Bump version in pyproject.toml

The version in the pyproject.toml file has been updated from 0.17.0rc2 to 0.17.0rc3.
Merge pull request #606 from xenteros/feature/Make_sv.MaskAnnotator_as_fast_as_annotators_in_popular_detection_frameworks_#472
2023-11-20 12:21:37 +01:00 · 2023-11-17 17:20:22 +01:00 · 2023-11-16 22:34:52 +01:00 · 2023-11-16 22:27:32 +01:00 · 2023-11-16 16:26:57 +01:00 · 2023-11-16 16:25:48 +01:00
30 changed files with 977 additions and 380 deletions
@@ -14,7 +14,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Clear cache
-        uses: actions/github-script@v6
+        uses: actions/github-script@v7
        with:
          script: |
            console.log("About to clear")
@@ -6,6 +6,12 @@ on:
      - master
      - main
      - develop
+
+permissions:
+  contents: write
+  pages: write
+  pull-requests: write
+
 jobs:
  deploy:
    runs-on: ubuntu-latest
@@ -11,6 +11,7 @@ repos:
    hooks:
      - id: end-of-file-fixer
      - id: trailing-whitespace
+        exclude: test/.*\.py
      - id: check-yaml
      - id: check-docstring-first
      - id: check-executables-have-shebangs
@@ -65,12 +66,12 @@ repos:


  -   repo: https://github.com/psf/black
-      rev: 23.10.0
+      rev: 23.11.0
      hooks:
      -   id: black

  -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.1.1
+      rev: v0.1.5
      hooks:
      -   id: ruff
          args: [--fix, --exit-non-zero-on-fix]
@@ -24,10 +24,6 @@

 </div>

-<a href="https://github.com/roboflow/supervision/issues?q=is%3Aissue+label%3Ahacktoberfest+">
-  <img width="100%" src="https://media.roboflow.com/open-source/supervision/hacktoberfest-banner-3.png">
-</a>
-
 ## 👋 hello

 **We write your reusable computer vision tools.** Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝
@@ -166,6 +166,27 @@

    </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>
+
 === "Label"

    ```python
@@ -304,6 +325,10 @@

 :::supervision.annotators.core.MaskAnnotator

+## PolygonAnnotator
+
+:::supervision.annotators.core.PolygonAnnotator
+
 ## LabelAnnotator

 :::supervision.annotators.core.LabelAnnotator
@@ -1 +1,183 @@
-🚧 Page under construction.
+Utilize Supervision to elevate your video analysis capabilities by effortlessly
+[tracking](https://supervision.roboflow.com/trackers/) objects identified by various
+object detection and segmentation models. This guide will walk you through the process
+of running inference using the [Ultralytics](https://github.com/ultralytics/ultralytics)
+YOLOv8 model, subsequently tracking these objects, and annotating the video.
+
+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.
+
+```{ .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
+)
+```
+
+<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.
+
+```{ .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
+)
+```
+
+## 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.
+
+```{ .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
+)
+```
+
+<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.
+
+```{ .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
+)
+```
+
+<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.
@@ -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);
+});
@@ -94,3 +94,7 @@ markdown_extensions:
      alternate_style: true
  - toc:
      permalink: true
+
+extra_javascript:
+  - "https://widget.kapa.ai/kapa-widget.bundle.js"
+  - "javascript/init_kapa_widget.js"
@@ -119,6 +119,20 @@ six = "*"
 [package.extras]
 test = ["astroid", "pytest"]

+[[package]]
+name = "async-lru"
+version = "2.0.4"
+description = "Simple LRU cache for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
+    {file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "attrs"
 version = "23.1.0"
@@ -185,33 +199,29 @@ lxml = ["lxml"]

 [[package]]
 name = "black"
-version = "23.7.0"
+version = "23.11.0"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -221,7 +231,7 @@ packaging = ">=22.0"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}

 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
@@ -745,19 +755,19 @@ testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "p

