bump version from 0.21.0rc1 to 0.21.0rc2

pin inference version in examples

.github/workflows/docs.yml

@@ -1,26 +0,0 @@
-name: Docs WorkFlow
-
-on:
-  push:
-    branches:
-      - master
-      - main
-      - develop
-
-permissions:
-  contents: write
-  pages: write
-  pull-requests: write
-
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
-        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

.github/workflows/publish-dev-docs.yml

@@ -0,0 +1,53 @@
+name: Docs WorkFlow - Develop Tag 📚
+
+on:
+  push:
+    branches:
+      - develop
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event_name == 'push' && github.ref}}
+  cancel-in-progress: true
+
+permissions:
+  contents: write
+  pages: write
+  pull-requests: write
+
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - name: 🔄 Checkout code
+        uses: actions/checkout@v4
+        with:
+          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"
+      - 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

.github/workflows/publish-release-docs.yml

@@ -0,0 +1,55 @@
+name: Supervision Release Documentation Workflow 📚
+on:
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event_name == 'push' && github.ref}}
+  cancel-in-progress: true
+
+permissions:
+  contents: write
+  pages: write
+  pull-requests: write
+
+
+jobs:
+  doc-build-deploy:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+    steps:
+      - name: 🛎️ Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.head_ref }}
+
+      - name: 🐍 Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+      - name: 📦 Install mkdocs-material
+        run: pip install "mkdocs-material"
+      - name: 📦 Install mkdocstrings[python]
+        run: pip install "mkdocstrings[python]"
+      - name: 📦 Install mkdocs-material[imaging]
+        run: pip install "mkdocs-material[imaging]"
+      - name: 📦 Install mike
+        run: pip install "mike"
+      - name: 📦 Install mkdocs-git-revision-date-localized-plugin
+        run: pip install "mkdocs-git-revision-date-localized-plugin"
+      - name: 📦 Install JupyterLab
+        run: pip install jupyterlab
+      - name: 📦 Install mkdocs-jupyter
+        run: pip install mkdocs-jupyter
+      - name: 📦 Install mkdocs-git-committers-plugin-2
+        run: pip install mkdocs-git-committers-plugin-2
+      - name: ⚙️ Configure git for github-actions 👷
+        run: |
+          git config --global user.name "github-actions[bot]"
+          git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com"
+      - name: 🚀 Deploy MkDoc-Material 📚
+        run: |
+            latest_tag=$(git describe --tags `git rev-list --tags --max-count=1`)
+            MKDOCS_GIT_COMMITTERS_APIKEY=${{ secrets.GITHUB_TOKEN }} mike deploy --push --update-aliases $latest_tag latest

.github/workflows/publish-test.yml

@@ -6,22 +6,24 @@ on:
       - '[0-9]+.[0-9]+[0-9]+.[0-9]+b[0-9]'
       - '[0-9]+.[0-9]+[0-9]+.[0-9]+rc[0-9]'
 
-  # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:
 
 jobs:
   build-n-publish:
     name: Build and publish to PyPI
     runs-on: ubuntu-latest
-
+    strategy:
+      matrix:
+        python-version: ["3.10"]
     steps:
-      - name: Checkout source
+      - name: 🛎️ Checkout
         uses: actions/checkout@v4
-
-      - name: 🐍 Set up Python 3.8 environment for build
+        with:
+          ref: ${{ github.head_ref }}
+      - name: 🐍 Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
-          python-version: "3.8"
+          python-version: ${{ matrix.python-version }}
 
       - name: 🏗️ Build source and wheel distributions
         run: |

.github/workflows/publish.yml

@@ -3,10 +3,7 @@ on:
   push:
     tags:
       - '[0-9]+.[0-9]+[0-9]+.[0-9]'
-      - '[0-9]+.[0-9]+[0-9]+.[0-9]'
-      - '[0-9]+.[0-9]+[0-9]+.[0-9]'
 
-  # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:
 
 jobs:
@@ -14,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8]
+        python-version: ["3.10"]
     steps:
       - name: 🛎️ Checkout
         uses: actions/checkout@v4

.github/workflows/test-doc.yml

@@ -0,0 +1,34 @@
+name: 🧪 Docs Test WorkFlow 📚
+
+on:
+  pull_request:
+    branches: [main, develop]
+
+jobs:
+  docs-build-test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: 🔄 Checkout code
+        uses: actions/checkout@v4
+      - 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: 🧪 Test documentation build
+        run: mkdocs build --verbose

.github/workflows/test-min.yml

@@ -37,6 +37,7 @@ jobs:
               matplotlib==3.5.0 \
               numpy==1.21.2 \
               opencv-python==4.5.5.64 \
+              Pillow==10.1.0 \
               packaging==23.2 \
               pluggy==1.3.0 \
               pyparsing==3.1.1 \

.gitignore

@@ -133,9 +133,6 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
-# Notebooks
-notebooks/
-
 # OSX folder attributes
 .DS_Store
 .AppleDouble

.pre-commit-config.yaml

@@ -7,14 +7,13 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
         exclude: test/.*\.py
       - id: check-yaml
         exclude: mkdocs.yml
-      - id: check-docstring-first
       - id: check-executables-have-shebangs
       - id: check-toml
       - id: check-case-conflict
@@ -28,9 +27,8 @@ repos:
       - id: mixed-line-ending
 
 
-
   -   repo: https://github.com/PyCQA/bandit
-      rev: '1.7.6'
+      rev: '1.7.8'
       hooks:
       -   id: bandit
           args: ["-c", "pyproject.toml"]
@@ -47,7 +45,7 @@ repos:
 
 
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.1.14
+      rev: v0.4.1
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]

CITATION.cff

@@ -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

CONTRIBUTING.md

@@ -112,6 +112,20 @@ To run the documentation, install the project requirements with `poetry install
 
 You can learn more about mkdocs on the [mkdocs website](https://www.mkdocs.org/).
 
+## 🧑‍🍳 cookbooks
+
+We are always looking for new examples and cookbooks to add to the `supervision`
+documentation. If you have a use case that you think would be helpful to others, please
+submit a PR with your example. Here are some guidelines for submitting a new example:
+
+- Create a new notebook in the [`docs/nodebooks`](https://github.com/roboflow/supervision/tree/develop/docs/notebooks) folder.
+- Add a link to the new notebook in [`docs/theme/cookbooks.html`](https://github.com/roboflow/supervision/blob/develop/docs/theme/cookbooks.html). Make sure to add the path to the new notebook, as well as a title, labels, author and supervision version.
+- Use the [Count Objects Crossing the Line](https://supervision.roboflow.com/develop/notebooks/count-objects-crossing-the-line/) example as a template for your new example.
+- Freeze the version of `supervision` you are using.
+- Place an appropriate Open in Colab button at the top of the notebook. You can find an example of such a button in the aforementioned `Count Objects Crossing the Line` cookbook.
+- Notebook should be self-contained. If you rely on external data ( videos, images, etc.) or libraries, include download and installation commands in the notebook.
+- Annotate the code with appropriate comments, including links to the documentation describing each of the tools you have used.
+
 ## 🧪 tests
 
 [`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests.

README.md

@@ -1,6 +1,6 @@
 <div align="center">
   <p>
-    <a align="center" href="" target="_blank">
+    <a align="center" href="" target="https://supervision.roboflow.com">
       <img
         width="100%"
         src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529"
@@ -10,7 +10,7 @@
 
   <br>
 
-[notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [collect](https://github.com/roboflow/roboflow-collect)
+[notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [maestro](https://github.com/roboflow/multimodal-maestro)
 
   <br>
 
@@ -18,16 +18,18 @@
 [![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)
 [![license](https://img.shields.io/pypi/l/supervision)](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
 [![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision)
-[![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)
-
+[![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
@@ -37,13 +39,13 @@ Pip install the supervision package in a
 pip install supervision
 ```
 
-Read more about desktop, headless, and local installation in our [guide](https://roboflow.github.io/supervision/).
+Read more about conda, mamba, and installing from source in our [guide](https://roboflow.github.io/supervision/).
 
 ## 🔥 quickstart
 
 ### 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
@@ -69,23 +71,22 @@ len(detections)
     ```python
     import cv2
     import supervision as sv
-    from inference.models.utils import get_roboflow_model
+    from inference import get_model
 
     image = cv2.imread(...)
-    model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
+    model = get_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
     result = model.infer(image)[0]
     detections = sv.Detections.from_inference(result)
 
     len(detections)
-    # 5
-
+    # 5
     ```
 
 </details>
 
 ### 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 +106,7 @@ 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
@@ -215,19 +216,23 @@ len(dataset)
 
 ## 🎬 tutorials
 
-<p align="left">
-<a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><img src="https://github.com/SkalskiP/SkalskiP/assets/26109316/61a444c8-b135-48ce-b979-2a5ab47c5a91" alt="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source" width="300px" align="left" /></a>
-<a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><strong>Speed Estimation & Vehicle Tracking | Computer Vision | Open Source</strong></a>
-<div><strong>Created: 11 Jan 2024</strong> | <strong>Updated: 11 Jan 2024</strong></div>
-<br/> Learn how to track and estimate the speed of vehicles using YOLO, ByteTrack, and Roboflow Inference. This comprehensive tutorial covers object detection, multi-object tracking, filtering detections, perspective transformation, speed estimation, visualization improvements, and more.</p>
+Want to learn how to use Supervision? Explore our [how-to guides](https://supervision.roboflow.com/develop/how_to/detect_and_annotate/), [end-to-end examples](https://github.com/roboflow/supervision/tree/develop/examples), and [cookbooks](https://supervision.roboflow.com/develop/cookbooks/)!
 
 <br/>
 
 <p align="left">
-<a href="https://youtu.be/4Q3ut7vqD5o" title="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking"><img src="https://github.com/roboflow/supervision/assets/26109316/54afdf1c-218c-4451-8f12-627fb85f1682" alt="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking" width="300px" align="left" /></a>
-<a href="https://youtu.be/4Q3ut7vqD5o" title="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking"><strong>Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking</strong></a>
-<div><strong>Created: 6 Sep 2023</strong> | <strong>Updated: 6 Sep 2023</strong></div>
-<br/> In this video, we explore real-time traffic analysis using YOLOv8 and ByteTrack to detect and track vehicles on aerial images. Harnessing the power of Python and Supervision, we delve deep into assigning cars to specific entry zones and understanding their direction of movement. By visualizing their paths, we gain insights into traffic flow across bustling roundabouts... </p>
+<a href="https://youtu.be/hAWpsIuem10" title="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing"><img src="https://github.com/SkalskiP/SkalskiP/assets/26109316/a742823d-c158-407d-b30f-063a5d11b4e1" alt="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing" width="300px" align="left" /></a>
+<a href="https://youtu.be/hAWpsIuem10" title="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing"><strong>Dwell Time Analysis with Computer Vision | Real-Time Stream Processing</strong></a>
+<div><strong>Created: 5 Apr 2024</strong></div>
+<br/>Learn how to use computer vision to analyze wait times and optimize processes. This tutorial covers object detection, tracking, and calculating time spent in designated zones. Use these techniques to improve customer experience in retail, traffic management, or other scenarios.</p>
+
+<br/>
+
+<p align="left">
+<a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><img src="https://github.com/SkalskiP/SkalskiP/assets/26109316/61a444c8-b135-48ce-b979-2a5ab47c5a91" alt="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source" width="300px" align="left" /></a>
+<a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><strong>Speed Estimation & Vehicle Tracking | Computer Vision | Open Source</strong></a>
+<div><strong>Created: 11 Jan 2024</strong></div>
+<br/>Learn how to track and estimate the speed of vehicles using YOLO, ByteTrack, and Roboflow Inference. This comprehensive tutorial covers object detection, multi-object tracking, filtering detections, perspective transformation, speed estimation, visualization improvements, and more.</p>
 
 ## 💜 built with supervision
 

docs/assets.md

@@ -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><a href="#supervision.assets.downloader.download_assets.download_assets">download_assets</a></h2>
+</div>
 
 :::supervision.assets.downloader.download_assets
 
-## VideoAssets
+<div class="md-typeset">
+    <h2><a href="#supervision.assets.downloader.download_assets.VideoAssets">VideoAssets</a></h2>
+</div>
 
 :::supervision.assets.list.VideoAssets

docs/changelog.md

@@ -1,6 +1,153 @@
+### 0.20.0 <small>April 24, 2024</small>
+
+- Added [#1128](https://github.com/roboflow/supervision/pull/1128): [`sv.KeyPoints`](/0.20.0/keypoint/core/#supervision.keypoint.core.KeyPoints) to provide initial support for pose estimation and broader keypoint detection models.
+
+- Added [#1128](https://github.com/roboflow/supervision/pull/1128): [`sv.EdgeAnnotator`](/0.20.0/keypoint/annotators/#supervision.keypoint.annotators.EdgeAnnotator) and [`sv.VertexAnnotator`](/0.20.0/keypoint/annotators/#supervision.keypoint.annotators.VertexAnnotator) to enable rendering of results from keypoint detection models.
+
+```python
+import cv2
+import supervision as sv
+from ultralytics import YOLO
+
+image = cv2.imread(<SOURCE_IMAGE_PATH>)
+model = YOLO('yolov8l-pose')
+
+result = model(image, verbose=False)[0]
+keypoints = sv.KeyPoints.from_ultralytics(result)
+
+edge_annotators = sv.EdgeAnnotator(color=sv.Color.GREEN, thickness=5)
+annotated_image = edge_annotators.annotate(image.copy(), keypoints)
+```
+
+- Changed [#1037](https://github.com/roboflow/supervision/pull/1037): [`sv.LabelAnnotator`](/0.20.0/annotators/#supervision.annotators.core.LabelAnnotator) by adding an additional `corner_radius` argument that allows for rounding the corners of the bounding box.
+
+- Changed [#1109](https://github.com/roboflow/supervision/pull/1109): [`sv.PolygonZone`](/0.20.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) such that the `frame_resolution_wh` argument is no longer required to initialize `sv.PolygonZone`.
+
+!!! failure "Deprecated"
+
+    The `frame_resolution_wh` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.24.0`.
+
+- Changed [#1084](https://github.com/roboflow/supervision/pull/1084): [`sv.get_polygon_center`](/0.20.0/utils/geometry/#supervision.geometry.core.utils.get_polygon_center) to calculate a more accurate polygon centroid.
+
+- Changed [#1069](https://github.com/roboflow/supervision/pull/1069): [`sv.Detections.from_transformers`](/0.20.0/detection/core/#supervision.detection.core.Detections.from_transformers) by adding support for Transformers segmentation models and extract class names values.
+
+```python
+import torch
+import supervision as sv
+from PIL import Image
+from transformers import DetrImageProcessor, DetrForSegmentation
+
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
+model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
+
+image = Image.open(<SOURCE_IMAGE_PATH>)
+inputs = processor(images=image, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+width, height = image.size
+target_size = torch.tensor([[height, width]])
+results = processor.post_process_segmentation(
+    outputs=outputs, target_sizes=target_size)[0]
+detections = sv.Detections.from_transformers(results, id2label=model.config.id2label)
+
+mask_annotator = sv.MaskAnnotator()
+label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
+
+annotated_image = mask_annotator.annotate(
+    scene=image, detections=detections)
+annotated_image = label_annotator.annotate(
+    scene=annotated_image, detections=detections)
+```
+
+- Fixed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack.update_with_detections`](/0.20.0/trackers/#supervision.tracker.byte_tracker.core.ByteTrack.update_with_detections) which was removing segmentation masks while tracking. Now, `ByteTrack` can be used alongside segmentation models.
+
+### 0.19.0 <small>March 15, 2024</small>
+
+- Added [#818](https://github.com/roboflow/supervision/pull/818): [`sv.CSVSink`](/0.19.0/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink) allowing for the straightforward saving of image, video, or stream inference results in a `.csv` file.
+
+```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)
+        csv_sink.append(detections, custom_data={<CUSTOM_LABEL>:<CUSTOM_DATA>})
+```
+
+- Added [#819](https://github.com/roboflow/supervision/pull/819): [`sv.JSONSink`](/0.19.0/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink) allowing for the straightforward saving of image, video, or stream inference results in a `.json` file.
+
+```python
+
+```python
+import supervision as sv
+from ultralytics import YOLO
+
+model = YOLO(<SOURCE_MODEL_PATH>)
+json_sink = sv.JSONSink(<RESULT_JSON_FILE_PATH>)
+frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+with json_sink:
+    for frame in frames_generator:
+        result = model(frame)[0]
+        detections = sv.Detections.from_ultralytics(result)
+        json_sink.append(detections, custom_data={<CUSTOM_LABEL>:<CUSTOM_DATA>})
+```
+
+- Added [#847](https://github.com/roboflow/supervision/pull/847): [`sv.mask_iou_batch`](/0.19.0/detection/utils/#supervision.detection.utils.mask_iou_batch) allowing to compute Intersection over Union (IoU) of two sets of masks.
+
+- Added [#847](https://github.com/roboflow/supervision/pull/847): [`sv.mask_non_max_suppression`](/0.19.0/detection/utils/#supervision.detection.utils.mask_non_max_suppression) allowing to perform Non-Maximum Suppression (NMS) on segmentation predictions.
+
+- Added [#888](https://github.com/roboflow/supervision/pull/888): [`sv.CropAnnotator`](/0.19.0/annotators/#supervision.annotators.core.CropAnnotator) allowing users to annotate the scene with scaled-up crops of detections.
+
+```python
+import cv2
+import supervision as sv
+from inference import get_model
+
+image = cv2.imread(<SOURCE_IMAGE_PATH>)
+model = get_model(model_id="yolov8n-640")
+
+result = model.infer(image)[0]
+detections = sv.Detections.from_inference(result)
+
+crop_annotator = sv.CropAnnotator()
+annotated_frame = crop_annotator.annotate(
+    scene=image.copy(),
+    detections=detections
+)
+```
+
+- Changed [#827](https://github.com/roboflow/supervision/pull/827): [`sv.ByteTrack.reset`](/0.19.0/tracking/#supervision.tracking.ByteTrack.reset) allowing users to clear trackers state, enabling the processing of multiple video files in sequence.
+
+- Changed [#802](https://github.com/roboflow/supervision/pull/802): [`sv.LineZoneAnnotator`](/0.19.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) allowing to hide in/out count using `display_in_count` and `display_out_count` properties.
+
+- Changed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack`](/0.19.0/tracking/#supervision.tracking.ByteTrack) input arguments and docstrings updated to improve readability and ease of use.
+
+!!! failure "Deprecated"
+
+    The `track_buffer`, `track_thresh`, and `match_thresh` parameters in `sv.ByterTrack` are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
+
+- Changed [#910](https://github.com/roboflow/supervision/pull/910): [`sv.PolygonZone`](/0.19.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) to now accept a list of specific box anchors that must be in zone for a detection to be counted.
+
+!!! failure "Deprecated"
+
+    The `triggering_position ` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors` instead.
+
+- Changed [#875](https://github.com/roboflow/supervision/pull/875): annotators adding support for Pillow images. All supervision Annotators can now accept an image as either a numpy array or a Pillow Image. They automatically detect its type, draw annotations, and return the output in the same format as the input.
+
+- Fixed [#944](https://github.com/roboflow/supervision/pull/944): [`sv.DetectionsSmoother`](/0.19.0/detection/tools/smoother/#supervision.detection.tools.smoother.DetectionsSmoother) removing `tracking_id` from `sv.Detections`.
+
 ### 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 [#720](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 +162,9 @@
 ... )
 ```
 
