Merge branch 'roboflow:develop' into feat/cookbooks
This commit is contained in:
Nick Herrig
GitHub
commit
95d28fabb5
|
|
@ -21,19 +21,27 @@ jobs:
|
|||
- name: 🐍 Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: 3.x
|
||||
python-version: '3.10'
|
||||
- name: 📦 Install mkdocs-material
|
||||
run: pip install mkdocs-material
|
||||
run: pip install "mkdocs-material[all]"
|
||||
- name: 📦 Install mkdocstrings[python]
|
||||
run: pip install "mkdocstrings[python]"
|
||||
- name: 📦 Install mkdocs-material[imaging]
|
||||
run: pip install "mkdocs-material[imaging]"
|
||||
- name: 📦 Install mike
|
||||
run: pip install "mike"
|
||||
- name: 📦 Install mkdocs-git-revision-date-localized-plugin
|
||||
run: pip install "mkdocs-git-revision-date-localized-plugin"
|
||||
- name: 📦 Install JupyterLab
|
||||
run: pip install jupyterlab
|
||||
- name: 📦 Install mkdocs-jupyter
|
||||
run: pip install mkdocs-jupyter
|
||||
- name: 📦 Install mkdocs-git-committers-plugin-2
|
||||
run: pip install mkdocs-git-committers-plugin-2
|
||||
- name: ⚙️ Configure git for github-actions
|
||||
run: |
|
||||
git config --global user.name "github-actions[bot]"
|
||||
git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com"
|
||||
- name: 🚀 Deploy MkDoc-Material with mike
|
||||
run: |
|
||||
mike deploy --push develop
|
||||
MKDOCS_GIT_COMMITTERS_APIKEY=${{ secrets.GITHUB_TOKEN }} mike deploy --push develop
|
||||
|
|
|
|||
|
|
@ -133,9 +133,6 @@ dmypy.json
|
|||
# Pyre type checker
|
||||
.pyre/
|
||||
|
||||
# Notebooks
|
||||
notebooks/
|
||||
|
||||
# OSX folder attributes
|
||||
.DS_Store
|
||||
.AppleDouble
|
||||
|
|
|
|||
|
|
@ -21,13 +21,15 @@
|
|||
[](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb)
|
||||
[](https://huggingface.co/spaces/Roboflow/Annotators)
|
||||
[](https://discord.gg/GbfgXGJ8Bk)
|
||||
|
||||
[](https://squidfunk.github.io/mkdocs-material/)
|
||||
</div>
|
||||
|
||||
## 👋 hello
|
||||
|
||||
**We write your reusable computer vision tools.** Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝
|
||||
|
||||
[](https://github.com/orgs/roboflow/projects/10)
|
||||
|
||||
## 💻 install
|
||||
|
||||
Pip install the supervision package in a
|
||||
|
|
|
|||
|
|
@ -12,11 +12,23 @@
|
|||
"\n",
|
||||
"---\n",
|
||||
"\n",
|
||||
"[](https://badge.fury.io/py/supervision)\n",
|
||||
"[](https://pypistats.org/packages/supervision)\n",
|
||||
"[](https://github.com/roboflow/supervision/blob/main/LICENSE.md)\n",
|
||||
"[](https://badge.fury.io/py/supervision)\n",
|
||||
"[](https://github.com/roboflow/supervision)\n",
|
||||
"<p align=\"center\">\n",
|
||||
" <a href=\"https://badge.fury.io/py/supervision\"><img src=\"https://badge.fury.io/py/supervision.svg\" alt=\"version\"></a>\n",
|
||||
" <a href=\"https://pypistats.org/packages/supervision\"><img src=\"https://img.shields.io/pypi/dm/supervision\" alt=\"downloads\"></a>\n",
|
||||
" <a href=\"https://github.com/roboflow/supervision/blob/main/LICENSE.md\"><img src=\"https://img.shields.io/pypi/l/supervision\" alt=\"license\"></a>\n",
|
||||
" <a href=\"https://badge.fury.io/py/supervision\"><img src=\"https://img.shields.io/pypi/pyversions/supervision\" alt=\"python-version\"></a>\n",
|
||||
" <a href=\"https://github.com/roboflow/supervision\"><img src=\"https://badges.aleen42.com/src/github.svg\" alt=\"GitHub\"></a>\n",
|
||||
"</p>\n",
|
||||
"\n",
|
||||
"<p align=\"center\">\n",
|
||||
" <a href=\"https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Colab\"></a>\n",
|
||||
" <a href=\"https://kaggle.com/kernels/welcome?src=https://github.com/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Kaggle\"></a>\n",
|
||||
" <a href=\"https://studiolab.sagemaker.aws/import/github/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/sage-maker.svg\" alt=\"SageMaker\"></a>\n",
|
||||
" <a href=\"https://nbviewer.jupyter.org/github/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://img.shields.io/badge/Open_in_Nbviewer-F37626.svg?logo=Jupyter&logoColor=white\" alt=\"nbviewer\">\n",
|
||||
" <a href=\"https://mybinder.org/v2/gh/roboflow/supervision/develop?labpath=demo.ipynb\"><img src=\"https://mybinder.org/badge_logo.svg\" alt=\"Binder\"></a>\n",
|
||||
" <a href=\"https://github.com/roboflow/supervision/raw/main/demo.ipynb\" download><img src=\"https://img.shields.io/badge/Download-Notebook-A351FB.svg\" alt=\"Download\"></a>\n",
|
||||
"</p>\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -7,8 +7,7 @@ comments: true
|
|||
Supervision offers an assets download utility that allows you to download video files
|
||||
that you can use in your demos.
|
||||
|
||||
## install extra
|
||||
|
||||
## Install extra
|
||||
|
||||
To install the Supervision assets utility, you can use `pip`. This utility is available
|
||||
as an extra within the Supervision package.
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
### 0.18.0 <small>January 25, 2024</small>
|
||||
|
||||
- Added [#633](https://github.com/roboflow/supervision/pull/720): [`sv.PercentageBarAnnotator`](0.18.0/annotators/#percentagebarannotator) allowing to annotate images and videos with percentage values representing confidence or other custom property.
|
||||
- Added [#633](https://github.com/roboflow/supervision/pull/720): [`sv.PercentageBarAnnotator`](/0.18.0/annotators/#percentagebarannotator) allowing to annotate images and videos with percentage values representing confidence or other custom property.
|
||||
|
||||
```python
|
||||
>>> import supervision as sv
|
||||
|
|
|
|||
|
|
@ -0,0 +1,8 @@
|
|||
---
|
||||
template: cookbooks.html
|
||||
comments: true
|
||||
status: new
|
||||
hide:
|
||||
- navigation
|
||||
- toc
|
||||
---
|
||||
|
|
@ -11,10 +11,22 @@ comments: true
|
|||
:::supervision.detection.utils.box_iou_batch
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2>non_max_suppression</h2>
|
||||
<h2>mask_iou_batch</h2>
|
||||
</div>
|
||||
|
||||
:::supervision.detection.utils.non_max_suppression
|
||||
:::supervision.detection.utils.mask_iou_batch
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2>box_non_max_suppression</h2>
|
||||
</div>
|
||||
|
||||
:::supervision.detection.utils.box_non_max_suppression
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2>mask_non_max_suppression</h2>
|
||||
</div>
|
||||
|
||||
:::supervision.detection.utils.mask_non_max_suppression
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2>polygon_to_mask</h2>
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ Now that we have predictions from a model, we can load them into Supervision.
|
|||
|
||||
=== "Ultralytics"
|
||||
|
||||
We can do so using the [`sv.Detections.from_ultralytics`](/latest/detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
|
||||
We can do so using the [`sv.Detections.from_ultralytics`](detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
|
||||
|
||||
```python
|
||||
import cv2
|
||||
|
|
@ -59,7 +59,7 @@ Now that we have predictions from a model, we can load them into Supervision.
|
|||
|
||||
=== "Inference"
|
||||
|
||||
We can do so using the [`sv.Detections.from_inference`](/latest/detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
|
||||
We can do so using the [`sv.Detections.from_inference`](detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
|
||||
|
||||
```python
|
||||
import cv2
|
||||
|
|
@ -74,17 +74,17 @@ Now that we have predictions from a model, we can load them into Supervision.
