supervision

🚀 Added

• Support for Python 3.12.

🏆 Contributors

@onuralpszr (Onuralp SEZER), @SkalskiP (Piotr Skalski)

🚀 Added

• sv.PixelateAnnotator allowing to pixelate objects on images and videos. (#633)

https://github.com/roboflow/supervision/assets/26109316/c2d4b3b1-fd19-44bb-94ec-f21b28dfd05f

• sv.TriangleAnnotator allowing to annotate images and videos with triangle markers. (#652)

• sv.PolygonAnnotator allowing to annotate images and videos with segmentation mask outline. (#602)

  >>> import supervision as sv
  
  >>> image = ...
  >>> detections = sv.Detections(...)
  
  >>> polygon_annotator = sv.PolygonAnnotator()
  >>> annotated_frame = polygon_annotator.annotate(
  ...     scene=image.copy(),
  ...     detections=detections
  ... )
  

https://github.com/roboflow/supervision/assets/26109316/c9236bf7-6ba4-4799-bf2a-b5532ad3591b

• sv.assets allowing download of video files that you can use in your demos. (#476)

  >>> from supervision.assets import download_assets, VideoAssets
  >>> download_assets(VideoAssets.VEHICLES)
  "vehicles.mp4"
  

• Position.CENTER_OF_MASS allowing to place labels in center of mass of segmentation masks. (#605)

• sv.scale_boxes allowing to scale sv.Detections.xyxy values. (#651)

• sv.calculate_dynamic_text_scale and sv.calculate_dynamic_line_thickness allowing text scale and line thickness to match image resolution. (#637)

• sv.Color.as_hex allowing to extract color value in HEX format. (#620)

• sv.Classifications.from_timm allowing to load classification result from timm models. (#572)

• sv.Classifications.from_clip allowing to load classification result from clip model. (#478)

• sv.Detections.from_azure_analyze_image allowing to load detection results from Azure Image Analysis. (#571)

🌱 Changed

• sv.BoxMaskAnnotator renaming it to sv.ColorAnnotator. (#646)

• sv.MaskAnnotator to make it 5x faster. (#606)

🛠️ Fixed

• sv.DetectionDataset.from_yolo to ignore empty lines in annotation files. (#584)

• sv.BlurAnnotator to trim negative coordinates before bluring detections. (#555)

• sv.TraceAnnotator to respect trace position. (#511)

🏆 Contributors

@onuralpszr (Onuralp SEZER), @hugoles (Hugo Dutra), @karanjakhar (Karan Jakhar), @kim-jeonghyun (Jeonghyun Kim), @fdloopes ( Felipe Lopes), @abhishek7kalra (Abhishek Kalra), @SummitStudiosDev, @xenteros @capjamesg (James Gallagher), @SkalskiP (Piotr Skalski)

🚀 Added

https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce

• sv.BoxMaskAnnotator allowing to annotate images and videos with mox masks. (#422)
• sv.HaloAnnotator allowing to annotate images and videos with halo effect. (#433)

>>> import supervision as sv

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

>>> halo_annotator = sv.HaloAnnotator()
>>> annotated_frame = halo_annotator.annotate(
...     scene=image.copy(),
...     detections=detections
... )

• sv.HeatMapAnnotator allowing to annotate videos with heat maps. (#466)
• sv.DotAnnotator allowing to annotate images and videos with dots. (#492)
• sv.draw_image allowing to draw an image onto a given scene with specified opacity and dimensions. (#449)
• sv.FPSMonitor for monitoring frames per second (FPS) to benchmark latency. (#280)
• 🤗 Hugging Face Annotators space. (#454)

🌱 Changed

• sv.LineZone.trigger now return Tuple[np.ndarray, np.ndarray]. The first array indicates which detections have crossed the line from outside to inside. The second array indicates which detections have crossed the line from inside to outside. (#482)
• Annotator argument name from color_map: str to color_lookup: ColorLookup enum to increase type safety. (#465)
• sv.MaskAnnotator allowing 2x faster annotation. (#426)

🛠️ Fixed

• Poetry env definition allowing proper local installation. (#477)
• sv.ByteTrack to return np.array([], dtype=int) when svDetections is empty. (#430)
• YOLONAS detection missing predication part added & fixed (#416)
• SAM detection at Demo Notebook MaskAnnotator(color_map="index") color_map set to index (#416)