-- 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 +184,9 @@ 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 +195,35 @@ 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 +238,7 @@ 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 +246,35 @@ 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 +289,46 @@ 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 +345,17 @@ 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 +374,23 @@ 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 +412,25 @@ 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 +457,23 @@ 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 +489,7 @@ 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 +513,13 @@ 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 +533,9 @@ 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 +543,7 @@ 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 +557,11 @@ 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 +571,12 @@ 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 +600,7 @@ 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

docs/classification/core.md

@@ -2,6 +2,6 @@
 comments: true
 ---
 
-## Classifications
+# Classifications
 
 :::supervision.classification.core.Classifications

docs/code_of_conduct.md

@@ -0,0 +1,5 @@
+# Code of conduct
+
+```
+--8<-- "CODE_OF_CONDUCT.md"
+```

docs/contributing.md

@@ -0,0 +1 @@
+--8<-- "CONTRIBUTING.md"

docs/cookbooks.md

@@ -0,0 +1,8 @@
+---
+template: cookbooks.html
+comments: true
+status: new
+hide:
+  - navigation
+  - toc
+---

docs/datasets.md

@@ -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

docs/deprecated.md

@@ -0,0 +1,19 @@
+---
+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.
+- The method `Color.white()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.WHITE` instead.
+- The method `Color.black()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.BLACK` instead.
+- The method `Color.red()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.RED` instead.
+- The method `Color.green()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.GREEN` instead.
+- The method `Color.blue()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.BLUE` instead.
+- The method [`ColorPalette.default()`](draw/color.md/#supervision.draw.color.ColorPalette.default) is deprecated and will be removed in `supervision-0.22.0`. Use the constant [`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`](detection/annotators.md/#supervision.annotators.core.BoundingBoxAnnotator) and [`LabelAnnotator`](detection/annotators.md/#supervision.annotators.core.LabelAnnotator) instead.
+- The method [`FPSMonitor.__call__`](utils/video.md/#supervision.utils.video.FPSMonitor.__call__) is deprecated and will be removed in `supervision-0.22.0`. Use the attribute [`FPSMonitor.fps`](utils/video.md/#supervision.utils.video.FPSMonitor.fps) instead.
+- The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByterTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
+- The `triggering_position ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors ` instead.
+- The `frame_resolution_wh ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) is deprecated and will be removed in `supervision-0.24.0`.

docs/detection/annotators.md

@@ -3,6 +3,8 @@ comments: true
 status: new
 ---
 
+# Annotators
+
 === "BoundingBox"
 
     ```python
@@ -258,15 +260,22 @@ status: new
 === "Label"
 
     ```python
-     import supervision as sv
+    import supervision as sv
 
     image = ...
     detections = sv.Detections(...)
 
+    labels = [
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(detections['class_name'], detections.confidence)
+    ]
+
     label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
     annotated_frame = label_annotator.annotate(
         scene=image.copy(),
-        detections=detections
+        detections=detections,
+        labels=labels
     )
     ```
 
@@ -276,6 +285,21 @@ status: new
 
     </div>
 
+=== "Crop"
+
+    ```python
+    import supervision as sv
+
+    image = ...
+    detections = sv.Detections(...)
+
+    crop_annotator = sv.CropAnnotator()
+    annotated_frame = crop_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
+    ```
+
 === "Blur"
 
     ```python
@@ -378,78 +402,122 @@ 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.CropAnnotator">CropAnnotator</a></h2>
+</div>
+
+:::supervision.annotators.core.CropAnnotator
+
+<div class="md-typeset">
+    <h2><a href="#supervision.annotators.core.ColorLookup">ColorLookup</a></h2>
+</div>
 
 :::supervision.annotators.utils.ColorLookup

docs/detection/core.md

@@ -3,6 +3,6 @@ comments: true
 status: new
 ---
 
-## Detections
+# Detections
 
 :::supervision.detection.core.Detections

docs/detection/metrics.md

@@ -0,0 +1,17 @@
+---
+comments: true
+---
+
+# Metrics
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.detection.ConfusionMatrix">ConfusionMatrix</a></h2>
+</div>
+
+:::supervision.metrics.detection.ConfusionMatrix
+
+<div class="md-typeset">
+    <h2><a href="#supervision.metrics.detection.MeanAveragePrecision">MeanAveragePrecision</a></h2>
+</div>
+
+:::supervision.metrics.detection.MeanAveragePrecision

docs/detection/tools/inference_slicer.md

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

docs/detection/tools/line_zone.md

@@ -2,6 +2,14 @@
 comments: true
 ---
 
-## LineZone
+<div class="md-typeset">
+  <h2>LineZone</h2>
+</div>
 
-:::supervision.detection.line_counter.LineZone
+:::supervision.detection.line_zone.LineZone
+
+<div class="md-typeset">
+  <h2>LineZoneAnnotator</h2>
+</div>
+
+:::supervision.detection.line_zone.LineZoneAnnotator

docs/detection/tools/polygon_zone.md

@@ -2,10 +2,14 @@
 comments: true
 ---
 
-## PolygonZone
+<div class="md-typeset">
+  <h2>PolygonZone</h2>
+</div>
 
 :::supervision.detection.tools.polygon_zone.PolygonZone
 
-## PolygonZoneAnnotator
+<div class="md-typeset">
+  <h2>PolygonZoneAnnotator</h2>
+</div>
 
 :::supervision.detection.tools.polygon_zone.PolygonZoneAnnotator

docs/detection/tools/save_detections.md

@@ -0,0 +1,18 @@
+---
+comments: true
+status: new
+---
+
+# Save Detections
+
+<div class="md-typeset">
+  <h2>CSV Sink</h2>
+</div>
+
+:::supervision.detection.tools.csv_sink.CSVSink
+
+<div class="md-typeset">
+  <h2>JSON Sink</h2>
+</div>
+
+:::supervision.detection.tools.json_sink.JSONSink

docs/detection/tools/smoother.md

@@ -1,8 +1,7 @@
 ---
 comments: true
-status: new
 ---
 
-## Detection Smoother
+# Detection Smoother
 
 :::supervision.detection.tools.smoother.DetectionsSmoother

docs/detection/utils.md

@@ -1,39 +1,72 @@
 ---
 comments: true
+status: new
 ---
 
-## box_iou_batch
+# Detection Utils
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.box_iou_batch">box_iou_batch</a></h2>
+</div>
 
 :::supervision.detection.utils.box_iou_batch
 
-## non_max_suppression
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.mask_iou_batch">mask_iou_batch</a></h2>
+</div>
 
-:::supervision.detection.utils.non_max_suppression
+:::supervision.detection.utils.mask_iou_batch
 
-## polygon_to_mask
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.box_non_max_suppression">box_non_max_suppression</a></h2>
+</div>
+
+:::supervision.detection.utils.box_non_max_suppression
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.mask_non_max_suppression">mask_non_max_suppression</a></h2>
+</div>
+
+:::supervision.detection.utils.mask_non_max_suppression
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.polygon_to_mask">polygon_to_mask</a></h2>
+</div>
 
 :::supervision.detection.utils.polygon_to_mask
 
-## mask_to_xyxy
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.mask_to_xyxy">mask_to_xyxy</a></h2>
+</div>
 
 :::supervision.detection.utils.mask_to_xyxy
 
-## mask_to_polygons
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.mask_to_polygons">mask_to_polygons</a></h2>
+</div>
 
 :::supervision.detection.utils.mask_to_polygons
 
-## polygon_to_xyxy
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.polygon_to_xyxy">polygon_to_xyxy</a></h2>
+</div>
 
 :::supervision.detection.utils.polygon_to_xyxy
 
-## filter_polygons_by_area
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.filter_polygons_by_area">filter_polygons_by_area</a></h2>
+</div>
 
 :::supervision.detection.utils.filter_polygons_by_area
 
-## move_boxes
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.move_boxes">move_boxes</a></h2>
+</div>
 
 :::supervision.detection.utils.move_boxes
 
-## scale_boxes
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.scale_boxes">scale_boxes</a></h2>
+</div>
 
 :::supervision.detection.utils.scale_boxes

docs/draw/color.md

@@ -1,11 +0,0 @@
----
-comments: true
----
-
-## Color
-
-:::supervision.draw.color.Color
-
-## ColorPalette
-
-:::supervision.draw.color.ColorPalette

docs/draw/utils.md

@@ -1,35 +0,0 @@
----
-comments: true
----
-
-## draw_line
-
-:::supervision.draw.utils.draw_line
-
-## draw_rectangle
-
-:::supervision.draw.utils.draw_rectangle
-
-## draw_filled_rectangle
-
-:::supervision.draw.utils.draw_filled_rectangle
-
-## draw_polygon
-
-:::supervision.draw.utils.draw_polygon
-
-## draw_text
-
-:::supervision.draw.utils.draw_text
-
-## draw_image
-
-:::supervision.draw.utils.draw_image
-
-## calculate_dynamic_font_scale
-
-:::supervision.draw.utils.calculate_dynamic_text_scale
-
-## calculate_dynamic_line_thickness
-
-:::supervision.draw.utils.calculate_dynamic_line_thickness

docs/geometry/core.md

@@ -1,7 +0,0 @@
----
-comments: true
----
-
-## Position
-
-:::supervision.geometry.core.Position

docs/how_to/detect_and_annotate.md

@@ -1,18 +1,35 @@
 ---
 comments: true
+status: new
 ---
 
-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
-[Inference](https://github.com/roboflow/inference) or
-[Ultralytics](https://github.com/ultralytics/ultralytics) packages. Following this,
-you'll learn how to import these predictions into Supervision for image annotation
-purposes.
+# Detect and Annotate
 
-## Run Inference
+Supervision provides a seamless process for annotating predictions generated by various
+object detection and segmentation models. This guide shows how to perform inference
+with the  [Inference](https://github.com/roboflow/inference),
+[Ultralytics](https://github.com/ultralytics/ultralytics) or
+[Transformers](https://github.com/huggingface/transformers) packages. Following this,
+you'll learn how to import these predictions into Supervision and use them to annotate
+source image.
 
-First, you'll need to obtain predictions from your object detection or segmentation model.
+![basic-annotation](https://media.roboflow.com/supervision_detect_and_annotate_example_1.png)
+
+## Run Detection
+
+First, you'll need to obtain predictions from your object detection or segmentation
+model.
+
+=== "Inference"
+
+    ```python
+    import cv2
+    from inference import get_model
+
+    model = get_model(model_id="yolov8n-640")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    results = model.infer(image)[0]
+    ```
 
 === "Ultralytics"
 
@@ -21,105 +38,115 @@ First, you'll need to obtain predictions from your object detection or segmentat
     from ultralytics import YOLO
 
     model = YOLO("yolov8n.pt")
-    image = cv2.imread(<PATH TO IMAGE>)
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
     results = model(image)[0]
     ```
 
-=== "Inference"
+=== "Transformers"
 
     ```python
-    import cv2
-    from inference.models.utils import get_roboflow_model
+    import torch
+    from PIL import Image
+    from transformers import DetrImageProcessor, DetrForObjectDetection
 
-    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
-    image = cv2.imread(<PATH TO IMAGE>)
-    results = model.infer(image)[0]
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+    image = Image.open(<SOURCE_IMAGE_PATH>)
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    width, height = image.size
+    target_size = torch.tensor([[height, width]])
+    results = processor.post_process_object_detection(
+        outputs=outputs, target_sizes=target_size)[0]
     ```
 
 ## Load Predictions into Supervision
 
 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.
-
-    ```python
-    import cv2
-    import supervision as sv
-    from ultralytics import YOLO
-
-    model = YOLO("yolov8n.pt")
-    image = cv2.imread(<PATH TO IMAGE>)
-    results = model(image)[0]
-    detections = sv.Detections.from_ultralytics(results)
-    ```
-
 === "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
+    ```{ .py hl_lines="2 8" }
     import cv2
     import supervision as sv
-    from inference.models.utils import get_roboflow_model
+    from inference import get_model
 
-    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>
-    image = cv2.imread(<PATH TO IMAGE>)
+    model = get_model(model_id="yolov8n-640")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
     results = model.infer(image)[0]
     detections = sv.Detections.from_inference(results)
     ```
 
-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))
-
-## 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.
-
 === "Ultralytics"
 
-    ```python
+    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.
+
+    ```{ .py hl_lines="2 8" }
     import cv2
     import supervision as sv
     from ultralytics import YOLO
 
     model = YOLO("yolov8n.pt")
-    image = cv2.imread(<PATH TO IMAGE>)
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
     results = model(image)[0]
     detections = sv.Detections.from_ultralytics(results)
-
-    bounding_box_annotator = sv.BoundingBoxAnnotator()
-    label_annotator = sv.LabelAnnotator()
-
-    labels = [
-        model.model.names[class_id]
-        for class_id
-        in detections.class_id
-    ]
-
-    annotated_image = bounding_box_annotator.annotate(
-        scene=image, detections=detections)
-    annotated_image = label_annotator.annotate(
-        scene=annotated_image, detections=detections, labels=labels)
     ```
 
+=== "Transformers"
+
+    We can do so using the [`sv.Detections.from_transformers`](detection/core/#supervision.detection.core.Detections.from_transformers) method, which accepts model results from both detection and segmentation models.
+
+    ```{ .py hl_lines="2 19-21" }
+    import torch
+    import supervision as sv
+    from PIL import Image
+    from transformers import DetrImageProcessor, DetrForObjectDetection
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+    image = Image.open(<SOURCE_IMAGE_PATH>)
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    width, height = image.size
+    target_size = torch.tensor([[height, width]])
+    results = processor.post_process_object_detection(
+        outputs=outputs, target_sizes=target_size)[0]
+    detections = sv.Detections.from_transformers(
+        transformers_results=results,
+        id2label=model.config.id2label)
+    ```
+
+You can load predictions from other computer vision frameworks and libraries using:
+
+- [`from_deepsparse`](/latest/detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
+- [`from_detectron2`](/latest/detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
+- [`from_mmdetection`](/latest/detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
+- [`from_sam`](/latest/detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
+- [`from_yolo_nas`](/latest/detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))
+
+## Annotate Image with Detections
+
+Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](/latest/annotators/#supervision.annotators.core.LabelAnnotator) classes.
+
 === "Inference"
 
-    ```python
+    ```{ .py hl_lines="10-16" }
     import cv2
     import supervision as sv
-    from inference.models.utils import get_roboflow_model
+    from inference import get_model
 
-    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>
-    image = cv2.imread(<PATH TO IMAGE>)
+    model = get_model(model_id="yolov8n-640")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
     results = model.infer(image)[0]
     detections = sv.Detections.from_inference(results)
 
@@ -132,12 +159,253 @@ Finally, we can annotate the image with the predictions. Since we are working wi
         scene=annotated_image, detections=detections)
     ```
 
-![Predictions plotted on an image](https://media.roboflow.com/supervision_annotate_example.png)
+=== "Ultralytics"
 
-## Display Annotated Image
+    ```{ .py hl_lines="10-16" }
+    import cv2
+    import supervision as sv
+    from ultralytics import YOLO
 