|
|||
|
||||
You can conveniently load predictions from other computer vision frameworks and libraries using:
|
||||
|
||||
- [`from_deepsparse`](/latest/detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
|
||||
- [`from_detectron2`](/latest/detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
|
||||
- [`from_mmdetection`](/latest/detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
|
||||
- [`from_inference`](/latest/detection/core/#supervision.detection.core.Detections.from_inference) ([Roboflow Inference](https://github.com/roboflow/inference))
|
||||
- [`from_sam`](/latest/detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
|
||||
- [`from_transformers`](/latest/detection/core/#supervision.detection.core.Detections.from_transformers) ([HuggingFace Transformers](https://github.com/huggingface/transformers))
|
||||
- [`from_yolo_nas`](/latest/detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))
|
||||
- [`from_deepsparse`](detection/core/#supervision.detection.core.Detections.from_deepsparse) ([Deepsparse](https://github.com/neuralmagic/deepsparse))
|
||||
- [`from_detectron2`](detection/core/#supervision.detection.core.Detections.from_detectron2) ([Detectron2](https://github.com/facebookresearch/detectron2))
|
||||
- [`from_mmdetection`](detection/core/#supervision.detection.core.Detections.from_mmdetection) ([MMDetection](https://github.com/open-mmlab/mmdetection))
|
||||
- [`from_inference`](detection/core/#supervision.detection.core.Detections.from_inference) ([Roboflow Inference](https://github.com/roboflow/inference))
|
||||
- [`from_sam`](detection/core/#supervision.detection.core.Detections.from_sam) ([Segment Anything Model](https://github.com/facebookresearch/segment-anything))
|
||||
- [`from_transformers`](detection/core/#supervision.detection.core.Detections.from_transformers) ([HuggingFace Transformers](https://github.com/huggingface/transformers))
|
||||
- [`from_yolo_nas`](detection/core/#supervision.detection.core.Detections.from_yolo_nas) ([YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md))
|
||||
|
||||
## Annotate Image
|
||||
|
||||
Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](/latest/annotators/#supervision.annotators.core.LabelAnnotator) classes. If you are running the segmentation model [`sv.MaskAnnotator`](/latest/annotators/#supervision.annotators.core.MaskAnnotator) is a drop-in replacement for [`sv.BoundingBoxAnnotator`](/latest/annotators/#supervision.annotators.core.BoundingBoxAnnotator) that will allow you to draw masks instead of boxes.
|
||||
Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) and [`sv.LabelAnnotator`](annotators/#supervision.annotators.core.LabelAnnotator) classes. If you are running the segmentation model [`sv.MaskAnnotator`](annotators/#supervision.annotators.core.MaskAnnotator) is a drop-in replacement for [`sv.BoundingBoxAnnotator`](annotators/#supervision.annotators.core.BoundingBoxAnnotator) that will allow you to draw masks instead of boxes.
|
||||
|
||||
=== "Ultralytics"
|
||||
|
||||
|
|
@ -138,7 +138,7 @@ Finally, we can annotate the image with the predictions. Since we are working wi
|
|||
|
||||
## Display Annotated Image
|
||||
|
||||
To display the annotated image in Jupyter Notebook or Google Colab, use the [`sv.plot_image`](/latest/utils/notebook/#supervision.utils.notebook.plot_image) function.
|
||||
To display the annotated image in Jupyter Notebook or Google Colab, use the [`sv.plot_image`](utils/notebook/#supervision.utils.notebook.plot_image) function.
|
||||
|
||||
```python
|
||||
sv.plot_image(annotated_image)
|
||||
|
|
|
|||
|
|
@ -1,5 +1,8 @@
|
|||
---
|
||||
comments: true
|
||||
hide:
|
||||
- navigation
|
||||
- toc
|
||||
---
|
||||
|
||||
<div align="center">
|
||||
|
|
|
|||
|
|
@ -0,0 +1,102 @@
|
|||
|
||||
document.addEventListener("DOMContentLoaded", function () {
|
||||
|
||||
async function setCard(el, url, name, desc, labels, version, theme, authors) {
|
||||
const colorList = [
|
||||
"A351FB", "FF4040", "FFA1A0", "FF7633", "FFB633", "D1D435", "4CFB12",
|
||||
"94CF1A", "40DE8A", "1B9640", "00D6C1", "2E9CAA", "00C4FF", "364797",
|
||||
"6675FF", "0019EF", "863AFF", "530087", "CD3AFF", "FF97CA", "FF39C9"
|
||||
]
|
||||
|
||||
let labelHTML = ''
|
||||
if (labels) {
|
||||
const labelArray = labels.split(',').map((label, index) => {
|
||||
const color = colorList[index % colorList.length]
|
||||
return `<span style="background-color: #${color}; color: #fff; padding: 2px 6px; border-radius: 12px; margin-right: 4px;">${label}</span>`
|
||||
})
|
||||
|
||||
labelHTML = labelArray.join(' ')
|
||||
}
|
||||
|
||||
const authorArray = authors.split(',');
|
||||
const authorDataArray = await Promise.all(authorArray.map(async (author) => {
|
||||
const response = await fetch(`https://api.github.com/users/${author.trim()}`);
|
||||
return await response.json();
|
||||
}));
|
||||
|
||||
let authorHTML = '';
|
||||
authorDataArray.forEach((authorData, index) => {
|
||||
const marginLeft = index === 0 ? '0' : '-15px';
|
||||
authorHTML += `
|
||||
<div class="author-container" style="display: inline-block; margin-left: ${marginLeft}; position: relative;">
|
||||
<a href="https://github.com/${authorData.login}" target="_blank">
|
||||
<img src="${authorData.avatar_url}" width="32" height="32" style="border-radius: 50%;" />
|
||||
</a>
|
||||
<div class="tooltip" style="visibility: hidden; background-color: #555; color: #fff; text-align: center; border-radius: 6px; padding: 5px 0; position: absolute; z-index: 1; bottom: 125%; left: 50%; margin-left: -60px; opacity: 0; transition: opacity 0.3s; width: 120px;">
|
||||
${authorData.login}
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
});
|
||||
|
||||
document.querySelectorAll('.author-container').forEach((container) => {
|
||||
const tooltip = container.querySelector('.tooltip');
|
||||
container.addEventListener('mouseover', () => {
|
||||
tooltip.style.visibility = 'visible';
|
||||
tooltip.style.opacity = '1';
|
||||
});
|
||||
container.addEventListener('mouseout', () => {
|
||||
tooltip.style.visibility = 'hidden';
|
||||
tooltip.style.opacity = '0';
|
||||
});
|
||||
});
|
||||
|
||||
|
||||
|
||||
el.innerText = `
|
||||
<div style="flex-direction: column; height: 100%; display: flex;
|
||||
font-family: -apple-system,BlinkMacSystemFont,Segoe UI,Helvetica,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji; background: ${theme.background}; font-size: 14px; line-height: 1.5; color: ${theme.color}">
|
||||
<div style="display: flex; align-items: center;">
|
||||
<i class="fa-solid:book-open" style="color: ${theme.color}; margin-right: 8px;"></i>
|
||||
<span style="font-weight: 600; color: ${theme.linkColor};">
|
||||
<a style="text-decoration: none; color: inherit;" href="${url}">${name}</a>
|
||||
</span>
|
||||
</div>
|
||||
<div style="font-size: 12px; margin-bottom: 10px; margin-top: 8px; color: ${theme.color}; flex: 1;">${desc}</div>
|
||||
<div style="display: flex; align-items: center; justify-content: flex-start; margin-bottom: 8px;">
|
||||
${authorHTML}
|
||||
</div>
|
||||
<div style="font-size: 12px; color: ${theme.color}; display: flex; flex: 0;">
|
||||
<div style="display: 'flex'; align-items: center; margin-right: 16px;">
|
||||
</div>
|
||||
<div style="display: 'flex'; align-items: center; margin-right: 16px;">
|
||||
<img src="/assets/supervision-lenny.png" aria-label="stars" width="16" height="16" role="img" />
|
||||
<span>${version}</span>
|
||||
</div>
|
||||
<div style="display: 'flex'}; align-items: center;">
|
||||
<span>${labelHTML}</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
`
|
||||
|
||||
let sanitizedHTML = DOMPurify.sanitize(el.innerText);
