+                    "0.85",
+                ],
+            ],
+        ),  # Inconsistent Data Types
+        (
+            mock_detections(
+                xyxy=[[20, 21, 22, 23]],
+            ),
+            {
+                "metadata": {"sensor_id": 101, "location": "north"},
+                "tags": ["urgent", "review"],
+            },
+            mock_detections(
+                xyxy=[[14, 15, 16, 17]],
+            ),
+            {
+                "metadata": {"sensor_id": 104, "location": "west"},
+                "tags": ["not-urgent", "done"],
+            },
+            "test_detections_complex_data.csv",
+            [
+                [
+                    "x_min",
+                    "y_min",
+                    "x_max",
+                    "y_max",
+                    "class_id",
+                    "confidence",
+                    "tracker_id",
+                    "metadata",
+                    "tags",
+                ],
+                [
+                    "20.0",
+                    "21.0",
+                    "22.0",
+                    "23.0",
+                    "",
+                    "",
+                    "",
+                    "{'sensor_id': 101, 'location': 'north'}",
+                    "['urgent', 'review']",
+                ],
+                [
+                    "14.0",
+                    "15.0",
+                    "16.0",
+                    "17.0",
+                    "",
+                    "",
+                    "",
+                    "{'sensor_id': 104, 'location': 'west'}",
+                    "['not-urgent', 'done']",
+                ],
+            ],
+        ),  # Complex Data
+    ],
+)
+def test_csv_sink_manual(
+    detections: mock_detections,
+    custom_data: Dict[str, Any],
+    second_detections: mock_detections,
+    second_custom_data: Dict[str, Any],
+    file_name: str,
+    expected_result: List[List[Any]],
+) -> None:
+    sink = sv.CSVSink(file_name)
+    sink.open()
+    sink.append(detections, custom_data)
+    sink.append(second_detections, second_custom_data)
+    sink.close()
+
+    assert_csv_equal(file_name, expected_result)
+
+
+def assert_csv_equal(file_name, expected_rows):
+    with open(file_name, mode="r", newline="") as file:
+        reader = csv.reader(file)
+        for i, row in enumerate(reader):
+            assert (
+                [str(item) for item in expected_rows[i]] == row
+            ), f"Row in CSV didn't match expected output: {row} != {expected_rows[i]}"
+
+    os.remove(file_name)
Author	SHA1	Message	Date
SkalskiP	beee1c715d	bump version from `0.19.0rc1` to `0.19.0rc2`	2024-02-05 23:49:09 +01:00
Onuralp SEZER	d9adc45ca9	Merge pull request #855 from roboflow/pre-commit-ci-update-config chore(pre_commit): ⬆ pre_commit autoupdate	2024-02-05 21:11:04 +03:00
pre-commit-ci[bot]	24df5aa78e	chore(pre_commit): ⬆ pre_commit autoupdate updates: - [github.com/astral-sh/ruff-pre-commit: v0.1.14 → v0.2.0](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.14...v0.2.0)	2024-02-05 17:37:15 +00:00
Onuralp SEZER	4a58c0b726	Merge pull request #852 from roboflow/dependabot/pip/mkdocs-material-9.5.7 ⬆️ Bump mkdocs-material from 9.5.6 to 9.5.7	2024-02-05 15:30:44 +03:00
dependabot[bot]	9fc789e6cf	⬆️ Bump mkdocs-material from 9.5.6 to 9.5.7 Bumps [mkdocs-material](https://github.com/squidfunk/mkdocs-material) from 9.5.6 to 9.5.7. - [Release notes](https://github.com/squidfunk/mkdocs-material/releases) - [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG) - [Commits](https://github.com/squidfunk/mkdocs-material/compare/9.5.6...9.5.7) --- updated-dependencies: - dependency-name: mkdocs-material dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com>	2024-02-05 00:47:50 +00:00
Onuralp SEZER	d423ff3b6c	Merge pull request #845 from Kadermiyanyedi/fix-linezone-in-out-values [LineZone] - flip in/out line crossing directions #838	2024-02-02 20:59:36 +03:00
Kader Miyanyedi	7fae4dbbb8	fix(line_zone): fix in/out values	2024-02-02 20:38:30 +03:00
Piotr Skalski	87a4927d03	Merge pull request #818 from AdonaiVera/allowing_serialise_detections_csv New function [CSVSink] - allowing to serialise Detections to a CSV file	2024-02-02 18:11:10 +01:00
pre-commit-ci[bot]	6bebd4e70d	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-02 17:03:05 +00:00
SkalskiP	2e76c66675	Merge remote-tracking branch 'origin/allowing_serialise_detections_csv' into allowing_serialise_detections_csv	2024-02-02 18:02:45 +01:00
SkalskiP	d6ee6440ad	final tests	2024-02-02 17:52:56 +01:00
pre-commit-ci[bot]	abb8daa352	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-02 15:45:40 +00:00
SkalskiP	ac6f3d6e7c	small docs updates	2024-02-02 16:45:05 +01:00
Piotr Skalski	0ccb0b85ad	Merge pull request #843 from roboflow/doc/urls-relative-fix docs(fix): 🐞📝 relative path fix for mike	2024-02-02 13:31:14 +01:00
Onuralp SEZER	e3c9ea5ee6	docs(fix): 🐞📝 changelog url fix /0.18.0/annotators/#percentagebarannotator Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-02 15:21:10 +03:00