-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.
+    model = YOLO("yolov8n.pt")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    results = model(image)[0]
+    detections = sv.Detections.from_ultralytics(results)
 
-```python
-sv.plot_image(annotated_image)
-```
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Transformers"
+
+    ```{ .py hl_lines="23-30" }
+    import torch
+    import supervision as sv
+    from PIL import Image
+    from transformers import DetrImageProcessor, DetrForObjectDetection
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+    image = Image.open(<SOURCE_IMAGE_PATH>)
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    width, height = image.size
+    target_size = torch.tensor([[height, width]])
+    results = processor.post_process_object_detection(
+        outputs=outputs, target_sizes=target_size)[0]
+    detections = sv.Detections.from_transformers(
+        transformers_results=results,
+        id2label=model.config.id2label)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+![basic-annotation](https://media.roboflow.com/supervision_detect_and_annotate_example_1.png)
+
+## Display Custom Labels
+
+By default, [`sv.LabelAnnotator`](/latest/annotators/#supervision.annotators.core.LabelAnnotator)
+will label each detection with its `class_name` (if possible) or `class_id`. You can
+override this behavior by passing a list of custom `labels` to the `annotate` method.
+
+=== "Inference"
+
+    ```{ .py hl_lines="13-17 22" }
+    import cv2
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8n-640")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    results = model.infer(image)[0]
+    detections = sv.Detections.from_inference(results)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    labels = [
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(detections['class_name'], detections.confidence)
+    ]
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections, labels=labels)
+    ```
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="13-17 22" }
+    import cv2
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    results = model(image)[0]
+    detections = sv.Detections.from_ultralytics(results)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    labels = [
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(detections['class_name'], detections.confidence)
+    ]
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections, labels=labels)
+    ```
+
+=== "Transformers"
+
+    ```{ .py hl_lines="26-30 35" }
+    import torch
+    import supervision as sv
+    from PIL import Image
+    from transformers import DetrImageProcessor, DetrForObjectDetection
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+    image = Image.open(<SOURCE_IMAGE_PATH>)
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    width, height = image.size
+    target_size = torch.tensor([[height, width]])
+    results = processor.post_process_object_detection(
+        outputs=outputs, target_sizes=target_size)[0]
+    detections = sv.Detections.from_transformers(
+        transformers_results=results,
+        id2label=model.config.id2label)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    labels = [
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(detections['class_name'], detections.confidence)
+    ]
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections, labels=labels)
+    ```
+
+![custom-label-annotation](https://media.roboflow.com/supervision_detect_and_annotate_example_2.png)
+
+## Annotate Image with Segmentations
+
+If you are running the segmentation model
+[`sv.MaskAnnotator`](/latest/annotators/#supervision.annotators.core.MaskAnnotator)
+is a drop-in replacement for
+[`sv.BoundingBoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoundingBoxAnnotator)
+that will allow you to draw masks instead of boxes.
+
+=== "Inference"
+
+    ```python
+    import cv2
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8n-seg-640")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    results = model.infer(image)[0]
+    detections = sv.Detections.from_inference(results)
+
+    mask_annotator = sv.MaskAnnotator()
+    label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER_OF_MASS)
+
+    annotated_image = mask_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Ultralytics"
+
+    ```python
+    import cv2
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n-seg.pt")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    results = model(image)[0]
+    detections = sv.Detections.from_ultralytics(results)
+
+    mask_annotator = sv.MaskAnnotator()
+    label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER_OF_MASS)
+
+    annotated_image = mask_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Transformers"
+
+    ```python
+    import torch
+    import supervision as sv
+    from PIL import Image
+    from transformers import DetrImageProcessor, DetrForSegmentation
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
+    model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
+
+    image = Image.open(<SOURCE_IMAGE_PATH>)
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    width, height = image.size
+    target_size = torch.tensor([[height, width]])
+    results = processor.post_process_segmentation(
+        outputs=outputs, target_sizes=target_size)[0]
+    detections = sv.Detections.from_transformers(
+        transformers_results=results,
+        id2label=model.config.id2label)
+
+    mask_annotator = sv.MaskAnnotator()
+    label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER_OF_MASS)
+
+    labels = [
+        f"{class_name} {confidence:.2f}"
+        for class_name, confidence
+        in zip(detections['class_name'], detections.confidence)
+    ]
+
+    annotated_image = mask_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections, labels=labels)
+    ```
+
+![segmentation-annotation](https://media.roboflow.com/supervision_detect_and_annotate_example_3.png)

docs/how_to/detect_small_objects.md

@@ -0,0 +1,266 @@
+---
+comments: true
+status: new
+---
+
+# Detect Small Objects
+
+This guide shows how to detect small objects
+with the  [Inference](https://github.com/roboflow/inference),
+[Ultralytics](https://github.com/ultralytics/ultralytics) or
+[Transformers](https://github.com/huggingface/transformers) packages using
+[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer).
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision_detect_small_objects_example.mp4" type="video/mp4">
+</video>
+
+## Baseline Detection
+
+Small object detection in high-resolution images presents challenges due to the objects'
+size relative to the image resolution.
+
+=== "Inference"
+
+    ```python
+    import cv2
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8x-640")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    results = model.infer(image)[0]
+    detections = sv.Detections.from_inference(results)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Ultralytics"
+
+    ```python
+    import cv2
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8x.pt")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    results = model(image)[0]
+    detections = sv.Detections.from_ultralytics(results)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Transformers"
+
+    ```python
+    import torch
+    import supervision as sv
+    from PIL import Image
+    from transformers import DetrImageProcessor, DetrForObjectDetection
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+    image = Image.open(<SOURCE_IMAGE_PATH>)
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    width, height = image.size
+    target_size = torch.tensor([[height, width]])
+    results = processor.post_process_object_detection(
+        outputs=outputs, target_sizes=target_size)[0]
+    detections = sv.Detections.from_transformers(results)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    labels = [
+        model.config.id2label[class_id]
+        for class_id
+        in detections.class_id
+    ]
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections, labels=labels)
+    ```
+
+![basic-detection](https://media.roboflow.com/supervision_detect_small_objects_example_1.png)
+
+## Input Resolution
+
+Modifying the input resolution of images before detection can enhance small object
+identification at the cost of processing speed and increased memory usage. This method
+is less effective for ultra-high-resolution images (4K and above).
+
+=== "Inference"
+
+    ```{ .py hl_lines="5" }
+    import cv2
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8x-1280")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    results = model.infer(image)[0]
+    detections = sv.Detections.from_inference(results)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="7" }
+    import cv2
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8x.pt")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+    results = model(image, imgsz=1280)[0]
+    detections = sv.Detections.from_ultralytics(results)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+![detection-with-high-input-resolution](https://media.roboflow.com/supervision_detect_small_objects_example_2.png)
+
+## Inference Slicer
+
+[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer)
+processes high-resolution images by dividing them into smaller segments, detecting
+objects within each, and aggregating the results.
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision_detect_small_objects_example_2.mp4" type="video/mp4">
+</video>
+
+=== "Inference"
+
+    ```{ .py hl_lines="9-14" }
+    import cv2
+    import numpy as np
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8x-640")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+
+    def callback(image_slice: np.ndarray) -> sv.Detections:
+        results = model.infer(image_slice)[0]
+        detections = sv.Detections.from_inference(results)
+
+    slicer = sv.InferenceSlicer(callback = callback)
+    detections = slicer(image)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="9-14" }
+    import cv2
+    import numpy as np
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8x.pt")
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+
+    def callback(image_slice: np.ndarray) -> sv.Detections:
+        result = model(image_slice)[0]
+        return sv.Detections.from_ultralytics(result)
+
+    slicer = sv.InferenceSlicer(callback = callback)
+    detections = slicer(image)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections)
+    ```
+
+=== "Transformers"
+
+    ```{ .py hl_lines="13-28" }
+    import cv2
+    import torch
+    import numpy as np
+    import supervision as sv
+    from PIL import Image
+    from transformers import DetrImageProcessor, DetrForObjectDetection
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+    image = cv2.imread(<SOURCE_IMAGE_PATH>)
+
+    def callback(image_slice: np.ndarray) -> sv.Detections:
+        image_slice = cv2.cvtColor(image_slice, cv2.COLOR_BGR2RGB)
+        image_slice = Image.fromarray(image_slice)
+        inputs = processor(images=image_slice, return_tensors="pt")
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        width, height = image.size
+        target_size = torch.tensor([[height, width]])
+        results = processor.post_process_object_detection(
+            outputs=outputs, target_sizes=target_size)[0]
+        return sv.Detections.from_transformers(results)
+
+    slicer = sv.InferenceSlicer(callback = callback)
+    detections = slicer(image)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    labels = [
+        model.config.id2label[class_id]
+        for class_id
+        in detections.class_id
+    ]
+
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    annotated_image = label_annotator.annotate(
+        scene=annotated_image, detections=detections, labels=labels)
+    ```
+
+![detection-with-inference-slicer](https://media.roboflow.com/supervision_detect_small_objects_example_3.png)

docs/how_to/filter_detections.md

@@ -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.

docs/how_to/save_detections.md

@@ -0,0 +1,299 @@
+---
+comments: true
+status: new
+---
+
+# Save Detections
+
+Supervision enables an easy way to save detections in .CSV and .JSON files for offline
+processing. This guide demonstrates how to perform video inference using the
+[Inference](https://github.com/roboflow/inference),
+[Ultralytics](https://github.com/ultralytics/ultralytics) or
+[Transformers](https://github.com/huggingface/transformers) packages and save their results with
+[`sv.CSVSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink) and
+[`sv.JSONSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink).
+
+## Run Detection
+
+First, you'll need to obtain predictions from your object detection or segmentation
+model. You can learn more on this topic in our
+[How to Detect and Annotate](/latest/how_to/detect_and_annotate.md) guide.
+
+=== "Inference"
+
+    ```python
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8n-640")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    for frame in frames_generator:
+
+        results = model.infer(image)[0]
+        detections = sv.Detections.from_inference(results)
+    ```
+
+=== "Ultralytics"
+
+    ```python
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    for frame in frames_generator:
+
+        results = model(frame)[0]
+        detections = sv.Detections.from_ultralytics(results)
+    ```
+
+=== "Transformers"
+
+    ```python
+    import torch
+    import supervision as sv
+    from transformers import DetrImageProcessor, DetrForObjectDetection
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    for frame in frames_generator:
+
+        frame = sv.cv2_to_pillow(frame)
+        inputs = processor(images=frame, return_tensors="pt")
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        width, height = frame.size
+        target_size = torch.tensor([[height, width]])
+        results = processor.post_process_object_detection(
+            outputs=outputs, target_sizes=target_size)[0]
+        detections = sv.Detections.from_transformers(results)
+    ```
+
+## Save Detections as CSV
+
+To save detections to a `.CSV` file, open our
+[`sv.CSVSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink)
+and then pass the
+[`sv.Detections`](/latest/detection/core/#supervision.detection.core.Detections)
+object resulting from the inference to it. Its fields are parsed and saved on disk.
+
+=== "Inference"
+
+    ```{ .py hl_lines="7 12" }
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8n-640")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
+        for frame in frames_generator:
+
+            results = model.infer(image)[0]
+            detections = sv.Detections.from_inference(results)
+            sink.append(detections, {})
+    ```
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="7 12" }
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
+        for frame in frames_generator:
+
+            results = model(frame)[0]
+            detections = sv.Detections.from_ultralytics(results)
+            sink.append(detections, {})
+    ```
+
+=== "Transformers"
+
+    ```{ .py hl_lines="9 23" }
+    import torch
+    import supervision as sv
+    from transformers import DetrImageProcessor, DetrForObjectDetection
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
+        for frame in frames_generator:
+
+            frame = sv.cv2_to_pillow(frame)
+            inputs = processor(images=frame, return_tensors="pt")
+
+            with torch.no_grad():
+                outputs = model(**inputs)
+
+            width, height = frame.size
+            target_size = torch.tensor([[height, width]])
+            results = processor.post_process_object_detection(
+                outputs=outputs, target_sizes=target_size)[0]
+            detections = sv.Detections.from_transformers(results)
+            sink.append(detections, {})
+    ```
+
+| x_min   | y_min    | x_max   | y_max    | class_id | confidence | tracker_id | class_name |
+|---------|----------|---------|----------|----------|------------|------------|------------|
+| 2941.14 | 1269.31  | 3220.77 | 1500.67  | 2        | 0.8517     |            | car        |
+| 944.889 | 899.641  | 1235.42 | 1308.80  | 7        | 0.6752     |            | truck      |
+| 1439.78 | 1077.79  | 1621.27 | 1231.40  | 2        | 0.6450     |            | car        |
+
+## Custom Fields
+
+Besides regular fields in
+[`sv.Detections`](/latest/detection/core/#supervision.detection.core.Detections),
+[`sv.CSVSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink)
+also allows you to add custom information to each row, which can be passed via the
+`custom_data` dictionary. Let's utilize this feature to save information about the
+frame index from which the detections originate.
+
+=== "Inference"
+
+    ```{ .py hl_lines="8 12" }
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8n-640")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
+        for frame_index, frame in enumerate(frames_generator):
+
+            results = model.infer(image)[0]
+            detections = sv.Detections.from_inference(results)
+            sink.append(detections, {"frame_index": frame_index})
+    ```
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="8 12" }
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
+        for frame_index, frame in enumerate(frames_generator):
+
+            results = model(frame)[0]
+            detections = sv.Detections.from_ultralytics(results)
+            sink.append(detections, {"frame_index": frame_index})
+    ```
+
+=== "Transformers"
+
+    ```{ .py hl_lines="10 23" }
+    import torch
+    import supervision as sv
+    from transformers import DetrImageProcessor, DetrForObjectDetection
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
+        for frame_index, frame in enumerate(frames_generator):
+
+            frame = sv.cv2_to_pillow(frame)
+            inputs = processor(images=frame, return_tensors="pt")
+
+            with torch.no_grad():
+                outputs = model(**inputs)
+
+            width, height = frame.size
+            target_size = torch.tensor([[height, width]])
+            results = processor.post_process_object_detection(
+                outputs=outputs, target_sizes=target_size)[0]
+            detections = sv.Detections.from_transformers(results)
+            sink.append(detections, {"frame_index": frame_index})
+    ```
+
+| x_min   | y_min    | x_max   | y_max    | class_id | confidence | tracker_id | class_name | frame_index |
+|---------|----------|---------|----------|----------|------------|------------|------------|-------------|
+| 2941.14 | 1269.31  | 3220.77 | 1500.67  | 2        | 0.8517     |            | car        | 0           |
+| 944.889 | 899.641  | 1235.42 | 1308.80  | 7        | 0.6752     |            | truck      | 0           |
+| 1439.78 | 1077.79  | 1621.27 | 1231.40  | 2        | 0.6450     |            | car        | 0           |
+
+## Save Detections as JSON
+
+If you prefer to save the result in a `.JSON` file instead of a `.CSV` file, all you
+need to do is replace
+[`sv.CSVSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink)
+with
+[`sv.JSONSink`](/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink).
+
+=== "Inference"
+
+    ```{ .py hl_lines="7" }
+    import supervision as sv
+    from inference import get_model
+
+    model = get_model(model_id="yolov8n-640")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    with sv.JSONSink(<TARGET_CSV_PATH>) as sink:
+        for frame_index, frame in enumerate(frames_generator):
+
+            results = model.infer(image)[0]
+            detections = sv.Detections.from_inference(results)
+            sink.append(detections, {"frame_index": frame_index})
+    ```
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="7" }
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    with sv.JSONSink(<TARGET_CSV_PATH>) as sink:
+        for frame_index, frame in enumerate(frames_generator):
+
+            results = model(frame)[0]
+            detections = sv.Detections.from_ultralytics(results)
+            sink.append(detections, {"frame_index": frame_index})
+    ```
+
+=== "Transformers"
+
+    ```{ .py hl_lines="9" }
+    import torch
+    import supervision as sv
+    from transformers import DetrImageProcessor, DetrForObjectDetection
+
+    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+    frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+    with sv.JSONSink(<TARGET_CSV_PATH>) as sink:
+        for frame_index, frame in enumerate(frames_generator):
+
+            frame = sv.cv2_to_pillow(frame)
+            inputs = processor(images=frame, return_tensors="pt")
+
+            with torch.no_grad():
+                outputs = model(**inputs)
+
+            width, height = frame.size
+            target_size = torch.tensor([[height, width]])
+            results = processor.post_process_object_detection(
+                outputs=outputs, target_sizes=target_size)[0]
+            detections = sv.Detections.from_transformers(results)
+            sink.append(detections, {"frame_index": frame_index})
+    ```

docs/how_to/track_objects.md

@@ -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.
 

docs/index.md

@@ -1,16 +1,20 @@
 ---
+template: index.html
 comments: true
+hide:
+  - navigation
+  - toc
 ---
 
-<div align="center">
-  <p>
-    <a align="center" href="" target="_blank">
-      <img
-        width="850"
-        src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529"
-      >
-    </a>
-  </p>
+<div class="md-typeset">
+  <h1></h1>
+</div>
+
+<div align="center" id="logo">
+  <a align="center" href="" target="_blank">
+      <img width="850"
+          src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529">
+  </a>
 </div>
 
 ## 👋 Hello
@@ -24,43 +28,9 @@ 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
+You can install `supervision` in a
 [**Python>=3.8**](https://www.python.org/) environment.
 