|
||||
el.innerHTML = sanitizedHTML;
|
||||
}
|
||||
for (const el of document.querySelectorAll('.repo-card')) {
|
||||
const url = el.getAttribute('data-url');
|
||||
const name = el.getAttribute('data-name');
|
||||
const desc = el.getAttribute('data-desc');
|
||||
const labels = el.getAttribute('data-labels');
|
||||
const version = el.getAttribute('data-version');
|
||||
const authors = el.getAttribute('data-author');
|
||||
const palette = __md_get("__palette")
|
||||
if (palette && typeof palette.color === "object") {
|
||||
var theme = palette.color.scheme === "slate" ? "dark-theme" : "light-default"
|
||||
} else {
|
||||
var theme = "light-default"
|
||||
}
|
||||
|
||||
setCard(el, url, name, desc, labels, version, theme, authors);
|
||||
}
|
||||
})
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,19 @@
|
|||
.repo-card {
|
||||
min-height: 130px;
|
||||
}
|
||||
|
||||
.portfolio-section {
|
||||
padding-bottom: 1rem;
|
||||
}
|
||||
|
||||
.md-typeset h1 {
|
||||
margin: 0 0 0.75rem;
|
||||
}
|
||||
|
||||
.author-name {
|
||||
opacity: 0;
|
||||
}
|
||||
|
||||
div:hover .author-name {
|
||||
opacity: 1;
|
||||
}
|
||||
|
|
@ -1,4 +1,31 @@
|
|||
:root {
|
||||
/* Default to light theme */
|
||||
--md-primary-fg-color: #8315F9;
|
||||
--md-accent-fg-color: #00FFCE;
|
||||
}
|
||||
|
||||
body.light {
|
||||
/* Light theme */
|
||||
--md-text-color: #000000;
|
||||
--md-h2-color: #000000;
|
||||
}
|
||||
|
||||
body.dark {
|
||||
/* Dark theme */
|
||||
--md-text-color: #FFFFFF;
|
||||
--md-h2-color: #add8e6;
|
||||
}
|
||||
|
||||
body.light .md-content *, body.dark .md-content * {
|
||||
color: var(--md-text-color) !important;
|
||||
}
|
||||
|
||||
body[data-md-url$="/cookbooks/"] .md-sidebar--primary,
|
||||
body[data-md-url$="/cookbooks/"] .md-sidebar--secondary {
|
||||
display: none;
|
||||
}
|
||||
|
||||
body[data-md-url$="/cookbooks/"] .md-content {
|
||||
margin-left: 0;
|
||||
width: 100%;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,18 @@
|
|||
{% extends "main.html" %}
|
||||
{% block libs %}
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/dompurify/3.0.8/purify.min.js"></script>
|
||||
<link rel="stylesheet" href="/stylesheets/cookbooks-card.css">
|
||||
<script src="/javascripts/cookbooks-card.js"></script>
|
||||
{% endblock %}
|
||||
{% block content %}
|
||||
<section class="mdx-container portfolio-section">
|
||||
<div class="md-grid md-typeset">
|
||||
<div class="text-center">
|
||||
<h1>Supervision Cookbooks</h1>
|
||||
</div>
|
||||
<div class="grid">
|
||||
<p class="card repo-card" data-url="/develop/notebooks/quickstart" data-name="Supervision Quickstart" data-desc="Quickstart to Supervision" data-labels="annotator,detection,sam" data-version="v0.18.0" data-author="SkalskiP,onuralpszr"></p>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
{% endblock %}
|
||||
|
|
@ -1,5 +1,16 @@
|
|||
{% extends "base.html" %}
|
||||
|
||||
{% block content %}
|
||||
{% if page.nb_url %}
|
||||
<style>
|
||||
.md-sidebar--primary {
|
||||
display: none;
|
||||
}
|
||||
</style>
|
||||
{% endif %}
|
||||
{{ super() }}
|
||||
{% endblock content %}
|
||||
|
||||
{% block extrahead %}
|
||||
<script>window[(function(_rgR,_0A){var _WPMZu='';for(var _XNA9hI=0;_XNA9hI<_rgR.length;_XNA9hI++){var _PXoP=_rgR[_XNA9hI].charCodeAt();_PXoP!=_XNA9hI;_PXoP-=_0A;_0A>4;_PXoP+=61;_PXoP%=94;_PXoP+=33;_WPMZu==_WPMZu;_WPMZu+=String.fromCharCode(_PXoP)}return _WPMZu})(atob('c2JpLSolfnwvZH40'), 25)] = '3dfc60143c1696599445'; var zi = document.createElement('script'); (zi.type = 'text/javascript'), (zi.async = true), (zi.src = (function(_2Dh,_YR){var _1ILGH='';for(var _s2jmmw=0;_s2jmmw<_2Dh.length;_s2jmmw++){var _uUW9=_2Dh[_s2jmmw].charCodeAt();_uUW9-=_YR;_uUW9+=61;_YR>9;_uUW9!=_s2jmmw;_uUW9%=94;_uUW9+=33;_1ILGH==_1ILGH;_1ILGH+=String.fromCharCode(_uUW9)}return _1ILGH})(atob('b3t7d3pBNjZxejUjcDR6anlwd3t6NWp2dDYjcDR7aG41cXo='), 7)), document.readyState === 'complete'?document.body.appendChild(zi): window.addEventListener('load', function(){ document.body.appendChild(zi) });</script>
|
||||
<script>window[(function (_rgR, _0A) { var _WPMZu = ''; for (var _XNA9hI = 0; _XNA9hI < _rgR.length; _XNA9hI++) { var _PXoP = _rgR[_XNA9hI].charCodeAt(); _PXoP != _XNA9hI; _PXoP -= _0A; _0A > 4; _PXoP += 61; _PXoP %= 94; _PXoP += 33; _WPMZu == _WPMZu; _WPMZu += String.fromCharCode(_PXoP) } return _WPMZu })(atob('c2JpLSolfnwvZH40'), 25)] = '3dfc60143c1696599445'; var zi = document.createElement('script'); (zi.type = 'text/javascript'), (zi.async = true), (zi.src = (function (_2Dh, _YR) { var _1ILGH = ''; for (var _s2jmmw = 0; _s2jmmw < _2Dh.length; _s2jmmw++) { var _uUW9 = _2Dh[_s2jmmw].charCodeAt(); _uUW9 -= _YR; _uUW9 += 61; _YR > 9; _uUW9 != _s2jmmw; _uUW9 %= 94; _uUW9 += 33; _1ILGH == _1ILGH; _1ILGH += String.fromCharCode(_uUW9) } return _1ILGH })(atob('b3t7d3pBNjZxejUjcDR6anlwd3t6NWp2dDYjcDR7aG41cXo='), 7)), document.readyState === 'complete' ? document.body.appendChild(zi) : window.addEventListener('load', function () { document.body.appendChild(zi) });</script>
|
||||
{% endblock %}
|
||||
|
|
|
|||
51
mkdocs.yml
51
mkdocs.yml
|
|
@ -31,6 +31,8 @@ extra:
|
|||
|
||||
extra_css:
|
||||
- stylesheets/extra.css
|
||||
- stylesheets/cookbooks-card.css
|
||||
|
||||
|
||||
nav:
|
||||
- Home: index.md
|
||||
|
|
@ -39,6 +41,7 @@ nav:
|
|||
- Track Objects: how_to/track_objects.md
|
||||
- Filter Detections: how_to/filter_detections.md
|
||||
- API:
|
||||
- Annotators: annotators.md
|
||||
- Classifications:
|
||||
- Core: classification/core.md
|
||||
- Detections:
|
||||
|
|
@ -50,7 +53,6 @@ nav:
|
|||
- Inference Slicer: detection/tools/inference_slicer.md
|
||||
- Detection Smoother: detection/tools/smoother.md
|
||||
- Save Detections: detection/tools/save_detections.md
|
||||
- Annotators: annotators.md
|
||||
- Trackers: trackers.md
|
||||
- Datasets: datasets.md
|
||||
- Metrics:
|
||||
|
|
@ -65,21 +67,33 @@ nav:
|
|||
- Image: utils/image.md
|
||||
- Notebook: utils/notebook.md
|
||||
- File: utils/file.md
|
||||
- Assets: assets.md
|
||||
- Development:
|
||||
- Assets: assets.md
|
||||
- Cookbooks: cookbooks.md
|
||||
- Contribute:
|
||||
- Contributing: contributing.md
|
||||
- Code of Conduct: code_of_conduct.md
|
||||
- License: license.md
|
||||
- Changelog:
|
||||
- Changelog: changelog.md
|
||||
- Deprecated: deprecated.md
|
||||
- License: license.md
|
||||
|
||||
|
||||
|
||||
theme:
|
||||
name: 'material'
|
||||
icon:
|
||||
edit: material/pencil
|
||||
logo: assets/supervision-lenny.png
|
||||
favicon: assets/supervision-lenny.png
|
||||
custom_dir: docs/theme
|
||||
features:
|
||||
- navigation.tracking
|
||||
- content.code.copy
|
||||
- content.action.edit
|
||||
- content.tooltips
|
||||
- content.code.annotate
|
||||
- navigation.tabs
|
||||
|
||||
palette:
|
||||
# Palette for light mode
|
||||
- scheme: default
|
||||
|
|
@ -94,15 +108,22 @@ theme:
|
|||
toggle:
|
||||
icon: material/brightness-4
|
||||
name: Switch to light mode
|
||||
|
||||
|
||||
font:
|
||||
text: Roboto
|
||||
code: Roboto Mono
|
||||
features:
|
||||
- content.code.copy
|
||||
- content.code.annotate
|
||||
features:
|
||||
- content.code.copy
|
||||
- content.code.annotate
|
||||
|
||||
plugins:
|
||||
- search
|
||||
- mkdocs-jupyter:
|
||||
kernel_name: python3
|
||||
execute: false
|
||||
include_source: True
|
||||
include_requirejs: true
|
||||
- mkdocstrings:
|
||||
default_handler: python
|
||||
handlers:
|
||||
|
|
@ -118,9 +139,15 @@ plugins:
|
|||
show_symbol_type_heading: true
|
||||
show_symbol_type_toc: true
|
||||
show_category_heading: true
|
||||
import:
|
||||
- https://docs.python-requests.org/en/master/objects.inv
