A set of easy-to-use utils that will come in handy in any Computer Vision project
Project description
👋 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 3.11>=Python>=3.7 environment.
pip install supervision
👉 install from source
# clone repository and navigate to root directory
git clone https://github.com/roboflow/supervision.git
cd supervision
# setup python environment and activate it
python3 -m venv venv
source venv/bin/activate
# install
pip install -e ".[dev]"
🔥 quickstart
detections processing
>>> import supervision as sv
>>> from ultralytics import YOLO
>>> model = YOLO('yolov8s.pt')
>>> result = model(IMAGE)[0]
>>> detections = sv.Detections.from_yolov8(result)
>>> len(detections)
5
👉 more detections utils
-
Easily switch inference pipeline between supported object detection / instance segmentation models
>>> import supervision as sv >>> from segment_anything import sam_model_registry, SamAutomaticMaskGenerator >>> sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(device=DEVICE) >>> mask_generator = SamAutomaticMaskGenerator(sam) >>> sam_result = mask_generator.generate(IMAGE) >>> detections = sv.Detections.from_sam(sam_result=sam_result)
-
>>> detections = detections[detections.class_id == 0] >>> detections = detections[detections.confidence > 0.5] >>> detections = detections[detections.area > 1000]
-
Image annotation
>>> import supervision as sv >>> box_annotator = sv.BoxAnnotator() >>> annotated_frame = box_annotator.annotate( ... scene=IMAGE, ... detections=detections ... )
datasets processing
>>> import supervision as sv
>>> dataset = sv.DetectionDataset.from_yolo(
... images_directory_path='...',
... annotations_directory_path='...',
... data_yaml_path='...'
... )
>>> dataset.classes
['dog', 'person']
>>> len(dataset)
1000
👉 more dataset utils
-
Load object detection / instance segmentation datasets in one of supported formats
>>> 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='...' ... )
-
Loop over dataset entries
>>> for name, image, labels in dataset: ... print(labels.xyxy) array([[404. , 719. , 538. , 884.5 ], [155. , 497. , 404. , 833.5 ], [ 20.154999, 347.825 , 416.125 , 915.895 ]], dtype=float32)
-
Split dataset for training, testing and validation
>>> 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)
-
Save object detection / instance segmentation datasets in one of supported formats
>>> dataset.as_yolo( ... images_directory_path='...', ... annotations_directory_path='...', ... data_yaml_path='...' ... ) >>> dataset.as_pascal_voc( ... images_directory_path='...', ... annotations_directory_path='...' ... )
-
Convert labels between suppoted formats
>>> sv.DetectionDataset.from_yolo( ... images_directory_path='...', ... annotations_directory_path='...', ... data_yaml_path='...' ... ).as_pascal_voc( ... images_directory_path='...', ... annotations_directory_path='...' ... )
🎬 tutorials
Accelerate Image Annotation with SAM and Grounding DINO
Discover how to speed up your image annotation process using Grounding DINO and Segment Anything Model (SAM). Learn how to convert object detection datasets into instance segmentation datasets, and see the potential of using these models to automatically annotate your datasets for real-time detectors like YOLOv8...
SAM - Segment Anything Model by Meta AI: Complete Guide
Discover the incredible potential of Meta AI's Segment Anything Model (SAM)! We dive into SAM, an efficient and promptable model for image segmentation, which has revolutionized computer vision tasks. With over 1 billion masks on 11M licensed and privacy-respecting images, SAM's zero-shot performance is often competitive with or even superior to prior fully supervised results...
📚 documentation
Curious how Supervision can help you solve problems on your project? Visit our documentation page!
🏆 contribution
We love your input! Please see our contributing guide to get started. Thank you 🙏 to all our contributors!
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