Metadata-Version: 2.1
Name: supervision
Version: 0.19.0rc3
Summary: A set of easy-to-use utils that will come in handy in any Computer Vision project
Home-page: https://github.com/roboflow/supervision
License: MIT
Keywords: machine-learning,deep-learning,vision,ML,DL,AI,Roboflow
Author: Piotr Skalski
Author-email: piotr.skalski92@gmail.com
Maintainer: Piotr Skalski
Maintainer-email: piotr.skalski92@gmail.com
Requires-Python: >=3.8,<4.0
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Software Development
Classifier: Typing :: Typed
Provides-Extra: assets
Provides-Extra: desktop
Requires-Dist: defusedxml (>=0.7.1,<0.8.0)
Requires-Dist: matplotlib (>=3.6.0)
Requires-Dist: numpy (>=1.21.2)
Requires-Dist: opencv-python (>=4.5.5.64) ; extra == "desktop"
Requires-Dist: opencv-python-headless (>=4.5.5.64)
Requires-Dist: pyyaml (>=5.3)
Requires-Dist: requests (>=2.26.0,<=2.31.0) ; extra == "assets"
Requires-Dist: scipy (==1.10.0) ; python_version < "3.9"
Requires-Dist: scipy (>=1.10.0,<2.0.0) ; python_version >= "3.9"
Requires-Dist: tqdm (>=4.62.3,<=4.66.1) ; extra == "assets"
Project-URL: Documentation, https://github.com/roboflow/supervision/blob/main/README.md
Project-URL: Repository, https://github.com/roboflow/supervision
Description-Content-Type: text/markdown

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  <br>

[notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [collect](https://github.com/roboflow/roboflow-collect)

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## 👋 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
[**Python>=3.8**](https://www.python.org/) environment.

```bash
pip install supervision
```

Read more about desktop, headless, and local installation in our [guide](https://roboflow.github.io/supervision/).

## 🔥 quickstart

### models

Supervision was designed to be model agnostic. Just plug in any classification, detection, or segmentation model. For your convenience, we have created [connectors](https://supervision.roboflow.com/latest/detection/core/#detections) for the most popular libraries like Ultralytics, Transformers, or MMDetection.

```python
import cv2
import supervision as sv
from ultralytics import YOLO

image = cv2.imread(...)
model = YOLO('yolov8s.pt')
result = model(image)[0]
detections = sv.Detections.from_ultralytics(result)

len(detections)
# 5
```

<details>
<summary>👉 more model connectors</summary>

- inference

    Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key).

    ```python
    import cv2
    import supervision as sv
    from inference.models.utils import get_roboflow_model

    image = cv2.imread(...)
    model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
    result = model.infer(image)[0]
    detections = sv.Detections.from_inference(result)

    len(detections)
    # 5

    ```

</details>

### annotators

Supervision offers a wide range of highly customizable [annotators](https://supervision.roboflow.com/latest/annotators/), allowing you to compose the perfect visualization for your use case.

```python
import cv2
import supervision as sv

image = cv2.imread(...)
detections = sv.Detections(...)

bounding_box_annotator = sv.BoundingBoxAnnotator()
annotated_frame = bounding_box_annotator.annotate(
    scene=image.copy(),
    detections=detections
)
```

https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce

### datasets

Supervision provides a set of [utils](https://supervision.roboflow.com/latest/datasets/) that allow you to load, split, merge, and save datasets in one of the supported formats.

```python
import supervision as sv

dataset = sv.DetectionDataset.from_yolo(
    images_directory_path=...,
    annotations_directory_path=...,
    data_yaml_path=...
)

dataset.classes
['dog', 'person']

len(dataset)
# 1000
```