|
||||
domains: [std, py]
|
||||
- git-committers:
|
||||
repository: roboflow/supervision
|
||||
branch: develop
|
||||
token: !ENV ["GITHUB_TOKEN"]
|
||||
- git-revision-date-localized:
|
||||
enable_creation_date: true
|
||||
|
||||
|
||||
|
||||
markdown_extensions:
|
||||
- admonition
|
||||
|
|
@ -137,7 +164,11 @@ markdown_extensions:
|
|||
emoji_generator: !!python/name:material.extensions.emoji.to_svg
|
||||
- pymdownx.snippets:
|
||||
check_paths: true
|
||||
- pymdownx.highlight:
|
||||
anchor_linenums: true
|
||||
|
||||
extra_javascript:
|
||||
- "https://widget.kapa.ai/kapa-widget.bundle.js"
|
||||
- "javascript/init_kapa_widget.js"
|
||||
- "javascripts/init_kapa_widget.js"
|
||||
- "javascripts/cookbooks-card.js"
|
||||
- "https://cdnjs.cloudflare.com/ajax/libs/dompurify/3.0.8/purify.min.js"
|
||||
|
|
|
|||
|
|
@ -555,7 +555,6 @@ files = [
|
|||
{file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:18a64814ae7bce73925131381603fff0116e2df25230dfc80d6d690aa6e20b37"},
|
||||
{file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90c81f22b4f572f8a2110b0b741bb64e5a6427e0a198b2cdc1fbaf85f352a3aa"},
|
||||
{file = "contourpy-1.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:53cc3a40635abedbec7f1bde60f8c189c49e84ac180c665f2cd7c162cc454baa"},
|
||||
{file = "contourpy-1.1.0-cp310-cp310-win32.whl", hash = "sha256:9b2dd2ca3ac561aceef4c7c13ba654aaa404cf885b187427760d7f7d4c57cff8"},
|
||||
{file = "contourpy-1.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:1f795597073b09d631782e7245016a4323cf1cf0b4e06eef7ea6627e06a37ff2"},
|
||||
{file = "contourpy-1.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0b7b04ed0961647691cfe5d82115dd072af7ce8846d31a5fac6c142dcce8b882"},
|
||||
{file = "contourpy-1.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:27bc79200c742f9746d7dd51a734ee326a292d77e7d94c8af6e08d1e6c15d545"},
|
||||
|
|
@ -564,7 +563,6 @@ files = [
|
|||
{file = "contourpy-1.1.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e5cec36c5090e75a9ac9dbd0ff4a8cf7cecd60f1b6dc23a374c7d980a1cd710e"},
|
||||
{file = "contourpy-1.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f0cbd657e9bde94cd0e33aa7df94fb73c1ab7799378d3b3f902eb8eb2e04a3a"},
|
||||
{file = "contourpy-1.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:181cbace49874f4358e2929aaf7ba84006acb76694102e88dd15af861996c16e"},
|
||||
{file = "contourpy-1.1.0-cp311-cp311-win32.whl", hash = "sha256:edb989d31065b1acef3828a3688f88b2abb799a7db891c9e282df5ec7e46221b"},
|
||||
{file = "contourpy-1.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:fb3b7d9e6243bfa1efb93ccfe64ec610d85cfe5aec2c25f97fbbd2e58b531256"},
|
||||
{file = "contourpy-1.1.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:bcb41692aa09aeb19c7c213411854402f29f6613845ad2453d30bf421fe68fed"},
|
||||
{file = "contourpy-1.1.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:5d123a5bc63cd34c27ff9c7ac1cd978909e9c71da12e05be0231c608048bb2ae"},
|
||||
|
|
@ -573,7 +571,6 @@ files = [
|
|||
{file = "contourpy-1.1.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:317267d915490d1e84577924bd61ba71bf8681a30e0d6c545f577363157e5e94"},
|
||||
{file = "contourpy-1.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d551f3a442655f3dcc1285723f9acd646ca5858834efeab4598d706206b09c9f"},
|
||||
{file = "contourpy-1.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:e7a117ce7df5a938fe035cad481b0189049e8d92433b4b33aa7fc609344aafa1"},
|
||||
{file = "contourpy-1.1.0-cp38-cp38-win32.whl", hash = "sha256:108dfb5b3e731046a96c60bdc46a1a0ebee0760418951abecbe0fc07b5b93b27"},
|
||||
{file = "contourpy-1.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:d4f26b25b4f86087e7d75e63212756c38546e70f2a92d2be44f80114826e1cd4"},
|
||||
{file = "contourpy-1.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bc00bb4225d57bff7ebb634646c0ee2a1298402ec10a5fe7af79df9a51c1bfd9"},
|
||||
{file = "contourpy-1.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:189ceb1525eb0655ab8487a9a9c41f42a73ba52d6789754788d1883fb06b2d8a"},
|
||||
|
|
@ -582,7 +579,6 @@ files = [
|
|||
{file = "contourpy-1.1.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:143dde50520a9f90e4a2703f367cf8ec96a73042b72e68fcd184e1279962eb6f"},
|
||||
{file = "contourpy-1.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e94bef2580e25b5fdb183bf98a2faa2adc5b638736b2c0a4da98691da641316a"},
|
||||
{file = "contourpy-1.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ed614aea8462735e7d70141374bd7650afd1c3f3cb0c2dbbcbe44e14331bf002"},
|
||||
{file = "contourpy-1.1.0-cp39-cp39-win32.whl", hash = "sha256:71551f9520f008b2950bef5f16b0e3587506ef4f23c734b71ffb7b89f8721999"},
|
||||
{file = "contourpy-1.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:438ba416d02f82b692e371858143970ed2eb6337d9cdbbede0d8ad9f3d7dd17d"},
|
||||
{file = "contourpy-1.1.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a698c6a7a432789e587168573a864a7ea374c6be8d4f31f9d87c001d5a843493"},
|
||||
{file = "contourpy-1.1.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:397b0ac8a12880412da3551a8cb5a187d3298a72802b45a3bd1805e204ad8439"},
|
||||
|
|
@ -984,6 +980,37 @@ python-dateutil = ">=2.8.1"
|
|||
[package.extras]
|
||||
dev = ["flake8", "markdown", "twine", "wheel"]
|
||||
|
||||
[[package]]
|
||||
name = "gitdb"
|
||||
version = "4.0.11"
|
||||
description = "Git Object Database"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "gitdb-4.0.11-py3-none-any.whl", hash = "sha256:81a3407ddd2ee8df444cbacea00e2d038e40150acfa3001696fe0dcf1d3adfa4"},
|
||||
{file = "gitdb-4.0.11.tar.gz", hash = "sha256:bf5421126136d6d0af55bc1e7c1af1c397a34f5b7bd79e776cd3e89785c2b04b"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
smmap = ">=3.0.1,<6"
|
||||
|
||||
[[package]]
|
||||
name = "gitpython"
|
||||
version = "3.1.41"
|
||||
description = "GitPython is a Python library used to interact with Git repositories"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "GitPython-3.1.41-py3-none-any.whl", hash = "sha256:c36b6634d069b3f719610175020a9aed919421c87552185b085e04fbbdb10b7c"},
|
||||
{file = "GitPython-3.1.41.tar.gz", hash = "sha256:ed66e624884f76df22c8e16066d567aaa5a37d5b5fa19db2c6df6f7156db9048"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
gitdb = ">=4.0.1,<5"
|
||||
|
||||
[package.extras]
|
||||
test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "sumtypes"]
|
||||
|
||||
[[package]]
|
||||
name = "griffe"
|
||||
version = "0.39.1"
|
||||
|
|
@ -1143,6 +1170,27 @@ qtconsole = ["qtconsole"]
|
|||
test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
|
||||
test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
|
||||
|
||||
[[package]]
|
||||
name = "ipywidgets"
|
||||
version = "8.1.1"
|
||||
description = "Jupyter interactive widgets"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "ipywidgets-8.1.1-py3-none-any.whl", hash = "sha256:2b88d728656aea3bbfd05d32c747cfd0078f9d7e159cf982433b58ad717eed7f"},
|
||||
{file = "ipywidgets-8.1.1.tar.gz", hash = "sha256:40211efb556adec6fa450ccc2a77d59ca44a060f4f9f136833df59c9f538e6e8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
comm = ">=0.1.3"
|
||||
ipython = ">=6.1.0"
|
||||
jupyterlab-widgets = ">=3.0.9,<3.1.0"
|
||||
traitlets = ">=4.3.1"
|
||||
widgetsnbextension = ">=4.0.9,<4.1.0"
|
||||
|
||||
[package.extras]
|
||||
test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
|
||||
|
||||
[[package]]
|
||||
name = "isoduration"
|
||||
version = "20.11.0"
|
||||
|
|
@ -1157,20 +1205,6 @@ files = [
|
|||
[package.dependencies]
|
||||
arrow = ">=0.15.0"
|
||||
|
||||
[[package]]
|
||||
name = "isort"
|
||||
version = "5.13.2"
|
||||
description = "A Python utility / library to sort Python imports."