 [[package]]
 name = "flake8"
-version = "6.0.0"
+version = "6.1.0"
 description = "the modular source code checker: pep8 pyflakes and co"
 optional = false
 python-versions = ">=3.8.1"
 files = [
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+    {file = "flake8-6.1.0-py2.py3-none-any.whl", hash = "sha256:ffdfce58ea94c6580c77888a86506937f9a1a227dfcd15f245d694ae20a6b6e5"},
+    {file = "flake8-6.1.0.tar.gz", hash = "sha256:d5b3857f07c030bdb5bf41c7f53799571d75c4491748a3adcd47de929e34cd23"},
 ]

 [package.dependencies]
 mccabe = ">=0.7.0,<0.8.0"
-pycodestyle = ">=2.10.0,<2.11.0"
-pyflakes = ">=3.0.0,<3.1.0"
+pycodestyle = ">=2.11.0,<2.12.0"
+pyflakes = ">=3.1.0,<3.2.0"

 [[package]]
 name = "fonttools"
@@ -1003,17 +1013,6 @@ qtconsole = ["qtconsole"]
 test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
 test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]

-[[package]]
-name = "ipython-genutils"
-version = "0.2.0"
-description = "Vestigial utilities from IPython"
-optional = false
-python-versions = "*"
-files = [
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-]
-
 [[package]]
 name = "isoduration"
 version = "20.11.0"
@@ -1114,6 +1113,20 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]

+[[package]]
+name = "json5"
+version = "0.9.14"
+description = "A Python implementation of the JSON5 data format."
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.extras]
+dev = ["hypothesis"]
+
 [[package]]
 name = "jsonpointer"
 version = "2.4"
@@ -1238,6 +1251,21 @@ cli = ["click", "rich"]
 docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
 test = ["click", "coverage", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "pytest-cov", "rich"]

+[[package]]
+name = "jupyter-lsp"
+version = "2.2.0"
+description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-server = ">=1.1.2"
+
 [[package]]
 name = "jupyter-server"
 version = "2.7.2"
@@ -1293,6 +1321,39 @@ terminado = ">=0.8.3"
 docs = ["jinja2", "jupyter-server", "mistune (<3.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"]
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+[[package]]
+name = "jupyterlab"
+version = "4.0.7"
+description = "JupyterLab computational environment"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "jupyterlab-4.0.7.tar.gz", hash = "sha256:48792efd9f962b2bcda1f87d72168ff122c288b1d97d32109e4a11b33dc862be"},
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+
+[package.dependencies]
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+importlib-resources = {version = ">=1.4", markers = "python_version < \"3.9\""}
+ipykernel = "*"
+jinja2 = ">=3.0.3"
+jupyter-core = "*"
+jupyter-lsp = ">=2.0.0"
+jupyter-server = ">=2.4.0,<3"
+jupyterlab-server = ">=2.19.0,<3"
+notebook-shim = ">=0.2"
+packaging = "*"
+tomli = {version = "*", markers = "python_version < \"3.11\""}
+tornado = ">=6.2.0"
+traitlets = "*"
+
+[package.extras]
+dev = ["black[jupyter] (==23.7.0)", "build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.0.286)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-tornasync", "sphinx (>=1.8,<7.2.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.0.1)", "ipython (==8.14.0)", "ipywidgets (==8.0.6)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post0)", "matplotlib (==3.7.1)", "nbconvert (>=7.0.0)", "pandas (==2.0.2)", "scipy (==1.10.1)", "vega-datasets (==0.9.0)"]
+test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
+
 [[package]]
 name = "jupyterlab-pygments"
 version = "0.2.2"
@@ -1304,6 +1365,32 @@ files = [
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 ]

+[[package]]
+name = "jupyterlab-server"
+version = "2.25.0"
+description = "A set of server components for JupyterLab and JupyterLab like applications."
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
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+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jinja2 = ">=3.0.3"
+json5 = ">=0.9.0"
+jsonschema = ">=4.18.0"
+jupyter-server = ">=1.21,<3"
+packaging = ">=21.3"
+requests = ">=2.31"
+
+[package.extras]
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+openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.7.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+
 [[package]]
 name = "keyring"
 version = "24.2.0"
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 [package.extras]
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 install-types = ["pip"]
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 reports = ["lxml"]