Onuralp SEZER	26135bd84d	docs(fix): 🐞📝 relative path fix for mike Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-02 15:15:18 +03:00
Piotr Skalski	cdd88dafeb	Merge pull request #829 from roboflow/doc/urls-fixes-and-others docs: ✨ fix url and coc and contributing.md, pythonic features of docs enabled, relative urls added.	2024-02-02 12:53:45 +01:00
Onuralp SEZER	177bcddcd9	Merge pull request #841 from roboflow/dependabot/pip/ruff-0.2.0 ⬆️ Bump ruff from 0.1.15 to 0.2.0	2024-02-02 09:12:13 +03:00
dependabot[bot]	e9bab7913f	⬆️ Bump ruff from 0.1.15 to 0.2.0 Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.15 to 0.2.0. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.15...v0.2.0) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com>	2024-02-02 00:54:14 +00:00
pre-commit-ci[bot]	b1034fe139	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-01 23:08:57 +00:00
Adonai Vera	e899b428ca	Fix length of the line	2024-02-01 18:07:24 -05:00
pre-commit-ci[bot]	aba5c0a07b	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-01 22:56:02 +00:00
Adonai Vera	788bc317fa	Improvements in thread #818 : documentation, handle unit test, improvements pattern directory	2024-02-01 17:53:15 -05:00
Onuralp SEZER	732aa09059	docs: 📝 linkedin url adjustment Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-02 01:22:57 +03:00
Onuralp SEZER	671b48df4b	docs: 📝 add social discord into footer Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-02 01:21:44 +03:00
Onuralp SEZER	71a18a12d7	docs: 📝 install and quick start position swapped Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-01 22:17:25 +03:00
Onuralp SEZER	3576f4f1a1	docs: 📝 versioned based change logs url and old url has been replace with new one Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-01 22:07:36 +03:00
Onuralp SEZER	df9616948b	docs(mkdocs): pythonic mkdocs features enabled Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-01 21:40:20 +03:00
Onuralp SEZER	edf35f4c6e	docs(link): add relative urls and fix readme urls Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-01 21:39:57 +03:00
Onuralp SEZER	ab5c8cfa68	docs(link): annotators now has proper linking Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-01 20:23:11 +03:00
Onuralp SEZER	847ebeb7b2	docs(fix): 🐞📝 removal of table of content duplicates for detection metrics Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-01 20:22:40 +03:00
Onuralp SEZERandPiotr Skalski	7a04a11524	Update docs/how_to/track_objects.md Co-authored-by: Piotr Skalski <piotr.skalski92@gmail.com>	2024-02-01 18:27:09 +03:00
Piotr Skalski	fc3bced56d	Merge pull request #832 from roboflow/fix/inference_and_yolo_nas_speed_estimation_examples fix inference and yolo nas speed estimation examples	2024-02-01 10:30:40 +01:00
SkalskiP	775efd0d41	fix inference and yolo nas speed estimation examples	2024-02-01 10:25:43 +01:00
Piotr Skalski	81d0c3a1a3	Merge pull request #827 from AdonaiVera/add_method_reset_state_tracker [ByteTrack] - add a method to reset the state of the tracker	2024-02-01 08:46:51 +01:00
SkalskiP	ee58e28d25	Small update in `reset` docstring.	2024-02-01 08:42:36 +01:00
Adonai Vera	889044cf06	Add docs to reset function and reduce the length	2024-02-01 00:37:43 -05:00
Adonai Vera	5f8127a1a5	Add docstring to the reset function	2024-02-01 00:30:48 -05:00
Onuralp SEZER	18c0ec4196	Merge branch 'develop' into doc/urls-fixes-and-others	2024-02-01 00:32:40 +03:00
Adonai Vera	9da2d9f761	Remove ultralytics example multiple video	2024-01-31 16:26:49 -05:00
Onuralp SEZER	833a9f117d	Merge branch 'develop' into doc/urls-fixes-and-others	2024-02-01 00:21:25 +03:00
SkalskiP	d00f5e4797	`deprecated.md` added	2024-02-01 00:18:53 +03:00
Onuralp SEZER	ff3df644c9	Merge branch 'develop' into doc/urls-fixes-and-others	2024-02-01 00:12:17 +03:00
Onuralp SEZER	fc5126f352	docs: ✨ docs urls in detection latest tag added Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-01 00:09:17 +03:00
pre-commit-ci[bot]	bef5fb02d0	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-01-31 20:32:49 +00:00
Adonai Vera	9ba0511610	Change import name	2024-01-31 15:30:03 -05:00
Adonai Vera	6993ca3d71	Reset function to process multiple videos	2024-01-31 15:07:27 -05:00
Onuralp SEZER	163aa785f9	docs: ✨ contributing.md/code_of_conduct/license added into docs Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-31 22:10:28 +03:00
Onuralp SEZER	d009f75c28	docs: 📝 pypi and docker url added to footer Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-01-31 21:47:07 +03:00
pre-commit-ci[bot]	91260bdfc9	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-01-31 17:11:18 +00:00
Adonai Vera	4ec353f5c2	Apply format	2024-01-31 12:10:56 -05:00
pre-commit-ci[bot]	fd00bd4ba2	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-01-31 16:41:57 +00:00
Adonai Vera	12568336ba	Applied feedback from SkalskiP in PR #818	2024-01-31 11:39:05 -05:00
Adonai Vera	5e339f99fc	[CSVSink] - allowing to serialise Detections to a CSV file ready for prod	2024-01-31 01:32:49 -05:00