supervision
Science Score: 44.0%
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○Scientific vocabulary similarity
Low similarity (12.4%) to scientific vocabulary
Last synced: 6 months ago
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Basic Info
- Host: GitHub
- Owner: gdlf13
- License: mit
- Language: Python
- Default Branch: main
- Size: 77.3 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 5
- Releases: 0
Created almost 2 years ago
· Last pushed over 1 year ago
Metadata Files
Readme
Contributing
License
Code of conduct
Citation
README.md
[notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [maestro](https://github.com/roboflow/multimodal-maestro)
[](https://badge.fury.io/py/supervision) [](https://pypistats.org/packages/supervision) [](https://github.com/roboflow/supervision/blob/main/LICENSE.md) [](https://badge.fury.io/py/supervision) [](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb) [](https://huggingface.co/spaces/Roboflow/Annotators) [](https://discord.gg/GbfgXGJ8Bk) [](https://squidfunk.github.io/mkdocs-material/)
👋 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 Python>=3.8 environment.
bash
pip install supervision
Read more about desktop, headless, and local installation 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, or MMDetection.
```python import cv2 import supervision as sv from ultralytics import YOLO
image = cv2.imread(...) model = YOLO('yolov8s.pt') result = model(image)[0] detections = sv.Detections.from_ultralytics(result)
len(detections)
5
```
👉 more model connectors
- inference Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key). ```python import cv2 import supervision as sv from inference.models.utils import get_roboflow_model image = cv2.imread(...) model = get_roboflow_model(model_id="yolov8s-640", api_key=annotators
Supervision offers a wide range of highly customizable annotators, allowing you to compose the perfect visualization for your use case.
```python import cv2 import supervision as sv
image = cv2.imread(...) detections = sv.Detections(...)
boundingboxannotator = sv.BoundingBoxAnnotator() annotatedframe = boundingbox_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.
```python import supervision as sv
dataset = sv.DetectionDataset.fromyolo( imagesdirectorypath=..., annotationsdirectorypath=..., datayaml_path=... )
dataset.classes ['dog', 'person']
len(dataset)
1000
```
👉 more dataset utils
- 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
Speed Estimation & Vehicle Tracking | Computer Vision | Open Source
Created: 11 Jan 2024 | Updated: 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.
Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking
Created: 6 Sep 2023 | Updated: 6 Sep 2023
In this video, we explore real-time traffic analysis using YOLOv8 and ByteTrack to detect and track vehicles on aerial images. Harnessing the power of Python and Supervision, we delve deep into assigning cars to specific entry zones and understanding their direction of movement. By visualizing their paths, we gain insights into traffic flow across bustling roundabouts... ## 💜 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 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](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!
Owner
- Login: gdlf13
- Kind: user
- Repositories: 1
- Profile: https://github.com/gdlf13
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: Supervision
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Roboflow
email: support@roboflow.com
repository-code: 'https://github.com/roboflow/supervision'
url: 'https://roboflow.github.io/supervision/'
abstract: >-
supervision features a range of utilities for use in
computer vision projects, from detections processing and
filtering to confusion matrix calculation.
keywords:
- computer vision
- image processing
- video processing
license: MIT
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examples/count_people_in_zone/requirements.txt
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