Update project version in pyproject.toml

The version of the project has been updated from "0.17.0rc3" to "0.17.0rc4".
Merge pull request #620 from roboflow/feature/add_color_as_hex
2023-11-26 11:33:56 +01:00 · 2023-11-26 11:29:25 +01:00 · 2023-11-26 10:27:43 +00:00 · 2023-11-26 11:25:59 +01:00 · 2023-11-21 18:06:51 +03:00 · 2023-11-21 15:18:54 +03:00
32 changed files with 1021 additions and 390 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: v0.1.6
      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"
 files = [
-    {file = "black-23.7.0-cp310-cp310-macosx_10_16_arm64.whl", hash = "sha256:5c4bc552ab52f6c1c506ccae05681fab58c3f72d59ae6e6639e8885e94fe2587"},
-    {file = "black-23.7.0-cp310-cp310-macosx_10_16_universal2.whl", hash = "sha256:552513d5cd5694590d7ef6f46e1767a4df9af168d449ff767b13b084c020e63f"},
-    {file = "black-23.7.0-cp310-cp310-macosx_10_16_x86_64.whl", hash = "sha256:86cee259349b4448adb4ef9b204bb4467aae74a386bce85d56ba4f5dc0da27be"},
-    {file = "black-23.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:501387a9edcb75d7ae8a4412bb8749900386eaef258f1aefab18adddea1936bc"},
-    {file = "black-23.7.0-cp310-cp310-win_amd64.whl", hash = "sha256:fb074d8b213749fa1d077d630db0d5f8cc3b2ae63587ad4116e8a436e9bbe995"},
-    {file = "black-23.7.0-cp311-cp311-macosx_10_16_arm64.whl", hash = "sha256:b5b0ee6d96b345a8b420100b7d71ebfdd19fab5e8301aff48ec270042cd40ac2"},
-    {file = "black-23.7.0-cp311-cp311-macosx_10_16_universal2.whl", hash = "sha256:893695a76b140881531062d48476ebe4a48f5d1e9388177e175d76234ca247cd"},
-    {file = "black-23.7.0-cp311-cp311-macosx_10_16_x86_64.whl", hash = "sha256:c333286dc3ddca6fdff74670b911cccedacb4ef0a60b34e491b8a67c833b343a"},
-    {file = "black-23.7.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:831d8f54c3a8c8cf55f64d0422ee875eecac26f5f649fb6c1df65316b67c8926"},
-    {file = "black-23.7.0-cp311-cp311-win_amd64.whl", hash = "sha256:7f3bf2dec7d541b4619b8ce526bda74a6b0bffc480a163fed32eb8b3c9aed8ad"},
-    {file = "black-23.7.0-cp38-cp38-macosx_10_16_arm64.whl", hash = "sha256:f9062af71c59c004cd519e2fb8f5d25d39e46d3af011b41ab43b9c74e27e236f"},
-    {file = "black-23.7.0-cp38-cp38-macosx_10_16_universal2.whl", hash = "sha256:01ede61aac8c154b55f35301fac3e730baf0c9cf8120f65a9cd61a81cfb4a0c3"},
-    {file = "black-23.7.0-cp38-cp38-macosx_10_16_x86_64.whl", hash = "sha256:327a8c2550ddc573b51e2c352adb88143464bb9d92c10416feb86b0f5aee5ff6"},
-    {file = "black-23.7.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d1c6022b86f83b632d06f2b02774134def5d4d4f1dac8bef16d90cda18ba28a"},
-    {file = "black-23.7.0-cp38-cp38-win_amd64.whl", hash = "sha256:27eb7a0c71604d5de083757fbdb245b1a4fae60e9596514c6ec497eb63f95320"},
-    {file = "black-23.7.0-cp39-cp39-macosx_10_16_arm64.whl", hash = "sha256:8417dbd2f57b5701492cd46edcecc4f9208dc75529bcf76c514864e48da867d9"},
-    {file = "black-23.7.0-cp39-cp39-macosx_10_16_universal2.whl", hash = "sha256:47e56d83aad53ca140da0af87678fb38e44fd6bc0af71eebab2d1f59b1acf1d3"},
-    {file = "black-23.7.0-cp39-cp39-macosx_10_16_x86_64.whl", hash = "sha256:25cc308838fe71f7065df53aedd20327969d05671bac95b38fdf37ebe70ac087"},
-    {file = "black-23.7.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:642496b675095d423f9b8448243336f8ec71c9d4d57ec17bf795b67f08132a91"},
-    {file = "black-23.7.0-cp39-cp39-win_amd64.whl", hash = "sha256:ad0014efc7acf0bd745792bd0d8857413652979200ab924fbf239062adc12491"},
-    {file = "black-23.7.0-py3-none-any.whl", hash = "sha256:9fd59d418c60c0348505f2ddf9609c1e1de8e7493eab96198fc89d9f865e7a96"},
-    {file = "black-23.7.0.tar.gz", hash = "sha256:022a582720b0d9480ed82576c920a8c1dde97cc38ff11d8d8859b3bd6ca9eedb"},
+    {file = "black-23.11.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dbea0bb8575c6b6303cc65017b46351dc5953eea5c0a59d7b7e3a2d2f433a911"},
+    {file = "black-23.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:412f56bab20ac85927f3a959230331de5614aecda1ede14b373083f62ec24e6f"},
+    {file = "black-23.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d136ef5b418c81660ad847efe0e55c58c8208b77a57a28a503a5f345ccf01394"},
+    {file = "black-23.11.0-cp310-cp310-win_amd64.whl", hash = "sha256:6c1cac07e64433f646a9a838cdc00c9768b3c362805afc3fce341af0e6a9ae9f"},
+    {file = "black-23.11.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:cf57719e581cfd48c4efe28543fea3d139c6b6f1238b3f0102a9c73992cbb479"},
+    {file = "black-23.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:698c1e0d5c43354ec5d6f4d914d0d553a9ada56c85415700b81dc90125aac244"},
+    {file = "black-23.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:760415ccc20f9e8747084169110ef75d545f3b0932ee21368f63ac0fee86b221"},
+    {file = "black-23.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:58e5f4d08a205b11800332920e285bd25e1a75c54953e05502052738fe16b3b5"},
+    {file = "black-23.11.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:45aa1d4675964946e53ab81aeec7a37613c1cb71647b5394779e6efb79d6d187"},
+    {file = "black-23.11.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4c44b7211a3a0570cc097e81135faa5f261264f4dfaa22bd5ee2875a4e773bd6"},
+    {file = "black-23.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2a9acad1451632021ee0d146c8765782a0c3846e0e0ea46659d7c4f89d9b212b"},
+    {file = "black-23.11.0-cp38-cp38-win_amd64.whl", hash = "sha256:fc7f6a44d52747e65a02558e1d807c82df1d66ffa80a601862040a43ec2e3142"},
+    {file = "black-23.11.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7f622b6822f02bfaf2a5cd31fdb7cd86fcf33dab6ced5185c35f5db98260b055"},
+    {file = "black-23.11.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:250d7e60f323fcfc8ea6c800d5eba12f7967400eb6c2d21ae85ad31c204fb1f4"},
+    {file = "black-23.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5133f5507007ba08d8b7b263c7aa0f931af5ba88a29beacc4b2dc23fcefe9c06"},
+    {file = "black-23.11.0-cp39-cp39-win_amd64.whl", hash = "sha256:421f3e44aa67138ab1b9bfbc22ee3780b22fa5b291e4db8ab7eee95200726b07"},
+    {file = "black-23.11.0-py3-none-any.whl", hash = "sha256:54caaa703227c6e0c87b76326d0862184729a69b73d3b7305b6288e1d830067e"},
+    {file = "black-23.11.0.tar.gz", hash = "sha256:4c68855825ff432d197229846f971bc4d6666ce90492e5b02013bcaca4d9ab05"},
 ]

 [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 = [
-    {file = "flake8-6.0.0-py2.py3-none-any.whl", hash = "sha256:3833794e27ff64ea4e9cf5d410082a8b97ff1a06c16aa3d2027339cd0f1195c7"},
-    {file = "flake8-6.0.0.tar.gz", hash = "sha256:c61007e76655af75e6785a931f452915b371dc48f56efd765247c8fe68f2b181"},
+    {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 = [
-    {file = "ipython_genutils-0.2.0-py2.py3-none-any.whl", hash = "sha256:72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8"},
-    {file = "ipython_genutils-0.2.0.tar.gz", hash = "sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8"},
-]
-
 [[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 = [
+    {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"},
+    {file = "json5-0.9.14.tar.gz", hash = "sha256:9ed66c3a6ca3510a976a9ef9b8c0787de24802724ab1860bc0153c7fdd589b02"},
+]
+
+[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 = [
+    {file = "jupyter-lsp-2.2.0.tar.gz", hash = "sha256:8ebbcb533adb41e5d635eb8fe82956b0aafbf0fd443b6c4bfa906edeeb8635a1"},
+    {file = "jupyter_lsp-2.2.0-py3-none-any.whl", hash = "sha256:9e06b8b4f7dd50300b70dd1a78c0c3b0c3d8fa68e0f2d8a5d1fbab62072aca3f"},
+]
+
+[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"]
 test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"]

