bump version from 0.19.0rc2 to 0.19.0rc3

Merge pull request #861 from NickHerrig/feat/cookbooks
Feat/cookbooks
2024-02-09 18:09:56 +01:00 · 2024-02-09 17:37:50 +01:00 · 2024-02-09 17:36:13 +01:00 · 2024-02-09 10:34:40 -05:00 · 2024-02-09 14:30:00 +00:00 · 2024-02-09 09:29:49 -05:00
29 changed files with 2844 additions and 107 deletions
@@ -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 @@
 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb)
 [![Gradio](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Roboflow/Annotators)
 [![Discord](https://img.shields.io/discord/1159501506232451173)](https://discord.gg/GbfgXGJ8Bk)
-
+[![Built with Material for MkDocs](https://img.shields.io/badge/Material_for_MkDocs-526CFE?logo=MaterialForMkDocs&logoColor=white)](https://squidfunk.github.io/mkdocs-material/)
 </div>

 ## 👋 hello

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

+[![supervision-hackfest](https://github.com/roboflow/supervision/assets/26109316/c05cc954-b9a6-4ed5-9a52-d0b4b619ff65)](https://github.com/orgs/roboflow/projects/10)
+
 ## 💻 install

 Pip install the supervision package in a
@@ -0,0 +1,19 @@
+# Supervision Cookbooks
+
+## About
+
+The Roboflow Cookbook is an open-source collection of examples and guides for building with the [Roboflow Ecosystem](https://roboflow.com/).
+
+Cookbooks are meant to be common and small scoped computer vision tasks that can allow a user to quickly hit the ground running. Learning is the main goal and purpose.
+
+Some examples require a Roboflow API key. You can [create a free account here](https://app.roboflow.com/login).
+
+## Other Resources
+
+Other than Cookbooks, Roboflow offers other learning resources including:
+
+- A set of open source tools found on [Github](https://github.com/roboflow).
+- A [blog](https://blog.roboflow.com/) detailing interesting tutorials, computer vision news, and product updates.
+- A [Youtube channel](https://www.youtube.com/roboflow) with end to end commputer vision projects.
+- A [forum](https://discuss.roboflow.com/) for sharing feedback, discussions, and other questions.
+- The worlds largest [open source collection](https://universe.roboflow.com/) of images, datasets, and fine-tuned models.
@@ -12,11 +12,23 @@
    "\n",
    "---\n",
    "\n",
-    "[![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)\n",
-    "[![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)\n",
-    "[![license](https://img.shields.io/pypi/l/supervision)](https://github.com/roboflow/supervision/blob/main/LICENSE.md)\n",
-    "[![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision)\n",
-    "[![GitHub](https://badges.aleen42.com/src/github.svg)](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.
@@ -16,7 +15,7 @@ as an extra within the Supervision package.
 !!! example "pip install"

    ```bash
-    pip install supervision[assets]
+    pip install "supervision[assets]"
    ```

 <div class="md-typeset">
@@ -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 +1,142 @@
+document.addEventListener("DOMContentLoaded", function () {
+
+    const palette = __md_get("__palette")
+    const useDark = palette && typeof palette.color === "object" && palette.color.scheme === "slate"
+    const theme = useDark ? "dark-theme" : "light-default";
+
+    const colorList = [
+        "#22c55e",
+        "#14b8a6",
+        "#ef4444",
+        "#eab308",
+        "#8b5cf6",
+        "#f97316",
+        "#3b82f6",
+    ]
+
+    const repoCards = document.querySelectorAll(".repo-card");
+    const labelsAll = Array
+        .from(repoCards)
+        .flatMap((element) => element.getAttribute('data-labels').split(','))
+        .map(label => label.trim())
+        .filter(label => label !== '');
+    const uniqueLabels = [...new Set(labelsAll)];
+
+    const labelToColor = uniqueLabels.reduce((map, label, index) => {
+        map[label] = colorList[index % colorList.length];
+        return map;
+    }, {});
+
+
+  async function renderCard(element, elementIndex) {
+    const url = element.getAttribute('data-url');
+    const name = element.getAttribute('data-name');
+    const labels = element.getAttribute('data-labels');
+    const version = element.getAttribute('data-version');
+    const authors = element.getAttribute('data-author');
+
+    const labelHTML = labels ? labels.split(',').filter(label => label !== '').map((label, index) => {
+        const color = labelToColor[label.trim()];
+        return `
+            <span
+                class="label non-selectable-text"
+                style="background-color: ${color}"
+            >
+                ${label.trim()}
+            </span>
+        `;
+    }).join(' ') : '';
+
+    const authorArray = authors.split(',');
+    const authorDataArray = await Promise.all(authorArray.map(async (author) => {
+      const response = await fetch(`https://api.github.com/users/${author.trim()}`);
+      return await response.json();
+    }));
+
+    let authorAvatarsHTML = authorDataArray.map((authorData, index) => {
+        const marginLeft = index === 0 ? '0' : '-10px';
+        const zIndex = 100 - index;
+        return `
+            <div
+                class="author-container"
+                data-login="${authorData.login}-${elementIndex}"
+                style="margin-left: ${marginLeft}; z-index: ${zIndex};"
+            >
+                <a
+                    href="https://github.com/${authorData.login}"
+                    target="_blank"
+                    style="line-height: 0;"
+                >
+                    <img
+                        class="author-avatar"
+                        src="${authorData.avatar_url}"
+                        alt="${authorData.login}'s avatar"
+                    >
+                </a>
+            </div>
+        `;
+    }).join('');
+
+    let authorNamesHTML = authorDataArray.map(
+        authorData => `
+            <span
+                class="author-name"
