bump version from 0.19.0rc2 to 0.19.0rc3

Merge pull request #861 from NickHerrig/feat/cookbooks
Feat/cookbooks
2024-02-09 18:09:56 +01:00 · 2024-02-09 17:37:50 +01:00 · 2024-02-09 17:36:13 +01:00 · 2024-02-09 10:34:40 -05:00 · 2024-02-09 14:30:00 +00:00 · 2024-02-09 09:29:49 -05:00
78 changed files with 4162 additions and 483 deletions
@@ -1,10 +1,8 @@
-name: Docs WorkFlow
+name: Docs WorkFlow - Develop Tag 📚

 on:
  push:
    branches:
-      - master
-      - main
      - develop

 permissions:
@@ -16,11 +14,34 @@ jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
+      - name: 🔄 Checkout code
+        uses: actions/checkout@v4
        with:
-          python-version: 3.x
-      - run: pip install mkdocs-material
-      - run: pip install "mkdocstrings[python]"
-      - run: pip install "mkdocs-material[imaging]"
-      - run: mkdocs gh-deploy --force
+          fetch-depth: 0
+      - name: 🐍 Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+      - name: 📦 Install mkdocs-material
+        run: pip install "mkdocs-material[all]"
+      - name: 📦 Install mkdocstrings[python]
+        run: pip install "mkdocstrings[python]"
+      - name: 📦 Install mkdocs-material[imaging]
+        run: pip install "mkdocs-material[imaging]"
+      - name: 📦 Install mike
+        run: pip install "mike"
+      - name: 📦 Install mkdocs-git-revision-date-localized-plugin
+        run: pip install "mkdocs-git-revision-date-localized-plugin"
+      - name: 📦 Install JupyterLab
+        run: pip install jupyterlab
+      - name: 📦 Install mkdocs-jupyter
+        run: pip install mkdocs-jupyter
+      - name: 📦 Install mkdocs-git-committers-plugin-2
+        run: pip install mkdocs-git-committers-plugin-2
+      - name: ⚙️ Configure git for github-actions
+        run: |
+          git config --global user.name "github-actions[bot]"
+          git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com"
+      - name: 🚀 Deploy MkDoc-Material with mike
+        run: |
+          MKDOCS_GIT_COMMITTERS_APIKEY=${{ secrets.GITHUB_TOKEN }} mike deploy --push develop
@@ -133,9 +133,6 @@ dmypy.json
 # Pyre type checker
 .pyre/

-# Notebooks
-notebooks/
-
 # OSX folder attributes
 .DS_Store
 .AppleDouble
@@ -30,7 +30,7 @@ repos:


  -   repo: https://github.com/PyCQA/bandit
-      rev: '1.7.6'
+      rev: '1.7.7'
      hooks:
      -   id: bandit
          args: ["-c", "pyproject.toml"]
@@ -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]
@@ -15,7 +15,7 @@ url: 'https://roboflow.github.io/supervision/'
 abstract: >-
  supervision features a range of utilities for use in
  computer vision projects, from detections processing and
-  filtering to confusion matrix calcuation.
+  filtering to confusion matrix calculation.
 keywords:
  - computer vision
  - image processing
@@ -21,13 +21,15 @@
 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb)
 [![Gradio](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Roboflow/Annotators)
 [![Discord](https://img.shields.io/discord/1159501506232451173)](https://discord.gg/GbfgXGJ8Bk)
-
+[![Built with Material for MkDocs](https://img.shields.io/badge/Material_for_MkDocs-526CFE?logo=MaterialForMkDocs&logoColor=white)](https://squidfunk.github.io/mkdocs-material/)
 </div>

 ## 👋 hello

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

+[![supervision-hackfest](https://github.com/roboflow/supervision/assets/26109316/c05cc954-b9a6-4ed5-9a52-d0b4b619ff65)](https://github.com/orgs/roboflow/projects/10)
+
 ## 💻 install

 Pip install the supervision package in a
@@ -43,7 +45,7 @@ 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 +87,7 @@ 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 +107,7 @@ 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
@@ -0,0 +1,19 @@
+# Supervision Cookbooks
+
+## About
+
+The Roboflow Cookbook is an open-source collection of examples and guides for building with the [Roboflow Ecosystem](https://roboflow.com/).
+
+Cookbooks are meant to be common and small scoped computer vision tasks that can allow a user to quickly hit the ground running. Learning is the main goal and purpose.
+
+Some examples require a Roboflow API key. You can [create a free account here](https://app.roboflow.com/login).
+
+## Other Resources
+
+Other than Cookbooks, Roboflow offers other learning resources including:
+
+- A set of open source tools found on [Github](https://github.com/roboflow).
+- A [blog](https://blog.roboflow.com/) detailing interesting tutorials, computer vision news, and product updates.
+- A [Youtube channel](https://www.youtube.com/roboflow) with end to end commputer vision projects.
+- A [forum](https://discuss.roboflow.com/) for sharing feedback, discussions, and other questions.
+- The worlds largest [open source collection](https://universe.roboflow.com/) of images, datasets, and fine-tuned models.
@@ -12,11 +12,23 @@
    "\n",
    "---\n",
    "\n",
-    "[![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)\n",
-    "[![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)\n",
-    "[![license](https://img.shields.io/pypi/l/supervision)](https://github.com/roboflow/supervision/blob/main/LICENSE.md)\n",
-    "[![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision)\n",
-    "[![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/roboflow/supervision)\n",
+    "<p align=\"center\">\n",
+    "  <a href=\"https://badge.fury.io/py/supervision\"><img src=\"https://badge.fury.io/py/supervision.svg\" alt=\"version\"></a>\n",
+    "  <a href=\"https://pypistats.org/packages/supervision\"><img src=\"https://img.shields.io/pypi/dm/supervision\" alt=\"downloads\"></a>\n",
+    "  <a href=\"https://github.com/roboflow/supervision/blob/main/LICENSE.md\"><img src=\"https://img.shields.io/pypi/l/supervision\" alt=\"license\"></a>\n",
+    "  <a href=\"https://badge.fury.io/py/supervision\"><img src=\"https://img.shields.io/pypi/pyversions/supervision\" alt=\"python-version\"></a>\n",
+    "  <a href=\"https://github.com/roboflow/supervision\"><img src=\"https://badges.aleen42.com/src/github.svg\" alt=\"GitHub\"></a>\n",
+    "</p>\n",
+    "\n",
+    "<p align=\"center\">\n",
+    "  <a href=\"https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Colab\"></a>\n",
+    "  <a href=\"https://kaggle.com/kernels/welcome?src=https://github.com/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Kaggle\"></a>\n",
+    "  <a href=\"https://studiolab.sagemaker.aws/import/github/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/sage-maker.svg\" alt=\"SageMaker\"></a>\n",
+    "  <a href=\"https://nbviewer.jupyter.org/github/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://img.shields.io/badge/Open_in_Nbviewer-F37626.svg?logo=Jupyter&logoColor=white\" alt=\"nbviewer\">\n",
+    "  <a href=\"https://mybinder.org/v2/gh/roboflow/supervision/develop?labpath=demo.ipynb\"><img src=\"https://mybinder.org/badge_logo.svg\" alt=\"Binder\"></a>\n",
+    "  <a href=\"https://github.com/roboflow/supervision/raw/main/demo.ipynb\" download><img src=\"https://img.shields.io/badge/Download-Notebook-A351FB.svg\" alt=\"Download\"></a>\n",
+    "</p>\n",
+    "\n",
    "\n",
    "We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝\n",
    "\n",
@@ -3,6 +3,8 @@ comments: true
 status: new
 ---

+# Annotators
+
 === "BoundingBox"

    ```python
@@ -378,78 +380,116 @@ status: new

    </div>

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

 :::supervision.annotators.core.BoundingBoxAnnotator

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

 :::supervision.annotators.core.RoundBoxAnnotator

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

 :::supervision.annotators.core.BoxCornerAnnotator

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

 :::supervision.annotators.core.OrientedBoxAnnotator

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

 :::supervision.annotators.core.ColorAnnotator

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

 :::supervision.annotators.core.CircleAnnotator

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

 :::supervision.annotators.core.DotAnnotator

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

 :::supervision.annotators.core.TriangleAnnotator

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

 :::supervision.annotators.core.EllipseAnnotator

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

 :::supervision.annotators.core.HaloAnnotator

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

 :::supervision.annotators.core.PercentageBarAnnotator

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

 :::supervision.annotators.core.HeatMapAnnotator

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

 :::supervision.annotators.core.MaskAnnotator

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

 :::supervision.annotators.core.PolygonAnnotator

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

 :::supervision.annotators.core.LabelAnnotator

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

 :::supervision.annotators.core.BlurAnnotator

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

 :::supervision.annotators.core.PixelateAnnotator

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

 :::supervision.annotators.core.TraceAnnotator

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

 :::supervision.annotators.utils.ColorLookup
@@ -2,10 +2,12 @@
 comments: true
 ---

+# Assets
+
 Supervision offers an assets download utility that allows you to download video files
 that you can use in your demos.

-## install extra
+## Install extra

 To install the Supervision assets utility, you can use `pip`. This utility is available
 as an extra within the Supervision package.
@@ -13,13 +15,17 @@ as an extra within the Supervision package.
 !!! example "pip install"

    ```bash
-    pip install supervision[assets]
+    pip install "supervision[assets]"
    ```

-## download_assets
+<div class="md-typeset">
+  <h2>download_assets</h2>
+</div>

 :::supervision.assets.downloader.download_assets

-## VideoAssets
+<div class="md-typeset">
+  <h2>VideoAssets</h2>
+</div>

 :::supervision.assets.list.VideoAssets
@@ -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`).

-!!! warning
+!!! failure "Deprecated"

-    `sv.ColorPalette.default()` is deprecated and will be removed in `supervision-0.21.0`. Use `sv.ColorPalette.DEFAULT` instead.
+    `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).

-!!! warning
+!!! failure "Deprecated"

-    `Detections.from_roboflow()` is deprecated and will be removed in `supervision-0.21.0`. Use `Detections.from_inference` instead.
+    `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.tigger`](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.

-!!! warning
+!!! 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.

-!!! warning
+!!! 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
@@ -265,25 +265,25 @@ 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.

-!!! warning
+!!! 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>

-!!! warning
+!!! failure "Python 3.7. Support Terminated"

    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 marge 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
@@ -2,6 +2,6 @@
 comments: true
 ---

-## Classifications
+# Classifications

 :::supervision.classification.core.Classifications
@@ -0,0 +1,5 @@
+# Code of conduct
+
+```
+--8<-- "CODE_OF_CONDUCT.md"
+```
@@ -0,0 +1 @@
+--8<-- "CONTRIBUTING.md"
@@ -0,0 +1,8 @@
+---
+template: cookbooks.html
+comments: true
+status: new
+hide:
+  - navigation
+  - toc
+---
@@ -2,15 +2,21 @@
 comments: true
 ---

+# Datasets
+
 !!! warning

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

-## DetectionDataset
+<div class="md-typeset">
+  <h2>DetectionDataset</h2>
+</div>

 :::supervision.dataset.core.DetectionDataset

-## ClassificationDataset
+<div class="md-typeset">
+  <h2>ClassificationDataset</h2>
+</div>

 :::supervision.dataset.core.ClassificationDataset
@@ -0,0 +1,16 @@
+---
+comments: true
+status: deprecated
+---
+
+These features are phased out due to better alternatives or potential issues in future versions. Deprecated functionalities are supported for **three subsequent releases**, providing time for users to transition to updated methods.
+
+- [`Detections.from_froboflow`](detection/core.md/#supervision.detection.core.Detections.from_roboflow) is deprecated and will be removed in `supervision-0.22.0`. Use [`Detections.from_inference`](detection/core.md/#supervision.detection.core.Detections.from_inference) instead.
+- `Color.white()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.WHITE` instead.
+- `Color.black()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.BLACK` instead.
+- `Color.red()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.RED` instead.
+- `Color.green()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.GREEN` instead.
+- `Color.blue()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.BLUE` instead.
+- [`ColorPalette.default()`](draw/color.md/#supervision.draw.color.ColorPalette.default) is deprecated and will be removed in `supervision-0.22.0`. Use [`ColorPalette.DEFAULT`](draw/color.md/#supervision.draw.color.ColorPalette.DEFAULT) instead.
+- `BoxAnnotator` is deprecated and will be removed in `supervision-0.22.0`. Use [`BoundingBoxAnnotator`](annotators.md/#supervision.annotators.core.BoundingBoxAnnotator) and [`LabelAnnotator`](annotators.md/#supervision.annotators.core.LabelAnnotator) instead.
+- [`FPSMonitor.__call__`](utils/video.md/#supervision.utils.video.FPSMonitor.__call__) is deprecated and will be removed in `supervision-0.22.0`. Use [`FPSMonitor.fps`](utils/video.md/#supervision.utils.video.FPSMonitor.fps) instead.
@@ -3,6 +3,6 @@ comments: true
 status: new
 ---

-## Detections
+# Detections

 :::supervision.detection.core.Detections
@@ -2,6 +2,6 @@
 comments: true
 ---

-## InferenceSlicer
+# InferenceSlicer

 :::supervision.detection.tools.inference_slicer.InferenceSlicer
@@ -2,6 +2,6 @@
 comments: true
 ---

-## LineZone
+# LineZone

 :::supervision.detection.line_counter.LineZone
@@ -2,10 +2,12 @@
 comments: true
 ---

-## PolygonZone
+# PolygonZone

 :::supervision.detection.tools.polygon_zone.PolygonZone

-## PolygonZoneAnnotator
+<div class="md-typeset">
+  <h2>PolygonZoneAnnotator</h2>
+</div>

 :::supervision.detection.tools.polygon_zone.PolygonZoneAnnotator
@@ -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
@@ -3,6 +3,6 @@ comments: true
 status: new
 ---

-## Detection Smoother
+# Detection Smoother

 :::supervision.detection.tools.smoother.DetectionsSmoother
@@ -2,38 +2,70 @@
 comments: true
 ---

-## box_iou_batch
+# Detection Utils
+
+<div class="md-typeset">
+  <h2>box_iou_batch</h2>
+</div>

 :::supervision.detection.utils.box_iou_batch

-## non_max_suppression
+<div class="md-typeset">
+  <h2>mask_iou_batch</h2>
+</div>

-:::supervision.detection.utils.non_max_suppression
+:::supervision.detection.utils.mask_iou_batch

-## polygon_to_mask
+<div class="md-typeset">
+  <h2>box_non_max_suppression</h2>
+</div>
+
+:::supervision.detection.utils.box_non_max_suppression
+
+<div class="md-typeset">
+  <h2>mask_non_max_suppression</h2>
+</div>
+
+:::supervision.detection.utils.mask_non_max_suppression
+
+<div class="md-typeset">
+    <h2>polygon_to_mask</h2>
+</div>

 :::supervision.detection.utils.polygon_to_mask

-## mask_to_xyxy
+<div class="md-typeset">
+    <h2>mask_to_xyxy</h2>
+</div>

 :::supervision.detection.utils.mask_to_xyxy

-## mask_to_polygons
+<div class="md-typeset">
+    <h2>mask_to_polygons</h2>
+</div>

