👊 bump version from 0.17.1 to 0.18.0rc1

⬆️ Bump mkdocs-material from 9.5.2 to 9.5.3

.github/dependabot.yml

@@ -0,0 +1,16 @@
+version: 2
+updates:
+  # GitHub Actions
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    commit-message:
+      prefix: ⬆️
+  # Python
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    commit-message:
+      prefix: ⬆️

.github/requirements-dev-min.txt

@@ -0,0 +1,28 @@
+attrs==23.1.0
+certifi==2023.7.22
+charset-normalizer==2.0.12
+cycler==0.12.1
+exceptiongroup==1.1.3
+fonttools==4.43.1
+idna==3.4
+iniconfig==2.0.0
+kiwisolver==1.4.5
+matplotlib==3.5.0
+numpy==1.21.2
+opencv-python==4.5.5.64
+packaging==23.2
+Pillow==10.1.0
+pluggy==1.3.0
+pyparsing==3.1.1
+pytest==7.2.0
+python-dateutil==2.8.2
+PyYAML==5.3
+requests==2.26.0
+scipy==1.9.0
+setuptools-scm==8.0.4
+six==1.16.0
+tomli==2.0.1
+tqdm==4.62.3
+typing_extensions==4.8.0
+urllib3==1.26.18
+defusedxml == 0.7.1

.github/workflows/clear-cache.yml

@@ -0,0 +1,33 @@
+
+name: Clear cache
+
+on:
+    schedule:
+      - cron: '0 0 1 * *'
+    workflow_dispatch:
+
+permissions:
+  actions: write
+
+jobs:
+  clear-cache:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Clear cache
+        uses: actions/github-script@v7
+        with:
+          script: |
+            console.log("About to clear")
+            const caches = await github.rest.actions.getActionsCacheList({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+            })
+            for (const cache of caches.data.actions_caches) {
+              console.log(cache)
+              github.rest.actions.deleteActionsCacheById({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                cache_id: cache.id,
+              })
+            }
+            console.log("Clear completed")

.github/workflows/docs.yml

@@ -6,14 +6,21 @@ on:
       - master
       - main
       - develop
+
+permissions:
+  contents: write
+  pages: write
+  pull-requests: write
+
 jobs:
   deploy:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v4
+      - 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-test.yml

@@ -16,10 +16,10 @@ jobs:
 
     steps:
       - name: Checkout source
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: 🐍 Set up Python 3.8 environment for build
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.8"
 

.github/workflows/publish.yml

@@ -17,11 +17,11 @@ jobs:
         python-version: [3.8]
     steps:
       - name: 🛎️ Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.head_ref }}
       - name: 🐍 Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 

.github/workflows/test-min.yml

@@ -0,0 +1,32 @@
+name: Python 3.8 - Min Dep Test WorkFlow
+
+on:
+  pull_request:
+    branches: [main, develop]
+
+jobs:
+  build-min-dep-test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8"]
+    steps:
+      - name: 🛎️ Checkout
+        uses: actions/checkout@v4
+      - name: 🐍 Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        # id based on python version
+        id: python-setup
+        with:
+          python-version: ${{ matrix.python-version }}
+          check-latest: true
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-dev-min.txt'
+
+      - name: 📦 Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r .github/requirements-dev-min.txt
+
+      - name: 🧪 Test
+        run: "python -m pytest ./test"

.github/workflows/test.yml

@@ -5,22 +5,25 @@ on:
     branches: [main, develop]
 
 jobs:
-  build:
+  build-dev-test:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10","3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     steps:
       - name: 🛎️ Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: 🐍 Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-      - name: 🦾 Install dependencies
+          check-latest: true
+
+      - name: 📦 Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install -e .
+          pip install .
           pip install pytest
+
       - name: 🧪 Test
         run: "python -m pytest ./test"

.github/workflows/welcome.yml

@@ -11,7 +11,7 @@ jobs:
     name: 👋 Welcome
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/first-interaction@v1.1.1
+      - uses: actions/first-interaction@v1.3.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap."

.pre-commit-config.yaml

@@ -7,74 +7,48 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
+        exclude: test/.*\.py
       - id: check-yaml
       - id: check-docstring-first
       - id: check-executables-have-shebangs
       - id: check-toml
       - id: check-case-conflict
       - id: check-added-large-files
-        args: ['--maxkb=2048']
-        exclude: ^logo/
       - id: detect-private-key
       - id: forbid-new-submodules
       - id: pretty-format-json
+        exclude: demo.ipynb
         args: ['--autofix', '--no-sort-keys', '--indent=4']
       - id: end-of-file-fixer
       - id: mixed-line-ending
 
-  # - repo: https://github.com/asottile/pyupgrade
-  #   rev: v3.9.0
-  #   hooks:
-  #     - id: pyupgrade
-  #       name: Upgrade code
-  #       args: [--py38-plus]
-
-  - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
-        name: Sort imports
-
-  - repo: https://github.com/PyCQA/flake8
-    rev: 6.1.0
-    hooks:
-      - id: flake8
-        name: Flake8 Checks
-        entry: flake8
-        additional_dependencies: [Flake8-pyproject]
 
 
   -   repo: https://github.com/PyCQA/bandit
-      rev: '1.7.5'
+      rev: '1.7.6'
       hooks:
       -   id: bandit
           args: ["-c", "pyproject.toml"]
           additional_dependencies: ["bandit[toml]"]
 
   -   repo: https://github.com/pycqa/isort
-      rev: 5.12.0
+      rev: 5.13.2
       hooks:
       -   id: isort
           name: isort (python)
-      -   id: isort
-          name: isort (cython)
-          types: [cython]
       -   id: isort
           name: isort (pyi)
           types: [pyi]
 
 
-  -   repo: https://github.com/psf/black
-      rev: 23.9.1
-      hooks:
-      -   id: black
-
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.0.292
+      rev: v0.1.9
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]
+      -   id: ruff-format
+          types_or: [ python, pyi, jupyter ]

CONTRIBUTING.md

@@ -76,7 +76,6 @@ All new functions and classes in `supervision` should include docstrings. This i
 
 So far, **there is no type checking with mypy**. See [issue](https://github.com/roboflow-ai/template-python/issues/4).
 
-
 Then, go back to your fork of the `supervision` repository, click "Pull Requests", and click "New Pull Request".
 
 ![Opening a pull request](https://media.roboflow.com/open_pr.png)
@@ -109,7 +108,7 @@ PRs must pass all tests and linting requirements before they can be merged.
 
 The `supervision` documentation is stored in a folder called `docs`. The project documentation is built using `mkdocs`.
 
-To run the documentation, install the project requirements with `poetry install dev`. Then, run `mkdocs serve` to start the documentation server.
+To run the documentation, install the project requirements with `poetry install --with dev`. Then, run `mkdocs serve` to start the documentation server.
 
 You can learn more about mkdocs on the [mkdocs website](https://www.mkdocs.org/).
 

Makefile

@@ -1,22 +0,0 @@
-.PHONY: style check_code_quality
-
-export PYTHONPATH = .
-check_dirs := supervision
-
-style:
-	black  $(check_dirs)
-	isort --profile black $(check_dirs)
-
-check_code_quality:
-	black --check $(check_dirs)
-	isort --check-only --profile black $(check_dirs)
-	# stop the build if there are Python syntax errors or undefined names
-	flake8 $(check_dirs) --count --select=E9,F63,F7,F82 --show-source --statistics
-	# exit-zero treats all errors as warnings. E203 for black, E501 for docstring, W503 for line breaks before logical operators
-	flake8 $(check_dirs) --count --max-line-length=88 --exit-zero  --ignore=D --extend-ignore=E203,E501,W503  --statistics
-
-publish:
-	poetry build
-	twine upload -r testpypi dist/* -u ${PYPI_USERNAME} -p ${PYPI_TEST_PASSWORD} --verbose
-	twine check dist/*
-	twine upload dist/* -u ${PYPI_USERNAME} -p ${PYPI_PASSWORD} --verbose

README.md

@@ -10,15 +10,17 @@
 
   <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) | [collect](https://github.com/roboflow/roboflow-collect)
 
   <br>
 
-  [![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)
-  [![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)
+[![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)
+[![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)
 
 </div>
 
@@ -29,76 +31,89 @@
 ## 💻 install
 
 Pip install the supervision package in a
-[**3.11>=Python>=3.8**](https://www.python.org/) environment.
+[**Python>=3.8**](https://www.python.org/) environment.
 
 ```bash
-pip install supervision[desktop]
+pip install supervision
 ```
 
 Read more about desktop, headless, and local installation in our [guide](https://roboflow.github.io/supervision/).
 
 ## 🔥 quickstart
 
-### [detections processing](https://roboflow.github.io/supervision/detection/core/)
+### 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.
 
 ```python
+>>> import cv2
 >>> import supervision as sv
 >>> from ultralytics import YOLO
 
+>>> image = cv2.imread(...)
 >>> model = YOLO('yolov8s.pt')
->>> result = model(IMAGE)[0]
+>>> result = model(image)[0]
 >>> detections = sv.Detections.from_ultralytics(result)
 
 >>> len(detections)
 5
 ```
 
-<details close>
-<summary>👉 more detections utils</summary>
+<details>
+<summary>👉 more model connectors</summary>
 
-- Easily switch inference pipeline between supported object detection/instance segmentation models
+- inference
+
+    Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key).
 
     ```python
+    >>> import cv2
     >>> import supervision as sv
-    >>> from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
+    >>> from inference.models.utils import get_roboflow_model
 
-    >>> sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
-    >>> mask_generator = SamAutomaticMaskGenerator(sam)
-    >>> sam_result = mask_generator.generate(IMAGE)
-    >>> detections = sv.Detections.from_sam(sam_result=sam_result)
-    ```
+    >>> image = cv2.imread(...)
+    >>> model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
+    >>> result = model.infer(image)[0]
+    >>> detections = sv.Detections.from_inference(result)
 
-- [Advanced filtering](https://roboflow.github.io/supervision/quickstart/detections/)
+    >>> len(detections)
+    >>> 5
 
-    ```python
-    >>> detections = detections[detections.class_id == 0]
-    >>> detections = detections[detections.confidence > 0.5]
-    >>> detections = detections[detections.area > 1000]
-    ```
-
-- Image annotation
-
-    ```python
-    >>> import supervision as sv
-
-    >>> box_annotator = sv.BoxAnnotator()
-    >>> annotated_frame = box_annotator.annotate(
-    ...     scene=IMAGE,
-    ...     detections=detections
-    ... )
     ```
 
 </details>
 
-### [datasets processing](https://roboflow.github.io/supervision/dataset/core/)
+### 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.
+
+```python
+>>> import cv2
+>>> import supervision as sv
+
+>>> image = cv2.imread(...)
+>>> detections = sv.Detections(...)
+
+>>> bounding_box_annotator = sv.BoundingBoxAnnotator()
+>>> annotated_frame = bounding_box_annotator.annotate(
+...     scene=image.copy(),
+...     detections=detections
+... )
+```
+
+https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce
+
+### 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.
 
 ```python
 >>> import supervision as sv
 
 >>> dataset = sv.DetectionDataset.from_yolo(
-...     images_directory_path='...',
-...     annotations_directory_path='...',
-...     data_yaml_path='...'
+...     images_directory_path=...,
+...     annotations_directory_path=...,
+...     data_yaml_path=...
 ... )
 
 >>> dataset.classes
@@ -111,165 +126,90 @@ Read more about desktop, headless, and local installation in our [guide](https:/
 <details close>
 <summary>👉 more dataset utils</summary>
 
-- Load object detection/instance segmentation datasets in one of the supported formats
+- load
 
-    ```python
-    >>> dataset = sv.DetectionDataset.from_yolo(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...',
-    ...     data_yaml_path='...'
-    ... )
+  ```python
+  >>> dataset = sv.DetectionDataset.from_yolo(
+  ...     images_directory_path=...,
+  ...     annotations_directory_path=...,
+  ...     data_yaml_path=...
+  ... )
 
-    >>> dataset = sv.DetectionDataset.from_pascal_voc(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...'
-    ... )
+  >>> dataset = sv.DetectionDataset.from_pascal_voc(
+  ...     images_directory_path=...,
+  ...     annotations_directory_path=...
+  ... )
 
-    >>> dataset = sv.DetectionDataset.from_coco(
-    ...     images_directory_path='...',
-    ...     annotations_path='...'
-    ... )
-    ```
+  >>> dataset = sv.DetectionDataset.from_coco(
+  ...     images_directory_path=...,
+  ...     annotations_path=...
+  ... )
+  ```
 
-- Loop over dataset entries
+- split
 
-    ```python
-    >>> for name, image, labels in dataset:
-    ...     print(labels.xyxy)
+  ```python
+  >>> train_dataset, test_dataset = dataset.split(split_ratio=0.7)
+  >>> test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
 
-    array([[404.      , 719.      , 538.      , 884.5     ],
-           [155.      , 497.      , 404.      , 833.5     ],
-           [ 20.154999, 347.825   , 416.125   , 915.895   ]], dtype=float32)
-    ```
+  >>> len(train_dataset), len(test_dataset), len(valid_dataset)
+  (700, 150, 150)
+  ```
 
-- Split dataset for training, testing, and validation
+- merge
 
-    ```python
-    >>> train_dataset, test_dataset = dataset.split(split_ratio=0.7)
-    >>> test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
+  ```python
+  >>> ds_1 = sv.DetectionDataset(...)
+  >>> len(ds_1)
+  100
+  >>> ds_1.classes
+  ['dog', 'person']
 
-    >>> len(train_dataset), len(test_dataset), len(valid_dataset)
-    (700, 150, 150)
-    ```
+  >>> ds_2 = sv.DetectionDataset(...)
+  >>> len(ds_2)
+  200
+  >>> ds_2.classes
+  ['cat']
 
-- Merge multiple datasets
+  >>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
+  >>> len(ds_merged)
+  300
+  >>> ds_merged.classes
+  ['cat', 'dog', 'person']
+  ```
 
-    ```python
-    >>> ds_1 = sv.DetectionDataset(...)
-    >>> len(ds_1)
-    100
-    >>> ds_1.classes
-    ['dog', 'person']
+- save
 
-    >>> ds_2 = sv.DetectionDataset(...)
-    >>> len(ds_2)
-    200
-    >>> ds_2.classes
-    ['cat']
+  ```python
+  >>> dataset.as_yolo(
+  ...     images_directory_path=...,
+  ...     annotations_directory_path=...,
+  ...     data_yaml_path=...
+  ... )
 
-    >>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
-    >>> len(ds_merged)
-    300
-    >>> ds_merged.classes
-    ['cat', 'dog', 'person']
-    ```
+  >>> dataset.as_pascal_voc(
+  ...     images_directory_path=...,
+  ...     annotations_directory_path=...
+  ... )
 
-- Save object detection/instance segmentation datasets in one of the supported formats
+  >>> dataset.as_coco(
+  ...     images_directory_path=...,
+  ...     annotations_path=...
+  ... )
+  ```
 
-    ```python
-    >>> dataset.as_yolo(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...',
-    ...     data_yaml_path='...'
-    ... )
+- convert
 
-    >>> dataset.as_pascal_voc(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...'
-    ... )
-
-    >>> dataset.as_coco(
-    ...     images_directory_path='...',
-    ...     annotations_path='...'
-    ... )
-    ```
-
-- Convert labels between supported formats
-
-    ```python
-    >>> sv.DetectionDataset.from_yolo(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...',
-    ...     data_yaml_path='...'
-    ... ).as_pascal_voc(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...'
-    ... )
-    ```
-
-- Load classification datasets in one of the supported formats
-
-    ```python
-    >>> cs = sv.ClassificationDataset.from_folder_structure(
-    ...     root_directory_path='...'
-    ... )
-    ```
-
-- Save classification datasets in one of the supported formats
-
-    ```python
-    >>> cs.as_folder_structure(
-    ...     root_directory_path='...'
-    ... )
-    ```
-
-</details>
-
-### [model evaluation](https://roboflow.github.io/supervision/metrics/detection/)
-
-```python
->>> import supervision as sv
-
->>> dataset = sv.DetectionDataset.from_yolo(...)
-
->>> def callback(image: np.ndarray) -> sv.Detections:
-...     ...
-
->>> confusion_matrix = sv.ConfusionMatrix.benchmark(
-...     dataset = dataset,
-...     callback = callback
-... )
-
->>> confusion_matrix.matrix
-array([
-    [0., 0., 0., 0.],
-    [0., 1., 0., 1.],
-    [0., 1., 1., 0.],
-    [1., 1., 0., 0.]
-])
-```
-
-<details close>
-<summary>👉 more metrics</summary>
-
-- Mean average precision (mAP) for object detection tasks.
-
-    ```python
-    >>> import supervision as sv
-
-    >>> dataset = sv.DetectionDataset.from_yolo(...)
-
-    >>> def callback(image: np.ndarray) -> sv.Detections:
-    ...     ...
-
-    >>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
-    ...     dataset = dataset,
-    ...     callback = callback
-    ... )
-
-    >>> mean_average_precision.map50_95
-    0.433
-    ```
+  ```python
+  >>> sv.DetectionDataset.from_yolo(
+  ...     images_directory_path=...,
+  ...     annotations_directory_path=...,
+  ...     data_yaml_path=...
+  ... ).as_pascal_voc(
+  ...     images_directory_path=...,
+  ...     annotations_directory_path=...
+  ... )
+  ```
 
 </details>
 
@@ -297,6 +237,8 @@ https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-
 
 https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900
 
+https://github.com/roboflow/supervision/assets/26109316/f84db7b5-79e2-4142-a1da-64daa43ce667
+
 ## 📚 documentation
 
 Visit our [documentation](https://roboflow.github.io/supervision) page to learn how supervision can help you build computer vision applications faster and more reliably.
@@ -305,6 +247,12 @@ Visit our [documentation](https://roboflow.github.io/supervision) page to learn
 
 We love your input! Please see our [contributing guide](https://github.com/roboflow/supervision/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
 
+<p align="center">
+    <a href="https://github.com/roboflow/supervision/graphs/contributors">
+      <img src="https://contrib.rocks/image?repo=roboflow/supervision" />
+    </a>
+</p>
+
 <br>
 
 <div align="center">
@@ -352,5 +300,4 @@ We love your input! Please see our [contributing guide](https://github.com/robof
       </a>
       </a>
   </div>
-
 </div>

docs/annotators.md

@@ -15,49 +15,7 @@
 
     <div class="result" markdown>
 
-    ![bounding-box-annotator-example](https://media.roboflow.com/supervision-annotator-examples/bounding-box-annotator-example.png){ align=center width="800" }
-
-    </div>
-
-=== "Mask"
-
-    ```python
-    >>> import supervision as sv
-
-    >>> image = ...
-    >>> detections = sv.Detections(...)
-
-    >>> mask_annotator = sv.MaskAnnotator()
-    >>> annotated_frame = mask_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
-    ```
-
-    <div class="result" markdown>
-
-    ![mask-annotator-example](https://media.roboflow.com/supervision-annotator-examples/mask-annotator-example.png){ align=center width="800" }
-
-    </div>
-
-=== "Ellipse"
-
-    ```python
-    >>> import supervision as sv
-
-    >>> image = ...
-    >>> detections = sv.Detections(...)
-
-    >>> ellipse_annotator = sv.EllipseAnnotator()
-    >>> annotated_frame = ellipse_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
-    ```
-
-    <div class="result" markdown>
-
-    ![ellipse-annotator-example](https://media.roboflow.com/supervision-annotator-examples/ellipse-annotator-example.png){ align=center width="800" }
+    ![bounding-box-annotator-example](https://media.roboflow.com/supervision-annotator-examples/bounding-box-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -78,7 +36,28 @@
 
     <div class="result" markdown>
 
-    ![box-corner-annotator-example](https://media.roboflow.com/supervision-annotator-examples/box-corner-annotator-example.png){ align=center width="800" }
+    ![box-corner-annotator-example](https://media.roboflow.com/supervision-annotator-examples/box-corner-annotator-example-purple.png){ align=center width="800" }
+
+    </div>
+
+=== "Color"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> color_annotator = sv.ColorAnnotator()
+    >>> annotated_frame = color_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![box-mask-annotator-example](https://media.roboflow.com/supervision-annotator-examples/box-mask-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -99,7 +78,133 @@
 
     <div class="result" markdown>
 
-    ![circle-annotator-example](https://media.roboflow.com/supervision-annotator-examples/circle-annotator-example.png){ align=center width="800" }
+    ![circle-annotator-example](https://media.roboflow.com/supervision-annotator-examples/circle-annotator-example-purple.png){ align=center width="800" }
+
+    </div>
+
+=== "Dot"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> dot_annotator = sv.DotAnnotator()
+    >>> annotated_frame = dot_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![circle-annotator-example](https://media.roboflow.com/supervision-annotator-examples/dot-annotator-example-purple.png){ align=center width="800" }
+
+    </div>
+
+=== "Triangle"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> triangle_annotator = sv.TriangleAnnotator()
+    >>> annotated_frame = triangle_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![triangle-annotator-example](https://media.roboflow.com/supervision-annotator-examples/triangle-annotator-example.png){ align=center width="800" }
+
+    </div>
+
+=== "Ellipse"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> ellipse_annotator = sv.EllipseAnnotator()
+    >>> annotated_frame = ellipse_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![ellipse-annotator-example](https://media.roboflow.com/supervision-annotator-examples/ellipse-annotator-example-purple.png){ align=center width="800" }
+
+    </div>
+
+=== "Halo"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> halo_annotator = sv.HaloAnnotator()
+    >>> annotated_frame = halo_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![ellipse-annotator-example](https://media.roboflow.com/supervision-annotator-examples/halo-annotator-example-purple.png){ align=center width="800" }
+
+    </div>
+
+=== "Mask"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> mask_annotator = sv.MaskAnnotator()
+    >>> annotated_frame = mask_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![mask-annotator-example](https://media.roboflow.com/supervision-annotator-examples/mask-annotator-example-purple.png){ align=center width="800" }
+
+    </div>
+
+=== "Polygon"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> polygon_annotator = sv.PolygonAnnotator()
+    >>> annotated_frame = polygon_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![polygon-annotator-example](https://media.roboflow.com/supervision-annotator-examples/polygon-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -120,7 +225,7 @@
 
     <div class="result" markdown>
 
-    ![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/label-annotator-example-2.png){ align=center width="800" }
+    ![label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/label-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -141,11 +246,11 @@
 
     <div class="result" markdown>
 
-    ![blur-annotator-example](https://media.roboflow.com/supervision-annotator-examples/blur-annotator-example-2.png){ align=center width="800" }
+    ![blur-annotator-example](https://media.roboflow.com/supervision-annotator-examples/blur-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
-=== "Trace"
+=== "Pixelate"
 
     ```python
     >>> import supervision as sv
@@ -153,8 +258,8 @@
     >>> image = ...
     >>> detections = sv.Detections(...)
 
-    >>> trace_annotator = sv.TraceAnnotator()
-    >>> annotated_frame = trace_annotator.annotate(
+    >>> pixelate_annotator = sv.PixelateAnnotator()
+    >>> annotated_frame = pixelate_annotator.annotate(
     ...     scene=image.copy(),
     ...     detections=detections
     ... )
@@ -162,7 +267,67 @@
 
     <div class="result" markdown>
 
-    ![trace-annotator-example](https://media.roboflow.com/supervision-annotator-examples/trace-annotator-example.png){ align=center width="800" }
+    ![pixelate-annotator-example](https://media.roboflow.com/supervision-annotator-examples/pixelate-annotator-example-10.png){ align=center width="800" }
+
+    </div>
+
+=== "Trace"
+
+    ```python
+    >>> import supervision as sv
+    >>> from ultralytics import YOLO
+
+    >>> model = YOLO('yolov8x.pt')
+
+    >>> trace_annotator = sv.TraceAnnotator()
+
+    >>> video_info = sv.VideoInfo.from_video_path(video_path='...')
+    >>> frames_generator = get_video_frames_generator(source_path='...')
+    >>> tracker = sv.ByteTrack()
+
+    >>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
+    ...    for frame in frames_generator:
+    ...        result = model(frame)[0]
+    ...        detections = sv.Detections.from_ultralytics(result)
+    ...        detections = tracker.update_with_detections(detections)
+    ...        annotated_frame = trace_annotator.annotate(
+    ...            scene=frame.copy(),
+    ...            detections=detections)
+    ...        sink.write_frame(frame=annotated_frame)
+    ```
+
+    <div class="result" markdown>
+
+    ![trace-annotator-example](https://media.roboflow.com/supervision-annotator-examples/trace-annotator-example-purple.png){ align=center width="800" }
+
+    </div>
+
+=== "HeatMap"
+
+    ```python
+    >>> import supervision as sv
+    >>> from ultralytics import YOLO
+
+    >>> model = YOLO('yolov8x.pt')
+
+    >>> heat_map_annotator = sv.HeatMapAnnotator()
+
+    >>> video_info = sv.VideoInfo.from_video_path(video_path='...')
+    >>> frames_generator = get_video_frames_generator(source_path='...')
+
+    >>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
+    ...    for frame in frames_generator:
+    ...        result = model(frame)[0]
+    ...        detections = sv.Detections.from_ultralytics(result)
+    ...        annotated_frame = heat_map_annotator.annotate(
+    ...            scene=frame.copy(),
+    ...            detections=detections)
+    ...        sink.write_frame(frame=annotated_frame)
+    ```
+
+    <div class="result" markdown>
+
+    ![trace-annotator-example](https://media.roboflow.com/supervision-annotator-examples/heat-map-annotator-example-purple.png){ align=center width="800" }
 
     </div>
 
@@ -170,30 +335,62 @@
 
 :::supervision.annotators.core.BoundingBoxAnnotator
 
-## MaskAnnotator
-
-:::supervision.annotators.core.MaskAnnotator
-
-## EllipseAnnotator
-
-:::supervision.annotators.core.EllipseAnnotator
-
 ## BoxCornerAnnotator
 
 :::supervision.annotators.core.BoxCornerAnnotator
 
+## ColorAnnotator
+
+:::supervision.annotators.core.ColorAnnotator
+
 ## CircleAnnotator
 
 :::supervision.annotators.core.CircleAnnotator
 
+## DotAnnotator
+
+:::supervision.annotators.core.DotAnnotator
+
+## TriangleAnnotator
+
+:::supervision.annotators.core.TriangleAnnotator
+
+## EllipseAnnotator
+
+:::supervision.annotators.core.EllipseAnnotator
+
+## HaloAnnotator
+
+:::supervision.annotators.core.HaloAnnotator
+
+## HeatMapAnnotator
+
+:::supervision.annotators.core.HeatMapAnnotator
+
+## MaskAnnotator
+
+:::supervision.annotators.core.MaskAnnotator
+
+## PolygonAnnotator
+
+:::supervision.annotators.core.PolygonAnnotator
+
 ## LabelAnnotator
 
 :::supervision.annotators.core.LabelAnnotator
 
+## BlurAnnotator
+
+:::supervision.annotators.core.BlurAnnotator
+
+## PixelateAnnotator
+
+:::supervision.annotators.core.PixelateAnnotator
+
 ## TraceAnnotator
 
 :::supervision.annotators.core.TraceAnnotator
 
-## BlurAnnotator
+## ColorLookup
 
-:::supervision.annotators.core.BlurAnnotator
+:::supervision.annotators.utils.ColorLookup

docs/assets.md

@@ -0,0 +1,21 @@
+Supervision offers an assets download utility that allows you to download video files
+that you can use in your demos.
+
+## install extra
+
+To install the Supervision assets utility, you can use `pip`. This utility is available
+as an extra within the Supervision package.
+
+!!! example "pip install"
+
+    ```bash
+    pip install supervision[assets]
+    ```
+
+## download_assets
+
+:::supervision.assets.downloader.download_assets
+
+## VideoAssets
+
+:::supervision.assets.list.VideoAssets

docs/changelog.md

@@ -1,3 +1,100 @@
+### 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 [#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 [#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.
+
+```python
+>>> import supervision as sv
+
+>>> image = ...
+>>> detections = sv.Detections(...)
+
+>>> polygon_annotator = sv.PolygonAnnotator()
+>>> annotated_frame = polygon_annotator.annotate(
+...     scene=image.copy(),
+...     detections=detections
+... )
+```
+
+- 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.
+
+```python
+>>> from supervision.assets import download_assets, VideoAssets
+>>> download_assets(VideoAssets.VEHICLES)
+"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 [#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 [#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 [#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 [#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 [#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 [#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).
+
+- 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 [#606](https://github.com/roboflow/supervision/pull/606): [`sv.MaskAnnotator`](https://supervision.roboflow.com/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 [#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 [#511](https://github.com/roboflow/supervision/pull/511): [`sv.TraceAnnotator`](https://supervision.roboflow.com/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 [#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.
+
+```python
+>>> import supervision as sv
+
+>>> image = ...
+>>> detections = sv.Detections(...)
+
+>>> halo_annotator = sv.HaloAnnotator()
+>>> annotated_frame = halo_annotator.annotate(
+...     scene=image.copy(),
+...     detections=detections
+... )
+```
+
+- 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 [#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 [#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 [#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 [#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 [#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.
+
+- 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.
+
+!!! warning
+
+    `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).
+
+
 ### 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.

docs/detection/tools/line_zone.md

@@ -0,0 +1,3 @@
+## LineZone
+
+:::supervision.detection.line_counter.LineZone

docs/detection/utils.md

@@ -25,3 +25,11 @@
 ## filter_polygons_by_area
 
 :::supervision.detection.utils.filter_polygons_by_area
+
+## move_boxes
+
+:::supervision.detection.utils.move_boxes
+
+## scale_boxes
+
+:::supervision.detection.utils.scale_boxes

docs/draw/color.md

@@ -0,0 +1,7 @@
+## Color
+
+:::supervision.draw.color.Color
+
+## ColorPalette
+
+:::supervision.draw.color.ColorPalette

docs/draw/utils.md

@@ -17,3 +17,15 @@
 ## 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/how_to/detect_and_annotate.md

@@ -0,0 +1,139 @@
+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.
+
+## Run Inference
+
+First, you'll need to obtain predictions from your object detection or segmentation model.
+
+=== "Ultralytics"
+
+    ```python
+    import cv2
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    image = cv2.imread(<PATH TO IMAGE>)
+    results = model(image)[0]
+    ```
+
+=== "Inference"
+
+    ```python
+    import cv2
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
+    image = cv2.imread(<PATH TO IMAGE>)
+    results = model.infer(image)[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.
+
+    ```python
+    import cv2
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>
+    image = cv2.imread(<PATH TO IMAGE>)
+    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
+    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)
+
+    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)
+    ```
+
+=== "Inference"
+
+    ```python
+    import cv2
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>
+    image = cv2.imread(<PATH TO IMAGE>)
+    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)
+    ```
+
+![Predictions plotted on an image](https://media.roboflow.com/supervision_annotate_example.png)
+
+## Display Annotated Image
+
+To display the annotated image in Jupyter Notebook or Google Colab, use the [`sv.plot_image`](https://supervision.roboflow.com/utils/notebook/#supervision.utils.notebook.plot_image) function.
+
+```python
+sv.plot_image(annotated_image)
+```