+                data-login="${authorData.login}-${elementIndex}"
+                style="color: ${theme.color}"
+            >
+            <a href="https://github.com/${authorData.login}" target="_blank">
+                ${authorData.login}
+            </a>
+        </span>`
+    ).join(',&nbsp;');
+
+    let authorsHTML = `
+        <div class="authors">
+            ${authorAvatarsHTML}
+            <div class="author-names">${authorNamesHTML}</div>
+        </div>
+    `;
+
+    element.innerText = `
+      <a style="text-decoration: none; color: inherit;" href="${url}">
+        <div style="flex-direction: column; height: 100%; display: flex;
+        font-family: -apple-system,BlinkMacSystemFont,Segoe UI,Helvetica,Arial,sans-serif,Apple Color Emoji,Segoe UI Emoji; background: ${theme.background}; font-size: 14px; line-height: 1.5; color: ${theme.color}">
+          <div style="display: flex; align-items: center;">
+            <span style="font-weight: 700; font-size: 1rem; color: ${theme.linkColor};">
+              ${name}
+            </span>
+          </div>
+          ${authorsHTML}
+          <div style="font-size: 12px; color: ${theme.color}; display: flex; flex: 0; justify-content: space-between">
+            <div style="display: flex; align-items: center;">
+              <img src="/assets/supervision-lenny.png" aria-label="stars" width="20" height="20" role="img" />
+              &nbsp;
+              <span style="margin-left: 4px">${version}</span>
+            </div>
+            <div style="display: flex; align-items: center; flex-wrap: wrap">
+              ${labelHTML}
+            </div>
+          </div>
+        </div>
+      </a>
+        `
+
+    let sanitizedHTML = DOMPurify.sanitize(element.innerText);
+    element.innerHTML = sanitizedHTML;
+
+    document.querySelectorAll('.author-name').forEach(element => {
+        element.addEventListener('mouseenter', function() {
+            const login = this.getAttribute('data-login');
+            document.querySelector(`.author-container[data-login="${login}"]`).classList.add('hover');
+        });
+
+        element.addEventListener('mouseleave', function() {
+            const login = this.getAttribute('data-login');
+            document.querySelector(`.author-container[data-login="${login}"]`).classList.remove('hover');
+        });
+    });
+  }
+    repoCards.forEach((element, index) => {
+        renderCard(element, index);
+    });
+})
@@ -0,0 +1,208 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# How to Display Bounding Boxes on Objects in a Video.\n",
+                "\n",
+                "---\n",
+                "\n",
+                "<p align=\"center\">\n",
+                "  <a href=\"https://colab.research.google.com/github/roboflow/supervision/blob/develop/docs/notebooks/bounding-boxes.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Colab\"></a>\n",
+                "</p>\n",
+                "\n",
+                "\n",
+                "One of the most common requirements of computer vision applications is detecting objects in images and displaying bounding boxes around those objects. In this cookbook we'll walk through the steps on how to utilize the open source Roboflow ecosystem to accomplish this task on a video. Let's dive in! \n",
+                "\n",
+                "## Installing Dependencies \n",
+                "\n",
+                "In this cookbook we'll be utilizing the open source packages [Inference](https://inference.roboflow.com/) and [Supervision](https://supervision.roboflow.com/latest/) to accomplish our goals. Let's get those installed in our notebook with pip."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "vscode": {
+                    "languageId": "shellscript"
+                }
+            },
+            "outputs": [],
+            "source": [
+                "\n",
+                "!pip install -q inference \"supervision[assets]\""
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Download a Video Asset\n",
+                "\n",
+                "First, let's download a video that we can detect objects in. Supervision comes with a great utility called Assets to help us hit the ground running. Wehn we run this script, the video is saved in our local directory and can be accessed with the variable `path_to_video`."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "\n",
+                "# Download a supervision video asset \n",
+                "path_to_video = download_assets(VideoAssets.PEOPLE_WALKING)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Detecting Objects\n",
+                "\n",
+                "For this example, the objects in the video that we'd like to detect are people. In order to display bounding boxes around the people in the video, we first need a way to detect them. We'll be using the open source [Inference](https://github.com/roboflow/inference) package for this task. Inference allows us to quickly use thousands of models, including fine tuned models from [Roboflow Universe](https://universe.roboflow.com/), with a few lines of code.  We'll also utilize a few utilities for working with our video data from the [Supervision](https://github.com/roboflow/supervision) package."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import supervision as sv\n",
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "from inference.models.utils import get_roboflow_model\n",
+                "\n",
+                "\n",
+                "if __name__ == \"__main__\":\n",
+                "    # Download the video asset from Supervision assets.\n",
+                "    PATH_TO_VIDEO = download_assets(VideoAssets.PEOPLE_WALKING)\n",
+                "\n",
+                "    # Load a yolov8 nano model from roboflow.\n",
+                "    model = get_roboflow_model(\"yolov8n-640\")\n",
+                "\n",