 :::supervision.detection.utils.mask_to_polygons

-## polygon_to_xyxy
+<div class="md-typeset">
+    <h2>polygon_to_xyxy</h2>
+</div>

 :::supervision.detection.utils.polygon_to_xyxy

-## filter_polygons_by_area
+<div class="md-typeset">
+    <h2>filter_polygons_by_area</h2>
+</div>

 :::supervision.detection.utils.filter_polygons_by_area

-## move_boxes
+<div class="md-typeset">
+    <h2>move_boxes</h2>
+</div>

 :::supervision.detection.utils.move_boxes

-## scale_boxes
+<div class="md-typeset">
+    <h2>scale_boxes</h2>
+</div>

 :::supervision.detection.utils.scale_boxes
@@ -2,10 +2,12 @@
 comments: true
 ---

-## Color
+# Color

 :::supervision.draw.color.Color

-## ColorPalette
+<div class="md-typeset">
+  <h2>ColorPalette</h2>
+</div>

 :::supervision.draw.color.ColorPalette
@@ -2,34 +2,52 @@
 comments: true
 ---

-## draw_line
+# Draw Utils
+
+<div class="md-typeset">
+  <h2>draw_line</h2>
+</div>

 :::supervision.draw.utils.draw_line

-## draw_rectangle
+<div class="md-typeset">
+  <h2>draw_rectangle</h2>
+</div>

 :::supervision.draw.utils.draw_rectangle

-## draw_filled_rectangle
+<div class="md-typeset">
+  <h2>draw_filled_rectangle</h2>
+</div>

 :::supervision.draw.utils.draw_filled_rectangle

-## draw_polygon
+<div class="md-typeset">
+  <h2>draw_polygon</h2>
+</div>

 :::supervision.draw.utils.draw_polygon

-## draw_text
+<div class="md-typeset">
+  <h2>draw_text</h2>
+</div>

 :::supervision.draw.utils.draw_text

-## draw_image
+<div class="md-typeset">
+  <h2>draw_image</h2>
+</div>

 :::supervision.draw.utils.draw_image

-## calculate_dynamic_font_scale
+<div class="md-typeset">
+  <h2>calculate_dynamic_font_scale</h2>
+</div>

 :::supervision.draw.utils.calculate_dynamic_text_scale

-## calculate_dynamic_line_thickness
+<div class="md-typeset">
+  <h2>calculate_dynamic_line_thickness</h2>
+</div>

 :::supervision.draw.utils.calculate_dynamic_line_thickness
@@ -2,6 +2,6 @@
 comments: true
 ---

-## Position
+# Position

 :::supervision.geometry.core.Position
@@ -2,6 +2,8 @@
 comments: true
 ---

+# Detect and Annotate
+
 Supervision offers a streamlined solution to effortlessly annotate predictions from a
 range of object detection and segmentation models. This guide demonstrates how to
 execute inference using the YOLOv8 model with either the
@@ -42,7 +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`](detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.

    ```python
    import cv2
@@ -57,7 +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`](detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.

    ```python
    import cv2
@@ -72,17 +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`](detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
+- [`from_detectron2`](detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
+- [`from_mmdetection`](detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
+- [`from_inference`](detection/core/#supervision.detection.core.Detections.from_inference) ([Roboflow Inference](https://github.com/roboflow/inference))
+- [`from_sam`](detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
+- [`from_transformers`](detection/core/#supervision.detection.core.Detections.from_transformers) ([HuggingFace Transformers](https://github.com/huggingface/transformers))
+- [`from_yolo_nas`](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`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](annotators/#supervision.annotators.core.LabelAnnotator) classes. If you are running the segmentation model [`sv.MaskAnnotator`](annotators/#supervision.annotators.core.MaskAnnotator) is a drop-in replacement for [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) that will allow you to draw masks instead of boxes.

 === "Ultralytics"