 !!! example "pip install (recommended)"
@@ -76,7 +46,25 @@ You can install `supervision` with pip in a
         If you require the full version of `supervision` with GUI support you can install the desktop version. This version includes the GUI components of OpenCV, allowing you to display images and videos on the screen.
 
         ```bash
-        pip install supervision[desktop]
+        pip install "supervision[desktop]"
+        ```
+
+!!! example "conda/mamba install"
+
+    === "conda"
+
+        [![conda-recipe](https://img.shields.io/badge/recipe-supervision-green.svg)](https://anaconda.org/conda-forge/supervision) [![conda-downloads](https://img.shields.io/conda/dn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision) [![conda-version](https://img.shields.io/conda/vn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision) [![conda-platforms](https://img.shields.io/conda/pn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision)
+
+        ```bash
+        conda install -c conda-forge supervision
+        ```
+
+    === "mamba"
+
+        [![mamba-recipe](https://img.shields.io/badge/recipe-supervision-green.svg)](https://anaconda.org/conda-forge/supervision) [![mamba-downloads](https://img.shields.io/conda/dn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision) [![mamba-version](https://img.shields.io/conda/vn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision) [![mamba-platforms](https://img.shields.io/conda/pn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision)
+
+        ```bash
+        mamba install -c conda-forge supervision
         ```
 
 !!! example "git clone (for development)"
@@ -117,3 +105,45 @@ 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)
+
+-   __Detect Small Objects__
+
+    ---
+
+    Learn how to detect small objects in images
+
+    [:octicons-arrow-right-24: Tutorial](how_to/detect_small_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>

docs/javascripts/cookbooks-card.js

@@ -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 = 4 - 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);
+    });
+})

docs/keypoint/annotators.md

@@ -0,0 +1,60 @@
+---
+comments: true
+status: new
+---
+
+# Annotators
+
+=== "VertexAnnotator"
+
+    ```python
+    import supervision as sv
+
+    image = ...
+    key_points = sv.KeyPoints(...)
+
+    vertex_annotator = sv.VertexAnnotator(color=sv.Color.GREEN, radius=10)
+    annotated_frame = vertex_annotator.annotate(
+        scene=image.copy(),
+        key_points=key_points
+    )
+    ```
+
+    <div class="result" markdown>
+
+    ![vertex-annotator-example](https://media.roboflow.com/supervision-annotator-examples/vertex-annotator-example.png){ align=center width="800" }
+
+    </div>
+
+=== "EdgeAnnotator"
+
+    ```python
+    import supervision as sv
+
+    image = ...
+    key_points = sv.KeyPoints(...)
+
+    edge_annotator = sv.EdgeAnnotator(color=sv.Color.GREEN, thickness=5)
+    annotated_frame = edge_annotator.annotate(
+        scene=image.copy(),
+        key_points=key_points
+    )
+    ```
+
+    <div class="result" markdown>
+
+    ![edge-annotator-example](https://media.roboflow.com/supervision-annotator-examples/edge-annotator-example.png){ align=center width="800" }
+
+    </div>
+
+<div class="md-typeset">
+  <h2><a href="#supervision.keypoint.annotators.VertexAnnotator">VertexAnnotator</a></h2>
+</div>
+
+:::supervision.keypoint.annotators.VertexAnnotator
+
+<div class="md-typeset">
+  <h2><a href="#supervision.keypoint.annotators.EdgeAnnotator">EdgeAnnotator</a></h2>
+</div>
+
+:::supervision.keypoint.annotators.EdgeAnnotator

docs/keypoint/core.md

@@ -0,0 +1,8 @@
+---
+comments: true
+status: new
+---
+
+# Keypoint Detection
+
+:::supervision.keypoint.core.KeyPoints

docs/license.md

@@ -0,0 +1,5 @@
+# License
+
+```
+--8<-- "LICENSE.md"
+```

docs/metrics/detection.md

@@ -1,16 +0,0 @@
----
-comments: true
----
-
-!!! 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
-
-:::supervision.metrics.detection.ConfusionMatrix
-
-## MeanAveragePrecision
-
-:::supervision.metrics.detection.MeanAveragePrecision

docs/notebooks/download-supervision-assets.ipynb

@@ -0,0 +1,102 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Download Supervision Assets\n",
+                "\n",
+                "---\n",
+                "\n",
+                "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/supervision/blob/develop/docs/notebooks/download-supervision-assets.ipynb)\n",
+                "\n",
+                "When experimenting with interesting and useful features of the Supervision package, it's 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 Dependencies"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "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 the a video of the subway.\n",
+                "path_to_video = download_assets(VideoAssets.SUBWAY)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "With this, we now have a high quality video asset for use in demos. Let's take a look at what we downloaded. Keep in mind that the video preview below works only in the web version of the cookbooks and not in Google Colab.\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "<div style=\"display: flex; justify-content: center;\">\n",
+                "    <video controls width=\"320\" height=\"240\">\n",
+                "        <source\n",
+                "            src=\"https://media.roboflow.com/supervision/video-examples/subway.mp4\"\n",
+                "            type=\"video/mp4\"\n",
+                "        >\n",
+                "    </video>\n",
+                "</div>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We're now equipt with a video asset from Supervision to run some experiments on! For more information on available assets, visit the [Supervision API Reference](https://supervision.roboflow.com/latest/assets). 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": "3.11.8"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}

docs/stylesheets/cookbooks-card.css

@@ -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 */
+}

docs/stylesheets/extra.css

@@ -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%;
+}

docs/theme/cookbooks.html

@@ -0,0 +1,25 @@
+{% 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/count-objects-crossing-the-line" data-name="Count Objects Crossing the Line" data-labels="ANNOTATORS,LINE ZONE,TRACKING" data-version="v0.18.0" data-author="SkalskiP"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/zero-shot-object-detection-with-yolo-world" data-name="Zero-Shot Object Detection with YOLO-World" data-labels="ANNOTATORS,DETECTION,INFERENCE" data-version="v0.19.0" data-author="SkalskiP"></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>
+      <p class="card repo-card" data-url="/develop/notebooks/occupancy_analytics" data-name="Analyzing Zone Occupancy"  data-labels="ANNOTATOR,DETECTION,ZONES" data-version="v0.19.0" data-author="stellasphere"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/evaluating-alignment-of-text-to-image-diffusion-models" data-name="Evaluating Alignment of Text-to-image Diffusion Models"  data-labels="ANNOTATORS,YOLO WORLD" data-version="v0.19.0rc5" data-author="iamhatesz"></p>
+    </div>
+  </div>
+</section>
+{% endblock %}

docs/theme/index.html

@@ -0,0 +1,15 @@
+{% extends "main.html" %}
+{% block content %}
+{{ super() }}
+<style>
+  .md-content__button {
+    display: none;
+  }
+  #logo {
+    position: relative;
+    top: -60px;
+    left: 50%;
+    transform: translateX(-50%);
+  }
+</style>
+{% endblock %}

docs/theme/main.html

@@ -1,5 +1,17 @@
 {% 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>
+<script>!function () {var reb2b = window.reb2b = window.reb2b || [];if (reb2b.invoked) return;reb2b.invoked = true;reb2b.methods = ["identify", "collect"];reb2b.factory = function (method) {return function () {var args = Array.prototype.slice.call(arguments);args.unshift(method);reb2b.push(args);return reb2b;};};for (var i = 0; i < reb2b.methods.length; i++) {var key = reb2b.methods[i];reb2b[key] = reb2b.factory(key);}reb2b.load = function (key) {var script = document.createElement("script");script.type = "text/javascript";script.async = true;script.src = "https://s3-us-west-2.amazonaws.com/b2bjsstore/b/" + key + "/reb2b.js.gz";var first = document.getElementsByTagName("script")[0];first.parentNode.insertBefore(script, first);};reb2b.SNIPPET_VERSION = "1.0.1";reb2b.load("L9NMMZHVD7NW");}();</script>
 {% endblock %}

docs/trackers.md

@@ -1,7 +1,8 @@
 ---
 comments: true
+status: new
 ---
 
-## ByteTrack
+# ByteTrack
 
 :::supervision.tracker.byte_tracker.core.ByteTrack

docs/utils/draw.md

@@ -0,0 +1,65 @@
+---
+comments: true
+---
+
+# Draw Utils
+
+<div class="md-typeset">
+    <h2><a href="#supervision.draw.utils.draw_line">draw_line</a></h2>
+</div>
+
+:::supervision.draw.utils.draw_line
+
+<div class="md-typeset">
+    <h2><a href="#supervision.draw.utils.draw_rectangle">draw_rectangle</a></h2>
+</div>
+
+:::supervision.draw.utils.draw_rectangle
+
+<div class="md-typeset">
+    <h2><a href="#supervision.draw.utils.draw_filled_rectangle">draw_filled_rectangle</a></h2>
+</div>
+
+:::supervision.draw.utils.draw_filled_rectangle
+
+<div class="md-typeset">
+    <h2><a href="#supervision.draw.utils.draw_polygon">draw_polygon</a></h2>
+</div>
+
+:::supervision.draw.utils.draw_polygon
+
+<div class="md-typeset">
+    <h2><a href="#supervision.draw.utils.draw_text">draw_text</a></h2>
+</div>
+
+:::supervision.draw.utils.draw_text
+
+<div class="md-typeset">
+    <h2><a href="#supervision.draw.utils.draw_image">draw_image</a></h2>
+</div>
+
+:::supervision.draw.utils.draw_image
+
+<div class="md-typeset">
+    <h2><a href="#supervision.draw.utils.calculate_optimal_font_scale">calculate_optimal_font_scale</a></h2>
+</div>
+
+:::supervision.draw.utils.calculate_optimal_text_scale
+
+<div class="md-typeset">
+    <h2><a href="#supervision.draw.utils.calculate_optimal_line_thickness">calculate_optimal_line_thickness</a></h2>
+</div>
+
+:::supervision.draw.utils.calculate_optimal_line_thickness
+
+<div class="md-typeset">
+    <h2><a href="#supervision.draw.color.Color">Color</a></h2>
+</div>
+
+:::supervision.draw.color.Color
+
+<div class="md-typeset">
+    <h2><a href="#supervision.draw.color.ColorPalette">ColorPalette</a></h2>
+</div>
+
+:::supervision.draw.color.ColorPalette

docs/utils/file.md

@@ -2,6 +2,10 @@
 comments: true
 ---
 
-## list_files_with_extensions
+# File Utils
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.file.list_files_with_extensions">list_files_with_extensions</a></h2>
+</div>
 
 :::supervision.utils.file.list_files_with_extensions

docs/utils/geometry.md

@@ -0,0 +1,15 @@
+---
+comments: true
+---
+
+<div class="md-typeset">
+    <h2><a href="#supervision.geometry.core.utils.get_polygon_center">get_polygon_center</a></h2>
+</div>
+
+:::supervision.geometry.utils.get_polygon_center
+
+<div class="md-typeset">
+    <h2><a href="#supervision.geometry.core.Position">Position</a></h2>
+</div>
+
+:::supervision.geometry.core.Position

docs/utils/image.md

@@ -1,11 +1,42 @@
 ---
 comments: true
+status: new
 ---
 
-## ImageSink
+# Image Utils
 
-:::supervision.utils.image.ImageSink
-
-## crop
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.image.crop_image">crop_image</a></h2>
+</div>
 
 :::supervision.utils.image.crop_image
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.image.scale_image">crop_image</a></h2>
+</div>
+
+:::supervision.utils.image.scale_image
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.image.resize_image">resize_image</a></h2>
+</div>
+
+:::supervision.utils.image.resize_image
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.image.letterbox_image">letterbox_image</a></h2>
+</div>
+
+:::supervision.utils.image.letterbox_image
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.image.overlay_image">overlay_image</a></h2>
+</div>
+
+:::supervision.utils.image.overlay_image
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.image.ImageSink">ImageSink</a></h2>
+</div>
+
+:::supervision.utils.image.ImageSink

docs/utils/iterables.md

@@ -0,0 +1,18 @@
+---
+comments: true
+status: new
+---
+
+# Iterables Utils
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.iterables.create_batches">create_batches</a></h2>
+</div>
+
+:::supervision.utils.iterables.create_batches
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.iterables.fill">fill</a></h2>
+</div>
+
+:::supervision.utils.iterables.fill

docs/utils/notebook.md

@@ -2,10 +2,16 @@
 comments: true
 ---
 
-## plot_image
+# Notebooks Utils
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.notebook.plot_image">plot_image</a></h2>
+</div>
 
 :::supervision.utils.notebook.plot_image
 
-## plot_images_grid
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.notebook.plot_images_grid">plot_images_grid</a></h2>
+</div>
 
 :::supervision.utils.notebook.plot_images_grid

docs/utils/video.md

@@ -2,22 +2,34 @@
 comments: true
 ---
 
-## VideoInfo
+# Video Utils
+
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.video.VideoInfo">VideoInfo</a></h2>
+</div>
 
 :::supervision.utils.video.VideoInfo
 
-## VideoSink
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.video.VideoSink">VideoSink</a></h2>
+</div>
 
 :::supervision.utils.video.VideoSink
 
-## FPSMonitor
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.video.FPSMonitor">FPSMonitor</a></h2>
+</div>
 
 :::supervision.utils.video.FPSMonitor
 
-## get_video_frames_generator
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.video.get_video_frames_generator">get_video_frames_generator</a></h2>
+</div>
 
 :::supervision.utils.video.get_video_frames_generator
 
-## process_video
+<div class="md-typeset">
+    <h2><a href="#supervision.utils.video.process_video">process_video</a></h2>
+</div>
 
 :::supervision.utils.video.process_video

examples/README.md

@@ -1,31 +1,12 @@
-## 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
-interfaces with diverse applications.
+Here, you'll find end-to-end examples that show how to solve common computer vision problems using Supervision.
+
+For more information and examples, visit our [documentation](https://supervision.roboflow.com/develop/annotators/) and explore our [how-to guides](https://supervision.roboflow.com/develop/how_to/detect_and_annotate/) and [cookbooks](https://supervision.roboflow.com/develop/cookbooks/). Join our [Discord](https://discord.com/invite/GbfgXGJ8Bk) and meet other Supervision power users!
 