+                "    # Create a frame generator and video info object from supervision utilities.\n",
+                "    frame_generator = sv.get_video_frames_generator(PATH_TO_VIDEO)\n",
+                "\n",
+                "    # Yield a single frame from the generator.\n",
+                "    frame = next(frame_generator)\n",
+                "\n",
+                "    # Run inference on our frame.\n",
+                "    result = model.infer(frame)[0]\n",
+                "\n",
+                "    # Parse result into detections data model.\n",
+                "    detections = sv.Detections.from_inference(result)\n",
+                "\n",
+                "    # Display the detections data model.\n",
+                "    print(detections)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "First, we load our model using the method `get_roboflow_model()`. Notice how we pass in a `model_id`? We're using an [alias](https://inference.roboflow.com/reference_pages/model_aliases/) here. This is where we can pass in other models from Roboflow Universe like this [rock, paper, scissors](https://universe.roboflow.com/roboflow-58fyf/rock-paper-scissors-sxsw) model utilizing our roboflow api key. \n",
+                "\n",
+                "```\n",
+                "model = get_roboflow_mode(\n",
+                "    model_id=\"rock-paper-scissors-sxsw/11\", \n",
+                "    api_key=\"roboflow_private_api_key\"\n",
+                ")\n",
+                "```\n",
+                "\n",
+                "If you don't have an api key, you can [create an free Roboflow account](https://app.roboflow.com/login). This model wouldn't be much help with detecting people, but it's a nice exercise to see how our code becomes model agnostic!\n",
+                "\n",
+                "We then create a `frame_generator` object and yeild a single frame for inference using `next()`. We pass our frame to `model.infer()` to run inference, then pass that data into a little helpfer function called `sv.Detections.from_inference()` to parse it. Lastly we print our detections to show we are in fact detecting a few people in the frame! \n",
+                "\n",
+                "## Saving Bounding Boxes to the Video\n",
+                "\n",
+                "Now comes the fun part. Let's wrap up our code by utilizing `Annotators` and a `VideoSink` to draw bounding boxes and save the resulting video respectively. Take a peak at the final code example below. This might can take up to a minute to run, since we're processing a full video. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import supervision as sv\n",
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "from inference.models.utils import get_roboflow_model\n",
+                "\n",
+                "\n",
+                "if __name__ == \"__main__\":\n",
+                "    # Download the video asset from Supervision assets.\n",
+                "    PATH_TO_VIDEO = download_assets(VideoAssets.PEOPLE_WALKING)\n",
+                "\n",
+                "    # Load a yolov8 nano model from roboflow.\n",
+                "    model = get_roboflow_model(\"yolov8n-640\")\n",
+                "\n",
+                "    # Initalize the bounding box frame annotator.\n",
+                "    box_annotator = sv.BoundingBoxAnnotator()\n",
+                "\n",
+                "    # Create a frame generator  object from video path.\n",
+                "    frame_generator = sv.get_video_frames_generator(PATH_TO_VIDEO)\n",
+                "\n",
+                "    # Create a video info object from video path.\n",
+                "    video_info = sv.VideoInfo.from_video_path(PATH_TO_VIDEO)\n",
+                "\n",
+                "    # Use a VideoSink context manager for saving frames of a video.\n",
+                "    with sv.VideoSink(target_path=\"output.mp4\", video_info=video_info) as sink:\n",
+                "\n",
+                "        # Iterate through frames yielded from the frame_generator.\n",
+                "        for frame in frame_generator:\n",
+                "\n",
+                "            # Run inference on our frame.\n",
+                "            result = model.infer(frame)[0]\n",
+                "\n",
+                "            # Parse the result into the detections data model.\n",
+                "            detections = sv.Detections.from_inference(result)\n",
+                "\n",
+                "            # Apply bounding box to detections on a copy of the frame.\n",
+                "            annotated_frame = box_annotator.annotate(\n",
+                "                scene=frame.copy(), \n",
+                "                detections=detections\n",
+                "            )\n",
+                "\n",
+                "            # Write the annotated frame to the video sink.\n",
+                "            sink.write_frame(frame=annotated_frame)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                " Notice that we create a `box_annoator` variable by initalizing a [BoundingBoxAnnotator](https://supervision.roboflow.com/latest/annotators/#boundingboxannotator). We can change the color and thickness, but for simplicity we keep the defaults. There are a ton of easy to use [annotators](https://supervision.roboflow.com/latest/annotators/) available in the Supervision package other than a bounding box that are fun to play with. Next, we create a `video_info` variable to pass information about the video to our `VideoSink`. The `VideoSink` is a cool little context manager that allows us to `write_frames()` to a video ouput file. Prior to writing the frame, we annotate a copy of it utilizing `box_annotator.annotate()`. Let's take a look at the resulting video. It will be installed locally and is called `output.mp4`. When run, you will see bounding boxes around the detections. Pretty awesome! "