@@ -136,7 +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`](utils/notebook/#supervision.utils.notebook.plot_image) function.

 ```python
 sv.plot_image(annotated_image)
@@ -2,6 +2,8 @@
 comments: true
 ---

+# Filter Detections
+
 The advanced filtering capabilities of the `Detections` class offer users a versatile and efficient way to narrow down
 and refine object detections. This section outlines various filtering methods, including filtering by specific class
 or a set of classes, confidence, object area, bounding box area, relative area, box dimensions, and designated zones.
@@ -42,7 +44,6 @@ Allows you to select detections that belong only to one selected class.

    </div>

-
 ### by set of classes

 Allows you to select detections that belong only to selected set of classes.
@@ -2,8 +2,10 @@
 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
@@ -13,7 +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
@@ -30,7 +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
@@ -90,7 +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.

@@ -146,7 +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.

@@ -228,7 +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.

@@ -1,5 +1,8 @@
 ---
 comments: true
+hide:
+  - navigation
+  - toc
 ---

 <div align="center">
@@ -24,40 +27,6 @@ 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)
-
-   __Track Objects__
-
-    ---
-
-    Discover how to enhance video analysis by implementing seamless object tracking
-
-    [:octicons-arrow-right-24: Tutorial](how_to/track_objects)
-
-   > __Count Objects Crossing Line__
-
-    ---
-
-    Explore methods to accurately count and analyze objects crossing a predefined line
-
-   > __Filter Objects in Zone__
-
-    ---
-
-    Master the techniques to selectively filter and focus on objects within a specific zone
-
-</div>
-
 ## 💻 Install

 You can install `supervision` with pip in a
@@ -117,3 +86,38 @@ 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 +1,142 @@
+document.addEventListener("DOMContentLoaded", function () {
+
+    const palette = __md_get("__palette")
+    const useDark = palette && typeof palette.color === "object" && palette.color.scheme === "slate"
+    const theme = useDark ? "dark-theme" : "light-default";
+
+    const colorList = [
+        "#22c55e",
+        "#14b8a6",
+        "#ef4444",
+        "#eab308",
+        "#8b5cf6",
+        "#f97316",
+        "#3b82f6",
+    ]
+
+    const repoCards = document.querySelectorAll(".repo-card");
+    const labelsAll = Array
+        .from(repoCards)
+        .flatMap((element) => element.getAttribute('data-labels').split(','))
+        .map(label => label.trim())
+        .filter(label => label !== '');
+    const uniqueLabels = [...new Set(labelsAll)];
+
+    const labelToColor = uniqueLabels.reduce((map, label, index) => {
+        map[label] = colorList[index % colorList.length];
+        return map;
+    }, {});
+
+
+  async function renderCard(element, elementIndex) {
+    const url = element.getAttribute('data-url');
+    const name = element.getAttribute('data-name');
+    const labels = element.getAttribute('data-labels');
+    const version = element.getAttribute('data-version');
+    const authors = element.getAttribute('data-author');
+
+    const labelHTML = labels ? labels.split(',').filter(label => label !== '').map((label, index) => {
+        const color = labelToColor[label.trim()];
+        return `
+            <span
+                class="label non-selectable-text"
+                style="background-color: ${color}"
+            >
+                ${label.trim()}
+            </span>
+        `;
+    }).join(' ') : '';
+
+    const authorArray = authors.split(',');
+    const authorDataArray = await Promise.all(authorArray.map(async (author) => {
+      const response = await fetch(`https://api.github.com/users/${author.trim()}`);
+      return await response.json();
+    }));
+
+    let authorAvatarsHTML = authorDataArray.map((authorData, index) => {
+        const marginLeft = index === 0 ? '0' : '-10px';
+        const zIndex = 100 - index;
+        return `
+            <div
+                class="author-container"
+                data-login="${authorData.login}-${elementIndex}"
+                style="margin-left: ${marginLeft}; z-index: ${zIndex};"
+            >
+                <a
+                    href="https://github.com/${authorData.login}"
+                    target="_blank"
+                    style="line-height: 0;"
+                >
+                    <img
+                        class="author-avatar"
+                        src="${authorData.avatar_url}"
+                        alt="${authorData.login}'s avatar"
+                    >
+                </a>
+            </div>
+        `;
+    }).join('');
+
+    let authorNamesHTML = authorDataArray.map(
+        authorData => `
+            <span
+                class="author-name"
+                data-login="${authorData.login}-${elementIndex}"
+                style="color: ${theme.color}"
+            >
+            <a href="https://github.com/${authorData.login}" target="_blank">
+                ${authorData.login}
+            </a>
+        </span>`
+    ).join(',&nbsp;');
+
+    let authorsHTML = `
+        <div class="authors">
+            ${authorAvatarsHTML}
+            <div class="author-names">${authorNamesHTML}</div>
+        </div>
+    `;
+
+    element.innerText = `
+      <a style="text-decoration: none; color: inherit;" href="${url}">
+        <div style="flex-direction: column; height: 100%; display: flex;
+        font-family: -apple-system,BlinkMacSystemFont,Segoe UI,Helvetica,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji; background: ${theme.background}; font-size: 14px; line-height: 1.5; color: ${theme.color}">
+          <div style="display: flex; align-items: center;">
+            <span style="font-weight: 700; font-size: 1rem; color: ${theme.linkColor};">
+              ${name}
+            </span>
+          </div>
+          ${authorsHTML}
+          <div style="font-size: 12px; color: ${theme.color}; display: flex; flex: 0; justify-content: space-between">
+            <div style="display: flex; align-items: center;">
+              <img src="/assets/supervision-lenny.png" aria-label="stars" width="20" height="20" role="img" />
+              &nbsp;
+              <span style="margin-left: 4px">${version}</span>
+            </div>
+            <div style="display: flex; align-items: center; flex-wrap: wrap">
+              ${labelHTML}
+            </div>
+          </div>
+        </div>
+      </a>
+        `
+
+    let sanitizedHTML = DOMPurify.sanitize(element.innerText);
+    element.innerHTML = sanitizedHTML;
+
+    document.querySelectorAll('.author-name').forEach(element => {
+        element.addEventListener('mouseenter', function() {
+            const login = this.getAttribute('data-login');
+            document.querySelector(`.author-container[data-login="${login}"]`).classList.add('hover');
+        });
+
+        element.addEventListener('mouseleave', function() {
+            const login = this.getAttribute('data-login');
+            document.querySelector(`.author-container[data-login="${login}"]`).classList.remove('hover');
+        });
+    });
+  }
+    repoCards.forEach((element, index) => {
+        renderCard(element, index);
+    });
+})
@@ -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
@@ -0,0 +1,208 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# How to Display Bounding Boxes on Objects in a Video.\n",
+                "\n",
+                "---\n",
+                "\n",
+                "<p align=\"center\">\n",
+                "  <a href=\"https://colab.research.google.com/github/roboflow/supervision/blob/develop/docs/notebooks/bounding-boxes.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Colab\"></a>\n",
+                "</p>\n",
+                "\n",
+                "\n",
+                "One of the most common requirements of computer vision applications is detecting objects in images and displaying bounding boxes around those objects. In this cookbook we'll walk through the steps on how to utilize the open source Roboflow ecosystem to accomplish this task on a video. Let's dive in! \n",
+                "\n",
+                "## Installing Dependencies \n",
+                "\n",
+                "In this cookbook we'll be utilizing the open source packages [Inference](https://inference.roboflow.com/) and [Supervision](https://supervision.roboflow.com/latest/) to accomplish our goals. Let's get those installed in our notebook with pip."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "vscode": {
+                    "languageId": "shellscript"
+                }
+            },
+            "outputs": [],
+            "source": [
+                "\n",
+                "!pip install -q inference \"supervision[assets]\""
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Download a Video Asset\n",
+                "\n",
+                "First, let's download a video that we can detect objects in. Supervision comes with a great utility called Assets to help us hit the ground running. Wehn we run this script, the video is saved in our local directory and can be accessed with the variable `path_to_video`."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "\n",
+                "# Download a supervision video asset \n",
+                "path_to_video = download_assets(VideoAssets.PEOPLE_WALKING)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Detecting Objects\n",
+                "\n",
+                "For this example, the objects in the video that we'd like to detect are people. In order to display bounding boxes around the people in the video, we first need a way to detect them. We'll be using the open source [Inference](https://github.com/roboflow/inference) package for this task. Inference allows us to quickly use thousands of models, including fine tuned models from [Roboflow Universe](https://universe.roboflow.com/), with a few lines of code.  We'll also utilize a few utilities for working with our video data from the [Supervision](https://github.com/roboflow/supervision) package."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import supervision as sv\n",
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "from inference.models.utils import get_roboflow_model\n",
+                "\n",
+                "\n",
+                "if __name__ == \"__main__\":\n",
+                "    # Download the video asset from Supervision assets.\n",
+                "    PATH_TO_VIDEO = download_assets(VideoAssets.PEOPLE_WALKING)\n",
+                "\n",
+                "    # Load a yolov8 nano model from roboflow.\n",
+                "    model = get_roboflow_model(\"yolov8n-640\")\n",
+                "\n",
+                "    # Create a frame generator and video info object from supervision utilities.\n",
+                "    frame_generator = sv.get_video_frames_generator(PATH_TO_VIDEO)\n",
+                "\n",
+                "    # Yield a single frame from the generator.\n",
+                "    frame = next(frame_generator)\n",
+                "\n",
+                "    # Run inference on our frame.\n",
+                "    result = model.infer(frame)[0]\n",
+                "\n",
+                "    # Parse result into detections data model.\n",
+                "    detections = sv.Detections.from_inference(result)\n",
+                "\n",
+                "    # Display the detections data model.\n",
+                "    print(detections)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "First, we load our model using the method `get_roboflow_model()`. Notice how we pass in a `model_id`? We're using an [alias](https://inference.roboflow.com/reference_pages/model_aliases/) here. This is where we can pass in other models from Roboflow Universe like this [rock, paper, scissors](https://universe.roboflow.com/roboflow-58fyf/rock-paper-scissors-sxsw) model utilizing our roboflow api key. \n",
+                "\n",
+                "```\n",
+                "model = get_roboflow_mode(\n",
+                "    model_id=\"rock-paper-scissors-sxsw/11\", \n",
+                "    api_key=\"roboflow_private_api_key\"\n",
+                ")\n",
+                "```\n",
+                "\n",
+                "If you don't have an api key, you can [create an free Roboflow account](https://app.roboflow.com/login). This model wouldn't be much help with detecting people, but it's a nice exercise to see how our code becomes model agnostic!\n",
+                "\n",
+                "We then create a `frame_generator` object and yeild a single frame for inference using `next()`. We pass our frame to `model.infer()` to run inference, then pass that data into a little helpfer function called `sv.Detections.from_inference()` to parse it. Lastly we print our detections to show we are in fact detecting a few people in the frame! \n",
+                "\n",
+                "## Saving Bounding Boxes to the Video\n",
+                "\n",
+                "Now comes the fun part. Let's wrap up our code by utilizing `Annotators` and a `VideoSink` to draw bounding boxes and save the resulting video respectively. Take a peak at the final code example below. This might can take up to a minute to run, since we're processing a full video. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import supervision as sv\n",
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "from inference.models.utils import get_roboflow_model\n",
+                "\n",
+                "\n",
+                "if __name__ == \"__main__\":\n",
+                "    # Download the video asset from Supervision assets.\n",
+                "    PATH_TO_VIDEO = download_assets(VideoAssets.PEOPLE_WALKING)\n",
+                "\n",
+                "    # Load a yolov8 nano model from roboflow.\n",
+                "    model = get_roboflow_model(\"yolov8n-640\")\n",
+                "\n",
+                "    # Initalize the bounding box frame annotator.\n",
+                "    box_annotator = sv.BoundingBoxAnnotator()\n",
+                "\n",
+                "    # Create a frame generator  object from video path.\n",
+                "    frame_generator = sv.get_video_frames_generator(PATH_TO_VIDEO)\n",
+                "\n",
+                "    # Create a video info object from video path.\n",
+                "    video_info = sv.VideoInfo.from_video_path(PATH_TO_VIDEO)\n",
+                "\n",
+                "    # Use a VideoSink context manager for saving frames of a video.\n",
+                "    with sv.VideoSink(target_path=\"output.mp4\", video_info=video_info) as sink:\n",
+                "\n",
+                "        # Iterate through frames yielded from the frame_generator.\n",
+                "        for frame in frame_generator:\n",
+                "\n",
+                "            # Run inference on our frame.\n",
+                "            result = model.infer(frame)[0]\n",
+                "\n",
+                "            # Parse the result into the detections data model.\n",
+                "            detections = sv.Detections.from_inference(result)\n",
+                "\n",
+                "            # Apply bounding box to detections on a copy of the frame.\n",
+                "            annotated_frame = box_annotator.annotate(\n",
+                "                scene=frame.copy(), \n",
+                "                detections=detections\n",
+                "            )\n",
+                "\n",
+                "            # Write the annotated frame to the video sink.\n",
+                "            sink.write_frame(frame=annotated_frame)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                " Notice that we create a `box_annoator` variable by initalizing a [BoundingBoxAnnotator](https://supervision.roboflow.com/latest/annotators/#boundingboxannotator). We can change the color and thickness, but for simplicity we keep the defaults. There are a ton of easy to use [annotators](https://supervision.roboflow.com/latest/annotators/) available in the Supervision package other than a bounding box that are fun to play with. Next, we create a `video_info` variable to pass information about the video to our `VideoSink`. The `VideoSink` is a cool little context manager that allows us to `write_frames()` to a video ouput file. Prior to writing the frame, we annotate a copy of it utilizing `box_annotator.annotate()`. Let's take a look at the resulting video. It will be installed locally and is called `output.mp4`. When run, you will see bounding boxes around the detections. Pretty awesome! "
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We only scratched the surface of all of the customizable Annotators and additional features that Supervision and Inference have to offer. Stay tuned for more cookbooks on how to take advantge of them in your computer vision applications. Happy building! "
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.9.7"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}
@@ -0,0 +1,99 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Experimenting with Supervision Assets\n",
+                "\n",
+                "---\n",
+                "\n",
+                "<p align=\"center\">\n",
+                "  <a href=\"https://colab.research.google.com/github/roboflow/supervision/blob/develop/docs/notebooks/supervision-assets.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Colab\"></a>\n",
+                "</p>\n",
+                "\n",