+[[package]]
+name = "jupyterlab"
+version = "4.0.7"
+description = "JupyterLab computational environment"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyterlab-4.0.7-py3-none-any.whl", hash = "sha256:08683045117cc495531fdb39c22ababb9aaac6977a45e67cfad20046564c9c7c"},
+    {file = "jupyterlab-4.0.7.tar.gz", hash = "sha256:48792efd9f962b2bcda1f87d72168ff122c288b1d97d32109e4a11b33dc862be"},
+]
+
+[package.dependencies]
+async-lru = ">=1.0.0"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+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 = [
    {file = "jupyterlab_pygments-0.2.2.tar.gz", hash = "sha256:7405d7fde60819d905a9fa8ce89e4cd830e318cdad22a0030f7a901da705585d"},
 ]

+[[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 = [
+    {file = "jupyterlab_server-2.25.0-py3-none-any.whl", hash = "sha256:c9f67a98b295c5dee87f41551b0558374e45d449f3edca153dd722140630dcb2"},
+    {file = "jupyterlab_server-2.25.0.tar.gz", hash = "sha256:77c2f1f282d610f95e496e20d5bf1d2a7706826dfb7b18f3378ae2870d272fb7"},
+]
+
+[package.dependencies]
+babel = ">=2.10"
+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]
+docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
+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"
@@ -1517,52 +1604,58 @@ files = [

 [[package]]
 name = "matplotlib"
-version = "3.7.2"
+version = "3.7.3"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "matplotlib-3.7.2-cp310-cp310-macosx_10_12_universal2.whl", hash = "sha256:2699f7e73a76d4c110f4f25be9d2496d6ab4f17345307738557d345f099e07de"},
-    {file = "matplotlib-3.7.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:a8035ba590658bae7562786c9cc6ea1a84aa49d3afab157e414c9e2ea74f496d"},
-    {file = "matplotlib-3.7.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2f8e4a49493add46ad4a8c92f63e19d548b2b6ebbed75c6b4c7f46f57d36cdd1"},
-    {file = "matplotlib-3.7.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71667eb2ccca4c3537d9414b1bc00554cb7f91527c17ee4ec38027201f8f1603"},
-    {file = "matplotlib-3.7.2-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:152ee0b569a37630d8628534c628456b28686e085d51394da6b71ef84c4da201"},
-    {file = "matplotlib-3.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:070f8dddd1f5939e60aacb8fa08f19551f4b0140fab16a3669d5cd6e9cb28fc8"},
-    {file = "matplotlib-3.7.2-cp310-cp310-win32.whl", hash = "sha256:fdbb46fad4fb47443b5b8ac76904b2e7a66556844f33370861b4788db0f8816a"},
-    {file = "matplotlib-3.7.2-cp310-cp310-win_amd64.whl", hash = "sha256:23fb1750934e5f0128f9423db27c474aa32534cec21f7b2153262b066a581fd1"},
-    {file = "matplotlib-3.7.2-cp311-cp311-macosx_10_12_universal2.whl", hash = "sha256:30e1409b857aa8a747c5d4f85f63a79e479835f8dffc52992ac1f3f25837b544"},
-    {file = "matplotlib-3.7.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:50e0a55ec74bf2d7a0ebf50ac580a209582c2dd0f7ab51bc270f1b4a0027454e"},
-    {file = "matplotlib-3.7.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ac60daa1dc83e8821eed155796b0f7888b6b916cf61d620a4ddd8200ac70cd64"},
-    {file = "matplotlib-3.7.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:305e3da477dc8607336ba10bac96986d6308d614706cae2efe7d3ffa60465b24"},
-    {file = "matplotlib-3.7.2-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1c308b255efb9b06b23874236ec0f10f026673ad6515f602027cc8ac7805352d"},
-    {file = "matplotlib-3.7.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:60c521e21031632aa0d87ca5ba0c1c05f3daacadb34c093585a0be6780f698e4"},
-    {file = "matplotlib-3.7.2-cp311-cp311-win32.whl", hash = "sha256:26bede320d77e469fdf1bde212de0ec889169b04f7f1179b8930d66f82b30cbc"},
-    {file = "matplotlib-3.7.2-cp311-cp311-win_amd64.whl", hash = "sha256:af4860132c8c05261a5f5f8467f1b269bf1c7c23902d75f2be57c4a7f2394b3e"},
-    {file = "matplotlib-3.7.2-cp38-cp38-macosx_10_12_universal2.whl", hash = "sha256:a1733b8e84e7e40a9853e505fe68cc54339f97273bdfe6f3ed980095f769ddc7"},
-    {file = "matplotlib-3.7.2-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:d9881356dc48e58910c53af82b57183879129fa30492be69058c5b0d9fddf391"},
-    {file = "matplotlib-3.7.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:f081c03f413f59390a80b3e351cc2b2ea0205839714dbc364519bcf51f4b56ca"},
-    {file = "matplotlib-3.7.2-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:1cd120fca3407a225168238b790bd5c528f0fafde6172b140a2f3ab7a4ea63e9"},
-    {file = "matplotlib-3.7.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:a2c1590b90aa7bd741b54c62b78de05d4186271e34e2377e0289d943b3522273"},
-    {file = "matplotlib-3.7.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6d2ff3c984b8a569bc1383cd468fc06b70d7b59d5c2854ca39f1436ae8394117"},
-    {file = "matplotlib-3.7.2-cp38-cp38-win32.whl", hash = "sha256:5dea00b62d28654b71ca92463656d80646675628d0828e08a5f3b57e12869e13"},
-    {file = "matplotlib-3.7.2-cp38-cp38-win_amd64.whl", hash = "sha256:0f506a1776ee94f9e131af1ac6efa6e5bc7cb606a3e389b0ccb6e657f60bb676"},
-    {file = "matplotlib-3.7.2-cp39-cp39-macosx_10_12_universal2.whl", hash = "sha256:6515e878f91894c2e4340d81f0911857998ccaf04dbc1bba781e3d89cbf70608"},
-    {file = "matplotlib-3.7.2-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:71f7a8c6b124e904db550f5b9fe483d28b896d4135e45c4ea381ad3b8a0e3256"},
-    {file = "matplotlib-3.7.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:12f01b92ecd518e0697da4d97d163b2b3aa55eb3eb4e2c98235b3396d7dad55f"},
-    {file = "matplotlib-3.7.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a7e28d6396563955f7af437894a36bf2b279462239a41028323e04b85179058b"},
-    {file = "matplotlib-3.7.2-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dbcf59334ff645e6a67cd5f78b4b2cdb76384cdf587fa0d2dc85f634a72e1a3e"},
-    {file = "matplotlib-3.7.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:318c89edde72ff95d8df67d82aca03861240512994a597a435a1011ba18dbc7f"},
-    {file = "matplotlib-3.7.2-cp39-cp39-win32.whl", hash = "sha256:ce55289d5659b5b12b3db4dc9b7075b70cef5631e56530f14b2945e8836f2d20"},
-    {file = "matplotlib-3.7.2-cp39-cp39-win_amd64.whl", hash = "sha256:2ecb5be2b2815431c81dc115667e33da0f5a1bcf6143980d180d09a717c4a12e"},
-    {file = "matplotlib-3.7.2-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:fdcd28360dbb6203fb5219b1a5658df226ac9bebc2542a9e8f457de959d713d0"},
-    {file = "matplotlib-3.7.2-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0c3cca3e842b11b55b52c6fb8bd6a4088693829acbfcdb3e815fa9b7d5c92c1b"},
-    {file = "matplotlib-3.7.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ebf577c7a6744e9e1bd3fee45fc74a02710b214f94e2bde344912d85e0c9af7c"},
-    {file = "matplotlib-3.7.2-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:936bba394682049919dda062d33435b3be211dc3dcaa011e09634f060ec878b2"},
-    {file = "matplotlib-3.7.2-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:bc221ffbc2150458b1cd71cdd9ddd5bb37962b036e41b8be258280b5b01da1dd"},
-    {file = "matplotlib-3.7.2-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:35d74ebdb3f71f112b36c2629cf32323adfbf42679e2751252acd468f5001c07"},
-    {file = "matplotlib-3.7.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:717157e61b3a71d3d26ad4e1770dc85156c9af435659a25ee6407dc866cb258d"},
-    {file = "matplotlib-3.7.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:20f844d6be031948148ba49605c8b96dfe7d3711d1b63592830d650622458c11"},
-    {file = "matplotlib-3.7.2.tar.gz", hash = "sha256:a8cdb91dddb04436bd2f098b8fdf4b81352e68cf4d2c6756fcc414791076569b"},
+    {file = "matplotlib-3.7.3-cp310-cp310-macosx_10_12_universal2.whl", hash = "sha256:085c33b27561d9c04386789d5aa5eb4a932ddef43cfcdd0e01735f9a6e85ce0c"},
+    {file = "matplotlib-3.7.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:c568e80e1c17f68a727f30f591926751b97b98314d8e59804f54f86ae6fa6a22"},
+    {file = "matplotlib-3.7.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:7baf98c5ad59c5c4743ea884bb025cbffa52dacdfdac0da3e6021a285a90377e"},
+    {file = "matplotlib-3.7.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:236024f582e40dac39bca592258888b38ae47a9fed7b8de652d68d3d02d47d2b"},
+    {file = "matplotlib-3.7.3-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:12b4f6795efea037ce2d41e7c417ad8bd02d5719c6ad4a8450a0708f4a1cfb89"},
+    {file = "matplotlib-3.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:78b2136cc6c5415b78977e0e8c608647d597204b05b1d9089ccf513c7d913733"},
+    {file = "matplotlib-3.7.3-cp310-cp310-win32.whl", hash = "sha256:122dcbf9be0086e2a95d9e5e0632dbf3bd5b65eaa68c369363310a6c87753059"},
+    {file = "matplotlib-3.7.3-cp310-cp310-win_amd64.whl", hash = "sha256:4aab27d9e33293389e3c1d7c881d414a72bdfda0fedc3a6bf46c6fa88d9b8015"},
+    {file = "matplotlib-3.7.3-cp311-cp311-macosx_10_12_universal2.whl", hash = "sha256:d5adc743de91e8e0b13df60deb1b1c285b8effea3d66223afceb14b63c9b05de"},
+    {file = "matplotlib-3.7.3-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:55de4cf7cd0071b8ebf203981b53ab64f988a0a1f897a2dff300a1124e8bcd8b"},
+    {file = "matplotlib-3.7.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ac03377fd908aaee2312d0b11735753e907adb6f4d1d102de5e2425249693f6c"},
+    {file = "matplotlib-3.7.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:755bafc10a46918ce9a39980009b54b02dd249594e5adf52f9c56acfddb5d0b7"},
+    {file = "matplotlib-3.7.3-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1a6094c6f8e8d18db631754df4fe9a34dec3caf074f6869a7db09f18f9b1d6b2"},
+    {file = "matplotlib-3.7.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:272dba2f1b107790ed78ebf5385b8d14b27ad9e90419de340364b49fe549a993"},
+    {file = "matplotlib-3.7.3-cp311-cp311-win32.whl", hash = "sha256:591c123bed1cb4b9996fb60b41a6d89c2ec4943244540776c5f1283fb6960a53"},
+    {file = "matplotlib-3.7.3-cp311-cp311-win_amd64.whl", hash = "sha256:3bf3a178c6504694cee8b88b353df0051583f2f6f8faa146f67115c27c856881"},
+    {file = "matplotlib-3.7.3-cp312-cp312-macosx_10_12_universal2.whl", hash = "sha256:edf54cac8ee3603f3093616b40a931e8c063969756a4d78a86e82c2fea9659f7"},
+    {file = "matplotlib-3.7.3-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:91e36a85ea639a1ba9f91427041eac064b04829945fe331a92617b6cb21d27e5"},
+    {file = "matplotlib-3.7.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:caf5eaaf7c68f8d7df269dfbcaf46f48a70ff482bfcebdcc97519671023f2a7d"},
+    {file = "matplotlib-3.7.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:74bf57f505efea376097e948b7cdd87191a7ce8180616390aef496639edf601f"},
+    {file = "matplotlib-3.7.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ee152a88a0da527840a426535514b6ed8ac4240eb856b1da92cf48124320e346"},
+    {file = "matplotlib-3.7.3-cp312-cp312-win_amd64.whl", hash = "sha256:67a410a9c9e07cbc83581eeea144bbe298870bf0ac0ee2f2e10a015ab7efee19"},
+    {file = "matplotlib-3.7.3-cp38-cp38-macosx_10_12_universal2.whl", hash = "sha256:259999c05285cb993d7f2a419cea547863fa215379eda81f7254c9e932963729"},
+    {file = "matplotlib-3.7.3-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:3f4e7fd5a6157e1d018ce2166ec8e531a481dd4a36f035b5c23edfe05a25419a"},
+    {file = "matplotlib-3.7.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:faa3d12d8811d08d14080a8b7b9caea9a457dc495350166b56df0db4b9909ef5"},
+    {file = "matplotlib-3.7.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:336e88900c11441e458da01c8414fc57e04e17f9d3bb94958a76faa2652bcf6b"},
+    {file = "matplotlib-3.7.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:12f4c0dd8aa280d796c8772ea8265a14f11a04319baa3a16daa5556065e8baea"},
+    {file = "matplotlib-3.7.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1990955b11e7918d256cf3b956b10997f405b7917a3f1c7d8e69c1d15c7b1930"},
+    {file = "matplotlib-3.7.3-cp38-cp38-win32.whl", hash = "sha256:e78707b751260b42b721507ad7aa60fe4026d7f51c74cca6b9cd8b123ebb633a"},
+    {file = "matplotlib-3.7.3-cp38-cp38-win_amd64.whl", hash = "sha256:e594ee43c59ea39ca5c6244667cac9d017a3527febc31f5532ad9135cf7469ec"},
+    {file = "matplotlib-3.7.3-cp39-cp39-macosx_10_12_universal2.whl", hash = "sha256:6eaa1cf0e94c936a26b78f6d756c5fbc12e0a58c8a68b7248a2a31456ce4e234"},
+    {file = "matplotlib-3.7.3-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:0a97af9d22e8ebedc9f00b043d9bbd29a375e9e10b656982012dded44c10fd77"},
+    {file = "matplotlib-3.7.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:1f9c6c16597af660433ab330b59ee2934b832ee1fabcaf5cbde7b2add840f31e"},
+    {file = "matplotlib-3.7.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a7240259b4b9cbc62381f6378cff4d57af539162a18e832c1e48042fabc40b6b"},
+    {file = "matplotlib-3.7.3-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:747c6191d2e88ae854809e69aa358dbf852ff1a5738401b85c1cc9012309897a"},
+    {file = "matplotlib-3.7.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ec726b08a5275d827aa91bb951e68234a4423adb91cf65bc0fcdc0f2777663f7"},
+    {file = "matplotlib-3.7.3-cp39-cp39-win32.whl", hash = "sha256:40e3b9b450c6534f07278310c4e34caff41c2a42377e4b9d47b0f8d3ac1083a2"},
+    {file = "matplotlib-3.7.3-cp39-cp39-win_amd64.whl", hash = "sha256:dfc118642903a23e309b1da32886bb39a4314147d013e820c86b5fb4cb2e36d0"},
+    {file = "matplotlib-3.7.3-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:165c8082bf8fc0360c24aa4724a22eaadbfd8c28bf1ccf7e94d685cad48261e4"},
+    {file = "matplotlib-3.7.3-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ebd8470cc2a3594746ff0513aecbfa2c55ff6f58e6cef2efb1a54eb87c88ffa2"},
+    {file = "matplotlib-3.7.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7153453669c9672b52095119fd21dd032d19225d48413a2871519b17db4b0fde"},
+    {file = "matplotlib-3.7.3-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:498a08267dc69dd8f24c4b5d7423fa584d7ce0027ba71f7881df05fc09b89bb7"},
+    {file = "matplotlib-3.7.3-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:d48999c4b19b5a0c058c9cd828ff6fc7748390679f6cf9a2ad653a3e802c87d3"},
+    {file = "matplotlib-3.7.3-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:22d65d18b4ee8070a5fea5761d59293f1f9e2fac37ec9ce090463b0e629432fd"},
+    {file = "matplotlib-3.7.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9c40cde976c36693cc0767e27cf5f443f91c23520060bd9496678364adfafe9c"},
+    {file = "matplotlib-3.7.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:39018a2b17592448fbfdf4b8352955e6c3905359939791d4ff429296494d1a0c"},
+    {file = "matplotlib-3.7.3.tar.gz", hash = "sha256:f09b3dd6bdeb588de91f853bbb2d6f0ff8ab693485b0c49035eaa510cb4f142e"},
 ]