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+    {file = "ruff-0.1.5-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:721f4b9d3b4161df8dc9f09aa8562e39d14e55a4dbaa451a8e55bdc9590e20f4"},
+    {file = "ruff-0.1.5-py3-none-win32.whl", hash = "sha256:f80c73bba6bc69e4fdc73b3991db0b546ce641bdcd5b07210b8ad6f64c79f1ab"},
+    {file = "ruff-0.1.5-py3-none-win_amd64.whl", hash = "sha256:c21fe20ee7d76206d290a76271c1af7a5096bc4c73ab9383ed2ad35f852a0087"},
+    {file = "ruff-0.1.5-py3-none-win_arm64.whl", hash = "sha256:82bfcb9927e88c1ed50f49ac6c9728dab3ea451212693fe40d08d314663e412f"},
+    {file = "ruff-0.1.5.tar.gz", hash = "sha256:5cbec0ef2ae1748fb194f420fb03fb2c25c3258c86129af7172ff8f198f125ab"},
 ]

 [[package]]
@@ -3225,6 +3278,28 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-g
 testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]

+[[package]]
+name = "setuptools-scm"
+version = "8.0.4"
+description = "the blessed package to manage your versions by scm tags"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "setuptools-scm-8.0.4.tar.gz", hash = "sha256:b5f43ff6800669595193fd09891564ee9d1d7dcb196cab4b2506d53a2e1c95c7"},
+    {file = "setuptools_scm-8.0.4-py3-none-any.whl", hash = "sha256:b47844cd2a84b83b3187a5782c71128c28b4c94cad8bfb871da2784a5cb54c4f"},
+]
+
+[package.dependencies]
+packaging = ">=20"
+setuptools = "*"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
+typing-extensions = "*"
+
+[package.extras]
+docs = ["entangled-cli[rich]", "mkdocs", "mkdocs-entangled-plugin", "mkdocs-material", "mkdocstrings[python]", "pygments"]
+rich = ["rich"]
+test = ["build", "pytest", "rich", "wheel"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -3559,17 +3634,17 @@ test = ["websockets"]

 [[package]]
 name = "wheel"
-version = "0.40.0"
+version = "0.41.3"
 description = "A built-package format for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "wheel-0.40.0-py3-none-any.whl", hash = "sha256:d236b20e7cb522daf2390fa84c55eea81c5c30190f90f29ae2ca1ad8355bf247"},
-    {file = "wheel-0.40.0.tar.gz", hash = "sha256:cd1196f3faee2b31968d626e1731c94f99cbdb67cf5a46e4f5656cbee7738873"},
+    {file = "wheel-0.41.3-py3-none-any.whl", hash = "sha256:488609bc63a29322326e05560731bf7bfea8e48ad646e1f5e40d366607de0942"},
+    {file = "wheel-0.41.3.tar.gz", hash = "sha256:4d4987ce51a49370ea65c0bfd2234e8ce80a12780820d9dc462597a6e60d0841"},
 ]

 [package.extras]
-test = ["pytest (>=6.0.0)"]
+test = ["pytest (>=6.0.0)", "setuptools (>=65)"]

 [[package]]
 name = "zipp"
@@ -3593,4 +3668,4 @@ desktop = ["opencv-python"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<3.12.0"
-content-hash = "7f29055b1cbde4699f27580f4ff0e115069821746674245274846b26ee48ce0b"
+content-hash = "f2d0f1827c652b0ae160a093050bca163b13f2d166968893a0c5a02d27bfb856"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.17.0rc2"
+version = "0.17.0rc3"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
@@ -54,10 +54,10 @@ assets = ["requests","tqdm"]
 [tool.poetry.group.dev.dependencies]
 twine = "^4.0.2"
 pytest = "^7.2.2"
-wheel = "^0.40.0"
-notebook = "^6.5.3"
+wheel = ">=0.40,<0.42"
+notebook = ">=6.5.3,<8.0.0"
 build = ">=0.10,<1.1"
-ruff = "^0.0.280"
+ruff = ">=0.0.280,<0.1.6"
 isort = "^5.12.0"
 black = "^23.7.0"
 mypy = "^1.4.1"
@@ -67,7 +67,7 @@ flake8 = { version = "*", python = ">=3.8.1,<3.12.0" }

