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Changelog
We write your reusable computer vision tools. π
Last updated 3 days ago
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
Stable Diffusion web UI
π€ Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
A feature-rich command-line audio/video downloader
sv.KeyPoints.from_mediapipeimport numpy as np
import mediapipe as mp
import supervision as sv
from PIL import Image
model = mp.solutions.face_mesh.FaceMesh()
edge_annotator = sv.EdgeAnnotator(color=sv.Color.BLACK, thickness=2)
image = Image.open(<PATH_TO_IMAGE>).convert('RGB')
results = model.process(np.array(image))
key_points = sv.KeyPoints.from_mediapipe(results, resolution_wh=image.size)
annotated_image = edge_annotator.annotate(scene=image, key_points=key_points)
https://github.com/user-attachments/assets/883a6bcc-5e39-41b0-9b6d-0348b5b2fe0e
sv.KeyPoints.from_detectron2 and sv.Detections.from_detectron2 extending support for Detectron2 models. (#1310, #1300)
sv.RichLabelAnnotator allowing to draw unicode characters (e.g. from non-latin languages), as long as you provide a compatible font. (#1277)
https://github.com/user-attachments/assets/de60eeb4-1259-421b-af66-f622a15988ea
sv.DetectionsDataset and sv.ClassificationDataset allowing to load the images into memory only when necessary (lazy loading). (#1326)import roboflow
from roboflow import Roboflow
import supervision as sv
roboflow.login()
rf = Roboflow()
project = rf.workspace(<WORKSPACE_ID>).project(<PROJECT_ID>)
dataset = project.version(<PROJECT_VERSION>).download("coco")
ds_train = sv.DetectionDataset.from_coco(
images_directory_path=f"{dataset.location}/train",
annotations_path=f"{dataset.location}/train/_annotations.coco.json",
)
path, image, annotation = ds_train[0]
# loads image on demand
for path, image, annotation in ds_train:
# loads image on demand
sv.Detections.from_lmm allowing to parse Florence-2 text result into sv.Detections object. (#1296)sv.DotAnnotator and sv.TriangleAnnotator allowing to add marker outlines. (#1294)sv.ColorAnnotator and sv.CropAnnotator buggy behaviours. (#1277, #1312)This release, @onuralpszr added two new Cookbooks to our collection. Check them out to learn how to save Detections to a file and convert it back to Detections!
@onuralpszr (Onuralp SEZER), @David-rn (David RedΓ³), @jeslinpjames (Jeslin P James), @Bhavay-2001 (Bhavay Malhotra), @hardikdava (Hardik Dava), @kirilman, @dsaha21 (Dripto Saha), @cdragos (Dragos Catarahia), @mqasim41 (Muhammad Qasim), @SkalskiP (Piotr Skalski), @LinasKo (Linas Kondrackis)
Special thanks to @rolson24 (Raif Olson) for helping the community with ByteTrack!