object_detection
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○Academic publication links
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (12.2%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: Rakeshbabu17
- License: mit
- Default Branch: main
- Size: 27.8 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
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://snyk.io/advisor/python/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 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, 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 import get_model image = cv2.imread(...) model = get_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(...)
boxannotator = sv.BoxAnnotator() annotatedframe = 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.
```python import supervision as sv from roboflow import Roboflow
project = Roboflow().workspace(
ds = sv.DetectionDataset.fromcoco( imagesdirectorypath=f"{dataset.location}/train", annotationspath=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 ```
👉 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=... ) ```
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.
💜 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.
Owner
- Login: Rakeshbabu17
- Kind: user
- Repositories: 1
- Profile: https://github.com/Rakeshbabu17
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