roboflow/supervision — a trending Python project

roboflow/supervision — Python · 50,409 stars · 4,803 forks · +217 today

We write your reusable computer vision tools. 💜

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📑 Table of Contents

• 👋 Hello
• 💻 Install
• 🔥 Quickstart
  • Models
  • Annotators
  • Datasets
• 🎬 Tutorials
• 💜 Built with Supervision
• 📚 Documentation
• 🏆 Contribution

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. 🤝

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.

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

👉 more model connectors

• inference

  Running with Inference requires a Roboflow API KEY.

  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
  

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

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

👉 more dataset utils

• load

  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

  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

  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

  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

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

Want to learn how to use Supervision? Explore our how-to guides, end-to-end examples, cheatsheet, and cookbooks!

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

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.

Speed Estimation & Vehicle Tracking | Computer Vision | Open Source

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.

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

Visit our documentation page to learn how supervision can help you build computer vision applications faster and more reliably.

We love your input! Please see our contributing guide to get started. Thank you 🙏 to all our contributors!

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Content above is the README of roboflow/supervision, reproduced from GitHub’s trending list. All rights remain with the original authors.