 - [tracking](./tracking) by [@SkalskiP](https://github.com/SkalskiP)
 - [count people in zone](./count_people_in_zone) by [@SkalskiP](https://github.com/SkalskiP)
 - [traffic analysis](./traffic_analysis) by [@SkalskiP](https://github.com/SkalskiP)
 - [speed estimation](./speed_estimation) by [@SkalskiP](https://github.com/SkalskiP)
+- [time in zone](./time_in_zone) by [@SkalskiP](https://github.com/SkalskiP)
 - [heatmap and track](./heatmap_and_track/) by [@HinePo](https://github.com/HinePo)
-
-## contributing
-
-We welcome contributions from the community in the form of examples, applications, and
-guides. To contribute, please follow these steps:
-
-1. Create a pull request (PR) with the `[Example]` prefix in the title, adding your
-project folder to the `examples/` directory in the repository.
-2. Confirm your project aligns with the following standards:
-   - Incorporates the `supervision` package.
-   - Provides a `README.md` file, detailing the instructions to execute the project.
-   - Showcases visual results, demonstrating the app's functionality.
-   - Avoids adding large assets or dependencies unless absolutely necessary.
-   - The contributor is expected to provide support for issues related to their
-examples.
-   - In case the presented model has licensing complications, kindly specify them to
-circumvent potential misunderstandings.
-
-For inquiries or concerns about these prerequisites, feel free to raise a PR. We are
-committed to assist and guide you.

examples/count_people_in_zone/README.md

@@ -1,5 +1,8 @@
 # count people in zone
 
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-detect-and-count-objects-in-polygon-zone.ipynb)
+[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://www.youtube.com/watch?v=l_kf9CfZ_8M)
+
 ## 👋 hello
 
 This demo is a video analysis tool that counts and highlights objects in specific zones

examples/count_people_in_zone/inference_example.py

@@ -38,15 +38,15 @@ def initiate_annotators(
 ) -> Tuple[
     List[sv.PolygonZone], List[sv.PolygonZoneAnnotator], List[sv.BoundingBoxAnnotator]
 ]:
-    line_thickness = sv.calculate_dynamic_line_thickness(resolution_wh=resolution_wh)
-    text_scale = sv.calculate_dynamic_text_scale(resolution_wh=resolution_wh)
+    line_thickness = sv.calculate_optimal_line_thickness(resolution_wh=resolution_wh)
+    text_scale = sv.calculate_optimal_text_scale(resolution_wh=resolution_wh)
 
     zones = []
     zone_annotators = []
     box_annotators = []
 
     for index, polygon in enumerate(polygons):
-        zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=resolution_wh)
+        zone = sv.PolygonZone(polygon=polygon)
         zone_annotator = sv.PolygonZoneAnnotator(
             zone=zone,
             color=COLORS.by_idx(index),

examples/count_people_in_zone/requirements.txt

@@ -1,5 +1,5 @@
 gdown
 inference
-supervision
+supervision>=0.20.0
 tqdm
 ultralytics

examples/count_people_in_zone/ultralytics_example.py

@@ -36,15 +36,15 @@ def initiate_annotators(
 ) -> Tuple[
     List[sv.PolygonZone], List[sv.PolygonZoneAnnotator], List[sv.BoundingBoxAnnotator]
 ]:
-    line_thickness = sv.calculate_dynamic_line_thickness(resolution_wh=resolution_wh)
-    text_scale = sv.calculate_dynamic_text_scale(resolution_wh=resolution_wh)
+    line_thickness = sv.calculate_optimal_line_thickness(resolution_wh=resolution_wh)
+    text_scale = sv.calculate_optimal_text_scale(resolution_wh=resolution_wh)
 
     zones = []
     zone_annotators = []
     box_annotators = []
 
     for index, polygon in enumerate(polygons):
-        zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=resolution_wh)
+        zone = sv.PolygonZone(polygon=polygon)
         zone_annotator = sv.PolygonZoneAnnotator(
             zone=zone,
             color=COLORS.by_idx(index),

examples/heatmap_and_track/requirements.txt

@@ -1,2 +1,2 @@
-supervision[assets]
+supervision[assets]==0.19.0
 ultralytics

examples/speed_estimation/inference_example.py

@@ -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)
@@ -95,10 +98,10 @@ if __name__ == "__main__":
         frame_rate=video_info.fps, track_thresh=args.confidence_threshold
     )
 
-    thickness = sv.calculate_dynamic_line_thickness(
+    thickness = sv.calculate_optimal_line_thickness(
         resolution_wh=video_info.resolution_wh
     )
-    text_scale = sv.calculate_dynamic_text_scale(resolution_wh=video_info.resolution_wh)
+    text_scale = sv.calculate_optimal_text_scale(resolution_wh=video_info.resolution_wh)
     bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
     label_annotator = sv.LabelAnnotator(
         text_scale=text_scale,
@@ -113,9 +116,7 @@ if __name__ == "__main__":
 
     frame_generator = sv.get_video_frames_generator(source_path=args.source_video_path)
 
-    polygon_zone = sv.PolygonZone(
-        polygon=SOURCE, frame_resolution_wh=video_info.resolution_wh
-    )
+    polygon_zone = sv.PolygonZone(polygon=SOURCE)
     view_transformer = ViewTransformer(source=SOURCE, target=TARGET)
 
     coordinates = defaultdict(lambda: deque(maxlen=video_info.fps))

examples/speed_estimation/requirements.txt

@@ -1,6 +1,6 @@
-supervision==0.18.0rc1
+supervision>=0.20.0
 tqdm==4.66.1
 requests
 ultralytics==8.0.237
 super-gradients==3.5.0
-inference==0.9.8
+inference<=0.9.21

examples/speed_estimation/ultralytics_example.py

@@ -76,10 +76,10 @@ if __name__ == "__main__":
         frame_rate=video_info.fps, track_thresh=args.confidence_threshold
     )
 
-    thickness = sv.calculate_dynamic_line_thickness(
+    thickness = sv.calculate_optimal_line_thickness(
         resolution_wh=video_info.resolution_wh
     )
-    text_scale = sv.calculate_dynamic_text_scale(resolution_wh=video_info.resolution_wh)
+    text_scale = sv.calculate_optimal_text_scale(resolution_wh=video_info.resolution_wh)
     bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
     label_annotator = sv.LabelAnnotator(
         text_scale=text_scale,
@@ -94,9 +94,7 @@ if __name__ == "__main__":
 
     frame_generator = sv.get_video_frames_generator(source_path=args.source_video_path)
 
-    polygon_zone = sv.PolygonZone(
-        polygon=SOURCE, frame_resolution_wh=video_info.resolution_wh
-    )
+    polygon_zone = sv.PolygonZone(polygon=SOURCE)
     view_transformer = ViewTransformer(source=SOURCE, target=TARGET)
 
     coordinates = defaultdict(lambda: deque(maxlen=video_info.fps))

examples/speed_estimation/yolo_nas_example.py

@@ -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)
@@ -74,10 +77,10 @@ if __name__ == "__main__":
         frame_rate=video_info.fps, track_thresh=args.confidence_threshold
     )
 
-    thickness = sv.calculate_dynamic_line_thickness(
+    thickness = sv.calculate_optimal_line_thickness(
         resolution_wh=video_info.resolution_wh
     )
-    text_scale = sv.calculate_dynamic_text_scale(resolution_wh=video_info.resolution_wh)
+    text_scale = sv.calculate_optimal_text_scale(resolution_wh=video_info.resolution_wh)
     bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=thickness)
     label_annotator = sv.LabelAnnotator(
         text_scale=text_scale,
@@ -92,9 +95,7 @@ if __name__ == "__main__":
 
     frame_generator = sv.get_video_frames_generator(source_path=args.source_video_path)
 
-    polygon_zone = sv.PolygonZone(
-        polygon=SOURCE, frame_resolution_wh=video_info.resolution_wh
-    )
+    polygon_zone = sv.PolygonZone(polygon=SOURCE)
     view_transformer = ViewTransformer(source=SOURCE, target=TARGET)
 
     coordinates = defaultdict(lambda: deque(maxlen=video_info.fps))

examples/time_in_zone/.gitignore

@@ -0,0 +1,9 @@
+data/
+venv*/
+*.pt
+*.pth
+*.mp4
+*.mov
+*.png
+*.jpg
+*.jpeg

examples/time_in_zone/README.md

@@ -0,0 +1,264 @@
+# time in zone
+
+[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://www.youtube.com/watch?v=hAWpsIuem10)
+
+## 👋 hello
+
+Practical demonstration on leveraging computer vision for analyzing wait times and
+monitoring the duration that objects or individuals spend in predefined areas of video
+frames. This example project, perfect for retail analytics or traffic management
+applications.
+
+https://github.com/roboflow/supervision/assets/26109316/d051cc8a-dd15-41d4-aa36-d38b86334c39
+
+## 💻 install
+
+- clone repository and navigate to example directory
+
+  ```bash
+  git clone https://github.com/roboflow/supervision.git
+  cd supervision/examples/time_in_zone
+  ```
+
+- setup python environment and activate it [optional]
+
+  ```bash
+  python3 -m venv venv
+  source venv/bin/activate
+  ```
+
+- install required dependencies
+
+  ```bash
+  pip install -r requirements.txt
+  ```
+
+## 🛠 scripts
+
+### `download_from_youtube`
+
+This script allows you to download a video from YouTube.
+
+- `--url`: The full URL of the YouTube video you wish to download.
+- `--output_path` (optional): Specifies the directory where the video will be saved.
+- `--file_name` (optional): Sets the name of the saved video file.
+
+```bash
+python scripts/download_from_youtube.py \
+--url "https://www.youtube.com/watch?v=-8zyEwAa50Q" \
+--output_path "data/checkout" \
+--file_name "video.mp4"
+```
+
+```bash
+python scripts/download_from_youtube.py \
+--url "https://www.youtube.com/watch?v=MNn9qKG2UFI" \
+--output_path "data/traffic" \
+--file_name "video.mp4"
+```
+
+### `stream_from_file`
+
+This script allows you to stream video files from a directory. It's an awesome way to
+mock a live video stream for local testing. Video will be streamed in a loop under
+`rtsp://localhost:8554/live0.stream` URL. This script requires docker to be installed.
+
+- `--video_directory`: Directory containing video files to stream.
+- `--number_of_streams`: Number of video files to stream.
+
+```bash
+python scripts/stream_from_file.py \
+--video_directory "data/checkout" \
+--number_of_streams 1
+```
+
+```bash
+python scripts/stream_from_file.py \
+--video_directory "data/traffic" \
+--number_of_streams 1
+```
+
+### `draw_zones`
+
+If you want to test zone time in zone analysis on your own video, you can use this
+script to design custom zones and save results as a JSON file. The script will open a
+window where you can draw polygons on the source image or video file. The polygons will
+be saved as a JSON file.
+
+- `--source_path`: Path to the source image or video file for drawing polygons.
+- `--zone_configuration_path`: Path where the polygon annotations will be saved as a JSON file.
+
+
+- `enter` - finish drawing the current polygon.
+- `escape` - cancel drawing the current polygon.
+- `q` - quit the drawing window.
+- `s` - save zone configuration to a JSON file.
+
+```bash
+python scripts/draw_zones.py \
+--source_path "data/checkout/video.mp4" \
+--zone_configuration_path "data/checkout/config.json"
+```
+
+```bash
+python scripts/draw_zones.py \
+--source_path "data/traffic/video.mp4" \
+--zone_configuration_path "data/traffic/custom_config.json"
+```
+
+https://github.com/roboflow/supervision/assets/26109316/9d514c9e-2a61-418b-ae49-6ac1ad6ae5ac
+
+## 🎬 video & stream processing
+
+### `inference_file_example`
+
+Script to run object detection on a video file using the Roboflow Inference model.
+
+  - `--zone_configuration_path`: Path to the zone configuration JSON file.
+  - `--source_video_path`: Path to the source video file.
+  - `--model_id`: Roboflow model ID.
+  - `--classes`: List of class IDs to track. If empty, all classes are tracked.
+  - `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
+  - `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
+
+```bash
+python inference_file_example.py \
+--zone_configuration_path "data/checkout/config.json" \
+--source_video_path "data/checkout/video.mp4" \
+--model_id "yolov8x-640" \
+--classes 0 \
+--confidence_threshold 0.3 \
+--iou_threshold 0.7
+```
+
+https://github.com/roboflow/supervision/assets/26109316/d051cc8a-dd15-41d4-aa36-d38b86334c39
+
+```bash
+python inference_file_example.py \
+--zone_configuration_path "data/traffic/config.json" \
+--source_video_path "data/traffic/video.mp4" \
+--model_id "yolov8x-640" \
+--classes 2 5 6 7 \
+--confidence_threshold 0.3 \
+--iou_threshold 0.7
+```
+
+https://github.com/roboflow/supervision/assets/26109316/5ec896d7-4b39-4426-8979-11e71666878b
+
+### `inference_stream_example`
+
+Script to run object detection on a video stream using the Roboflow Inference model.
+
+  - `--zone_configuration_path`: Path to the zone configuration JSON file.
+  - `--rtsp_url`: Complete RTSP URL for the video stream.
+  - `--model_id`: Roboflow model ID.
+  - `--classes`: List of class IDs to track. If empty, all classes are tracked.
+  - `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
+  - `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
+
+```bash
+python inference_file_example.py \
+--zone_configuration_path "data/checkout/config.json" \
+--rtsp_url "rtsp://localhost:8554/live0.stream" \
+--model_id "yolov8x-640" \
+--classes 0 \
+--confidence_threshold 0.3 \
+--iou_threshold 0.7
+```
+
+```bash
+python inference_file_example.py \
+--zone_configuration_path "data/traffic/config.json" \
+--rtsp_url "rtsp://localhost:8554/live0.stream" \
+--model_id "yolov8x-640" \
+--classes 2 5 6 7 \
+--confidence_threshold 0.3 \
+--iou_threshold 0.7
+```
+
+<details>
+<summary>👉 show ultralytics examples</summary>
+
+### `ultralytics_file_example`
+
+Script to run object detection on a video file using the Ultralytics YOLOv8 model.
+
+  - `--zone_configuration_path`: Path to the zone configuration JSON file.
+  - `--source_video_path`: Path to the source video file.
+  - `--weights`: Path to the model weights file. Default is `'yolov8s.pt'`.
+  - `--device`: Computation device (`'cpu'`, `'mps'` or `'cuda'`). Default is `'cpu'`.
+  - `--classes`: List of class IDs to track. If empty, all classes are tracked.
+  - `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
+  - `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
+
+```bash
+python inference_file_example.py \
+--zone_configuration_path "data/checkout/config.json" \
+--source_video_path "data/checkout/video.mp4" \
+--weights "yolov8x.pt" \
+--device "cpu" \
+--classes 0 \
+--confidence_threshold 0.3 \
+--iou_threshold 0.7
+```
+
+```bash
+python inference_file_example.py \
+--zone_configuration_path "data/traffic/config.json" \
+--source_video_path "data/traffic/video.mp4" \
+--weights "yolov8x.pt" \
+--device "cpu" \
+--classes 2 5 6 7 \
+--confidence_threshold 0.3 \
+--iou_threshold 0.7
+```
+
+### `ultralytics_stream_example`
+
+Script to run object detection on a video stream using the Ultralytics YOLOv8 model.
+
+  - `--zone_configuration_path`: Path to the zone configuration JSON file.
+  - `--rtsp_url`: Complete RTSP URL for the video stream.
+  - `--weights`: Path to the model weights file. Default is `'yolov8s.pt'`.
+  - `--device`: Computation device (`'cpu'`, `'mps'` or `'cuda'`). Default is `'cpu'`.
+  - `--classes`: List of class IDs to track. If empty, all classes are tracked.
+  - `--confidence_threshold`: Confidence level for detections (`0` to `1`). Default is `0.3`.
+  - `--iou_threshold`: IOU threshold for non-max suppression. Default is `0.7`.
+
+```bash
+python inference_file_example.py \
+--zone_configuration_path "data/checkout/config.json" \
+--rtsp_url "rtsp://localhost:8554/live0.stream" \
+--weights "yolov8x.pt" \
+--device "cpu" \
+--classes 0 \
+--confidence_threshold 0.3 \
+--iou_threshold 0.7
+```
+
+```bash
+python inference_file_example.py \
+--zone_configuration_path "data/traffic/config.json" \
+--rtsp_url "rtsp://localhost:8554/live0.stream" \
+--weights "yolov8x.pt" \
+--device "cpu" \
+--classes 2 5 6 7 \
+--confidence_threshold 0.3 \
+--iou_threshold 0.7
+```
+
+</details>
+
+## © license
+
+This demo integrates two main components, each with its own licensing:
+
+- ultralytics: The object detection model used in this demo, YOLOv8, is distributed
+  under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
+  You can find more details about this license here.
+
+- supervision: The analytics code that powers the zone-based analysis in this demo is
+  based on the Supervision library, which is licensed under the
+  [MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
+  makes the Supervision part of the code fully open source and freely usable in your
+  projects.