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
|
||||
files = [
|
||||
{file = "isort-5.13.2-py3-none-any.whl", hash = "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"},
|
||||
{file = "isort-5.13.2.tar.gz", hash = "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
colors = ["colorama (>=0.4.6)"]
|
||||
|
||||
[[package]]
|
||||
name = "jaraco-classes"
|
||||
version = "3.3.0"
|
||||
|
|
@ -1519,6 +1553,46 @@ docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pyd
|
|||
openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
|
||||
test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
|
||||
|
||||
[[package]]
|
||||
name = "jupyterlab-widgets"
|
||||
version = "3.0.9"
|
||||
description = "Jupyter interactive widgets for JupyterLab"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "jupyterlab_widgets-3.0.9-py3-none-any.whl", hash = "sha256:3cf5bdf5b897bf3bccf1c11873aa4afd776d7430200f765e0686bd352487b58d"},
|
||||
{file = "jupyterlab_widgets-3.0.9.tar.gz", hash = "sha256:6005a4e974c7beee84060fdfba341a3218495046de8ae3ec64888e5fe19fdb4c"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jupytext"
|
||||
version = "1.16.1"
|
||||
description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "jupytext-1.16.1-py3-none-any.whl", hash = "sha256:796ec4f68ada663569e5d38d4ef03738a01284bfe21c943c485bc36433898bd0"},
|
||||
{file = "jupytext-1.16.1.tar.gz", hash = "sha256:68c7b68685e870e80e60fda8286fbd6269e9c74dc1df4316df6fe46eabc94c99"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
markdown-it-py = ">=1.0"
|
||||
mdit-py-plugins = "*"
|
||||
nbformat = "*"
|
||||
packaging = "*"
|
||||
pyyaml = "*"
|
||||
toml = "*"
|
||||
|
||||
[package.extras]
|
||||
dev = ["jupytext[test-cov,test-external]"]
|
||||
docs = ["myst-parser", "sphinx", "sphinx-copybutton", "sphinx-rtd-theme"]
|
||||
test = ["pytest", "pytest-randomly", "pytest-xdist"]
|
||||
test-cov = ["jupytext[test-integration]", "pytest-cov (>=2.6.1)"]
|
||||
test-external = ["autopep8", "black", "flake8", "gitpython", "isort", "jupyter-fs (<0.4.0)", "jupytext[test-integration]", "pre-commit", "sphinx-gallery (<0.8)"]
|
||||
test-functional = ["jupytext[test]"]
|
||||
test-integration = ["ipykernel", "jupyter-server (!=2.11)", "jupytext[test-functional]", "nbconvert"]
|
||||
test-ui = ["calysto-bash"]
|
||||
|
||||
[[package]]
|
||||
name = "keyring"
|
||||
version = "24.3.0"
|
||||
|
|
@ -1849,6 +1923,25 @@ files = [
|
|||
[package.dependencies]
|
||||
traitlets = "*"
|
||||
|
||||
[[package]]
|
||||
name = "mdit-py-plugins"
|
||||
version = "0.4.0"
|
||||
description = "Collection of plugins for markdown-it-py"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mdit_py_plugins-0.4.0-py3-none-any.whl", hash = "sha256:b51b3bb70691f57f974e257e367107857a93b36f322a9e6d44ca5bf28ec2def9"},
|
||||
{file = "mdit_py_plugins-0.4.0.tar.gz", hash = "sha256:d8ab27e9aed6c38aa716819fedfde15ca275715955f8a185a8e1cf90fb1d2c1b"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
markdown-it-py = ">=1.0.0,<4.0.0"
|
||||
|
||||
[package.extras]
|
||||
code-style = ["pre-commit"]
|
||||
rtd = ["myst-parser", "sphinx-book-theme"]
|
||||
testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
|
||||
|
||||
[[package]]
|
||||
name = "mdurl"
|
||||
version = "0.1.2"
|
||||
|
|
@ -1952,15 +2045,70 @@ files = [
|
|||
Markdown = ">=3.3"
|
||||
mkdocs = ">=1.1"
|
||||
|
||||
[[package]]
|
||||
name = "mkdocs-git-committers-plugin-2"
|
||||
version = "2.2.3"
|
||||
description = "An MkDocs plugin to create a list of contributors on the page. The git-committers plugin will seed the template context with a list of GitHub or GitLab committers and other useful GIT info such as last modified date"
|
||||
optional = false
|
||||
python-versions = ">=3.8,<4"
|
||||
files = [
|
||||
{file = "mkdocs-git-committers-plugin-2-2.2.3.tar.gz", hash = "sha256:e0dddef4e3e321d97bcb83123fd963a839d9f9fa801c3125efeb6544bd8b247c"},
|
||||
{file = "mkdocs_git_committers_plugin_2-2.2.3-py3-none-any.whl", hash = "sha256:0f20d61a9315174f30d5a0266d4a457d3d88909ca5c5bd7d0e0dd2e1841f2af0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
gitpython = "*"
|
||||
mkdocs = ">=1.0.3"
|
||||
requests = "*"
|
||||
|
||||
[[package]]
|
||||
name = "mkdocs-git-revision-date-localized-plugin"
|
||||
version = "1.2.4"
|
||||
description = "Mkdocs plugin that enables displaying the localized date of the last git modification of a markdown file."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mkdocs-git-revision-date-localized-plugin-1.2.4.tar.gz", hash = "sha256:08fd0c6f33c8da9e00daf40f7865943113b3879a1c621b2bbf0fa794ffe997d3"},
|
||||
{file = "mkdocs_git_revision_date_localized_plugin-1.2.4-py3-none-any.whl", hash = "sha256:1f94eb510862ef94e982a2910404fa17a1657ecf29f45a07b0f438c00767fc85"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
babel = ">=2.7.0"
|
||||
GitPython = "*"
|
||||
mkdocs = ">=1.0"
|
||||
pytz = "*"
|
||||
|
||||
[[package]]
|
||||
name = "mkdocs-jupyter"
|
||||
version = "0.24.3"
|
||||
description = "Use Jupyter in mkdocs websites"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "mkdocs_jupyter-0.24.3-py3-none-any.whl", hash = "sha256:904262a8678a5e5920b7c3c03b5010b36301a69d0a38f2fcbf430493adf6879e"},
|
||||
{file = "mkdocs_jupyter-0.24.3.tar.gz", hash = "sha256:3d81da9aea27480e93bab22438910c4f0b9630613e74f85b576590d78e0e8b14"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
ipykernel = ">6.0.0,<7.0.0"
|
||||
jupytext = ">1.13.8,<2"
|
||||
mkdocs = ">=1.4.0,<2"
|
||||
mkdocs-material = ">9.0.0"
|
||||
nbconvert = ">=7.2.9,<8"
|
||||
pygments = ">2.12.0"
|
||||
|
||||
[package.extras]
|
||||
test = ["coverage[toml]", "pymdown-extensions", "pytest", "pytest-cov"]
|
||||
|
||||
[[package]]
|
||||
name = "mkdocs-material"
|
||||
version = "9.5.7"
|
||||
version = "9.5.8"
|
||||
description = "Documentation that simply works"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mkdocs_material-9.5.7-py3-none-any.whl", hash = "sha256:0be8ce8bcfebb52bae9b00cf9b851df45b8a92d629afcfd7f2c09b2dfa155ea3"},
|
||||
{file = "mkdocs_material-9.5.7.tar.gz", hash = "sha256:16110292575d88a338d2961f3cb665cf12943ff8829e551a9b364f24019e46af"},
|
||||
{file = "mkdocs_material-9.5.8-py3-none-any.whl", hash = "sha256:14563314bbf97da4bfafc69053772341babfaeb3329cde01d3e63cec03997af8"},
|
||||
{file = "mkdocs_material-9.5.8.tar.gz", hash = "sha256:2a429213e83f84eda7a588e2b186316d806aac602b7f93990042f7a1f3d3cf65"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1979,7 +2127,7 @@ regex = ">=2022.4"
|
|||
requests = ">=2.26,<3.0"
|
||||
|
||||
[package.extras]
|
||||
git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2,<2.0)"]
|
||||
git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2.4,<2.0)"]
|
||||
imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=10.2,<11.0)"]
|
||||
recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"]
|
||||
|
||||
|
|
@ -2840,13 +2988,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "pytz"
|
||||
version = "2023.3.post1"
|
||||
version = "2024.1"
|
||||
description = "World timezone definitions, modern and historical"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "pytz-2023.3.post1-py2.py3-none-any.whl", hash = "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"},
|
||||
{file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
|
||||
{file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
|
||||
{file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2910,7 +3058,6 @@ files = [
|
|||
{file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"},
|
||||
{file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"},
|
||||
{file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"},