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We only scratched the surface of all of the customizable Annotators and additional features that Supervision and Inference have to offer. Stay tuned for more cookbooks on how to take advantge of them in your computer vision applications. Happy building! "
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.9.7"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}
@@ -0,0 +1,99 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Experimenting with Supervision Assets\n",
+                "\n",
+                "---\n",
+                "\n",
+                "<p align=\"center\">\n",
+                "  <a href=\"https://colab.research.google.com/github/roboflow/supervision/blob/develop/docs/notebooks/supervision-assets.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Colab\"></a>\n",
+                "</p>\n",
+                "\n",
+                "When experimenting with interesting and useful features of the Supervision package, its important to have some sort of image or video data to experiment with. Luckily for us, Supervision ships with [Assets](https://supervision.roboflow.com/latest/assets/)! Assets is a collection of videos that you can utilize to start experimenting with the various features Supervision has to offer. Let's take a look at how to use this resource.\n",
+                "\n",
+                "## Install with Pip\n",
+                "\n",
+                "If you're running this code outside a Jupyter notebook, it's generally a best practice to create a virtual environment so that your projects deppendencies are isolated. Run the following ing your command line of choice to create and source one.\n",
+                "\n",
+                "```shell\n",
+                "\n",
+                "python -m venv venv\n",
+                "source venv/bin/activate\n",
+                "\n",
+                "``` "
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Now that we've got a virtual enviornment installed and sourced, let's install the pip package with the assets \"extra\" package. Make sure to remove the \"!\" if running this in your command line."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "vscode": {
+                    "languageId": "shellscript"
+                }
+            },
+            "outputs": [],
+            "source": [
+                "!pip install -q \"supervision[assets]\""
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Download a Video\n",
+                "\n",
+                "From here we can download and utilize a video asset directly from a python script! Note below that we're utilizing the method `download_assets` to download the `VideoAssets.SUBWAY` video to our local directory. This method returns the file path, so we can then utilize this path for additional experimentation. From here, you will see a video asset to experiment with in your local directory. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "\n",
+                "# Download a video asset from the supervision package.\n",
+                "path_to_video = download_assets(VideoAssets.SUBWAY)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We're now equipt with a video asset from Supervision to run some experiements on! For more information on available video assets, visit the [Supervision API Reference](https://supervision.roboflow.com/latest/assets/#videoassets). Happy building!"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.11.7"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}
@@ -0,0 +1,154 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Tracking Objects in a Video\n",
+                "\n",
+                "---\n",
+                "\n",
+                "<p align=\"center\">\n",
+                "  <a href=\"https://colab.research.google.com/github/roboflow/supervision/blob/main/docs/notebooks/tracking.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Colab\"></a>\n",
+                "</p>\n",
+                "\n",
+                "In some cases, it's important for us to track objects across multiple frames of a video. For example, we may need to figure out the direction a vehicle is moving, or count objects in a frame. Some Supvervision [Annotators](https://supervision.roboflow.com/latest/annotators/) and Tools like [LineZone](https://supervision.roboflow.com/latest/detection/tools/line_zone/) require tracking to be setup.  In this cookbook, we'll cover how to get a tracker up and running for use in your computer vision applications.\n",
+                "\n",
+                "## What is a Tracker?\n",
+                "\n",
+                "Trackers are a piece of code that identifies objects across frames and assigns them a unique `tracker_id`. There are a few popular trackers at the time of writing this including ByteTrack and Bot-SORT. Supervision makes using trackers a breeze and comes with ByteTrack built-in. But first, let's get our deppendencies installed. \n",
+                "\n",
+                "## Install Dependencies"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "vscode": {
+                    "languageId": "powershell"
+                }
+            },
+            "outputs": [],
+            "source": [
+                "!pip install -q inference \"supervision[assets]\""
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Download a Video Asset\n",
+                "\n",
+                "Now that we have our enviornment setup, lets download a video that we can detect objects in. Supervision comes with a great utility to help us hit the ground running. We can use the below snippet to he video is save a video asset in our local directory. It can also be accessed with the variable `path_to_video` for additional application logic."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "\n",
+                "# Download a supervision video asset \n",