+                "When experimenting with interesting and useful features of the Supervision package, its important to have some sort of image or video data to experiment with. Luckily for us, Supervision ships with [Assets](https://supervision.roboflow.com/latest/assets/)! Assets is a collection of videos that you can utilize to start experimenting with the various features Supervision has to offer. Let's take a look at how to use this resource.\n",
+                "\n",
+                "## Install with Pip\n",
+                "\n",
+                "If you're running this code outside a Jupyter notebook, it's generally a best practice to create a virtual environment so that your projects deppendencies are isolated. Run the following ing your command line of choice to create and source one.\n",
+                "\n",
+                "```shell\n",
+                "\n",
+                "python -m venv venv\n",
+                "source venv/bin/activate\n",
+                "\n",
+                "``` "
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Now that we've got a virtual enviornment installed and sourced, let's install the pip package with the assets \"extra\" package. Make sure to remove the \"!\" if running this in your command line."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "vscode": {
+                    "languageId": "shellscript"
+                }
+            },
+            "outputs": [],
+            "source": [
+                "!pip install -q \"supervision[assets]\""
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Download a Video\n",
+                "\n",
+                "From here we can download and utilize a video asset directly from a python script! Note below that we're utilizing the method `download_assets` to download the `VideoAssets.SUBWAY` video to our local directory. This method returns the file path, so we can then utilize this path for additional experimentation. From here, you will see a video asset to experiment with in your local directory. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "\n",
+                "# Download a video asset from the supervision package.\n",
+                "path_to_video = download_assets(VideoAssets.SUBWAY)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We're now equipt with a video asset from Supervision to run some experiements on! For more information on available video assets, visit the [Supervision API Reference](https://supervision.roboflow.com/latest/assets/#videoassets). Happy building!"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.11.7"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}
@@ -0,0 +1,154 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Tracking Objects in a Video\n",
+                "\n",
+                "---\n",
+                "\n",
+                "<p align=\"center\">\n",
+                "  <a href=\"https://colab.research.google.com/github/roboflow/supervision/blob/main/docs/notebooks/tracking.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Colab\"></a>\n",
+                "</p>\n",
+                "\n",
+                "In some cases, it's important for us to track objects across multiple frames of a video. For example, we may need to figure out the direction a vehicle is moving, or count objects in a frame. Some Supvervision [Annotators](https://supervision.roboflow.com/latest/annotators/) and Tools like [LineZone](https://supervision.roboflow.com/latest/detection/tools/line_zone/) require tracking to be setup.  In this cookbook, we'll cover how to get a tracker up and running for use in your computer vision applications.\n",
+                "\n",
+                "## What is a Tracker?\n",
+                "\n",
+                "Trackers are a piece of code that identifies objects across frames and assigns them a unique `tracker_id`. There are a few popular trackers at the time of writing this including ByteTrack and Bot-SORT. Supervision makes using trackers a breeze and comes with ByteTrack built-in. But first, let's get our deppendencies installed. \n",
+                "\n",
+                "## Install Dependencies"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "vscode": {
+                    "languageId": "powershell"
+                }
+            },
+            "outputs": [],
+            "source": [
+                "!pip install -q inference \"supervision[assets]\""
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Download a Video Asset\n",
+                "\n",
+                "Now that we have our enviornment setup, lets download a video that we can detect objects in. Supervision comes with a great utility to help us hit the ground running. We can use the below snippet to he video is save a video asset in our local directory. It can also be accessed with the variable `path_to_video` for additional application logic."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "\n",
+                "# Download a supervision video asset \n",
+                "path_to_video = download_assets(VideoAssets.PEOPLE_WALKING)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Tracking Objects\n",
+                "\n",
+                "Now that we have our video installed, let's get to work on tracking objects. We'll first pull in a model from roboflow Inference to detect people in our video. Then let's create a `byte_tracker` object that we'll pass our detections to. This will give us a `tracker_id`. We'll then utilize that tracker id to label our detections with a `label_annotator` to display the tracker id. Finally, we'll use a utility called `VideoSink` to save the annotated frames to a video. Let's dive in to the code."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import supervision as sv\n",
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "from inference.models.utils import get_roboflow_model\n",
+                "\n",
+                "\n",
+                "if __name__ == '__main__':\n",
+                "\n",
+                "    # Install our video from supervision assets.\n",
+                "    PATH_TO_VIDEO = download_assets(VideoAssets.PEOPLE_WALKING)\n",
+                "\n",
+                "    # Load a pre trained yolov8 nano model from Roboflow Inference.\n",
+                "    model = get_roboflow_model('yolov8n-640')\n",
+                "\n",
+                "    # Create a video info object from the video path.\n",
+                "    video_info = sv.VideoInfo.from_video_path(PATH_TO_VIDEO)\n",
+                "\n",
+                "    # Create a label annotator for labeling detections with our tracker_id.\n",
+                "    label = sv.LabelAnnotator()\n",
+                "\n",
+                "    # Create a ByteTrack object to track detections.\n",
+                "    byte_tracker = sv.ByteTrack(frame_rate=video_info.fps)\n",
+                "\n",
+                "    # Create a frame generator from video path for iteration of frames.\n",
+                "    frame_generator = sv.get_video_frames_generator(PATH_TO_VIDEO)\n",
+                "\n",
+                "    # Create a video sink context manager to save resulting video.\n",
+                "    with sv.VideoSink(target_path=\"output.mp4\", video_info=video_info) as sink:\n",
+                "\n",
+                "        # Iterate over frames of the video.\n",
+                "        for frame in frame_generator:\n",
+                "\n",
+                "            # Run inference on the frame by passing it to our model.\n",
+                "            result = model.infer(frame)[0]\n",
+                "\n",
+                "            # Convert model results to a supervision detection object.\n",
+                "            detections = sv.Detections.from_inference(result)\n",
+                "\n",
+                "            # Update detections with tracker ids fro byte_tracker.\n",
+                "            tracked_detections = byte_tracker.update_with_detections(detections)\n",
+                "\n",
+                "            # Create labels with tracker_id for label annotator.\n",
+                "            labels = [ f\"{tracker_id}\" for tracker_id in tracked_detections.tracker_id ]\n",
+                "\n",
+                "            # Apply label annotator to frame.\n",
+                "            annotated_frame = label.annotate(scene=frame.copy(), detections=tracked_detections, labels=labels)\n",
+                "\n",
+                "            # Save the annotated frame to an output video.\n",
+                "            sink.write_frame(frame=annotated_frame)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# View the Video\n",
+                "\n",
+                "Let's take a look at our resulting video. It will be created in your current directory with the name `output.mp4` Notice how even with a little flicker, we can see the `tracker_id` on the people walking in the video. With trackers under your belt, there are now a wide variety of use cases you can solve for! Happy building! "
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "venv",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.9.7"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}
@@ -0,0 +1,107 @@
+.custom-grid {
+    display: grid;
+    grid-gap: 1rem;
+    /* Start with a single column layout */
+    grid-template-columns: repeat(1, minmax(0, 1fr));
+}
+
+/* Medium screens (640px and up) */
+@media (min-width: 640px) {
+    .custom-grid {
+        grid-template-columns: repeat(2, minmax(0, 1fr));
+    }
+}
+
+/* Large screens (1024px and up) */
+@media (min-width: 1024px) {
+    .custom-grid {
+        grid-template-columns: repeat(3, minmax(0, 1fr));
+    }
+}
+
+.repo-card {
+    background: radial-gradient(at right top, #A351FB25, #A5F9EA25);
+    border-radius: 0.5rem;
+    padding: 1rem;
+    transition: transform 0.2s ease-in-out; /* Smooth transition for the transform */
+    cursor: pointer;
+}
+
+.repo-card:hover {
+    transform: translateY(-0.125rem); /* Move up by -0.125rem on hover */
+}
+
+.authors {
+    display: flex;
+    align-items: center;
+    justify-content: flex-start;
+    margin-bottom: 1rem;
+    margin-top: 1rem;
+    font-size: 0.75rem;
+    line-height: 1rem;
+}
+
+.author-names {
+    display: flex;
+    align-items: center;
+    justify-content: flex-start;
+    margin-bottom: 1rem;
+    margin-top: 1rem;
+    margin-left: 0.5rem;
+}
+
+.author-container {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    width: 32px;
+    height: 32px;
+    padding: 0;
+    margin: 0;
+    transition: transform 0.2s;
+}
+
+.author-container.hover {
+    transform: translateY(-0.125rem);
+}
+
+.author-container:hover {
+    transform: translateY(-0.125rem);
+}
+
+.author-container a {
+    padding: 0;
+    margin: 0;
+    display: block;
+}
+
+.author-avatar {
+    width: 32px;
+    height: 32px;
+    border-radius: 50%;
+    display: block;
+}
+
+.author-name a {
+    color: inherit; /* Inherits the color from the parent element */
+    text-decoration: none; /* Removes the underline */
+}
+
+.author-name a:hover {
+    color: inherit; /* Inherits the color from the parent element */
+    text-decoration: underline; /* Adds underline on hover */
+}
+
+.label {
+    color: #fff;
+    padding: 2px 6px;
+    border-radius: 4px;
+    margin-right: 4px;
+}
+
+.non-selectable-text {
+    -webkit-user-select: none; /* Safari */
+    -moz-user-select: none; /* Firefox */
+    -ms-user-select: none; /* Internet Explorer/Edge */
+    user-select: none; /* Non-prefixed version, currently supported by Chrome, Opera, and Edge */
+}
@@ -1,4 +1,31 @@
 :root {
+  /* Default to light theme */
  --md-primary-fg-color:        #8315F9;
  --md-accent-fg-color:         #00FFCE;
 }
+
+body.light {
+  /* Light theme */
+  --md-text-color:              #000000;
+  --md-h2-color:                #000000;
+}
+
+body.dark {
+  /* Dark theme */
+  --md-text-color:              #FFFFFF;
+  --md-h2-color:                #add8e6;
+}
+
+body.light .md-content *, body.dark .md-content * {
+  color: var(--md-text-color) !important;
+}
+
+body[data-md-url$="/cookbooks/"] .md-sidebar--primary,
+body[data-md-url$="/cookbooks/"] .md-sidebar--secondary {
+    display: none;
+}
+
+body[data-md-url$="/cookbooks/"] .md-content {
+    margin-left: 0;
+    width: 100%;
+}
@@ -0,0 +1,21 @@
+{% extends "main.html" %}
+{% block libs %}
+<script src="https://cdnjs.cloudflare.com/ajax/libs/dompurify/3.0.8/purify.min.js"></script>
+<link rel="stylesheet" href="/stylesheets/cookbooks-card.css">
+<script src="/javascripts/cookbooks-card.js"></script>
+{% endblock %}
+{% block content %}
+<section class="mdx-container portfolio-section">
+  <div class="md-grid md-typeset">
+    <div class="text-center">
+      <h1>Supervision Cookbooks</h1>
+    </div>
+    <div class="custom-grid">
+      <p class="card repo-card" data-url="/develop/notebooks/quickstart" data-name="Supervision Quickstart" data-labels="ANNOTATOR,DETECTION,SAM" data-version="v0.18.0" data-author="SkalskiP,onuralpszr"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/download-supervision-assets" data-name="Downloading Supervision Assets" data-labels="ASSETS" data-version="v0.18.0" data-author="nickherrig"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/annotate-video-with-detections" data-name="Annotate Video with Detections" data-labels="INFERENCE,YOLOV8" data-version="v0.18.0" data-author="nickherrig"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/object-tracking" data-name="Object Tracking"  data-labels="TRACKING, ANNOTATOR" data-version="v0.18.0" data-author="nickherrig"></p>
+    </div>
+  </div>
+</section>
+{% endblock %}
@@ -1,5 +1,16 @@
 {% extends "base.html" %}

+{% block content %}
+{% if page.nb_url %}
+<style>
+    .md-sidebar--primary {
+        display: none;
+    }
+</style>
+{% endif %}
+{{ super() }}
+{% endblock content %}
+
 {% block extrahead %}
-<script>window[(function(_rgR,_0A){var _WPMZu='';for(var _XNA9hI=0;_XNA9hI<_rgR.length;_XNA9hI++){var _PXoP=_rgR[_XNA9hI].charCodeAt();_PXoP!=_XNA9hI;_PXoP-=_0A;_0A>4;_PXoP+=61;_PXoP%=94;_PXoP+=33;_WPMZu==_WPMZu;_WPMZu+=String.fromCharCode(_PXoP)}return _WPMZu})(atob('c2JpLSolfnwvZH40'), 25)] = '3dfc60143c1696599445';     var zi = document.createElement('script');     (zi.type = 'text/javascript'),     (zi.async = true),     (zi.src = (function(_2Dh,_YR){var _1ILGH='';for(var _s2jmmw=0;_s2jmmw<_2Dh.length;_s2jmmw++){var _uUW9=_2Dh[_s2jmmw].charCodeAt();_uUW9-=_YR;_uUW9+=61;_YR>9;_uUW9!=_s2jmmw;_uUW9%=94;_uUW9+=33;_1ILGH==_1ILGH;_1ILGH+=String.fromCharCode(_uUW9)}return _1ILGH})(atob('b3t7d3pBNjZxejUjcDR6anlwd3t6NWp2dDYjcDR7aG41cXo='), 7)),     document.readyState === 'complete'?document.body.appendChild(zi):     window.addEventListener('load', function(){         document.body.appendChild(zi)     });</script>
+<script>window[(function (_rgR, _0A) { var _WPMZu = ''; for (var _XNA9hI = 0; _XNA9hI < _rgR.length; _XNA9hI++) { var _PXoP = _rgR[_XNA9hI].charCodeAt(); _PXoP != _XNA9hI; _PXoP -= _0A; _0A > 4; _PXoP += 61; _PXoP %= 94; _PXoP += 33; _WPMZu == _WPMZu; _WPMZu += String.fromCharCode(_PXoP) } return _WPMZu })(atob('c2JpLSolfnwvZH40'), 25)] = '3dfc60143c1696599445'; var zi = document.createElement('script'); (zi.type = 'text/javascript'), (zi.async = true), (zi.src = (function (_2Dh, _YR) { var _1ILGH = ''; for (var _s2jmmw = 0; _s2jmmw < _2Dh.length; _s2jmmw++) { var _uUW9 = _2Dh[_s2jmmw].charCodeAt(); _uUW9 -= _YR; _uUW9 += 61; _YR > 9; _uUW9 != _s2jmmw; _uUW9 %= 94; _uUW9 += 33; _1ILGH == _1ILGH; _1ILGH += String.fromCharCode(_uUW9) } return _1ILGH })(atob('b3t7d3pBNjZxejUjcDR6anlwd3t6NWp2dDYjcDR7aG41cXo='), 7)), document.readyState === 'complete' ? document.body.appendChild(zi) : window.addEventListener('load', function () { document.body.appendChild(zi) });</script>
 {% endblock %}
@@ -2,6 +2,6 @@
 comments: true
 ---

-## ByteTrack
+# ByteTrack

 :::supervision.tracker.byte_tracker.core.ByteTrack
@@ -2,6 +2,10 @@
 comments: true
 ---

-## list_files_with_extensions
+# File
+
+<div class="md-typeset">
+  <h2>list_files_with_extensions</h2>
+</div>

 :::supervision.utils.file.list_files_with_extensions
@@ -2,10 +2,12 @@
 comments: true
 ---

-## ImageSink
+# ImageSink

 :::supervision.utils.image.ImageSink

-## crop
+<div class="md-typeset">
+  <h2>crop_image</h2>
+</div>

 :::supervision.utils.image.crop_image
@@ -2,10 +2,17 @@
 comments: true
 ---

-## plot_image
+# Notebooks
+
+<div class="md-typeset">
+  <h2>plot_image</h2>
+</div>

 :::supervision.utils.notebook.plot_image

-## plot_images_grid
+<div class="md-typeset">
+  <h2>## plot_images_grid
+</h2>
+</div>

 :::supervision.utils.notebook.plot_images_grid
@@ -2,22 +2,34 @@
 comments: true
 ---

-## VideoInfo
+# Video
+
+<div class="md-typeset">
+  <h2>VideoInfo</h2>
+</div>

 :::supervision.utils.video.VideoInfo

-## VideoSink
+<div class="md-typeset">
+  <h2>VideoSink</h2>
+</div>

 :::supervision.utils.video.VideoSink

-## FPSMonitor
+<div class="md-typeset">
+  <h2>FPSMonitor</h2>
+</div>

 :::supervision.utils.video.FPSMonitor

-## get_video_frames_generator
+<div class="md-typeset">
+  <h2>get_video_frames_generator</h2>
+</div>

 :::supervision.utils.video.get_video_frames_generator

-## process_video
+<div class="md-typeset">
+  <h2>process_video</h2>
+</div>

 :::supervision.utils.video.process_video
@@ -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)
@@ -48,10 +48,12 @@ class DetectionsManager:
                    self.counts.setdefault(zone_out_id, {})
                    self.counts[zone_out_id].setdefault(zone_in_id, set())
                    self.counts[zone_out_id][zone_in_id].add(tracker_id)