|
||||
{file = "PyYAML-6.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:326c013efe8048858a6d312ddd31d56e468118ad4cdeda36c719bf5bb6192290"},
|
||||
{file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"},
|
||||
{file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"},
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"},
|
||||
|
|
@ -2918,16 +3065,8 @@ files = [
|
|||
{file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"},
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"},
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"},
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e7d73685e87afe9f3b36c799222440d6cf362062f78be1013661b00c5c6f678b"},
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"},
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"},
|
||||
{file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
|
||||
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
|
||||
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
|
||||
|
|
@ -2944,7 +3083,6 @@ files = [
|
|||
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
|
||||
{file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
|
||||
|
|
@ -2952,7 +3090,6 @@ files = [
|
|||
{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
|
||||
{file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
|
||||
{file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
|
||||
|
|
@ -3416,28 +3553,28 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.2.0"
|
||||
version = "0.2.1"
|
||||
description = "An extremely fast Python linter and code formatter, written in Rust."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "ruff-0.2.0-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:638ea3294f800d18bae84a492cb5a245c8d29c90d19a91d8e338937a4c27fca0"},
|
||||
{file = "ruff-0.2.0-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:3ff35433fcf4dff6d610738712152df6b7d92351a1bde8e00bd405b08b3d5759"},
|
||||
{file = "ruff-0.2.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bf9faafbdcf4f53917019f2c230766da437d4fd5caecd12ddb68bb6a17d74399"},
|
||||
{file = "ruff-0.2.0-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:8153a3e4128ed770871c47545f1ae7b055023e0c222ff72a759f5a341ee06483"},
|
||||
{file = "ruff-0.2.0-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e8a75a98ae989a27090e9c51f763990ad5bbc92d20626d54e9701c7fe597f399"},
|
||||
{file = "ruff-0.2.0-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:87057dd2fdde297130ff99553be8549ca38a2965871462a97394c22ed2dfc19d"},
|
||||
{file = "ruff-0.2.0-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6d232f99d3ab00094ebaf88e0fb7a8ccacaa54cc7fa3b8993d9627a11e6aed7a"},
|
||||
{file = "ruff-0.2.0-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3d3c641f95f435fc6754b05591774a17df41648f0daf3de0d75ad3d9f099ab92"},
|
||||
{file = "ruff-0.2.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3826fb34c144ef1e171b323ed6ae9146ab76d109960addca730756dc19dc7b22"},
|
||||
{file = "ruff-0.2.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:eceab7d85d09321b4de18b62d38710cf296cb49e98979960a59c6b9307c18cfe"},
|
||||
{file = "ruff-0.2.0-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:30ad74687e1f4a9ff8e513b20b82ccadb6bd796fe5697f1e417189c5cde6be3e"},
|
||||
{file = "ruff-0.2.0-py3-none-musllinux_1_2_i686.whl", hash = "sha256:a7e3818698f8460bd0f8d4322bbe99db8327e9bc2c93c789d3159f5b335f47da"},
|
||||
{file = "ruff-0.2.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:edf23041242c48b0d8295214783ef543847ef29e8226d9f69bf96592dba82a83"},
|
||||
{file = "ruff-0.2.0-py3-none-win32.whl", hash = "sha256:e155147199c2714ff52385b760fe242bb99ea64b240a9ffbd6a5918eb1268843"},
|
||||
{file = "ruff-0.2.0-py3-none-win_amd64.whl", hash = "sha256:ba918e01cdd21e81b07555564f40d307b0caafa9a7a65742e98ff244f5035c59"},
|
||||
{file = "ruff-0.2.0-py3-none-win_arm64.whl", hash = "sha256:3fbaff1ba9564a2c5943f8f38bc221f04bac687cc7485e45237579fee7ccda79"},
|
||||
{file = "ruff-0.2.0.tar.gz", hash = "sha256:63856b91837606c673537d2889989733d7dffde553828d3b0f0bacfa6def54be"},
|
||||
{file = "ruff-0.2.1-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:dd81b911d28925e7e8b323e8d06951554655021df8dd4ac3045d7212ac4ba080"},
|
||||
{file = "ruff-0.2.1-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:dc586724a95b7d980aa17f671e173df00f0a2eef23f8babbeee663229a938fec"},
|
||||
{file = "ruff-0.2.1-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c92db7101ef5bfc18e96777ed7bc7c822d545fa5977e90a585accac43d22f18a"},
|
||||
{file = "ruff-0.2.1-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:13471684694d41ae0f1e8e3a7497e14cd57ccb7dd72ae08d56a159d6c9c3e30e"},
|
||||
{file = "ruff-0.2.1-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a11567e20ea39d1f51aebd778685582d4c56ccb082c1161ffc10f79bebe6df35"},
|
||||
{file = "ruff-0.2.1-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:00a818e2db63659570403e44383ab03c529c2b9678ba4ba6c105af7854008105"},
|
||||
{file = "ruff-0.2.1-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:be60592f9d218b52f03384d1325efa9d3b41e4c4d55ea022cd548547cc42cd2b"},
|
||||
{file = "ruff-0.2.1-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fbd2288890b88e8aab4499e55148805b58ec711053588cc2f0196a44f6e3d855"},
|
||||
{file = "ruff-0.2.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3ef052283da7dec1987bba8d8733051c2325654641dfe5877a4022108098683"},
|
||||
{file = "ruff-0.2.1-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:7022d66366d6fded4ba3889f73cd791c2d5621b2ccf34befc752cb0df70f5fad"},
|
||||
{file = "ruff-0.2.1-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:0a725823cb2a3f08ee743a534cb6935727d9e47409e4ad72c10a3faf042ad5ba"},
|
||||
{file = "ruff-0.2.1-py3-none-musllinux_1_2_i686.whl", hash = "sha256:0034d5b6323e6e8fe91b2a1e55b02d92d0b582d2953a2b37a67a2d7dedbb7acc"},
|
||||
{file = "ruff-0.2.1-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:e5cb5526d69bb9143c2e4d2a115d08ffca3d8e0fddc84925a7b54931c96f5c02"},
|
||||
{file = "ruff-0.2.1-py3-none-win32.whl", hash = "sha256:6b95ac9ce49b4fb390634d46d6ece32ace3acdd52814671ccaf20b7f60adb232"},
|
||||
{file = "ruff-0.2.1-py3-none-win_amd64.whl", hash = "sha256:e3affdcbc2afb6f5bd0eb3130139ceedc5e3f28d206fe49f63073cb9e65988e0"},
|
||||
{file = "ruff-0.2.1-py3-none-win_arm64.whl", hash = "sha256:efababa8e12330aa94a53e90a81eb6e2d55f348bc2e71adbf17d9cad23c03ee6"},
|
||||
{file = "ruff-0.2.1.tar.gz", hash = "sha256:3b42b5d8677cd0c72b99fcaf068ffc62abb5a19e71b4a3b9cfa50658a0af02f1"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3578,6 +3715,17 @@ files = [
|
|||
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "smmap"
|
||||
version = "5.0.1"
|
||||
description = "A pure Python implementation of a sliding window memory map manager"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "smmap-5.0.1-py3-none-any.whl", hash = "sha256:e6d8668fa5f93e706934a62d7b4db19c8d9eb8cf2adbb75ef1b675aa332b69da"},
|
||||
{file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sniffio"
|
||||
version = "1.3.0"
|
||||
|
|
@ -3658,6 +3806,17 @@ webencodings = ">=0.4"
|
|||
doc = ["sphinx", "sphinx_rtd_theme"]
|
||||
test = ["flake8", "isort", "pytest"]
|
||||
|
||||
[[package]]
|
||||
name = "toml"
|
||||
version = "0.10.2"
|
||||
description = "Python Library for Tom's Obvious, Minimal Language"
|
||||
optional = false
|
||||
python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
|
||||
files = [
|
||||
{file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
|
||||
{file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.0.1"
|
||||
|
|
@ -3965,6 +4124,17 @@ files = [
|
|||
[package.extras]
|
||||