 [package.dependencies]
@@ -1571,11 +1664,12 @@ cycler = ">=0.10"
 fonttools = ">=4.22.0"
 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
 kiwisolver = ">=1.0.1"
-numpy = ">=1.20"
+numpy = ">=1.20,<2"
 packaging = ">=20.0"
 pillow = ">=6.2.0"
-pyparsing = ">=2.3.1,<3.1"
+pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
+setuptools_scm = ">=7"

 [[package]]
 name = "matplotlib-inline"
@@ -1683,13 +1777,13 @@ mkdocs = ">=1.1"

 [[package]]
 name = "mkdocs-material"
-version = "9.4.6"
+version = "9.4.8"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.4.6-py3-none-any.whl", hash = "sha256:78802035d5768a78139c84ad7dce0c6493e8f7dc4861727d36ed91d1520a54da"},
-    {file = "mkdocs_material-9.4.6.tar.gz", hash = "sha256:09665e60df7ee9e5ff3a54af173f6d45be718b1ee7dd962bcff3102b81fb0c14"},
+    {file = "mkdocs_material-9.4.8-py3-none-any.whl", hash = "sha256:8b20f6851bddeef37dced903893cd176cf13a21a482e97705a103c45f06ce9b9"},
+    {file = "mkdocs_material-9.4.8.tar.gz", hash = "sha256:f0c101453e8bc12b040e8b64ca39a405d950d8402609b1378cc2b98976e74b5f"},
 ]

 [package.dependencies]
@@ -1698,7 +1792,7 @@ colorama = ">=0.4,<1.0"
 jinja2 = ">=3.0,<4.0"
 markdown = ">=3.2,<4.0"
 mkdocs = ">=1.5.3,<2.0"
-mkdocs-material-extensions = ">=1.2,<2.0"
+mkdocs-material-extensions = ">=1.3,<2.0"
 paginate = ">=0.5,<1.0"
 pygments = ">=2.16,<3.0"
 pymdown-extensions = ">=10.2,<11.0"
@@ -1723,23 +1817,27 @@ files = [