 [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"
@@ -173,6 +173,7 @@ convention = "google"
 "__init__.py" = ["E402","F401"]
 "supervision/assets/list.py" = ["E501"]

+
 [tool.ruff.pylint]
 max-args = 20

@@ -18,6 +18,7 @@ from supervision.annotators.core import (
    HeatMapAnnotator,
    LabelAnnotator,
    MaskAnnotator,
+    PolygonAnnotator,
    TraceAnnotator,
 )
 from supervision.annotators.utils import ColorLookup
@@ -34,6 +35,7 @@ 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,
@@ -7,7 +7,9 @@ import numpy as np
 from supervision.annotators.base import BaseAnnotator
 from supervision.annotators.utils import ColorLookup, Trace, resolve_color
 from supervision.detection.core import Detections
+from supervision.detection.utils import clip_boxes, mask_to_polygons
 from supervision.draw.color import Color, ColorPalette
+from supervision.draw.utils import draw_polygon
 from supervision.geometry.core import Position


@@ -50,7 +52,7 @@ class BoundingBoxAnnotator(BaseAnnotator):
                Allows to override the default color mapping strategy.

        Returns:
-            np.ndarray: The annotated image.
+            The annotated image.

        Example:
            ```python
@@ -92,6 +94,10 @@ class BoundingBoxAnnotator(BaseAnnotator):
 class MaskAnnotator(BaseAnnotator):
    """
    A class for drawing masks on an image using provided detections.
+
+    !!! warning
+
+        This annotator utilizes the `sv.Detections.mask`.
    """

    def __init__(
@@ -128,7 +134,7 @@ class MaskAnnotator(BaseAnnotator):
                Allows to override the default color mapping strategy.

        Returns:
-            np.ndarray: The annotated image.
+            The annotated image.

        Example:
            ```python
@@ -150,6 +156,8 @@ class MaskAnnotator(BaseAnnotator):
        if detections.mask is None:
            return scene

