Metadata-Version: 2.4
Name: superiorvision
Version: 0.30.0.dev0
Summary: A tensor-native, inference-focused fork of Supervision
Author-email: "Roboflow et al." <develop@roboflow.com>
Maintainer-email: Piotr Skalski <piotr@roboflow.com>
License-Expression: MIT
Project-URL: Documentation, https://supervision.roboflow.com/latest/
Project-URL: Homepage, https://github.com/roboflow/supervision
Project-URL: Repository, https://github.com/roboflow/supervision
Keywords: AI,deep-learning,DL,machine-learning,ML,Roboflow,vision
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Multimedia :: Graphics
Classifier: Topic :: Multimedia :: Video
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Software Development
Classifier: Typing :: Typed
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: defusedxml>=0.7.1
Requires-Dist: matplotlib>=3.6
Requires-Dist: numpy>=1.21.2
Requires-Dist: opencv-python>=4.5.5.64
Requires-Dist: pillow>=9.4
Requires-Dist: pydeprecate<0.11,>=0.9
Requires-Dist: pyyaml>=5.3
Requires-Dist: requests>=2.26
Requires-Dist: scipy>=1.10
Requires-Dist: torch<3,>=2
Requires-Dist: tqdm>=4.62.3
Provides-Extra: geotiff
Requires-Dist: rasterio>=1.3; extra == "geotiff"
Provides-Extra: metrics
Requires-Dist: pandas>=2; extra == "metrics"
Dynamic: license-file

<div align="center">
  <p>
    <a align="center" href="https://supervision.roboflow.com" target="_blank">
      <img
        width="100%"
        src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529"
      >
    </a>
  </p>

> Supervision, except the numbers have finally been informed that GPUs exist.

SuperiorVision is Roboflow's tensor-native, inference-focused fork of
[Supervision](https://github.com/roboflow/supervision). It preserves the
`import supervision as sv` API used by
[Roboflow Inference](https://github.com/roboflow/inference), while keeping
rectangular numeric state in `torch.Tensor` objects throughout the supported
code paths.

If detections begin as tensors, converting them to NumPy so the next operation
can turn them back into tensors is not "compatibility." It is cardio.
SuperiorVision declines the workout.

[![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision) [![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision) [![license](https://img.shields.io/pypi/l/supervision)](LICENSE.md) [![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision) [![codecov](https://codecov.io/gh/roboflow/supervision/graph/badge.svg?token=HMNJ5FVZ36)](https://codecov.io/gh/roboflow/supervision)

[![snyk](https://snyk.io/advisor/python/supervision/badge.svg)](https://snyk.io/advisor/python/supervision) [![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?logo=discord&label=discord&labelColor=fff&color=5865f2&link=https%3A%2F%2Fdiscord.gg%2FGbfgXGJ8Bk)](https://discord.gg/GbfgXGJ8Bk)

<div align="center">
    <a href="https://trendshift.io/repositories/124"  target="_blank"><img src="https://trendshift.io/api/badge/repositories/124" alt="roboflow%2Fsupervision | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
  </div>

Same API. Fewer NumPy vacations.

For the external multi-object trackers used by Inference, pair SuperiorVision
with the private [Tracktors](https://github.com/roboflow/tracktors) fork. The
dependency points one way—Tracktors consumes tensor-native `sv.Detections`—so
SuperiorVision keeps its existing `sv.ByteTrack` compatibility API without a
circular package dependency.

<details>
<summary><strong>📑 Table of Contents</strong></summary>

- [👋 Hello](#-hello)
- [💻 Install](#-install)
- [🔥 Quickstart](#-quickstart)
    - [Models](#models)
    - [Annotators](#annotators)
    - [Datasets](#datasets)
- [🎬 Tutorials](#-tutorials)
- [💜 Built with Supervision](#-built-with-supervision)
- [📚 Documentation](#-documentation)
- [🏆 Contribution](#-contribution)

</details>

## 👋 Hello

**We are your essential toolkit for computer vision.** From data loading to real-time zone counting, we provide the building blocks so you can focus on building applications around your models. 🤝

## 💻 Install

Pip install the supervision package in a [**Python>=3.10**](https://www.python.org/) environment.

```bash
git clone git@github.com:roboflow/superiorvision.git
cd superiorvision
pip install -e .
```

Both imports are supported:

```python
import supervision as sv       # existing Inference code
import superiorvision as sv    # cheekier spelling, same API
```

Because SuperiorVision provides the `supervision` namespace, install it as a
replacement for upstream Supervision, not beside it. Two packages cannot both
own the same trench coat and pretend everything is fine.

The upstream documentation below remains useful for the compatible API surface.

## 🔥 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, MMDetection, or Inference. Other integrations, like `rfdetr`, already return `sv.Detections` directly.

Install the optional dependencies for this example with `pip install pillow rfdetr`.

```python
import supervision as sv
from PIL import Image
from rfdetr import RFDETRSmall

image = Image.open("path/to/image.jpg")
model = RFDETRSmall()
detections = model.predict(image, threshold=0.5)

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 supervision as sv
    from PIL import Image
    from inference import get_model

    image = Image.open("path/to/image.jpg")
    model = get_model(model_id="rfdetr-small", 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/detection/annotators/), allowing you to compose the perfect visualization for your use case.

```python
import cv2
import supervision as sv

image = cv2.imread("path/to/image.jpg")
# Assuming detections are obtained from a model
detections = sv.Detections(...)

box_annotator = sv.BoxAnnotator()
annotated_frame = 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/core/) that allow you to load, split, merge, and save datasets in one of the supported formats.

```python
import supervision as sv
from roboflow import Roboflow

project = Roboflow().workspace("WORKSPACE_ID").project("PROJECT_ID")
dataset = project.version("PROJECT_VERSION").download("coco")

ds = sv.DetectionDataset.from_coco(
    images_directory_path=f"{dataset.location}/train",
    annotations_path=f"{dataset.location}/train/_annotations.coco.json",
)

path, image, annotation = ds[0]
# loads image on demand

for path, image, annotation in ds:
    # loads image on demand
    pass
```

<details>
<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

Want to learn how to use Supervision? Explore our [how-to guides](https://supervision.roboflow.com/develop/how_to/detect_and_annotate/), [end-to-end examples](./examples), [cheatsheet](https://roboflow.github.io/cheatsheet-supervision/), and [cookbooks](https://supervision.roboflow.com/develop/cookbooks/)!

<br/>

<p align="left">
<a href="https://youtu.be/hAWpsIuem10" title="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing"><img src="https://github.com/user-attachments/assets/014cffc7-72b3-4c0a-bb89-6de265b2c06b" alt="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing" width="300px" align="left" /></a>
<a href="https://youtu.be/hAWpsIuem10" title="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing"><strong>Dwell Time Analysis with Computer Vision | Real-Time Stream Processing</strong></a>
<div><strong>Created: 5 Apr 2024</strong></div>
<br/>Learn how to use computer vision to analyze wait times and optimize processes. This tutorial covers object detection, tracking, and calculating time spent in designated zones. Use these techniques to improve customer experience in retail, traffic management, or other scenarios.</p>

<br/>

<p align="left">
<a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><img src="https://github.com/user-attachments/assets/b16b8e21-dc6c-4a73-a678-2f7d5d374793" 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></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>

## 💜 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](.github/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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