 [[package]]
 name = "mkdocstrings"
-version = "0.20.0"
+version = "0.24.0"
 description = "Automatic documentation from sources, for MkDocs."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "mkdocstrings-0.20.0-py3-none-any.whl", hash = "sha256:f17fc2c4f760ec302b069075ef9e31045aa6372ca91d2f35ded3adba8e25a472"},
-    {file = "mkdocstrings-0.20.0.tar.gz", hash = "sha256:c757f4f646d4f939491d6bc9256bfe33e36c5f8026392f49eaa351d241c838e5"},
+    {file = "mkdocstrings-0.24.0-py3-none-any.whl", hash = "sha256:f4908560c10f587326d8f5165d1908817b2e280bbf707607f601c996366a2264"},
+    {file = "mkdocstrings-0.24.0.tar.gz", hash = "sha256:222b1165be41257b494a9d29b14135d2b7ca43f38161d5b10caae03b87bd4f7e"},
 ]

 [package.dependencies]
+click = ">=7.0"
+importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
 Jinja2 = ">=2.11.1"
 Markdown = ">=3.3"
 MarkupSafe = ">=1.1"
-mkdocs = ">=1.2"
+mkdocs = ">=1.4"
 mkdocs-autorefs = ">=0.3.1"
 mkdocstrings-python = {version = ">=0.5.2", optional = true, markers = "extra == \"python\""}
+platformdirs = ">=2.2.0"
 pymdown-extensions = ">=6.3"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.10\""}

 [package.extras]
 crystal = ["mkdocstrings-crystal (>=0.3.4)"]
@@ -1774,37 +1872,38 @@ files = [

 [[package]]
 name = "mypy"
-version = "1.4.1"
+version = "1.7.0"
 description = "Optional static typing for Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "mypy-1.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:566e72b0cd6598503e48ea610e0052d1b8168e60a46e0bfd34b3acf2d57f96a8"},
-    {file = "mypy-1.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ca637024ca67ab24a7fd6f65d280572c3794665eaf5edcc7e90a866544076878"},
-    {file = "mypy-1.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0dde1d180cd84f0624c5dcaaa89c89775550a675aff96b5848de78fb11adabcd"},
-    {file = "mypy-1.4.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:8c4d8e89aa7de683e2056a581ce63c46a0c41e31bd2b6d34144e2c80f5ea53dc"},
-    {file = "mypy-1.4.1-cp310-cp310-win_amd64.whl", hash = "sha256:bfdca17c36ae01a21274a3c387a63aa1aafe72bff976522886869ef131b937f1"},
-    {file = "mypy-1.4.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:7549fbf655e5825d787bbc9ecf6028731973f78088fbca3a1f4145c39ef09462"},
-    {file = "mypy-1.4.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:98324ec3ecf12296e6422939e54763faedbfcc502ea4a4c38502082711867258"},
-    {file = "mypy-1.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:141dedfdbfe8a04142881ff30ce6e6653c9685b354876b12e4fe6c78598b45e2"},
-    {file = "mypy-1.4.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:8207b7105829eca6f3d774f64a904190bb2231de91b8b186d21ffd98005f14a7"},
-    {file = "mypy-1.4.1-cp311-cp311-win_amd64.whl", hash = "sha256:16f0db5b641ba159eff72cff08edc3875f2b62b2fa2bc24f68c1e7a4e8232d01"},
-    {file = "mypy-1.4.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:470c969bb3f9a9efcedbadcd19a74ffb34a25f8e6b0e02dae7c0e71f8372f97b"},
-    {file = "mypy-1.4.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e5952d2d18b79f7dc25e62e014fe5a23eb1a3d2bc66318df8988a01b1a037c5b"},
-    {file = "mypy-1.4.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:190b6bab0302cec4e9e6767d3eb66085aef2a1cc98fe04936d8a42ed2ba77bb7"},
-    {file = "mypy-1.4.1-cp37-cp37m-win_amd64.whl", hash = "sha256:9d40652cc4fe33871ad3338581dca3297ff5f2213d0df345bcfbde5162abf0c9"},
-    {file = "mypy-1.4.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:01fd2e9f85622d981fd9063bfaef1aed6e336eaacca00892cd2d82801ab7c042"},
-    {file = "mypy-1.4.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2460a58faeea905aeb1b9b36f5065f2dc9a9c6e4c992a6499a2360c6c74ceca3"},
-    {file = "mypy-1.4.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2746d69a8196698146a3dbe29104f9eb6a2a4d8a27878d92169a6c0b74435b6"},
-    {file = "mypy-1.4.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:ae704dcfaa180ff7c4cfbad23e74321a2b774f92ca77fd94ce1049175a21c97f"},
-    {file = "mypy-1.4.1-cp38-cp38-win_amd64.whl", hash = "sha256:43d24f6437925ce50139a310a64b2ab048cb2d3694c84c71c3f2a1626d8101dc"},
-    {file = "mypy-1.4.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c482e1246726616088532b5e964e39765b6d1520791348e6c9dc3af25b233828"},
-    {file = "mypy-1.4.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:43b592511672017f5b1a483527fd2684347fdffc041c9ef53428c8dc530f79a3"},
-    {file = "mypy-1.4.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:34a9239d5b3502c17f07fd7c0b2ae6b7dd7d7f6af35fbb5072c6208e76295816"},
-    {file = "mypy-1.4.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5703097c4936bbb9e9bce41478c8d08edd2865e177dc4c52be759f81ee4dd26c"},
-    {file = "mypy-1.4.1-cp39-cp39-win_amd64.whl", hash = "sha256:e02d700ec8d9b1859790c0475df4e4092c7bf3272a4fd2c9f33d87fac4427b8f"},
-    {file = "mypy-1.4.1-py3-none-any.whl", hash = "sha256:45d32cec14e7b97af848bddd97d85ea4f0db4d5a149ed9676caa4eb2f7402bb4"},
-    {file = "mypy-1.4.1.tar.gz", hash = "sha256:9bbcd9ab8ea1f2e1c8031c21445b511442cc45c89951e49bbf852cbb70755b1b"},
+    {file = "mypy-1.7.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5da84d7bf257fd8f66b4f759a904fd2c5a765f70d8b52dde62b521972a0a2357"},
+    {file = "mypy-1.7.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a3637c03f4025f6405737570d6cbfa4f1400eb3c649317634d273687a09ffc2f"},
+    {file = "mypy-1.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b633f188fc5ae1b6edca39dae566974d7ef4e9aaaae00bc36efe1f855e5173ac"},
+    {file = "mypy-1.7.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:d6ed9a3997b90c6f891138e3f83fb8f475c74db4ccaa942a1c7bf99e83a989a1"},
+    {file = "mypy-1.7.0-cp310-cp310-win_amd64.whl", hash = "sha256:1fe46e96ae319df21359c8db77e1aecac8e5949da4773c0274c0ef3d8d1268a9"},
+    {file = "mypy-1.7.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:df67fbeb666ee8828f675fee724cc2cbd2e4828cc3df56703e02fe6a421b7401"},
+    {file = "mypy-1.7.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a79cdc12a02eb526d808a32a934c6fe6df07b05f3573d210e41808020aed8b5d"},
+    {file = "mypy-1.7.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f65f385a6f43211effe8c682e8ec3f55d79391f70a201575def73d08db68ead1"},
+    {file = "mypy-1.7.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:0e81ffd120ee24959b449b647c4b2fbfcf8acf3465e082b8d58fd6c4c2b27e46"},
+    {file = "mypy-1.7.0-cp311-cp311-win_amd64.whl", hash = "sha256:f29386804c3577c83d76520abf18cfcd7d68264c7e431c5907d250ab502658ee"},
+    {file = "mypy-1.7.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:87c076c174e2c7ef8ab416c4e252d94c08cd4980a10967754f91571070bf5fbe"},
+    {file = "mypy-1.7.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6cb8d5f6d0fcd9e708bb190b224089e45902cacef6f6915481806b0c77f7786d"},
+    {file = "mypy-1.7.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d93e76c2256aa50d9c82a88e2f569232e9862c9982095f6d54e13509f01222fc"},
+    {file = "mypy-1.7.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:cddee95dea7990e2215576fae95f6b78a8c12f4c089d7e4367564704e99118d3"},
+    {file = "mypy-1.7.0-cp312-cp312-win_amd64.whl", hash = "sha256:d01921dbd691c4061a3e2ecdbfbfad029410c5c2b1ee88946bf45c62c6c91210"},
+    {file = "mypy-1.7.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:185cff9b9a7fec1f9f7d8352dff8a4c713b2e3eea9c6c4b5ff7f0edf46b91e41"},
+    {file = "mypy-1.7.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7a7b1e399c47b18feb6f8ad4a3eef3813e28c1e871ea7d4ea5d444b2ac03c418"},
+    {file = "mypy-1.7.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc9fe455ad58a20ec68599139ed1113b21f977b536a91b42bef3ffed5cce7391"},
+    {file = "mypy-1.7.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:d0fa29919d2e720c8dbaf07d5578f93d7b313c3e9954c8ec05b6d83da592e5d9"},
+    {file = "mypy-1.7.0-cp38-cp38-win_amd64.whl", hash = "sha256:2b53655a295c1ed1af9e96b462a736bf083adba7b314ae775563e3fb4e6795f5"},
+    {file = "mypy-1.7.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c1b06b4b109e342f7dccc9efda965fc3970a604db70f8560ddfdee7ef19afb05"},
+    {file = "mypy-1.7.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:bf7a2f0a6907f231d5e41adba1a82d7d88cf1f61a70335889412dec99feeb0f8"},
+    {file = "mypy-1.7.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:551d4a0cdcbd1d2cccdcc7cb516bb4ae888794929f5b040bb51aae1846062901"},
+    {file = "mypy-1.7.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:55d28d7963bef00c330cb6461db80b0b72afe2f3c4e2963c99517cf06454e665"},
+    {file = "mypy-1.7.0-cp39-cp39-win_amd64.whl", hash = "sha256:870bd1ffc8a5862e593185a4c169804f2744112b4a7c55b93eb50f48e7a77010"},
+    {file = "mypy-1.7.0-py3-none-any.whl", hash = "sha256:96650d9a4c651bc2a4991cf46f100973f656d69edc7faf91844e87fe627f7e96"},
+    {file = "mypy-1.7.0.tar.gz", hash = "sha256:1e280b5697202efa698372d2f39e9a6713a0395a756b1c6bd48995f8d72690dc"},
 ]

