roboflow / supervision

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notebooks | inference | autodistill | maestro

    

   

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

👋 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 environment.

pip install supervision

Read more about conda, mamba, and installing from source in our guide.

🔥 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 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.

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

• inference

Running with Inference requires a Roboflow 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

```

Annotators

Supervision offers a wide range of highly customizable annotators, allowing you to compose the perfect visualization for your use case.

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 that allow you to load, split, merge, and save datasets in one of the supported formats.

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

• 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=...,

)

```

🎬 Tutorials

Want to learn how to use Supervision? Explore our how-to guides, [end-to-end examples](./examples), cheatsheet, and cookbooks!

Dwell Time Analysis with Computer Vision | Real-Time Stream Processing

Speed Estimation & Vehicle Tracking | Computer Vision | Open Source

💜 Built with Supervision

Did you build something cool using supervision? Let us know!

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 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!

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