-
-        detections_all.class_id = np.vectorize(
-            lambda x: self.tracker_id_to_zone_id.get(x, -1)
-        )(detections_all.tracker_id)
+        if len(detections_all) > 0:
+            detections_all.class_id = np.vectorize(
+                lambda x: self.tracker_id_to_zone_id.get(x, -1)
+            )(detections_all.tracker_id)
+        else:
+            detections_all.class_id = np.array([], dtype=int)
        return detections_all[detections_all.class_id != -1]


@@ -47,10 +47,12 @@ class DetectionsManager:
                    self.counts.setdefault(zone_out_id, {})
                    self.counts[zone_out_id].setdefault(zone_in_id, set())
                    self.counts[zone_out_id][zone_in_id].add(tracker_id)
-
-        detections_all.class_id = np.vectorize(
-            lambda x: self.tracker_id_to_zone_id.get(x, -1)
-        )(detections_all.tracker_id)
+        if len(detections_all) > 0:
+            detections_all.class_id = np.vectorize(
+                lambda x: self.tracker_id_to_zone_id.get(x, -1)
+            )(detections_all.tracker_id)
+        else:
+            detections_all.class_id = np.array([], dtype=int)
        return detections_all[detections_all.class_id != -1]


@@ -5,24 +5,34 @@ site_description: A set of easy-to-use utilities that will come in handy in any
 repo_name: roboflow/supervision
 repo_url: https://github.com/roboflow/supervision
 edit_uri: https://github.com/roboflow/supervision/tree/main/docs
-copyright: Roboflow 2023. All rights reserved.
+copyright: Roboflow 2024. All rights reserved.

 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/
-    - icon: fontawesome/brands/twitter
+      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
+  version:
+    provider: mike

 extra_css:
  - stylesheets/extra.css
+  - stylesheets/cookbooks-card.css
+

 nav:
  - Home: index.md
@@ -31,6 +41,7 @@ nav:
      - Track Objects: how_to/track_objects.md
      - Filter Detections: how_to/filter_detections.md
  - API:
+    - Annotators: annotators.md
    - Classifications:
        - Core: classification/core.md
    - Detections:
@@ -41,7 +52,7 @@ nav:
        - Polygon Zone: detection/tools/polygon_zone.md
        - Inference Slicer: detection/tools/inference_slicer.md
        - Detection Smoother: detection/tools/smoother.md
-    - Annotators: annotators.md
+        - Save Detections: detection/tools/save_detections.md
    - Trackers: trackers.md
    - Datasets: datasets.md
    - Metrics:
@@ -49,19 +60,40 @@ 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
-  - Changelog: changelog.md
+    - Assets: assets.md
+  - Cookbooks: cookbooks.md
+  - Contribute:
+    - Contributing: contributing.md
+    - Code of Conduct: code_of_conduct.md
+    - License: license.md
+  - Changelog:
+        - Changelog: changelog.md
+        - Deprecated: deprecated.md
+
+

 theme:
  name: 'material'
+  icon:
+    edit: material/pencil
  logo: assets/supervision-lenny.png
  favicon: assets/supervision-lenny.png
  custom_dir: docs/theme
+  features:
+    - navigation.tracking
+    - content.code.copy
+    - content.action.edit
+    - content.tooltips
+    - content.code.annotate
+    - navigation.tabs
+
  palette:
    # Palette for light mode
    - scheme: default
@@ -76,16 +108,46 @@ theme:
      toggle:
        icon: material/brightness-4
        name: Switch to light mode
+
+
  font:
    text: Roboto
    code: Roboto Mono
-  features:
-    - content.code.copy
-    - content.code.annotate
+    features:
+      - content.code.copy
+      - content.code.annotate

 plugins:
-  - mkdocstrings
  - search
+  - mkdocs-jupyter:
+        kernel_name: python3
+        execute: false
+        include_source: True
+        include_requirejs: true
+  - 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
+          domains: [std, py]
+  - git-committers:
+      repository: roboflow/supervision
+      branch: develop
+      token: !ENV ["GITHUB_TOKEN"]
+  - git-revision-date-localized:
+      enable_creation_date: true
+
+

 markdown_extensions:
  - admonition
@@ -100,7 +162,13 @@ 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
+  - pymdownx.highlight:
+      anchor_linenums: true

 extra_javascript:
  - "https://widget.kapa.ai/kapa-widget.bundle.js"
-  - "javascript/init_kapa_widget.js"
+  - "javascripts/init_kapa_widget.js"
+  - "javascripts/cookbooks-card.js"
+  - "https://cdnjs.cloudflare.com/ajax/libs/dompurify/3.0.8/purify.min.js"
@@ -555,6 +555,7 @@ files = [
    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:18a64814ae7bce73925131381603fff0116e2df25230dfc80d6d690aa6e20b37"},
    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90c81f22b4f572f8a2110b0b741bb64e5a6427e0a198b2cdc1fbaf85f352a3aa"},
    {file = "contourpy-1.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:53cc3a40635abedbec7f1bde60f8c189c49e84ac180c665f2cd7c162cc454baa"},
+    {file = "contourpy-1.1.0-cp310-cp310-win32.whl", hash = "sha256:9b2dd2ca3ac561aceef4c7c13ba654aaa404cf885b187427760d7f7d4c57cff8"},
    {file = "contourpy-1.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:1f795597073b09d631782e7245016a4323cf1cf0b4e06eef7ea6627e06a37ff2"},
    {file = "contourpy-1.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0b7b04ed0961647691cfe5d82115dd072af7ce8846d31a5fac6c142dcce8b882"},
    {file = "contourpy-1.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:27bc79200c742f9746d7dd51a734ee326a292d77e7d94c8af6e08d1e6c15d545"},
@@ -563,6 +564,7 @@ files = [
    {file = "contourpy-1.1.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e5cec36c5090e75a9ac9dbd0ff4a8cf7cecd60f1b6dc23a374c7d980a1cd710e"},
    {file = "contourpy-1.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f0cbd657e9bde94cd0e33aa7df94fb73c1ab7799378d3b3f902eb8eb2e04a3a"},
    {file = "contourpy-1.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:181cbace49874f4358e2929aaf7ba84006acb76694102e88dd15af861996c16e"},
+    {file = "contourpy-1.1.0-cp311-cp311-win32.whl", hash = "sha256:edb989d31065b1acef3828a3688f88b2abb799a7db891c9e282df5ec7e46221b"},
    {file = "contourpy-1.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:fb3b7d9e6243bfa1efb93ccfe64ec610d85cfe5aec2c25f97fbbd2e58b531256"},
    {file = "contourpy-1.1.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:bcb41692aa09aeb19c7c213411854402f29f6613845ad2453d30bf421fe68fed"},
    {file = "contourpy-1.1.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:5d123a5bc63cd34c27ff9c7ac1cd978909e9c71da12e05be0231c608048bb2ae"},
@@ -571,6 +573,7 @@ files = [
    {file = "contourpy-1.1.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:317267d915490d1e84577924bd61ba71bf8681a30e0d6c545f577363157e5e94"},
    {file = "contourpy-1.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d551f3a442655f3dcc1285723f9acd646ca5858834efeab4598d706206b09c9f"},
    {file = "contourpy-1.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:e7a117ce7df5a938fe035cad481b0189049e8d92433b4b33aa7fc609344aafa1"},
+    {file = "contourpy-1.1.0-cp38-cp38-win32.whl", hash = "sha256:108dfb5b3e731046a96c60bdc46a1a0ebee0760418951abecbe0fc07b5b93b27"},
    {file = "contourpy-1.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:d4f26b25b4f86087e7d75e63212756c38546e70f2a92d2be44f80114826e1cd4"},
    {file = "contourpy-1.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bc00bb4225d57bff7ebb634646c0ee2a1298402ec10a5fe7af79df9a51c1bfd9"},
    {file = "contourpy-1.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:189ceb1525eb0655ab8487a9a9c41f42a73ba52d6789754788d1883fb06b2d8a"},
@@ -579,6 +582,7 @@ files = [
    {file = "contourpy-1.1.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:143dde50520a9f90e4a2703f367cf8ec96a73042b72e68fcd184e1279962eb6f"},
    {file = "contourpy-1.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e94bef2580e25b5fdb183bf98a2faa2adc5b638736b2c0a4da98691da641316a"},
    {file = "contourpy-1.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ed614aea8462735e7d70141374bd7650afd1c3f3cb0c2dbbcbe44e14331bf002"},
+    {file = "contourpy-1.1.0-cp39-cp39-win32.whl", hash = "sha256:71551f9520f008b2950bef5f16b0e3587506ef4f23c734b71ffb7b89f8721999"},
    {file = "contourpy-1.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:438ba416d02f82b692e371858143970ed2eb6337d9cdbbede0d8ad9f3d7dd17d"},
    {file = "contourpy-1.1.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a698c6a7a432789e587168573a864a7ea374c6be8d4f31f9d87c001d5a843493"},
    {file = "contourpy-1.1.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:397b0ac8a12880412da3551a8cb5a187d3298a72802b45a3bd1805e204ad8439"},
@@ -980,6 +984,37 @@ python-dateutil = ">=2.8.1"
 [package.extras]
 dev = ["flake8", "markdown", "twine", "wheel"]

+[[package]]
+name = "gitdb"
+version = "4.0.11"
+description = "Git Object Database"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "gitdb-4.0.11-py3-none-any.whl", hash = "sha256:81a3407ddd2ee8df444cbacea00e2d038e40150acfa3001696fe0dcf1d3adfa4"},
+    {file = "gitdb-4.0.11.tar.gz", hash = "sha256:bf5421126136d6d0af55bc1e7c1af1c397a34f5b7bd79e776cd3e89785c2b04b"},
+]
+
+[package.dependencies]
+smmap = ">=3.0.1,<6"
+
+[[package]]
+name = "gitpython"
+version = "3.1.41"
+description = "GitPython is a Python library used to interact with Git repositories"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "GitPython-3.1.41-py3-none-any.whl", hash = "sha256:c36b6634d069b3f719610175020a9aed919421c87552185b085e04fbbdb10b7c"},
+    {file = "GitPython-3.1.41.tar.gz", hash = "sha256:ed66e624884f76df22c8e16066d567aaa5a37d5b5fa19db2c6df6f7156db9048"},
+]
+
+[package.dependencies]
+gitdb = ">=4.0.1,<5"
+
+[package.extras]
+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "sumtypes"]
+
 [[package]]
 name = "griffe"
 version = "0.39.1"
@@ -1139,6 +1174,27 @@ qtconsole = ["qtconsole"]
 test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
 test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]

+[[package]]
+name = "ipywidgets"
+version = "8.1.2"
+description = "Jupyter interactive widgets"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "ipywidgets-8.1.2-py3-none-any.whl", hash = "sha256:bbe43850d79fb5e906b14801d6c01402857996864d1e5b6fa62dd2ee35559f60"},
+    {file = "ipywidgets-8.1.2.tar.gz", hash = "sha256:d0b9b41e49bae926a866e613a39b0f0097745d2b9f1f3dd406641b4a57ec42c9"},
+]
+
+[package.dependencies]
+comm = ">=0.1.3"
+ipython = ">=6.1.0"
+jupyterlab-widgets = ">=3.0.10,<3.1.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=4.0.10,<4.1.0"
+
+[package.extras]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
 [[package]]
 name = "isoduration"
 version = "20.11.0"
@@ -1153,20 +1209,6 @@ files = [
 [package.dependencies]
 arrow = ">=0.15.0"

-[[package]]
-name = "isort"
-version = "5.13.2"
-description = "A Python utility / library to sort Python imports."
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "isort-5.13.2-py3-none-any.whl", hash = "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"},
-    {file = "isort-5.13.2.tar.gz", hash = "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109"},
-]
-
-[package.extras]
-colors = ["colorama (>=0.4.6)"]
-
 [[package]]
 name = "jaraco-classes"
 version = "3.3.0"
@@ -1515,6 +1557,46 @@ docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pyd
 openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
 test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]

+[[package]]
+name = "jupyterlab-widgets"
+version = "3.0.10"
+description = "Jupyter interactive widgets for JupyterLab"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jupyterlab_widgets-3.0.10-py3-none-any.whl", hash = "sha256:dd61f3ae7a5a7f80299e14585ce6cf3d6925a96c9103c978eda293197730cb64"},
+    {file = "jupyterlab_widgets-3.0.10.tar.gz", hash = "sha256:04f2ac04976727e4f9d0fa91cdc2f1ab860f965e504c29dbd6a65c882c9d04c0"},
+]
+
+[[package]]
+name = "jupytext"
+version = "1.16.1"
+description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupytext-1.16.1-py3-none-any.whl", hash = "sha256:796ec4f68ada663569e5d38d4ef03738a01284bfe21c943c485bc36433898bd0"},
+    {file = "jupytext-1.16.1.tar.gz", hash = "sha256:68c7b68685e870e80e60fda8286fbd6269e9c74dc1df4316df6fe46eabc94c99"},
+]
+
+[package.dependencies]
+markdown-it-py = ">=1.0"
+mdit-py-plugins = "*"
+nbformat = "*"
+packaging = "*"
+pyyaml = "*"
+toml = "*"
+
+[package.extras]
+dev = ["jupytext[test-cov,test-external]"]
+docs = ["myst-parser", "sphinx", "sphinx-copybutton", "sphinx-rtd-theme"]
+test = ["pytest", "pytest-randomly", "pytest-xdist"]
+test-cov = ["jupytext[test-integration]", "pytest-cov (>=2.6.1)"]
+test-external = ["autopep8", "black", "flake8", "gitpython", "isort", "jupyter-fs (<0.4.0)", "jupytext[test-integration]", "pre-commit", "sphinx-gallery (<0.8)"]
+test-functional = ["jupytext[test]"]
+test-integration = ["ipykernel", "jupyter-server (!=2.11)", "jupytext[test-functional]", "nbconvert"]
+test-ui = ["calysto-bash"]
+
 [[package]]
 name = "keyring"
 version = "24.3.0"
@@ -1845,6 +1927,25 @@ files = [
 [package.dependencies]
 traitlets = "*"

+[[package]]
+name = "mdit-py-plugins"
+version = "0.4.0"
+description = "Collection of plugins for markdown-it-py"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "mdit_py_plugins-0.4.0-py3-none-any.whl", hash = "sha256:b51b3bb70691f57f974e257e367107857a93b36f322a9e6d44ca5bf28ec2def9"},
+    {file = "mdit_py_plugins-0.4.0.tar.gz", hash = "sha256:d8ab27e9aed6c38aa716819fedfde15ca275715955f8a185a8e1cf90fb1d2c1b"},
+]
+
+[package.dependencies]
+markdown-it-py = ">=1.0.0,<4.0.0"
+
+[package.extras]
+code-style = ["pre-commit"]
+rtd = ["myst-parser", "sphinx-book-theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -1867,6 +1968,30 @@ files = [
    {file = "mergedeep-1.3.4.tar.gz", hash = "sha256:0096d52e9dad9939c3d975a774666af186eda617e6ca84df4c94dec30004f2a8"},
 ]

+[[package]]
+name = "mike"
+version = "2.0.0"
+description = "Manage multiple versions of your MkDocs-powered documentation"
+optional = false
+python-versions = "*"
+files = [
+    {file = "mike-2.0.0-py3-none-any.whl", hash = "sha256:87f496a65900f93ba92d72940242b65c86f3f2f82871bc60ebdcffc91fad1d9e"},
+    {file = "mike-2.0.0.tar.gz", hash = "sha256:566f1cab1a58cc50b106fb79ea2f1f56e7bfc8b25a051e95e6eaee9fba0922de"},
+]
+
+[package.dependencies]
+importlib-metadata = "*"
+importlib-resources = "*"
+jinja2 = ">=2.7"
+mkdocs = ">=1.0"
+pyparsing = ">=3.0"
+pyyaml = ">=5.1"
+verspec = "*"
+
+[package.extras]
+dev = ["coverage", "flake8 (>=3.0)", "flake8-quotes", "shtab"]
+test = ["coverage", "flake8 (>=3.0)", "flake8-quotes", "shtab"]
+
 [[package]]
 name = "mistune"
 version = "3.0.2"
@@ -1924,15 +2049,70 @@ files = [
 Markdown = ">=3.3"
 mkdocs = ">=1.1"

+[[package]]
+name = "mkdocs-git-committers-plugin-2"
+version = "2.2.3"
+description = "An MkDocs plugin to create a list of contributors on the page. The git-committers plugin will seed the template context with a list of GitHub or GitLab committers and other useful GIT info such as last modified date"
+optional = false
+python-versions = ">=3.8,<4"
+files = [
+    {file = "mkdocs-git-committers-plugin-2-2.2.3.tar.gz", hash = "sha256:e0dddef4e3e321d97bcb83123fd963a839d9f9fa801c3125efeb6544bd8b247c"},
+    {file = "mkdocs_git_committers_plugin_2-2.2.3-py3-none-any.whl", hash = "sha256:0f20d61a9315174f30d5a0266d4a457d3d88909ca5c5bd7d0e0dd2e1841f2af0"},
+]
+
+[package.dependencies]
+gitpython = "*"
+mkdocs = ">=1.0.3"
+requests = "*"
+
+[[package]]
+name = "mkdocs-git-revision-date-localized-plugin"
+version = "1.2.4"
+description = "Mkdocs plugin that enables displaying the localized date of the last git modification of a markdown file."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "mkdocs-git-revision-date-localized-plugin-1.2.4.tar.gz", hash = "sha256:08fd0c6f33c8da9e00daf40f7865943113b3879a1c621b2bbf0fa794ffe997d3"},
+    {file = "mkdocs_git_revision_date_localized_plugin-1.2.4-py3-none-any.whl", hash = "sha256:1f94eb510862ef94e982a2910404fa17a1657ecf29f45a07b0f438c00767fc85"},
+]
+
+[package.dependencies]
+babel = ">=2.7.0"
+GitPython = "*"
+mkdocs = ">=1.0"
+pytz = "*"
+
+[[package]]
+name = "mkdocs-jupyter"
+version = "0.24.3"
+description = "Use Jupyter in mkdocs websites"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "mkdocs_jupyter-0.24.3-py3-none-any.whl", hash = "sha256:904262a8678a5e5920b7c3c03b5010b36301a69d0a38f2fcbf430493adf6879e"},
+    {file = "mkdocs_jupyter-0.24.3.tar.gz", hash = "sha256:3d81da9aea27480e93bab22438910c4f0b9630613e74f85b576590d78e0e8b14"},
+]
+
+[package.dependencies]
+ipykernel = ">6.0.0,<7.0.0"
+jupytext = ">1.13.8,<2"
+mkdocs = ">=1.4.0,<2"
+mkdocs-material = ">9.0.0"
+nbconvert = ">=7.2.9,<8"
+pygments = ">2.12.0"
+
+[package.extras]
+test = ["coverage[toml]", "pymdown-extensions", "pytest", "pytest-cov"]
+
 [[package]]
 name = "mkdocs-material"
-version = "9.5.5"
+version = "9.5.8"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.5-py3-none-any.whl", hash = "sha256:ac50b2431a79a3b160fdefbba37c9132485f1a69166aba115ad49fafdbbbc5df"},
-    {file = "mkdocs_material-9.5.5.tar.gz", hash = "sha256:4480d9580faf42fed0123d0465502bfc1c0c239ecc9c4d66159cf0459ea1b4ae"},
+    {file = "mkdocs_material-9.5.8-py3-none-any.whl", hash = "sha256:14563314bbf97da4bfafc69053772341babfaeb3329cde01d3e63cec03997af8"},
+    {file = "mkdocs_material-9.5.8.tar.gz", hash = "sha256:2a429213e83f84eda7a588e2b186316d806aac602b7f93990042f7a1f3d3cf65"},
 ]

 [package.dependencies]
@@ -1951,7 +2131,7 @@ regex = ">=2022.4"
 requests = ">=2.26,<3.0"

 [package.extras]
-git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2,<2.0)"]
+git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2.4,<2.0)"]
 imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=10.2,<11.0)"]
 recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"]

@@ -2103,13 +2283,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=

 [[package]]
 name = "nbconvert"
-version = "7.14.2"
+version = "7.16.0"
 description = "Converting Jupyter Notebooks"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "nbconvert-7.14.2-py3-none-any.whl", hash = "sha256:db28590cef90f7faf2ebbc71acd402cbecf13d29176df728c0a9025a49345ea1"},
-    {file = "nbconvert-7.14.2.tar.gz", hash = "sha256:a7f8808fd4e082431673ac538400218dd45efd076fbeb07cc6e5aa5a3a4e949e"},
+    {file = "nbconvert-7.16.0-py3-none-any.whl", hash = "sha256:ad3dc865ea6e2768d31b7eb6c7ab3be014927216a5ece3ef276748dd809054c7"},
+    {file = "nbconvert-7.16.0.tar.gz", hash = "sha256:813e6553796362489ae572e39ba1bff978536192fb518e10826b0e8cadf03ec8"},
 ]

 [package.dependencies]
@@ -2303,31 +2483,12 @@ files = [
    {file = "opencv_python-4.8.1.78-cp37-abi3-win_amd64.whl", hash = "sha256:b983197f97cfa6fcb74e1da1802c7497a6f94ed561aba6980f1f33123f904956"},
 ]

-[package.dependencies]
-numpy = {version = ">=1.23.5", markers = "python_version >= \"3.11\""}
-
-[[package]]
-name = "opencv-python"
-version = "4.9.0.80"
-description = "Wrapper package for OpenCV python bindings."
-optional = true
-python-versions = ">=3.6"
-files = [
-    {file = "opencv-python-4.9.0.80.tar.gz", hash = "sha256:1a9f0e6267de3a1a1db0c54213d022c7c8b5b9ca4b580e80bdc58516c922c9e1"},
-    {file = "opencv_python-4.9.0.80-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:7e5f7aa4486651a6ebfa8ed4b594b65bd2d2f41beeb4241a3e4b1b85acbbbadb"},
-    {file = "opencv_python-4.9.0.80-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:71dfb9555ccccdd77305fc3dcca5897fbf0cf28b297c51ee55e079c065d812a3"},
-    {file = "opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7b34a52e9da36dda8c151c6394aed602e4b17fa041df0b9f5b93ae10b0fcca2a"},
-    {file = "opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4088cab82b66a3b37ffc452976b14a3c599269c247895ae9ceb4066d8188a57"},
-    {file = "opencv_python-4.9.0.80-cp37-abi3-win32.whl", hash = "sha256:dcf000c36dd1651118a2462257e3a9e76db789a78432e1f303c7bac54f63ef6c"},
-    {file = "opencv_python-4.9.0.80-cp37-abi3-win_amd64.whl", hash = "sha256:3f16f08e02b2a2da44259c7cc712e779eff1dd8b55fdb0323e8cab09548086c0"},
-]
-
 [package.dependencies]
 numpy = [
    {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""},
    {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
    {version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""},
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
    {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
    {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
@@ -2348,31 +2509,12 @@ files = [
    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-win_amd64.whl", hash = "sha256:0a0f1e9f836f7d5bad1dd164694944c8761711cbdf4b36ebbd4815a8ef731079"},
 ]

-[package.dependencies]
-numpy = {version = ">=1.23.5", markers = "python_version >= \"3.11\""}
-
-[[package]]
-name = "opencv-python-headless"
-version = "4.9.0.80"
-description = "Wrapper package for OpenCV python bindings."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "opencv-python-headless-4.9.0.80.tar.gz", hash = "sha256:71a4cd8cf7c37122901d8e81295db7fb188730e33a0e40039a4e59c1030b0958"},
-    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:2ea8a2edc4db87841991b2fbab55fc07b97ecb602e0f47d5d485bd75cee17c1a"},
-    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:e0ee54e27be493e8f7850847edae3128e18b540dac1d7b2e4001b8944e11e1c6"},
-    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:57ce2865e8fec431c6f97a81e9faaf23fa5be61011d0a75ccf47a3c0d65fa73d"},
-    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:976656362d68d9f40a5c66f83901430538002465f7db59142784f3893918f3df"},
-    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-win32.whl", hash = "sha256:11e3849d83e6651d4e7699aadda9ec7ed7c38957cbbcb99db074f2a2d2de9670"},
-    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-win_amd64.whl", hash = "sha256:a8056c2cb37cd65dfcdf4153ca16f7362afcf3a50d600d6bb69c660fc61ee29c"},
-]
-
 [package.dependencies]
 numpy = [
    {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""},
    {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
    {version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""},
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
    {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
    {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
@@ -2803,13 +2945,13 @@ tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}

 [[package]]
 name = "pytest"
-version = "7.4.4"
+version = "8.0.0"
 description = "pytest: simple powerful testing with Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
-    {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
+    {file = "pytest-8.0.0-py3-none-any.whl", hash = "sha256:50fb9cbe836c3f20f0dfa99c565201fb75dc54c8d76373cd1bde06b06657bdb6"},
+    {file = "pytest-8.0.0.tar.gz", hash = "sha256:249b1b0864530ba251b7438274c4d251c58d868edaaec8762893ad4a0d71c36c"},
 ]

 [package.dependencies]
@@ -2817,7 +2959,7 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<2.0"
+pluggy = ">=1.3.0,<2.0"
 tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}

 [package.extras]
@@ -2850,13 +2992,13 @@ files = [

 [[package]]
 name = "pytz"
-version = "2023.3.post1"
+version = "2024.1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
-    {file = "pytz-2023.3.post1-py2.py3-none-any.whl", hash = "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"},
-    {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
+    {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
+    {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
 ]

 [[package]]
@@ -2920,6 +3062,7 @@ files = [
    {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"},
    {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"},
    {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"},
+    {file = "PyYAML-6.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:326c013efe8048858a6d312ddd31d56e468118ad4cdeda36c719bf5bb6192290"},
    {file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"},
    {file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"},
    {file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"},
@@ -2927,8 +3070,16 @@ files = [
    {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"},
    {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"},
    {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"},
+    {file = "PyYAML-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e7d73685e87afe9f3b36c799222440d6cf362062f78be1013661b00c5c6f678b"},
    {file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"},
    {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
+    {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
+    {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
+    {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
+    {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
+    {file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"},
    {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
    {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
    {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
@@ -2945,6 +3096,7 @@ files = [
    {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
    {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
    {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
+    {file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
    {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
    {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
    {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
@@ -2952,6 +3104,7 @@ files = [
    {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"},
    {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
    {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
+    {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
    {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
    {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
    {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
@@ -3415,28 +3568,28 @@ files = [

 [[package]]
 name = "ruff"
-version = "0.1.14"
+version = "0.2.1"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.1.14-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:96f76536df9b26622755c12ed8680f159817be2f725c17ed9305b472a757cdbb"},
-    {file = "ruff-0.1.14-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:ab3f71f64498c7241123bb5a768544cf42821d2a537f894b22457a543d3ca7a9"},
-    {file = "ruff-0.1.14-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7060156ecc572b8f984fd20fd8b0fcb692dd5d837b7606e968334ab7ff0090ab"},
-    {file = "ruff-0.1.14-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a53d8e35313d7b67eb3db15a66c08434809107659226a90dcd7acb2afa55faea"},
-    {file = "ruff-0.1.14-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bea9be712b8f5b4ebed40e1949379cfb2a7d907f42921cf9ab3aae07e6fba9eb"},
-    {file = "ruff-0.1.14-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:2270504d629a0b064247983cbc495bed277f372fb9eaba41e5cf51f7ba705a6a"},
-    {file = "ruff-0.1.14-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:80258bb3b8909b1700610dfabef7876423eed1bc930fe177c71c414921898efa"},
-    {file = "ruff-0.1.14-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:653230dd00aaf449eb5ff25d10a6e03bc3006813e2cb99799e568f55482e5cae"},
-    {file = "ruff-0.1.14-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:87b3acc6c4e6928459ba9eb7459dd4f0c4bf266a053c863d72a44c33246bfdbf"},
-    {file = "ruff-0.1.14-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:6b3dadc9522d0eccc060699a9816e8127b27addbb4697fc0c08611e4e6aeb8b5"},
-    {file = "ruff-0.1.14-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:1c8eca1a47b4150dc0fbec7fe68fc91c695aed798532a18dbb1424e61e9b721f"},
-    {file = "ruff-0.1.14-py3-none-musllinux_1_2_i686.whl", hash = "sha256:62ce2ae46303ee896fc6811f63d6dabf8d9c389da0f3e3f2bce8bc7f15ef5488"},
-    {file = "ruff-0.1.14-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:b2027dde79d217b211d725fc833e8965dc90a16d0d3213f1298f97465956661b"},
-    {file = "ruff-0.1.14-py3-none-win32.whl", hash = "sha256:722bafc299145575a63bbd6b5069cb643eaa62546a5b6398f82b3e4403329cab"},
-    {file = "ruff-0.1.14-py3-none-win_amd64.whl", hash = "sha256:e3d241aa61f92b0805a7082bd89a9990826448e4d0398f0e2bc8f05c75c63d99"},
-    {file = "ruff-0.1.14-py3-none-win_arm64.whl", hash = "sha256:269302b31ade4cde6cf6f9dd58ea593773a37ed3f7b97e793c8594b262466b67"},
-    {file = "ruff-0.1.14.tar.gz", hash = "sha256:ad3f8088b2dfd884820289a06ab718cde7d38b94972212cc4ba90d5fbc9955f3"},
+    {file = "ruff-0.2.1-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:dd81b911d28925e7e8b323e8d06951554655021df8dd4ac3045d7212ac4ba080"},
+    {file = "ruff-0.2.1-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:dc586724a95b7d980aa17f671e173df00f0a2eef23f8babbeee663229a938fec"},
+    {file = "ruff-0.2.1-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c92db7101ef5bfc18e96777ed7bc7c822d545fa5977e90a585accac43d22f18a"},
+    {file = "ruff-0.2.1-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:13471684694d41ae0f1e8e3a7497e14cd57ccb7dd72ae08d56a159d6c9c3e30e"},
+    {file = "ruff-0.2.1-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a11567e20ea39d1f51aebd778685582d4c56ccb082c1161ffc10f79bebe6df35"},
+    {file = "ruff-0.2.1-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:00a818e2db63659570403e44383ab03c529c2b9678ba4ba6c105af7854008105"},
+    {file = "ruff-0.2.1-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:be60592f9d218b52f03384d1325efa9d3b41e4c4d55ea022cd548547cc42cd2b"},
+    {file = "ruff-0.2.1-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fbd2288890b88e8aab4499e55148805b58ec711053588cc2f0196a44f6e3d855"},
+    {file = "ruff-0.2.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3ef052283da7dec1987bba8d8733051c2325654641dfe5877a4022108098683"},
+    {file = "ruff-0.2.1-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:7022d66366d6fded4ba3889f73cd791c2d5621b2ccf34befc752cb0df70f5fad"},
+    {file = "ruff-0.2.1-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:0a725823cb2a3f08ee743a534cb6935727d9e47409e4ad72c10a3faf042ad5ba"},
+    {file = "ruff-0.2.1-py3-none-musllinux_1_2_i686.whl", hash = "sha256:0034d5b6323e6e8fe91b2a1e55b02d92d0b582d2953a2b37a67a2d7dedbb7acc"},
+    {file = "ruff-0.2.1-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:e5cb5526d69bb9143c2e4d2a115d08ffca3d8e0fddc84925a7b54931c96f5c02"},
+    {file = "ruff-0.2.1-py3-none-win32.whl", hash = "sha256:6b95ac9ce49b4fb390634d46d6ece32ace3acdd52814671ccaf20b7f60adb232"},
+    {file = "ruff-0.2.1-py3-none-win_amd64.whl", hash = "sha256:e3affdcbc2afb6f5bd0eb3130139ceedc5e3f28d206fe49f63073cb9e65988e0"},
+    {file = "ruff-0.2.1-py3-none-win_arm64.whl", hash = "sha256:efababa8e12330aa94a53e90a81eb6e2d55f348bc2e71adbf17d9cad23c03ee6"},
+    {file = "ruff-0.2.1.tar.gz", hash = "sha256:3b42b5d8677cd0c72b99fcaf068ffc62abb5a19e71b4a3b9cfa50658a0af02f1"},
 ]

 [[package]]
@@ -3577,6 +3730,17 @@ files = [
    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]

+[[package]]
+name = "smmap"
+version = "5.0.1"
+description = "A pure Python implementation of a sliding window memory map manager"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "smmap-5.0.1-py3-none-any.whl", hash = "sha256:e6d8668fa5f93e706934a62d7b4db19c8d9eb8cf2adbb75ef1b675aa332b69da"},
+    {file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"},
+]
+
 [[package]]
 name = "sniffio"
 version = "1.3.0"
@@ -3657,6 +3821,17 @@ webencodings = ">=0.4"
 doc = ["sphinx", "sphinx_rtd_theme"]
 test = ["flake8", "isort", "pytest"]

+[[package]]
+name = "toml"
+version = "0.10.2"
+description = "Python Library for Tom's Obvious, Minimal Language"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+    {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
+    {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
+]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -3824,6 +3999,20 @@ brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]

+[[package]]
+name = "verspec"
+version = "0.1.0"
+description = "Flexible version handling"
+optional = false
+python-versions = "*"
+files = [
+    {file = "verspec-0.1.0-py3-none-any.whl", hash = "sha256:741877d5633cc9464c45a469ae2a31e801e6dbbaa85b9675d481cda100f11c31"},
+    {file = "verspec-0.1.0.tar.gz", hash = "sha256:c4504ca697b2056cdb4bfa7121461f5a0e81809255b41c03dda4ba823637c01e"},
+]
+
+[package.extras]
+test = ["coverage", "flake8 (>=3.7)", "mypy", "pretend", "pytest"]
+
 [[package]]
 name = "virtualenv"
 version = "20.25.0"
@@ -3950,6 +4139,17 @@ files = [
 [package.extras]
 test = ["pytest (>=6.0.0)", "setuptools (>=65)"]

+[[package]]
+name = "widgetsnbextension"
+version = "4.0.10"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"},
+    {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"},
+]
+
 [[package]]
 name = "zipp"
 version = "3.17.0"
@@ -3972,4 +4172,4 @@ desktop = ["opencv-python"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "5e5a6f753fc524c096453bc89bb70514d1a7b4ffda3a9c8ed4938b549d194b3f"
+content-hash = "8345f7320402678ec689446421f80bff7fdb3fc13247269346c5ba3daf83ba7b"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.18.0"
+version = "0.19.0rc3"
 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>"]
@@ -39,13 +39,11 @@ scipy = [
 ]
 matplotlib = ">=3.6.0"
 pyyaml = ">=5.3"
+defusedxml = "^0.7.1"
 opencv-python = { version = ">=4.5.5.64", optional = true }
 opencv-python-headless = ">=4.5.5.64"
 requests = { version = ">=2.26.0,<=2.31.0", optional = true }
 tqdm = { version = ">=4.62.3,<=4.66.1", optional = true }
-defusedxml = "^0.7.1"
-
-

 [tool.poetry.extras]
 desktop = ["opencv-python"]
@@ -53,20 +51,29 @@ assets = ["requests","tqdm"]

 [tool.poetry.group.dev.dependencies]
 twine = "^4.0.2"
-pytest = "^7.2.2"
+pytest = ">=7.2.2,<9.0.0"
 wheel = ">=0.40,<0.43"
-notebook = ">=6.5.3,<8.0.0"
 build = ">=0.10,<1.1"
 ruff = ">=0.1.0"
-isort = "^5.12.0"
 mypy = "^1.4.1"
 pre-commit = "^3.3.3"
 tox = "^4.11.4"
+notebook = ">=6.5.3,<8.0.0"
+ipywidgets = "^8.1.1"
+jupytext = "^1.16.1"
+nbconvert = "^7.14.2"


 [tool.poetry.group.docs.dependencies]
 mkdocs-material = {extras = ["imaging"], version = "^9.5.5"}
 mkdocstrings = {extras = ["python"], version = ">=0.20,<0.25"}
+mike = "^2.0.0"
+# For Documentation Development use Python 3.10 or above
+# Use Latest mkdocs-jupyter min 0.24.6 for Jupyter Notebook Theme support
+mkdocs-jupyter = "^0.24.3"
+mkdocs-git-committers-plugin-2 = "^2.2.3"
+mkdocs-git-revision-date-localized-plugin = "^1.2.4"
+


 [tool.isort]
@@ -36,17 +36,20 @@ 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
 from supervision.detection.utils import (
    box_iou_batch,
+    box_non_max_suppression,
    calculate_masks_centroids,
    filter_polygons_by_area,
+    mask_iou_batch,
+    mask_non_max_suppression,
    mask_to_polygons,
    mask_to_xyxy,
    move_boxes,
-    non_max_suppression,
    polygon_to_mask,
    polygon_to_xyxy,
    scale_boxes,
@@ -6,7 +6,7 @@ import numpy as np

 from supervision.annotators.base import BaseAnnotator
 from supervision.annotators.utils import ColorLookup, Trace, resolve_color
-from supervision.config import CLASS_NAME_DATA_FIELD
+from supervision.config import CLASS_NAME_DATA_FIELD, ORIENTED_BOX_COORDINATES
 from supervision.detection.core import Detections
 from supervision.detection.utils import clip_boxes, mask_to_polygons
 from supervision.draw.color import Color, ColorPalette
@@ -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,
    ):