 [package.dependencies]
@@ -1815,7 +1914,7 @@ typing-extensions = ">=4.1.0"
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
 install-types = ["pip"]
-python2 = ["typed-ast (>=1.4.0,<2)"]
+mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]

 [[package]]
@@ -1829,41 +1928,6 @@ files = [
    {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]

-[[package]]
-name = "nbclassic"
-version = "1.0.0"
-description = "Jupyter Notebook as a Jupyter Server extension."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "nbclassic-1.0.0-py3-none-any.whl", hash = "sha256:f99e4769b4750076cd4235c044b61232110733322384a94a63791d2e7beacc66"},
-    {file = "nbclassic-1.0.0.tar.gz", hash = "sha256:0ae11eb2319455d805596bf320336cda9554b41d99ab9a3c31bf8180bffa30e3"},
-]
-
-[package.dependencies]
-argon2-cffi = "*"
-ipykernel = "*"
-ipython-genutils = "*"
-jinja2 = "*"
-jupyter-client = ">=6.1.1"
-jupyter-core = ">=4.6.1"
-jupyter-server = ">=1.8"
-nbconvert = ">=5"
-nbformat = "*"
-nest-asyncio = ">=1.5"
-notebook-shim = ">=0.2.3"
-prometheus-client = "*"
-pyzmq = ">=17"
-Send2Trash = ">=1.8.0"
-terminado = ">=0.8.3"
-tornado = ">=6.1"
-traitlets = ">=4.2.1"
-
-[package.extras]
-docs = ["myst-parser", "nbsphinx", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
-json-logging = ["json-logging"]
-test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-jupyter", "pytest-playwright", "pytest-tornasync", "requests", "requests-unixsocket", "testpath"]
-
 [[package]]
 name = "nbclient"
 version = "0.8.0"
@@ -1972,37 +2036,26 @@ setuptools = "*"

 [[package]]
 name = "notebook"
-version = "6.5.4"
-description = "A web-based notebook environment for interactive computing"
+version = "7.0.6"
+description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "notebook-6.5.4-py3-none-any.whl", hash = "sha256:dd17e78aefe64c768737b32bf171c1c766666a21cc79a44d37a1700771cab56f"},
-    {file = "notebook-6.5.4.tar.gz", hash = "sha256:517209568bd47261e2def27a140e97d49070602eea0d226a696f42a7f16c9a4e"},
+    {file = "notebook-7.0.6-py3-none-any.whl", hash = "sha256:0fe8f67102fea3744fedf652e4c15339390902ca70c5a31c4f547fa23da697cc"},
+    {file = "notebook-7.0.6.tar.gz", hash = "sha256:ec6113b06529019f7f287819af06c97a2baf7a95ac21a8f6e32192898e9f9a58"},
 ]

 [package.dependencies]
-argon2-cffi = "*"
-ipykernel = "*"
-ipython-genutils = "*"
-jinja2 = "*"
-jupyter-client = ">=5.3.4"
-jupyter-core = ">=4.6.1"
-nbclassic = ">=0.4.7"
-nbconvert = ">=5"
-nbformat = "*"
-nest-asyncio = ">=1.5"
-prometheus-client = "*"
-pyzmq = ">=17"
-Send2Trash = ">=1.8.0"
-terminado = ">=0.8.3"
-tornado = ">=6.1"
-traitlets = ">=4.2.1"
+jupyter-server = ">=2.4.0,<3"
+jupyterlab = ">=4.0.2,<5"
+jupyterlab-server = ">=2.22.1,<3"
+notebook-shim = ">=0.2,<0.3"
+tornado = ">=6.2.0"

 [package.extras]
-docs = ["myst-parser", "nbsphinx", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
-json-logging = ["json-logging"]
-test = ["coverage", "nbval", "pytest", "pytest-cov", "requests", "requests-unixsocket", "selenium (==4.1.5)", "testpath"]
+dev = ["hatch", "pre-commit"]
+docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.22.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]

 [[package]]
 name = "notebook-shim"
@@ -2086,18 +2139,18 @@ numpy = [

 [[package]]
 name = "opencv-python-headless"
-version = "4.8.0.74"
+version = "4.8.1.78"
 description = "Wrapper package for OpenCV python bindings."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "opencv-python-headless-4.8.0.74.tar.gz", hash = "sha256:a72770b8f2e08358b1faa41c8372b17d040aa9bb3e446ab7090e358f6f4e91ba"},
-    {file = "opencv_python_headless-4.8.0.74-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:f645b17667fc9bffec8c3b3a72a217430c8d7090ed3ebb80fd8efc657f372d6c"},
-    {file = "opencv_python_headless-4.8.0.74-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:7c88744ce922b81747c86f2b7f1b3ec84ff2f53477ee9219a5e75ae2012b443f"},
-    {file = "opencv_python_headless-4.8.0.74-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:34e92fef90e8f768d92dca875aefaff8b11556427f82484378a3848eb690d4c0"},
-    {file = "opencv_python_headless-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f8a88620c43a5d17f7df8317767da93b935656ab9236df92e1e2e0dd23195756"},
-    {file = "opencv_python_headless-4.8.0.74-cp37-abi3-win32.whl", hash = "sha256:b52a6eb2d92fff0f660e6aade958cb4373fbfcbafa0488f7ae88c4e6ce3f47f6"},
-    {file = "opencv_python_headless-4.8.0.74-cp37-abi3-win_amd64.whl", hash = "sha256:19d30269ae710f67e0ae549361a68b755e28599a572d5f72d0bbb177ab438a28"},
+    {file = "opencv-python-headless-4.8.1.78.tar.gz", hash = "sha256:bc7197b42352f6f865c302a49140b889ec7cd957dd697e2d7fc016ad0d3f28f1"},
+    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:f3a33f644249f9ce1c913eac580e4b3ef4ce7cab0a71900274708959c2feb5e3"},
+    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:2c7d45721df9801c4dcd34683a15caa0e30f38b185263fec04a6eb274bc720f0"},
+    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b6bd6e1132b6f5dcb3a5bfe30fc4d341a7bfb26134da349a06c9255288ded94"},
+    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58e70d2f0915fe23e02c6e405588276c9397844a47d38b9c87fac5f7f9ba2dcc"},
+    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-win32.whl", hash = "sha256:382f8c7a6a14f80091284eecedd52cee4812231ee0eff1118592197b538d9252"},
+    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-win_amd64.whl", hash = "sha256:0a0f1e9f836f7d5bad1dd164694944c8761711cbdf4b36ebbd4815a8ef731079"},
 ]