🗑️ Deleted

Warning Deleted sv.Detections.from_yolov8 and sv.Classifications.from_yolov8 as those are now replaced by sv.Detections.from_ultralytics and sv.Classifications.from_ultralytics. (#438)

🏆 Contributors

@hardikdava (Hardik Dava), @onuralpszr (Onuralp SEZER), @kapter, @keshav278 (Keshav Subramanian), @akashpambhar (Akash Pambhar), @AntonioConsiglio (Antonio Consiglio), @ashishdatta, @mario-dg (Mario da Graca), @ jayaBalaR (JAYABALAMBIKA.R), @abhishek7kalra (Abhishek Kalra), @PankajKrana (Pankaj Kumar Rana), @capjamesg (James Gallagher), @SkalskiP (Piotr Skalski)

🚀 Added

https://github.com/roboflow/supervision/assets/26109316/4d6c4a70-b40e-48fc-9e58-23b7e67bf94a

• sv.LabelAnnotator allowing to annotate images and videos with text. (#170)

• sv.BoundingBoxAnnotator allowing to annotate images and videos with bounding boxes. (#170)

• sv.BoxCornerAnnotator allowing to annotate images and videos with just bounding box corners. (#170)

• sv.MaskAnnotator allowing to annotate images and videos with segmentation masks. (#170)

• sv.EllipseAnnotator allowing to annotate images and videos with ellipses (sports game style). (#170)

• sv.CircleAnnotator allowing to annotate images and videos with circles. (#386)

• sv.TraceAnnotator allowing to draw path of moving objects on videos. (#354)

• sv.BlurAnnotator allowing to blur objects on images and videos. (#405)

>>> import supervision as sv

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

>>> bounding_box_annotator = sv.BoundingBoxAnnotator()
>>> annotated_frame = bounding_box_annotator.annotate(
...     scene=image.copy(),
...     detections=detections
... )

• Supervision usage example. You can now learn how to perform traffic flow analysis with Supervision. (#354)

https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900

🌱 Changed

• sv.Detections.from_roboflow now does not require class_list to be specified. The class_id value can be extracted directly from the inference response. (#399)

• sv.VideoSink now allows to customize the output codec. (#381)

• sv.InferenceSlicer can now operate in multithreading mode. (#361)

🛠️ Fixed

• sv.Detections.from_deepsparse to allow processing empty deepsparse result object. (#348)

🏆 Contributors

@hardikdava (Hardik Dava), @onuralpszr (Onuralp SEZER), @Killua7362 (Akshay Bhat), @fcakyon (Fatih C. Akyon), @akashAD98 (Akash A Desai), @Rajarshi-Misra (Rajarshi Misra), @capjamesg (James Gallagher), @SkalskiP (Piotr Skalski)

🚀 Added

• Support for SAHI inference technique with sv.InferenceSlicer. (#282)

>>> import cv2
>>> import supervision as sv
>>> import numpy as np
>>> from ultralytics import YOLO

>>> image = cv2.imread(SOURCE_IMAGE_PATH)
>>> model = YOLO(...)

>>> def callback(image_slice: np.ndarray) -> sv.Detections:
...     result = model(image_slice)[0]
...     return sv.Detections.from_ultralytics(result)

>>> slicer = sv.InferenceSlicer(callback = callback)

>>> detections = slicer(image)

https://github.com/roboflow/supervision/assets/26109316/da665575-4d74-469c-a1f7-a43b7ee7e214

• Detections.from_deepsparse to enable seamless integration with DeepSparse framework. (#297)

• sv.Classifications.from_ultralytics to enable seamless integration with Ultralytics framework. This will enable you to use supervision with all models that Ultralytics supports. (#281)

  Warning sv.Detections.from_yolov8 and sv.Classifications.from_yolov8 are now deprecated and will be removed with supervision-0.16.0 release.

• First supervision usage example script showing how to detect and track objects on video using YOLOv8 + Supervision. (#341)

https://github.com/roboflow/supervision/assets/26109316/d8128440-6bd7-491a-8c7d-519254b76ec5

🌱 Changed

• sv.ClassificationDataset and sv.DetectionDataset now use image path (not image name) as dataset keys. (#296)

🛠️ Fixed

• Detections.from_roboflow to filter out polygons with less than 3 points. (#300)

🏆 Contributors

@hardikdava (Hardik Dava), @onuralpszr (Onuralp SEZER), @mayankagarwals (Mayank Agarwal), @rizavelioglu (Riza Velioglu), @arjun-234 (Arjun D.), @mwitiderrick (Derrick Mwiti), @ShubhamKanitkar32, @gasparitiago (Tiago De Gaspari), @capjamesg (James Gallagher), @SkalskiP (Piotr Skalski)

🚀 Added

• Support for mean average precision (mAP) for object detection models with sv.MeanAveragePrecision. (#236)

>>> import supervision as sv
>>> from ultralytics import YOLO

>>> dataset = sv.DetectionDataset.from_yolo(...)