docs/how_to/evaluate_model.md

@@ -0,0 +1 @@
+🚧 Page under construction.

docs/how_to/filter_detections.md

@@ -1,6 +1,3 @@
-## advanced filtering
-
-
 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.

docs/how_to/process_video.md

@@ -0,0 +1 @@
+🚧 Page under construction.

docs/how_to/track_objects.md

@@ -0,0 +1,313 @@
+Leverage Supervision's advanced capabilities for enhancing your video analysis by
+seamlessly [tracking](https://supervision.roboflow.com/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
+[Ultralytics](https://github.com/ultralytics/ultralytics) packages. Following this,
+you'll discover how to track these objects efficiently and annotate your video content
+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.
+
+```python
+from supervision.assets import download_assets, VideoAssets
+
+download_assets(VideoAssets.PEOPLE_WALKING)
+```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/people-walking.mp4" type="video/mp4">
+</video>
+
+## Run Inference
+
+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)
+for guidance on how to plug in other models.
+
+We will define a `callback` function, which will process each frame of the video
+by obtaining model predictions and then annotating the frame based on these predictions.
+This `callback` function will be essential in the subsequent steps of the tutorial, as
+it will be modified to include tracking, labeling, and trace annotations.
+
+=== "Ultralytics"
+
+    ```{ .py }
+    import numpy as np
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    box_annotator = sv.BoundingBoxAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model(frame)[0]
+        detections = sv.Detections.from_ultralytics(results)
+        return box_annotator.annotate(frame.copy(), detections=detections)
+
+    sv.process_video(
+        source_path="people-walking.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+=== "Inference"
+
+    ```{ .py }
+    import numpy as np
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
+    box_annotator = sv.BoundingBoxAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model.infer(frame)[0]
+        detections = sv.Detections.from_inference(results)
+        return box_annotator.annotate(frame.copy(), detections=detections)
+
+    sv.process_video(
+        source_path="people-walking.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/run-inference.mp4" type="video/mp4">
+</video>
+
+## Tracking
+
+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)
+functionality, each detected object is assigned a unique tracker ID,
+enabling the continuous following of the object's motion path across different frames.
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="6 12" }
+    import numpy as np
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoundingBoxAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model(frame)[0]
+        detections = sv.Detections.from_ultralytics(results)
+        detections = tracker.update_with_detections(detections)
+        return box_annotator.annotate(frame.copy(), detections=detections)
+
+    sv.process_video(
+        source_path="people-walking.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+=== "Inference"
+
+    ```{ .py hl_lines="6 12" }
+    import numpy as np
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoundingBoxAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model.infer(frame)[0]
+        detections = sv.Detections.from_inference(results)
+        detections = tracker.update_with_detections(detections)
+        return box_annotator.annotate(frame.copy(), detections=detections)
+
+    sv.process_video(
+        source_path="people-walking.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+## Annotate Video with Tracking IDs
+
+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)
+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.
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="8 15-19 23-24" }
+    import numpy as np
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model(frame)[0]
+        detections = sv.Detections.from_ultralytics(results)
+        detections = tracker.update_with_detections(detections)
+
+        labels = [
+            f"#{tracker_id} {results.names[class_id]}"
+            for class_id, tracker_id
+            in zip(detections.class_id, detections.tracker_id)
+        ]
+
+        annotated_frame = box_annotator.annotate(
+            frame.copy(), detections=detections)
+        return label_annotator.annotate(
+            annotated_frame, detections=detections, labels=labels)
+
+    sv.process_video(
+        source_path="people-walking.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+=== "Inference"
+
+    ```{ .py hl_lines="8 15-19 23-24" }
+    import numpy as np
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model.infer(frame)[0]
+        detections = sv.Detections.from_inference(results)
+        detections = tracker.update_with_detections(detections)
+
+        labels = [
+            f"#{tracker_id} {results.names[class_id]}"
+            for class_id, tracker_id
+            in zip(detections.class_id, detections.tracker_id)
+        ]
+
+        annotated_frame = box_annotator.annotate(
+            frame.copy(), detections=detections)
+        return label_annotator.annotate(
+            annotated_frame, detections=detections, labels=labels)
+
+    sv.process_video(
+        source_path="people-walking.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/annotate-video-with-tracking-ids.mp4" type="video/mp4">
+</video>
+
+## Annotate Video with Traces
+
+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),
+allows for visualizing the trajectories of objects, helping in understanding the
+movement patterns and interactions between objects in the video.
+
+=== "Ultralytics"
+
+    ```{ .py hl_lines="9 26-27" }
+    import numpy as np
+    import supervision as sv
+    from ultralytics import YOLO
+
+    model = YOLO("yolov8n.pt")
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+    trace_annotator = sv.TraceAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model(frame)[0]
+        detections = sv.Detections.from_ultralytics(results)
+        detections = tracker.update_with_detections(detections)
+
+        labels = [
+            f"#{tracker_id} {results.names[class_id]}"
+            for class_id, tracker_id
+            in zip(detections.class_id, detections.tracker_id)
+        ]
+
+        annotated_frame = box_annotator.annotate(
+            frame.copy(), detections=detections)
+        annotated_frame = label_annotator.annotate(
+            annotated_frame, detections=detections, labels=labels)
+        return trace_annotator.annotate(
+            annotated_frame, detections=detections)
+
+    sv.process_video(
+        source_path="people-walking.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+=== "Inference"
+
+    ```{ .py hl_lines="9 26-27" }
+    import numpy as np
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
+
+    model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+    trace_annotator = sv.TraceAnnotator()
+
+    def callback(frame: np.ndarray, _: int) -> np.ndarray:
+        results = model.infer(frame)[0]
+        detections = sv.Detections.from_inference(results)
+        detections = tracker.update_with_detections(detections)
+
+        labels = [
+            f"#{tracker_id} {results.names[class_id]}"
+            for class_id, tracker_id
+            in zip(detections.class_id, detections.tracker_id)
+        ]
+
+        annotated_frame = box_annotator.annotate(
+            frame.copy(), detections=detections)
+        annotated_frame = label_annotator.annotate(
+            annotated_frame, detections=detections, labels=labels)
+        return trace_annotator.annotate(
+            annotated_frame, detections=detections)
+
+    sv.process_video(
+        source_path="people-walking.mp4",
+        target_path="result.mp4",
+        callback=callback
+    )
+    ```
+
+<video controls>
+    <source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/annotate-video-with-traces.mp4" type="video/mp4">
+</video>
+
+This structured walkthrough should give a detailed pathway to annotate videos
+effectively using Supervision’s various functionalities, including object tracking and
+trace annotations.

docs/index.md

@@ -16,7 +16,7 @@ We write your reusable computer vision tools. Whether you need to load your data
 ## 💻 Install
 
 You can install `supervision` with pip in a
-[**3.11>=Python>=3.8**](https://www.python.org/) environment.
+[**Python>=3.8**](https://www.python.org/) environment.
 
 !!! example "pip install (recommended)"
 
@@ -46,6 +46,7 @@ You can install `supervision` with pip in a
         # setup python environment and activate it
         python3 -m venv venv
         source venv/bin/activate
+        pip install --upgrade pip
 
         # headless install
         pip install -e "."
@@ -62,7 +63,7 @@ You can install `supervision` with pip in a
         cd supervision
 
         # setup python environment and activate it
-        poetry env use python 3.10
+        poetry env use python3.10
         poetry shell
 
         # headless install

docs/javascript/init_kapa_widget.js

@@ -0,0 +1,10 @@
+document.addEventListener("DOMContentLoaded", function () {
+    var script = document.createElement("script");
+    script.src = "https://widget.kapa.ai/kapa-widget.bundle.js";
+    script.setAttribute("data-website-id", "e83c5c60-2968-410b-a2da-08fb104f23df");
+    script.setAttribute("data-project-name", "Roboflow");
+    script.setAttribute("data-project-color", "#6405C9");
+    script.setAttribute("data-project-logo", "https://media.roboflow.com/chat.png");
+    script.async = true;
+    document.head.appendChild(script);
+});

docs/javascript/zoominfo.js

@@ -0,0 +1 @@
+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)     });

docs/theme/main.html

@@ -0,0 +1,5 @@
+{% extends "base.html" %}
+
+{% 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>
+{% endblock %}

docs/utils/video.md

@@ -6,6 +6,10 @@
 
 :::supervision.utils.video.VideoSink
 
+## FPSMonitor
+
+:::supervision.utils.video.FPSMonitor
+
 ## get_video_frames_generator
 
 :::supervision.utils.video.get_video_frames_generator

examples/README.md

@@ -0,0 +1,30 @@
+## 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.
+
+- [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)
+- [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/.gitignore

@@ -0,0 +1,5 @@
+# Ignore everything in the data directory
+data/*
+
+# But re-include all .json files
+!data/*.json

examples/count_people_in_zone/README.md

@@ -0,0 +1,120 @@
+# count people in zone
+
+## 👋 hello
+
+This demo is a video analysis tool that counts and highlights objects in specific zones
+of a video. Each zone and the objects within it are marked in different colors, making
+it easy to see and count the objects in each area. The tool can save this enhanced
+video or display it live on the screen.
+
+https://github.com/roboflow/supervision/assets/26109316/f84db7b5-79e2-4142-a1da-64daa43ce667
+
+## 💻 install
+
+- clone repository and navigate to example directory
+
+  ```bash
+  git clone https://github.com/roboflow/supervision.git
+  cd supervision/examples/count_people_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
+  ```
+
+- download `traffic_analysis.pt` and `traffic_analysis.mov` files
+
+  ```bash
+  ./setup.sh
+  ```
+
+## 🛠️ script arguments
+
+- ultralytics
+
+  - `--source_weights_path` (optional): The path to the YOLO model's weights file.
+    Defaults to `"yolov8x.pt"` if not specified.
+
+  - `--zone_configuration_path`: Specifies the path to the JSON file containing zone
+    configurations. This file defines the polygonal areas in the video where objects will
+    be counted.
+  - `--source_video_path`: The path to the source video file that will be analyzed.
+  - `--target_video_path` (optional): The path to save the output video with annotations.
+    If not provided, the processed video will be displayed in real-time.
+  - `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
+    to filter detections. Default is `0.3`.
+  - `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
+    for the model. Default is `0.7`.
+
+- inference
+
+  - `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
+    directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
+    variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
+    to acquire your `API KEY`.
+  - `--model_id` (optional): Designates the Roboflow model ID to be used. The default
+    value is `"yolov8x-1280"`.
+
+  - `--zone_configuration_path`: Specifies the path to the JSON file containing zone
+    configurations. This file defines the polygonal areas in the video where objects will
+    be counted.
+  - `--source_video_path`: The path to the source video file that will be analyzed.
+  - `--target_video_path` (optional): The path to save the output video with annotations.
+    If not provided, the processed video will be displayed in real-time.
+  - `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
+    to filter detections. Default is `0.3`.
+  - `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
+    for the model. Default is `0.7`.
+
+## 📌 zone configuration
+
+- `horizontal-zone-config.json`: Defines zones divided horizontally across the frame.
+- `multi-zone-config.json`: Configures multiple zones with custom shapes and positions.
+- `quarters-zone-config.json`: Splits the frame into four equal quarters.
+- `vertical-zone-config.json`: Divides the frame into vertical zones of equal width.
+
+## ⚙️ run example
+
+- ultralytics
+
+  ```bash
+  python ultralytics_example.py \
+  --zone_configuration_path data/multi-zone-config.json \
+  --source_video_path data/market-square.mp4 \
+  --confidence_threshold 0.3 \
+  --iou_threshold 0.5
+  ```
+
+- inference
+
+  ```bash
+  python inference_example.py \
+  --roboflow_api_key <ROBOFLOW API KEY> \
+  --zone_configuration_path data/multi-zone-config.json \
+  --source_video_path data/market-square.mp4 \
+  --confidence_threshold 0.3 \
+  --iou_threshold 0.5
+  ```
+
+## © 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/count_people_in_zone/data/horizontal-zone-config.json

@@ -0,0 +1,76 @@
+{
+    "polygons": [
+        [
+            [
+                0,
+                0
+            ],
+            [
+                2160,
+                0
+            ],
+            [
+                2160,
+                960
+            ],
+            [
+                0,
+                960
+            ]
+        ],
+        [
+            [
+                0,
+                960
+            ],
+            [
+                2160,
+                960
+            ],
+            [
+                2160,
+                1920
+            ],
+            [
+                0,
+                1920
+            ]
+        ],
+        [
+            [
+                0,
+                1920
+            ],
+            [
+                2160,
+                1920
+            ],
+            [
+                2160,
+                2880
+            ],
+            [
+                0,
+                2880
+            ]
+        ],
+        [
+            [
+                0,
+                2880
+            ],
+            [
+                2160,
+                2880
+            ],
+            [
+                2160,
+                3840
+            ],
+            [
+                0,
+                3840
+            ]
+        ]
+    ]
+}

examples/count_people_in_zone/data/multi-zone-config.json

@@ -0,0 +1,122 @@
+{
+    "polygons": [
+        [
+            [
+                540,
+                985
+            ],
+            [
+                1620,
+                985
+            ],
+            [
+                2160,
+                1920
+            ],
+            [
+                1620,
+                2855
+            ],
+            [
+                540,
+                2855
+            ],
+            [
+                0,
+                1920
+            ]
+        ],
+        [
+            [
+                0,
+                1920
+            ],
+            [
+                540,
+                985
+            ],
+            [
+                0,
+                0
+            ]
+        ],
+        [
+            [
+                1620,
+                985
+            ],
+            [
+                2160,
+                1920
+            ],
+            [
+                2160,
+                0
+            ]
+        ],
+        [
+            [
+                540,
+                985
+            ],
+            [
+                0,
+                0
+            ],
+            [
+                2160,
+                0
+            ],
+            [
+                1620,
+                985
+            ]
+        ],
+        [
+            [
+                0,
+                1920
+            ],
+            [
+                0,
+                3840
+            ],
+            [
+                540,
+                2855
+            ]
+        ],
+        [
+            [
+                2160,
+                1920
+            ],
+            [
+                1620,
+                2855
+            ],
+            [
+                2160,
+                3840
+            ]
+        ],
+        [
+            [
+                1620,
+                2855
+            ],
+            [
+                540,
+                2855
+            ],
+            [
+                0,
+                3840
+            ],
+            [
+                2160,
+                3840
+            ]
+        ]
+    ]
+}

examples/count_people_in_zone/data/quarters-zone-config.json

@@ -0,0 +1,76 @@
+{
+    "polygons": [
+        [
+            [
+                0,
+                0
+            ],
+            [
+                1080,
+                0
+            ],
+            [
+                1080,
+                1920
+            ],
+            [
+                0,
+                1920
+            ]
+        ],
+        [
+            [
+                1080,
+                0
+            ],
+            [
+                2160,
+                0
+            ],
+            [
+                2160,
+                1920
+            ],
+            [
+                1080,
+                1920
+            ]
+        ],
+        [
+            [
+                0,
+                1920
+            ],
+            [
+                1080,
+                1920
+            ],
+            [
+                1080,
+                3840
+            ],
+            [
+                0,
+                3840
+            ]
+        ],
+        [
+            [
+                1080,
+                1920
+            ],
+            [
+                2160,
+                1920
+            ],
+            [
+                2160,
+                3840
+            ],
+            [
+                1080,
+                3840
+            ]
+        ]
+    ]
+}

examples/count_people_in_zone/data/vertical-zone-config.json

@@ -0,0 +1,76 @@
+{
+    "polygons": [
+        [
+            [
+                0,
+                0
+            ],
+            [
+                540,
+                0
+            ],
+            [
+                540,
+                3840
+            ],
+            [
+                0,
+                3840
+            ]
+        ],
+        [
+            [
+                540,
+                0
+            ],
+            [
+                1080,
+                0
+            ],
+            [
+                1080,
+                3840
+            ],
+            [
+                540,
+                3840
+            ]
+        ],
+        [
+            [
+                1080,
+                0
+            ],
+            [
+                1620,
+                0
+            ],
+            [
+                1620,
+                3840
+            ],
+            [
+                1080,
+                3840
+            ]
+        ],
+        [
+            [
+                1620,
+                0
+            ],
+            [
+                2160,
+                0
+            ],
+            [
+                2160,
+                3840
+            ],
+            [
+                1620,
+                3840
+            ]
+        ]
+    ]
+}

examples/count_people_in_zone/inference_example.py

@@ -0,0 +1,224 @@
+import argparse
+import json
+import os
+from typing import List, Tuple
+
+import cv2
+import numpy as np
+from inference.core.models.roboflow import RoboflowInferenceModel
+from inference.models.utils import get_roboflow_model
+from tqdm import tqdm
+
+import supervision as sv
+
+COLORS = sv.ColorPalette.default()
+
+
+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["polygons"]]
+
+
+def initiate_annotators(
+    polygons: List[np.ndarray], resolution_wh: Tuple[int, int]
+) -> 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)
+
+    zones = []
+    zone_annotators = []
+    box_annotators = []
+
+    for index, polygon in enumerate(polygons):
+        zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=resolution_wh)
+        zone_annotator = sv.PolygonZoneAnnotator(
+            zone=zone,
+            color=COLORS.by_idx(index),
+            thickness=line_thickness,
+            text_thickness=line_thickness * 2,
+            text_scale=text_scale * 2,
+        )
+        box_annotator = sv.BoundingBoxAnnotator(
+            color=COLORS.by_idx(index), thickness=line_thickness
+        )
+        zones.append(zone)
+        zone_annotators.append(zone_annotator)
+        box_annotators.append(box_annotator)
+
+    return zones, zone_annotators, box_annotators
+
+
+def detect(
+    frame: np.ndarray, model: RoboflowInferenceModel, confidence_threshold: float = 0.5
+) -> sv.Detections:
+    """
+    Detect objects in a frame using Inference model, filtering detections by class ID
+        and confidence threshold.
+
+    Args:
+        frame (np.ndarray): The frame to process, expected to be a NumPy array.
+        model (RoboflowInferenceModel): The Inference model used for processing the
+            frame.
+        confidence_threshold (float, optional): The confidence threshold for filtering
+            detections. Default is 0.5.
+
+    Returns:
+        sv.Detections: Filtered detections after processing the frame with the Inference
+            model.
+
+    Note:
+        This function is specifically tailored for an Inference model and assumes class
+        ID 0 for filtering.
+    """
+    results = model.infer(frame)[0]
+    detections = sv.Detections.from_inference(results)
+    filter_by_class = detections.class_id == 0
+    filter_by_confidence = detections.confidence > confidence_threshold
+    return detections[filter_by_class & filter_by_confidence]
+
+
+def annotate(
+    frame: np.ndarray,
+    zones: List[sv.PolygonZone],
+    zone_annotators: List[sv.PolygonZoneAnnotator],
+    box_annotators: List[sv.BoundingBoxAnnotator],
+    detections: sv.Detections,
+) -> np.ndarray:
+    """
+    Annotate a frame with zone and box annotations based on given detections.
+
+    Args:
+        frame (np.ndarray): The original frame to be annotated.
+        zones (List[sv.PolygonZone]): A list of polygon zones used for detection.
+        zone_annotators (List[sv.PolygonZoneAnnotator]): A list of annotators for
+            drawing zone annotations.
+        box_annotators (List[sv.BoundingBoxAnnotator]): A list of annotators for
+            drawing box annotations.
+        detections (sv.Detections): Detections to be used for annotation.
+
+    Returns:
+        np.ndarray: The annotated frame.
+    """
+    annotated_frame = frame.copy()
+    for zone, zone_annotator, box_annotator in zip(
+        zones, zone_annotators, box_annotators
+    ):
+        detections_in_zone = detections[zone.trigger(detections=detections)]
+        annotated_frame = zone_annotator.annotate(scene=annotated_frame)
+        annotated_frame = box_annotator.annotate(
+            scene=annotated_frame, detections=detections_in_zone
+        )
+    return annotated_frame
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Counting people in zones with Inference and Supervision"
+    )
+
+    parser.add_argument(
+        "--zone_configuration_path",
+        required=True,
+        help="Path to the zone configuration JSON file",
+        type=str,
+    )
+    parser.add_argument(
+        "--model_id",
+        default="yolov8x-1280",
+        help="Roboflow model ID",
+        type=str,
+    )
+    parser.add_argument(
+        "--roboflow_api_key",
+        default=None,
+        help="Roboflow API KEY",
+        type=str,
+    )
+    parser.add_argument(
+        "--source_video_path",
+        required=True,
+        help="Path to the source video file",
+        type=str,
+    )
+    parser.add_argument(
+        "--target_video_path",
+        default=None,
+        help="Path to the target video file (output)",
+        type=str,
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        default=0.3,
+        help="Confidence threshold for the model",
+        type=float,
+    )
+    parser.add_argument(
+        "--iou_threshold",
+        default=0.7,
+        help="IOU threshold for the model",
+        type=float,
+    )
+
+    args = parser.parse_args()
+
+    api_key = args.roboflow_api_key
+    api_key = os.environ.get("ROBOFLOW_API_KEY", api_key)
+    if api_key is None:
+        raise ValueError(
+            "Roboflow API key is missing. Please provide it as an argument or set the "
+            "ROBOFLOW_API_KEY environment variable."
+        )
+    args.roboflow_api_key = api_key
+
+    video_info = sv.VideoInfo.from_video_path(args.source_video_path)
+    polygons = load_zones_config(args.zone_configuration_path)
+    zones, zone_annotators, box_annotators = initiate_annotators(
+        polygons=polygons, resolution_wh=video_info.resolution_wh
+    )
+
+    model = get_roboflow_model(model_id=args.model_id, api_key=args.roboflow_api_key)
+
+    frames_generator = sv.get_video_frames_generator(args.source_video_path)
+    if args.target_video_path is not None:
+        with sv.VideoSink(args.target_video_path, video_info) as sink:
+            for frame in tqdm(frames_generator, total=video_info.total_frames):
+                detections = detect(frame, model, args.confidence_threshold)
+                annotated_frame = annotate(
+                    frame=frame,
+                    zones=zones,
+                    zone_annotators=zone_annotators,
+                    box_annotators=box_annotators,
+                    detections=detections,
+                )
+                sink.write_frame(annotated_frame)
+    else:
+        for frame in tqdm(frames_generator, total=video_info.total_frames):
+            detections = detect(frame, model, args.confidence_threshold)
+            annotated_frame = annotate(
+                frame=frame,
+                zones=zones,
+                zone_annotators=zone_annotators,
+                box_annotators=box_annotators,
+                detections=detections,
+            )
+            cv2.imshow("Processed Video", annotated_frame)
+            if cv2.waitKey(1) & 0xFF == ord("q"):
+                break
+
+        cv2.destroyAllWindows()

examples/count_people_in_zone/requirements.txt

@@ -0,0 +1,5 @@
+gdown
+inference
+supervision
+tqdm
+ultralytics

examples/count_people_in_zone/setup.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+
+# Get the directory where the script is located
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+
+# Check if 'data' directory does not exist and then create it
+if [[ ! -e $DIR/data ]]; then
+    mkdir "$DIR/data"
+else
+    echo "'data' directory already exists."
+fi
+
+# Download the market-square.mp4 file from Google Drive
+gdown -O "$DIR/data/market-square.mp4" "https://drive.google.com/uc?id=1vVrEVMxucHgqGd7vAa501ASojbeGPhIr"

examples/count_people_in_zone/ultralytics_example.py

@@ -0,0 +1,206 @@
+import argparse
+import json
+from typing import List, Tuple
+
+import cv2
+import numpy as np
+from tqdm import tqdm
+from ultralytics import YOLO
+
+import supervision as sv
+
+COLORS = sv.ColorPalette.default()
+
+
+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["polygons"]]
+
+
+def initiate_annotators(
+    polygons: List[np.ndarray], resolution_wh: Tuple[int, int]
+) -> 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)
+
+    zones = []
+    zone_annotators = []
+    box_annotators = []
+
+    for index, polygon in enumerate(polygons):
+        zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=resolution_wh)
+        zone_annotator = sv.PolygonZoneAnnotator(
+            zone=zone,
+            color=COLORS.by_idx(index),
+            thickness=line_thickness,
+            text_thickness=line_thickness * 2,
+            text_scale=text_scale * 2,
+        )
+        box_annotator = sv.BoundingBoxAnnotator(
+            color=COLORS.by_idx(index), thickness=line_thickness
+        )
+        zones.append(zone)
+        zone_annotators.append(zone_annotator)
+        box_annotators.append(box_annotator)
+
+    return zones, zone_annotators, box_annotators
+
+
+def detect(
+    frame: np.ndarray, model: YOLO, confidence_threshold: float = 0.5
+) -> sv.Detections:
+    """
+    Detect objects in a frame using a YOLO model, filtering detections by class ID and
+        confidence threshold.
+
+    Args:
+        frame (np.ndarray): The frame to process, expected to be a NumPy array.
+        model (YOLO): The YOLO model used for processing the frame.
+        confidence_threshold (float, optional): The confidence threshold for filtering
+            detections. Default is 0.5.
+
+    Returns:
+        sv.Detections: Filtered detections after processing the frame with the YOLO
+            model.
+
+    Note:
+        This function is specifically tailored for a YOLO model and assumes class ID 0
+            for filtering.
+    """
+    results = model(frame, imgsz=1280, verbose=False)[0]
+    detections = sv.Detections.from_ultralytics(results)
+    filter_by_class = detections.class_id == 0
+    filter_by_confidence = detections.confidence > confidence_threshold
+    return detections[filter_by_class & filter_by_confidence]
+
+
+def annotate(
+    frame: np.ndarray,
+    zones: List[sv.PolygonZone],
+    zone_annotators: List[sv.PolygonZoneAnnotator],
+    box_annotators: List[sv.BoundingBoxAnnotator],
+    detections: sv.Detections,
+) -> np.ndarray:
+    """
+    Annotate a frame with zone and box annotations based on given detections.
+
+    Args:
+        frame (np.ndarray): The original frame to be annotated.
+        zones (List[sv.PolygonZone]): A list of polygon zones used for detection.
+        zone_annotators (List[sv.PolygonZoneAnnotator]): A list of annotators for
+            drawing zone annotations.
+        box_annotators (List[sv.BoundingBoxAnnotator]): A list of annotators for
+            drawing box annotations.
+        detections (sv.Detections): Detections to be used for annotation.
+
+    Returns:
+        np.ndarray: The annotated frame.
+    """
+    annotated_frame = frame.copy()
+    for zone, zone_annotator, box_annotator in zip(
+        zones, zone_annotators, box_annotators
+    ):
+        detections_in_zone = detections[zone.trigger(detections=detections)]
+        annotated_frame = zone_annotator.annotate(scene=annotated_frame)
+        annotated_frame = box_annotator.annotate(
+            scene=annotated_frame, detections=detections_in_zone
+        )
+    return annotated_frame
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Counting people in zones with YOLO and Supervision"
+    )
+
+    parser.add_argument(
+        "--zone_configuration_path",
+        required=True,
+        help="Path to the zone configuration JSON file",
+        type=str,
+    )
+    parser.add_argument(
+        "--source_weights_path",
+        default="yolov8x.pt",
+        help="Path to the source weights file",
+        type=str,
+    )
+    parser.add_argument(
+        "--source_video_path",
+        required=True,
+        help="Path to the source video file",
+        type=str,
+    )
+    parser.add_argument(
+        "--target_video_path",
+        default=None,
+        help="Path to the target video file (output)",
+        type=str,
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        default=0.3,
+        help="Confidence threshold for the model",
+        type=float,
+    )
+    parser.add_argument(
+        "--iou_threshold",
+        default=0.7,
+        help="IOU threshold for the model",
+        type=float,
+    )
+
+    args = parser.parse_args()
+
+    video_info = sv.VideoInfo.from_video_path(args.source_video_path)
+    polygons = load_zones_config(args.zone_configuration_path)
+    zones, zone_annotators, box_annotators = initiate_annotators(
+        polygons=polygons, resolution_wh=video_info.resolution_wh
+    )
+
+    model = YOLO(args.source_weights_path)
+
+    frames_generator = sv.get_video_frames_generator(args.source_video_path)
+    if args.target_video_path is not None:
+        with sv.VideoSink(args.target_video_path, video_info) as sink:
+            for frame in tqdm(frames_generator, total=video_info.total_frames):
+                detections = detect(frame, model, args.confidence_threshold)
+                annotated_frame = annotate(
+                    frame=frame,
+                    zones=zones,
+                    zone_annotators=zone_annotators,
+                    box_annotators=box_annotators,
+                    detections=detections,
+                )
+                sink.write_frame(annotated_frame)
+    else:
+        for frame in tqdm(frames_generator, total=video_info.total_frames):
+            detections = detect(frame, model, args.confidence_threshold)
+            annotated_frame = annotate(
+                frame=frame,
+                zones=zones,
+                zone_annotators=zone_annotators,
+                box_annotators=box_annotators,
+                detections=detections,
+            )
+            cv2.imshow("Processed Video", annotated_frame)
+            if cv2.waitKey(1) & 0xFF == ord("q"):
+                break
+
+        cv2.destroyAllWindows()

examples/heatmap_and_track/README.md

@@ -0,0 +1,75 @@
+# heatmap and tracking
+
+## 👋 hello
+
+This script performs heatmap and tracking analysis using YOLOv8, an object-detection method and
+ByteTrack, a simple yet effective online multi-object tracking method. It uses the
+supervision package for multiple tasks such as drawing heatmap annotations, tracking objects, etc.
+
+
+## 💻 install
+
+- clone repository and navigate to example directory
+
+    ```bash
+    git clone https://github.com/roboflow/supervision.git
+    cd supervision/examples/heatmap_and_track
+    ```
+
+- 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
+    ```
+
+## 🛠️ script arguments
+
+- `--source_weights_path`: Required. Specifies the path to the weights file for the
+YOLO model. This file contains the trained model data necessary for object detection.
+- `--source_video_path` (optional): The path to the source video file that will be
+analyzed. This is the input video on which crowd analysis will be performed.
+If not specified default is `people-walking.mp4` from supervision assets
+- `--target_video_path` (optional): The path to save the output.mp4 video with annotations.
+- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
+to filter detections. Default is `0.3`. This determines how confident the model should
+be to recognize an object in the video.
+- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
+for the model. Default is 0.7. This value is used to manage object detection accuracy,
+particularly in distinguishing between different objects.
+- `--heatmap_alpha` (optional): Opacity of the overlay mask, between 0 and 1.
+- `--radius` (optional): Radius of the heat circle.
+- `--track_threshold` (optional): Detection confidence threshold for track activation.
+- `--track_seconds` (optional): Number of seconds to buffer when a track is lost.
+- `--match_threshold` (optional): Threshold for matching tracks with detections.
+
+## ⚙️ run
+
+```bash
+python script.py \
+--source_weights_path weight.pt \
+--source_video_path  input_video.mp4 \
+--confidence_threshold 0.3 \
+--iou_threshold 0.5 \
+--target_video_path  output_video.mp4
+```
+
+## © 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/heatmap_and_track/requirements.txt