<details close>
<summary>👉 more dataset utils</summary>

- load

  ```python
  dataset = sv.DetectionDataset.from_yolo(
      images_directory_path=...,
      annotations_directory_path=...,
      data_yaml_path=...
  )

  dataset = sv.DetectionDataset.from_pascal_voc(
      images_directory_path=...,
      annotations_directory_path=...
  )

  dataset = sv.DetectionDataset.from_coco(
      images_directory_path=...,
      annotations_path=...
  )
  ```

- split

  ```python
  train_dataset, test_dataset = dataset.split(split_ratio=0.7)
  test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)

  len(train_dataset), len(test_dataset), len(valid_dataset)
  # (700, 150, 150)
  ```

- merge

  ```python
  ds_1 = sv.DetectionDataset(...)
  len(ds_1)
  # 100
  ds_1.classes
  # ['dog', 'person']

  ds_2 = sv.DetectionDataset(...)
  len(ds_2)
  # 200
  ds_2.classes
  # ['cat']

  ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
  len(ds_merged)
  # 300
  ds_merged.classes
  # ['cat', 'dog', 'person']
  ```

- save

  ```python
  dataset.as_yolo(
      images_directory_path=...,
      annotations_directory_path=...,
      data_yaml_path=...
  )

  dataset.as_pascal_voc(
      images_directory_path=...,
      annotations_directory_path=...
  )

  dataset.as_coco(
      images_directory_path=...,
      annotations_path=...
  )
  ```

- convert

  ```python
  sv.DetectionDataset.from_yolo(
      images_directory_path=...,
      annotations_directory_path=...,
      data_yaml_path=...
  ).as_pascal_voc(
      images_directory_path=...,
      annotations_directory_path=...
  )
  ```

</details>

## 🎬 tutorials

<p align="left">
<a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><img src="https://github.com/SkalskiP/SkalskiP/assets/26109316/61a444c8-b135-48ce-b979-2a5ab47c5a91" alt="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source" width="300px" align="left" /></a>
<a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><strong>Speed Estimation & Vehicle Tracking | Computer Vision | Open Source</strong></a>
<div><strong>Created: 11 Jan 2024</strong> | <strong>Updated: 11 Jan 2024</strong></div>
<br/> Learn how to track and estimate the speed of vehicles using YOLO, ByteTrack, and Roboflow Inference. This comprehensive tutorial covers object detection, multi-object tracking, filtering detections, perspective transformation, speed estimation, visualization improvements, and more.</p>

<br/>

<p align="left">
<a href="https://youtu.be/4Q3ut7vqD5o" title="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking"><img src="https://github.com/roboflow/supervision/assets/26109316/54afdf1c-218c-4451-8f12-627fb85f1682" alt="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking" width="300px" align="left" /></a>
<a href="https://youtu.be/4Q3ut7vqD5o" title="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking"><strong>Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking</strong></a>
<div><strong>Created: 6 Sep 2023</strong> | <strong>Updated: 6 Sep 2023</strong></div>
<br/> In this video, we explore real-time traffic analysis using YOLOv8 and ByteTrack to detect and track vehicles on aerial images. Harnessing the power of Python and Supervision, we delve deep into assigning cars to specific entry zones and understanding their direction of movement. By visualizing their paths, we gain insights into traffic flow across bustling roundabouts... </p>

## 💜 built with supervision

Did you build something cool using supervision? [Let us know!](https://github.com/roboflow/supervision/discussions/categories/built-with-supervision)

https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4

https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900

https://github.com/roboflow/supervision/assets/26109316/3ac6982f-4943-4108-9b7f-51787ef1a69f

## 📚 documentation

Visit our [documentation](https://roboflow.github.io/supervision) page to learn how supervision can help you build computer vision applications faster and more reliably.

## 🏆 contribution

We love your input! Please see our [contributing guide](https://github.com/roboflow/supervision/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!

<p align="center">
    <a href="https://github.com/roboflow/supervision/graphs/contributors">
      <img src="https://contrib.rocks/image?repo=roboflow/supervision" />
    </a>
</p>

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