+                "path_to_video = download_assets(VideoAssets.PEOPLE_WALKING)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Tracking Objects\n",
+                "\n",
+                "Now that we have our video installed, let's get to work on tracking objects. We'll first pull in a model from roboflow Inference to detect people in our video. Then let's create a `byte_tracker` object that we'll pass our detections to. This will give us a `tracker_id`. We'll then utilize that tracker id to label our detections with a `label_annotator` to display the tracker id. Finally, we'll use a utility called `VideoSink` to save the annotated frames to a video. Let's dive in to the code."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import supervision as sv\n",
+                "from supervision.assets import download_assets, VideoAssets\n",
+                "from inference.models.utils import get_roboflow_model\n",
+                "\n",
+                "\n",
+                "if __name__ == '__main__':\n",
+                "\n",
+                "    # Install our video from supervision assets.\n",
+                "    PATH_TO_VIDEO = download_assets(VideoAssets.PEOPLE_WALKING)\n",
+                "\n",
+                "    # Load a pre trained yolov8 nano model from Roboflow Inference.\n",
+                "    model = get_roboflow_model('yolov8n-640')\n",
+                "\n",
+                "    # Create a video info object from the video path.\n",
+                "    video_info = sv.VideoInfo.from_video_path(PATH_TO_VIDEO)\n",
+                "\n",
+                "    # Create a label annotator for labeling detections with our tracker_id.\n",
+                "    label = sv.LabelAnnotator()\n",
+                "\n",
+                "    # Create a ByteTrack object to track detections.\n",
+                "    byte_tracker = sv.ByteTrack(frame_rate=video_info.fps)\n",
+                "\n",
+                "    # Create a frame generator from video path for iteration of frames.\n",
+                "    frame_generator = sv.get_video_frames_generator(PATH_TO_VIDEO)\n",
+                "\n",
+                "    # Create a video sink context manager to save resulting video.\n",
+                "    with sv.VideoSink(target_path=\"output.mp4\", video_info=video_info) as sink:\n",
+                "\n",
+                "        # Iterate over frames of the video.\n",
+                "        for frame in frame_generator:\n",
+                "\n",
+                "            # Run inference on the frame by passing it to our model.\n",
+                "            result = model.infer(frame)[0]\n",
+                "\n",
+                "            # Convert model results to a supervision detection object.\n",
+                "            detections = sv.Detections.from_inference(result)\n",
+                "\n",
+                "            # Update detections with tracker ids fro byte_tracker.\n",
+                "            tracked_detections = byte_tracker.update_with_detections(detections)\n",
+                "\n",
+                "            # Create labels with tracker_id for label annotator.\n",
+                "            labels = [ f\"{tracker_id}\" for tracker_id in tracked_detections.tracker_id ]\n",
+                "\n",
+                "            # Apply label annotator to frame.\n",
+                "            annotated_frame = label.annotate(scene=frame.copy(), detections=tracked_detections, labels=labels)\n",
+                "\n",
+                "            # Save the annotated frame to an output video.\n",
+                "            sink.write_frame(frame=annotated_frame)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# View the Video\n",
+                "\n",
+                "Let's take a look at our resulting video. It will be created in your current directory with the name `output.mp4` Notice how even with a little flicker, we can see the `tracker_id` on the people walking in the video. With trackers under your belt, there are now a wide variety of use cases you can solve for! Happy building! "
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "venv",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.9.7"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}
@@ -0,0 +1,107 @@
+.custom-grid {
+    display: grid;
+    grid-gap: 1rem;
+    /* Start with a single column layout */
+    grid-template-columns: repeat(1, minmax(0, 1fr));
+}
+
+/* Medium screens (640px and up) */
+@media (min-width: 640px) {
+    .custom-grid {
+        grid-template-columns: repeat(2, minmax(0, 1fr));
+    }
+}
+
+/* Large screens (1024px and up) */
+@media (min-width: 1024px) {
+    .custom-grid {
+        grid-template-columns: repeat(3, minmax(0, 1fr));
+    }
+}
+
+.repo-card {
+    background: radial-gradient(at right top, #A351FB25, #A5F9EA25);
+    border-radius: 0.5rem;
+    padding: 1rem;
+    transition: transform 0.2s ease-in-out; /* Smooth transition for the transform */
+    cursor: pointer;
+}
+
+.repo-card:hover {
+    transform: translateY(-0.125rem); /* Move up by -0.125rem on hover */
+}
+
+.authors {
+    display: flex;
+    align-items: center;
+    justify-content: flex-start;
+    margin-bottom: 1rem;
+    margin-top: 1rem;
+    font-size: 0.75rem;
+    line-height: 1rem;
+}
+
+.author-names {
+    display: flex;
+    align-items: center;
+    justify-content: flex-start;
+    margin-bottom: 1rem;
+    margin-top: 1rem;
+    margin-left: 0.5rem;
+}
+
+.author-container {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    width: 32px;
+    height: 32px;
+    padding: 0;
+    margin: 0;
+    transition: transform 0.2s;
+}
+
+.author-container.hover {
+    transform: translateY(-0.125rem);
+}
+
+.author-container:hover {
+    transform: translateY(-0.125rem);
+}
+
+.author-container a {
+    padding: 0;
+    margin: 0;
+    display: block;
+}
+
+.author-avatar {
+    width: 32px;
+    height: 32px;
+    border-radius: 50%;