+        colored_mask = np.array(scene, copy=True, dtype=np.uint8)
+
        for detection_idx in np.flip(np.argsort(detections.area)):
            color = resolve_color(
                color=self.color,
@@ -160,11 +168,94 @@ class MaskAnnotator(BaseAnnotator):
                else custom_color_lookup,
            )
            mask = detections.mask[detection_idx]
-            colored_mask = np.zeros_like(scene, dtype=np.uint8)
-            colored_mask[:] = color.as_bgr()
-            scene[mask] = cv2.addWeighted(
-                colored_mask, self.opacity, scene, 1 - self.opacity, 0
-            )[mask]
+            colored_mask[mask] = color.as_bgr()
+
+        scene = cv2.addWeighted(colored_mask, self.opacity, scene, 1 - self.opacity, 0)
+        return scene.astype(np.uint8)
+
+
+class PolygonAnnotator(BaseAnnotator):
+    """
+    A class for drawing polygons on an image using provided detections.
+
+    !!! warning
+
+        This annotator utilizes the `sv.Detections.mask`.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        thickness: int = 2,
+        color_lookup: ColorLookup = ColorLookup.CLASS,
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating detections.
+            thickness (int): Thickness of the polygon lines.
+            color_lookup (str): Strategy for mapping colors to annotations.
+                Options are `INDEX`, `CLASS`, `TRACE`.
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.thickness: int = thickness
+        self.color_lookup: ColorLookup = color_lookup
+
+    def annotate(
+        self,
+        scene: np.ndarray,
+        detections: Detections,
+        custom_color_lookup: Optional[np.ndarray] = None,
+    ) -> np.ndarray:
+        """
+        Annotates the given scene with polygons based on the provided detections.
+
+        Args:
+            scene (np.ndarray): The image where polygons will be drawn.
+            detections (Detections): Object detections to annotate.
+            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
+                Allows to override the default color mapping strategy.
+
+        Returns:
+            The annotated image.
+
+        Example:
+            ```python
+            >>> import supervision as sv
+
+            >>> image = ...
+            >>> detections = sv.Detections(...)
+
+            >>> polygon_annotator = sv.PolygonAnnotator()
+            >>> annotated_frame = polygon_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+        ![polygon-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/polygon-annotator-example-purple.png)
+        """
+        if detections.mask is None:
+            return scene
+
+        for detection_idx in range(len(detections)):
+            mask = detections.mask[detection_idx]
+            color = resolve_color(
+                color=self.color,
+                detections=detections,
+                detection_idx=detection_idx,
+                color_lookup=self.color_lookup
+                if custom_color_lookup is None
+                else custom_color_lookup,
+            )
+            for polygon in mask_to_polygons(mask=mask):
+                scene = draw_polygon(
+                    scene=scene,
+                    polygon=polygon,
+                    color=color,
+                    thickness=self.thickness,
+                )

        return scene

@@ -208,7 +299,7 @@ class BoxMaskAnnotator(BaseAnnotator):
                Allows to override the default color mapping strategy.

        Returns:
-            np.ndarray: The annotated image.
+            The annotated image.

        Example:
            ```python
@@ -254,6 +345,10 @@ class BoxMaskAnnotator(BaseAnnotator):
 class HaloAnnotator(BaseAnnotator):
    """
    A class for drawing Halos on an image using provided detections.
+
+    !!! warning
+
+        This annotator utilizes the `sv.Detections.mask`.
    """

    def __init__(
@@ -294,7 +389,7 @@ class HaloAnnotator(BaseAnnotator):
                Allows to override the default color mapping strategy.

        Returns:
-            np.ndarray: The annotated image.
+            The annotated image.

        Example:
            ```python
@@ -388,7 +483,7 @@ class EllipseAnnotator(BaseAnnotator):
                Allows to override the default color mapping strategy.

        Returns:
-            np.ndarray: The annotated image.
+            The annotated image.

        Example:
            ```python
@@ -475,7 +570,7 @@ class BoxCornerAnnotator(BaseAnnotator):
                Allows to override the default color mapping strategy.

        Returns:
-            np.ndarray: The annotated image.
+            The annotated image.

        Example:
            ```python
@@ -559,7 +654,7 @@ class CircleAnnotator(BaseAnnotator):
                Allows to override the default color mapping strategy.

        Returns:
-            np.ndarray: The annotated image.
+            The annotated image.

        Example:
            ```python
@@ -645,7 +740,7 @@ class DotAnnotator(BaseAnnotator):
                Allows to override the default color mapping strategy.

        Returns:
-            np.ndarray: The annotated image.
+            The annotated image.

        Example:
            ```python
@@ -664,7 +759,7 @@ class DotAnnotator(BaseAnnotator):
        ![dot-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/dot-annotator-example-purple.png)
        """
-        xy = detections.get_anchor_coordinates(anchor=self.position)
+        xy = detections.get_anchors_coordinates(anchor=self.position)
        for detection_idx in range(len(detections)):
            color = resolve_color(
                color=self.color,
@@ -712,49 +807,46 @@ class LabelAnnotator:
        self.text_scale: float = text_scale
        self.text_thickness: int = text_thickness
        self.text_padding: int = text_padding
-        self.text_position: Position = text_position
+        self.text_anchor: Position = text_position
        self.color_lookup: ColorLookup = color_lookup

    @staticmethod
    def resolve_text_background_xyxy(
-        detection_xyxy: Tuple[int, int, int, int],
+        center_coordinates: Tuple[int, int],
        text_wh: Tuple[int, int],
-        text_padding: int,
        position: Position,
    ) -> Tuple[int, int, int, int]:
-        padded_text_wh = (text_wh[0] + 2 * text_padding, text_wh[1] + 2 * text_padding)
-        x1, y1, x2, y2 = detection_xyxy
-        center_x = (x1 + x2) // 2
-        center_y = (y1 + y2) // 2
+        center_x, center_y = center_coordinates
+        text_w, text_h = text_wh

        if position == Position.TOP_LEFT:
-            return x1, y1 - padded_text_wh[1], x1 + padded_text_wh[0], y1
+            return center_x, center_y - text_h, center_x + text_w, center_y
        elif position == Position.TOP_RIGHT:
-            return x2 - padded_text_wh[0], y1 - padded_text_wh[1], x2, y1
+            return center_x - text_w, center_y - text_h, center_x, center_y
        elif position == Position.TOP_CENTER:
            return (
-                center_x - padded_text_wh[0] // 2,
-                y1 - padded_text_wh[1],
-                center_x + padded_text_wh[0] // 2,
-                y1,
+                center_x - text_w // 2,
+                center_y - text_h,
+                center_x + text_w // 2,
+                center_y,
            )
-        elif position == Position.CENTER:
+        elif position == Position.CENTER or position == Position.CENTER_OF_MASS:
            return (
-                center_x - padded_text_wh[0] // 2,
-                center_y - padded_text_wh[1] // 2,
-                center_x + padded_text_wh[0] // 2,
-                center_y + padded_text_wh[1] // 2,
+                center_x - text_w // 2,
+                center_y - text_h // 2,
+                center_x + text_w // 2,
+                center_y + text_h // 2,
            )
        elif position == Position.BOTTOM_LEFT:
-            return x1, y2, x1 + padded_text_wh[0], y2 + padded_text_wh[1]
+            return center_x, center_y, center_x + text_w, center_y + text_h
        elif position == Position.BOTTOM_RIGHT:
-            return x2 - padded_text_wh[0], y2, x2, y2 + padded_text_wh[1]
+            return center_x - text_w, center_y, center_x, center_y + text_h
        elif position == Position.BOTTOM_CENTER:
            return (
-                center_x - padded_text_wh[0] // 2,
-                y2,
-                center_x + padded_text_wh[0] // 2,
-                y2 + padded_text_wh[1],
+                center_x - text_w // 2,
+                center_y,
+                center_x + text_w // 2,
+                center_y + text_h,
            )

    def annotate(
@@ -775,7 +867,7 @@ class LabelAnnotator:
                Allows to override the default color mapping strategy.

        Returns:
-            np.ndarray: The annotated image.
+            The annotated image.

        Example:
            ```python
@@ -795,8 +887,10 @@ class LabelAnnotator:
        supervision-annotator-examples/label-annotator-example-purple.png)
        """
        font = cv2.FONT_HERSHEY_SIMPLEX
-        for detection_idx in range(len(detections)):
-            detection_xyxy = detections.xyxy[detection_idx].astype(int)
+        anchors_coordinates = detections.get_anchors_coordinates(
+            anchor=self.text_anchor
+        ).astype(int)
+        for detection_idx, center_coordinates in enumerate(anchors_coordinates):
            color = resolve_color(
                color=self.color,
                detections=detections,
@@ -810,22 +904,22 @@ class LabelAnnotator:
                if (labels is None or len(detections) != len(labels))
                else labels[detection_idx]
            )
-            text_wh = cv2.getTextSize(
+            text_w, text_h = cv2.getTextSize(
                text=text,
                fontFace=font,
                fontScale=self.text_scale,
                thickness=self.text_thickness,
            )[0]
-
+            text_w_padded = text_w + 2 * self.text_padding
+            text_h_padded = text_h + 2 * self.text_padding
            text_background_xyxy = self.resolve_text_background_xyxy(
-                detection_xyxy=detection_xyxy,
-                text_wh=text_wh,
-                text_padding=self.text_padding,
-                position=self.text_position,
+                center_coordinates=tuple(center_coordinates),