>>> model = YOLO(...)
>>> def callback(image: np.ndarray) -> sv.Detections:
...     result = model(image)[0]
...     return sv.Detections.from_yolov8(result)

>>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
...     dataset = dataset,
...     callback = callback
... )

>>> mean_average_precision.map50_95
0.433

• Support for ByteTrack for object tracking with sv.ByteTrack. (#256)

>>> import supervision as sv
>>> from ultralytics import YOLO

>>> model = YOLO(...)
>>> byte_tracker = sv.ByteTrack()
>>> annotator = sv.BoxAnnotator()

>>> def callback(frame: np.ndarray, index: int) -> np.ndarray:
...     results = model(frame)[0]
...     detections = sv.Detections.from_yolov8(results)
...     detections = byte_tracker.update_from_detections(detections=detections)
...     labels = [
...         f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}"
...         for _, _, confidence, class_id, tracker_id
...         in detections
...     ]
...     return annotator.annotate(scene=frame.copy(), detections=detections, labels=labels)

>>> sv.process_video(
...     source_path='...',
...     target_path='...',
...     callback=callback
... )

https://github.com/roboflow/supervision/assets/26109316/d5d393f5-e577-474a-bc8c-82483ef8a578

• sv.Detections.from_ultralytics to enable seamless integration with Ultralytics framework. This will enable you to use supervision with all models that Ultralytics supports. (#222)

  Warning sv.Detections.from_yolov8 is now deprecated and will be removed with supervision-0.15.0 release.

• sv.Detections.from_paddledet to enable seamless integration with PaddleDetection framework. (#191)

• Support for loading PASCAL VOC segmentation datasets with sv.DetectionDataset.. (#245)

🏆 Contributors

@hardikdava (Hardik Dava), @kirilllzaitsev (Kirill Zaitsev), @onuralpszr (Onuralp SEZER), @dbroboflow, @mayankagarwals (Mayank Agarwal), @danigarciaoca (Daniel M. García-Ocaña), @capjamesg (James Gallagher), @SkalskiP (Piotr Skalski)

Warning With the supervision-0.12.0 release, we are terminating official support for Python 3.7. (#179)

🚀 Added

• Initial support for object detection model benchmarking with sv.ConfusionMatrix. (#177)

>>> import supervision as sv
>>> from ultralytics import YOLO

>>> dataset = sv.DetectionDataset.from_yolo(...)

>>> model = YOLO(...)
>>> def callback(image: np.ndarray) -> sv.Detections:
...     result = model(image)[0]
...     return sv.Detections.from_yolov8(result)

>>> confusion_matrix = sv.ConfusionMatrix.benchmark(
...     dataset = dataset,
...     callback = callback
... )

>>> confusion_matrix.matrix
array([
    [0., 0., 0., 0.],
    [0., 1., 0., 1.],
    [0., 1., 1., 0.],
    [1., 1., 0., 0.]
])

• Detections.from_mmdetection to enable seamless integration with MMDetection framework. (#173)

• Ability to install package in headless or desktop mode. (#130)

🌱 Changed

• Packing method from setup.py to pyproject.toml. (#180)

🛠️ Fixed

• sv.DetectionDataset.from_cooc can't be loaded when there are images without annotations. (#188)
• sv.DetectionDataset.from_yolo can't load background instances. (#226)

🏆 Contributors

@kirilllzaitsev @hardikdava @onuralpszr @Ucag @SkalskiP @capjamesg

🛠️ Fixed

• as_folder_structure fails to save sv.ClassificationDataset when it is result of inference. (https://github.com/roboflow/supervision/pull/165)

🏆 Contributors

@capjamesg @SkalskiP

🚀 Added

• Ability to load and save sv.DetectionDataset in COCO format using as_coco and from_coco methods. (https://github.com/roboflow/supervision/pull/150)

