📑 Table of Contents
👋 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
👉 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
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
👉 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=...,
)
🎬 Tutorials
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
Created: 5 Apr 2024
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
Created: 11 Jan 2024
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.
💜 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 to get started. Thank you 🙏 to all our contributors!
Supervision 是一个 Python 库，提供可复用的计算机视觉基础模块：处理检测、分割、分类结果，以及标注器、跟踪器和数据集工具，可接入任意模型。