examples/time_in_zone/inference_file_example.py

@@ -0,0 +1,128 @@
+import argparse
+from typing import List
+
+import cv2
+import numpy as np
+from inference import get_model
+from utils.general import find_in_list, load_zones_config
+from utils.timers import FPSBasedTimer
+
+import supervision as sv
+
+COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
+COLOR_ANNOTATOR = sv.ColorAnnotator(color=COLORS)
+LABEL_ANNOTATOR = sv.LabelAnnotator(
+    color=COLORS, text_color=sv.Color.from_hex("#000000")
+)
+
+
+def main(
+    source_video_path: str,
+    zone_configuration_path: str,
+    model_id: str,
+    confidence: float,
+    iou: float,
+    classes: List[int],
+) -> None:
+    model = get_model(model_id=model_id)
+    tracker = sv.ByteTrack(minimum_matching_threshold=0.5)
+    video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
+    frames_generator = sv.get_video_frames_generator(source_video_path)
+
+    polygons = load_zones_config(file_path=zone_configuration_path)
+    zones = [
+        sv.PolygonZone(
+            polygon=polygon,
+            triggering_anchors=(sv.Position.CENTER,),
+        )
+        for polygon in polygons
+    ]
+    timers = [FPSBasedTimer(video_info.fps) for _ in zones]
+
+    for frame in frames_generator:
+        results = model.infer(frame, confidence=confidence, iou_threshold=iou)[0]
+        detections = sv.Detections.from_inference(results)
+        detections = detections[find_in_list(detections.class_id, classes)]
+        detections = tracker.update_with_detections(detections)
+
+        annotated_frame = frame.copy()
+
+        for idx, zone in enumerate(zones):
+            annotated_frame = sv.draw_polygon(
+                scene=annotated_frame, polygon=zone.polygon, color=COLORS.by_idx(idx)
+            )
+
+            detections_in_zone = detections[zone.trigger(detections)]
+            time_in_zone = timers[idx].tick(detections_in_zone)
+            custom_color_lookup = np.full(detections_in_zone.class_id.shape, idx)
+
+            annotated_frame = COLOR_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                custom_color_lookup=custom_color_lookup,
+            )
+            labels = [
+                f"#{tracker_id} {int(time // 60):02d}:{int(time % 60):02d}"
+                for tracker_id, time in zip(detections_in_zone.tracker_id, time_in_zone)
+            ]
+            annotated_frame = LABEL_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                labels=labels,
+                custom_color_lookup=custom_color_lookup,
+            )
+
+        cv2.imshow("Processed Video", annotated_frame)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Calculating detections dwell time in zones, using video file."
+    )
+    parser.add_argument(
+        "--zone_configuration_path",
+        type=str,
+        required=True,
+        help="Path to the zone configuration JSON file.",
+    )
+    parser.add_argument(
+        "--source_video_path",
+        type=str,
+        required=True,
+        help="Path to the source video file.",
+    )
+    parser.add_argument(
+        "--model_id", type=str, default="yolov8s-640", help="Roboflow model ID."
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        type=float,
+        default=0.3,
+        help="Confidence level for detections (0 to 1). Default is 0.3.",
+    )
+    parser.add_argument(
+        "--iou_threshold",
+        default=0.7,
+        type=float,
+        help="IOU threshold for non-max suppression. Default is 0.7.",
+    )
+    parser.add_argument(
+        "--classes",
+        nargs="*",
+        type=int,
+        default=[],
+        help="List of class IDs to track. If empty, all classes are tracked.",
+    )
+    args = parser.parse_args()
+
+    main(
+        source_video_path=args.source_video_path,
+        zone_configuration_path=args.zone_configuration_path,
+        model_id=args.model_id,
+        confidence=args.confidence_threshold,
+        iou=args.iou_threshold,
+        classes=args.classes,
+    )

examples/time_in_zone/inference_naive_stream_example.py

@@ -0,0 +1,138 @@
+import argparse
+from typing import List
+
+import cv2
+import numpy as np
+from inference import get_model
+from utils.general import find_in_list, get_stream_frames_generator, load_zones_config
+from utils.timers import ClockBasedTimer
+
+import supervision as sv
+
+COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
+COLOR_ANNOTATOR = sv.ColorAnnotator(color=COLORS)
+LABEL_ANNOTATOR = sv.LabelAnnotator(
+    color=COLORS, text_color=sv.Color.from_hex("#000000")
+)
+
+
+def main(
+    rtsp_url: str,
+    zone_configuration_path: str,
+    model_id: str,
+    confidence: float,
+    iou: float,
+    classes: List[int],
+) -> None:
+    model = get_model(model_id=model_id)
+    tracker = sv.ByteTrack(minimum_matching_threshold=0.5)
+    frames_generator = get_stream_frames_generator(rtsp_url=rtsp_url)
+    fps_monitor = sv.FPSMonitor()
+
+    polygons = load_zones_config(file_path=zone_configuration_path)
+    zones = [
+        sv.PolygonZone(
+            polygon=polygon,
+            triggering_anchors=(sv.Position.CENTER,),
+        )
+        for polygon in polygons
+    ]
+    timers = [ClockBasedTimer() for _ in zones]
+
+    for frame in frames_generator:
+        fps_monitor.tick()
+        fps = fps_monitor.fps
+
+        results = model.infer(frame, confidence=confidence, iou_threshold=iou)[0]
+        detections = sv.Detections.from_inference(results)
+        detections = detections[find_in_list(detections.class_id, classes)]
+        detections = tracker.update_with_detections(detections)
+
+        annotated_frame = frame.copy()
+        annotated_frame = sv.draw_text(
+            scene=annotated_frame,
+            text=f"{fps:.1f}",
+            text_anchor=sv.Point(40, 30),
+            background_color=sv.Color.from_hex("#A351FB"),
+            text_color=sv.Color.from_hex("#000000"),
+        )
+
+        for idx, zone in enumerate(zones):
+            annotated_frame = sv.draw_polygon(
+                scene=annotated_frame, polygon=zone.polygon, color=COLORS.by_idx(idx)
+            )
+
+            detections_in_zone = detections[zone.trigger(detections)]
+            time_in_zone = timers[idx].tick(detections_in_zone)
+            custom_color_lookup = np.full(detections_in_zone.class_id.shape, idx)
+
+            annotated_frame = COLOR_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                custom_color_lookup=custom_color_lookup,
+            )
+            labels = [
+                f"#{tracker_id} {int(time // 60):02d}:{int(time % 60):02d}"
+                for tracker_id, time in zip(detections_in_zone.tracker_id, time_in_zone)
+            ]
+            annotated_frame = LABEL_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                labels=labels,
+                custom_color_lookup=custom_color_lookup,
+            )
+
+        cv2.imshow("Processed Video", annotated_frame)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Calculating detections dwell time in zones, using RTSP stream."
+    )
+    parser.add_argument(
+        "--zone_configuration_path",
+        type=str,
+        required=True,
+        help="Path to the zone configuration JSON file.",
+    )
+    parser.add_argument(
+        "--rtsp_url",
+        type=str,
+        required=True,
+        help="Complete RTSP URL for the video stream.",
+    )
+    parser.add_argument(
+        "--model_id", type=str, default="yolov8s-640", help="Roboflow model ID."
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        type=float,
+        default=0.3,
+        help="Confidence level for detections (0 to 1). Default is 0.3.",
+    )
+    parser.add_argument(
+        "--iou_threshold",
+        default=0.7,
+        type=float,
+        help="IOU threshold for non-max suppression. Default is 0.7.",
+    )
+    parser.add_argument(
+        "--classes",
+        nargs="*",
+        type=int,
+        default=[],
+        help="List of class IDs to track. If empty, all classes are tracked.",
+    )
+    args = parser.parse_args()
+
+    main(
+        rtsp_url=args.rtsp_url,
+        zone_configuration_path=args.zone_configuration_path,
+        model_id=args.model_id,
+        confidence=args.confidence_threshold,
+        iou=args.iou_threshold,
+        classes=args.classes,
+    )

examples/time_in_zone/inference_stream_example.py

@@ -0,0 +1,153 @@
+import argparse
+from typing import List
+
+import cv2
+import numpy as np
+from inference import InferencePipeline
+from inference.core.interfaces.camera.entities import VideoFrame
+from utils.general import find_in_list, load_zones_config
+from utils.timers import ClockBasedTimer
+
+import supervision as sv
+
+COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
+COLOR_ANNOTATOR = sv.ColorAnnotator(color=COLORS)
+LABEL_ANNOTATOR = sv.LabelAnnotator(
+    color=COLORS, text_color=sv.Color.from_hex("#000000")
+)
+
+
+class CustomSink:
+    def __init__(self, zone_configuration_path: str, classes: List[int]):
+        self.classes = classes
+        self.tracker = sv.ByteTrack(minimum_matching_threshold=0.5)
+        self.fps_monitor = sv.FPSMonitor()
+        self.polygons = load_zones_config(file_path=zone_configuration_path)
+        self.timers = [ClockBasedTimer() for _ in self.polygons]
+        self.zones = [
+            sv.PolygonZone(
+                polygon=polygon,
+                triggering_anchors=(sv.Position.CENTER,),
+            )
+            for polygon in self.polygons
+        ]
+
+    def on_prediction(self, result: dict, frame: VideoFrame) -> None:
+        self.fps_monitor.tick()
+        fps = self.fps_monitor.fps
+
+        detections = sv.Detections.from_inference(result)
+        detections = detections[find_in_list(detections.class_id, self.classes)]
+        detections = self.tracker.update_with_detections(detections)
+
+        annotated_frame = frame.image.copy()
+        annotated_frame = sv.draw_text(
+            scene=annotated_frame,
+            text=f"{fps:.1f}",
+            text_anchor=sv.Point(40, 30),
+            background_color=sv.Color.from_hex("#A351FB"),
+            text_color=sv.Color.from_hex("#000000"),
+        )
+
+        for idx, zone in enumerate(self.zones):
+            annotated_frame = sv.draw_polygon(
+                scene=annotated_frame, polygon=zone.polygon, color=COLORS.by_idx(idx)
+            )
+
+            detections_in_zone = detections[zone.trigger(detections)]
+            time_in_zone = self.timers[idx].tick(detections_in_zone)
+            custom_color_lookup = np.full(detections_in_zone.class_id.shape, idx)
+
+            annotated_frame = COLOR_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                custom_color_lookup=custom_color_lookup,
+            )
+            labels = [
+                f"#{tracker_id} {int(time // 60):02d}:{int(time % 60):02d}"
+                for tracker_id, time in zip(detections_in_zone.tracker_id, time_in_zone)
+            ]
+            annotated_frame = LABEL_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                labels=labels,
+                custom_color_lookup=custom_color_lookup,
+            )
+        cv2.imshow("Processed Video", annotated_frame)
+        cv2.waitKey(1)
+
+
+def main(
+    rtsp_url: str,
+    zone_configuration_path: str,
+    model_id: str,
+    confidence: float,
+    iou: float,
+    classes: List[int],
+) -> None:
+    sink = CustomSink(zone_configuration_path=zone_configuration_path, classes=classes)
+
+    pipeline = InferencePipeline.init(
+        model_id=model_id,
+        video_reference=rtsp_url,
+        on_prediction=sink.on_prediction,
+        confidence=confidence,
+        iou_threshold=iou,
+    )
+
+    pipeline.start()
+
+    try:
+        pipeline.join()
+    except KeyboardInterrupt:
+        pipeline.terminate()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Calculating detections dwell time in zones, using RTSP stream."
+    )
+    parser.add_argument(
+        "--zone_configuration_path",
+        type=str,
+        required=True,
+        help="Path to the zone configuration JSON file.",
+    )
+    parser.add_argument(
+        "--rtsp_url",
+        type=str,
+        required=True,
+        help="Complete RTSP URL for the video stream.",
+    )
+    parser.add_argument(
+        "--model_id", type=str, default="yolov8s-640", help="Roboflow model ID."
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        type=float,
+        default=0.3,
+        help="Confidence level for detections (0 to 1). Default is 0.3.",
+    )
+    parser.add_argument(
+        "--iou_threshold",
+        default=0.7,
+        type=float,
+        help="IOU threshold for non-max suppression. Default is 0.7.",
+    )
+    parser.add_argument(
+        "--classes",
+        nargs="*",
+        type=int,
+        default=[],
+        help="List of class IDs to track. If empty, all classes are tracked.",
+    )
+    args = parser.parse_args()
+
+    main(
+        rtsp_url=args.rtsp_url,
+        zone_configuration_path=args.zone_configuration_path,
+        model_id=args.model_id,
+        confidence=args.confidence_threshold,
+        iou=args.iou_threshold,
+        classes=args.classes,
+    )

examples/time_in_zone/requirements.txt

@@ -0,0 +1,5 @@
+opencv-python
+supervision>=0.20.0
+ultralytics
+inference<=0.9.21
+pytube

examples/time_in_zone/scripts/download_from_youtube.py

@@ -0,0 +1,46 @@
+import argparse
+import os
+from typing import Optional
+
+from pytube import YouTube
+
+
+def main(url: str, output_path: Optional[str], file_name: Optional[str]) -> None:
+    yt = YouTube(url)
+    stream = yt.streams.get_highest_resolution()
+
+    if not os.path.exists(output_path):
+        os.makedirs(output_path)
+
+    stream.download(output_path=output_path, filename=file_name)
+    final_name = file_name if file_name else yt.title
+    final_path = output_path if output_path else "current directory"
+    print(f"Download completed! Video saved as '{final_name}' in '{final_path}'.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Download a specific YouTube video by providing its URL."
+    )
+    parser.add_argument(
+        "--url",
+        type=str,
+        required=True,
+        help="The full URL of the YouTube video you wish to download.",
+    )
+    parser.add_argument(
+        "--output_path",
+        type=str,
+        default="data/source",
+        required=False,
+        help="Optional. Specifies the directory where the video will be saved.",
+    )
+    parser.add_argument(
+        "--file_name",
+        type=str,
+        default="video.mp4",
+        required=False,
+        help="Optional. Sets the name of the saved video file.",
+    )
+    args = parser.parse_args()
+    main(url=args.url, output_path=args.output_path, file_name=args.file_name)

examples/time_in_zone/scripts/draw_zones.py

@@ -0,0 +1,176 @@
+import argparse
+import json
+import os
+from typing import Any, Optional, Tuple
+
+import cv2
+import numpy as np
+
+import supervision as sv
+
+KEY_ENTER = 13
+KEY_NEWLINE = 10
+KEY_ESCAPE = 27
+KEY_QUIT = ord("q")
+KEY_SAVE = ord("s")
+
+THICKNESS = 2
+COLORS = sv.ColorPalette.DEFAULT
+WINDOW_NAME = "Draw Zones"
+POLYGONS = [[]]
+
+current_mouse_position: Optional[Tuple[int, int]] = None
+
+
+def resolve_source(source_path: str) -> Optional[np.ndarray]:
+    if not os.path.exists(source_path):
+        return None
+
+    image = cv2.imread(source_path)
+    if image is not None:
+        return image
+
+    frame_generator = sv.get_video_frames_generator(source_path=source_path)
+    frame = next(frame_generator)
+    return frame
+
+
+def mouse_event(event: int, x: int, y: int, flags: int, param: Any) -> None:
+    global current_mouse_position
+    if event == cv2.EVENT_MOUSEMOVE:
+        current_mouse_position = (x, y)
+    elif event == cv2.EVENT_LBUTTONDOWN:
+        POLYGONS[-1].append((x, y))
+
+
+def redraw(image: np.ndarray, original_image: np.ndarray) -> None:
+    global POLYGONS, current_mouse_position
+    image[:] = original_image.copy()
+    for idx, polygon in enumerate(POLYGONS):
+        color = (
+            COLORS.by_idx(idx).as_bgr()
+            if idx < len(POLYGONS) - 1
+            else sv.Color.WHITE.as_bgr()
+        )
+
+        if len(polygon) > 1:
+            for i in range(1, len(polygon)):
+                cv2.line(
+                    img=image,
+                    pt1=polygon[i - 1],
+                    pt2=polygon[i],
+                    color=color,
+                    thickness=THICKNESS,
+                )
+            if idx < len(POLYGONS) - 1:
+                cv2.line(
+                    img=image,
+                    pt1=polygon[-1],
+                    pt2=polygon[0],
+                    color=color,
+                    thickness=THICKNESS,
+                )
+        if idx == len(POLYGONS) - 1 and current_mouse_position is not None and polygon:
+            cv2.line(
+                img=image,
+                pt1=polygon[-1],
+                pt2=current_mouse_position,
+                color=color,
+                thickness=THICKNESS,
+            )
+    cv2.imshow(WINDOW_NAME, image)
+
+
+def close_and_finalize_polygon(image: np.ndarray, original_image: np.ndarray) -> None:
+    if len(POLYGONS[-1]) > 2:
+        cv2.line(
+            img=image,
+            pt1=POLYGONS[-1][-1],
+            pt2=POLYGONS[-1][0],
+            color=COLORS.by_idx(0).as_bgr(),
+            thickness=THICKNESS,
+        )
+    POLYGONS.append([])
+    image[:] = original_image.copy()
+    redraw_polygons(image)
+    cv2.imshow(WINDOW_NAME, image)
+
+
+def redraw_polygons(image: np.ndarray) -> None:
+    for idx, polygon in enumerate(POLYGONS[:-1]):
+        if len(polygon) > 1:
+            color = COLORS.by_idx(idx).as_bgr()
+            for i in range(len(polygon) - 1):
+                cv2.line(
+                    img=image,
+                    pt1=polygon[i],
+                    pt2=polygon[i + 1],
+                    color=color,
+                    thickness=THICKNESS,
+                )
+            cv2.line(
+                img=image,
+                pt1=polygon[-1],
+                pt2=polygon[0],
+                color=color,
+                thickness=THICKNESS,
+            )
+
+
+def save_polygons_to_json(polygons, target_path):
+    data_to_save = polygons if polygons[-1] else polygons[:-1]
+    with open(target_path, "w") as f:
+        json.dump(data_to_save, f)
+
+
+def main(source_path: str, zone_configuration_path: str) -> None:
+    global current_mouse_position
+    original_image = resolve_source(source_path=source_path)
+    if original_image is None:
+        print("Failed to load source image.")
+        return
+
+    image = original_image.copy()
+    cv2.imshow(WINDOW_NAME, image)
+    cv2.setMouseCallback(WINDOW_NAME, mouse_event, image)
+
+    while True:
+        key = cv2.waitKey(1) & 0xFF
+        if key == KEY_ENTER or key == KEY_NEWLINE:
+            close_and_finalize_polygon(image, original_image)
+        elif key == KEY_ESCAPE:
+            POLYGONS[-1] = []
+            current_mouse_position = None
+        elif key == KEY_SAVE:
+            save_polygons_to_json(POLYGONS, zone_configuration_path)
+            print(f"Polygons saved to {zone_configuration_path}")
+            break
+        redraw(image, original_image)
+        if key == KEY_QUIT:
+            break
+
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Interactively draw polygons on images or video frames and save "
+        "the annotations."
+    )
+    parser.add_argument(
+        "--source_path",
+        type=str,
+        required=True,
+        help="Path to the source image or video file for drawing polygons.",
+    )
+    parser.add_argument(
+        "--zone_configuration_path",
+        type=str,
+        required=True,
+        help="Path where the polygon annotations will be saved as a JSON file.",
+    )
+    arguments = parser.parse_args()
+    main(
+        source_path=arguments.source_path,
+        zone_configuration_path=arguments.zone_configuration_path,
+    )