 [package.dependencies]
@@ -2206,65 +2259,65 @@ files = [

 [[package]]
 name = "pillow"
-version = "10.0.1"
+version = "10.1.0"
 description = "Python Imaging Library (Fork)"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "Pillow-10.0.1-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:8f06be50669087250f319b706decf69ca71fdecd829091a37cc89398ca4dc17a"},
-    {file = "Pillow-10.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:50bd5f1ebafe9362ad622072a1d2f5850ecfa44303531ff14353a4059113b12d"},
-    {file = "Pillow-10.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6a90167bcca1216606223a05e2cf991bb25b14695c518bc65639463d7db722d"},
-    {file = "Pillow-10.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f11c9102c56ffb9ca87134bd025a43d2aba3f1155f508eff88f694b33a9c6d19"},
-    {file = "Pillow-10.0.1-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:186f7e04248103482ea6354af6d5bcedb62941ee08f7f788a1c7707bc720c66f"},
-    {file = "Pillow-10.0.1-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:0462b1496505a3462d0f35dc1c4d7b54069747d65d00ef48e736acda2c8cbdff"},
-    {file = "Pillow-10.0.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:d889b53ae2f030f756e61a7bff13684dcd77e9af8b10c6048fb2c559d6ed6eaf"},
-    {file = "Pillow-10.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:552912dbca585b74d75279a7570dd29fa43b6d93594abb494ebb31ac19ace6bd"},
-    {file = "Pillow-10.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:787bb0169d2385a798888e1122c980c6eff26bf941a8ea79747d35d8f9210ca0"},
-    {file = "Pillow-10.0.1-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:fd2a5403a75b54661182b75ec6132437a181209b901446ee5724b589af8edef1"},
-    {file = "Pillow-10.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2d7e91b4379f7a76b31c2dda84ab9e20c6220488e50f7822e59dac36b0cd92b1"},
-    {file = "Pillow-10.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:19e9adb3f22d4c416e7cd79b01375b17159d6990003633ff1d8377e21b7f1b21"},
-    {file = "Pillow-10.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:93139acd8109edcdeffd85e3af8ae7d88b258b3a1e13a038f542b79b6d255c54"},
-    {file = "Pillow-10.0.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:92a23b0431941a33242b1f0ce6c88a952e09feeea9af4e8be48236a68ffe2205"},
-    {file = "Pillow-10.0.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:cbe68deb8580462ca0d9eb56a81912f59eb4542e1ef8f987405e35a0179f4ea2"},
-    {file = "Pillow-10.0.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:522ff4ac3aaf839242c6f4e5b406634bfea002469656ae8358644fc6c4856a3b"},
-    {file = "Pillow-10.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:84efb46e8d881bb06b35d1d541aa87f574b58e87f781cbba8d200daa835b42e1"},
-    {file = "Pillow-10.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:898f1d306298ff40dc1b9ca24824f0488f6f039bc0e25cfb549d3195ffa17088"},
-    {file = "Pillow-10.0.1-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:bcf1207e2f2385a576832af02702de104be71301c2696d0012b1b93fe34aaa5b"},
-    {file = "Pillow-10.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:5d6c9049c6274c1bb565021367431ad04481ebb54872edecfcd6088d27edd6ed"},
-    {file = "Pillow-10.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:28444cb6ad49726127d6b340217f0627abc8732f1194fd5352dec5e6a0105635"},
-    {file = "Pillow-10.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de596695a75496deb3b499c8c4f8e60376e0516e1a774e7bc046f0f48cd620ad"},
-    {file = "Pillow-10.0.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:2872f2d7846cf39b3dbff64bc1104cc48c76145854256451d33c5faa55c04d1a"},
-    {file = "Pillow-10.0.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:4ce90f8a24e1c15465048959f1e94309dfef93af272633e8f37361b824532e91"},
-    {file = "Pillow-10.0.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ee7810cf7c83fa227ba9125de6084e5e8b08c59038a7b2c9045ef4dde61663b4"},
-    {file = "Pillow-10.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:b1be1c872b9b5fcc229adeadbeb51422a9633abd847c0ff87dc4ef9bb184ae08"},
-    {file = "Pillow-10.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:98533fd7fa764e5f85eebe56c8e4094db912ccbe6fbf3a58778d543cadd0db08"},
-    {file = "Pillow-10.0.1-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:764d2c0daf9c4d40ad12fbc0abd5da3af7f8aa11daf87e4fa1b834000f4b6b0a"},
-    {file = "Pillow-10.0.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:fcb59711009b0168d6ee0bd8fb5eb259c4ab1717b2f538bbf36bacf207ef7a68"},
-    {file = "Pillow-10.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:697a06bdcedd473b35e50a7e7506b1d8ceb832dc238a336bd6f4f5aa91a4b500"},
-    {file = "Pillow-10.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9f665d1e6474af9f9da5e86c2a3a2d2d6204e04d5af9c06b9d42afa6ebde3f21"},
-    {file = "Pillow-10.0.1-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:2fa6dd2661838c66f1a5473f3b49ab610c98a128fc08afbe81b91a1f0bf8c51d"},
-    {file = "Pillow-10.0.1-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:3a04359f308ebee571a3127fdb1bd01f88ba6f6fb6d087f8dd2e0d9bff43f2a7"},
-    {file = "Pillow-10.0.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:723bd25051454cea9990203405fa6b74e043ea76d4968166dfd2569b0210886a"},
-    {file = "Pillow-10.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:71671503e3015da1b50bd18951e2f9daf5b6ffe36d16f1eb2c45711a301521a7"},
-    {file = "Pillow-10.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:44e7e4587392953e5e251190a964675f61e4dae88d1e6edbe9f36d6243547ff3"},
-    {file = "Pillow-10.0.1-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:3855447d98cced8670aaa63683808df905e956f00348732448b5a6df67ee5849"},
-    {file = "Pillow-10.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ed2d9c0704f2dc4fa980b99d565c0c9a543fe5101c25b3d60488b8ba80f0cce1"},
-    {file = "Pillow-10.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f5bb289bb835f9fe1a1e9300d011eef4d69661bb9b34d5e196e5e82c4cb09b37"},
-    {file = "Pillow-10.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a0d3e54ab1df9df51b914b2233cf779a5a10dfd1ce339d0421748232cea9876"},
-    {file = "Pillow-10.0.1-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:2cc6b86ece42a11f16f55fe8903595eff2b25e0358dec635d0a701ac9586588f"},
-    {file = "Pillow-10.0.1-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:ca26ba5767888c84bf5a0c1a32f069e8204ce8c21d00a49c90dabeba00ce0145"},
-    {file = "Pillow-10.0.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:f0b4b06da13275bc02adfeb82643c4a6385bd08d26f03068c2796f60d125f6f2"},
-    {file = "Pillow-10.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:bc2e3069569ea9dbe88d6b8ea38f439a6aad8f6e7a6283a38edf61ddefb3a9bf"},
-    {file = "Pillow-10.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:8b451d6ead6e3500b6ce5c7916a43d8d8d25ad74b9102a629baccc0808c54971"},
-    {file = "Pillow-10.0.1-pp310-pypy310_pp73-macosx_10_10_x86_64.whl", hash = "sha256:32bec7423cdf25c9038fef614a853c9d25c07590e1a870ed471f47fb80b244db"},
-    {file = "Pillow-10.0.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b7cf63d2c6928b51d35dfdbda6f2c1fddbe51a6bc4a9d4ee6ea0e11670dd981e"},
-    {file = "Pillow-10.0.1-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:f6d3d4c905e26354e8f9d82548475c46d8e0889538cb0657aa9c6f0872a37aa4"},
-    {file = "Pillow-10.0.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:847e8d1017c741c735d3cd1883fa7b03ded4f825a6e5fcb9378fd813edee995f"},
-    {file = "Pillow-10.0.1-pp39-pypy39_pp73-macosx_10_10_x86_64.whl", hash = "sha256:7f771e7219ff04b79e231d099c0a28ed83aa82af91fd5fa9fdb28f5b8d5addaf"},
-    {file = "Pillow-10.0.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:459307cacdd4138edee3875bbe22a2492519e060660eaf378ba3b405d1c66317"},
-    {file = "Pillow-10.0.1-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:b059ac2c4c7a97daafa7dc850b43b2d3667def858a4f112d1aa082e5c3d6cf7d"},
-    {file = "Pillow-10.0.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:d6caf3cd38449ec3cd8a68b375e0c6fe4b6fd04edb6c9766b55ef84a6e8ddf2d"},
-    {file = "Pillow-10.0.1.tar.gz", hash = "sha256:d72967b06be9300fed5cfbc8b5bafceec48bf7cdc7dab66b1d2549035287191d"},
+    {file = "Pillow-10.1.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:1ab05f3db77e98f93964697c8efc49c7954b08dd61cff526b7f2531a22410106"},
+    {file = "Pillow-10.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6932a7652464746fcb484f7fc3618e6503d2066d853f68a4bd97193a3996e273"},
+    {file = "Pillow-10.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5f63b5a68daedc54c7c3464508d8c12075e56dcfbd42f8c1bf40169061ae666"},
+    {file = "Pillow-10.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c0949b55eb607898e28eaccb525ab104b2d86542a85c74baf3a6dc24002edec2"},
+    {file = "Pillow-10.1.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:ae88931f93214777c7a3aa0a8f92a683f83ecde27f65a45f95f22d289a69e593"},
+    {file = "Pillow-10.1.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:b0eb01ca85b2361b09480784a7931fc648ed8b7836f01fb9241141b968feb1db"},
+    {file = "Pillow-10.1.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:d27b5997bdd2eb9fb199982bb7eb6164db0426904020dc38c10203187ae2ff2f"},
+    {file = "Pillow-10.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:7df5608bc38bd37ef585ae9c38c9cd46d7c81498f086915b0f97255ea60c2818"},
+    {file = "Pillow-10.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:41f67248d92a5e0a2076d3517d8d4b1e41a97e2df10eb8f93106c89107f38b57"},
+    {file = "Pillow-10.1.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:1fb29c07478e6c06a46b867e43b0bcdb241b44cc52be9bc25ce5944eed4648e7"},
+    {file = "Pillow-10.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2cdc65a46e74514ce742c2013cd4a2d12e8553e3a2563c64879f7c7e4d28bce7"},
+    {file = "Pillow-10.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50d08cd0a2ecd2a8657bd3d82c71efd5a58edb04d9308185d66c3a5a5bed9610"},
+    {file = "Pillow-10.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:062a1610e3bc258bff2328ec43f34244fcec972ee0717200cb1425214fe5b839"},
+    {file = "Pillow-10.1.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:61f1a9d247317fa08a308daaa8ee7b3f760ab1809ca2da14ecc88ae4257d6172"},
+    {file = "Pillow-10.1.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a646e48de237d860c36e0db37ecaecaa3619e6f3e9d5319e527ccbc8151df061"},
+    {file = "Pillow-10.1.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:47e5bf85b80abc03be7455c95b6d6e4896a62f6541c1f2ce77a7d2bb832af262"},
+    {file = "Pillow-10.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:a92386125e9ee90381c3369f57a2a50fa9e6aa8b1cf1d9c4b200d41a7dd8e992"},
+    {file = "Pillow-10.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:0f7c276c05a9767e877a0b4c5050c8bee6a6d960d7f0c11ebda6b99746068c2a"},
+    {file = "Pillow-10.1.0-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:a89b8312d51715b510a4fe9fc13686283f376cfd5abca8cd1c65e4c76e21081b"},
+    {file = "Pillow-10.1.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:00f438bb841382b15d7deb9a05cc946ee0f2c352653c7aa659e75e592f6fa17d"},
+    {file = "Pillow-10.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3d929a19f5469b3f4df33a3df2983db070ebb2088a1e145e18facbc28cae5b27"},
+    {file = "Pillow-10.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9a92109192b360634a4489c0c756364c0c3a2992906752165ecb50544c251312"},
+    {file = "Pillow-10.1.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:0248f86b3ea061e67817c47ecbe82c23f9dd5d5226200eb9090b3873d3ca32de"},
+    {file = "Pillow-10.1.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:9882a7451c680c12f232a422730f986a1fcd808da0fd428f08b671237237d651"},
+    {file = "Pillow-10.1.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:1c3ac5423c8c1da5928aa12c6e258921956757d976405e9467c5f39d1d577a4b"},
+    {file = "Pillow-10.1.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:806abdd8249ba3953c33742506fe414880bad78ac25cc9a9b1c6ae97bedd573f"},
+    {file = "Pillow-10.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:eaed6977fa73408b7b8a24e8b14e59e1668cfc0f4c40193ea7ced8e210adf996"},
+    {file = "Pillow-10.1.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:fe1e26e1ffc38be097f0ba1d0d07fcade2bcfd1d023cda5b29935ae8052bd793"},
+    {file = "Pillow-10.1.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7a7e3daa202beb61821c06d2517428e8e7c1aab08943e92ec9e5755c2fc9ba5e"},
+    {file = "Pillow-10.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:24fadc71218ad2b8ffe437b54876c9382b4a29e030a05a9879f615091f42ffc2"},
+    {file = "Pillow-10.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fa1d323703cfdac2036af05191b969b910d8f115cf53093125e4058f62012c9a"},
+    {file = "Pillow-10.1.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:912e3812a1dbbc834da2b32299b124b5ddcb664ed354916fd1ed6f193f0e2d01"},
+    {file = "Pillow-10.1.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:7dbaa3c7de82ef37e7708521be41db5565004258ca76945ad74a8e998c30af8d"},
+    {file = "Pillow-10.1.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:9d7bc666bd8c5a4225e7ac71f2f9d12466ec555e89092728ea0f5c0c2422ea80"},
+    {file = "Pillow-10.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:baada14941c83079bf84c037e2d8b7506ce201e92e3d2fa0d1303507a8538212"},
+    {file = "Pillow-10.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:2ef6721c97894a7aa77723740a09547197533146fba8355e86d6d9a4a1056b14"},
+    {file = "Pillow-10.1.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:0a026c188be3b443916179f5d04548092e253beb0c3e2ee0a4e2cdad72f66099"},
+    {file = "Pillow-10.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:04f6f6149f266a100374ca3cc368b67fb27c4af9f1cc8cb6306d849dcdf12616"},
+    {file = "Pillow-10.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb40c011447712d2e19cc261c82655f75f32cb724788df315ed992a4d65696bb"},
+    {file = "Pillow-10.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1a8413794b4ad9719346cd9306118450b7b00d9a15846451549314a58ac42219"},
+    {file = "Pillow-10.1.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:c9aeea7b63edb7884b031a35305629a7593272b54f429a9869a4f63a1bf04c34"},
+    {file = "Pillow-10.1.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:b4005fee46ed9be0b8fb42be0c20e79411533d1fd58edabebc0dd24626882cfd"},
+    {file = "Pillow-10.1.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:4d0152565c6aa6ebbfb1e5d8624140a440f2b99bf7afaafbdbf6430426497f28"},
+    {file = "Pillow-10.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:d921bc90b1defa55c9917ca6b6b71430e4286fc9e44c55ead78ca1a9f9eba5f2"},
+    {file = "Pillow-10.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:cfe96560c6ce2f4c07d6647af2d0f3c54cc33289894ebd88cfbb3bcd5391e256"},
+    {file = "Pillow-10.1.0-pp310-pypy310_pp73-macosx_10_10_x86_64.whl", hash = "sha256:937bdc5a7f5343d1c97dc98149a0be7eb9704e937fe3dc7140e229ae4fc572a7"},
+    {file = "Pillow-10.1.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b1c25762197144e211efb5f4e8ad656f36c8d214d390585d1d21281f46d556ba"},
+    {file = "Pillow-10.1.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:afc8eef765d948543a4775f00b7b8c079b3321d6b675dde0d02afa2ee23000b4"},
+    {file = "Pillow-10.1.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:883f216eac8712b83a63f41b76ddfb7b2afab1b74abbb413c5df6680f071a6b9"},
+    {file = "Pillow-10.1.0-pp39-pypy39_pp73-macosx_10_10_x86_64.whl", hash = "sha256:b920e4d028f6442bea9a75b7491c063f0b9a3972520731ed26c83e254302eb1e"},
+    {file = "Pillow-10.1.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1c41d960babf951e01a49c9746f92c5a7e0d939d1652d7ba30f6b3090f27e412"},
+    {file = "Pillow-10.1.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:1fafabe50a6977ac70dfe829b2d5735fd54e190ab55259ec8aea4aaea412fa0b"},
+    {file = "Pillow-10.1.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:3b834f4b16173e5b92ab6566f0473bfb09f939ba14b23b8da1f54fa63e4b623f"},
+    {file = "Pillow-10.1.0.tar.gz", hash = "sha256:e6bf8de6c36ed96c86ea3b6e1d5273c53f46ef518a062464cd7ef5dd2cf92e38"},
 ]