>>> import supervision as sv

>>> ds = sv.DetectionDataset.from_coco(
...     images_directory_path='...',
...     annotations_path='...'
... )

>>> ds.as_coco(
...     images_directory_path='...',
...     annotations_path='...'
... )

• Ability to marge multiple sv.DetectionDataset together using merge method. (https://github.com/roboflow/supervision/pull/158)

>>> import supervision as sv

>>> 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']

• Additional start and end arguments to sv.get_video_frames_generator allowing to generate frames only for a selected part of the video. (https://github.com/roboflow/supervision/pull/162)

🛠️ Fixed

• Incorrect loading of YOLO dataset class names from data.yaml. (https://github.com/roboflow/supervision/pull/157)

🏆 Contributors

@SkalskiP @hardikdava

🚀 Added

• Ability to load and save sv.ClassificationDataset in a folder structure format. (https://github.com/roboflow/supervision/pull/125)

>>> import supervision as sv

>>> cs = sv.ClassificationDataset.from_folder_structure(
...     root_directory_path='...'
... )

>>> cs.as_folder_structure(
...     root_directory_path='...'
... )

• Support for sv.ClassificationDataset.split allowing to divide sv.ClassificationDataset into two parts. (https://github.com/roboflow/supervision/pull/125)

>>> import supervision as sv

>>> cs = sv.ClassificationDataset(...)
>>> train_cs, test_cs = cs.split(split_ratio=0.7, random_state=42, shuffle=True)

>>> len(train_cs), len(test_cs)
(700, 300)

• Ability to extract masks from Roboflow API results using sv.Detections.from_roboflow. (https://github.com/roboflow/supervision/pull/110)

• Supervision Quickstart notebook where you can learn more about Detection, Dataset and Video APIs.

🌱 Changed

• sv.get_video_frames_generator documentation to better describe actual behavior. (https://github.com/roboflow/supervision/pull/135)

🏆 Contributors

@capjamesg @dankresio @SkalskiP

🚀 Added

• Ability to select sv.Detections by index, list of indexes or slice. Here is an example illustrating the new selection methods. (https://github.com/roboflow/supervision/pull/118)

>>> import supervision as sv

>>> detections = sv.Detections(...)
>>> len(detections[0])
1
>>> len(detections[[0, 1]])
2
>>> len(detections[0:2])
2

• Ability to extract masks from YOLOv8 results using sv.Detections.from_yolov8. Here is an example illustrating how to extract boolean masks from the result of the YOLOv8 model inference. (https://github.com/roboflow/supervision/pull/101)

>>> import cv2
>>> from ultralytics import YOLO
>>> import supervision as sv

>>> image = cv2.imread(...)
>>> image.shape
(640, 640, 3)

>>> model = YOLO('yolov8s-seg.pt')
>>> result = model(image)[0]
>>> detections = sv.Detections.from_yolov8(result)
>>> detections.mask.shape
(2, 640, 640)

• Ability to crop the image using sv.crop. Here is an example showing how to get a separate crop for each detection in sv.Detections. (https://github.com/roboflow/supervision/pull/122)

>>> import cv2
>>> import supervision as sv

>>> image = cv2.imread(...)
>>> detections = sv.Detections(...)
>>> len(detections)
2
>>> crops = [
...     sv.crop(image=image, xyxy=xyxy) 
...     for xyxy 
...     in detections.xyxy
... ]
>>> len(crops)
2

• Ability to conveniently save multiple images into directory using sv.ImageSink. An example shows how to save every tenth video frame as a separate image. (https://github.com/roboflow/supervision/pull/120)

>>> import supervision as sv

>>> with sv.ImageSink(target_dir_path='target/directory/path') as sink:
...     for image in sv.get_video_frames_generator(source_path='source_video.mp4', stride=10):
...         sink.save_image(image=image)

🛠️ Fixed

• Inconvenient handling of sv.PolygonZone coordinates. Now sv.PolygonZone accepts coordinates in the form of [[x1, y1], [x2, y2], ...] that can be both integers and floats. (https://github.com/roboflow/supervision/issues/106)

🏆 Contributors

@SkalskiP @lomnes-atlast-food @hardikdava

🚀 Added

• Support for dataset inheritance. The current Dataset got renamed to DetectionDataset. Now DetectionDataset inherits from BaseDataset. This change was made to enforce the future consistency of APIs of different types of computer vision datasets. (https://github.com/roboflow/supervision/pull/100)
• Ability to save datasets in YOLO format using DetectionDataset.as_yolo. (https://github.com/roboflow/supervision/pull/100)