+    display: block;
+}
+
+.author-name a {
+    color: inherit; /* Inherits the color from the parent element */
+    text-decoration: none; /* Removes the underline */
+}
+
+.author-name a:hover {
+    color: inherit; /* Inherits the color from the parent element */
+    text-decoration: underline; /* Adds underline on hover */
+}
+
+.label {
+    color: #fff;
+    padding: 2px 6px;
+    border-radius: 4px;
+    margin-right: 4px;
+}
+
+.non-selectable-text {
+    -webkit-user-select: none; /* Safari */
+    -moz-user-select: none; /* Firefox */
+    -ms-user-select: none; /* Internet Explorer/Edge */
+    user-select: none; /* Non-prefixed version, currently supported by Chrome, Opera, and Edge */
+}
@@ -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 +1,21 @@
+{% extends "main.html" %}
+{% block libs %}
+<script src="https://cdnjs.cloudflare.com/ajax/libs/dompurify/3.0.8/purify.min.js"></script>
+<link rel="stylesheet" href="/stylesheets/cookbooks-card.css">
+<script src="/javascripts/cookbooks-card.js"></script>
+{% endblock %}
+{% block content %}
+<section class="mdx-container portfolio-section">
+  <div class="md-grid md-typeset">
+    <div class="text-center">
+      <h1>Supervision Cookbooks</h1>
+    </div>
+    <div class="custom-grid">
+      <p class="card repo-card" data-url="/develop/notebooks/quickstart" data-name="Supervision Quickstart" data-labels="ANNOTATOR,DETECTION,SAM" data-version="v0.18.0" data-author="SkalskiP,onuralpszr"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/download-supervision-assets" data-name="Downloading Supervision Assets" data-labels="ASSETS" data-version="v0.18.0" data-author="nickherrig"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/annotate-video-with-detections" data-name="Annotate Video with Detections" data-labels="INFERENCE,YOLOV8" data-version="v0.18.0" data-author="nickherrig"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/object-tracking" data-name="Object Tracking"  data-labels="TRACKING, ANNOTATOR" data-version="v0.18.0" data-author="nickherrig"></p>
+    </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 %}
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+<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 %}
@@ -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"
@@ -984,6 +984,37 @@ 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 +1174,27 @@ 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 = "8.1.2"
+description = "Jupyter interactive widgets"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "ipywidgets-8.1.2-py3-none-any.whl", hash = "sha256:bbe43850d79fb5e906b14801d6c01402857996864d1e5b6fa62dd2ee35559f60"},
+    {file = "ipywidgets-8.1.2.tar.gz", hash = "sha256:d0b9b41e49bae926a866e613a39b0f0097745d2b9f1f3dd406641b4a57ec42c9"},
+]
+
+[package.dependencies]
+comm = ">=0.1.3"
+ipython = ">=6.1.0"
+jupyterlab-widgets = ">=3.0.10,<3.1.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=4.0.10,<4.1.0"
+
+[package.extras]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
 [[package]]
 name = "isoduration"
 version = "20.11.0"
@@ -1157,20 +1209,6 @@ 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 +1557,46 @@ 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 = "3.0.10"
+description = "Jupyter interactive widgets for JupyterLab"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jupyterlab_widgets-3.0.10-py3-none-any.whl", hash = "sha256:dd61f3ae7a5a7f80299e14585ce6cf3d6925a96c9103c978eda293197730cb64"},
+    {file = "jupyterlab_widgets-3.0.10.tar.gz", hash = "sha256:04f2ac04976727e4f9d0fa91cdc2f1ab860f965e504c29dbd6a65c882c9d04c0"},
+]
+
+[[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 +1927,25 @@ 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 +2049,70 @@ 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 +2131,7 @@ 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)"]

@@ -2131,13 +2283,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=

 [[package]]
 name = "nbconvert"
-version = "7.14.2"
+version = "7.16.0"
 description = "Converting Jupyter Notebooks"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "nbconvert-7.14.2-py3-none-any.whl", hash = "sha256:db28590cef90f7faf2ebbc71acd402cbecf13d29176df728c0a9025a49345ea1"},
-    {file = "nbconvert-7.14.2.tar.gz", hash = "sha256:a7f8808fd4e082431673ac538400218dd45efd076fbeb07cc6e5aa5a3a4e949e"},
+    {file = "nbconvert-7.16.0-py3-none-any.whl", hash = "sha256:ad3dc865ea6e2768d31b7eb6c7ab3be014927216a5ece3ef276748dd809054c7"},
+    {file = "nbconvert-7.16.0.tar.gz", hash = "sha256:813e6553796362489ae572e39ba1bff978536192fb518e10826b0e8cadf03ec8"},
 ]

 [package.dependencies]
@@ -2840,13 +2992,13 @@ 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]]
@@ -3416,28 +3568,28 @@ 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"},
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+    {file = "ruff-0.2.1-py3-none-win_amd64.whl", hash = "sha256:e3affdcbc2afb6f5bd0eb3130139ceedc5e3f28d206fe49f63073cb9e65988e0"},
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+    {file = "ruff-0.2.1.tar.gz", hash = "sha256:3b42b5d8677cd0c72b99fcaf068ffc62abb5a19e71b4a3b9cfa50658a0af02f1"},
 ]

 [[package]]
@@ -3578,6 +3730,17 @@ 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 +3821,17 @@ 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 +4139,17 @@ files = [
 [package.extras]
 test = ["pytest (>=6.0.0)", "setuptools (>=65)"]

+[[package]]
+name = "widgetsnbextension"
+version = "4.0.10"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"},
+    {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"},
+]
+
 [[package]]
 name = "zipp"
 version = "3.17.0"
@@ -3987,4 +4172,4 @@ desktop = ["opencv-python"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "07b8d86e8a1e4688ed81283e49b3969889b5374ca079cbafc1b4b7038b01b949"
+content-hash = "8345f7320402678ec689446421f80bff7fdb3fc13247269346c5ba3daf83ba7b"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.19.0rc2"
