supervision-0.22.0

🚀 Added

• Supervision Cheatsheet 🔥

• sv.KeyPoints.from_mediapipe adding support for Mediapipe keypoint models (both legacy and modern), along with default visualizers for face and body pose keypoints. (#1232, #1316)

import 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)

IMG_1777-result-refined-optimized.mp4

• 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)

rich-label-annotator-2.mp4

🌱 Changed

• 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)

🛠️ Fixed

• sv.ColorAnnotator and sv.CropAnnotator buggy behaviours. (#1277, #1312)

🧑‍🍳 Cookbooks

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!

• Serialize detections to JSON (#975)
• Serialize detections to CSV (#975).

🏆 Contributors

@onuralpszr (Onuralp SEZER), @David-rn (David Redó), @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!