supervision-0.10.0

🚀 Added

• Ability to load and save sv.ClassificationDataset in a folder structure format. (#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. (#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. (#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. (#135)

🏆 Contributors

@capjamesg @dankresio @SkalskiP

supervision-0.9.0

🚀 Added

• Ability to select sv.Detections by index, list of indexes or slice. Here is an example illustrating the new selection methods. (#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. (#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. (#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. (#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. (#106)

🏆 Contributors

@SkalskiP @lomnes-atlast-food @hardikdava

supervision-0.8.0

🚀 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. (#100)
• Ability to save datasets in YOLO format using DetectionDataset.as_yolo. (#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. (#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. (#100)

🏆 Contributors

• @SkalskiP

supervision-0.7.0

🚀 Added

• Detections.from_yolo_nas to enable seamless integration with YOLO-NAS model. (#91)
• Ability to load datasets in YOLO format using Dataset.from_yolo. (#86)
• Detections.merge to merge multiple Detections objects together. (#84)

🌱 Changed

• LineZoneAnnotator.annotate to allow for the custom text for the in and out tags. (#44)

🛠️ Fixed

• LineZoneAnnotator.annotate does not return annotated frame. (#81)

🏆 Contributors

• @SkalskiP
• @iPoe
• @hardikdava

supervision-0.6.0

🚀 Added

• Initial Dataset support and ability to save Detections in Pascal VOC XML format. (#71)
• New mask_to_polygons, filter_polygons_by_area, polygon_to_xyxy and approximate_polygon utilities. (#71)
• Ability to load Pascal VOC XML object detections dataset as Dataset. (#72)

🌱 Changed

• order of Detections attributes to make it consistent with order of objects in __iter__ tuple. (#70)
• generate_2d_mask to polygon_to_mask. (#71)

🏆 Contributors

• @SkalskiP
• @alexandercarruthers

supervision-0.5.2

🛠️ Fixed

• Fixed LineZone.trigger function expects 4 values instead of 5 (#63)

🏆 Contributors

• @SkalskiP @ChaseDDevelopment

supervision-0.5.1

🛠️ Fixed

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

🏆 Contributors

• @SkalskiP

supervision-0.5.0

🚀 Added

• Detections.mask to enable segmentation support. (#58)
• MaskAnnotator to allow easy Detections.mask annotation. (#58)
• Detections.from_sam to enable native Segment Anything Model (SAM) support. (#58)

🌱 Changed

• Detections.area behaviour to work not only with boxes but also with masks. (#58)

🏆 Contributors

• @SkalskiP

supervision-0.4.0

🚀 Added

• Detections.empty to allow easy creation of empty Detections objects. (#48)
• Detections.from_roboflow to allow easy creation of Detections objects from Roboflow API inference results. (#56)
• plot_images_grid to allow easy plotting of multiple images on single plot. (#56)
• Initial support for Pascal VOC XML format with detections_to_voc_xml method. (#56)

🌱 Changed

• show_frame_in_notebook refactored and renamed to plot_image. (#56)

🏆 Contributors

• @SkalskiP

supervision-0.3.2

🌱 Changed

• Drop requirement for class_id in sv.Detections (#50) to make it more flexible

🏆 Contributors

• @SkalskiP