@@ -0,0 +1,2 @@
+supervision[assets]
+ultralytics

examples/heatmap_and_track/script.py

@@ -0,0 +1,181 @@
+import argparse
+
+import cv2
+from ultralytics import YOLO
+
+import supervision as sv
+from supervision.assets import VideoAssets, download_assets
+
+
+def download_video() -> str:
+    download_assets(VideoAssets.PEOPLE_WALKING)
+    return VideoAssets.PEOPLE_WALKING.value
+
+
+def heatmap_and_track(
+    source_weights_path: str,
+    source_video_path: str,
+    target_video_path: str,
+    confidence_threshold: float = 0.35,
+    iou_threshold: float = 0.5,
+    heatmap_alpha: float = 0.5,
+    radius: int = 25,
+    track_threshold: float = 0.35,
+    track_seconds: int = 5,
+    match_threshold: float = 0.99,
+) -> None:
+    ### instantiate model
+    model = YOLO(source_weights_path)
+
+    ### heatmap config
+    heat_map_annotator = sv.HeatMapAnnotator(
+        position=sv.Position.BOTTOM_CENTER,
+        opacity=heatmap_alpha,
+        radius=radius,
+        kernel_size=25,
+        top_hue=0,
+        low_hue=125,
+    )
+
+    ### annotation config
+    label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
+
+    ### get the video fps
+    cap = cv2.VideoCapture(source_video_path)
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    cap.release()
+
+    ### tracker config
+    byte_tracker = sv.ByteTrack(
+        track_thresh=track_threshold,
+        track_buffer=track_seconds * fps,
+        match_thresh=match_threshold,
+        frame_rate=fps,
+    )
+
+    ### video config
+    video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
+    frames_generator = sv.get_video_frames_generator(
+        source_path=source_video_path, stride=1
+    )
+
+    ### Detect, track, annotate, save
+    with sv.VideoSink(target_path=target_video_path, video_info=video_info) as sink:
+        for frame in frames_generator:
+            result = model(
+                source=frame,
+                classes=[0],  # only person class
+                conf=confidence_threshold,
+                iou=iou_threshold,
+                # show_conf = True,
+                # save_txt = True,
+                # save_conf = True,
+                # save = True,
+                device=None,  # use None = CPU, 0 = single GPU, or [0,1] = dual GPU
+            )[0]
+
+            detections = sv.Detections.from_ultralytics(result)  # get detections
+
+            detections = byte_tracker.update_with_detections(
+                detections
+            )  # update tracker
+
+            ### draw heatmap
+            annotated_frame = heat_map_annotator.annotate(
+                scene=frame.copy(), detections=detections
+            )
+
+            ### draw other attributes from `detections` object
+            labels = [
+                f"#{tracker_id}"
+                for class_id, tracker_id in zip(
+                    detections.class_id, detections.tracker_id
+                )
+            ]
+
+            label_annotator.annotate(
+                scene=annotated_frame, detections=detections, labels=labels
+            )
+
+            sink.write_frame(frame=annotated_frame)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Heatmap and Tracking with Supervision"
+    )
+    parser.add_argument(
+        "--source_weights_path",
+        required=True,
+        help="Path to the source weights file",
+        type=str,
+    )
+    parser.add_argument(
+        "--source_video_path",
+        default=download_video(),
+        help="Path to the source video file",
+        type=str,
+    )
+    parser.add_argument(
+        "--target_video_path",
+        default="output.mp4",
+        help="Path to the target video file (output)",
+        type=str,
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        default=0.35,
+        help="Confidence threshold for the model",
+        type=float,
+    )
+    parser.add_argument(
+        "--iou_threshold",
+        default=0.5,
+        help="IOU threshold for the model",
+        type=float,
+    )
+    parser.add_argument(
+        "--heatmap_alpha",
+        default=0.5,
+        help="Opacity of the overlay mask, between 0 and 1",
+        type=float,
+    )
+    parser.add_argument(
+        "--radius",
+        default=25,
+        help="Radius of the heat circle",
+        type=float,
+    )
+    parser.add_argument(
+        "--track_threshold",
+        default=0.35,
+        help="Detection confidence threshold for track activation",
+        type=float,
+    )
+    parser.add_argument(
+        "--track_seconds",
+        default=5,
+        help="Number of seconds to buffer when a track is lost",
+        type=int,
+    )
+    parser.add_argument(
+        "--match_threshold",
+        default=0.99,
+        help="Threshold for matching tracks with detections",
+        type=float,
+    )
+
+    args = parser.parse_args()
+
+    heatmap_and_track(
+        source_weights_path=args.source_weights_path,
+        source_video_path=args.source_video_path,
+        target_video_path=args.target_video_path,
+        confidence_threshold=args.confidence_threshold,
+        iou_threshold=args.iou_threshold,
+        heatmap_alpha=args.heatmap_alpha,
+        radius=args.radius,
+        track_threshold=args.track_threshold,
+        track_seconds=args.track_seconds,
+        match_threshold=args.match_threshold,
+    )

examples/tracking/README.md

@@ -1,3 +1,5 @@
+# tracking
+
 ## 👋 hello
 
 This script provides functionality for processing videos using YOLOv8 for object
@@ -5,16 +7,95 @@ detection and Supervision for tracking and annotation.
 
 ## 💻 install
 
-```bash
-pip install -r requirements.txt
-```
+- clone repository and navigate to example directory
 
-## ⚙️ parameters
+  ```bash
+  git clone https://github.com/roboflow/supervision.git
+  cd supervision/examples/tracking
+  ```
 
-| parameter                | required | description                                                                       |
-|:-------------------------|:--------:|:----------------------------------------------------------------------------------|
-| `--source_weights_path`  |    ✓     | Path to the source weights file for YOLOv8.                                       |
-| `--source_video_path`    |    ✓     | Path to the source video file to be processed.                                    |
-| `--target_video_path`    |    ✓     | Path to the target video file (output).                                           |
-| `--confidence_threshold` |    ✗     | Confidence threshold for YOLO model detection. Default is 0.3.                    |
-| `--iou_threshold`        |    ✗     | IOU (Intersection over Union) threshold for YOLO model detection. Default is 0.7. |
+- 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
+  ```
+
+## 🛠️ script arguments
+
+- ultralytics
+
+  - `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
+    file, which is essential for the object detection process. This file contains the data
+    that the model uses to identify objects in the video.
+
+  - `--source_video_path`: Required. The path to the source video file to be processed.
+    This is the video on which object detection and annotation will be performed.
+  - `--target_video_path`: Required. The path where the processed video, with annotations
+    added, will be saved. This is your output video file.
+  - `--confidence_threshold` (optional): Sets the confidence level at which the model
+    identifies objects in the video. Default is `0.3`. A higher threshold makes the model
+    more selective, while a lower threshold makes it more inclusive in identifying objects.
+  - `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
+    for the model, defaulting to `0.7`. This parameter helps in differentiating between
+    distinct objects, especially in crowded scenes.
+
+- inference
+
+  - `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
+    directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
+    variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
+    to acquire your `API KEY`.
+  - `--model_id` (optional): Designates the Roboflow model ID to be used. The default
+    value is `"yolov8x-1280"`.
+
+  - `--source_video_path`: Required. The path to the source video file to be processed.
+    This is the video on which object detection and annotation will be performed.
+  - `--target_video_path`: Required. The path where the processed video, with annotations
+    added, will be saved. This is your output video file.
+  - `--confidence_threshold` (optional): Sets the confidence level at which the model
+    identifies objects in the video. Default is `0.3`. A higher threshold makes the model
+    more selective, while a lower threshold makes it more inclusive in identifying objects.
+  - `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
+    for the model, defaulting to `0.7`. This parameter helps in differentiating between
+    distinct objects, especially in crowded scenes.
+
+## ⚙️ run
+
+- inference
+
+  ```bash
+  python inference_example.py \
+  --roboflow_api_key <ROBOFLOW API KEY> \
+  --source_video_path input.mp4 \
+  --target_video_path tracking_result.mp4
+  ```
+
+- ultralytics
+
+  ```bash
+  python ultralytics_example.py \
+  --source_weights_path yolov8s.pt \
+  --source_video_path input.mp4 \
+  --target_video_path tracking_result.mp4
+  ```
+
+## © 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/tracking/inference_example.py

@@ -0,0 +1,101 @@
+import argparse
+import os
+
+from inference.models.utils import get_roboflow_model
+from tqdm import tqdm
+
+import supervision as sv
+
+
+def process_video(
+    roboflow_api_key: str,
+    model_id: str,
+    source_video_path: str,
+    target_video_path: str,
+    confidence_threshold: float = 0.3,
+    iou_threshold: float = 0.7,
+) -> None:
+    model = get_roboflow_model(model_id=model_id, api_key=roboflow_api_key)
+
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+    frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
+    video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
+
+    with sv.VideoSink(target_path=target_video_path, video_info=video_info) as sink:
+        for frame in tqdm(frame_generator, total=video_info.total_frames):
+            results = model.infer(
+                frame, confidence=confidence_threshold, iou_threshold=iou_threshold
+            )[0]
+            detections = sv.Detections.from_inference(results)
+            detections = tracker.update_with_detections(detections)
+
+            annotated_frame = box_annotator.annotate(
+                scene=frame.copy(), detections=detections
+            )
+
+            annotated_labeled_frame = label_annotator.annotate(
+                scene=annotated_frame, detections=detections
+            )
+
+            sink.write_frame(frame=annotated_labeled_frame)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Video Processing with Inference and ByteTrack"
+    )
+    parser.add_argument(
+        "--model_id",
+        default="yolov8x-1280",
+        help="Roboflow model ID",
+        type=str,
+    )
+    parser.add_argument(
+        "--source_video_path",
+        required=True,
+        help="Path to the source video file",
+        type=str,
+    )
+    parser.add_argument(
+        "--target_video_path",
+        required=True,
+        help="Path to the target video file (output)",
+        type=str,
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        default=0.3,
+        help="Confidence threshold for the model",
+        type=float,
+    )
+    parser.add_argument(
+        "--iou_threshold", default=0.7, help="IOU threshold for the model", type=float
+    )
+    parser.add_argument(
+        "--roboflow_api_key",
+        default=None,
+        help="Roboflow API key",
+        type=str,
+    )
+
+    args = parser.parse_args()
+
+    api_key = args.roboflow_api_key
+    api_key = os.environ.get("ROBOFLOW_API_KEY", api_key)
+    if api_key is None:
+        raise ValueError(
+            "Roboflow API key is missing. Please provide it as an argument or set the "
+            "ROBOFLOW_API_KEY environment variable."
+        )
+    args.roboflow_api_key = api_key
+
+    process_video(
+        roboflow_api_key=args.roboflow_api_key,
+        model_id=args.model_id,
+        source_video_path=args.source_video_path,
+        target_video_path=args.target_video_path,
+        confidence_threshold=args.confidence_threshold,
+        iou_threshold=args.iou_threshold,
+    )

examples/tracking/requirements.txt

@@ -1,3 +1,4 @@
+inference
 supervision
 tqdm
 ultralytics

examples/tracking/ultralytics_example.py

@@ -17,7 +17,7 @@ def process_video(
 
     tracker = sv.ByteTrack()
     box_annotator = sv.BoundingBoxAnnotator()
-    label_annotator = sv.LabelAnnotator(classes=model.names)
+    label_annotator = sv.LabelAnnotator()
     frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
     video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
 

examples/traffic_analysis/README.md

@@ -1,6 +1,10 @@
+# traffic analysis
+
 ## 👋 hello
 
-This script performs traffic flow analysis using YOLOv8, an object-detection method and ByteTrack, a simple yet effective online multi-object tracking method. It uses the supervision package for multiple tasks such as tracking, annotations, etc.
+This script performs traffic flow analysis using YOLOv8, an object-detection method and
+ByteTrack, a simple yet effective online multi-object tracking method. It uses the
+supervision package for multiple tasks such as tracking, annotations, etc.
 
 https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900
 
@@ -8,37 +12,105 @@ https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-
 
 - clone repository and navigate to example directory
 
-    ```bash
-    git clone https://github.com/roboflow/supervision.git
-    cd supervision/examples/traffic_analysis
-    ```
+  ```bash
+  git clone https://github.com/roboflow/supervision.git
+  cd supervision/examples/traffic_analysis
+  ```
 
 - setup python environment and activate it [optional]
 
-    ```bash
-    python3 -m venv venv
-    source venv/bin/activate
-    ```
+  ```bash
+  python3 -m venv venv
+  source venv/bin/activate
+  ```
 
 - install required dependencies
 
-    ```bash
-    pip install -r requirements.txt
-    ```
+  ```bash
+  pip install -r requirements.txt
+  ```
 
 - download `traffic_analysis.pt` and `traffic_analysis.mov` files
 
