roboflow/supervision 0.18.0

supervision-0.18.0

on GitHub

🚀 Added

• sv.PercentageBarAnnotator allowing to annotate images and videos with percentage values representing confidence or other custom property. (#720)

import supervision as sv

image = ...
detections = sv.Detections(...)

percentage_bar_annotator = sv.PercentageBarAnnotator()
annotated_frame = percentage_bar_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

• sv.RoundBoxAnnotator allowing to annotate images and videos with rounded corners bounding boxes. (#702)
• sv.DetectionsSmoother allowing for smoothing detections over multiple frames in video tracking. (#696)

• sv.OrientedBoxAnnotator allowing to annotate images and videos with OBB (Oriented Bounding Boxes). (#770)

import cv2
import supervision as sv
from ultralytics import YOLO

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO("yolov8n-obb.pt")

result = model(image)[0]
detections = sv.Detections.from_ultralytics(result)

oriented_box_annotator = sv.OrientedBoxAnnotator()
annotated_frame = oriented_box_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

• sv.ColorPalette.from_matplotlib allowing users to create a sv.ColorPalette instance from a Matplotlib color palette. (#769)

import supervision as sv

sv.ColorPalette.from_matplotlib('viridis', 5)
# ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=59, g=82, b=139), ...])

🌱 Changed

• sv.Detections.from_ultralytics adding support for OBB (Oriented Bounding Boxes). (#770)
• sv.LineZone to now accept a list of specific box anchors that must cross the line for a detection to be counted. This update marks a significant improvement from the previous requirement, where all four box corners were necessary. Users can now specify a single anchor, such as sv.Position.BOTTOM_CENTER, or any other combination of anchors defined as List[sv.Position]. (#735)
• sv.Detections to support custom payload. (#700)
• sv.Color's and sv.ColorPalette's method of accessing predefined colors, transitioning from a function-based approach (sv.Color.red()) to a more intuitive and conventional property-based method (sv.Color.RED). (#756) (#769)

• sv.ColorPalette.DEFAULT value, giving users a more extensive set of annotation colors. (#769)

• sv.Detections.from_roboflow to sv.Detections.from_inference streamlining its functionality to be compatible with both the both inference pip package and the Roboflow hosted API. (#677)

import cv2
import supervision as sv
from inference.models.utils import get_roboflow_model

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_roboflow_model(model_id="yolov8s-640")

result = model.infer(image)[0]
detections = sv.Detections.from_inference(result)

🛠️ Fixed

• sv.LineZone functionality to accurately update the counter when an object crosses a line from any direction, including from the side. This enhancement enables more precise tracking and analytics, such as calculating individual in/out counts for each lane on the road. (#735)

🏆 Contributors

@onuralpszr (Onuralp SEZER), @HinePo (Rafael Levy), @xaristeidou (Christoforos Aristeidou), @revtheundead (Utku Özbek), @paulguerrie (Paul Guerrie), @yeldarby (Brad Dwyer), @capjamesg (James Gallagher), @SkalskiP (Piotr Skalski)