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Piotr Skalski authored and GitHub committed 2023-05-18 01:10:05 +02:00
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<br>
[![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)](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
[![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-detect-and-count-objects-in-polygon-zone.ipynb)
</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! 🤝
## 💻 install
Pip install the supervision package in a
[**3.10>=Python>=3.7**](https://www.python.org/) environment.
```bash
pip install supervision
```
<details close>
<summary>👉 install from source</summary>
```bash
# clone repository and navigate to root directory
git clone https://github.com/roboflow/supervision.git
cd supervision
# setup python environment and activate it
python3 -m venv venv
source venv/bin/activate
# install
pip install -e ".[dev]"
```
</details>
## 🔥 quickstart
### [detections processing](https://roboflow.github.io/supervision/detection/core/)
```python
>>> import supervision as sv
>>> from ultralytics import YOLO
>>> model = YOLO('yolov8s.pt')
>>> result = model(IMAGE)[0]
>>> detections = sv.Detections.from_yolov8(result)
>>> len(detections)
5
```
<details close>
<summary>👉 more detections utils</summary>
- Easily switch inference pipeline between supported object detection / instance segmentation models
```python
>>> import supervision as sv
>>> from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
>>> sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
>>> mask_generator = SamAutomaticMaskGenerator(sam)
>>> sam_result = mask_generator.generate(IMAGE)
>>> detections = sv.Detections.from_sam(sam_result=sam_result)
```
- [Advanced filtering](https://roboflow.github.io/supervision/quickstart/detections/)
```python
>>> detections = detections[detections.class_id == 0]
>>> detections = detections[detections.confidence > 0.5]
>>> detections = detections[detections.area > 1000]
```
- Image annotation
```python
>>> import supervision as sv
>>> box_annotator = sv.BoxAnnotator()
>>> annotated_frame = box_annotator.annotate(
... scene=IMAGE,
... detections=detections
... )
```
</details>
### [datasets processing](https://roboflow.github.io/supervision/dataset/core/)
```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 object detection / instance segmentation datasets in one of supported formats
```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='...'
... )
```
- Loop over dataset entries
```python
>>> for image, labels in dataset:
... print(labels.xyxy)
array([[404. , 719. , 538. , 884.5 ],
[155. , 497. , 404. , 833.5 ],
[ 20.154999, 347.825 , 416.125 , 915.895 ]], dtype=float32)
```
- Split dataset for training, testing and validation
```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)
```
- Save object detection / instance segmentation datasets in one of supported formats
```python
>>> dataset.as_yolo(
... images_directory_path='...',
... annotations_directory_path='...',
... data_yaml_path='...'
... )
>>> dataset.as_pascal_voc(
... images_directory_path='...',
... annotations_directory_path='...'
... )
```
- Convert labels between suppoted formats
```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/oEQYStnF2l8" title="Accelerate Image Annotation with SAM and Grounding DINO"><img src="https://github.com/SkalskiP/SkalskiP/assets/26109316/ae1ca38e-40b7-4b35-8582-e8ea5de3806e" alt="Accelerate Image Annotation with SAM and Grounding DINO" width="300px" align="left" /></a>
<a href="https://youtu.be/oEQYStnF2l8" title="Accelerate Image Annotation with SAM and Grounding DINO"><strong>Accelerate Image Annotation with SAM and Grounding DINO</strong></a>
<div><strong>Created: 20 Apr 2023</strong> | <strong>Updated: 20 Apr 2023</strong></div>
<br/> Discover how to speed up your image annotation process using Grounding DINO and Segment Anything Model (SAM). Learn how to convert object detection datasets into instance segmentation datasets, and see the potential of using these models to automatically annotate your datasets for real-time detectors like YOLOv8... </p>
<br/>
<p align="left">
<a href="https://youtu.be/oEQYStnF2l8" title="SAM - Segment Anything Model by Meta AI: Complete Guide"><img src="https://github.com/SkalskiP/SkalskiP/assets/26109316/6913ff11-53c6-4341-8d90-eaff3023c3fd" alt="SAM - Segment Anything Model by Meta AI: Complete Guide" width="300px" align="left" /></a>
<a href="https://youtu.be/oEQYStnF2l8" title="SAM - Segment Anything Model by Meta AI: Complete Guide"><strong>SAM - Segment Anything Model by Meta AI: Complete Guide</strong></a>
<div><strong>Created: 11 Apr 2023</strong> | <strong>Updated: 11 Apr 2023</strong></div>
<br/> Discover the incredible potential of Meta AI's Segment Anything Model (SAM)! We dive into SAM, an efficient and promptable model for image segmentation, which has revolutionized computer vision tasks. With over 1 billion masks on 11M licensed and privacy-respecting images, SAM's zero-shot performance is often competitive with or even superior to prior fully supervised results... </p>
## 📚 documentation
Curious how Supervision can help you solve problems on your project? Visit our [documentation](https://roboflow.github.io/supervision) page!
## 🏆 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!
<br>
<div align="center">
<div align="center">
<a href="https://youtube.com/roboflow">
<img
@@ -54,77 +261,4 @@
</a>
</div>
<br>
[![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)](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
[![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-detect-and-count-objects-in-polygon-zone.ipynb)
</div>
## 👋 hello
A set of easy-to-use utils that will come in handy in any computer vision project. **Supervision is still in
pre-release stage. 🚧 Keep your eyes open for potential bugs and be aware that at this stage our API is still fluid
and may change.**
## 💻 install
Pip install the supervision package in a
[**3.10>=Python>=3.7**](https://www.python.org/) environment.
```bash
pip install supervision
```
<details close>
<summary>Install from source</summary>
```bash
# clone repository and navigate to root directory
git clone https://github.com/roboflow/supervision.git
cd supervision
# setup python environment and activate it
python3 -m venv venv
source venv/bin/activate
# install
pip install -e ".[dev]"
```
</details>
## 📖 documentation
Curious how Supervision can help you solve problems on your project? Visit our [documentation](https://roboflow.github.io/supervision) page!
## 🎬 tutorials
🔥 [Subscribe](https://www.youtube.com/@Roboflow) and stay up to date with the latest changes to the Supervision library.
<p align="center">
<a href="https://youtu.be/l_kf9CfZ_8M">
<img
width="90%"
src="https://user-images.githubusercontent.com/26109316/217950212-311de186-1862-4b4c-a86e-89cafd68b233.jpg"
alt="count-objects-in-zone"
>
</a>
</p>
<p align="center">
<a href="https://youtu.be/OS5qI9YBkfk">
<img
width="90%"
src="https://user-images.githubusercontent.com/26109316/213702005-ddd568f0-b902-46c2-9af9-e6ff33db23bf.jpg"
alt="count-objects-crossing-line"
>
</a>
</p>
## 🏆 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!