 [package.extras]
@@ -2328,13 +2381,13 @@ testing = ["pytest", "pytest-benchmark"]

 [[package]]
 name = "pre-commit"
-version = "3.3.3"
+version = "3.5.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pre_commit-3.3.3-py2.py3-none-any.whl", hash = "sha256:10badb65d6a38caff29703362271d7dca483d01da88f9d7e05d0b97171c136cb"},
-    {file = "pre_commit-3.3.3.tar.gz", hash = "sha256:a2256f489cd913d575c145132ae196fe335da32d91a8294b7afe6622335dd023"},
+    {file = "pre_commit-3.5.0-py2.py3-none-any.whl", hash = "sha256:841dc9aef25daba9a0238cd27984041fa0467b4199fc4852e27950664919f660"},
+    {file = "pre_commit-3.5.0.tar.gz", hash = "sha256:5804465c675b659b0862f07907f96295d490822a450c4c40e747d0b1c6ebcb32"},
 ]

 [package.dependencies]
@@ -2425,13 +2478,13 @@ tests = ["pytest"]

 [[package]]
 name = "pycodestyle"
-version = "2.10.0"
+version = "2.11.1"
 description = "Python style guide checker"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "pycodestyle-2.10.0-py2.py3-none-any.whl", hash = "sha256:8a4eaf0d0495c7395bdab3589ac2db602797d76207242c17d470186815706610"},
-    {file = "pycodestyle-2.10.0.tar.gz", hash = "sha256:347187bdb476329d98f695c213d7295a846d1152ff4fe9bacb8a9590b8ee7053"},
+    {file = "pycodestyle-2.11.1-py2.py3-none-any.whl", hash = "sha256:44fe31000b2d866f2e41841b18528a505fbd7fef9017b04eff4e2648a0fadc67"},
+    {file = "pycodestyle-2.11.1.tar.gz", hash = "sha256:41ba0e7afc9752dfb53ced5489e89f8186be00e599e712660695b7a75ff2663f"},
 ]

 [[package]]
@@ -2447,13 +2500,13 @@ files = [

 [[package]]
 name = "pyflakes"
-version = "3.0.1"
+version = "3.1.0"
 description = "passive checker of Python programs"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "pyflakes-3.0.1-py2.py3-none-any.whl", hash = "sha256:ec55bf7fe21fff7f1ad2f7da62363d749e2a470500eab1b555334b67aa1ef8cf"},
-    {file = "pyflakes-3.0.1.tar.gz", hash = "sha256:ec8b276a6b60bd80defed25add7e439881c19e64850afd9b346283d4165fd0fd"},
+    {file = "pyflakes-3.1.0-py2.py3-none-any.whl", hash = "sha256:4132f6d49cb4dae6819e5379898f2b8cce3c5f23994194c24b77d5da2e36f774"},
+    {file = "pyflakes-3.1.0.tar.gz", hash = "sha256:a0aae034c444db0071aa077972ba4768d40c830d9539fd45bf4cd3f8f6992efc"},
 ]

 [[package]]
@@ -2518,13 +2571,13 @@ tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}

 [[package]]
 name = "pytest"
-version = "7.4.2"
+version = "7.4.3"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pytest-7.4.2-py3-none-any.whl", hash = "sha256:1d881c6124e08ff0a1bb75ba3ec0bfd8b5354a01c194ddd5a0a870a48d99b002"},
-    {file = "pytest-7.4.2.tar.gz", hash = "sha256:a766259cfab564a2ad52cb1aae1b881a75c3eb7e34ca3779697c23ed47c47069"},
+    {file = "pytest-7.4.3-py3-none-any.whl", hash = "sha256:0d009c083ea859a71b76adf7c1d502e4bc170b80a8ef002da5806527b9591fac"},
+    {file = "pytest-7.4.3.tar.gz", hash = "sha256:d989d136982de4e3b29dabcc838ad581c64e8ed52c11fbe86ddebd9da0818cd5"},
 ]

