Metadata-Version: 2.1
Name: supervision
Version: 0.14.0b2
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,YOLOv5,YOLOv8,Roboflow
Author: Piotr Skalski
Author-email: piotr.skalski92@gmail.com
Maintainer: Piotr Skalski
Maintainer-email: piotr.skalski92@gmail.com
Requires-Python: >=3.8,<3.12.0
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
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 :: 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: desktop
Requires-Dist: matplotlib (>=3.7.1,<4.0.0)
Requires-Dist: numpy (>=1.20.0,<2.0.0)
Requires-Dist: opencv-python (>=4.8.0.74,<5.0.0.0) ; extra == "desktop"
Requires-Dist: opencv-python-headless (>=4.8.0.74,<5.0.0.0)
Requires-Dist: pillow (>=9.4.0,<10.0.0)
Requires-Dist: pyyaml (>=6.0,<7.0)
Requires-Dist: scipy (>=1.9.0,<2.0.0)
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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  [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! 🤝

## 💻 install

Pip install the supervision package in a
[**3.11>=Python>=3.8**](https://www.python.org/) environment.

```bash
pip install supervision[desktop]
```

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

## 🔥 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_ultralytics(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 the 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='...'
    ... )

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

- Loop over dataset entries

    ```python
    >>> for name, 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)
    ```

- Merge multiple datasets

    ```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 object detection/instance segmentation datasets in one of the 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='...'
    ... )

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

- Convert labels between supported formats

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

- Load classification datasets in one of the supported formats

    ```python
    >>> cs = sv.ClassificationDataset.from_folder_structure(
    ...     root_directory_path='...'
    ... )
    ```

- Save classification datasets in one of the supported formats

    ```python
    >>> cs.as_folder_structure(
    ...     root_directory_path='...'
    ... )
    ```

</details>

### [model evaluation](https://roboflow.github.io/supervision/metrics/detection/)

```python
>>> import supervision as sv

>>> dataset = sv.DetectionDataset.from_yolo(...)

>>> def callback(image: np.ndarray) -> sv.Detections:
...     ...

>>> confusion_matrix = sv.ConfusionMatrix.benchmark(
...     dataset = dataset,
...     callback = callback
... )

>>> confusion_matrix.matrix
array([
    [0., 0., 0., 0.],
    [0., 1., 0., 1.],
    [0., 1., 1., 0.],
    [1., 1., 0., 0.]
])
```

<details close>
<summary>👉 more metrics</summary>

- Mean average precision (mAP) for object detection tasks.

    ```python
    >>> import supervision as sv

    >>> dataset = sv.DetectionDataset.from_yolo(...)

    >>> def callback(image: np.ndarray) -> sv.Detections:
    ...     ...

    >>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
    ...     dataset = dataset,
    ...     callback = callback
    ... )

    >>> mean_average_precision.map50_95
    0.433
    ```

</details>

## 🛠️ 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

## 🎬 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/D-D6ZmadzPE" 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/D-D6ZmadzPE" 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

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!

<br>

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