+version = "0.19.0rc3"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
@@ -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",
    [
Author	SHA1	Message	Date
SkalskiP	11197f8a4e	bump version from `0.19.0rc2` to `0.19.0rc3`	2024-02-09 18:09:56 +01:00
Piotr Skalski	8529a3ec18	Merge pull request #861 from NickHerrig/feat/cookbooks Feat/cookbooks	2024-02-09 17:37:50 +01:00
SkalskiP	115d4492da	changing colab names, card names, and card labels	2024-02-09 17:36:13 +01:00
Nick Herrig	84b54d31bf	Add colab notebook button, test in notebooks	2024-02-09 10:34:40 -05:00
pre-commit-ci[bot]	57c7109498	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-09 14:30:00 +00:00
Nick Herrig	008dbcb0b4	Merge branch 'develop' into feat/cookbooks	2024-02-09 09:29:49 -05:00
Nick Herrig	122ca82cad	Update cookbooks html to match new format -> no desc, cap tags	2024-02-09 09:27:07 -05:00
Nick Herrig	9ca7d3eb75	Finish and fix grammar of tracking cookbook	2024-02-09 09:21:14 -05:00
Piotr Skalski	5b2bdf58e7	Merge pull request #868 from roboflow/cookbooks/cards-improvements [Cookbooks] - Improved functionality of cookbook cards	2024-02-09 14:24:51 +01:00
pre-commit-ci[bot]	b7070ae574	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-09 13:23:07 +00:00
SkalskiP	85f09e3323	same label have same color across multiple cards; fixed small bug with hover effect	2024-02-09 14:19:41 +01:00
SkalskiP	03f7001a2b	cookbooks-card.js refactor	2024-02-09 13:26:32 +01:00
SkalskiP	36a825b7a0	author styling improvements	2024-02-09 12:47:02 +01:00
Onuralp SEZER	0950f90ddf	Merge pull request #867 from roboflow/dependabot/pip/nbconvert-7.16.0 ⬆️ Bump nbconvert from 7.14.2 to 7.16.0	2024-02-09 14:22:44 +03:00
Onuralp SEZER	cb22b94fe1	Merge pull request #866 from roboflow/dependabot/pip/ipywidgets-8.1.2 ⬆️ Bump ipywidgets from 8.1.1 to 8.1.2	2024-02-09 14:22:29 +03:00
pre-commit-ci[bot]	019c41bf1a	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-09 04:37:54 +00:00
Nick Herrig	607d94dad9	Merge branch 'develop' into feat/cookbooks	2024-02-08 23:37:45 -05:00
Nick Herrig	aacdbe76f0	Add html entries for cookbooks, add tracking cookbook	2024-02-08 23:27:53 -05:00
Nick Herrig	f340c49a70	Update bounding box cookbook name, Fix grammar and spelling	2024-02-08 20:30:57 -05:00
dependabot[bot]	5bd30f4c72	⬆️ Bump nbconvert from 7.14.2 to 7.16.0 Bumps [nbconvert](https://github.com/jupyter/nbconvert) from 7.14.2 to 7.16.0. - [Release notes](https://github.com/jupyter/nbconvert/releases) - [Changelog](https://github.com/jupyter/nbconvert/blob/main/CHANGELOG.md) - [Commits](https://github.com/jupyter/nbconvert/compare/v7.14.2...v7.16.0) --- updated-dependencies: - dependency-name: nbconvert dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com>	2024-02-09 01:04:56 +00:00
dependabot[bot]	d0f31448b9	⬆️ Bump ipywidgets from 8.1.1 to 8.1.2 Bumps [ipywidgets](https://github.com/jupyter-widgets/ipywidgets) from 8.1.1 to 8.1.2. - [Release notes](https://github.com/jupyter-widgets/ipywidgets/releases) - [Commits](https://github.com/jupyter-widgets/ipywidgets/compare/8.1.1...8.1.2) --- updated-dependencies: - dependency-name: ipywidgets dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com>	2024-02-09 01:01:04 +00:00
Nick Herrig	7b4cf2f0f6	Reviewed supervision assets cookbook and renamed	2024-02-08 19:51:08 -05:00
Piotr Skalski	fb7dd6aefd	Merge pull request #865 from roboflow/cookbooks/html-optimization-and-css-update cookbooks page HTML optimisation + CSS update	2024-02-08 22:42:00 +01:00
pre-commit-ci[bot]	ce70266d23	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-08 21:39:07 +00:00
SkalskiP	f1a2b99931	ready for review	2024-02-08 22:36:57 +01:00
Nick Herrig	e3adcbeec1	Move cookbooks to new webpage and configure html file	2024-02-08 15:30:32 -05:00
SkalskiP	48af41377f	move notebook inside the grid	2024-02-08 20:32:37 +01:00
Nick Herrig	0b7b8bb439	Move assets cookbook to docs, Update theme/cookbooks	2024-02-08 14:13:14 -05:00
Nick Herrig	95d28fabb5	Merge branch 'roboflow:develop' into feat/cookbooks	2024-02-08 13:30:40 -05:00
pre-commit-ci[bot]	efd06ac77d	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-08 17:22:01 +00:00
Nick Herrig	1ac497a3e2	Finished bounding box annotation cookbook	2024-02-08 12:20:56 -05:00
Piotr Skalski	3dd3781b1b	Add hackfest baner to README.md	2024-02-08 18:20:13 +01:00
Nick Herrig	a015fc176e	Fix object detection conflicts	2024-02-08 12:18:09 -05:00
Piotr Skalski	775c1b65f8	Merge pull request #863 from roboflow/fix/doc-env2 docs(fix): 🐞📝 MKDOCS_GIT_COMMITTERS_APIKEY fix attempt v2	2024-02-08 17:56:10 +01:00
Onuralp SEZER	8514f086b7	docs(fix): 🐞📝 MKDOCS_GIT_COMMITTERS_APIKEY fix attempt v2 Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 19:53:51 +03:00
Nick Herrig	4685a0a2ff	Update bounding box conflict	2024-02-08 11:38:14 -05:00
Piotr Skalski	a26dd9416e	Merge pull request #862 from roboflow/fix/doc-env docs(fix): 🐞📝 MKDOCS_GIT_COMMITTERS_APIKEY value added	2024-02-08 17:37:57 +01:00
Onuralp SEZER	a6466d13a9	docs(fix): 🐞📝 MKDOCS_GIT_COMMITTERS_APIKEY value added Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 19:33:20 +03:00
Piotr Skalski	82b18ae3f8	Merge pull request #860 from roboflow/docs/notebooks docs(page): 🚀 Supervision Cookbook	2024-02-08 17:26:24 +01:00
Onuralp SEZER	08a204d872	docs(api): 📝 api/annotators position adjusted and small typo fix Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 19:20:15 +03:00
Onuralp SEZER	4e5331b92d	docs(version): 🐞 restore version part and revert hide h1 on home for now Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 18:57:39 +03:00
Onuralp SEZER	985624e35e	docs(assets): 🐞 missing js and css files added into mkdocs.yml Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 18:05:07 +03:00
Onuralp SEZER	6a091b58a8	ci(docs): 👷 GH_TOKEN added for git-committers Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 17:51:19 +03:00