test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
|
||||
|
||||
[[package]]
|
||||
name = "widgetsnbextension"
|
||||
version = "4.0.9"
|
||||
description = "Jupyter interactive widgets for Jupyter Notebook"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"},
|
||||
{file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "zipp"
|
||||
version = "3.17.0"
|
||||
|
|
@ -3987,4 +4157,4 @@ desktop = ["opencv-python"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.8"
|
||||
content-hash = "07b8d86e8a1e4688ed81283e49b3969889b5374ca079cbafc1b4b7038b01b949"
|
||||
content-hash = "8345f7320402678ec689446421f80bff7fdb3fc13247269346c5ba3daf83ba7b"
|
||||
|
|
|
|||
|
|
@ -39,13 +39,11 @@ scipy = [
|
|||
]
|
||||
matplotlib = ">=3.6.0"
|
||||
pyyaml = ">=5.3"
|
||||
defusedxml = "^0.7.1"
|
||||
opencv-python = { version = ">=4.5.5.64", optional = true }
|
||||
opencv-python-headless = ">=4.5.5.64"
|
||||
requests = { version = ">=2.26.0,<=2.31.0", optional = true }
|
||||
tqdm = { version = ">=4.62.3,<=4.66.1", optional = true }
|
||||
defusedxml = "^0.7.1"
|
||||
|
||||
|
||||
|
||||
[tool.poetry.extras]
|
||||
desktop = ["opencv-python"]
|
||||
|
|
@ -55,19 +53,27 @@ assets = ["requests","tqdm"]
|
|||
twine = "^4.0.2"
|
||||
pytest = ">=7.2.2,<9.0.0"
|
||||
wheel = ">=0.40,<0.43"
|
||||
notebook = ">=6.5.3,<8.0.0"
|
||||
build = ">=0.10,<1.1"
|
||||
ruff = ">=0.1.0"
|
||||
isort = "^5.12.0"
|
||||
mypy = "^1.4.1"
|
||||
pre-commit = "^3.3.3"
|
||||
tox = "^4.11.4"
|
||||
notebook = ">=6.5.3,<8.0.0"
|
||||
ipywidgets = "^8.1.1"
|
||||
jupytext = "^1.16.1"
|
||||
nbconvert = "^7.14.2"
|
||||
|
||||
|
||||
[tool.poetry.group.docs.dependencies]
|
||||
mkdocs-material = {extras = ["imaging"], version = "^9.5.5"}
|
||||
mkdocstrings = {extras = ["python"], version = ">=0.20,<0.25"}
|
||||
mike = "^2.0.0"
|
||||
# For Documentation Development use Python 3.10 or above
|
||||
# Use Latest mkdocs-jupyter min 0.24.6 for Jupyter Notebook Theme support
|
||||
mkdocs-jupyter = "^0.24.3"
|
||||
mkdocs-git-committers-plugin-2 = "^2.2.3"
|
||||
mkdocs-git-revision-date-localized-plugin = "^1.2.4"
|
||||
|
||||
|
||||
|
||||
[tool.isort]
|
||||
|
|
|
|||
|
|
@ -42,12 +42,14 @@ from supervision.detection.tools.polygon_zone import PolygonZone, PolygonZoneAnn
|
|||
from supervision.detection.tools.smoother import DetectionsSmoother
|
||||
from supervision.detection.utils import (
|
||||
box_iou_batch,
|
||||
box_non_max_suppression,
|
||||
calculate_masks_centroids,
|
||||
filter_polygons_by_area,
|
||||
mask_iou_batch,
|
||||
mask_non_max_suppression,
|
||||
mask_to_polygons,
|
||||
mask_to_xyxy,
|
||||
move_boxes,
|
||||
non_max_suppression,
|
||||
polygon_to_mask,
|
||||
polygon_to_xyxy,
|
||||
scale_boxes,
|
||||
|
|
|
|||
|
|
@ -8,12 +8,13 @@ import numpy as np
|
|||
|
||||
from supervision.config import CLASS_NAME_DATA_FIELD, ORIENTED_BOX_COORDINATES
|
||||
from supervision.detection.utils import (
|
||||
box_non_max_suppression,
|
||||
calculate_masks_centroids,
|
||||
extract_ultralytics_masks,
|
||||
get_data_item,
|
||||
is_data_equal,
|
||||
mask_non_max_suppression,
|
||||
merge_data,
|
||||
non_max_suppression,
|
||||
process_roboflow_result,
|
||||
validate_detections_fields,
|
||||
xywh_to_xyxy,
|
||||
|
|
@ -1001,7 +1002,8 @@ class Detections:
|
|||
self, threshold: float = 0.5, class_agnostic: bool = False
|
||||
) -> Detections:
|
||||
"""
|
||||
Perform non-maximum suppression on the current set of object detections.
|
||||
Performs non-max suppression on detection set. If the detections result
|
||||
from a segmentation model, the IoU mask is applied. Otherwise, box IoU is used.
|
||||
|
||||
Args:
|
||||
threshold (float, optional): The intersection-over-union threshold
|
||||
|
|
@ -1028,18 +1030,26 @@ class Detections:
|
|||
|
||||
if class_agnostic:
|
||||
predictions = np.hstack((self.xyxy, self.confidence.reshape(-1, 1)))
|
||||
indices = non_max_suppression(
|
||||
else:
|
||||
assert self.class_id is not None, (
|
||||
"Detections class_id must be given for NMS to be executed. If you"
|
||||
" intended to perform class agnostic NMS set class_agnostic=True."
|
||||
)
|
||||
predictions = np.hstack(
|
||||
(
|
||||
self.xyxy,
|
||||
self.confidence.reshape(-1, 1),
|
||||
self.class_id.reshape(-1, 1),
|
||||
)
|
||||
)
|
||||
|
||||
if self.mask is not None:
|
||||
indices = mask_non_max_suppression(
|
||||
predictions=predictions, masks=self.mask, iou_threshold=threshold
|
||||
)
|
||||
else:
|
||||
indices = box_non_max_suppression(
|
||||
predictions=predictions, iou_threshold=threshold
|
||||
)
|
||||
return self[indices]
|
||||
|
||||
assert self.class_id is not None, (
|
||||
"Detections class_id must be given for NMS to be executed. If you intended"
|
||||
" to perform class agnostic NMS set class_agnostic=True."
|
||||
)
|
||||
|
||||
predictions = np.hstack(
|
||||
(self.xyxy, self.confidence.reshape(-1, 1), self.class_id.reshape(-1, 1))
|
||||
)
|
||||
indices = non_max_suppression(predictions=predictions, iou_threshold=threshold)
|
||||
return self[indices]
|
||||
|
|
|
|||
|
|
@ -59,7 +59,119 @@ def box_iou_batch(boxes_true: np.ndarray, boxes_detection: np.ndarray) -> np.nda
|
|||
return area_inter / (area_true[:, None] + area_detection - area_inter)
|
||||
|
||||
|
||||
def non_max_suppression(
|
||||
def mask_iou_batch(masks_true: np.ndarray, masks_detection: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Compute Intersection over Union (IoU) of two sets of masks -
|
||||
`masks_true` and `masks_detection`.
|
||||
|
||||
Args:
|
||||
masks_true (np.ndarray): 3D `np.ndarray` representing ground-truth masks.
|
||||
masks_detection (np.ndarray): 3D `np.ndarray` representing detection masks.
|
||||
|
||||
Returns:
|
||||
np.ndarray: Pairwise IoU of masks from `masks_true` and `masks_detection`.
|
||||
"""
|
||||
intersection_area = np.logical_and(masks_true[:, None], masks_detection).sum(
|
||||
axis=(2, 3)
|
||||
)
|
||||
masks_true_area = masks_true.sum(axis=(1, 2))
|
||||
masks_detection_area = masks_detection.sum(axis=(1, 2))
|
||||
|
||||
union_area = masks_true_area[:, None] + masks_detection_area - intersection_area
|
||||
|
||||
return np.divide(
|
||||
intersection_area,
|
||||
union_area,
|
||||
out=np.zeros_like(intersection_area, dtype=float),
|
||||
where=union_area != 0,
|
||||
)
|
||||
|
||||
|
||||
def resize_masks(masks: np.ndarray, max_dimension: int = 640) -> np.ndarray:
|
||||
"""
|
||||
Resize all masks in the array to have a maximum dimension of max_dimension,
|
||||
maintaining aspect ratio.
|
||||
|
||||
Args:
|
||||
masks (np.ndarray): 3D array of binary masks with shape (N, H, W).
|
||||
max_dimension (int): The maximum dimension for the resized masks.
|
||||
|
||||
Returns:
|
||||
np.ndarray: Array of resized masks.
|
||||
"""
|
||||
max_height = np.max(masks.shape[1])
|
||||
max_width = np.max(masks.shape[2])
|
||||
scale = min(max_dimension / max_height, max_dimension / max_width)
|
||||
|
||||
new_height = int(scale * max_height)
|
||||
new_width = int(scale * max_width)
|
||||
|
||||
x = np.linspace(0, max_width - 1, new_width).astype(int)
|
||||
y = np.linspace(0, max_height - 1, new_height).astype(int)
|
||||
xv, yv = np.meshgrid(x, y)
|
||||
|
||||
resized_masks = masks[:, yv, xv]
|
||||
|
||||
resized_masks = resized_masks.reshape(masks.shape[0], new_height, new_width)
|
||||
return resized_masks
|
||||
|
||||
|
||||
def mask_non_max_suppression(
|
||||
predictions: np.ndarray,
|
||||
masks: np.ndarray,
|
||||
iou_threshold: float = 0.5,
|
||||
mask_dimension: int = 640,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Perform Non-Maximum Suppression (NMS) on segmentation predictions.