examples/time_in_zone/scripts/stream_from_file.py

@@ -0,0 +1,104 @@
+import argparse
+import os
+import subprocess
+import tempfile
+from glob import glob
+from threading import Thread
+
+import yaml
+
+SERVER_CONFIG = {"protocols": ["tcp"], "paths": {"all": {"source": "publisher"}}}
+BASE_STREAM_URL = "rtsp://localhost:8554/live"
+
+
+def main(video_directory: str, number_of_streams: int) -> None:
+    video_files = find_video_files_in_directory(video_directory, number_of_streams)
+    try:
+        with tempfile.TemporaryDirectory() as temporary_directory:
+            config_file_path = create_server_config_file(temporary_directory)
+            run_rtsp_server(config_path=config_file_path)
+            stream_videos(video_files)
+    finally:
+        stop_rtsp_server()
+
+
+def find_video_files_in_directory(directory: str, limit: int) -> list:
+    video_formats = ["*.mp4", "*.webm"]
+    video_paths = []
+    for video_format in video_formats:
+        video_paths.extend(glob(os.path.join(directory, video_format)))
+    return video_paths[:limit]
+
+
+def create_server_config_file(directory: str) -> str:
+    config_path = os.path.join(directory, "rtsp-simple-server.yml")
+    with open(config_path, "w") as config_file:
+        yaml.dump(SERVER_CONFIG, config_file)
+    return config_path
+
+
+def run_rtsp_server(config_path: str) -> None:
+    command = (
+        "docker run --rm --name rtsp_server -d -v "
+        f"{config_path}:/rtsp-simple-server.yml -p 8554:8554 "
+        "aler9/rtsp-simple-server:v1.3.0"
+    )
+    if run_command(command.split()) != 0:
+        raise RuntimeError("Could not start the RTSP server!")
+
+
+def stop_rtsp_server() -> None:
+    run_command("docker kill rtsp_server".split())
+
+
+def stream_videos(video_files: list) -> None:
+    threads = []
+    for index, video_file in enumerate(video_files):
+        stream_url = f"{BASE_STREAM_URL}{index}.stream"
+        print(f"Streaming {video_file} under {stream_url}")
+        thread = stream_video_to_url(video_file, stream_url)
+        threads.append(thread)
+    for thread in threads:
+        thread.join()
+
+
+def stream_video_to_url(video_path: str, stream_url: str) -> Thread:
+    command = (
+        f"ffmpeg -re -stream_loop -1 -i {video_path} "
+        f"-f rtsp -rtsp_transport tcp {stream_url}"
+    )
+    return run_command_in_thread(command.split())
+
+
+def run_command_in_thread(command: list) -> Thread:
+    thread = Thread(target=run_command, args=(command,))
+    thread.start()
+    return thread
+
+
+def run_command(command: list) -> int:
+    process = subprocess.run(command)
+    return process.returncode
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Script to stream videos using RTSP protocol."
+    )
+    parser.add_argument(
+        "--video_directory",
+        type=str,
+        required=True,
+        help="Directory containing video files to stream.",
+    )
+    parser.add_argument(
+        "--number_of_streams",
+        type=int,
+        default=6,
+        help="Number of video files to stream.",
+    )
+    arguments = parser.parse_args()
+    main(
+        video_directory=arguments.video_directory,
+        number_of_streams=arguments.number_of_streams,
+    )

examples/time_in_zone/ultralytics_file_example.py

@@ -0,0 +1,140 @@
+import argparse
+from typing import List
+
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from utils.general import find_in_list, load_zones_config
+from utils.timers import FPSBasedTimer
+
+import supervision as sv
+
+COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
+COLOR_ANNOTATOR = sv.ColorAnnotator(color=COLORS)
+LABEL_ANNOTATOR = sv.LabelAnnotator(
+    color=COLORS, text_color=sv.Color.from_hex("#000000")
+)
+
+
+def main(
+    source_video_path: str,
+    zone_configuration_path: str,
+    weights: str,
+    device: str,
+    confidence: float,
+    iou: float,
+    classes: List[int],
+) -> None:
+    model = YOLO(weights)
+    tracker = sv.ByteTrack(minimum_matching_threshold=0.5)
+    video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
+    frames_generator = sv.get_video_frames_generator(source_video_path)
+
+    polygons = load_zones_config(file_path=zone_configuration_path)
+    zones = [
+        sv.PolygonZone(
+            polygon=polygon,
+            triggering_anchors=(sv.Position.CENTER,),
+        )
+        for polygon in polygons
+    ]
+    timers = [FPSBasedTimer(video_info.fps) for _ in zones]
+
+    for frame in frames_generator:
+        results = model(frame, verbose=False, device=device, conf=confidence)[0]
+        detections = sv.Detections.from_ultralytics(results)
+        detections = detections[find_in_list(detections.class_id, classes)]
+        detections = detections.with_nms(threshold=iou)
+        detections = tracker.update_with_detections(detections)
+
+        annotated_frame = frame.copy()
+
+        for idx, zone in enumerate(zones):
+            annotated_frame = sv.draw_polygon(
+                scene=annotated_frame, polygon=zone.polygon, color=COLORS.by_idx(idx)
+            )
+
+            detections_in_zone = detections[zone.trigger(detections)]
+            time_in_zone = timers[idx].tick(detections_in_zone)
+            custom_color_lookup = np.full(detections_in_zone.class_id.shape, idx)
+
+            annotated_frame = COLOR_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                custom_color_lookup=custom_color_lookup,
+            )
+            labels = [
+                f"#{tracker_id} {int(time // 60):02d}:{int(time % 60):02d}"
+                for tracker_id, time in zip(detections_in_zone.tracker_id, time_in_zone)
+            ]
+            annotated_frame = LABEL_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                labels=labels,
+                custom_color_lookup=custom_color_lookup,
+            )
+
+        cv2.imshow("Processed Video", annotated_frame)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Calculating detections dwell time in zones, using video file."
+    )
+    parser.add_argument(
+        "--zone_configuration_path",
+        type=str,
+        required=True,
+        help="Path to the zone configuration JSON file.",
+    )
+    parser.add_argument(
+        "--source_video_path",
+        type=str,
+        required=True,
+        help="Path to the source video file.",
+    )
+    parser.add_argument(
+        "--weights",
+        type=str,
+        default="yolov8s.pt",
+        help="Path to the model weights file. Default is 'yolov8s.pt'.",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cpu",
+        help="Computation device ('cpu', 'mps' or 'cuda'). Default is 'cpu'.",
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        type=float,
+        default=0.3,
+        help="Confidence level for detections (0 to 1). Default is 0.3.",
+    )
+    parser.add_argument(
+        "--iou_threshold",
+        default=0.7,
+        type=float,
+        help="IOU threshold for non-max suppression. Default is 0.7.",
+    )
+    parser.add_argument(
+        "--classes",
+        nargs="*",
+        type=int,
+        default=[],
+        help="List of class IDs to track. If empty, all classes are tracked.",
+    )
+    args = parser.parse_args()
+
+    main(
+        source_video_path=args.source_video_path,
+        zone_configuration_path=args.zone_configuration_path,
+        weights=args.weights,
+        device=args.device,
+        confidence=args.confidence_threshold,
+        iou=args.iou_threshold,
+        classes=args.classes,
+    )

examples/time_in_zone/ultralytics_naive_stream_example.py

@@ -0,0 +1,150 @@
+import argparse
+from typing import List
+
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from utils.general import find_in_list, get_stream_frames_generator, load_zones_config
+from utils.timers import ClockBasedTimer
+
+import supervision as sv
+
+COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
+COLOR_ANNOTATOR = sv.ColorAnnotator(color=COLORS)
+LABEL_ANNOTATOR = sv.LabelAnnotator(
+    color=COLORS, text_color=sv.Color.from_hex("#000000")
+)
+
+
+def main(
+    rtsp_url: str,
+    zone_configuration_path: str,
+    weights: str,
+    device: str,
+    confidence: float,
+    iou: float,
+    classes: List[int],
+) -> None:
+    model = YOLO(weights)
+    tracker = sv.ByteTrack(minimum_matching_threshold=0.5)
+    frames_generator = get_stream_frames_generator(rtsp_url=rtsp_url)
+    fps_monitor = sv.FPSMonitor()
+
+    polygons = load_zones_config(file_path=zone_configuration_path)
+    zones = [
+        sv.PolygonZone(
+            polygon=polygon,
+            triggering_anchors=(sv.Position.CENTER,),
+        )
+        for polygon in polygons
+    ]
+    timers = [ClockBasedTimer() for _ in zones]
+
+    for frame in frames_generator:
+        fps_monitor.tick()
+        fps = fps_monitor.fps
+
+        results = model(frame, verbose=False, device=device, conf=confidence)[0]
+        detections = sv.Detections.from_ultralytics(results)
+        detections = detections[find_in_list(detections.class_id, classes)]
+        detections = detections.with_nms(threshold=iou)
+        detections = tracker.update_with_detections(detections)
+
+        annotated_frame = frame.copy()
+        annotated_frame = sv.draw_text(
+            scene=annotated_frame,
+            text=f"{fps:.1f}",
+            text_anchor=sv.Point(40, 30),
+            background_color=sv.Color.from_hex("#A351FB"),
+            text_color=sv.Color.from_hex("#000000"),
+        )
+
+        for idx, zone in enumerate(zones):
+            annotated_frame = sv.draw_polygon(
+                scene=annotated_frame, polygon=zone.polygon, color=COLORS.by_idx(idx)
+            )
+
+            detections_in_zone = detections[zone.trigger(detections)]
+            time_in_zone = timers[idx].tick(detections_in_zone)
+            custom_color_lookup = np.full(detections_in_zone.class_id.shape, idx)
+
+            annotated_frame = COLOR_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                custom_color_lookup=custom_color_lookup,
+            )
+            labels = [
+                f"#{tracker_id} {int(time // 60):02d}:{int(time % 60):02d}"
+                for tracker_id, time in zip(detections_in_zone.tracker_id, time_in_zone)
+            ]
+            annotated_frame = LABEL_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                labels=labels,
+                custom_color_lookup=custom_color_lookup,
+            )
+
+        cv2.imshow("Processed Video", annotated_frame)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Calculating detections dwell time in zones, using RTSP stream."
+    )
+    parser.add_argument(
+        "--zone_configuration_path",
+        type=str,
+        required=True,
+        help="Path to the zone configuration JSON file.",
+    )
+    parser.add_argument(
+        "--rtsp_url",
+        type=str,
+        required=True,
+        help="Complete RTSP URL for the video stream.",
+    )
+    parser.add_argument(
+        "--weights",
+        type=str,
+        default="yolov8s.pt",
+        help="Path to the model weights file. Default is 'yolov8s.pt'.",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cpu",
+        help="Computation device ('cpu', 'mps' or 'cuda'). Default is 'cpu'.",
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        type=float,
+        default=0.3,
+        help="Confidence level for detections (0 to 1). Default is 0.3.",
+    )
+    parser.add_argument(
+        "--iou_threshold",
+        default=0.7,
+        type=float,
+        help="IOU threshold for non-max suppression. Default is 0.7.",
+    )
+    parser.add_argument(
+        "--classes",
+        nargs="*",
+        type=int,
+        default=[],
+        help="List of class IDs to track. If empty, all classes are tracked.",
+    )
+    args = parser.parse_args()
+
+    main(
+        rtsp_url=args.rtsp_url,
+        zone_configuration_path=args.zone_configuration_path,
+        weights=args.weights,
+        device=args.device,
+        confidence=args.confidence_threshold,
+        iou=args.iou_threshold,
+        classes=args.classes,
+    )

examples/time_in_zone/ultralytics_stream_example.py

@@ -0,0 +1,168 @@
+import argparse
+from typing import List
+
+import cv2
+import numpy as np
+from inference import InferencePipeline
+from inference.core.interfaces.camera.entities import VideoFrame
+from ultralytics import YOLO
+from utils.general import find_in_list, load_zones_config
+from utils.timers import ClockBasedTimer
+
+import supervision as sv
+
+COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
+COLOR_ANNOTATOR = sv.ColorAnnotator(color=COLORS)
+LABEL_ANNOTATOR = sv.LabelAnnotator(
+    color=COLORS, text_color=sv.Color.from_hex("#000000")
+)
+
+
+class CustomSink:
+    def __init__(self, zone_configuration_path: str, classes: List[int]):
+        self.classes = classes
+        self.tracker = sv.ByteTrack(minimum_matching_threshold=0.8)
+        self.fps_monitor = sv.FPSMonitor()
+        self.polygons = load_zones_config(file_path=zone_configuration_path)
+        self.timers = [ClockBasedTimer() for _ in self.polygons]
+        self.zones = [
+            sv.PolygonZone(
+                polygon=polygon,
+                triggering_anchors=(sv.Position.CENTER,),
+            )
+            for polygon in self.polygons
+        ]
+
+    def on_prediction(self, detections: sv.Detections, frame: VideoFrame) -> None:
+        self.fps_monitor.tick()
+        fps = self.fps_monitor.fps
+
+        detections = detections[find_in_list(detections.class_id, self.classes)]
+        detections = self.tracker.update_with_detections(detections)
+
+        annotated_frame = frame.image.copy()
+        annotated_frame = sv.draw_text(
+            scene=annotated_frame,
+            text=f"{fps:.1f}",
+            text_anchor=sv.Point(40, 30),
+            background_color=sv.Color.from_hex("#A351FB"),
+            text_color=sv.Color.from_hex("#000000"),
+        )
+
+        for idx, zone in enumerate(self.zones):
+            annotated_frame = sv.draw_polygon(
+                scene=annotated_frame, polygon=zone.polygon, color=COLORS.by_idx(idx)
+            )
+
+            detections_in_zone = detections[zone.trigger(detections)]
+            time_in_zone = self.timers[idx].tick(detections_in_zone)
+            custom_color_lookup = np.full(detections_in_zone.class_id.shape, idx)
+
+            annotated_frame = COLOR_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                custom_color_lookup=custom_color_lookup,
+            )
+            labels = [
+                f"#{tracker_id} {int(time // 60):02d}:{int(time % 60):02d}"
+                for tracker_id, time in zip(detections_in_zone.tracker_id, time_in_zone)
+            ]
+            annotated_frame = LABEL_ANNOTATOR.annotate(
+                scene=annotated_frame,
+                detections=detections_in_zone,
+                labels=labels,
+                custom_color_lookup=custom_color_lookup,
+            )
+        cv2.imshow("Processed Video", annotated_frame)
+        cv2.waitKey(1)
+
+
+def main(
+    rtsp_url: str,
+    zone_configuration_path: str,
+    weights: str,
+    device: str,
+    confidence: float,
+    iou: float,
+    classes: List[int],
+) -> None:
+    model = YOLO(weights)
+
+    def inference_callback(frame: VideoFrame) -> sv.Detections:
+        results = model(frame.image, verbose=False, conf=confidence, device=device)[0]
+        return sv.Detections.from_ultralytics(results).with_nms(threshold=iou)
+
+    sink = CustomSink(zone_configuration_path=zone_configuration_path, classes=classes)
+
+    pipeline = InferencePipeline.init_with_custom_logic(
+        video_reference=rtsp_url,
+        on_video_frame=inference_callback,
+        on_prediction=sink.on_prediction,
+    )
+
+    pipeline.start()
+
+    try:
+        pipeline.join()
+    except KeyboardInterrupt:
+        pipeline.terminate()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Calculating detections dwell time in zones, using RTSP stream."
+    )
+    parser.add_argument(
+        "--zone_configuration_path",
+        type=str,
+        required=True,
+        help="Path to the zone configuration JSON file.",
+    )
+    parser.add_argument(
+        "--rtsp_url",
+        type=str,
+        required=True,
+        help="Complete RTSP URL for the video stream.",
+    )
+    parser.add_argument(
+        "--weights",
+        type=str,
+        default="yolov8s.pt",
+        help="Path to the model weights file. Default is 'yolov8s.pt'.",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cpu",
+        help="Computation device ('cpu', 'mps' or 'cuda'). Default is 'cpu'.",
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        type=float,
+        default=0.3,
+        help="Confidence level for detections (0 to 1). Default is 0.3.",
+    )
+    parser.add_argument(
+        "--iou_threshold",
+        default=0.7,
+        type=float,
+        help="IOU threshold for non-max suppression. Default is 0.7.",
+    )
+    parser.add_argument(
+        "--classes",
+        nargs="*",
+        type=int,
+        default=[],
+        help="List of class IDs to track. If empty, all classes are tracked.",
+    )
+    args = parser.parse_args()
+
+    main(
+        rtsp_url=args.rtsp_url,
+        zone_configuration_path=args.zone_configuration_path,
+        weights=args.weights,
+        device=args.device,
+        confidence=args.confidence_threshold,
+        iou=args.iou_threshold,
+        classes=args.classes,
+    )