-    ```bash
-    ./setup.sh
-    ```
+  ```bash
+  ./setup.sh
+  ```
+
+## 🛠️ script arguments
+
+- ultralytics
+
+  - `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
+    file, which is essential for the object detection process. This file contains the
+    data that the model uses to identify objects in the video.
+
+  - `--source_video_path`: Required. The path to the source video file that will be
+    analyzed. This is the input video on which traffic flow analysis will be performed.
+  - `--target_video_path` (optional): The path to save the output video with
+    annotations. If not specified, the processed video will be displayed in real-time
+    without being saved.
+  - `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
+    model to filter detections. Default is `0.3`. This determines how confident the
+    model should be to recognize an object in the video.
+  - `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
+    for the model. Default is 0.7. This value is used to manage object detection
+    accuracy, particularly in distinguishing between different objects.
+
+- inference
+
+  - `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
+    directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
+    variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
+    to acquire your `API KEY`.
+  - `--model_id` (optional): Designates the Roboflow model ID to be used. The default
+    value is `"vehicle-count-in-drone-video/6"`.
+
+  - `--source_video_path`: Required. The path to the source video file that will be
+    analyzed. This is the input video on which traffic flow analysis will be performed.
+  - `--target_video_path` (optional): The path to save the output video with
+    annotations. If not specified, the processed video will be displayed in real-time
+    without being saved.
+  - `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
+    model to filter detections. Default is `0.3`. This determines how confident the
+    model should be to recognize an object in the video.
+  - `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
+    for the model. Default is 0.7. This value is used to manage object detection
+    accuracy, particularly in distinguishing between different objects.
 
 ## ⚙️ run
 
-```bash
-python script.py \
---source_weights_path data/traffic_analysis.pt \
---source_video_path data/traffic_analysis.mov \
---confidence_threshold 0.3 \
---iou_threshold 0.5 \
---target_video_path data/traffic_analysis_result.mov
-```
+- ultralytics
+
+  ```bash
+  python ultralytics_example.py \
+  --source_weights_path data/traffic_analysis.pt \
+  --source_video_path data/traffic_analysis.mov \
+  --confidence_threshold 0.3 \
+  --iou_threshold 0.5 \
+  --target_video_path data/traffic_analysis_result.mov
+  ```
+
+- inference
+
+  ```bash
+  python inference_example.py \
+  --roboflow_api_key <ROBOFLOW API KEY> \
+  --source_video_path data/traffic_analysis.mov \
+  --confidence_threshold 0.3 \
+  --iou_threshold 0.5 \
+  --target_video_path data/traffic_analysis_result.mov
+  ```
+
+## © 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/traffic_analysis/inference_example.py

@@ -0,0 +1,238 @@
+import argparse
+import os
+from typing import Dict, List, Set, Tuple
+
+import cv2
+import numpy as np
+from inference.models.utils import get_roboflow_model
+from tqdm import tqdm
+
+import supervision as sv
+
+COLORS = sv.ColorPalette.default()
+
+ZONE_IN_POLYGONS = [
+    np.array([[592, 282], [900, 282], [900, 82], [592, 82]]),
+    np.array([[950, 860], [1250, 860], [1250, 1060], [950, 1060]]),
+    np.array([[592, 582], [592, 860], [392, 860], [392, 582]]),
+    np.array([[1250, 282], [1250, 530], [1450, 530], [1450, 282]]),
+]
+
+ZONE_OUT_POLYGONS = [
+    np.array([[950, 282], [1250, 282], [1250, 82], [950, 82]]),
+    np.array([[592, 860], [900, 860], [900, 1060], [592, 1060]]),
+    np.array([[592, 282], [592, 550], [392, 550], [392, 282]]),
+    np.array([[1250, 860], [1250, 560], [1450, 560], [1450, 860]]),
+]
+
+
+class DetectionsManager:
+    def __init__(self) -> None:
+        self.tracker_id_to_zone_id: Dict[int, int] = {}
+        self.counts: Dict[int, Dict[int, Set[int]]] = {}
+
+    def update(
+        self,
+        detections_all: sv.Detections,
+        detections_in_zones: List[sv.Detections],
+        detections_out_zones: List[sv.Detections],
+    ) -> sv.Detections:
+        for zone_in_id, detections_in_zone in enumerate(detections_in_zones):
+            for tracker_id in detections_in_zone.tracker_id:
+                self.tracker_id_to_zone_id.setdefault(tracker_id, zone_in_id)
+
+        for zone_out_id, detections_out_zone in enumerate(detections_out_zones):
+            for tracker_id in detections_out_zone.tracker_id:
+                if tracker_id in self.tracker_id_to_zone_id:
+                    zone_in_id = self.tracker_id_to_zone_id[tracker_id]
+                    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)
+        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,
+) -> List[sv.PolygonZone]:
+    return [
+        sv.PolygonZone(
+            polygon=polygon,
+            frame_resolution_wh=frame_resolution_wh,
+            triggering_position=triggering_position,
+        )
+        for polygon in polygons
+    ]
+
+
+class VideoProcessor:
+    def __init__(
+        self,
+        roboflow_api_key: str,
+        model_id: str,
+        source_video_path: str,
+        target_video_path: str = None,
+        confidence_threshold: float = 0.3,
+        iou_threshold: float = 0.7,
+    ) -> None:
+        self.conf_threshold = confidence_threshold
+        self.iou_threshold = iou_threshold
+        self.source_video_path = source_video_path
+        self.target_video_path = target_video_path
+
+        self.model = get_roboflow_model(model_id=model_id, api_key=roboflow_api_key)
+        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.box_annotator = sv.BoxAnnotator(color=COLORS)
+        self.trace_annotator = sv.TraceAnnotator(
+            color=COLORS, position=sv.Position.CENTER, trace_length=100, thickness=2
+        )
+        self.detections_manager = DetectionsManager()
+
+    def process_video(self):
+        frame_generator = sv.get_video_frames_generator(
+            source_path=self.source_video_path
+        )
+
+        if self.target_video_path:
+            with sv.VideoSink(self.target_video_path, self.video_info) as sink:
+                for frame in tqdm(frame_generator, total=self.video_info.total_frames):
+                    annotated_frame = self.process_frame(frame)
+                    sink.write_frame(annotated_frame)
+        else:
+            for frame in tqdm(frame_generator, total=self.video_info.total_frames):
+                annotated_frame = self.process_frame(frame)
+                cv2.imshow("Processed Video", annotated_frame)
+                if cv2.waitKey(1) & 0xFF == ord("q"):
+                    break
+            cv2.destroyAllWindows()
+
+    def annotate_frame(
+        self, frame: np.ndarray, detections: sv.Detections
+    ) -> np.ndarray:
+        annotated_frame = frame.copy()
+        for i, (zone_in, zone_out) in enumerate(zip(self.zones_in, self.zones_out)):
+            annotated_frame = sv.draw_polygon(
+                annotated_frame, zone_in.polygon, COLORS.colors[i]
+            )
+            annotated_frame = sv.draw_polygon(
+                annotated_frame, zone_out.polygon, COLORS.colors[i]
+            )
+
+        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, detections, labels
+        )
+
+        for zone_out_id, zone_out in enumerate(self.zones_out):
+            zone_center = sv.get_polygon_center(polygon=zone_out.polygon)
+            if zone_out_id in self.detections_manager.counts:
+                counts = self.detections_manager.counts[zone_out_id]
+                for i, zone_in_id in enumerate(counts):
+                    count = len(self.detections_manager.counts[zone_out_id][zone_in_id])
+                    text_anchor = sv.Point(x=zone_center.x, y=zone_center.y + 40 * i)
+                    annotated_frame = sv.draw_text(
+                        scene=annotated_frame,
+                        text=str(count),
+                        text_anchor=text_anchor,
+                        background_color=COLORS.colors[zone_in_id],
+                    )
+
+        return annotated_frame
+
+    def process_frame(self, frame: np.ndarray) -> np.ndarray:
+        results = self.model.infer(
+            frame, confidence=self.conf_threshold, iou_threshold=self.iou_threshold
+        )[0]
+        detections = sv.Detections.from_inference(results)
+        detections.class_id = np.zeros(len(detections))
+        detections = self.tracker.update_with_detections(detections)
+
+        detections_in_zones = []
+        detections_out_zones = []
+
+        for i, (zone_in, zone_out) in enumerate(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)]
+            detections_out_zones.append(detections_out_zone)
+
+        detections = self.detections_manager.update(
+            detections, detections_in_zones, detections_out_zones
+        )
+        return self.annotate_frame(frame, detections)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Traffic Flow Analysis with Inference and ByteTrack"
+    )
+
+    parser.add_argument(
+        "--model_id",
+        default="vehicle-count-in-drone-video/6",
+        help="Roboflow model ID",
+        type=str,
+    )
+    parser.add_argument(
+        "--roboflow_api_key",
+        default=None,
+        help="Roboflow API KEY",
+        type=str,
+    )
+    parser.add_argument(
+        "--source_video_path",
+        required=True,
+        help="Path to the source video file",
+        type=str,
+    )
+    parser.add_argument(
+        "--target_video_path",
+        default=None,
+        help="Path to the target video file (output)",
+        type=str,
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        default=0.3,
+        help="Confidence threshold for the model",
+        type=float,
+    )
+    parser.add_argument(
+        "--iou_threshold", default=0.7, help="IOU threshold for the model", type=float
+    )
+
+    args = parser.parse_args()
+
+    api_key = args.roboflow_api_key
+    api_key = os.environ.get("ROBOFLOW_API_KEY", api_key)
+    if api_key is None:
+        raise ValueError(
+            "Roboflow API KEY is missing. Please provide it as an argument or set the "
+            "ROBOFLOW_API_KEY environment variable."
+        )
+    args.roboflow_api_key = api_key
+
+    processor = VideoProcessor(
+        roboflow_api_key=args.roboflow_api_key,
+        model_id=args.model_id,
+        source_video_path=args.source_video_path,
+        target_video_path=args.target_video_path,
+        confidence_threshold=args.confidence_threshold,
+        iou_threshold=args.iou_threshold,
+    )
+    processor.process_video()

examples/traffic_analysis/requirements.txt

@@ -1,4 +1,5 @@
-supervision>=0.15.0rc1
+gdown
+inference
+supervision
 tqdm
 ultralytics
-gdown

mkdocs.yml

@@ -1,7 +1,7 @@
 site_name: Supervision
-site_url: https://roboflow.github.io/supervision
+site_url: https://supervision.roboflow.com/
 site_author: Roboflow
-site_description: A set of easy-to-use utils that will come in handy in any Computer Vision project
+site_description: A set of easy-to-use utilities that will come in handy in any computer vision project.
 repo_name: roboflow/supervision
 repo_url: https://github.com/roboflow/supervision
 edit_uri: https://github.com/roboflow/supervision/tree/main/docs
@@ -26,35 +26,43 @@ extra_css:
 
 nav:
   - Home: index.md
-  - Quickstart:
-    - Detections: quickstart/detections.md
-  - Classifications:
-      - Core: classification/core.md
-  - Detections:
-      - Core: detection/core.md
-      - Annotate: detection/annotate.md
-      - Utils: detection/utils.md
-      - Tools:
-          - Polygon Zone: detection/tools/polygon_zone.md
-          - Inference Slicer: detection/tools/inference_slicer.md
-  - Annotators: annotators.md
-  - Trackers: trackers.md
-  - Datasets: datasets.md
-  - Metrics:
-    - Object Detection: metrics/detection.md
-  - Draw:
-      - Utils: draw/utils.md
-  - Utils:
-      - Video: utils/video.md
-      - Image: utils/image.md
-      - Notebook: utils/notebook.md
-      - File: utils/file.md
+  - How to:
+      - Detect and Annotate: how_to/detect_and_annotate.md
+      - Process Video: how_to/process_video.md
+      - Track Objects: how_to/track_objects.md
+      - Filter Detections: how_to/filter_detections.md
+      - Evaluate Model: how_to/evaluate_model.md
+  - API:
+    - Classifications:
+        - Core: classification/core.md
+    - Detections:
+        - Core: detection/core.md
+        - Utils: detection/utils.md
+    - Tools:
+        - Line Zone: detection/tools/line_zone.md
+        - Polygon Zone: detection/tools/polygon_zone.md
+        - Inference Slicer: detection/tools/inference_slicer.md
+    - Annotators: annotators.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
+        - Notebook: utils/notebook.md
+        - File: utils/file.md
+  - Assets: assets.md
   - Changelog: changelog.md
 
 theme:
   name: 'material'
-  logo: https://media.roboflow.com/open-source/supervision/supervision-lenny.png
-  favicon: https://media.roboflow.com/open-source/supervision/supervision-lenny.png
+  logo: assets/supervision-lenny.png
+  favicon: assets/supervision-lenny.png
+  custom_dir: docs/theme
   palette:
     # Palette for light mode
     - scheme: default
@@ -72,6 +80,8 @@ theme:
   font:
     text: Roboto
     code: Roboto Mono
+  features:
+    - content.code.copy
 
 plugins:
   - mkdocstrings
@@ -87,3 +97,7 @@ markdown_extensions:
       alternate_style: true
   - toc:
       permalink: true
+
+extra_javascript:
+  - "https://widget.kapa.ai/kapa-widget.bundle.js"
+  - "javascript/init_kapa_widget.js"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.15.0"
+version = "0.18.0rc1"
 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>"]
@@ -10,7 +10,7 @@ packages = [{include = "supervision"}]
 homepage = "https://github.com/roboflow/supervision"
 repository = "https://github.com/roboflow/supervision"
 documentation = "https://github.com/roboflow/supervision/blob/main/README.md"
-keywords = ["machine-learning", "deep-learning", "vision", "ML", "DL", "AI", "YOLOv5", "YOLOv8", "Roboflow"]
+keywords = ["machine-learning", "deep-learning", "vision", "ML", "DL", "AI", "Roboflow"]
 
 classifiers=[
     'Development Status :: 4 - Beta',
@@ -19,10 +19,6 @@ classifiers=[
     'Intended Audience :: Science/Research',
     'License :: OSI Approved :: MIT License',
     'Programming Language :: Python :: 3',
-    'Programming Language :: Python :: 3.8',
-    'Programming Language :: Python :: 3.9',
-    'Programming Language :: Python :: 3.10',
-    'Programming Language :: Python :: 3.11',
     'Programming Language :: Python :: 3 :: Only',
     'Topic :: Software Development',
     'Topic :: Scientific/Engineering',
@@ -35,51 +31,40 @@ classifiers=[
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.8,<3.12.0"
-numpy = "^1.20.0"
-matplotlib = "^3.7.1"
-pyyaml = "^6.0"
-pillow = ">=9.4,<11.0"
-opencv-python = { version = "^4.8.0.74", optional = true }
-opencv-python-headless = "^4.8.0.74"
-scipy = "^1.9.0"
+python = "^3.8"
+numpy = ">=1.21.2"
+scipy =  ">=1.9.0"
+matplotlib = ">=3.5.0"
+pyyaml = ">=5.3"
+pillow = ">=9.4"
+opencv-python = { version = ">=4.5.5.64,<=4.8.1.78", optional = true }
+opencv-python-headless = ">=4.5.5.64,<=4.8.1.78"
+requests = { version = ">=2.26.0,<=2.31.0", optional = true }
+tqdm = { version = ">=4.62.3,<=4.66.1", optional = true }
+defusedxml = "^0.7.1"
 
 
 [tool.poetry.extras]
 desktop = ["opencv-python"]
-
+assets = ["requests","tqdm"]
 
 [tool.poetry.group.dev.dependencies]
 twine = "^4.0.2"
 pytest = "^7.2.2"
-wheel = "^0.40.0"
-notebook = "^6.5.3"
-build = "^0.10.0"
-ruff = "^0.0.280"
+wheel = ">=0.40,<0.43"
+notebook = ">=6.5.3,<8.0.0"
+build = ">=0.10,<1.1"
+ruff = ">=0.0.280,<0.1.12"
 isort = "^5.12.0"
-black = "^23.7.0"
 mypy = "^1.4.1"
 pre-commit = "^3.3.3"
-flake8 = { version = "*", python = ">=3.8.1,<3.12.0" }
+tox = "^4.11.4"
 
 
 [tool.poetry.group.docs.dependencies]
 mkdocs-material = "^9.1.4"
-mkdocstrings = {extras = ["python"], version = "^0.20.0"}
+mkdocstrings = {extras = ["python"], version = ">=0.20,<0.25"}
 
-[tool.flake8]
-exclude = ".venv"
-max-complexity = 10
-max-line-length = 88
-extend-ignore = """
-    W503,
-    E203,
-    E701,
-    C901,
-"""
-per-file-ignores = """
-    __init__.py: F401
-"""
 
 [tool.isort]
 line_length = 88
@@ -87,7 +72,7 @@ profile = "black"
 
 [tool.bandit]
 target = ["test", "supervision"]
-tests = ["B201", "B301"]
+tests = ["B201", "B301", "B318", "B314", "B303", "B413", "B412", "B410"]
 
 [tool.autoflake]
 check = true
@@ -153,6 +138,7 @@ exclude = [
 
 # Same as Black.
 line-length = 88
+indent-width = 4
 
 # Allow unused variables when underscore-prefixed.
 dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
@@ -167,16 +153,34 @@ convention = "google"
 
 [tool.ruff.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"
+
+# Like Black, indent with spaces, rather than tabs.
+indent-style = "space"
+
+# Like Black, respect magic trailing commas.
+skip-magic-trailing-comma = false
+
+# Like Black, automatically detect the appropriate line ending.
+line-ending = "auto"
 
 [tool.setuptools]
 include-package-data = false
 
 [tool.setuptools.packages.find]
-exclude = ["docs*", "test*"]
+exclude = ["docs*", "test*","examples*"]
 
 [build-system]
 requires = ["poetry-core"]

supervision/__init__.py

@@ -11,11 +11,19 @@ from supervision.annotators.core import (
     BoundingBoxAnnotator,
     BoxCornerAnnotator,
     CircleAnnotator,
+    ColorAnnotator,
+    DotAnnotator,
     EllipseAnnotator,
+    HaloAnnotator,
+    HeatMapAnnotator,
     LabelAnnotator,
     MaskAnnotator,
+    PixelateAnnotator,
+    PolygonAnnotator,
     TraceAnnotator,
+    TriangleAnnotator,
 )
+from supervision.annotators.utils import ColorLookup
 from supervision.classification.core import Classifications
 from supervision.dataset.core import (
     BaseDataset,
@@ -29,15 +37,27 @@ from supervision.detection.tools.inference_slicer import InferenceSlicer
 from supervision.detection.tools.polygon_zone import PolygonZone, PolygonZoneAnnotator
 from supervision.detection.utils import (
     box_iou_batch,
+    calculate_masks_centroids,
     filter_polygons_by_area,
     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 draw_filled_rectangle, draw_polygon, draw_text
+from supervision.draw.utils import (
+    calculate_dynamic_line_thickness,
+    calculate_dynamic_text_scale,
+    draw_filled_rectangle,
+    draw_image,
+    draw_line,
+    draw_polygon,
+    draw_rectangle,
+    draw_text,
+)
 from supervision.geometry.core import Point, Position, Rect
 from supervision.geometry.utils import get_polygon_center
 from supervision.metrics.detection import ConfusionMatrix, MeanAveragePrecision
@@ -46,6 +66,7 @@ from supervision.utils.file import list_files_with_extensions
 from supervision.utils.image import ImageSink, crop_image
 from supervision.utils.notebook import plot_image, plot_images_grid
 from supervision.utils.video import (
+    FPSMonitor,
     VideoInfo,
     VideoSink,
     get_video_frames_generator,

supervision/annotators/utils.py

@@ -8,18 +8,29 @@ from supervision.draw.color import Color, ColorPalette
 from supervision.geometry.core import Position
 
 
-class ColorMap(Enum):
+class ColorLookup(Enum):
     """
-    Enum for annotator color mapping.
+    Enumeration class to define strategies for mapping colors to annotations.
+
+    This enum supports three different lookup strategies:
+        - `INDEX`: Colors are determined by the index of the detection within the scene.
+        - `CLASS`: Colors are determined by the class label of the detected object.
+        - `TRACK`: Colors are determined by the tracking identifier of the object.
     """
 
     INDEX = "index"
     CLASS = "class"
     TRACK = "track"
 
+    @classmethod
+    def list(cls):
+        return list(map(lambda c: c.value, cls))
+
 
 def resolve_color_idx(
-    detections: Detections, detection_idx: int, color_map: ColorMap = ColorMap.CLASS
+    detections: Detections,
+    detection_idx: int,
+    color_lookup: Union[ColorLookup, np.ndarray] = ColorLookup.CLASS,
 ) -> int:
     if detection_idx >= len(detections):
         raise ValueError(
@@ -27,16 +38,23 @@ def resolve_color_idx(
             f"is out of bounds for detections of length {len(detections)}"
         )
 
-    if color_map == ColorMap.INDEX:
+    if isinstance(color_lookup, np.ndarray):
+        if len(color_lookup) != len(detections):
+            raise ValueError(
+                f"Length of color lookup {len(color_lookup)}"
+                f"does not match length of detections {len(detections)}"
+            )
+        return color_lookup[detection_idx]
+    elif color_lookup == ColorLookup.INDEX:
         return detection_idx
-    elif color_map == ColorMap.CLASS:
+    elif color_lookup == ColorLookup.CLASS:
         if detections.class_id is None:
             raise ValueError(
                 "Could not resolve color by class because"
                 "Detections do not have class_id"
             )
         return detections.class_id[detection_idx]
-    elif color_map == ColorMap.TRACK:
+    elif color_lookup == ColorLookup.TRACK:
         if detections.tracker_id is None:
             raise ValueError(
                 "Could not resolve color by track because"
@@ -45,12 +63,26 @@ def resolve_color_idx(
         return detections.tracker_id[detection_idx]
 
 
-def resolve_color(color: Union[Color, ColorPalette], idx: int) -> Color:
+def get_color_by_index(color: Union[Color, ColorPalette], idx: int) -> Color:
     if isinstance(color, ColorPalette):
         return color.by_idx(idx)
     return color
 
 
+def resolve_color(
+    color: Union[Color, ColorPalette],
+    detections: Detections,
+    detection_idx: int,
+    color_lookup: Union[ColorLookup, np.ndarray] = ColorLookup.CLASS,
+) -> Color:
+    idx = resolve_color_idx(
+        detections=detections,
+        detection_idx=detection_idx,
+        color_lookup=color_lookup,
+    )
+    return get_color_by_index(color=color, idx=idx)
+
+
 class Trace:
     def __init__(
         self,
@@ -70,7 +102,7 @@ class Trace:
         frame_id = np.full(len(detections), self.current_frame_id, dtype=int)
         self.frame_id = np.concatenate([self.frame_id, frame_id])
         self.xy = np.concatenate(
-            [self.xy, detections.get_anchor_coordinates(self.anchor)]
+            [self.xy, detections.get_anchors_coordinates(self.anchor)]
         )
         self.tracker_id = np.concatenate([self.tracker_id, detections.tracker_id])
 

supervision/assets/__init__.py

@@ -0,0 +1,2 @@
+from supervision.assets.downloader import download_assets
+from supervision.assets.list import VideoAssets

supervision/assets/downloader.py

@@ -0,0 +1,95 @@
+import os
+from hashlib import new as hash_new
+from pathlib import Path
+from shutil import copyfileobj
+from typing import Union
+
+from supervision.assets.list import VIDEO_ASSETS, VideoAssets
+
+try:
+    from requests import get
+    from tqdm.auto import tqdm
+except ImportError:
+    raise ValueError(
+        "\n"
+        "Please install requests and tqdm to download assets \n"
+        "or install supervision with assets \n"
+        "pip install supervision[assets] \n"
+        "\n"
+    )
+
+
+def is_md5_hash_matching(filename: str, original_md5_hash: str) -> bool:
+    """
+    Check if the MD5 hash of a file matches the original hash.
+
+    Parameters:
+        filename (str): The path to the file to be checked as a string.
+        original_md5_hash (str): The original MD5 hash to compare against.
+
+    Returns:
+        bool: True if the hashes match, False otherwise.
+    """
+    if not os.path.exists(filename):
+        return False
+
+    with open(filename, "rb") as file:
+        file_contents = file.read()
+        computed_md5_hash = hash_new(name="MD5")
+        computed_md5_hash.update(file_contents)
+
+    return computed_md5_hash.hexdigest() == original_md5_hash
+
+
+def download_assets(asset_name: Union[VideoAssets, str]) -> str:
+    """
+    Download a specified asset if it doesn't already exist or is corrupted.
+
+    Parameters:
+        asset_name (Union[VideoAssets, str]): The name or type of the asset to be
+            downloaded.
+
+    Returns:
+        str: The filename of the downloaded asset.
+
+    Example:
+        ```python
+        >>> from supervision.assets import download_assets, VideoAssets
+
+        >>> download_assets(VideoAssets.VEHICLES)
+        "vehicles.mp4"
+        ```
+    """
+
+    filename = asset_name.value if isinstance(asset_name, VideoAssets) else asset_name
+
+    if not Path(filename).exists() and filename in VIDEO_ASSETS:
+        print(f"Downloading {filename} assets \n")
+        response = get(VIDEO_ASSETS[filename][0], stream=True, allow_redirects=True)
+        response.raise_for_status()
+
+        file_size = int(response.headers.get("Content-Length", 0))
+        folder_path = Path(filename).expanduser().resolve()
+        folder_path.parent.mkdir(parents=True, exist_ok=True)
+
+        with tqdm.wrapattr(
+            response.raw, "read", total=file_size, desc="", colour="#a351fb"
+        ) as raw_resp:
+            with folder_path.open("wb") as file:
+                copyfileobj(raw_resp, file)
+
+    elif Path(filename).exists():
+        if not is_md5_hash_matching(filename, VIDEO_ASSETS[filename][1]):
+            print("File corrupted. Re-downloading... \n")
+            os.remove(filename)
+            return download_assets(filename)
+
+        print(f"{filename} asset download complete. \n")
+
+    else:
+        valid_assets = ", ".join(asset.value for asset in VideoAssets)
+        raise ValueError(
+            f"Invalid asset. It should be one of the following: {valid_assets}."
+        )
+
+    return filename

supervision/assets/list.py

@@ -0,0 +1,65 @@
+from enum import Enum
+from typing import Dict, Tuple
+
+BASE_VIDEO_URL = "https://media.roboflow.com/supervision/video-examples/"
+
+
+class VideoAssets(Enum):
+    """
+    Each member of this enum represents a video asset. The value associated with each
+    member is the filename of the video.
+
+    | Enum Member            | Video Filename             | Video URL                                                                             |