 [package.dependencies]
@@ -3116,28 +3169,28 @@ files = [

 [[package]]
 name = "ruff"
-version = "0.0.280"
-description = "An extremely fast Python linter, written in Rust."
+version = "0.1.6"
+description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.0.280-py3-none-macosx_10_7_x86_64.whl", hash = "sha256:48ed5aca381050a4e2f6d232db912d2e4e98e61648b513c350990c351125aaec"},
-    {file = "ruff-0.0.280-py3-none-macosx_10_9_x86_64.macosx_11_0_arm64.macosx_10_9_universal2.whl", hash = "sha256:ef6ee3e429fd29d6a5ceed295809e376e6ece5b0f13c7e703efaf3d3bcb30b96"},
-    {file = "ruff-0.0.280-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d878370f7e9463ac40c253724229314ff6ebe4508cdb96cb536e1af4d5a9cd4f"},
-    {file = "ruff-0.0.280-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:83e8f372fa5627eeda5b83b5a9632d2f9c88fc6d78cead7e2a1f6fb05728d137"},
-    {file = "ruff-0.0.280-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7008fc6ca1df18b21fa98bdcfc711dad5f94d0fc3c11791f65e460c48ef27c82"},
-    {file = "ruff-0.0.280-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:fe7118c1eae3fda17ceb409629c7f3b5a22dffa7caf1f6796776936dca1fe653"},
-    {file = "ruff-0.0.280-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:37359cd67d2af8e09110a546507c302cbea11c66a52d2a9b6d841d465f9962d4"},
-    {file = "ruff-0.0.280-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bd58af46b0221efb95966f1f0f7576df711cb53e50d2fdb0e83c2f33360116a4"},
-    {file = "ruff-0.0.280-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2e7c15828d09f90e97bea8feefcd2907e8c8ce3a1f959c99f9b4b3469679f33c"},
-    {file = "ruff-0.0.280-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:2dae8f2d9c44c5c49af01733c2f7956f808db682a4193180dedb29dd718d7bbe"},
-    {file = "ruff-0.0.280-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:5f972567163a20fb8c2d6afc60c2ea5ef8b68d69505760a8bd0377de8984b4f6"},
-    {file = "ruff-0.0.280-py3-none-musllinux_1_2_i686.whl", hash = "sha256:8ffa7347ad11643f29de100977c055e47c988cd6d9f5f5ff83027600b11b9189"},
-    {file = "ruff-0.0.280-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:7a37dab70114671d273f203268f6c3366c035fe0c8056614069e90a65e614bfc"},
-    {file = "ruff-0.0.280-py3-none-win32.whl", hash = "sha256:7784e3606352fcfb193f3cd22b2e2117c444cb879ef6609ec69deabd662b0763"},
-    {file = "ruff-0.0.280-py3-none-win_amd64.whl", hash = "sha256:4a7d52457b5dfcd3ab24b0b38eefaead8e2dca62b4fbf10de4cd0938cf20ce30"},
-    {file = "ruff-0.0.280-py3-none-win_arm64.whl", hash = "sha256:b7de5b8689575918e130e4384ed9f539ce91d067c0a332aedef6ca7188adac2d"},
-    {file = "ruff-0.0.280.tar.gz", hash = "sha256:581c43e4ac5e5a7117ad7da2120d960a4a99e68ec4021ec3cd47fe1cf78f8380"},
+    {file = "ruff-0.1.6-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:88b8cdf6abf98130991cbc9f6438f35f6e8d41a02622cc5ee130a02a0ed28703"},
+    {file = "ruff-0.1.6-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:5c549ed437680b6105a1299d2cd30e4964211606eeb48a0ff7a93ef70b902248"},
+    {file = "ruff-0.1.6-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1cf5f701062e294f2167e66d11b092bba7af6a057668ed618a9253e1e90cfd76"},
+    {file = "ruff-0.1.6-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:05991ee20d4ac4bb78385360c684e4b417edd971030ab12a4fbd075ff535050e"},
+    {file = "ruff-0.1.6-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:87455a0c1f739b3c069e2f4c43b66479a54dea0276dd5d4d67b091265f6fd1dc"},
+    {file = "ruff-0.1.6-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:683aa5bdda5a48cb8266fcde8eea2a6af4e5700a392c56ea5fb5f0d4bfdc0240"},
+    {file = "ruff-0.1.6-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:137852105586dcbf80c1717facb6781555c4e99f520c9c827bd414fac67ddfb6"},
+    {file = "ruff-0.1.6-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bd98138a98d48a1c36c394fd6b84cd943ac92a08278aa8ac8c0fdefcf7138f35"},
+    {file = "ruff-0.1.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a0cd909d25f227ac5c36d4e7e681577275fb74ba3b11d288aff7ec47e3ae745"},
+    {file = "ruff-0.1.6-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:e8fd1c62a47aa88a02707b5dd20c5ff20d035d634aa74826b42a1da77861b5ff"},
+    {file = "ruff-0.1.6-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:fd89b45d374935829134a082617954120d7a1470a9f0ec0e7f3ead983edc48cc"},
+    {file = "ruff-0.1.6-py3-none-musllinux_1_2_i686.whl", hash = "sha256:491262006e92f825b145cd1e52948073c56560243b55fb3b4ecb142f6f0e9543"},
+    {file = "ruff-0.1.6-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:ea284789861b8b5ca9d5443591a92a397ac183d4351882ab52f6296b4fdd5462"},
+    {file = "ruff-0.1.6-py3-none-win32.whl", hash = "sha256:1610e14750826dfc207ccbcdd7331b6bd285607d4181df9c1c6ae26646d6848a"},
+    {file = "ruff-0.1.6-py3-none-win_amd64.whl", hash = "sha256:4558b3e178145491e9bc3b2ee3c4b42f19d19384eaa5c59d10acf6e8f8b57e33"},
+    {file = "ruff-0.1.6-py3-none-win_arm64.whl", hash = "sha256:03910e81df0d8db0e30050725a5802441c2022ea3ae4fe0609b76081731accbc"},
+    {file = "ruff-0.1.6.tar.gz", hash = "sha256:1b09f29b16c6ead5ea6b097ef2764b42372aebe363722f1605ecbcd2b9207184"},
 ]

 [[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 = "abbd8c3b0bbed2349aa2ba8f4a822ab44d1092ea8b60118dc83fe11737e4ca12"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.17.0rc2"
+version = "0.17.0rc4"
 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,<0.1.7"
 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


@@ -28,7 +30,7 @@ class BoundingBoxAnnotator(BaseAnnotator):
                annotating detections.
            thickness (int): Thickness of the bounding box lines.
            color_lookup (str): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
+                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.thickness: int = thickness
@@ -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__(
@@ -106,7 +112,7 @@ class MaskAnnotator(BaseAnnotator):
                annotating detections.
            opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
            color_lookup (str): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
+                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.opacity = opacity
@@ -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`, `TRACK`.
+        """
+        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

@@ -186,7 +277,7 @@ class BoxMaskAnnotator(BaseAnnotator):
                annotating detections.
            opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
            color_lookup (str): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
+                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.color_lookup: ColorLookup = color_lookup
@@ -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__(
@@ -271,7 +366,7 @@ class HaloAnnotator(BaseAnnotator):
            kernel_size (int): The size of the average pooling kernel used for creating
                the halo.
            color_lookup (str): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
+                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.opacity = opacity
@@ -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
@@ -364,7 +459,7 @@ class EllipseAnnotator(BaseAnnotator):
            start_angle (int): Starting angle of the ellipse.
            end_angle (int): Ending angle of the ellipse.
            color_lookup (str): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
+                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.thickness: int = thickness
@@ -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
@@ -452,7 +547,7 @@ class BoxCornerAnnotator(BaseAnnotator):
            thickness (int): Thickness of the corner lines.
            corner_length (int): Length of each corner line.
            color_lookup (str): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
+                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.thickness: int = thickness
@@ -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
@@ -536,7 +631,7 @@ class CircleAnnotator(BaseAnnotator):
                annotating detections.
            thickness (int): Thickness of the circle line.
            color_lookup (str): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
+                Options are `INDEX`, `CLASS`, `TRACK`.
        """

        self.color: Union[Color, ColorPalette] = color
@@ -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
@@ -622,7 +717,7 @@ class DotAnnotator(BaseAnnotator):
            radius (int): Radius of the drawn dots.
            position (Position): The anchor position for placing the dot.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
+                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.radius: int = radius
@@ -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,
@@ -705,56 +800,53 @@ class LabelAnnotator:
            text_position (Position): Position of the text relative to the detection.
                Possible values are defined in the `Position` enum.
            color_lookup (str): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
+                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.text_color: Color = text_color
        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.
    """
@@ -930,7 +1027,7 @@ class TraceAnnotator:
                points. Defaults to `30`.
            thickness (int): The thickness of the trace lines. Defaults to `2`.
            color_lookup (str): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
+                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.position = position
@@ -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)
@@ -71,6 +71,21 @@ 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 an RGB tuple.
@@ -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)
@@ -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
@@ -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