>>> import supervision as sv

>>> ds = sv.DetectionDataset(...)
>>> ds.as_yolo(
...     images_directory_path='...',
...     annotations_directory_path='...',
...     data_yaml_path='...'
... )

• Support for DetectionDataset.split allowing to divide DetectionDataset into two parts. (https://github.com/roboflow/supervision/pull/102)

>>> import supervision as sv

>>> ds = sv.DetectionDataset(...)
>>> train_ds, test_ds = ds.split(split_ratio=0.7, random_state=42, shuffle=True)

>>> len(train_ds), len(test_ds)
(700, 300)

🌱 Changed

• Default value of approximation_percentage parameter from 0.75 to 0.0 in DetectionDataset.as_yolo and DetectionDataset.as_pascal_voc. (https://github.com/roboflow/supervision/pull/100)

🏆 Contributors

• @SkalskiP

🚀 Added

• Detections.from_yolo_nas to enable seamless integration with YOLO-NAS model. (https://github.com/roboflow/supervision/pull/91)
• Ability to load datasets in YOLO format using Dataset.from_yolo. (https://github.com/roboflow/supervision/pull/86)
• Detections.merge to merge multiple Detections objects together. (https://github.com/roboflow/supervision/pull/84)

🌱 Changed

• LineZoneAnnotator.annotate to allow for the custom text for the in and out tags. (https://github.com/roboflow/supervision/pull/44)

🛠️ Fixed

• LineZoneAnnotator.annotate does not return annotated frame. (https://github.com/roboflow/supervision/pull/81)

🏆 Contributors

• @SkalskiP
• @iPoe
• @hardikdava

🚀 Added

• Initial Dataset support and ability to save Detections in Pascal VOC XML format. (https://github.com/roboflow/supervision/pull/71)
• New mask_to_polygons, filter_polygons_by_area, polygon_to_xyxy and approximate_polygon utilities. (https://github.com/roboflow/supervision/pull/71)
• Ability to load Pascal VOC XML object detections dataset as Dataset. (https://github.com/roboflow/supervision/pull/72)

🌱 Changed

• order of Detections attributes to make it consistent with order of objects in __iter__ tuple. (https://github.com/roboflow/supervision/pull/70)
• generate_2d_mask to polygon_to_mask. (https://github.com/roboflow/supervision/pull/71)

🏆 Contributors

• @SkalskiP
• @alexandercarruthers

🛠️ Fixed

• Fixed LineZone.trigger function expects 4 values instead of 5 (https://github.com/roboflow/supervision/pull/63)

🏆 Contributors

• @SkalskiP @ChaseDDevelopment

🛠️ Fixed

• Fixed Detections.__getitem__ method did not return mask for selected item.
• Fixed Detections.area crashed for mask detections.

🏆 Contributors

• @SkalskiP

🚀 Added

• Detections.mask to enable segmentation support. (https://github.com/roboflow/supervision/pull/58)
• MaskAnnotator to allow easy Detections.mask annotation. (https://github.com/roboflow/supervision/pull/58)
• Detections.from_sam to enable native Segment Anything Model (SAM) support. (https://github.com/roboflow/supervision/pull/58)

🌱 Changed

• Detections.area behaviour to work not only with boxes but also with masks. (https://github.com/roboflow/supervision/pull/58)

🏆 Contributors

• @SkalskiP

🚀 Added

• Detections.empty to allow easy creation of empty Detections objects. (https://github.com/roboflow/supervision/discussions/48)
• Detections.from_roboflow to allow easy creation of Detections objects from Roboflow API inference results. (https://github.com/roboflow/supervision/pull/56)
• plot_images_grid to allow easy plotting of multiple images on single plot. (https://github.com/roboflow/supervision/pull/56)
• Initial support for Pascal VOC XML format with detections_to_voc_xml method. (https://github.com/roboflow/supervision/pull/56)

🌱 Changed

• show_frame_in_notebook refactored and renamed to plot_image. (https://github.com/roboflow/supervision/pull/56)

🏆 Contributors

• @SkalskiP

🌱 Changed

• Drop requirement for class_id in sv.Detections (https://github.com/roboflow/supervision/pull/50) to make it more flexible

🏆 Contributors

• @SkalskiP

🌱 Changed

• Detections.wth_nms support class agnostic and non-class agnostic case (https://github.com/roboflow/supervision/pull/36)