+    |------------------------|----------------------------|---------------------------------------------------------------------------------------|
+    | `VEHICLES`             | `vehicles.mp4`             | [Link](https://media.roboflow.com/supervision/video-examples/vehicles.mp4)            |
+    | `MILK_BOTTLING_PLANT`  | `milk-bottling-plant.mp4`  | [Link](https://media.roboflow.com/supervision/video-examples/milk-bottling-plant.mp4) |
+    | `VEHICLES_2`           | `vehicles-2.mp4`           | [Link](https://media.roboflow.com/supervision/video-examples/vehicles-2.mp4)          |
+    | `GROCERY_STORE`        | `grocery-store.mp4`        | [Link](https://media.roboflow.com/supervision/video-examples/grocery-store.mp4)       |
+    | `SUBWAY`               | `subway.mp4`               | [Link](https://media.roboflow.com/supervision/video-examples/subway.mp4)              |
+    | `MARKET_SQUARE`        | `market-square.mp4`        | [Link](https://media.roboflow.com/supervision/video-examples/market-square.mp4)       |
+    | `PEOPLE_WALKING`       | `people-walking.mp4`       | [Link](https://media.roboflow.com/supervision/video-examples/people-walking.mp4)      |
+    """  # noqa: E501 // docs
+
+    VEHICLES = "vehicles.mp4"
+    MILK_BOTTLING_PLANT = "milk-bottling-plant.mp4"
+    VEHICLES_2 = "vehicles-2.mp4"
+    GROCERY_STORE = "grocery-store.mp4"
+    SUBWAY = "subway.mp4"
+    MARKET_SQUARE = "market-square.mp4"
+    PEOPLE_WALKING = "people-walking.mp4"
+
+    @classmethod
+    def list(cls):
+        return list(map(lambda c: c.value, cls))
+
+
+VIDEO_ASSETS: Dict[str, Tuple[str, str]] = {
+    VideoAssets.VEHICLES.value: (
+        f"{BASE_VIDEO_URL}{VideoAssets.VEHICLES.value}",
+        "8155ff4e4de08cfa25f39de96483f918",
+    ),
+    VideoAssets.VEHICLES_2.value: (
+        f"{BASE_VIDEO_URL}{VideoAssets.VEHICLES_2.value}",
+        "830af6fba21ffbf14867a7fea595937b",
+    ),
+    VideoAssets.MILK_BOTTLING_PLANT.value: (
+        f"{BASE_VIDEO_URL}{VideoAssets.MILK_BOTTLING_PLANT.value}",
+        "9e8fb6e883f842a38b3d34267290bdc7",
+    ),
+    VideoAssets.GROCERY_STORE.value: (
+        f"{BASE_VIDEO_URL}{VideoAssets.GROCERY_STORE.value}",
+        "11402e7b861c1980527d3d74cbe3b366",
+    ),
+    VideoAssets.SUBWAY.value: (
+        f"{BASE_VIDEO_URL}{VideoAssets.SUBWAY.value}",
+        "453475750691fb23c56a0cffef089194",
+    ),
+    VideoAssets.MARKET_SQUARE.value: (
+        f"{BASE_VIDEO_URL}{VideoAssets.MARKET_SQUARE.value}",
+        "859179bf4a21f80a8baabfdb2ed716dc",
+    ),
+    VideoAssets.PEOPLE_WALKING.value: (
+        f"{BASE_VIDEO_URL}{VideoAssets.PEOPLE_WALKING.value}",
+        "0574c053c8686c3f1dc0aa3743e45cb9",
+    ),
+}

supervision/classification/core.py

@@ -5,8 +5,6 @@ from typing import Any, Optional, Tuple
 
 import numpy as np
 
-from supervision.utils.internal import deprecated
-
 
 def _validate_class_ids(class_id: Any, n: int) -> None:
     """
@@ -41,50 +39,58 @@ class Classifications:
         _validate_class_ids(self.class_id, n)
         _validate_confidence(self.confidence, n)
 
+    def __len__(self) -> int:
+        """
+        Returns the number of classifications.
+        """
+        return len(self.class_id)
+
     @classmethod
-    @deprecated(
-        """
-        This method is deprecated and removed in 0.16.0 release.
-        Use sv.Classifications.from_ultralytics() instead as it is more generic and
-        can be used for detections from any ultralytics.engine.results.Results Object
-        """
-    )
-    def from_yolov8(cls, yolov8_results) -> Classifications:
+    def from_clip(cls, clip_results) -> Classifications:
         """
         Creates a Classifications instance from a
-        [YOLOv8](https://github.com/ultralytics/ultralytics) inference result.
+        [clip](https://github.com/openai/clip) inference result.
 
         Args:
-            yolov8_results (ultralytics.yolo.engine.results.Results):
-                The output Results instance from YOLOv8
+            clip_results (np.ndarray): The inference result from clip model.
 
         Returns:
             Classifications: A new Classifications object.
 
         Example:
             ```python
-            >>> import cv2
-            >>> from ultralytics import YOLO
+            >>> from PIL import Image
+            >>> import clip
             >>> import supervision as sv
 
+            >>> model, preprocess = clip.load('ViT-B/32')
+
             >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> model = YOLO('yolov8s-cls.pt')
-            >>> result = model(image)[0]
-            >>> classifications = sv.Classifications.from_yolov8(result)
+            >>> image = preprocess(image).unsqueeze(0)
+
+            >>> text = clip.tokenize(["a diagram", "a dog", "a cat"])
+            >>> output, _ = model(image, text)
+            >>> classifications = sv.Classifications.from_clip(output)
             ```
         """
-        confidence = yolov8_results.probs.data.cpu().numpy()
-        return cls(class_id=np.arange(confidence.shape[0]), confidence=confidence)
+
+        confidence = clip_results.softmax(dim=-1).cpu().detach().numpy()[0]
+
+        if len(confidence) == 0:
+            return cls(class_id=np.array([]), confidence=np.array([]))
+
+        class_ids = np.arange(len(confidence))
+        return cls(class_id=class_ids, confidence=confidence)
 
     @classmethod
     def from_ultralytics(cls, ultralytics_results) -> Classifications:
         """
         Creates a Classifications instance from a
-        (https://github.com/ultralytics/ultralytics) inference result.
+        [ultralytics](https://github.com/ultralytics/ultralytics) inference result.
 
         Args:
             ultralytics_results (ultralytics.engine.results.Results):
-            The output Results instance from ultralytics model
+                The inference result from ultralytics model.
 
         Returns:
             Classifications: A new Classifications object.
@@ -97,15 +103,57 @@ class Classifications:
 
             >>> image = cv2.imread(SOURCE_IMAGE_PATH)
             >>> model = YOLO('yolov8n-cls.pt')
-            >>> model = YOLO('yolov8s-cls.pt')
 
-            >>> result = model(image)[0]
-            >>> classifications = sv.Classifications.from_ultralytics(result)
+            >>> output = model(image)[0]
+            >>> classifications = sv.Classifications.from_ultralytics(output)
             ```
         """
         confidence = ultralytics_results.probs.data.cpu().numpy()
         return cls(class_id=np.arange(confidence.shape[0]), confidence=confidence)
 
+    @classmethod
+    def from_timm(cls, timm_results) -> Classifications:
+        """
+        Creates a Classifications instance from a
+        [timm](https://huggingface.co/docs/hub/timm) inference result.
+
+        Args:
+            timm_results: The inference result from timm model.
+
+        Returns:
+            Classifications: A new Classifications object.
+
+        Example:
+            ```python
+            >>> from PIL import Image
+            >>> import timm
+            >>> from timm.data import resolve_data_config, create_transform
+            >>> import supervision as sv
+
+            >>> model = timm.create_model(
+            ...     model_name='hf-hub:nateraw/resnet50-oxford-iiit-pet',
+            ...     pretrained=True
+            ... ).eval()
+
+            >>> config = resolve_data_config({}, model=model)
+            >>> transform = create_transform(**config)
+
+            >>> image = Image.open(SOURCE_IMAGE_PATH).convert('RGB')
+            >>> x = transform(image).unsqueeze(0)
+
+            >>> output = model(x)
+
+            >>> classifications = sv.Classifications.from_timm(output)
+            ```
+        """
+        confidence = timm_results.cpu().detach().numpy()[0]
+
+        if len(confidence) == 0:
+            return cls(class_id=np.array([]), confidence=np.array([]))
+
+        class_id = np.arange(len(confidence))
+        return cls(class_id=class_id, confidence=confidence)
+
     def get_top_k(self, k: int) -> Tuple[np.ndarray, np.ndarray]:
         """
         Retrieve the top k class IDs and confidences,

supervision/config.py

@@ -0,0 +1 @@
+CLASS_NAME_DATA_FIELD = "class_name"

supervision/dataset/core.py

@@ -165,9 +165,8 @@ class DetectionDataset(BaseDataset):
                 where the images should be saved.
                 If not provided, images will not be saved.
             annotations_directory_path (Optional[str]): The path to
-            the directory where the annotations in
-                PASCAL VOC format should be saved. If not provided,
-                annotations will not be saved.
+                the directory where the annotations in PASCAL VOC format should be
+                saved. If not provided, annotations will not be saved.
             min_image_area_percentage (float): The minimum percentage of
                 detection area relative to
                 the image area for a detection to be included.
@@ -178,7 +177,7 @@ class DetectionDataset(BaseDataset):
                 Argument is used only for segmentation datasets.
             approximation_percentage (float): The percentage of
                 polygon points to be removed from the input polygon,
-            in the range [0, 1). Argument is used only for segmentation datasets.
+                in the range [0, 1). Argument is used only for segmentation datasets.
         """
         if images_directory_path:
             save_dataset_images(
@@ -563,8 +562,7 @@ class ClassificationDataset(BaseDataset):
             split_ratio (float, optional): The ratio of the training
                 set to the entire dataset.
             random_state (int, optional): The seed for the
-                random number generator.
-            This is used for reproducibility.
+                random number generator. This is used for reproducibility.
             shuffle (bool, optional): Whether to shuffle the data before splitting.
 
         Returns:

supervision/dataset/formats/pascal_voc.py

@@ -1,11 +1,11 @@
 import os
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
-from xml.dom.minidom import parseString
-from xml.etree.ElementTree import Element, SubElement, parse, tostring
+from xml.etree.ElementTree import Element, SubElement
 
 import cv2
 import numpy as np
+from defusedxml.ElementTree import fromstring, parse, tostring
 
 from supervision.dataset.utils import approximate_mask_with_polygons
 from supervision.detection.core import Detections
@@ -129,7 +129,7 @@ def detections_to_pascal_voc(
             annotation.append(next_object)
 
     # Generate XML string
-    xml_string = parseString(tostring(annotation)).toprettyxml(indent="  ")
+    xml_string = fromstring(tostring(annotation)).toprettyxml(indent="  ")
 
     return xml_string
 
@@ -268,12 +268,12 @@ def detections_from_xml_obj(
         [extended_classes.index(class_name) for class_name in class_names]
     )
 
-    if with_masks:
-        annotation = Detections(
-            xyxy=xyxy, mask=np.array(masks).astype(bool), class_id=class_id
-        )
-    else:
-        annotation = Detections(xyxy=xyxy, class_id=class_id)
+    annotation = Detections(
+        xyxy=xyxy.astype(np.float32),
+        mask=np.array(masks).astype(bool) if with_masks else None,
+        class_id=class_id,
+    )
+
     return annotation, extended_classes
 
 

supervision/dataset/formats/yolo.py

@@ -149,7 +149,7 @@ def load_yolo_annotations(
             annotations[image_path] = Detections.empty()
             continue
 
-        lines = read_txt_file(str(annotation_path))
+        lines = read_txt_file(file_path=annotation_path, skip_empty=True)
         h, w, _ = image.shape
         resolution_wh = (w, h)
 

supervision/detection/core.py

@@ -1,69 +1,31 @@
 from __future__ import annotations
 
-from dataclasses import astuple, dataclass
-from typing import Any, Iterator, List, Optional, Tuple, Union
+from contextlib import suppress
+from dataclasses import dataclass, field
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
 import numpy as np
 
 from supervision.detection.utils import (
+    calculate_masks_centroids,
     extract_ultralytics_masks,
+    get_data_item,
+    is_data_equal,
+    merge_data,
     non_max_suppression,
     process_roboflow_result,
+    validate_detections_fields,
     xywh_to_xyxy,
 )
 from supervision.geometry.core import Position
 from supervision.utils.internal import deprecated
 
 
-def _validate_xyxy(xyxy: Any, n: int) -> None:
-    is_valid = isinstance(xyxy, np.ndarray) and xyxy.shape == (n, 4)
-    if not is_valid:
-        raise ValueError("xyxy must be 2d np.ndarray with (n, 4) shape")
-
-
-def _validate_mask(mask: Any, n: int) -> None:
-    is_valid = mask is None or (
-        isinstance(mask, np.ndarray) and len(mask.shape) == 3 and mask.shape[0] == n
-    )
-    if not is_valid:
-        raise ValueError("mask must be 3d np.ndarray with (n, H, W) shape")
-
-
-def validate_inference_callback(callback) -> None:
-    tmp_img = np.zeros((256, 256, 3), dtype=np.uint8)
-    res = callback(tmp_img)
-    if not isinstance(res, Detections):
-        raise ValueError("Callback function must return sv.Detection type")
-
-
-def _validate_class_id(class_id: Any, n: int) -> None:
-    is_valid = class_id is None or (
-        isinstance(class_id, np.ndarray) and class_id.shape == (n,)
-    )
-    if not is_valid:
-        raise ValueError("class_id must be None or 1d np.ndarray with (n,) shape")
-
-
-def _validate_confidence(confidence: Any, n: int) -> None:
-    is_valid = confidence is None or (
-        isinstance(confidence, np.ndarray) and confidence.shape == (n,)
-    )
-    if not is_valid:
-        raise ValueError("confidence must be None or 1d np.ndarray with (n,) shape")
-
-
-def _validate_tracker_id(tracker_id: Any, n: int) -> None:
-    is_valid = tracker_id is None or (
-        isinstance(tracker_id, np.ndarray) and tracker_id.shape == (n,)
-    )
-    if not is_valid:
-        raise ValueError("tracker_id must be None or 1d np.ndarray with (n,) shape")
-
-
 @dataclass
 class Detections:
     """
-    Data class containing information about the detections in a video frame.
+    A dataclass representing detection results.
+
     Attributes:
         xyxy (np.ndarray): An array of shape `(n, 4)` containing
             the bounding boxes coordinates in format `[x1, y1, x2, y2]`
@@ -75,6 +37,17 @@ class Detections:
             `(n,)` containing the class ids of the detections.
         tracker_id (Optional[np.ndarray]): An array of shape
             `(n,)` containing the tracker ids of the detections.
+        data (Dict[str, Union[np.ndarray, List]]): A dictionary containing additional
+            data where each key is a string representing the data type, and the value
+            is either a NumPy array or a list of corresponding data.
+
+    !!! warning
+
+        The `data` field in the `sv.Detections` class is currently in an experimental
+        phase. Please be aware that its API and functionality are subject to change in
+        future updates as we continue to refine and improve its capabilities.
+        We encourage users to experiment with this feature and provide feedback, but
+        also to be prepared for potential modifications in upcoming releases.
     """
 
     xyxy: np.ndarray
@@ -82,14 +55,17 @@ class Detections:
     confidence: Optional[np.ndarray] = None
     class_id: Optional[np.ndarray] = None
     tracker_id: Optional[np.ndarray] = None
+    data: Dict[str, Union[np.ndarray, List]] = field(default_factory=dict)
 
     def __post_init__(self):
-        n = len(self.xyxy)
-        _validate_xyxy(xyxy=self.xyxy, n=n)
-        _validate_mask(mask=self.mask, n=n)
-        _validate_class_id(class_id=self.class_id, n=n)
-        _validate_confidence(confidence=self.confidence, n=n)
-        _validate_tracker_id(tracker_id=self.tracker_id, n=n)
+        validate_detections_fields(
+            xyxy=self.xyxy,
+            mask=self.mask,
+            confidence=self.confidence,
+            class_id=self.class_id,
+            tracker_id=self.tracker_id,
+            data=self.data,
+        )
 
     def __len__(self):
         """
@@ -106,11 +82,12 @@ class Detections:
             Optional[float],
             Optional[int],
             Optional[int],
+            Dict[str, Union[np.ndarray, List]],
         ]
     ]:
         """
         Iterates over the Detections object and yield a tuple of
-        `(xyxy, mask, confidence, class_id, tracker_id)` for each detection.
+        `(xyxy, mask, confidence, class_id, tracker_id, data)` for each detection.
         """
         for i in range(len(self.xyxy)):
             yield (
@@ -119,36 +96,18 @@ class Detections:
                 self.confidence[i] if self.confidence is not None else None,
                 self.class_id[i] if self.class_id is not None else None,
                 self.tracker_id[i] if self.tracker_id is not None else None,
+                get_data_item(self.data, i),
             )
 
     def __eq__(self, other: Detections):
         return all(
             [
                 np.array_equal(self.xyxy, other.xyxy),
-                any(
-                    [
-                        self.mask is None and other.mask is None,
-                        np.array_equal(self.mask, other.mask),
-                    ]
-                ),
-                any(
-                    [
-                        self.class_id is None and other.class_id is None,
-                        np.array_equal(self.class_id, other.class_id),
-                    ]
-                ),
-                any(
-                    [
-                        self.confidence is None and other.confidence is None,
-                        np.array_equal(self.confidence, other.confidence),
-                    ]
-                ),
-                any(
-                    [
-                        self.tracker_id is None and other.tracker_id is None,
-                        np.array_equal(self.tracker_id, other.tracker_id),
-                    ]
-                ),
+                np.array_equal(self.mask, other.mask),
+                np.array_equal(self.class_id, other.class_id),
+                np.array_equal(self.confidence, other.confidence),
+                np.array_equal(self.tracker_id, other.tracker_id),
+                is_data_equal(self.data, other.data),
             ]
         )
 
@@ -185,46 +144,6 @@ class Detections:
             class_id=yolov5_detections_predictions[:, 5].astype(int),
         )
 
-    @classmethod
-    @deprecated(
-        """
-        This method is deprecated and removed in 0.15.0 release.
-        Use sv.Detections.from_ultralytics() instead as it is more generic and
-        can be used for detections from any ultralytics.engine.results.Results Object
-        """
-    )
-    def from_yolov8(cls, yolov8_results) -> Detections:
-        """
-        Creates a Detections instance from a
-        [YOLOv8](https://github.com/ultralytics/ultralytics) inference result.
-
-        Args:
-            yolov8_results (ultralytics.yolo.engine.results.Results):
-                The output Results instance from YOLOv8
-
-        Returns:
-            Detections: A new Detections object.
-
-        Example:
-            ```python
-            >>> import cv2
-            >>> from ultralytics import YOLO
-            >>> import supervision as sv
-
-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> model = YOLO('yolov8s.pt')
-            >>> result = model(image)[0]
-            >>> detections = sv.Detections.from_yolov8(result)
-            ```
-        """
-
-        return cls(
-            xyxy=yolov8_results.boxes.xyxy.cpu().numpy(),
-            confidence=yolov8_results.boxes.conf.cpu().numpy(),
-            class_id=yolov8_results.boxes.cls.cpu().numpy().astype(int),
-            mask=extract_ultralytics_masks(yolov8_results),
-        )
-
     @classmethod
     def from_ultralytics(cls, ultralytics_results) -> Detections:
         """
@@ -241,19 +160,12 @@ class Detections:
         Example:
             ```python
             >>> import cv2
-            >>> from ultralytics import YOLO, FastSAM, SAM, RTDETR
             >>> import supervision as sv
+            >>> from ultralytics import YOLO
 
-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
+            >>> image = cv2.imread(...)
             >>> model = YOLO('yolov8s.pt')
-            >>> model = SAM('sam_b.pt')
-            >>> model = SAM('mobile_sam.pt')
-            >>> model = FastSAM('FastSAM-s.pt')
-            >>> model = RTDETR('rtdetr-l.pt')
-            >>> # model inferences
             >>> result = model(image)[0]
-            >>> # if tracker is enabled
-            >>> result = model.track(image)[0]
             >>> detections = sv.Detections.from_ultralytics(result)
             ```
         """
@@ -296,7 +208,7 @@ class Detections:
             >>> detections = sv.Detections.from_yolo_nas(result)
             ```
         """
-        if np.asarray(yolo_nas_results.bboxes_xyxy).shape[0] == 0:
+        if np.asarray(yolo_nas_results.prediction.bboxes_xyxy).shape[0] == 0:
             return cls.empty()
 
         return cls(
@@ -305,6 +217,51 @@ class Detections:
             class_id=yolo_nas_results.prediction.labels.astype(int),
         )
 
+    @classmethod
+    def from_tensorflow(
+        cls, tensorflow_results: dict, resolution_wh: tuple
+    ) -> Detections:
+        """
+        Creates a Detections instance from a
+        [Tensorflow Hub](https://www.tensorflow.org/hub/tutorials/tf2_object_detection)
+        inference result.
+
+        Args:
+            tensorflow_results (dict):
+                The output results from Tensorflow Hub.
+
+        Returns:
+            Detections: A new Detections object.
+
+        Example:
+            ```python
+            >>> import tensorflow as tf
+            >>> import tensorflow_hub as hub
+            >>> import numpy as np
+            >>> import cv2
+
+            >>> module_handle = "https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1"
+
+            >>> model = hub.load(module_handle)
+
+            >>> img = np.array(cv2.imread(SOURCE_IMAGE_PATH))
+
+            >>> result = model(img)
+
+            >>> detections = sv.Detections.from_tensorflow(result)
+            ```
+        """  # noqa: E501 // docs
+
+        boxes = tensorflow_results["detection_boxes"][0].numpy()
+        boxes[:, [0, 2]] *= resolution_wh[0]
+        boxes[:, [1, 3]] *= resolution_wh[1]
+        boxes = boxes[:, [1, 0, 3, 2]]
+        return cls(
+            xyxy=boxes,
+            confidence=tensorflow_results["detection_scores"][0].numpy(),
+            class_id=tensorflow_results["detection_classes"][0].numpy().astype(int),
+        )
+
     @classmethod
     def from_deepsparse(cls, deepsparse_results) -> Detections:
         """
@@ -321,18 +278,18 @@ class Detections:
 
         Example:
             ```python
-            >>> from deepsparse import Pipeline
             >>> import supervision as sv
+            >>> from deepsparse import Pipeline
 
             >>> yolo_pipeline = Pipeline.create(
             ...     task="yolo",
-            ...     model_path = "zoo:cv/detection/yolov5-l/pytorch/" \
-            ...                  "ultralytics/coco/pruned80_quant-none"
-            >>> pipeline_outputs = yolo_pipeline(SOURCE_IMAGE_PATH,
-            ...                         iou_thres=0.6, conf_thres=0.001)
+            ...     model_path = "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned80_quant-none"
+            ... )
+            >>> result = yolo_pipeline(<SOURCE IMAGE PATH>)
             >>> detections = sv.Detections.from_deepsparse(result)
             ```
-        """
+        """  # noqa: E501 // docs
+
         if np.asarray(deepsparse_results.boxes[0]).shape[0] == 0:
             return cls.empty()
 
@@ -364,8 +321,8 @@ class Detections:
 
             >>> inferencer = DetInferencer(model_name, checkpoint, device)
             >>> mmdet_result = inferencer(SOURCE_IMAGE_PATH, out_dir='./output',
-            ...                           return_datasample=True)["predictions"][0]
-            >>> detections = sv.Detections.from_mmdet(mmdet_result)
+            ...                           return_datasamples=True)["predictions"][0]
+            >>> detections = sv.Detections.from_mmdetection(mmdet_result)
             ```
         """
 
@@ -432,14 +389,22 @@ class Detections:
         )
 
     @classmethod
-    def from_roboflow(cls, roboflow_result: dict) -> Detections:
+    def from_inference(cls, roboflow_result: Union[dict, Any]) -> Detections:
         """
         Create a Detections object from the [Roboflow](https://roboflow.com/)
-            API inference result.
+        API inference result or the [Inference](https://inference.roboflow.com/)
+        package results. This method extracts bounding boxes, class IDs,
+        confidences, and class names from the Roboflow API result and encapsulates
+        them into a Detections object.
+
+        !!! note
+
+            Class names can be accessed using the key 'class_name' in the returned
+            object's data attribute.
 
         Args:
-            roboflow_result (dict): The result from the
-                Roboflow API containing predictions.
+            roboflow_result (dict, any): The result from the
+                Roboflow API or Inference package containing predictions.
 
         Returns:
             (Detections): A Detections object containing the bounding boxes, class IDs,
@@ -447,27 +412,19 @@ class Detections:
 
         Example:
             ```python
+            >>> import cv2
             >>> import supervision as sv
+            >>> from inference.models.utils import get_roboflow_model
 
-            >>> roboflow_result = {
-            ...     "predictions": [
-            ...         {
-            ...             "x": 0.5,
-            ...             "y": 0.5,
-            ...             "width": 0.2,
-            ...             "height": 0.3,
-            ...             "class_id": 0,
-            ...             "class": "person",
-            ...             "confidence": 0.9
-            ...         },
-            ...         # ... more predictions ...
-            ...     ]
-            ... }
-
-            >>> detections = sv.Detections.from_roboflow(roboflow_result)
+            >>> image = cv2.imread(...)
+            >>> model = get_roboflow_model(model_id="yolov8s-640")
+            >>> result = model.infer(image)[0]
