supervision-0.20.0 release

[SkalskiP]

📅 Timeline

The supervision-0.20.0 release is around the corner. Here is the timeline:

• 24 Apr 2024 08:00 PM CEST (UTC +2) / 24 Apr 2024 11:00 AM PDT (UTC -7) - merge develop into main - closing list supervision-0.20.0 features
• 25 Apr 2023 11:00 AM CEST (UTC +2) / 25 Apr 2023 02:00 AM PDT (UTC -7) - release supervision-0.20.0

🪵 Changelog

🚀 Added

• sv.KeyPoints to provide initial support for pose estimation and broader keypoint detection models. (#1128)

• sv.EdgeAnnotator and sv.VertexAnnotator to enable rendering of results from keypoint detection models. (#1128)

import cv2
import supervision as sv
from ultralytics import YOLO

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO('yolov8l-pose')

result = model(image, verbose=False)[0]
keypoints = sv.KeyPoints.from_ultralytics(result)

edge_annotators = sv.EdgeAnnotator(color=sv.Color.GREEN, thickness=5)
annotated_image = edge_annotators.annotate(image.copy(), keypoints)

import cv2
import supervision as sv
from ultralytics import YOLO

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO('yolov8l-pose')

result = model(image, verbose=False)[0]
keypoints = sv.KeyPoints.from_ultralytics(result)

vertex_annotators = sv.VertexAnnotator(color=sv.Color.GREEN, radius=10)
annotated_image = vertex_annotators.annotate(image.copy(), keypoints)

🌱 Changed

• sv.LabelAnnotator by adding an additional corner_radius argument that allows for rounding the corners of the bounding box. (#1037)

• sv.PolygonZone such that the frame_resolution_wh argument is no longer required to initialize sv.PolygonZone. (#1109)

Warning

The frame_resolution_wh parameter in sv.PolygonZone is deprecated and will be removed in supervision-0.24.0.

• sv.get_polygon_center to calculate a more accurate polygon centroid. (#1084)

• sv.Detections.from_transformers by adding support for Transformers segmentation models and extract class names values. (#1069)

import torch
import supervision as sv
from PIL import Image
from transformers import DetrImageProcessor, DetrForSegmentation

processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")

image = Image.open(<SOURCE_IMAGE_PATH>)
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

width, height = image.size
target_size = torch.tensor([[height, width]])
results = processor.post_process_segmentation(
    outputs=outputs, target_sizes=target_size)[0]
detections = sv.Detections.from_transformers(results, id2label=model.config.id2label)

mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)

annotated_image = mask_annotator.annotate(
    scene=image, detections=detections)
annotated_image = label_annotator.annotate(
    scene=annotated_image, detections=detections)

🛠️ Fixed

• sv.ByteTrack.update_with_detections which was removing segmentation masks while tracking. Now, ByteTrack can be used alongside segmentation models. (#787)

🏆 Contributors

@onuralpszr (Onuralp SEZER), @rolson24 (Raif Olson), @xaristeidou (Christoforos Aristeidou), @jeslinpjames (Jeslin P James), @Griffin-Sullivan (Griffin Sullivan), @PawelPeczek-Roboflow (Paweł Pęczek), @pirnerjonas (Jonas Pirner), @sharingan000, @macc-n, @LinasKo (Linas Kondrackis), @SkalskiP (Piotr Skalski)