Show HN: Plug-and-play Python utils for any computer-vision pipeline

3 months ago 4

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! 🤝

Pip install the supervision package in a Python>=3.9 environment.

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, or MMDetection.

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 requires a Roboflow API KEY.

import cv2 import supervision as sv from inference import get_model image = cv2.imread(...) model = get_model(model_id="yolov8s-640", 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(...) detections = sv.Detections(...) box_annotator = sv.BoxAnnotator() annotated_frame = box_annotator.annotate( scene=image.copy(), detections=detections)

supervision-0.16.0-annotators.mp4

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

👉 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

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.

Did you build something cool using supervision? Let us know!

football-players-tracking-25.mp4