+            >>> detections = sv.Detections.from_inference(result)
             ```
         """
-        xyxy, confidence, class_id, masks = process_roboflow_result(
+        with suppress(AttributeError):
+            roboflow_result = roboflow_result.dict(exclude_none=True, by_alias=True)
+        xyxy, confidence, class_id, masks, trackers, data = process_roboflow_result(
             roboflow_result=roboflow_result
         )
 
@@ -479,8 +436,43 @@ class Detections:
             confidence=confidence,
             class_id=class_id,
             mask=masks,
+            tracker_id=trackers,
+            data=data,
         )
 
+    @classmethod
+    @deprecated(
+        "`Detections.from_roboflow` is deprecated and will be removed in "
+        "`supervision-0.21.0`. Use `Detections.from_inference` instead."
+    )
+    def from_roboflow(cls, roboflow_result: Union[dict, Any]) -> Detections:
+        """
+        Create a Detections object from the [Roboflow](https://roboflow.com/)
+            API inference result or the [Inference](https://inference.roboflow.com/)
+            package results.
+
+        Args:
+            roboflow_result (dict): The result from the
+                Roboflow API containing predictions.
+
+        Returns:
+            (Detections): A Detections object containing the bounding boxes, class IDs,
+                and confidences of the predictions.
+
+        Example:
+            ```python
+            >>> import cv2
+            >>> import supervision as sv
+            >>> from inference.models.utils import get_roboflow_model
+
+            >>> image = cv2.imread(...)
+            >>> model = get_roboflow_model(model_id="yolov8s-640")
+            >>> result = model.infer(image)[0]
+            >>> detections = sv.Detections.from_roboflow(result)
+            ```
+        """
+        return cls.from_inference(roboflow_result)
+
     @classmethod
     def from_sam(cls, sam_result: List[dict]) -> Detections:
         """
@@ -500,7 +492,7 @@ class Detections:
             >>> from segment_anything import (
             ...     sam_model_registry,
             ...     SamAutomaticMaskGenerator
-            ...     )
+            ... )
 
             >>> sam_model_reg = sam_model_registry[MODEL_TYPE]
             >>> sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
@@ -523,6 +515,93 @@ class Detections:
         xyxy = xywh_to_xyxy(boxes_xywh=xywh)
         return cls(xyxy=xyxy, mask=mask)
 
+    @classmethod
+    def from_azure_analyze_image(
+        cls, azure_result: dict, class_map: Optional[Dict[int, str]] = None
+    ) -> Detections:
+        """
+        Creates a Detections instance from [Azure Image Analysis 4.0](
+        https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/
+        concept-object-detection-40).
+
+        Args:
+            azure_result (dict): The result from Azure Image Analysis. It should
+                contain detected objects and their bounding box coordinates.
+            class_map (Optional[Dict[int, str]]): A mapping ofclass IDs (int) to class
+                names (str). If None, a new mapping is created dynamically.
+
+        Returns:
+            Detections: A new Detections object.
+
+        Example:
+            ```python
+            >>> import requests
+            >>> import supervision as sv
+
+            >>> image = open(input, "rb").read()
+
+            >>> endpoint = "https://.cognitiveservices.azure.com/"
+            >>> subscription_key = "..."
+
+            >>> headers = {
+            ...    "Content-Type": "application/octet-stream",
+            ...    "Ocp-Apim-Subscription-Key": subscription_key
+            ... }
+
+            >>> response = requests.post(endpoint,
+            ...     headers=self.headers,
+            ...     data=image
+            ... ).json()
+
+            >>> detections = sv.Detections.from_azure_analyze_image(response)
+            ```
+        """
+        if "error" in azure_result:
+            raise ValueError(
+                f'Azure API returned an error {azure_result["error"]["message"]}'
+            )
+
+        xyxy, confidences, class_ids = [], [], []
+
+        is_dynamic_mapping = class_map is None
+        if is_dynamic_mapping:
+            class_map = {}
+
+        class_map = {value: key for key, value in class_map.items()}
+
+        for detection in azure_result["objectsResult"]["values"]:
+            bbox = detection["boundingBox"]
+
+            tags = detection["tags"]
+
+            x0 = bbox["x"]
+            y0 = bbox["y"]
+            x1 = x0 + bbox["w"]
+            y1 = y0 + bbox["h"]
+
+            for tag in tags:
+                confidence = tag["confidence"]
+                class_name = tag["name"]
+                class_id = class_map.get(class_name, None)
+
+                if is_dynamic_mapping and class_id is None:
+                    class_id = len(class_map)
+                    class_map[class_name] = class_id
+
+                if class_id is not None:
+                    xyxy.append([x0, y0, x1, y1])
+                    confidences.append(confidence)
+                    class_ids.append(class_id)
+
+        if len(xyxy) == 0:
+            return Detections.empty()
+
+        return cls(
+            xyxy=np.array(xyxy),
+            class_id=np.array(class_ids),
+            confidence=np.array(confidences),
+        )
+
     @classmethod
     def from_paddledet(cls, paddledet_result) -> Detections:
         """
@@ -607,30 +686,67 @@ class Detections:
 
         Example:
             ```python
-            >>> from supervision import Detections
+            import numpy as np
+            import supervision as sv
 
-            >>> detections_1 = Detections(...)
-            >>> detections_2 = Detections(...)
+            >>> detections_1 = sv.Detections(
+            ...     xyxy=np.array([[15, 15, 100, 100], [200, 200, 300, 300]]),
+            ...     class_id=np.array([1, 2]),
+            ...     data={'feature_vector': np.array([0.1, 0.2)])}
+            ... )
+
+            >>> detections_2 = sv.Detections(
+            ...     xyxy=np.array([[30, 30, 120, 120]]),
+            ...     class_id=np.array([1]),
+            ...     data={'feature_vector': [np.array([0.3])]}
+            ... )
 
             >>> merged_detections = Detections.merge([detections_1, detections_2])
+
+            >>> merged_detections.xyxy
+            array([[ 15,  15, 100, 100],
+                   [200, 200, 300, 300],
+                   [ 30,  30, 120, 120]])
+
+            >>> merged_detections.class_id
+            array([1, 2, 1])
+
+            >>> merged_detections.data['feature_vector']
+            array([0.1, 0.2, 0.3])
             ```
         """
         if len(detections_list) == 0:
             return Detections.empty()
 
-        detections_tuples_list = [astuple(detection) for detection in detections_list]
-        xyxy, mask, confidence, class_id, tracker_id = [
-            list(field) for field in zip(*detections_tuples_list)
-        ]
+        for detections in detections_list:
+            validate_detections_fields(
+                xyxy=detections.xyxy,
+                mask=detections.mask,
+                confidence=detections.confidence,
+                class_id=detections.class_id,
+                tracker_id=detections.tracker_id,
+                data=detections.data,
+            )
 
-        def __all_not_none(item_list: List[Any]):
-            return all(x is not None for x in item_list)
+        xyxy = np.vstack([d.xyxy for d in detections_list])
 
-        xyxy = np.vstack(xyxy)
-        mask = np.vstack(mask) if __all_not_none(mask) else None
-        confidence = np.hstack(confidence) if __all_not_none(confidence) else None
-        class_id = np.hstack(class_id) if __all_not_none(class_id) else None
-        tracker_id = np.hstack(tracker_id) if __all_not_none(tracker_id) else None
+        def stack_or_none(name: str):
+            if all(d.__getattribute__(name) is None for d in detections_list):
+                return None
+            if any(d.__getattribute__(name) is None for d in detections_list):
+                raise ValueError(f"All or none of the '{name}' fields must be None")
+            return (
+                np.vstack([d.__getattribute__(name) for d in detections_list])
+                if name == "mask"
+                else np.hstack([d.__getattribute__(name) for d in detections_list])
+            )
+
+        mask = stack_or_none("mask")
+        confidence = stack_or_none("confidence")
+        class_id = stack_or_none("class_id")
+        tracker_id = stack_or_none("tracker_id")
+
+        data = merge_data([d.data for d in detections_list])
 
         return cls(
             xyxy=xyxy,
@@ -638,9 +754,10 @@ class Detections:
             confidence=confidence,
             class_id=class_id,
             tracker_id=tracker_id,
+            data=data,
         )
 
-    def get_anchor_coordinates(self, anchor: Position) -> np.ndarray:
+    def get_anchors_coordinates(self, anchor: Position) -> np.ndarray:
         """
         Calculates and returns the coordinates of a specific anchor point
         within the bounding boxes defined by the `xyxy` attribute. The anchor
@@ -667,6 +784,12 @@ class Detections:
                     (self.xyxy[:, 1] + self.xyxy[:, 3]) / 2,
                 ]
             ).transpose()
+        elif anchor == Position.CENTER_OF_MASS:
+            if self.mask is None:
+                raise ValueError(
+                    "Cannot use `Position.CENTER_OF_MASS` without a detection mask."
+                )
+            return calculate_masks_centroids(masks=self.mask)
         elif anchor == Position.CENTER_LEFT:
             return np.array(
                 [
@@ -701,17 +824,23 @@ class Detections:
         raise ValueError(f"{anchor} is not supported.")
 
     def __getitem__(
-        self, index: Union[int, slice, List[int], np.ndarray]
-    ) -> Detections:
+        self, index: Union[int, slice, List[int], np.ndarray, str]
+    ) -> Union[Detections, List, np.ndarray, None]:
         """
-        Get a subset of the Detections object.
+        Get a subset of the Detections object or access an item from its data field.
+
+        When provided with an integer, slice, list of integers, or a numpy array, this
+        method returns a new Detections object that represents a subset of the original
+        detections. When provided with a string, it accesses the corresponding item in
+        the data dictionary.
 
         Args:
-            index (Union[int, slice, List[int], np.ndarray]):
-                The index or indices of the subset of the Detections
+            index (Union[int, slice, List[int], np.ndarray, str]): The index, indices,
+                or key to access a subset of the Detections or an item from the data.
 
         Returns:
-            (Detections): A subset of the Detections object.
+            Union[Detections, Any]: A subset of the Detections object or an item from
+                the data field.
 
         Example:
             ```python
@@ -720,16 +849,16 @@ class Detections:
             >>> detections = sv.Detections(...)
 
             >>> first_detection = detections[0]
-
             >>> first_10_detections = detections[0:10]
-
             >>> some_detections = detections[[0, 2, 4]]
-
             >>> class_0_detections = detections[detections.class_id == 0]
-
             >>> high_confidence_detections = detections[detections.confidence > 0.5]
+
+            >>> feature_vector = detections['feature_vector']
             ```
         """
+        if isinstance(index, str):
+            return self.data.get(index)
         if isinstance(index, int):
             index = [index]
         return Detections(
@@ -738,8 +867,45 @@ class Detections:
             confidence=self.confidence[index] if self.confidence is not None else None,
             class_id=self.class_id[index] if self.class_id is not None else None,
             tracker_id=self.tracker_id[index] if self.tracker_id is not None else None,
+            data=get_data_item(self.data, index),
         )
 
+    def __setitem__(self, key: str, value: Union[np.ndarray, List]):
+        """
+        Set a value in the data dictionary of the Detections object.
+
+        Args:
+            key (str): The key in the data dictionary to set.
+            value (Union[np.ndarray, List]): The value to set for the key.
+
+        Example:
+            ```python
+            >>> import cv2
+            >>> from ultralytics import YOLO
+            >>> import supervision as sv
+
+            >>> model = YOLO('yolov8s.pt')
+
+            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
+
+            >>> result = model(image)[0]
+            >>> detections = sv.Detections.from_ultralytics(result)
+
+            >>> detections['names'] = [
+            ...     model.model.names[class_id]
+            ...     for class_id
+            ...     in detections.class_id
+            ... ]
+            ```
+        """
+        if not isinstance(value, (np.ndarray, list)):
+            raise TypeError("Value must be a np.ndarray or a list")
+
+        if isinstance(value, list):
+            value = np.array(value)
+
+        self.data[key] = value
+
     @property
     def area(self) -> np.ndarray:
         """

supervision/detection/line_counter.py

@@ -1,4 +1,4 @@
-from typing import Dict, Optional
+from typing import Dict, Optional, Tuple
 
 import cv2
 import numpy as np
@@ -10,37 +10,54 @@ from supervision.geometry.core import Point, Rect, Vector
 
 class LineZone:
     """
-    Count the number of objects that cross a line.
+    This class is responsible for counting the number of objects that cross a
+    predefined line.
+
+    !!! warning
+
+        LineZone utilizes the `tracker_id`. Read
+        [here](https://supervision.roboflow.com/trackers/) to learn how to plug
+        tracking into your inference pipeline.
+
+    Attributes:
+        in_count (int): The number of objects that have crossed the line from outside
+            to inside.
+        out_count (int): The number of objects that have crossed the line from inside
+            to outside.
     """
 
     def __init__(self, start: Point, end: Point):
         """
-        Initialize a LineCounter object.
-
-        Attributes:
+        Args:
             start (Point): The starting point of the line.
             end (Point): The ending point of the line.
-
         """
         self.vector = Vector(start=start, end=end)
         self.tracker_state: Dict[str, bool] = {}
         self.in_count: int = 0
         self.out_count: int = 0
 
-    def trigger(self, detections: Detections):
+    def trigger(self, detections: Detections) -> Tuple[np.ndarray, np.ndarray]:
         """
-        Update the in_count and out_count for the detections that cross the line.
+        Update the `in_count` and `out_count` based on the objects that cross the line.
 
-        Attributes:
-            detections (Detections): The detections for which to update the counts.
+        Args:
+            detections (Detections): A list of detections for which to update the
+                counts.
 
+        Returns:
+            A tuple of two boolean NumPy arrays. 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.
         """
-        for xyxy, _, confidence, class_id, tracker_id in detections:
-            # handle detections with no tracker_id
+        crossed_in = np.full(len(detections), False)
+        crossed_out = np.full(len(detections), False)
+
+        for i, (xyxy, _, confidence, class_id, tracker_id) in enumerate(detections):
             if tracker_id is None:
                 continue
 
-            # we check if all four anchors of bbox are on the same side of vector
             x1, y1, x2, y2 = xyxy
             anchors = [
                 Point(x=x1, y=y1),
@@ -50,25 +67,27 @@ class LineZone:
             ]
             triggers = [self.vector.is_in(point=anchor) for anchor in anchors]
 
-            # detection is partially in and partially out
             if len(set(triggers)) == 2:
                 continue
 
             tracker_state = triggers[0]
-            # handle new detection
+
             if tracker_id not in self.tracker_state:
                 self.tracker_state[tracker_id] = tracker_state
                 continue
 
-            # handle detection on the same side of the line
             if self.tracker_state.get(tracker_id) == tracker_state:
                 continue
 
             self.tracker_state[tracker_id] = tracker_state
             if tracker_state:
                 self.in_count += 1
+                crossed_in[i] = True
             else:
                 self.out_count += 1
+                crossed_out[i] = True
+
+        return crossed_in, crossed_out
 
 
 class LineZoneAnnotator:

supervision/detection/tools/inference_slicer.py

@@ -3,7 +3,7 @@ from typing import Callable, Optional, Tuple
 
 import numpy as np
 
-from supervision.detection.core import Detections, validate_inference_callback
+from supervision.detection.core import Detections
 from supervision.detection.utils import move_boxes
 from supervision.utils.image import crop_image
 
@@ -60,7 +60,6 @@ class InferenceSlicer:
         self.iou_threshold = iou_threshold
         self.callback = callback
         self.thread_workers = thread_workers
-        validate_inference_callback(callback=callback)
 
     def __call__(self, image: np.ndarray) -> Detections:
         """

supervision/detection/tools/polygon_zone.py

@@ -56,11 +56,11 @@ class PolygonZone:
         """
 
         clipped_xyxy = clip_boxes(
-            boxes_xyxy=detections.xyxy, frame_resolution_wh=self.frame_resolution_wh
+            xyxy=detections.xyxy, resolution_wh=self.frame_resolution_wh
         )
         clipped_detections = replace(detections, xyxy=clipped_xyxy)
         clipped_anchors = np.ceil(
-            clipped_detections.get_anchor_coordinates(anchor=self.triggering_position)
+            clipped_detections.get_anchors_coordinates(anchor=self.triggering_position)
         ).astype(int)
         is_in_zone = self.mask[clipped_anchors[:, 1], clipped_anchors[:, 0]]
         self.current_count = int(np.sum(is_in_zone))

supervision/detection/utils.py

@@ -1,8 +1,11 @@
-from typing import List, Optional, Tuple
+from itertools import chain
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import cv2
 import numpy as np
 
+from supervision.config import CLASS_NAME_DATA_FIELD
+
 MIN_POLYGON_POINT_COUNT = 3
 
 
@@ -110,17 +113,15 @@ def non_max_suppression(
     return keep[sort_index.argsort()]
 
 
-def clip_boxes(
-    boxes_xyxy: np.ndarray, frame_resolution_wh: Tuple[int, int]
-) -> np.ndarray:
+def clip_boxes(xyxy: np.ndarray, resolution_wh: Tuple[int, int]) -> np.ndarray:
     """
     Clips bounding boxes coordinates to fit within the frame resolution.
 
     Args:
-        boxes_xyxy (np.ndarray): A numpy array of shape `(N, 4)` where each
+        xyxy (np.ndarray): A numpy array of shape `(N, 4)` where each
             row corresponds to a bounding box in
         the format `(x_min, y_min, x_max, y_max)`.
-        frame_resolution_wh (Tuple[int, int]): A tuple of the form `(width, height)`
+        resolution_wh (Tuple[int, int]): A tuple of the form `(width, height)`
             representing the resolution of the frame.
 
     Returns:
@@ -128,8 +129,8 @@ def clip_boxes(
             corresponds to a bounding box with coordinates clipped to fit
             within the frame resolution.
     """
-    result = np.copy(boxes_xyxy)
-    width, height = frame_resolution_wh
+    result = np.copy(xyxy)
+    width, height = resolution_wh
     result[:, [0, 2]] = result[:, [0, 2]].clip(0, width)
     result[:, [1, 3]] = result[:, [1, 3]].clip(0, height)
     return result
@@ -301,7 +302,6 @@ def extract_ultralytics_masks(yolov8_results) -> Optional[np.ndarray]:
     orig_shape = yolov8_results.orig_shape
     inference_shape = tuple(yolov8_results.masks.data.shape[1:])
 
-    gain = 0
     pad = (0, 0)
 
     if inference_shape != orig_shape:
@@ -333,14 +333,30 @@ def extract_ultralytics_masks(yolov8_results) -> Optional[np.ndarray]:
 
 def process_roboflow_result(
     roboflow_result: dict,
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray, Optional[np.ndarray]]:
+) -> Tuple[
+    np.ndarray,
+    np.ndarray,
+    np.ndarray,
+    Optional[np.ndarray],
+    Optional[np.ndarray],
+    Dict[str, List[np.ndarray]],
+]:
     if not roboflow_result["predictions"]:
-        return np.empty((0, 4)), np.empty(0), np.empty(0), None
+        return (
+            np.empty((0, 4)),
+            np.empty(0),
+            np.empty(0),
+            None,
+            None,
+            {CLASS_NAME_DATA_FIELD: np.empty(0)},
+        )
 
     xyxy = []
     confidence = []
     class_id = []
+    class_name = []
     masks = []
+    tracker_ids = []
 
     image_width = int(roboflow_result["image"]["width"])
     image_height = int(roboflow_result["image"]["height"])
@@ -358,7 +374,10 @@ def process_roboflow_result(
         if "points" not in prediction:
             xyxy.append([x_min, y_min, x_max, y_max])
             class_id.append(prediction["class_id"])
+            class_name.append(prediction["class"])
             confidence.append(prediction["confidence"])
+            if "tracker_id" in prediction:
+                tracker_ids.append(prediction["tracker_id"])
         elif len(prediction["points"]) >= 3:
             polygon = np.array(
                 [[point["x"], point["y"]] for point in prediction["points"]], dtype=int
@@ -366,26 +385,310 @@ def process_roboflow_result(
             mask = polygon_to_mask(polygon, resolution_wh=(image_width, image_height))
             xyxy.append([x_min, y_min, x_max, y_max])
             class_id.append(prediction["class_id"])
+            class_name.append(prediction["class"])
             confidence.append(prediction["confidence"])
             masks.append(mask)
+            if "tracker_id" in prediction:
+                tracker_ids.append(prediction["tracker_id"])
 
     xyxy = np.array(xyxy) if len(xyxy) > 0 else np.empty((0, 4))
     confidence = np.array(confidence) if len(confidence) > 0 else np.empty(0)
     class_id = np.array(class_id).astype(int) if len(class_id) > 0 else np.empty(0)
+    class_name = np.array(class_name) if len(class_name) > 0 else np.empty(0)
     masks = np.array(masks, dtype=bool) if len(masks) > 0 else None
+    tracker_id = np.array(tracker_ids).astype(int) if len(tracker_ids) > 0 else None
+    data = {CLASS_NAME_DATA_FIELD: class_name}
 
-    return xyxy, confidence, class_id, masks
+    return xyxy, confidence, class_id, masks, tracker_id, data
 
 
 def move_boxes(xyxy: np.ndarray, offset: np.ndarray) -> np.ndarray:
     """
-    Args:
+    Parameters:
         xyxy (np.ndarray): An array of shape `(n, 4)` containing the bounding boxes
             coordinates in format `[x1, y1, x2, y2]`
         offset (np.array): An array of shape `(2,)` containing offset values in format
             is `[dx, dy]`.
 
     Returns:
-        (np.ndarray) repositioned bounding boxes
+        np.ndarray: Repositioned bounding boxes.
+
+    Example:
+        ```python
+        >>> import numpy as np
+        >>> import supervision as sv
+
+        >>> boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
+        >>> offset = np.array([5, 5])
+        >>> sv.move_boxes(boxes, offset)
+        ... array([
+        ...     [15, 15, 25, 25],
+        ...     [35, 35, 45, 45]
+        ... ])
+        ```
     """
     return xyxy + np.hstack([offset, offset])
+
+
+def scale_boxes(xyxy: np.ndarray, factor: float) -> np.ndarray:
+    """
+    Scale the dimensions of bounding boxes.
+
+    Parameters:
+        xyxy (np.ndarray): An array of shape `(n, 4)` containing the bounding boxes
+            coordinates in format `[x1, y1, x2, y2]`
+        factor (float): A float value representing the factor by which the box
+            dimensions are scaled. A factor greater than 1 enlarges the boxes, while a
+            factor less than 1 shrinks them.
+
+    Returns:
+        np.ndarray: Scaled bounding boxes.
+
+    Example:
+        ```python
+        >>> import numpy as np
+        >>> import supervision as sv
+
+        >>> boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
+        >>> factor = 1.5
+        >>> sv.scale_boxes(boxes, factor)
+        ... array([
+        ...     [ 7.5,  7.5, 22.5, 22.5],
+        ...     [27.5, 27.5, 42.5, 42.5]
+        ... ])
+        ```
+    """
+    centers = (xyxy[:, :2] + xyxy[:, 2:]) / 2
+    new_sizes = (xyxy[:, 2:] - xyxy[:, :2]) * factor
+    return np.concatenate((centers - new_sizes / 2, centers + new_sizes / 2), axis=1)
+
+
+def calculate_masks_centroids(masks: np.ndarray) -> np.ndarray:
+    """
+    Calculate the centroids of binary masks in a tensor.
+
+    Parameters:
+        masks (np.ndarray): A 3D NumPy array of shape (num_masks, height, width).
+            Each 2D array in the tensor represents a binary mask.
+
+    Returns:
+        A 2D NumPy array of shape (num_masks, 2), where each row contains the x and y
+            coordinates (in that order) of the centroid of the corresponding mask.
+    """
+    num_masks, height, width = masks.shape
+    total_pixels = masks.sum(axis=(1, 2))
+
+    # offset for 1-based indexing
+    vertical_indices, horizontal_indices = np.indices((height, width)) + 0.5
+    # avoid division by zero for empty masks
+    total_pixels[total_pixels == 0] = 1
+
+    def sum_over_mask(indices: np.ndarray, axis: tuple) -> np.ndarray:
+        return np.tensordot(masks, indices, axes=axis)
+
+    aggregation_axis = ([1, 2], [0, 1])
+    centroid_x = sum_over_mask(horizontal_indices, aggregation_axis) / total_pixels
+    centroid_y = sum_over_mask(vertical_indices, aggregation_axis) / total_pixels
+
+    return np.column_stack((centroid_x, centroid_y)).astype(int)
+
+
+def validate_xyxy(xyxy: Any) -> None:
+    expected_shape = "(_, 4)"
+    actual_shape = str(getattr(xyxy, "shape", None))
+    is_valid = isinstance(xyxy, np.ndarray) and xyxy.ndim == 2 and xyxy.shape[1] == 4
+    if not is_valid:
+        raise ValueError(
+            f"xyxy must be a 2D np.ndarray with shape {expected_shape}, but got shape "
+            f"{actual_shape}"
+        )
+
+
+def validate_mask(mask: Any, n: int) -> None:
+    expected_shape = f"({n}, H, W)"
+    actual_shape = str(getattr(mask, "shape", None))
+    is_valid = mask is None or (
+        isinstance(mask, np.ndarray) and len(mask.shape) == 3 and mask.shape[0] == n
+    )