Onuralp SEZER	4ef2d04dec	ci(docs): 👷 new packages added for build document Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 17:39:16 +03:00
pre-commit-ci[bot]	f81ccbcc5f	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-08 14:34:47 +00:00
Onuralp SEZER	d900e68787	docs(home): 🚀 hide table of content for home page only and give looks as more like home of supervision Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 17:33:52 +03:00
Onuralp SEZER	ab38cb1531	docs(nb): 🚀 initial notebook added as quickstart and other examples are removed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 17:30:04 +03:00
pre-commit-ci[bot]	7f51984063	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-08 14:22:52 +00:00
pre-commit-ci[bot]	1ad26b5527	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-08 14:13:38 +00:00
Onuralp SEZER	d2ccc4f270	docs(nav): 🚀 nav-tab enabled and new order of pages for better understanding Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 17:13:03 +03:00
Onuralp SEZER	15b4914448	docs(js): 🔥 unused params has been removed from js files for cookbook page Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 17:06:22 +03:00
Onuralp SEZER	3ddeed1945	docs: ✨ data-url param added Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 16:48:46 +03:00
Onuralp SEZER	1a20bb5745	docs: ✨ add demo notebook and remove notebooks folder from ignore list Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 16:33:05 +03:00
Onuralp SEZER	c4365871b5	Merge changes from develop branch	2024-02-08 15:31:06 +03:00
Onuralp SEZER	1fba192319	docs: ✨ folder rename to javascript to javascripts (plural) Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 15:26:03 +03:00
Onuralp SEZER	6ffd159b77	docs: ✨ supervision cookbook initial page design landed Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-08 15:20:43 +03:00
Onuralp SEZER	16d3214db9	Merge pull request #857 from roboflow/dependabot/pip/mkdocs-material-9.5.8 ⬆️ Bump mkdocs-material from 9.5.7 to 9.5.8	2024-02-08 03:41:16 +03:00
dependabot[bot]	381bc8aed6	⬆️ Bump mkdocs-material from 9.5.7 to 9.5.8 Bumps [mkdocs-material](https://github.com/squidfunk/mkdocs-material) from 9.5.7 to 9.5.8. - [Release notes](https://github.com/squidfunk/mkdocs-material/releases) - [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG) - [Commits](https://github.com/squidfunk/mkdocs-material/compare/9.5.7...9.5.8) --- updated-dependencies: - dependency-name: mkdocs-material dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com>	2024-02-08 00:37:30 +00:00
Nick Herrig	3150f5a672	Add rough draft of bounding box displays	2024-02-07 18:55:24 -05:00
Onuralp SEZER	eb35fdd1b7	Merge pull request #856 from roboflow/dependabot/pip/ruff-0.2.1 ⬆️ Bump ruff from 0.2.0 to 0.2.1	2024-02-08 00:43:46 +03:00
Nick Herrig	9e95e1f0b3	Add supervision assets cookbook, correct assets documentation	2024-02-07 14:56:08 -05:00
Nick Herrig	4bfbe3c8f7	Add readme for cookbooks directory	2024-02-07 14:52:43 -05:00
Onuralp SEZER	a6d9961b2b	docs: ✨ jupyter notebook support added Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>	2024-02-07 20:06:25 +03:00
Piotr Skalski	a670faf646	Merge pull request #847 from AdonaiVera/segmentation_nms [NMS] - add segmentation models support	2024-02-07 10:09:01 +01:00
SkalskiP	824a9140b6	tests fixed	2024-02-07 09:59:58 +01:00
SkalskiP	36ec1b6481	Merge remote-tracking branch 'origin/segmentation_nms' into segmentation_nms	2024-02-07 09:43:20 +01:00
SkalskiP	79a13d0432	final tests	2024-02-07 09:42:56 +01:00
pre-commit-ci[bot]	cbbfb453f2	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-07 08:41:34 +00:00
SkalskiP	bbe0bfc760	final tests	2024-02-07 09:41:21 +01:00
SkalskiP	98e7310038	test vectorized mask_non_max_suppression	2024-02-07 09:31:02 +01:00
SkalskiP	71836ed1ee	Merge remote-tracking branch 'origin/segmentation_nms' into segmentation_nms	2024-02-07 09:28:03 +01:00
SkalskiP	b681274954	Merge remote-tracking branch 'origin/segmentation_nms' into segmentation_nms	2024-02-07 09:27:49 +01:00
SkalskiP	619fa832c5	Merge remote-tracking branch 'origin/segmentation_nms' into segmentation_nms	2024-02-07 09:24:55 +01:00
SkalskiP	f2e65cf6fc	refactored `mask_non_max_suppression`	2024-02-07 09:24:45 +01:00
pre-commit-ci[bot]	8f6887d994	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-07 08:21:36 +00:00
SkalskiP	c46a2e9c08	Merge remote-tracking branch 'origin/segmentation_nms' into segmentation_nms # Conflicts: # supervision/detection/utils.py	2024-02-07 09:21:22 +01:00
SkalskiP	7e44838e51	refactored `mask_non_max_suppression`	2024-02-07 09:20:54 +01:00
pre-commit-ci[bot]	c98dbbf2f6	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-07 08:18:54 +00:00
SkalskiP	239f7333f1	refactored `mask_non_max_suppression`	2024-02-07 09:18:41 +01:00
SkalskiP	35c9ebdd60	refactored `mask_non_max_suppression`	2024-02-07 08:51:57 +01:00
pre-commit-ci[bot]	879cf6d9dc	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-07 05:15:10 +00:00
Adonai Vera	fd70f3ce5d	Applied inclusion-exclusion principle to mask_iou_batch and resize function to improve speed	2024-02-07 00:14:54 -05:00
pre-commit-ci[bot]	cf63c838b2	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-06 08:55:15 +00:00
SkalskiP	9ee42e5257	mask_iou_batch speed test	2024-02-06 09:54:07 +01:00
dependabot[bot]	9253da570f	⬆️ Bump ruff from 0.2.0 to 0.2.1 Bumps [ruff](https://github.com/astral-sh/ruff) from 0.2.0 to 0.2.1. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.2.0...v0.2.1) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com>	2024-02-06 01:03:21 +00:00
pre-commit-ci[bot]	1e87a99259	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-03 03:19:57 +00:00
Adonai Vera	ebe2785287	Removed issue of length sentence	2024-02-02 22:19:40 -05:00
pre-commit-ci[bot]	aaf3621841	fix(pre_commit): 🎨 auto format pre-commit hooks	2024-02-03 03:10:29 +00:00
Adonai Vera	6a9c013949	Added nms for segmentation algorithm, added unit test, added init, added withnms conditional depends if contains mask	2024-02-02 21:33:15 -05:00