🛠️ Fixed

• PolygonZone throws an exception when the object touches the bottom edge of the image (https://github.com/roboflow/supervision/issues/41)
• Detections.wth_nms method throws an exception when Detections is empty (https://github.com/roboflow/supervision/issues/42)

🏆 Contributors

• @SkalskiP

🚀 Added

New methods in sv.Detections API:

• from_transformers - convert Object Detection 🤗 Transformer result into sv.Detections
• from_detectron2 - convert Detectron2 result into sv.Detections
• from_coco_annotations - convert COCO annotation into sv.Detections
• area - dynamically calculated property storing bbox area
• with_nms - initial implementation (only class agnostic) of sv.Detections NMS

🌱 Changed

• Make sv.Detections.confidence field Optional.

🏆 Contributors

• @SkalskiP

🔪 Killer features

• Support for PolygonZone and PolygonZoneAnnotator 🔥

<details> <summary>👉 Code example</summary>

import numpy as np
import supervision as sv
from ultralytics import YOLO

# initiate polygon zone
polygon = np.array([
    [1900, 1250],
    [2350, 1250],
    [3500, 2160],
    [1250, 2160]
])
video_info = sv.VideoInfo.from_video_path(MALL_VIDEO_PATH)
zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=video_info.resolution_wh)

# initiate annotators
box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
zone_annotator = sv.PolygonZoneAnnotator(zone=zone, color=sv.Color.white(), thickness=6, text_thickness=6, text_scale=4)

# extract video frame
generator = sv.get_video_frames_generator(MALL_VIDEO_PATH)
iterator = iter(generator)
frame = next(iterator)

# detect
model = YOLO('yolov8s.pt')
results = model(frame, imgsz=1280)[0]
detections = sv.Detections.from_yolov8(results)
detections = detections[detections.class_id == 0]
zone.trigger(detections=detections)

# annotate
box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
labels = [f"{model.names[class_id]} {confidence:0.2f}" for _, confidence, class_id, _ in detections]
frame = box_annotator.annotate(scene=frame, detections=detections, labels=labels)
frame = zone_annotator.annotate(scene=frame)

</details>

• Advanced vs.Detections filtering with pandas-like API.

detections = detections[(detections.class_id == 0) & (detections.confidence > 0.5)]

• Improved integration with YOLOv5 and YOLOv8 models.

import torch
import supervision as sv

model = torch.hub.load('ultralytics/yolov5', 'yolov5x6')
results = model(frame, size=1280)
detections = sv.Detections.from_yolov5(results)

from ultralytics import YOLO
import supervision as sv

model = YOLO('yolov8s.pt')
results = model(frame, imgsz=1280)[0]
detections = sv.Detections.from_yolov8(results)

🚀 Added

• supervision.get_polygon_center function - takes in a polygon as a 2-dimensional numpy.ndarray and returns the center of the polygon as a Point object
• supervision.draw_polygon function - draw a polygon on a scene
• supervision.draw_text function - draw a text on a scene
• supervision.ColorPalette.default() - class method - to generate default ColorPalette
• supervision.generate_2d_mask function - generate a 2D mask from a polygon
• supervision.PolygonZone class - to define polygon zones and validate if supervision.Detections are in the zone
• supervision.PolygonZoneAnnotator class - to draw supervision.PolygonZone on scene

🌱 Changed

• VideoInfo API - change the property name resolution -> resolution_wh to make it more descriptive; convert VideoInfo to dataclass
• process_frame API - change argument name frame -> scene to make it consistent with other classes and methods
• LineCounter API - rename class LineCounter -> LineZone to make it consistent with PolygonZone
• LineCounterAnnotator API - rename class LineCounterAnnotator -> LineZoneAnnotator

🏆 Contributors

• @SkalskiP
• @capjamesg

🚀 Added

• ⓒ Add project license
• 🎨 DEFAULT_COLOR_PALETTE, Color, and ColorPalette classes
• 📐 initial implementation of Point, Vector, and Rect classes
• 🎬 VideoInfo and VideoSink classes as well as get_video_frames_generator -📓 show_frame_in_notebook util
• 🖌️ draw_line, draw_rectangle, draw_filled_rectangle utils added
• 📦 Initial version Detections and BoxAnnotator added
• 🧮 initial implementation of LineCounter and LineCounterAnnotator classes

🏆 Contributors

@SkalskiP