+                text_wh=(text_w_padded, text_h_padded),
+                position=self.text_anchor,
            )

            text_x = text_background_xyxy[0] + self.text_padding
-            text_y = text_background_xyxy[1] + self.text_padding + text_wh[1]
+            text_y = text_background_xyxy[1] + self.text_padding + text_h

            cv2.rectangle(
                img=scene,
@@ -872,7 +966,7 @@ class BlurAnnotator(BaseAnnotator):
            detections (Detections): Object detections to annotate.

        Returns:
-            np.ndarray: The annotated image.
+            The annotated image.

        Example:
            ```python
@@ -882,7 +976,7 @@ class BlurAnnotator(BaseAnnotator):
            >>> detections = sv.Detections(...)

            >>> blur_annotator = sv.BlurAnnotator()
-            >>> annotated_frame = blur_annotator.annotate(
+            >>> annotated_frame = circle_annotator.annotate(
            ...     scene=image.copy(),
            ...     detections=detections
            ... )
@@ -891,10 +985,13 @@ class BlurAnnotator(BaseAnnotator):
        ![blur-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/blur-annotator-example-purple.png)
        """
-        for detection_idx in range(len(detections)):
-            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
-            roi = scene[y1:y2, x1:x2]
+        image_height, image_width = scene.shape[:2]
+        clipped_xyxy = clip_boxes(
+            xyxy=detections.xyxy, resolution_wh=(image_width, image_height)
+        ).astype(int)

+        for x1, y1, x2, y2 in clipped_xyxy:
+            roi = scene[y1:y2, x1:x2]
            roi = cv2.blur(roi, (self.kernel_size, self.kernel_size))
            scene[y1:y2, x1:x2] = roi

@@ -907,7 +1004,7 @@ class TraceAnnotator:

    !!! warning

-        This annotator utilizes the `tracker_id`. Read
+        This annotator utilizes the `sv.Detections.tracker_id`. Read
        [here](https://supervision.roboflow.com/trackers/) to learn how to plug
        tracking into your inference pipeline.
    """
@@ -955,7 +1052,7 @@ class TraceAnnotator:
                Allows to override the default color mapping strategy.

        Returns:
-            np.ndarray: The image with the trace paths drawn on it.
+            The annotated image.

        Example:
            ```python
@@ -1051,7 +1148,7 @@ class HeatMapAnnotator:
            detections (Detections): Object detections to annotate.

        Returns:
-            np.ndarray: Annotated image.
+            Annotated image.

        Example:
            ```python
@@ -1082,7 +1179,7 @@ class HeatMapAnnotator:
        if self.heat_mask is None:
            self.heat_mask = np.zeros(scene.shape[:2])
        mask = np.zeros(scene.shape[:2])
-        for xy in detections.get_anchor_coordinates(self.position):
+        for xy in detections.get_anchors_coordinates(self.position):
            cv2.circle(mask, (int(xy[0]), int(xy[1])), self.radius, 1, -1)
        self.heat_mask = mask + self.heat_mask
        temp = self.heat_mask.copy()
@@ -102,7 +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])

@@ -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)

@@ -6,6 +6,7 @@ from typing import Any, Iterator, List, Optional, Tuple, Union
 import numpy as np

 from supervision.detection.utils import (
+    calculate_masks_centroids,
    extract_ultralytics_masks,
    non_max_suppression,
    process_roboflow_result,
@@ -600,7 +601,7 @@ class Detections:
            tracker_id=tracker_id,
        )

-    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
@@ -627,6 +628,12 @@ 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(
                [
@@ -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))
@@ -110,17 +110,15 @@ 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 +126,8 @@ 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
@@ -395,3 +393,33 @@ def move_boxes(xyxy: np.ndarray, offset: np.ndarray) -> np.ndarray:
        (np.ndarray) repositioned bounding boxes
    """
    return xyxy + np.hstack([offset, offset])
+
+
+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)
@@ -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):
@@ -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

@@ -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 Optional

 import numpy as np
@@ -1,6 +1,6 @@
 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 numpy as np
@@ -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
@@ -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
@@ -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
@@ -270,6 +270,6 @@ 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)
@@ -5,6 +5,7 @@ import numpy as np
 import pytest

 from supervision.detection.utils import (
+    calculate_masks_centroids,
    clip_boxes,
    filter_polygons_by_area,
    move_boxes,
@@ -122,7 +123,7 @@ 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 +158,11 @@ 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)


@@ -498,5 +499,97 @@ 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(
+    "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)
@@ -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
@@ -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