examples/time_in_zone/utils/general.py

@@ -0,0 +1,66 @@
+import json
+from typing import Generator, List
+
+import cv2
+import numpy as np
+
+
+def load_zones_config(file_path: str) -> List[np.ndarray]:
+    """
+    Load polygon zone configurations from a JSON file.
+
+    This function reads a JSON file which contains polygon coordinates, and
+    converts them into a list of NumPy arrays. Each polygon is represented as
+    a NumPy array of coordinates.
+
+    Args:
+        file_path (str): The path to the JSON configuration file.
+
+    Returns:
+        List[np.ndarray]: A list of polygons, each represented as a NumPy array.
+    """
+    with open(file_path, "r") as file:
+        data = json.load(file)
+        return [np.array(polygon, np.int32) for polygon in data]
+
+
+def find_in_list(array: np.ndarray, search_list: List[int]) -> np.ndarray:
+    """Determines if elements of a numpy array are present in a list.
+
+    Args:
+        array (np.ndarray): The numpy array of integers to check.
+        search_list (List[int]): The list of integers to search within.
+
+    Returns:
+        np.ndarray: A numpy array of booleans, where each boolean indicates whether
+        the corresponding element in `array` is found in `search_list`.
+    """
+    if not search_list:
+        return np.ones(array.shape, dtype=bool)
+    else:
+        return np.isin(array, search_list)
+
+
+def get_stream_frames_generator(rtsp_url: str) -> Generator[np.ndarray, None, None]:
+    """
+    Generator function to yield frames from an RTSP stream.
+
+    Args:
+        rtsp_url (str): URL of the RTSP video stream.
+
+    Yields:
+        np.ndarray: The next frame from the video stream.
+    """
+    cap = cv2.VideoCapture(rtsp_url)
+    if not cap.isOpened():
+        raise Exception("Error: Could not open video stream.")
+
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                print("End of stream or error reading frame.")
+                break
+            yield frame
+    finally:
+        cap.release()

examples/time_in_zone/utils/timers.py

@@ -0,0 +1,88 @@
+from datetime import datetime
+from typing import Dict
+
+import numpy as np
+
+import supervision as sv
+
+
+class FPSBasedTimer:
+    """
+    A timer that calculates the duration each object has been detected based on frames
+    per second (FPS).
+
+    Attributes:
+        fps (int): The frame rate of the video stream, used to calculate time durations.
+        frame_id (int): The current frame number in the sequence.
+        tracker_id2frame_id (Dict[int, int]): Maps each tracker's ID to the frame number
+            at which it was first detected.
+    """
+
+    def __init__(self, fps: int = 30) -> None:
+        """Initializes the FPSBasedTimer with the specified frames per second rate.
+
+        Args:
+            fps (int, optional): The frame rate of the video stream. Defaults to 30.
+        """
+        self.fps = fps
+        self.frame_id = 0
+        self.tracker_id2frame_id: Dict[int, int] = {}
+
+    def tick(self, detections: sv.Detections) -> np.ndarray:
+        """Processes the current frame, updating time durations for each tracker.
+
+        Args:
+            detections: The detections for the current frame, including tracker IDs.
+
+        Returns:
+            np.ndarray: Time durations (in seconds) for each detected tracker, since
+                their first detection.
+        """
+        self.frame_id += 1
+        times = []
+
+        for tracker_id in detections.tracker_id:
+            self.tracker_id2frame_id.setdefault(tracker_id, self.frame_id)
+
+            start_frame_id = self.tracker_id2frame_id[tracker_id]
+            time_duration = (self.frame_id - start_frame_id) / self.fps
+            times.append(time_duration)
+
+        return np.array(times)
+
+
+class ClockBasedTimer:
+    """
+    A timer that calculates the duration each object has been detected based on the
+    system clock.
+
+    Attributes:
+        tracker_id2start_time (Dict[int, datetime]): Maps each tracker's ID to the
+            datetime when it was first detected.
+    """
+
+    def __init__(self) -> None:
+        """Initializes the ClockBasedTimer."""
+        self.tracker_id2start_time: Dict[int, datetime] = {}
+
+    def tick(self, detections: sv.Detections) -> np.ndarray:
+        """Processes the current frame, updating time durations for each tracker.
+
+        Args:
+            detections: The detections for the current frame, including tracker IDs.
+
+        Returns:
+            np.ndarray: Time durations (in seconds) for each detected tracker, since
+                their first detection.
+        """
+        current_time = datetime.now()
+        times = []
+
+        for tracker_id in detections.tracker_id:
+            self.tracker_id2start_time.setdefault(tracker_id, current_time)
+
+            start_time = self.tracker_id2start_time[tracker_id]
+            time_duration = (current_time - start_time).total_seconds()
+            times.append(time_duration)
+
+        return np.array(times)

examples/tracking/requirements.txt

@@ -1,4 +1,4 @@
 inference
-supervision
+supervision==0.19.0
 tqdm
 ultralytics

examples/traffic_analysis/inference_example.py

@@ -1,6 +1,6 @@
 import argparse
 import os
-from typing import Dict, List, Set, Tuple
+from typing import Dict, Iterable, List, Set
 
 import cv2
 import numpy as np
@@ -9,7 +9,8 @@ from tqdm import tqdm
 
 import supervision as sv
 
-COLORS = sv.ColorPalette.default()
+COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
+
 
 ZONE_IN_POLYGONS = [
     np.array([[592, 282], [900, 282], [900, 82], [592, 82]]),
@@ -48,23 +49,23 @@ 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]
 
 
 def initiate_polygon_zones(
     polygons: List[np.ndarray],
-    frame_resolution_wh: Tuple[int, int],
-    triggering_position: sv.Position = sv.Position.CENTER,
+    triggering_anchors: Iterable[sv.Position] = [sv.Position.CENTER],
 ) -> List[sv.PolygonZone]:
     return [
         sv.PolygonZone(
             polygon=polygon,
-            frame_resolution_wh=frame_resolution_wh,
-            triggering_position=triggering_position,
+            triggering_anchors=triggering_anchors,
         )
         for polygon in polygons
     ]
@@ -89,14 +90,13 @@ class VideoProcessor:
         self.tracker = sv.ByteTrack()
 
         self.video_info = sv.VideoInfo.from_video_path(source_video_path)
-        self.zones_in = initiate_polygon_zones(
-            ZONE_IN_POLYGONS, self.video_info.resolution_wh, sv.Position.CENTER
-        )
-        self.zones_out = initiate_polygon_zones(
-            ZONE_OUT_POLYGONS, self.video_info.resolution_wh, sv.Position.CENTER
-        )
+        self.zones_in = initiate_polygon_zones(ZONE_IN_POLYGONS, [sv.Position.CENTER])
+        self.zones_out = initiate_polygon_zones(ZONE_OUT_POLYGONS, [sv.Position.CENTER])
 
-        self.box_annotator = sv.BoxAnnotator(color=COLORS)
+        self.bounding_box_annotator = sv.BoundingBoxAnnotator(color=COLORS)
+        self.label_annotator = sv.LabelAnnotator(
+            color=COLORS, text_color=sv.Color.BLACK
+        )
         self.trace_annotator = sv.TraceAnnotator(
             color=COLORS, position=sv.Position.CENTER, trace_length=100, thickness=2
         )
@@ -134,7 +134,10 @@ class VideoProcessor:
 
         labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]
         annotated_frame = self.trace_annotator.annotate(annotated_frame, detections)
-        annotated_frame = self.box_annotator.annotate(
+        annotated_frame = self.bounding_box_annotator.annotate(
+            annotated_frame, detections
+        )
+        annotated_frame = self.label_annotator.annotate(
             annotated_frame, detections, labels
         )
 
@@ -165,7 +168,7 @@ class VideoProcessor:
         detections_in_zones = []
         detections_out_zones = []
 
-        for i, (zone_in, zone_out) in enumerate(zip(self.zones_in, self.zones_out)):
+        for zone_in, zone_out in zip(self.zones_in, self.zones_out):
             detections_in_zone = detections[zone_in.trigger(detections=detections)]
             detections_in_zones.append(detections_in_zone)
             detections_out_zone = detections[zone_out.trigger(detections=detections)]

examples/traffic_analysis/requirements.txt

@@ -1,5 +1,5 @@
 gdown
-inference
-supervision
+inference<=0.9.21
+supervision>=0.20.0
 tqdm
 ultralytics

examples/traffic_analysis/ultralytics_example.py

@@ -1,5 +1,5 @@
 import argparse
-from typing import Dict, List, Set, Tuple
+from typing import Dict, Iterable, List, Set
 
 import cv2
 import numpy as np
@@ -8,7 +8,7 @@ from ultralytics import YOLO
 
 import supervision as sv
 
-COLORS = sv.ColorPalette.default()
+COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
 
 ZONE_IN_POLYGONS = [
     np.array([[592, 282], [900, 282], [900, 82], [592, 82]]),
@@ -47,23 +47,23 @@ 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]
 
 
 def initiate_polygon_zones(
     polygons: List[np.ndarray],
-    frame_resolution_wh: Tuple[int, int],
-    triggering_position: sv.Position = sv.Position.CENTER,
+    triggering_anchors: Iterable[sv.Position] = [sv.Position.CENTER],
 ) -> List[sv.PolygonZone]:
     return [
         sv.PolygonZone(
             polygon=polygon,
-            frame_resolution_wh=frame_resolution_wh,
-            triggering_position=triggering_position,
+            triggering_anchors=triggering_anchors,
         )
         for polygon in polygons
     ]
@@ -87,14 +87,13 @@ class VideoProcessor:
         self.tracker = sv.ByteTrack()
 
         self.video_info = sv.VideoInfo.from_video_path(source_video_path)
-        self.zones_in = initiate_polygon_zones(
-            ZONE_IN_POLYGONS, self.video_info.resolution_wh, sv.Position.CENTER
-        )
-        self.zones_out = initiate_polygon_zones(
-            ZONE_OUT_POLYGONS, self.video_info.resolution_wh, sv.Position.CENTER
-        )
+        self.zones_in = initiate_polygon_zones(ZONE_IN_POLYGONS, [sv.Position.CENTER])
+        self.zones_out = initiate_polygon_zones(ZONE_OUT_POLYGONS, [sv.Position.CENTER])
 
-        self.box_annotator = sv.BoxAnnotator(color=COLORS)
+        self.bounding_box_annotator = sv.BoundingBoxAnnotator(color=COLORS)
+        self.label_annotator = sv.LabelAnnotator(
+            color=COLORS, text_color=sv.Color.BLACK
+        )
         self.trace_annotator = sv.TraceAnnotator(
             color=COLORS, position=sv.Position.CENTER, trace_length=100, thickness=2
         )
@@ -132,7 +131,10 @@ class VideoProcessor:
 
         labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]
         annotated_frame = self.trace_annotator.annotate(annotated_frame, detections)
-        annotated_frame = self.box_annotator.annotate(
+        annotated_frame = self.bounding_box_annotator.annotate(
+            annotated_frame, detections
+        )
+        annotated_frame = self.label_annotator.annotate(
             annotated_frame, detections, labels
         )
 
@@ -163,7 +165,7 @@ class VideoProcessor:
         detections_in_zones = []
         detections_out_zones = []
 
-        for i, (zone_in, zone_out) in enumerate(zip(self.zones_in, self.zones_out)):
+        for zone_in, zone_out in zip(self.zones_in, self.zones_out):
             detections_in_zone = detections[zone_in.trigger(detections=detections)]
             detections_in_zones.append(detections_in_zone)
             detections_out_zone = detections[zone_out.trigger(detections=detections)]

mkdocs.yml

@@ -5,63 +5,98 @@ 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
   - How to:
       - Detect and Annotate: how_to/detect_and_annotate.md
-      - Track Objects: how_to/track_objects.md
+      - Save Detections: how_to/save_detections.md
       - Filter Detections: how_to/filter_detections.md
+      - Detect Small Objects: how_to/detect_small_objects.md
+      - Track Objects: how_to/track_objects.md
+
   - API:
-    - Classifications:
-        - Core: classification/core.md
-    - Detections:
+    - Detection and Segmentation:
         - Core: detection/core.md
+        - Annotators: detection/annotators.md
+        - Metrics: detection/metrics.md
         - Utils: detection/utils.md
+    - Keypoint Detection:
+        - Core: keypoint/core.md
+        - Annotators: keypoint/annotators.md
+    - Classification:
+        - Core: classification/core.md
     - Tools:
         - Line Zone: detection/tools/line_zone.md
         - Polygon Zone: detection/tools/polygon_zone.md
         - Inference Slicer: detection/tools/inference_slicer.md
         - Detection Smoother: detection/tools/smoother.md
-    - Annotators: annotators.md
+        - Save Detections: detection/tools/save_detections.md
     - Trackers: trackers.md
     - Datasets: datasets.md
-    - Metrics:
-      - Object Detection: metrics/detection.md
-    - Draw:
-        - Color: draw/color.md
-        - Utils: draw/utils.md
     - Utils:
         - Video: utils/video.md
         - Image: utils/image.md
+        - Iterables: utils/iterables.md
         - Notebook: utils/notebook.md
         - File: utils/file.md
-  - Assets: assets.md
-  - Changelog: changelog.md
+        - Draw: utils/draw.md
+        - Geometry: utils/geometry.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 +111,46 @@ 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 +165,13 @@ 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"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.18.0"
+version = "0.21.0rc2"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
@@ -39,34 +39,42 @@ 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"
-
-
+tqdm = { version = ">=4.62.3,<=4.66.2", optional = true }
+pillow = ">=9.4"
 
 [tool.poetry.extras]
 desktop = ["opencv-python"]
 assets = ["requests","tqdm"]
 
 [tool.poetry.group.dev.dependencies]
-twine = "^4.0.2"
-pytest = "^7.2.2"
-wheel = ">=0.40,<0.43"
-notebook = ">=6.5.3,<8.0.0"
-build = ">=0.10,<1.1"
+twine = ">=4.0.2,<6.0.0"
+pytest = ">=7.2.2,<9.0.0"
+wheel = ">=0.40,<0.44"
+build = ">=0.10,<1.3"
 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"}
+mkdocstrings = {extras = ["python"], version = ">=0.20,<0.26"}
+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]
@@ -103,13 +111,6 @@ exclude = '''
 
 [tool.ruff]
 target-version = "py38"
-# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
-select = ["E", "F"]
-ignore = []
-
-# Allow autofix for all enabled rules (when `--fix`) is provided.
-fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
-unfixable = []
 
 # Exclude a variety of commonly ignored directories.
 exclude = [
@@ -139,33 +140,35 @@ exclude = [
     "docs",
 ]
 
-# Same as Black.
 line-length = 88
 indent-width = 4
 
+[tool.ruff.lint]
+# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
+select = ["E", "F"]
+ignore = []
+# Allow autofix for all enabled rules (when `--fix`) is provided.
+fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
+unfixable = []
 # Allow unused variables when underscore-prefixed.
 dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+pylint.max-args = 20
 
-[tool.ruff.flake8-quotes]
+[tool.ruff.lint.flake8-quotes]
 inline-quotes = "double"
 multiline-quotes = "double"
 docstring-quotes = "double"
 
-[tool.ruff.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 convention = "google"
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 "__init__.py" = ["E402","F401"]
-"supervision/assets/list.py" = ["E501"]
 
 [tool.ruff.lint.mccabe]
 # Flag errors (`C901`) whenever the complexity level exceeds 5.
 max-complexity = 20
 
-
-[tool.ruff.pylint]
-max-args = 20
-
 [tool.ruff.format]
 # Like Black, use double quotes for strings.
 quote-style = "double"

supervision/__init__.py

@@ -12,6 +12,7 @@ from supervision.annotators.core import (
     BoxCornerAnnotator,
     CircleAnnotator,
     ColorAnnotator,
+    CropAnnotator,
     DotAnnotator,
     EllipseAnnotator,
     HaloAnnotator,
@@ -35,26 +36,30 @@ 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.line_zone import LineZone, LineZoneAnnotator
+from supervision.detection.tools.csv_sink import CSVSink
 from supervision.detection.tools.inference_slicer import InferenceSlicer
+from supervision.detection.tools.json_sink import JSONSink
 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,
 )
 from supervision.draw.color import Color, ColorPalette
 from supervision.draw.utils import (
-    calculate_dynamic_line_thickness,
-    calculate_dynamic_text_scale,
+    calculate_optimal_line_thickness,
+    calculate_optimal_text_scale,
     draw_filled_rectangle,
     draw_image,
     draw_line,
@@ -64,10 +69,21 @@ from supervision.draw.utils import (
 )
 from supervision.geometry.core import Point, Position, Rect
 from supervision.geometry.utils import get_polygon_center
+from supervision.keypoint.annotators import EdgeAnnotator, VertexAnnotator
+from supervision.keypoint.core import KeyPoints
 from supervision.metrics.detection import ConfusionMatrix, MeanAveragePrecision
 from supervision.tracker.byte_tracker.core import ByteTrack
+from supervision.utils.conversion import cv2_to_pillow, pillow_to_cv2
 from supervision.utils.file import list_files_with_extensions
-from supervision.utils.image import ImageSink, crop_image
+from supervision.utils.image import (
+    ImageSink,
+    create_tiles,
+    crop_image,
+    letterbox_image,
+    overlay_image,
+    resize_image,
+    scale_image,
+)
 from supervision.utils.notebook import plot_image, plot_images_grid
 from supervision.utils.video import (
     FPSMonitor,