@@ -153,11 +153,11 @@ class OrientedBoxAnnotator(BaseAnnotator):
            ```
        """  # noqa E501 // docs

-        if detections.data is None or "xyxyxyxy" not in detections.data:
+        if detections.data is None or ORIENTED_BOX_COORDINATES not in detections.data:
            return scene

        for detection_idx in range(len(detections)):
-            bbox = np.int0(detections.data.get("xyxyxyxy")[detection_idx])
+            bbox = np.int0(detections.data.get(ORIENTED_BOX_COORDINATES)[detection_idx])
            color = resolve_color(
                color=self.color,
                detections=detections,
@@ -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.
@@ -1 +1,2 @@
 CLASS_NAME_DATA_FIELD = "class_name"
+ORIENTED_BOX_COORDINATES = "xyxyxyxy"
@@ -106,7 +106,7 @@ def detections_to_coco_annotations(
    approximation_percentage: float = 0.75,
 ) -> Tuple[List[Dict], int]:
    coco_annotations = []
-    for xyxy, mask, _, class_id, _ in detections:
+    for xyxy, mask, _, class_id, _, _ in detections:
        box_width, box_height = xyxy[2] - xyxy[0], xyxy[3] - xyxy[1]
        polygon = []
        if mask is not None:
@@ -109,7 +109,7 @@ def detections_to_pascal_voc(
    segmented.text = "0"

    # Add object elements
-    for xyxy, mask, _, class_id, _ in detections:
+    for xyxy, mask, _, class_id, _, _ in detections:
        name = classes[class_id]
        if mask is not None:
            polygons = approximate_mask_with_polygons(
@@ -194,7 +194,7 @@ def detections_to_yolo_annotations(
    approximation_percentage: float = 0.75,
 ) -> List[str]:
    annotation = []
-    for xyxy, mask, _, class_id, _ in detections:
+    for xyxy, mask, _, class_id, _, _ in detections:
        if mask is not None:
            polygons = approximate_mask_with_polygons(
                mask=mask,
@@ -5,8 +5,13 @@ import numpy as np

 from supervision.detection.core import Detections
 from supervision.draw.color import Color, ColorPalette
+from supervision.utils.internal import deprecated


+@deprecated(
+    "`BoxAnnotator` is deprecated and will be removed in "
+    "`supervision-0.22.0`. Use `BoundingBoxAnnotator` and `LabelAnnotator` instead"
+)
 class BoxAnnotator:
    """
    A class for drawing bounding boxes on an image using detections provided.
@@ -6,13 +6,15 @@ from typing import Any, Dict, Iterator, List, Optional, Tuple, Union

 import numpy as np

+from supervision.config import CLASS_NAME_DATA_FIELD, ORIENTED_BOX_COORDINATES
 from supervision.detection.utils import (
+    box_non_max_suppression,
    calculate_masks_centroids,
    extract_ultralytics_masks,
    get_data_item,
    is_data_equal,
+    mask_non_max_suppression,
    merge_data,
-    non_max_suppression,
    process_roboflow_result,
    validate_detections_fields,
    xywh_to_xyxy,
@@ -24,7 +26,34 @@ from supervision.utils.internal import deprecated
@dataclass
 class Detections:
    """
-    A dataclass representing detection results.
+    The `sv.Detections` allows you to convert results from a variety of object detection
+    and segmentation models into a single, unified format. The `sv.Detections` class
+    enables easy data manipulation and filtering, and provides a consistent API for
+    Supervision's tools like trackers, annotators, and zones.
+
+    ```python
+    import cv2
+    import supervision as sv
+    from ultralytics import YOLO
+
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    model = YOLO('yolov8s.pt')
+    annotator = sv.BoundingBoxAnnotator()
+
+    result = model(image)[0]
+    detections = sv.Detections.from_ultralytics(result)
+
+    annotated_image = annotator.annotate(image, detections)
+    ```
+
+    !!! tip
+
+        In `sv.Detections`, detection data is categorized into two main field types:
+        fixed and custom. The fixed fields include `xyxy`, `mask`, `confidence`,
+        `class_id`, and `tracker_id`. For any additional data requirements, custom
+        fields come into play, stored in the data field. These custom fields are easily
+        accessible using the `detections[<FIELD_NAME>]` syntax, providing flexibility
+        for diverse data handling needs.

    Attributes:
        xyxy (np.ndarray): An array of shape `(n, 4)` containing
@@ -179,24 +208,33 @@ class Detections:
        """  # noqa: E501 // docs

        if ultralytics_results.obb is not None:
+            class_id = ultralytics_results.obb.cls.cpu().numpy().astype(int)
+            class_names = np.array([ultralytics_results.names[i] for i in class_id])
+            oriented_box_coordinates = ultralytics_results.obb.xyxyxyxy.cpu().numpy()
            return cls(
                xyxy=ultralytics_results.obb.xyxy.cpu().numpy(),
-                data={"xyxyxyxy": ultralytics_results.obb.xyxyxyxy.cpu().numpy()},
                confidence=ultralytics_results.obb.conf.cpu().numpy(),
-                class_id=ultralytics_results.obb.cls.cpu().numpy().astype(int),
+                class_id=class_id,
                tracker_id=ultralytics_results.obb.id.int().cpu().numpy()
                if ultralytics_results.obb.id is not None
                else None,
+                data={
+                    ORIENTED_BOX_COORDINATES: oriented_box_coordinates,
+                    CLASS_NAME_DATA_FIELD: class_names,
+                },
            )

+        class_id = ultralytics_results.boxes.cls.cpu().numpy().astype(int)
+        class_names = np.array([ultralytics_results.names[i] for i in class_id])
        return cls(
            xyxy=ultralytics_results.boxes.xyxy.cpu().numpy(),
            confidence=ultralytics_results.boxes.conf.cpu().numpy(),
-            class_id=ultralytics_results.boxes.cls.cpu().numpy().astype(int),
+            class_id=class_id,
            mask=extract_ultralytics_masks(ultralytics_results),
            tracker_id=ultralytics_results.boxes.id.int().cpu().numpy()
            if ultralytics_results.boxes.id is not None
            else None,
+            data={CLASS_NAME_DATA_FIELD: class_names},
        )

    @classmethod
@@ -463,10 +501,15 @@ class Detections:
    @classmethod
    @deprecated(
        "`Detections.from_roboflow` is deprecated and will be removed in "
-        "`supervision-0.21.0`. Use `Detections.from_inference` instead."
+        "`supervision-0.22.0`. Use `Detections.from_inference` instead."
    )
    def from_roboflow(cls, roboflow_result: Union[dict, Any]) -> Detections:
        """
+        !!! failure "Deprecated"
+
+            `Detections.from_roboflow` is deprecated and will be removed in
+            `supervision-0.22.0`. Use `Detections.from_inference` instead.
+
        Create a Detections object from the [Roboflow](https://roboflow.com/)
            API inference result or the [Inference](https://inference.roboflow.com/)
            package results.
@@ -959,7 +1002,8 @@ class Detections:
        self, threshold: float = 0.5, class_agnostic: bool = False
    ) -> Detections:
        """
-        Perform non-maximum suppression on the current set of object detections.
+        Performs non-max suppression on detection set. If the detections result
+        from a segmentation model, the IoU mask is applied. Otherwise, box IoU is used.

        Args:
            threshold (float, optional): The intersection-over-union threshold
@@ -986,18 +1030,26 @@ class Detections:

        if class_agnostic:
            predictions = np.hstack((self.xyxy, self.confidence.reshape(-1, 1)))
-            indices = non_max_suppression(
+        else:
+            assert self.class_id is not None, (
+                "Detections class_id must be given for NMS to be executed. If you"
+                " intended to perform class agnostic NMS set class_agnostic=True."
+            )
+            predictions = np.hstack(
+                (
+                    self.xyxy,
+                    self.confidence.reshape(-1, 1),
+                    self.class_id.reshape(-1, 1),
+                )
+            )
+
+        if self.mask is not None:
+            indices = mask_non_max_suppression(
+                predictions=predictions, masks=self.mask, iou_threshold=threshold
+            )
+        else:
+            indices = box_non_max_suppression(
                predictions=predictions, iou_threshold=threshold
            )
-            return self[indices]

-        assert self.class_id is not None, (
-            "Detections class_id must be given for NMS to be executed. If you intended"
-            " to perform class agnostic NMS set class_agnostic=True."
-        )
-
-        predictions = np.hstack(
-            (self.xyxy, self.confidence.reshape(-1, 1), self.class_id.reshape(-1, 1))
-        )
-        indices = non_max_suppression(predictions=predictions, iou_threshold=threshold)
        return self[indices]
@@ -5,7 +5,8 @@ import numpy as np

 from supervision.detection.core import Detections
 from supervision.draw.color import Color
-from supervision.geometry.core import Point, Position, Rect, Vector
+from supervision.draw.utils import draw_text
+from supervision.geometry.core import Point, Position, Vector


 class LineZone:
@@ -16,7 +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:
@@ -136,7 +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:
@@ -174,6 +175,8 @@ class LineZoneAnnotator:
        text_padding: int = 10,
        custom_in_text: Optional[str] = None,
        custom_out_text: Optional[str] = None,
+        display_in_count: bool = True,
+        display_out_count: bool = True,
    ):
        """
        Initialize the LineCounterAnnotator object with default values.
@@ -186,6 +189,8 @@ class LineZoneAnnotator:
            text_scale (float): The scale of the text that will be drawn.
            text_offset (float): The offset of the text that will be drawn.
            text_padding (int): The padding of the text that will be drawn.
+            display_in_count (bool): Whether to display the in count or not.
+            display_out_count (bool): Whether to display the out count or not.

        """
        self.thickness: float = thickness
@@ -197,6 +202,43 @@ class LineZoneAnnotator:
        self.text_padding: int = text_padding
        self.custom_in_text: str = custom_in_text
        self.custom_out_text: str = custom_out_text
+        self.display_in_count: bool = display_in_count
+        self.display_out_count: bool = display_out_count
+
+    def _annotate_count(
+        self,
+        frame: np.ndarray,
+        center_text_anchor: Point,
+        text: str,
+        is_in_count: bool,
+    ) -> None:
+        """This method is drawing the text on the frame.
+
+        Args:
+            frame (np.ndarray): The image on which the text will be drawn.
+            center_text_anchor: The center point that the text will be drawn.
+            text (str): The text that will be drawn.
+            is_in_count (bool): Whether to display the in count or out count.
+        """
+        _, text_height = cv2.getTextSize(
+            text, cv2.FONT_HERSHEY_SIMPLEX, self.text_scale, self.text_thickness
+        )[0]
+
+        if is_in_count:
+            center_text_anchor.y -= int(self.text_offset * text_height)
+        else:
+            center_text_anchor.y += int(self.text_offset * text_height)
+
+        draw_text(
+            scene=frame,
+            text=text,
+            text_anchor=center_text_anchor,
+            text_color=self.text_color,
+            text_scale=self.text_scale,
+            text_thickness=self.text_thickness,
+            text_padding=self.text_padding,
+            background_color=self.color,
+        )

    def annotate(self, frame: np.ndarray, line_counter: LineZone) -> np.ndarray:
        """
@@ -237,90 +279,33 @@ class LineZoneAnnotator:
            lineType=cv2.LINE_AA,
        )

-        in_text = (
-            f"{self.custom_in_text}: {line_counter.in_count}"
-            if self.custom_in_text is not None
-            else f"in: {line_counter.in_count}"
-        )
-        out_text = (
-            f"{self.custom_out_text}: {line_counter.out_count}"
-            if self.custom_out_text is not None
-            else f"out: {line_counter.out_count}"
+        text_anchor = Vector(
+            start=line_counter.vector.start, end=line_counter.vector.end
        )

-        (in_text_width, in_text_height), _ = cv2.getTextSize(
-            in_text, cv2.FONT_HERSHEY_SIMPLEX, self.text_scale, self.text_thickness
-        )
-        (out_text_width, out_text_height), _ = cv2.getTextSize(
-            out_text, cv2.FONT_HERSHEY_SIMPLEX, self.text_scale, self.text_thickness
-        )
+        if self.display_in_count:
+            in_text = (
+                f"{self.custom_in_text}: {line_counter.in_count}"
+                if self.custom_in_text is not None
+                else f"in: {line_counter.in_count}"
+            )
+            self._annotate_count(
+                frame=frame,
+                center_text_anchor=text_anchor.center,
+                text=in_text,
+                is_in_count=True,
+            )

-        in_text_x = int(
-            (line_counter.vector.end.x + line_counter.vector.start.x - in_text_width)
-            / 2
-        )
-        in_text_y = int(
-            (line_counter.vector.end.y + line_counter.vector.start.y + in_text_height)
-            / 2
-            - self.text_offset * in_text_height
-        )
-
-        out_text_x = int(
-            (line_counter.vector.end.x + line_counter.vector.start.x - out_text_width)
-            / 2
-        )
-        out_text_y = int(
-            (line_counter.vector.end.y + line_counter.vector.start.y + out_text_height)
-            / 2
-            + self.text_offset * out_text_height
-        )
-
-        in_text_background_rect = Rect(
-            x=in_text_x,
-            y=in_text_y - in_text_height,
-            width=in_text_width,
-            height=in_text_height,
-        ).pad(padding=self.text_padding)
-        out_text_background_rect = Rect(
-            x=out_text_x,
-            y=out_text_y - out_text_height,
-            width=out_text_width,
-            height=out_text_height,
-        ).pad(padding=self.text_padding)
-
-        cv2.rectangle(
-            frame,
-            in_text_background_rect.top_left.as_xy_int_tuple(),
-            in_text_background_rect.bottom_right.as_xy_int_tuple(),
-            self.color.as_bgr(),
-            -1,
-        )
-        cv2.rectangle(
-            frame,
-            out_text_background_rect.top_left.as_xy_int_tuple(),
-            out_text_background_rect.bottom_right.as_xy_int_tuple(),
-            self.color.as_bgr(),
-            -1,
-        )
-
-        cv2.putText(
-            frame,
-            in_text,
-            (in_text_x, in_text_y),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            self.text_scale,
-            self.text_color.as_bgr(),
-            self.text_thickness,
-            cv2.LINE_AA,
-        )
-        cv2.putText(
-            frame,
-            out_text,
-            (out_text_x, out_text_y),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            self.text_scale,
-            self.text_color.as_bgr(),
-            self.text_thickness,
-            cv2.LINE_AA,
-        )
+        if self.display_out_count:
+            out_text = (
+                f"{self.custom_out_text}: {line_counter.out_count}"
+                if self.custom_out_text is not None
+                else f"out: {line_counter.out_count}"
+            )
+            self._annotate_count(
+                frame=frame,
+                center_text_anchor=text_anchor.center,
+                text=out_text,
+                is_in_count=False,
+            )
        return frame
@@ -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

@@ -59,7 +59,119 @@ def box_iou_batch(boxes_true: np.ndarray, boxes_detection: np.ndarray) -> np.nda
    return area_inter / (area_true[:, None] + area_detection - area_inter)


-def non_max_suppression(
+def mask_iou_batch(masks_true: np.ndarray, masks_detection: np.ndarray) -> np.ndarray:
+    """
+    Compute Intersection over Union (IoU) of two sets of masks -
+        `masks_true` and `masks_detection`.
+
+    Args:
+        masks_true (np.ndarray): 3D `np.ndarray` representing ground-truth masks.
+        masks_detection (np.ndarray): 3D `np.ndarray` representing detection masks.
+
+    Returns:
+        np.ndarray: Pairwise IoU of masks from `masks_true` and `masks_detection`.
+    """
+    intersection_area = np.logical_and(masks_true[:, None], masks_detection).sum(
+        axis=(2, 3)
+    )
+    masks_true_area = masks_true.sum(axis=(1, 2))
+    masks_detection_area = masks_detection.sum(axis=(1, 2))
+
+    union_area = masks_true_area[:, None] + masks_detection_area - intersection_area
+
+    return np.divide(
+        intersection_area,
+        union_area,
+        out=np.zeros_like(intersection_area, dtype=float),
+        where=union_area != 0,
+    )
+
+
+def resize_masks(masks: np.ndarray, max_dimension: int = 640) -> np.ndarray:
+    """
+    Resize all masks in the array to have a maximum dimension of max_dimension,
+    maintaining aspect ratio.
+
+    Args:
+        masks (np.ndarray): 3D array of binary masks with shape (N, H, W).
+        max_dimension (int): The maximum dimension for the resized masks.
+
+    Returns:
+        np.ndarray: Array of resized masks.
+    """
+    max_height = np.max(masks.shape[1])
+    max_width = np.max(masks.shape[2])
+    scale = min(max_dimension / max_height, max_dimension / max_width)
+
+    new_height = int(scale * max_height)
+    new_width = int(scale * max_width)
+
+    x = np.linspace(0, max_width - 1, new_width).astype(int)
+    y = np.linspace(0, max_height - 1, new_height).astype(int)