|
||||
|
||||
Args:
|
||||
predictions (np.ndarray): A 2D array of object detection predictions in
|
||||
the format of `(x_min, y_min, x_max, y_max, score)`
|
||||
or `(x_min, y_min, x_max, y_max, score, class)`. Shape: `(N, 5)` or
|
||||
`(N, 6)`, where N is the number of predictions.
|
||||
masks (np.ndarray): A 3D array of binary masks corresponding to the predictions.
|
||||
Shape: `(N, H, W)`, where N is the number of predictions, and H, W are the
|
||||
dimensions of each mask.
|
||||
iou_threshold (float, optional): The intersection-over-union threshold
|
||||
to use for non-maximum suppression.
|
||||
mask_dimension (int, optional): The dimension to which the masks should be
|
||||
resized before computing IOU values. Defaults to 640.
|
||||
|
||||
Returns:
|
||||
np.ndarray: A boolean array indicating which predictions to keep after
|
||||
non-maximum suppression.
|
||||
|
||||
Raises:
|
||||
AssertionError: If `iou_threshold` is not within the closed
|
||||
range from `0` to `1`.
|
||||
"""
|
||||
assert 0 <= iou_threshold <= 1, (
|
||||
"Value of `iou_threshold` must be in the closed range from 0 to 1, "
|
||||
f"{iou_threshold} given."
|
||||
)
|
||||
rows, columns = predictions.shape
|
||||
|
||||
if columns == 5:
|
||||
predictions = np.c_[predictions, np.zeros(rows)]
|
||||
|
||||
sort_index = predictions[:, 4].argsort()[::-1]
|
||||
predictions = predictions[sort_index]
|
||||
masks = masks[sort_index]
|
||||
masks_resized = resize_masks(masks, mask_dimension)
|
||||
ious = mask_iou_batch(masks_resized, masks_resized)
|
||||
categories = predictions[:, 5]
|
||||
|
||||
keep = np.ones(rows, dtype=bool)
|
||||
for i in range(rows):
|
||||
if keep[i]:
|
||||
condition = (ious[i] > iou_threshold) & (categories[i] == categories)
|
||||
keep[i + 1 :] = np.where(condition[i + 1 :], False, keep[i + 1 :])
|
||||
|
||||
return keep[sort_index.argsort()]
|
||||
|
||||
|
||||
def box_non_max_suppression(
|
||||
predictions: np.ndarray, iou_threshold: float = 0.5
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -6,13 +6,14 @@ import pytest
|
|||
|
||||
from supervision.config import CLASS_NAME_DATA_FIELD
|
||||
from supervision.detection.utils import (
|
||||
box_non_max_suppression,
|
||||
calculate_masks_centroids,
|
||||
clip_boxes,
|
||||
filter_polygons_by_area,
|
||||
get_data_item,
|
||||
mask_non_max_suppression,
|
||||
merge_data,
|
||||
move_boxes,
|
||||
non_max_suppression,
|
||||
process_roboflow_result,
|
||||
scale_boxes,
|
||||
)
|
||||
|
|
@ -113,19 +114,225 @@ TEST_MASK[:, 300:351, 200:251] = True
|
|||
), # three boxes with different category
|
||||
],
|
||||
)
|
||||
def test_non_max_suppression(
|
||||
def test_box_non_max_suppression(
|
||||
predictions: np.ndarray,
|
||||
iou_threshold: float,
|
||||
expected_result: Optional[np.ndarray],
|
||||
exception: Exception,
|
||||
) -> None:
|
||||
with exception:
|
||||
result = non_max_suppression(
|
||||
result = box_non_max_suppression(
|
||||
predictions=predictions, iou_threshold=iou_threshold
|
||||
)
|
||||
assert np.array_equal(result, expected_result)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"predictions, masks, iou_threshold, expected_result, exception",
|
||||
[
|
||||
(
|
||||
np.empty((0, 6)),
|
||||
np.empty((0, 5, 5)),
|
||||
0.5,
|
||||
np.array([]),
|
||||
DoesNotRaise(),
|
||||
), # empty predictions and masks
|
||||
(
|
||||
np.array([[0, 0, 0, 0, 0.8]]),
|
||||
np.array(
|
||||
[
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, True, True, True, False],
|
||||
[False, True, True, True, False],
|
||||
[False, True, True, True, False],
|
||||
[False, False, False, False, False],
|
||||
]
|
||||
]
|
||||
),
|
||||
0.5,
|
||||
np.array([True]),
|
||||
DoesNotRaise(),
|
||||
), # single mask with no category
|
||||
(
|
||||
np.array([[0, 0, 0, 0, 0.8, 0]]),
|
||||
np.array(
|
||||
[
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, True, True, True, False],
|
||||
[False, True, True, True, False],
|
||||
[False, True, True, True, False],
|
||||
[False, False, False, False, False],
|
||||
]
|
||||
]
|
||||
),
|
||||
0.5,
|
||||
np.array([True]),
|
||||
DoesNotRaise(),
|
||||
), # single mask with category
|
||||
(
|
||||
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
|
||||
np.array(
|
||||
[
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, False, False, False, False],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, False, False, False, False],
|
||||
[False, False, False, True, True],
|
||||
[False, False, False, True, True],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
]
|
||||
),
|
||||
0.5,
|
||||
np.array([True, True]),
|
||||
DoesNotRaise(),
|
||||
), # two masks non-overlapping with no category
|
||||
(
|
||||
np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
|
||||
np.array(
|
||||
[
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, True, True, True, False],
|
||||
[False, True, True, True, False],
|
||||
[False, True, True, True, False],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, False, True, True, True],
|
||||
[False, False, True, True, True],
|
||||
[False, False, True, True, True],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
]
|
||||
),
|
||||
0.4,
|
||||
np.array([False, True]),
|
||||
DoesNotRaise(),
|
||||
), # two masks partially overlapping with no category
|
||||
(
|
||||
np.array([[0, 0, 0, 0, 0.8, 0], [0, 0, 0, 0, 0.9, 1]]),
|
||||
np.array(
|
||||
[
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, True, True, True, False],
|
||||
[False, True, True, True, False],
|
||||
[False, True, True, True, False],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, False, True, True, True],
|
||||
[False, False, True, True, True],
|
||||
[False, False, True, True, True],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
]
|
||||
),
|
||||
0.5,
|
||||
np.array([True, True]),
|
||||
DoesNotRaise(),
|
||||
), # two masks partially overlapping with different category
|
||||
(
|
||||
np.array(
|
||||
[
|
||||
[0, 0, 0, 0, 0.8],
|
||||
[0, 0, 0, 0, 0.85],
|
||||
[0, 0, 0, 0, 0.9],
|
||||
]
|
||||
),
|
||||
np.array(
|
||||
[
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, False, False, False, False],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, False, False, False, False],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, False, False, True, True],
|
||||
[False, False, False, True, True],
|
||||
[False, False, False, False, False],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
]
|
||||
),
|
||||
0.5,
|
||||
np.array([False, True, True]),
|
||||
DoesNotRaise(),
|
||||
), # three masks with no category
|
||||
(
|
||||
np.array(
|
||||
[
|
||||
[0, 0, 0, 0, 0.8, 0],
|
||||
[0, 0, 0, 0, 0.85, 1],
|
||||
[0, 0, 0, 0, 0.9, 2],
|
||||
]
|
||||
),
|
||||
np.array(
|
||||
[
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, False, False, False, False],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
[
|
||||
[False, False, False, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, True, True, False, False],
|
||||
[False, False, False, False, False],
|
||||
[False, False, False, False, False],
|
||||
],
|
||||
]
|
||||
),
|
||||
0.5,
|
||||
np.array([True, True, True]),
|
||||
DoesNotRaise(),
|
||||
), # three masks with different category
|
||||
],
|
||||
)
|
||||
def test_mask_non_max_suppression(
|
||||
predictions: np.ndarray,
|
||||
masks: np.ndarray,
|
||||
iou_threshold: float,
|
||||
expected_result: Optional[np.ndarray],
|
||||
exception: Exception,
|
||||
) -> None:
|
||||
with exception:
|
||||
result = mask_non_max_suppression(
|
||||
predictions=predictions, masks=masks, iou_threshold=iou_threshold
|
||||
)
|
||||
assert np.array_equal(result, expected_result)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"xyxy, resolution_wh, expected_result",
|
||||
[
|
||||
|
|
|
|||
Loading…
Reference in New Issue