📅 Timeline

supervision-0.19.0 release is around the corner. Here is the timeline:

• 14 Mar 2024 08:00 PM CEST (UTC +2) / 23 Jan 2024 11:00 AM PDT (UTC -7) - merge develop into main - closing list supervision-0.19.0 features
• 15 Mar 2023 11:00 AM CEST (UTC +2) / 24 Jan 2023 02:00 AM PDT (UTC -7) - release supervision-0.19.0

🪵 Changelog

🧑‍🍳 Cookbooks

Supervision Cookbooks - A curated open-source collection crafted by the community, offering practical examples, comprehensive guides, and walkthroughs for leveraging Supervision alongside diverse Computer Vision models. (#860)

🚀 Added

• sv.CSVSink allowing for the straightforward saving of image, video, or stream inference results in a .csv file. (#818)

import supervision as sv
from ultralytics import YOLO

model = YOLO(<SOURCE_MODEL_PATH>)
csv_sink = sv.CSVSink(<RESULT_CSV_FILE_PATH>)
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with csv_sink:
    for frame in frames_generator:
        result = model(frame)[0]
        detections = sv.Detections.from_ultralytics(result)
        csv_sink.append(detections, custom_data={<CUSTOM_LABEL>:<CUSTOM_DATA>})

• sv.JSONSink allowing for the straightforward saving of image, video, or stream inference results in a .json file. (#819)

import supervision as sv
from ultralytics import YOLO

model = YOLO(<SOURCE_MODEL_PATH>)
json_sink = sv.JSONSink(<RESULT_JSON_FILE_PATH>)
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with json_sink:
    for frame in frames_generator:
        result = model(frame)[0]
        detections = sv.Detections.from_ultralytics(result)
        json_sink.append(detections, custom_data={<CUSTOM_LABEL>:<CUSTOM_DATA>})

• sv.mask_iou_batch allowing to compute Intersection over Union (IoU) of two sets of masks. (#847)
• sv.mask_non_max_suppression allowing to perform Non-Maximum Suppression (NMS) on segmentation predictions. (#847)
• sv.CropAnnotator allowing users to annotate the scene with scaled-up crops of detections. (#888)

import cv2
import supervision as sv
from inference import get_model

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_model(model_id="yolov8n-640")

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

crop_annotator = sv.CropAnnotator()
annotated_frame = crop_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

🌱 Changed

• sv.ByteTrack.reset allowing users to clear trackers state, enabling the processing of multiple video files in sequence. (#827)
• sv.LineZoneAnnotator allowing to hide in/out count using display_in_count and display_out_count properties. (#802)
• sv.ByteTrack input arguments and docstrings updated to improve readability and ease of use. (#787)

• sv.PolygonZone to now accept a list of specific box anchors that must be in zone for a detection to be counted. (#910)

• Annotators adding support for Pillow images. All supervision Annotators can now accept an image as either a numpy array or a Pillow Image. They automatically detect its type, draw annotations, and return the output in the same format as the input. (#875)

🛠️ Fixed

• sv.DetectionsSmoother removing tracking_id from sv.Detections. (#944)
• sv.DetectionDataset which, after changes introduced in supervision-0.18.0, failed to load datasets in YOLO, PASCAL VOC, and COCO formats.

🏆 Contributors

@onuralpszr (Onuralp SEZER), @LinasKo (Linas Kondrackis), @LeviVasconcelos (Levi Vasconcelos), @AdonaiVera (Adonai Vera), @xaristeidou (Christoforos Aristeidou), @Kadermiyanyedi (Kader Miyanyedi), @NickHerrig (Nick Herrig), @PacificDou (Shuyang Dou), @iamhatesz (Tomasz Wrona), @capjamesg (James Gallagher), @sansyo, @SkalskiP (Piotr Skalski)