+    xv, yv = np.meshgrid(x, y)
+
+    resized_masks = masks[:, yv, xv]
+
+    resized_masks = resized_masks.reshape(masks.shape[0], new_height, new_width)
+    return resized_masks
+
+
+def mask_non_max_suppression(
+    predictions: np.ndarray,
+    masks: np.ndarray,
+    iou_threshold: float = 0.5,
+    mask_dimension: int = 640,
+) -> np.ndarray:
+    """
+    Perform Non-Maximum Suppression (NMS) on segmentation predictions.
+
+    Args:
+        predictions (np.ndarray): A 2D array of object detection predictions in
+            the format of `(x_min, y_min, x_max, y_max, score)`
+            or `(x_min, y_min, x_max, y_max, score, class)`. Shape: `(N, 5)` or
+            `(N, 6)`, where N is the number of predictions.
+        masks (np.ndarray): A 3D array of binary masks corresponding to the predictions.
+            Shape: `(N, H, W)`, where N is the number of predictions, and H, W are the
+            dimensions of each mask.
+        iou_threshold (float, optional): The intersection-over-union threshold
+            to use for non-maximum suppression.
+        mask_dimension (int, optional): The dimension to which the masks should be
+            resized before computing IOU values. Defaults to 640.
+
+    Returns:
+        np.ndarray: A boolean array indicating which predictions to keep after
+            non-maximum suppression.
+
+    Raises:
+        AssertionError: If `iou_threshold` is not within the closed
+        range from `0` to `1`.
+    """
+    assert 0 <= iou_threshold <= 1, (
+        "Value of `iou_threshold` must be in the closed range from 0 to 1, "
+        f"{iou_threshold} given."
+    )
+    rows, columns = predictions.shape
+
+    if columns == 5:
+        predictions = np.c_[predictions, np.zeros(rows)]
+
+    sort_index = predictions[:, 4].argsort()[::-1]
+    predictions = predictions[sort_index]
+    masks = masks[sort_index]
+    masks_resized = resize_masks(masks, mask_dimension)
+    ious = mask_iou_batch(masks_resized, masks_resized)
+    categories = predictions[:, 5]
+
+    keep = np.ones(rows, dtype=bool)
+    for i in range(rows):
+        if keep[i]:
+            condition = (ious[i] > iou_threshold) & (categories[i] == categories)
+            keep[i + 1 :] = np.where(condition[i + 1 :], False, keep[i + 1 :])
+
+    return keep[sort_index.argsort()]
+
+
+def box_non_max_suppression(
    predictions: np.ndarray, iou_threshold: float = 0.5
 ) -> np.ndarray:
    """
@@ -206,7 +206,7 @@ class Color:
    @classmethod
    @deprecated(
        "`Color.white()` is deprecated and will be removed in "
-        "`supervision-0.20.0`. Use `Color.WHITE` instead."
+        "`supervision-0.22.0`. Use `Color.WHITE` instead."
    )
    def white(cls) -> Color:
        return Color.from_hex(color_hex="#ffffff")
@@ -214,7 +214,7 @@ class Color:
    @classmethod
    @deprecated(
        "`Color.black()` is deprecated and will be removed in "
-        "`supervision-0.20.0`. Use `Color.BLACK` instead."
+        "`supervision-0.22.0`. Use `Color.BLACK` instead."
    )
    def black(cls) -> Color:
        return Color.from_hex(color_hex="#000000")
@@ -222,7 +222,7 @@ class Color:
    @classmethod
    @deprecated(
        "`Color.red()` is deprecated and will be removed in "
-        "`supervision-0.20.0`. Use `Color.RED` instead."
+        "`supervision-0.22.0`. Use `Color.RED` instead."
    )
    def red(cls) -> Color:
        return Color.from_hex(color_hex="#ff0000")
@@ -230,7 +230,7 @@ class Color:
    @classmethod
    @deprecated(
        "`Color.green()` is deprecated and will be removed in "
-        "`supervision-0.20.0`. Use `Color.GREEN` instead."
+        "`supervision-0.22.0`. Use `Color.GREEN` instead."
    )
    def green(cls) -> Color:
        return Color.from_hex(color_hex="#00ff00")
@@ -238,7 +238,7 @@ class Color:
    @classmethod
    @deprecated(
        "`Color.blue()` is deprecated and will be removed in "
-        "`supervision-0.20.0`. Use `Color.BLUE` instead."
+        "`supervision-0.22.0`. Use `Color.BLUE` instead."
    )
    def blue(cls) -> Color:
        return Color.from_hex(color_hex="#0000ff")
@@ -297,10 +297,15 @@ class ColorPalette:
    @classmethod
    @deprecated(
        "`ColorPalette.default()` is deprecated and will be removed in "
-        "`supervision-0.20.0`. Use `Color.DEFAULT` instead."
+        "`supervision-0.22.0`. Use `Color.DEFAULT` instead."
    )
    def default(cls) -> ColorPalette:
        """
+        !!! failure "Deprecated"
+
+            `ColorPalette.default()` is deprecated and will be removed in
+            `supervision-0.22.0`. Use `Color.DEFAULT` instead.
+
        Returns a default color palette.

        Returns:
@@ -148,9 +148,12 @@ def draw_text(
        fontScale=text_scale,
        thickness=text_thickness,
    )[0]
+
+    text_anchor_x, text_anchor_y = text_anchor.as_xy_int_tuple()
+
    text_rect = Rect(
-        x=text_anchor.x - text_width // 2,
-        y=text_anchor.y - text_height // 2,
+        x=text_anchor_x - text_width // 2,
+        y=text_anchor_y - text_height // 2,
        width=text_width,
        height=text_height,
    ).pad(text_padding)
@@ -163,7 +166,7 @@ def draw_text(
    cv2.putText(
        img=scene,
        text=text,
-        org=(text_anchor.x - text_width // 2, text_anchor.y + text_height // 2),
+        org=(text_anchor_x - text_width // 2, text_anchor_y + text_height // 2),
        fontFace=text_font,
        fontScale=text_scale,
        color=text_color.as_bgr(),
@@ -56,6 +56,19 @@ class Vector:
        dy = self.end.y - self.start.y
        return sqrt(dx**2 + dy**2)

+    @property
+    def center(self) -> Point:
+        """
+        Calculate the center point of the vector.
+
+        Returns:
+            Point: The center point of the vector.
+        """
+        return Point(
+            x=(self.start.x + self.end.x) / 2,
+            y=(self.start.y + self.end.y) / 2,
+        )
+
    def cross_product(self, point: Point) -> float:
        """
        Calculate the 2D cross product (also known as the vector product or outer
@@ -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

@@ -220,7 +220,7 @@ class ByteTrack:
            def callback(frame: np.ndarray, index: int) -> np.ndarray:
                results = model(frame)[0]
                detections = sv.Detections.from_ultralytics(results)
-                detections = byte_tracker.update_with_detections(detections)
+                detections = tracker.update_with_detections(detections)

                labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]

@@ -260,6 +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.
@@ -305,6 +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)
@@ -26,7 +26,7 @@ def crop_image(image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:
        with sv.ImageSink(target_dir_path='target/directory/path') as sink:
            for xyxy in detection.xyxy:
                cropped_image = sv.crop_image(image=image, xyxy=xyxy)
-                sink.save_image(image=image)
+                sink.save_image(image=cropped_image)
        ```
    """

@@ -8,6 +8,8 @@ from typing import Callable, Generator, Optional, Tuple
 import cv2
 import numpy as np

+from supervision.utils.internal import deprecated
+

@dataclass
 class VideoInfo:
@@ -219,25 +221,43 @@ class FPSMonitor:
            ```python
            import supervision as sv

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

            for frame in frames_generator:
                # your processing code here
                fps_monitor.tick()
-                fps = fps_monitor()
+                fps = fps_monitor.fps
            ```
-        """
+        """  # noqa: E501 // docs
        self.all_timestamps = deque(maxlen=sample_size)

+    @deprecated(
+        "`FPSMonitor.__call__` is deprecated and will be removed in "
+        "`supervision-0.22.0`. Use `FPSMonitor.fps` instead."
+    )
    def __call__(self) -> float:
+        """
+        !!! failure "Deprecated"
+
+            `FPSMonitor.__call__` is deprecated and will be removed in
+            `supervision-0.22.0`. Use `FPSMonitor.fps` instead.
+
+        Computes and returns the average FPS based on the stored time stamps.
+
+        Returns:
+            float: The average FPS. Returns 0.0 if no time stamps are stored.
+        """
+        return self.fps
+
+    @property
+    def fps(self) -> float:
        """
        Computes and returns the average FPS based on the stored time stamps.

        Returns:
            float: The average FPS. Returns 0.0 if no time stamps are stored.
        """
-
        if not self.all_timestamps:
            return 0.0
        taken_time = self.all_timestamps[-1] - self.all_timestamps[0]
@@ -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)
@@ -6,13 +6,14 @@ import pytest

 from supervision.config import CLASS_NAME_DATA_FIELD
 from supervision.detection.utils import (
+    box_non_max_suppression,
    calculate_masks_centroids,
    clip_boxes,
    filter_polygons_by_area,
    get_data_item,
+    mask_non_max_suppression,
    merge_data,
    move_boxes,
-    non_max_suppression,
    process_roboflow_result,
    scale_boxes,
 )
@@ -113,19 +114,225 @@ TEST_MASK[:, 300:351, 200:251] = True
        ),  # three boxes with different category
    ],
 )
-def test_non_max_suppression(
+def test_box_non_max_suppression(
    predictions: np.ndarray,
    iou_threshold: float,
    expected_result: Optional[np.ndarray],
    exception: Exception,
 ) -> None:
    with exception:
-        result = non_max_suppression(
+        result = box_non_max_suppression(
            predictions=predictions, iou_threshold=iou_threshold
        )
        assert np.array_equal(result, expected_result)


+@pytest.mark.parametrize(
+    "predictions, masks, iou_threshold, expected_result, exception",
+    [
+        (
+            np.empty((0, 6)),
+            np.empty((0, 5, 5)),
+            0.5,
+            np.array([]),
+            DoesNotRaise(),
+        ),  # empty predictions and masks
+        (
+            np.array([[0, 0, 0, 0, 0.8]]),
+            np.array(
+                [
+                    [
+                        [False, False, False, False, False],
+                        [False, True, True, True, False],
+                        [False, True, True, True, False],
+                        [False, True, True, True, False],
+                        [False, False, False, False, False],
+                    ]
+                ]
+            ),
+            0.5,
+            np.array([True]),
+            DoesNotRaise(),
+        ),  # single mask with no category
+        (
+            np.array([[0, 0, 0, 0, 0.8, 0]]),
+            np.array(
+                [
+                    [
+                        [False, False, False, False, False],
+                        [False, True, True, True, False],
+                        [False, True, True, True, False],
+                        [False, True, True, True, False],
+                        [False, False, False, False, False],
+                    ]
+                ]
+            ),
+            0.5,
+            np.array([True]),
+            DoesNotRaise(),
+        ),  # single mask with category
+        (
+            np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
+            np.array(
+                [
+                    [
+                        [False, False, False, False, False],
+                        [False, True, True, False, False],
+                        [False, True, True, False, False],
+                        [False, False, False, False, False],
+                        [False, False, False, False, False],
+                    ],
+                    [
+                        [False, False, False, False, False],
+                        [False, False, False, False, False],
+                        [False, False, False, True, True],
+                        [False, False, False, True, True],
+                        [False, False, False, False, False],
+                    ],
+                ]
+            ),
+            0.5,
+            np.array([True, True]),
+            DoesNotRaise(),
+        ),  # two masks non-overlapping with no category
+        (
+            np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
+            np.array(
+                [
+                    [
+                        [False, False, False, False, False],
+                        [False, True, True, True, False],
+                        [False, True, True, True, False],
+                        [False, True, True, True, False],
+                        [False, False, False, False, False],
+                    ],
+                    [
+                        [False, False, False, False, False],
+                        [False, False, True, True, True],
+                        [False, False, True, True, True],
+                        [False, False, True, True, True],
+                        [False, False, False, False, False],
+                    ],
+                ]
+            ),
+            0.4,
+            np.array([False, True]),
+            DoesNotRaise(),
+        ),  # two masks partially overlapping with no category
+        (
+            np.array([[0, 0, 0, 0, 0.8, 0], [0, 0, 0, 0, 0.9, 1]]),
+            np.array(
+                [
+                    [
+                        [False, False, False, False, False],
+                        [False, True, True, True, False],
+                        [False, True, True, True, False],
+                        [False, True, True, True, False],
+                        [False, False, False, False, False],
+                    ],
+                    [
+                        [False, False, False, False, False],
+                        [False, False, True, True, True],
+                        [False, False, True, True, True],
+                        [False, False, True, True, True],
+                        [False, False, False, False, False],
+                    ],
+                ]
+            ),
+            0.5,
+            np.array([True, True]),
+            DoesNotRaise(),
+        ),  # two masks partially overlapping with different category
+        (
+            np.array(
+                [
+                    [0, 0, 0, 0, 0.8],
+                    [0, 0, 0, 0, 0.85],
+                    [0, 0, 0, 0, 0.9],
+                ]
+            ),
+            np.array(
+                [
+                    [
+                        [False, False, False, False, False],
+                        [False, True, True, False, False],
+                        [False, True, True, False, False],
+                        [False, False, False, False, False],
+                        [False, False, False, False, False],
+                    ],
+                    [
+                        [False, False, False, False, False],
+                        [False, True, True, False, False],
+                        [False, True, True, False, False],
+                        [False, False, False, False, False],
+                        [False, False, False, False, False],
+                    ],
+                    [
+                        [False, False, False, False, False],
+                        [False, False, False, True, True],
+                        [False, False, False, True, True],
+                        [False, False, False, False, False],
+                        [False, False, False, False, False],
+                    ],
+                ]
+            ),
+            0.5,
+            np.array([False, True, True]),
+            DoesNotRaise(),
+        ),  # three masks with no category
+        (
+            np.array(
+                [
+                    [0, 0, 0, 0, 0.8, 0],
+                    [0, 0, 0, 0, 0.85, 1],
+                    [0, 0, 0, 0, 0.9, 2],
+                ]
+            ),
+            np.array(
+                [
+                    [
+                        [False, False, False, False, False],
+                        [False, True, True, False, False],
+                        [False, True, True, False, False],
+                        [False, False, False, False, False],
+                        [False, False, False, False, False],
+                    ],
+                    [
+                        [False, False, False, False, False],
+                        [False, True, True, False, False],
+                        [False, True, True, False, False],
+                        [False, True, True, False, False],
+                        [False, False, False, False, False],
+                    ],
+                    [
+                        [False, False, False, False, False],
+                        [False, True, True, False, False],
+                        [False, True, True, False, False],
+                        [False, False, False, False, False],
+                        [False, False, False, False, False],
+                    ],
+                ]
+            ),
+            0.5,
+            np.array([True, True, True]),
+            DoesNotRaise(),
+        ),  # three masks with different category
+    ],
+)
+def test_mask_non_max_suppression(
+    predictions: np.ndarray,
+    masks: np.ndarray,
+    iou_threshold: float,
+    expected_result: Optional[np.ndarray],
+    exception: Exception,
+) -> None:
+    with exception:
+        result = mask_non_max_suppression(
+            predictions=predictions, masks=masks, iou_threshold=iou_threshold
+        )
+        assert np.array_equal(result, expected_result)
+
+
@pytest.mark.parametrize(
    "xyxy, resolution_wh, expected_result",
    [