+    if not is_valid:
+        raise ValueError(
+            f"mask must be a 3D np.ndarray with shape {expected_shape}, but got shape "
+            f"{actual_shape}"
+        )
+
+
+def validate_class_id(class_id: Any, n: int) -> None:
+    expected_shape = f"({n},)"
+    actual_shape = str(getattr(class_id, "shape", None))
+    is_valid = class_id is None or (
+        isinstance(class_id, np.ndarray) and class_id.shape == (n,)
+    )
+    if not is_valid:
+        raise ValueError(
+            f"class_id must be a 1D np.ndarray with shape {expected_shape}, but got "
+            f"shape {actual_shape}"
+        )
+
+
+def validate_confidence(confidence: Any, n: int) -> None:
+    expected_shape = f"({n},)"
+    actual_shape = str(getattr(confidence, "shape", None))
+    is_valid = confidence is None or (
+        isinstance(confidence, np.ndarray) and confidence.shape == (n,)
+    )
+    if not is_valid:
+        raise ValueError(
+            f"confidence must be a 1D np.ndarray with shape {expected_shape}, but got "
+            f"shape {actual_shape}"
+        )
+
+
+def validate_tracker_id(tracker_id: Any, n: int) -> None:
+    expected_shape = f"({n},)"
+    actual_shape = str(getattr(tracker_id, "shape", None))
+    is_valid = tracker_id is None or (
+        isinstance(tracker_id, np.ndarray) and tracker_id.shape == (n,)
+    )
+    if not is_valid:
+        raise ValueError(
+            f"tracker_id must be a 1D np.ndarray with shape {expected_shape}, but got "
+            f"shape {actual_shape}"
+        )
+
+
+def validate_data(data: Dict[str, Any], n: int) -> None:
+    for key, value in data.items():
+        if isinstance(value, list):
+            if len(value) != n:
+                raise ValueError(f"Length of list for key '{key}' must be {n}")
+        elif isinstance(value, np.ndarray):
+            if value.ndim == 1 and value.shape[0] != n:
+                raise ValueError(f"Shape of np.ndarray for key '{key}' must be ({n},)")
+            elif value.ndim > 1 and value.shape[0] != n:
+                raise ValueError(
+                    f"First dimension of np.ndarray for key '{key}' must have size {n}"
+                )
+        else:
+            raise ValueError(f"Value for key '{key}' must be a list or np.ndarray")
+
+
+def validate_detections_fields(
+    xyxy: Any,
+    mask: Any,
+    class_id: Any,
+    confidence: Any,
+    tracker_id: Any,
+    data: Dict[str, Any],
+) -> None:
+    validate_xyxy(xyxy)
+    n = len(xyxy)
+    validate_mask(mask, n)
+    validate_class_id(class_id, n)
+    validate_confidence(confidence, n)
+    validate_tracker_id(tracker_id, n)
+    validate_data(data, n)
+
+
+def is_data_equal(data_a: Dict[str, np.ndarray], data_b: Dict[str, np.ndarray]) -> bool:
+    """
+    Compares the data payloads of two Detections instances.
+
+    Args:
+        data_a, data_b: The data payloads of the instances.
+
+    Returns:
+        True if the data payloads are equal, False otherwise.
+    """
+    return set(data_a.keys()) == set(data_b.keys()) and all(
+        np.array_equal(data_a[key], data_b[key]) for key in data_a
+    )
+
+
+def merge_data(
+    data_list: List[Dict[str, Union[np.ndarray, List]]],
+) -> Dict[str, Union[np.ndarray, List]]:
+    """
+    Merges the data payloads of a list of Detections instances.
+
+    Args:
+        data_list: The data payloads of the instances.
+
+    Returns:
+        A single data payload containing the merged data, preserving the original data
+            types (list or np.ndarray).
+
+    Raises:
+        ValueError: If data values within a single object have different lengths or if
+            dictionaries have different keys.
+    """
+    if not data_list:
+        return {}
+
+    all_keys_sets = [set(data.keys()) for data in data_list]
+    if not all(keys_set == all_keys_sets[0] for keys_set in all_keys_sets):
+        raise ValueError("All data dictionaries must have the same keys to merge.")
+
+    for data in data_list:
+        lengths = [len(value) for value in data.values()]
+        if len(set(lengths)) > 1:
+            raise ValueError(
+                "All data values within a single object must have equal length."
+            )
+
+    merged_data = {key: [] for key in all_keys_sets[0]}
+
+    for data in data_list:
+        for key in merged_data:
+            merged_data[key].append(data[key])
+
+    for key in merged_data:
+        if all(isinstance(item, list) for item in merged_data[key]):
+            merged_data[key] = list(chain.from_iterable(merged_data[key]))
+        elif all(isinstance(item, np.ndarray) for item in merged_data[key]):
+            ndim = merged_data[key][0].ndim
+            if ndim == 1:
+                merged_data[key] = np.hstack(merged_data[key])
+            elif ndim > 1:
+                merged_data[key] = np.vstack(merged_data[key])
+            else:
+                raise ValueError(f"Unexpected array dimension for key '{key}'.")
+        else:
+            raise ValueError(
+                f"Inconsistent data types for key '{key}'. Only np.ndarray and list "
+                f"types are allowed."
+            )
+
+    return merged_data
+
+
+def get_data_item(
+    data: Dict[str, Union[np.ndarray, List]],
+    index: Union[int, slice, List[int], np.ndarray],
+) -> Dict[str, Union[np.ndarray, List]]:
+    """
+    Retrieve a subset of the data dictionary based on the given index.
+
+    Args:
+        data: The data dictionary of the Detections object.
+        index: The index or indices specifying the subset to retrieve.
+
+    Returns:
+        A subset of the data dictionary corresponding to the specified index.
+    """
+    subset_data = {}
+    for key, value in data.items():
+        if isinstance(value, np.ndarray):
+            subset_data[key] = value[index]
+        elif isinstance(value, list):
+            if isinstance(index, slice):
+                subset_data[key] = value[index]
+            elif isinstance(index, (list, np.ndarray)):
+                subset_data[key] = [value[i] for i in index]
+            elif isinstance(index, int):
+                subset_data[key] = [value[index]]
+            else:
+                raise TypeError(f"Unsupported index type: {type(index)}")
+        else:
+            raise TypeError(f"Unsupported data type for key '{key}': {type(value)}")
+
+    return subset_data

supervision/draw/color.py

@@ -4,6 +4,7 @@ from dataclasses import dataclass
 from typing import List, Tuple
 
 DEFAULT_COLOR_PALETTE = [
+    "#a351fb",
     "#e6194b",
     "#3cb44b",
     "#ffe119",
@@ -33,21 +34,35 @@ def _validate_color_hex(color_hex: str):
 
 @dataclass
 class Color:
+    """
+    Represents a color in RGB format.
+
+    Attributes:
+        r (int): Red channel.
+        g (int): Green channel.
+        b (int): Blue channel.
+    """
+
     r: int
     g: int
     b: int
 
     @classmethod
-    def from_hex(cls, color_hex: str):
+    def from_hex(cls, color_hex: str) -> Color:
         """
-        Creates a Color instance from a color hex string
+        Create a Color instance from a hex string.
 
-        :param color_hex: str : The color hex string in the format
-            of "fff", "ffffff", "#fff", or "#ffffff"
-        :return: Color : A Color instance representing the color
+        Args:
+            color_hex (str): Hex string of the color.
+
+        Returns:
+            Color: Instance representing the color.
 
         Example:
-        color = Color.from_hex('#ff00ff')
+            ```
+            >>> Color.from_hex('#ff00ff')
+            Color(r=255, g=0, b=255)
+            ```
         """
         _validate_color_hex(color_hex)
         color_hex = color_hex.lstrip("#")
@@ -56,19 +71,48 @@ class Color:
         r, g, b = (int(color_hex[i : i + 2], 16) for i in range(0, 6, 2))
         return cls(r, g, b)
 
+    def as_hex(self) -> str:
+        """
+        Converts the Color instance to a hex string.
+
+        Returns:
+            str: The hexadecimal color string.
+
+        Example:
+            ```
+            >>> Color(r=255, g=0, b=255).as_hex()
+            '#ff00ff'
+            ```
+        """
+        return f"#{self.r:02x}{self.g:02x}{self.b:02x}"
+
     def as_rgb(self) -> Tuple[int, int, int]:
         """
-        Returns the color as a tuple of integers in the RGB format
+        Returns the color as an RGB tuple.
 
-        :return: Tuple[int, int, int] : The color in the RGB format
+        Returns:
+            Tuple[int, int, int]: RGB tuple.
+
+        Example:
+            ```
+            >>> color.as_rgb()
+            (255, 0, 255)
+            ```
         """
         return self.r, self.g, self.b
 
     def as_bgr(self) -> Tuple[int, int, int]:
         """
-        Returns the color as a tuple of integers in the BGR format
+        Returns the color as a BGR tuple.
 
-        :return: Tuple[int, int, int] : The color in the BGR format
+        Returns:
+            Tuple[int, int, int]: BGR tuple.
+
+        Example:
+            ```
+            >>> color.as_bgr()
+            (255, 0, 255)
+            ```
         """
         return self.b, self.g, self.r
 
@@ -99,33 +143,55 @@ class ColorPalette:
 
     @classmethod
     def default(cls) -> ColorPalette:
+        """
+        Returns a default color palette.
+
+        Returns:
+            ColorPalette: A ColorPalette instance with default colors.
+
+        Example:
+            ```
+            >>> ColorPalette.default()
+            ColorPalette(colors=[Color(r=255, g=0, b=0), Color(r=0, g=255, b=0), ...])
+            ```
+        """
         return ColorPalette.from_hex(color_hex_list=DEFAULT_COLOR_PALETTE)
 
     @classmethod
-    def from_hex(cls, color_hex_list: List[str]):
+    def from_hex(cls, color_hex_list: List[str]) -> ColorPalette:
         """
-        Creates a ColorPalette instance from a list of color hex strings
+        Create a ColorPalette instance from a list of hex strings.
 
-        :param color_hex_list: List[str] : A list of color hex strings in the
-            format of "fff", "ffffff", "#fff", or "#ffffff"
-        :return: ColorPalette : A ColorPalette instance representing the color palette
+        Args:
+            color_hex_list (List[str]): List of color hex strings.
+
+        Returns:
+            ColorPalette: A ColorPalette instance.
 
         Example:
-        color_palette = ColorPalette.from_hex(['#ff0000', '#00ff00', '#0000ff'])
+            ```
+            >>> ColorPalette.from_hex(['#ff0000', '#00ff00', '#0000ff'])
+            ColorPalette(colors=[Color(r=255, g=0, b=0), Color(r=0, g=255, b=0), ...])
+            ```
         """
         colors = [Color.from_hex(color_hex) for color_hex in color_hex_list]
         return cls(colors)
 
     def by_idx(self, idx: int) -> Color:
         """
-        Returns the color at a given index in the color palette.
+        Return the color at a given index in the palette.
 
-        :param idx: int : The index of the color in the color palette
-        :return: Color : The color at the given index
+        Args:
+            idx (int): Index of the color in the palette.
+
+        Returns:
+            Color: Color at the given index.
 
         Example:
-        color_palette = ColorPalette.from_hex(['#ff0000', '#00ff00', '#0000ff'])
-        color = color_palette.by_idx(1)
+            ```
+            >>> color_palette.by_idx(1)
+            Color(r=0, g=255, b=0)
+            ```
         """
         if idx < 0:
             raise ValueError("idx argument should not be negative")

supervision/draw/utils.py

@@ -1,4 +1,5 @@
-from typing import Optional
+import os
+from typing import Optional, Tuple, Union
 
 import cv2
 import numpy as np
@@ -168,3 +169,103 @@ def draw_text(
         lineType=cv2.LINE_AA,
     )
     return scene
+
+
+def draw_image(
+    scene: np.ndarray, image: Union[str, np.ndarray], opacity: float, rect: Rect
+) -> np.ndarray:
+    """
+    Draws an image onto a given scene with specified opacity and dimensions.
+
+    Args:
+        scene (np.ndarray): Background image where the new image will be drawn.
+        image (Union[str, np.ndarray]): Image to draw.
+        opacity (float): Opacity of the image to be drawn.
+        rect (Rect): Rectangle specifying where to draw the image.
+
+    Returns:
+        np.ndarray: The updated scene.
+
+    Raises:
+        FileNotFoundError: If the image path does not exist.
+        ValueError: For invalid opacity or rectangle dimensions.
+    """
+
+    # Validate and load image
+    if isinstance(image, str):
+        if not os.path.exists(image):
+            raise FileNotFoundError(f"Image path ('{image}') does not exist.")
+        image = cv2.imread(image, cv2.IMREAD_UNCHANGED)
+
+    # Validate opacity
+    if not 0.0 <= opacity <= 1.0:
+        raise ValueError("Opacity must be between 0.0 and 1.0.")
+
+    # Validate rectangle dimensions
+    if (
+        rect.x < 0
+        or rect.y < 0
+        or rect.x + rect.width > scene.shape[1]
+        or rect.y + rect.height > scene.shape[0]
+    ):
+        raise ValueError("Invalid rectangle dimensions.")
+
+    # Resize and isolate alpha channel
+    image = cv2.resize(image, (rect.width, rect.height))
+    alpha_channel = (
+        image[:, :, 3]
+        if image.shape[2] == 4
+        else np.ones((rect.height, rect.width), dtype=image.dtype) * 255
+    )
+    alpha_scaled = cv2.convertScaleAbs(alpha_channel * opacity)
+
+    # Perform blending
+    scene_roi = scene[rect.y : rect.y + rect.height, rect.x : rect.x + rect.width]
+    alpha_float = alpha_scaled.astype(np.float32) / 255.0
+    blended_roi = cv2.convertScaleAbs(
+        (1 - alpha_float[..., np.newaxis]) * scene_roi
+        + alpha_float[..., np.newaxis] * image[:, :, :3]
+    )
+
+    # Update the scene
+    scene[rect.y : rect.y + rect.height, rect.x : rect.x + rect.width] = blended_roi
+
+    return scene
+
+
+def calculate_dynamic_text_scale(resolution_wh: Tuple[int, int]) -> float:
+    """
+    Calculate a dynamic font scale based on the resolution of an image.
+
+    Parameters:
+         resolution_wh (Tuple[int, int]): A tuple representing the width and height
+                 of the image.
+
+    Returns:
+         float: The calculated font scale factor.
+    """
+    return min(resolution_wh) * 1e-3
+
+
+def calculate_dynamic_line_thickness(resolution_wh: Tuple[int, int]) -> int:
+    """
+    Calculate a dynamic line thickness based on the resolution of an image.
+
+    Parameters:
+        resolution_wh (Tuple[int, int]): A tuple representing the width and height
+                of the image.
+
+    Returns:
+        int: The calculated line thickness in pixels.
+    """
+    min_dimension = min(resolution_wh)
+    if min_dimension < 480:
+        return 2
+    if min_dimension < 720:
+        return 2
+    if min_dimension < 1080:
+        return 2
+    if min_dimension < 2160:
+        return 4
+    else:
+        return 4

supervision/geometry/core.py

@@ -19,6 +19,7 @@ class Position(Enum):
     BOTTOM_LEFT = "BOTTOM_LEFT"
     BOTTOM_CENTER = "BOTTOM_CENTER"
     BOTTOM_RIGHT = "BOTTOM_RIGHT"
+    CENTER_OF_MASS = "CENTER_OF_MASS"
 
     @classmethod
     def list(cls):

supervision/tracker/byte_tracker/core.py

@@ -249,6 +249,8 @@ class ByteTrack:
             detections.confidence = np.array(
                 [t.score for t in tracks], dtype=np.float32
             )
+        else:
+            detections.tracker_id = np.array([], dtype=int)
 
         return detections
 

supervision/utils/file.py

@@ -57,19 +57,24 @@ def list_files_with_extensions(
     return files_with_extensions
 
 
-def read_txt_file(file_path: str) -> List[str]:
+def read_txt_file(file_path: str, skip_empty: bool = False) -> List[str]:
     """
     Read a text file and return a list of strings without newline characters.
+    Optionally skip empty lines.
 
     Args:
         file_path (str): The path to the text file.
+        skip_empty (bool): If True, skip lines that are empty or contain only
+            whitespace. Default is False.
 
     Returns:
         List[str]: A list of strings representing the lines in the text file.
     """
     with open(file_path, "r") as file:
-        lines = file.readlines()
-        lines = [line.rstrip("\n") for line in lines]
+        if skip_empty:
+            lines = [line.rstrip("\n") for line in file if line.strip()]
+        else:
+            lines = [line.rstrip("\n") for line in file]
 
     return lines
 

supervision/utils/video.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import time
+from collections import deque
 from dataclasses import dataclass
 from typing import Callable, Generator, Optional, Tuple
 
@@ -71,13 +73,11 @@ class VideoSink:
         ```python
         >>> import supervision as sv
 
-        >>> video_info = sv.VideoInfo.from_video_path(video_path='source_video.mp4')
+        >>> video_info = sv.VideoInfo.from_video_path('source.mp4')
+        >>> frames_generator = sv.get_video_frames_generator('source.mp4')
 
-        >>> with sv.VideoSink(target_path='target_video.mp4',
-        ...                   video_info=video_info,
-                              codec='H264') as sink:
-        ...     for frame in get_video_frames_generator(source_path='source_video.mp4',
-        ...                                             stride=2):
+        >>> with sv.VideoSink(target_path='target.mp4', video_info=video_info) as sink:
+        ...     for frame in frames_generator:
         ...         sink.write_frame(frame=frame)
         ```
     """
@@ -202,3 +202,55 @@ def process_video(
         ):
             result_frame = callback(frame, index)
             sink.write_frame(frame=result_frame)
+
+
+class FPSMonitor:
+    """
+    A class for monitoring frames per second (FPS) to benchmark latency.
+    """
+
+    def __init__(self, sample_size: int = 30):
+        """
+        Args:
+            sample_size (int): The maximum number of observations for latency
+                benchmarking.
+
+        Examples:
+            ```python
+            >>> import supervision as sv
+
+            >>> frames_generator = sv.get_video_frames_generator('source.mp4')
+            >>> fps_monitor = sv.FPSMonitor()
+
+            >>> for frame in frames_generator:
+            ...     # your processing code here
+            ...     fps_monitor.tick()
+            ...     fps = fps_monitor()
+            ```
+        """
+        self.all_timestamps = deque(maxlen=sample_size)
+
+    def __call__(self) -> float:
+        """
+        Computes and returns the average FPS based on the stored time stamps.
+
+        Returns:
+            float: The average FPS. Returns 0.0 if no time stamps are stored.
+        """
+
+        if not self.all_timestamps:
+            return 0.0
+        taken_time = self.all_timestamps[-1] - self.all_timestamps[0]
+        return (len(self.all_timestamps)) / taken_time if taken_time != 0 else 0.0
+
+    def tick(self) -> None:
+        """
+        Adds a new time stamp to the deque for FPS calculation.
+        """
+        self.all_timestamps.append(time.monotonic())
+
+    def reset(self) -> None:
+        """
+        Clears all the time stamps from the deque.
+        """
+        self.all_timestamps.clear()

test/annotators/test_utils.py

@@ -1,15 +1,16 @@
 from contextlib import ExitStack as DoesNotRaise
-from test.utils import mock_detections
+from test.test_utils import mock_detections
 from typing import Optional
 
+import numpy as np
 import pytest
 
-from supervision.annotators.utils import ColorMap, resolve_color_idx
+from supervision.annotators.utils import ColorLookup, resolve_color_idx
 from supervision.detection.core import Detections
 
 
 @pytest.mark.parametrize(
-    "detections, detection_idx, color_map, expected_result, exception",
+    "detections, detection_idx, color_lookup, expected_result, exception",
     [
         (
             mock_detections(
@@ -18,10 +19,10 @@ from supervision.detection.core import Detections
                 tracker_id=[2, 6],
             ),
             0,
-            ColorMap.INDEX,
+            ColorLookup.INDEX,
             0,
             DoesNotRaise(),
-        ),  # multiple detections; index mapping
+        ),  # multiple detections; index lookup
         (
             mock_detections(
                 xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]],
@@ -29,10 +30,10 @@ from supervision.detection.core import Detections
                 tracker_id=[2, 6],
             ),
             0,
-            ColorMap.CLASS,
+            ColorLookup.CLASS,
             5,
             DoesNotRaise(),
-        ),  # multiple detections; class mapping
+        ),  # multiple detections; class lookup
         (
             mock_detections(
                 xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]],
@@ -40,17 +41,17 @@ from supervision.detection.core import Detections
                 tracker_id=[2, 6],
             ),
             0,
-            ColorMap.TRACK,
+            ColorLookup.TRACK,
             2,
             DoesNotRaise(),
-        ),  # multiple detections; track mapping
+        ),  # multiple detections; track lookup
         (
             Detections.empty(),
             0,
-            ColorMap.INDEX,
+            ColorLookup.INDEX,
             None,
             pytest.raises(ValueError),
-        ),  # no detections; index mapping; out of bounds
+        ),  # no detections; index lookup; out of bounds
         (
             mock_detections(
                 xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]],
@@ -58,30 +59,44 @@ from supervision.detection.core import Detections
                 tracker_id=[2, 6],
             ),
             2,
-            ColorMap.INDEX,
+            ColorLookup.INDEX,
             None,
             pytest.raises(ValueError),
-        ),  # multiple detections; index mapping; out of bounds
+        ),  # multiple detections; index lookup; out of bounds
         (
             mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
             0,
-            ColorMap.CLASS,
+            ColorLookup.CLASS,
             None,
             pytest.raises(ValueError),
-        ),  # multiple detections; class mapping; no class_id
+        ),  # multiple detections; class lookup; no class_id
         (
             mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
             0,
-            ColorMap.TRACK,
+            ColorLookup.TRACK,
             None,
             pytest.raises(ValueError),
-        ),  # multiple detections; class mapping; no track_id
+        ),  # multiple detections; class lookup; no track_id
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            0,
+            np.array([1, 0]),
+            1,
+            DoesNotRaise(),
+        ),  # multiple detections; custom lookup; correct length
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
+            0,
+            np.array([1]),
+            None,
+            pytest.raises(ValueError),
+        ),  # multiple detections; custom lookup; wrong length
     ],
 )
 def test_resolve_color_idx(
     detections: Detections,
     detection_idx: int,
-    color_map: ColorMap,
+    color_lookup: ColorLookup,
     expected_result: Optional[int],
     exception: Exception,
 ) -> None:
@@ -89,6 +104,6 @@ def test_resolve_color_idx(
         result = resolve_color_idx(
             detections=detections,
             detection_idx=detection_idx,
-            color_map=color_map,
+            color_lookup=color_lookup,
         )
         assert result == expected_result

test/dataset/formats/test_pascal_voc.py

@@ -1,8 +1,8 @@
-import xml.etree.ElementTree as ET
 from contextlib import ExitStack as DoesNotRaise
-from test.utils import mock_detections
+from test.test_utils import mock_detections
 from typing import List, Optional
 
+import defusedxml.ElementTree as ET
 import numpy as np
 import pytest
 
@@ -122,7 +122,7 @@ NO_DETECTIONS = """<annotation></annotation>"""
             ["test"],
             (100, 100),
             False,
-            mock_detections(np.array([[0, 0, 10, 10]]), None, [0]),
+            mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[0]),
             DoesNotRaise(),
         ),
         (
@@ -130,7 +130,9 @@ NO_DETECTIONS = """<annotation></annotation>"""
             ["test"],
             (100, 100),
             False,
-            mock_detections(np.array([[0, 0, 10, 10], [10, 10, 20, 20]]), None, [0, 0]),
+            mock_detections(
+                xyxy=np.array([[0, 0, 10, 10], [10, 10, 20, 20]]), class_id=[0, 0]
+            ),
             DoesNotRaise(),
         ),
         (
@@ -139,9 +141,8 @@ NO_DETECTIONS = """<annotation></annotation>"""
             (100, 100),
             False,
             mock_detections(
-                np.array([[0, 0, 10, 10], [20, 30, 30, 40], [10, 10, 20, 20]]),
-                None,
-                [0, 0, 1],
+                xyxy=np.array([[0, 0, 10, 10], [20, 30, 30, 40], [10, 10, 20, 20]]),
+                class_id=[0, 0, 1],
             ),
             DoesNotRaise(),
         ),
@@ -150,7 +151,7 @@ NO_DETECTIONS = """<annotation></annotation>"""
             [],
             (100, 100),
             False,
-            mock_detections(np.empty((0, 4)), None, []),
+            mock_detections(xyxy=np.empty((0, 4)), class_id=[]),
             DoesNotRaise(),
         ),
     ],

test/dataset/test_core.py

@@ -1,5 +1,5 @@
 from contextlib import ExitStack as DoesNotRaise
-from test.utils import mock_detections
+from test.test_utils import mock_detections
 from typing import List, Optional
 
 import numpy as np

test/dataset/test_utils.py

@@ -1,5 +1,5 @@
 from contextlib import ExitStack as DoesNotRaise
-from test.utils import mock_detections
+from test.test_utils import mock_detections
 from typing import Dict, List, Optional, Tuple, TypeVar
 
 import pytest

test/detection/test_core.py

@@ -1,5 +1,5 @@
 from contextlib import ExitStack as DoesNotRaise
-from test.utils import mock_detections
+from test.test_utils import mock_detections
 from typing import List, Optional, Union
 
 import numpy as np
@@ -153,7 +153,10 @@ def test_getitem(
             DoesNotRaise(),
         ),  # single detection with xyxy field
         (
-            [mock_detections(xyxy=[[10, 10, 20, 20]]), Detections.empty()],
+            [
+                mock_detections(xyxy=[[10, 10, 20, 20]]),
+                mock_detections(xyxy=np.empty((0, 4), dtype=np.float32)),
+            ],
             mock_detections(xyxy=[[10, 10, 20, 20]]),
             DoesNotRaise(),
         ),  # single detection with xyxy field + empty detection
@@ -171,7 +174,7 @@ def test_getitem(
                 mock_detections(xyxy=[[20, 20, 30, 30]]),
             ],
             mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]]),
-            DoesNotRaise(),
+            pytest.raises(ValueError),
         ),  # detection with xyxy, class_id fields + detection with xyxy field
         (
             [
@@ -181,6 +184,16 @@ def test_getitem(
             mock_detections(xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]], class_id=[0, 1]),
             DoesNotRaise(),
         ),  # two detections with xyxy, class_id fields
+        (
+            [
+                mock_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
+                mock_detections(xyxy=[[20, 20, 30, 30]], data={"test": [2]}),
+            ],
+            mock_detections(
+                xyxy=[[10, 10, 20, 20], [20, 20, 30, 30]], data={"test": [1, 2]}
+            ),
+            DoesNotRaise(),
+        ),  # two detections with xyxy, data fields
     ],
 )
 def test_merge(
@@ -270,6 +283,57 @@ def test_get_anchor_coordinates(
     expected_result: np.ndarray,
     exception: Exception,
 ) -> None:
-    result = detections.get_anchor_coordinates(anchor)
+    result = detections.get_anchors_coordinates(anchor)
     with exception:
         assert np.array_equal(result, expected_result)
+
+
+@pytest.mark.parametrize(
+    "detections_a, detections_b, expected_result",
+    [
+        (
+            Detections.empty(),
+            Detections.empty(),
+            True,
+        ),  # empty detections
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20]]),
+            mock_detections(xyxy=[[10, 10, 20, 20]]),
+            True,
+        ),  # detections with xyxy field
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20]], confidence=[0.5]),
+            mock_detections(xyxy=[[10, 10, 20, 20]], confidence=[0.5]),
+            True,
+        ),  # detections with xyxy, confidence fields
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20]], confidence=[0.5]),
+            mock_detections(xyxy=[[10, 10, 20, 20]]),
+            False,
+        ),  # detection with xyxy field + detection with xyxy, confidence fields
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
+            mock_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
+            True,
+        ),  # detections with xyxy, data fields
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20]], data={"test": [1]}),
+            mock_detections(xyxy=[[10, 10, 20, 20]]),
+            False,
+        ),  # detection with xyxy field + detection with xyxy, data fields
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20]], data={"test_1": [1]}),
+            mock_detections(xyxy=[[10, 10, 20, 20]], data={"test_2": [1]}),
+            False,
+        ),  # detections with xyxy, and different data field names
+        (
+            mock_detections(xyxy=[[10, 10, 20, 20]], data={"test_1": [1]}),
+            mock_detections(xyxy=[[10, 10, 20, 20]], data={"test_1": [3]}),
+            False,
+        ),  # detections with xyxy, and different data field values
+    ],
+)
+def test_equal(
+    detections_a: Detections, detections_b: Detections, expected_result: bool
+) -> None:
+    assert (detections_a == detections_b) == expected_result

test/detection/test_utils.py

@@ -1,15 +1,20 @@
 from contextlib import ExitStack as DoesNotRaise
-from typing import List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
 import pytest
 
+from supervision.config import CLASS_NAME_DATA_FIELD
 from supervision.detection.utils import (
+    calculate_masks_centroids,
     clip_boxes,
     filter_polygons_by_area,
+    get_data_item,
+    merge_data,
     move_boxes,
     non_max_suppression,
     process_roboflow_result,
+    scale_boxes,
 )
 
 TEST_MASK = np.zeros((1, 1000, 1000), dtype=bool)
@@ -122,7 +127,7 @@ def test_non_max_suppression(
 
 
 @pytest.mark.parametrize(
-    "boxes_xyxy, frame_resolution_wh, expected_result",
+    "xyxy, resolution_wh, expected_result",
     [
         (
             np.empty(shape=(0, 4)),
@@ -157,11 +162,11 @@ def test_non_max_suppression(
     ],
 )
 def test_clip_boxes(
-    boxes_xyxy: np.ndarray,
-    frame_resolution_wh: Tuple[int, int],
+    xyxy: np.ndarray,
+    resolution_wh: Tuple[int, int],
     expected_result: np.ndarray,
 ) -> None:
-    result = clip_boxes(boxes_xyxy=boxes_xyxy, frame_resolution_wh=frame_resolution_wh)
+    result = clip_boxes(xyxy=xyxy, resolution_wh=resolution_wh)
     assert np.array_equal(result, expected_result)
 
 
@@ -264,7 +269,14 @@ def test_filter_polygons_by_area(
     [
         (
             {"predictions": [], "image": {"width": 1000, "height": 1000}},
-            (np.empty((0, 4)), np.empty(0), np.empty(0), None),
+            (
+                np.empty((0, 4)),
+                np.empty(0),
+                np.empty(0),
+                None,
+                None,
+                {CLASS_NAME_DATA_FIELD: np.empty(0)},
+            ),
             DoesNotRaise(),
         ),  # empty result
         (
@@ -287,6 +299,8 @@ def test_filter_polygons_by_area(
                 np.array([0.9]),
                 np.array([0]),
                 None,
+                None,
+                {CLASS_NAME_DATA_FIELD: np.array(["person"])},
             ),
             DoesNotRaise(),
         ),  # single correct object detection result
@@ -301,6 +315,7 @@ def test_filter_polygons_by_area(
                         "confidence": 0.9,
                         "class_id": 0,
                         "class": "person",
+                        "tracker_id": 1,
                     },
                     {
                         "x": 500.0,
@@ -310,6 +325,7 @@ def test_filter_polygons_by_area(
                         "confidence": 0.8,
                         "class_id": 7,
                         "class": "truck",
+                        "tracker_id": 2,
                     },
                 ],
                 "image": {"width": 1000, "height": 1000},
@@ -319,6 +335,8 @@ def test_filter_polygons_by_area(
                 np.array([0.9, 0.8]),
                 np.array([0, 7]),
                 None,
+                np.array([1, 2]),
+                {CLASS_NAME_DATA_FIELD: np.array(["person", "truck"])},
             ),
             DoesNotRaise(),
         ),  # two correct object detection result
@@ -334,11 +352,19 @@ def test_filter_polygons_by_area(
                         "class_id": 0,
                         "class": "person",
                         "points": [],
+                        "tracker_id": None,
                     }
                 ],
                 "image": {"width": 1000, "height": 1000},
             },
-            (np.empty((0, 4)), np.empty(0), np.empty(0), None),
+            (
+                np.empty((0, 4)),
+                np.empty(0),
+                np.empty(0),
+                None,
+                None,
+                {CLASS_NAME_DATA_FIELD: np.empty(0)},
+            ),
             DoesNotRaise(),
         ),  # single incorrect instance segmentation result with no points
         (
@@ -357,7 +383,14 @@ def test_filter_polygons_by_area(
                 ],
                 "image": {"width": 1000, "height": 1000},
             },
-            (np.empty((0, 4)), np.empty(0), np.empty(0), None),
+            (
+                np.empty((0, 4)),
+                np.empty(0),
+                np.empty(0),
+                None,
+                None,
+                {CLASS_NAME_DATA_FIELD: np.empty(0)},
+            ),
             DoesNotRaise(),
         ),  # single incorrect instance segmentation result with no enough points
         (
@@ -386,6 +419,8 @@ def test_filter_polygons_by_area(
                 np.array([0.9]),
                 np.array([0]),
                 TEST_MASK,
+                None,
+                {CLASS_NAME_DATA_FIELD: np.array(["person"])},
             ),
             DoesNotRaise(),
         ),  # single incorrect instance segmentation result with no enough points
@@ -425,6 +460,8 @@ def test_filter_polygons_by_area(
                 np.array([0.9]),
                 np.array([0]),
                 TEST_MASK,
+                None,
+                {CLASS_NAME_DATA_FIELD: np.array(["person"])},
             ),
             DoesNotRaise(),
         ),  # two instance segmentation results - one correct, one incorrect
@@ -432,7 +469,9 @@ def test_filter_polygons_by_area(
 )
 def test_process_roboflow_result(
     roboflow_result: dict,
-    expected_result: Tuple[np.ndarray, np.ndarray, np.ndarray, Optional[np.ndarray]],
+    expected_result: Tuple[
+        np.ndarray, np.ndarray, np.ndarray, Optional[np.ndarray], np.ndarray
+    ],
     exception: Exception,
 ) -> None:
     with exception:
@@ -443,6 +482,18 @@ def test_process_roboflow_result(
         assert (result[3] is None and expected_result[3] is None) or (
             np.array_equal(result[3], expected_result[3])
         )
+        assert (result[4] is None and expected_result[4] is None) or (
+            np.array_equal(result[4], expected_result[4])
+        )
+        for key in result[5]:
+            if isinstance(result[5][key], np.ndarray):
+                assert np.array_equal(
+                    result[5][key], expected_result[5][key]
+                ), f"Mismatch in arrays for key {key}"
+            else:
+                assert (
+                    result[5][key] == expected_result[5][key]
+                ), f"Mismatch in non-array data for key {key}"
 
 
 @pytest.mark.parametrize(
@@ -486,5 +537,414 @@ def test_move_boxes(
     expected_result: np.ndarray,
     exception: Exception,
 ) -> None:
-    result = move_boxes(xyxy=xyxy, offset=offset)
-    assert np.array_equal(result, expected_result)
+    with exception:
+        result = move_boxes(xyxy=xyxy, offset=offset)
+        assert np.array_equal(result, expected_result)
+
+
+@pytest.mark.parametrize(
+    "xyxy, factor, expected_result, exception",
+    [
+        (
+            np.empty(shape=(0, 4)),
+            2.0,
+            np.empty(shape=(0, 4)),
+            DoesNotRaise(),
+        ),  # empty xyxy array
+        (
+            np.array([[0, 0, 10, 10]]),
+            1.0,
+            np.array([[0, 0, 10, 10]]),
+            DoesNotRaise(),
+        ),  # single box with factor equal to 1.0
+        (
+            np.array([[0, 0, 10, 10]]),
+            2.0,
+            np.array([[-5, -5, 15, 15]]),
+            DoesNotRaise(),
+        ),  # single box with factor equal to 2.0
+        (
+            np.array([[0, 0, 10, 10]]),
+            0.5,
+            np.array([[2.5, 2.5, 7.5, 7.5]]),
+            DoesNotRaise(),
+        ),  # single box with factor equal to 0.5
+        (
+            np.array([[0, 0, 10, 10], [10, 10, 30, 30]]),
+            2.0,
+            np.array([[-5, -5, 15, 15], [0, 0, 40, 40]]),
+            DoesNotRaise(),
+        ),  # two boxes with factor equal to 2.0
+    ],
+)
+def test_scale_boxes(
+    xyxy: np.ndarray,
+    factor: float,
+    expected_result: np.ndarray,
+    exception: Exception,
+) -> None:
+    with exception:
+        result = scale_boxes(xyxy=xyxy, factor=factor)
+        assert np.array_equal(result, expected_result)
+
+
+@pytest.mark.parametrize(
+    "masks, expected_result, exception",
+    [
+        (
+            np.array(
+                [
+                    [
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                    ]
+                ]
+            ),
+            np.array([[0, 0]]),
+            DoesNotRaise(),
+        ),  # single mask with all zeros
+        (
+            np.array(
+                [
+                    [
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                    ]
+                ]
+            ),
+            np.array([[2, 2]]),
+            DoesNotRaise(),
+        ),  # single mask with all ones
+        (
+            np.array(
+                [
+                    [
+                        [0, 1, 1, 0],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [0, 1, 1, 0],
+                    ]
+                ]
+            ),
+            np.array([[2, 2]]),
+            DoesNotRaise(),
+        ),  # single mask with symmetric ones
+        (
+            np.array(
+                [
+                    [
+                        [0, 0, 0, 0],
+                        [0, 0, 1, 1],
+                        [0, 0, 1, 1],
+                        [0, 0, 0, 0],
+                    ]
+                ]
+            ),
+            np.array([[3, 2]]),
+            DoesNotRaise(),
+        ),  # single mask with asymmetric ones
+        (
+            np.array(
+                [
+                    [
+                        [0, 1, 1, 0],
+                        [1, 1, 1, 1],
+                        [1, 1, 1, 1],
+                        [0, 1, 1, 0],
+                    ],
+                    [
+                        [0, 0, 0, 0],
+                        [0, 0, 1, 1],
+                        [0, 0, 1, 1],
+                        [0, 0, 0, 0],
+                    ],
+                ]
+            ),
+            np.array([[2, 2], [3, 2]]),
+            DoesNotRaise(),
+        ),  # two masks
+    ],
+)
+def test_calculate_masks_centroids(
+    masks: np.ndarray,
+    expected_result: np.ndarray,
+    exception: Exception,
+) -> None:
+    with exception:
+        result = calculate_masks_centroids(masks=masks)
+        assert np.array_equal(result, expected_result)
+
+
+@pytest.mark.parametrize(
+    "data_list, expected_result, exception",
+    [
+        (
+            [],
+            {},
+            DoesNotRaise(),
+        ),  # empty data list
+        (
+            [{}],
+            {},
+            DoesNotRaise(),
+        ),  # single empty data dict
+        (
+            [{}, {}],
+            {},
+            DoesNotRaise(),
+        ),  # two empty data dicts
+        (
+            [
+                {"test_1": []},
+            ],
+            {"test_1": []},
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and empty list values
+        (
+            [
+                {"test_1": np.array([])},
+            ],
+            {"test_1": np.array([])},
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and empty np.array values
+        (
+            [
+                {"test_1": [1, 2, 3]},
+            ],
+            {"test_1": [1, 2, 3]},
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and list values
+        (
+            [
+                {"test_1": []},
+                {"test_1": [3, 2, 1]},
+            ],
+            {"test_1": [3, 2, 1]},
+            DoesNotRaise(),
+        ),  # two data dicts with the same field name and empty and list values
+        (
+            [
+                {"test_1": [1, 2, 3]},
+                {"test_1": [3, 2, 1]},
+            ],
+            {"test_1": [1, 2, 3, 3, 2, 1]},
+            DoesNotRaise(),
+        ),  # two data dicts with the same field name and list values
+        (
+            [
+                {"test_1": [1, 2, 3]},
+                {"test_1": [3, 2, 1]},
+                {"test_1": [1, 2, 3]},
+            ],
+            {"test_1": [1, 2, 3, 3, 2, 1, 1, 2, 3]},
+            DoesNotRaise(),
+        ),  # three data dicts with the same field name and list values
+        (
+            [
+                {"test_1": [1, 2, 3]},
+                {"test_2": [3, 2, 1]},
+            ],
+            None,
+            pytest.raises(ValueError),
+        ),  # two data dicts with different field names
+        (
+            [
+                {"test_1": np.array([1, 2, 3])},
+                {"test_1": np.array([3, 2, 1])},
+            ],
+            {"test_1": np.array([1, 2, 3, 3, 2, 1])},
+            DoesNotRaise(),
+        ),  # two data dicts with the same field name and np.array values as 1D arrays
+        (
+            [
+                {"test_1": np.array([[1, 2, 3]])},
+                {"test_1": np.array([[3, 2, 1]])},
+            ],
+            {"test_1": np.array([[1, 2, 3], [3, 2, 1]])},
+            DoesNotRaise(),
+        ),  # two data dicts with the same field name and np.array values as 2D arrays
+        (
+            [
+                {"test_1": np.array([1, 2, 3]), "test_2": np.array(["a", "b", "c"])},
+                {"test_1": np.array([3, 2, 1]), "test_2": np.array(["c", "b", "a"])},
+            ],
+            {
+                "test_1": np.array([1, 2, 3, 3, 2, 1]),
+                "test_2": np.array(["a", "b", "c", "c", "b", "a"]),
+            },
+            DoesNotRaise(),
+        ),  # two data dicts with the same field names and np.array values
+        (
+            [
+                {"test_1": [1, 2, 3], "test_2": np.array(["a", "b", "c"])},
+                {"test_1": [3, 2, 1], "test_2": np.array(["c", "b", "a"])},
+            ],
+            {
+                "test_1": [1, 2, 3, 3, 2, 1],
+                "test_2": np.array(["a", "b", "c", "c", "b", "a"]),
+            },
+            DoesNotRaise(),
+        ),  # two data dicts with the same field names and mixed values
+        (
+            [
+                {"test_1": np.array([1, 2, 3])},
+                {"test_1": np.array([[3, 2, 1]])},
+            ],
+            None,
+            pytest.raises(ValueError),
+        ),  # two data dicts with the same field name and 1D and 2D arrays values
+        (
+            [
+                {"test_1": np.array([1, 2, 3]), "test_2": np.array(["a", "b"])},
+                {"test_1": np.array([3, 2, 1]), "test_2": np.array(["c", "b", "a"])},
+            ],
+            None,
+            pytest.raises(ValueError),
+        ),  # two data dicts with the same field name and different length arrays values
+    ],
+)
+def test_merge_data(
+    data_list: List[Dict[str, Any]],
+    expected_result: Optional[Dict[str, Any]],
+    exception: Exception,
+):
+    with exception:
+        result = merge_data(data_list=data_list)
+        for key in result:
+            if isinstance(result[key], np.ndarray):
+                assert np.array_equal(
+                    result[key], expected_result[key]
+                ), f"Mismatch in arrays for key {key}"
+            else:
+                assert (
+                    result[key] == expected_result[key]
+                ), f"Mismatch in non-array data for key {key}"
+
+
+@pytest.mark.parametrize(
+    "data, index, expected_result, exception",
+    [
+        ({}, 0, {}, DoesNotRaise()),  # empty data dict
+        (
+            {
+                "test_1": [1, 2, 3],
+            },
+            0,
+            {
+                "test_1": [1],
+            },
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and list values
+        (
+            {
+                "test_1": np.array([1, 2, 3]),
+            },
+            0,
+            {
+                "test_1": np.array([1]),
+            },
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and np.array values as 1D arrays
+        (
+            {
+                "test_1": [1, 2, 3],
+            },
+            slice(0, 2),
+            {
+                "test_1": [1, 2],
+            },
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and list values
+        (
+            {
+                "test_1": np.array([1, 2, 3]),
+            },
+            slice(0, 2),
+            {
+                "test_1": np.array([1, 2]),
+            },
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and np.array values as 1D arrays
+        (
+            {
+                "test_1": [1, 2, 3],
+            },
+            -1,
+            {
+                "test_1": [3],
+            },
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and list values
+        (
+            {
+                "test_1": np.array([1, 2, 3]),
+            },
+            -1,
+            {
+                "test_1": np.array([3]),
+            },
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and np.array values as 1D arrays
+        (
+            {
+                "test_1": [1, 2, 3],
+            },
+            [0, 2],
+            {
+                "test_1": [1, 3],
+            },
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and list values
+        (
+            {
+                "test_1": np.array([1, 2, 3]),
+            },
+            [0, 2],
+            {
+                "test_1": np.array([1, 3]),
+            },
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and np.array values as 1D arrays
+        (
+            {
+                "test_1": [1, 2, 3],
+            },
+            np.array([0, 2]),
+            {
+                "test_1": [1, 3],
+            },
+            DoesNotRaise(),
+        ),  # single data dict with a single field name and list values
+        (
+            {
+                "test_1": np.array([1, 2, 3]),
+            },
+            np.array([0, 2]),
+            {
+                "test_1": np.array([1, 3]),
+            },
+            DoesNotRaise(),
+        ),
+    ],
+)
+def test_get_data_item(
+    data: Dict[str, Any],
+    index: Any,
+    expected_result: Optional[Dict[str, Any]],
+    exception: Exception,
+):
+    with exception:
+        result = get_data_item(data=data, index=index)
+        for key in result:
+            if isinstance(result[key], np.ndarray):
+                assert np.array_equal(
+                    result[key], expected_result[key]
+                ), f"Mismatch in arrays for key {key}"
+            else:
+                assert (
+                    result[key] == expected_result[key]
+                ), f"Mismatch in non-array data for key {key}"

test/draw/test_color.py

@@ -30,3 +30,22 @@ def test_color_from_hex(
     with exception:
         result = Color.from_hex(color_hex=color_hex)
         assert result == expected_result
+
+
+@pytest.mark.parametrize(
+    "color, expected_result, exception",
+    [
+        (Color.white(), "#ffffff", DoesNotRaise()),
+        (Color.black(), "#000000", DoesNotRaise()),
+        (Color.red(), "#ff0000", DoesNotRaise()),
+        (Color.green(), "#00ff00", DoesNotRaise()),
+        (Color.blue(), "#0000ff", DoesNotRaise()),
+        (Color(r=128, g=128, b=0), "#808000", DoesNotRaise()),
+    ],
+)
+def test_color_as_hex(
+    color: Color, expected_result: Optional[str], exception: Exception
+) -> None:
+    with exception:
+        result = color.as_hex()
+        assert result == expected_result

test/metrics/test_detection.py

@@ -1,5 +1,5 @@
 from contextlib import ExitStack as DoesNotRaise
-from test.utils import assert_almost_equal, mock_detections
+from test.test_utils import assert_almost_equal, mock_detections
 from typing import Optional, Union
 
 import numpy as np

test/test_utils.py

@@ -0,0 +1,34 @@
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+
+from supervision.detection.core import Detections
+
+
+def mock_detections(
+    xyxy: List[List[float]],
+    mask: Optional[List[np.ndarray]] = None,
+    confidence: Optional[List[float]] = None,
+    class_id: Optional[List[int]] = None,
+    tracker_id: Optional[List[int]] = None,
+    data: Optional[Dict[str, List[Any]]] = None,
+) -> Detections:
+    def convert_data(data: Dict[str, List[Any]]):
+        return {k: np.array(v) for k, v in data.items()}
+
+    return Detections(
+        xyxy=np.array(xyxy, dtype=np.float32),
+        mask=(mask if mask is None else np.array(mask, dtype=bool)),
+        confidence=(
+            confidence if confidence is None else np.array(confidence, dtype=np.float32)
+        ),
+        class_id=(class_id if class_id is None else np.array(class_id, dtype=int)),
+        tracker_id=(
+            tracker_id if tracker_id is None else np.array(tracker_id, dtype=int)
+        ),
+        data=convert_data(data) if data else {},
+    )
+
+
+def assert_almost_equal(actual, expected, tolerance=1e-5):
+    assert abs(actual - expected) < tolerance, f"Expected {expected}, but got {actual}."

test/utils.py

@@ -1,27 +0,0 @@
-from typing import List
-
-import numpy as np
-
-from supervision.detection.core import Detections
-
-
-def mock_detections(
-    xyxy: List[List[float]],
-    confidence: List[float] = None,
-    class_id: List[int] = None,
-    tracker_id: List[int] = None,
-) -> Detections:
-    return Detections(
-        xyxy=np.array(xyxy, dtype=np.float32),
-        confidence=confidence
-        if confidence is None
-        else np.array(confidence, dtype=np.float32),
-        class_id=class_id if class_id is None else np.array(class_id, dtype=int),
-        tracker_id=tracker_id
-        if tracker_id is None
-        else np.array(tracker_id, dtype=int),
-    )
-
-
-def assert_almost_equal(actual, expected, tolerance=1e-5):
-    assert abs(actual - expected) < tolerance, f"Expected {expected}, but got {actual}."

test/utils/test_file.py

@@ -0,0 +1,64 @@
+import os
+from contextlib import ExitStack as DoesNotRaise
+from typing import List, Optional
+
+import pytest
+
+from supervision.utils.file import read_txt_file
+
+FILE_1_CONTENT = """Line 1
+Line 2
+Line 3
+"""
+
+FILE_2_CONTENT = """   
+Line 2
+
+Line 4
+
+"""  # noqa
+
+FILE_3_CONTENT = """
+Line 2
+
+Line 4
+
+"""
+
+
+@pytest.fixture(scope="module", autouse=True)
+def setup_and_teardown_files():
+    with open("file_1.txt", "w") as file:
+        file.write(FILE_1_CONTENT)
+    with open("file_2.txt", "w") as file:
+        file.write(FILE_2_CONTENT)
+    with open("file_3.txt", "w") as file:
+        file.write(FILE_3_CONTENT)
+
+    yield
+
+    os.remove("file_1.txt")
+    os.remove("file_2.txt")
+    os.remove("file_3.txt")
+
+
+@pytest.mark.parametrize(
+    "file_name, skip_empty, expected_result, exception",
+    [
+        ("file_1.txt", False, ["Line 1", "Line 2", "Line 3"], DoesNotRaise()),
+        ("file_2.txt", True, ["Line 2", "Line 4"], DoesNotRaise()),
+        ("file_2.txt", False, ["   ", "Line 2", "", "Line 4", ""], DoesNotRaise()),
+        ("file_3.txt", True, ["Line 2", "Line 4"], DoesNotRaise()),
+        ("file_3.txt", False, ["", "Line 2", "", "Line 4", ""], DoesNotRaise()),
+        ("file_4.txt", True, None, pytest.raises(FileNotFoundError)),
+    ],
+)
+def test_read_txt_file(
+    file_name: str,
+    skip_empty: bool,
+    expected_result: Optional[List[str]],
+    exception: Exception,
+):
+    with exception:
+        result = read_txt_file(file_name, skip_empty)
+        assert result == expected_result

tox.ini

@@ -0,0 +1,7 @@
+[tox]
+envlist = py38,py39,py310,py311,py312
+
+[testenv]
+changedir = test
+deps = pytest
+commands = pytest