supervision-0.23.0

🚀 Added

• BackgroundOverlayAnnotator annotates the background of your image! #1385

pexels-squirrel-short-result-optim.mp4

(video by Pexels)

• We're introducing metrics, which currently supports xyxy boxes and masks. Over the next few releases, supervision will focus on adding more metrics, allowing you to evaluate your model performance. We plan to support not just boxes, masks, but oriented bounding boxes as well! #1442

Tip

Help in implementing metrics is very welcome! Keep an eye on our issue board if you'd like to contribute!

import supervision as sv
from supervision.metrics import MeanAveragePrecision

predictions = sv.Detections(...)
targets = sv.Detections(...)

map_metric = MeanAveragePrecision()
map_result = map_metric.update(predictions, targets).compute()

print(map_result)
print(map_result.map50_95)
print(map_result.large_objects.map50_95)
map_result.plot()

Here's a very basic way to compare model results:

📊 Example code

  import supervision as sv
  from supervision.metrics import MeanAveragePrecision
  from inference import get_model
  import matplotlib.pyplot as plt
  
  # !wget https://media.roboflow.com/notebooks/examples/dog.jpeg
  image = "dog.jpeg"
  
  model_1 = get_model("yolov8n-640")
  model_2 = get_model("yolov8s-640")
  model_3 = get_model("yolov8m-640")
  model_4 = get_model("yolov8l-640")
  
  results_1 = model_1.infer(image)[0]
  results_2 = model_2.infer(image)[0]
  results_3 = model_3.infer(image)[0]
  results_4 = model_4.infer(image)[0]
  
  detections_1 = sv.Detections.from_inference(results_1)
  detections_2 = sv.Detections.from_inference(results_2)
  detections_3 = sv.Detections.from_inference(results_3)
  detections_4 = sv.Detections.from_inference(results_4)
  
  map_n_metric = MeanAveragePrecision().update([detections_1], [detections_4]).compute()
  map_s_metric = MeanAveragePrecision().update([detections_2], [detections_4]).compute()
  map_m_metric = MeanAveragePrecision().update([detections_3], [detections_4]).compute()
  
  labels = ["YOLOv8n", "YOLOv8s", "YOLOv8m"]
  map_values = [map_n_metric.map50_95, map_s_metric.map50_95, map_m_metric.map50_95]
  
  plt.title("YOLOv8 Model Comparison")
  plt.bar(labels, map_values)
  ax = plt.gca()
  ax.set_ylim([0, 1])
  plt.show()

• Added the IconAnnotator, which allows you to place icons on your images. #930

example-icon-annotator-optim.mp4

(Video by Pexels, icons by Icons8)

import supervision as sv
from inference import get_model

image = <SOURCE_IMAGE_PATH>
icon_dog = <DOG_PNG_PATH>
icon_cat = <CAT_PNG_PATH>

model = get_model(model_id="yolov8n-640")
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)

icon_paths = []
for class_name in detections.data["class_name"]:
    if class_name == "dog":
        icon_paths.append(icon_dog)
    elif class_name == "cat":
        icon_paths.append(icon_cat)
    else:
        icon_paths.append("")

icon_annotator = sv.IconAnnotator()
annotated_frame = icon_annotator.annotate(
    scene=image.copy(),
    detections=detections,
    icon_path=icon_paths
)

• Segment Anything 2 was released this month. And while you can load its results via from_sam, we've added support to from_ultralytics for loading the results if you ran it with Ultralytics. #1354

import cv2
import supervision as sv
from ultralytics import SAM

image = cv2.imread("...")

model = SAM("mobile_sam.pt")
results = model(image, bboxes=[[588, 163, 643, 220]])
detections = sv.Detections.from_ultralytics(results[0])

polygon_annotator = sv.PolygonAnnotator()
mask_annotator = sv.MaskAnnotator()

annoated_image = mask_annotator.annotate(image.copy(), detections)
annoated_image = polygon_annotator.annotate(annoated_image, detections)

sv.plot_image(annoated_image, (12,12))

SAM2 with our annotators:

pexels_cheetah-result-optim-halfsized.mp4

• TriangleAnnotator and DotAnnotator contour color customization #1458
• VertexLabelAnnotator for keypoints now has text_color parameter #1409

🌱 Changed

• Updated sv.Detections.from_transformers to support the transformers v5 functions. This includes the DetrImageProcessor methods post_process_object_detection, post_process_panoptic_segmentation, post_process_semantic_segmentation, and post_process_instance_segmentation. #1386
• InferenceSlicer now features an overlap_ratio_wh parameter, making it easier to compute slice sizes when handling overlapping slices. #1434

image_with_small_objects = cv2.imread("...")
model = get_model("yolov8n-640")

def callback(image_slice: np.ndarray) -> sv.Detections:
    print("image_slice.shape:", image_slice.shape)
    result = model.infer(image_slice)[0]
    return sv.Detections.from_inference(result)

slicer = sv.InferenceSlicer(
    callback=callback,
    slice_wh=(128, 128),
    overlap_ratio_wh=(0.2, 0.2),
)

detections = slicer(image_with_small_objects)

🛠️ Fixed

• Annotator type fixes #1448
• New way of seeking to a specific video frame, where other methods don't work #1348
• plot_image now clearly states the size is in inches. #1424

⚠️ Deprecated

• overlap_filter_strategy in InferenceSlicer.__init__ is deprecated and will be removed in supervision-0.27.0. Use overlap_strategy instead.
• overlap_ratio_wh in InferenceSlicer.__init__ is deprecated and will be removed in supervision-0.27.0. Use overlap_wh instead.

❌ Removed

• The track_buffer, track_thresh, and match_thresh parameters in ByteTrack are deprecated and were removed as of supervision-0.23.0. Use lost_track_buffer, track_activation_threshold, and minimum_matching_threshold instead.
• The triggering_position parameter in sv.PolygonZone was removed as of supervision-0.23.0. Use triggering_anchors instead.

🏆 Contributors

@shaddu, @onuralpszr (Onuralp SEZER), @Kadermiyanyedi (Kader Miyanyedi), @xaristeidou (Christoforos Aristeidou), @Gk-rohan (Rohan Gupta), @Bhavay-2001 (Bhavay Malhotra), @arthurcerveira (Arthur Cerveira), @J4BEZ (Ju Hoon Park), @venkatram-dev, @eric220, @capjamesg (James), @yeldarby (Brad Dwyer), @SkalskiP (Piotr Skalski), @LinasKo (LinasKo)

supervision-0.22.0

🚀 Added

• Supervision Cheatsheet 🔥

• sv.KeyPoints.from_mediapipe adding support for Mediapipe keypoint models (both legacy and modern), along with default visualizers for face and body pose keypoints. (#1232, #1316)

import numpy as np
import mediapipe as mp
import supervision as sv
from PIL import Image

model = mp.solutions.face_mesh.FaceMesh()

edge_annotator = sv.EdgeAnnotator(color=sv.Color.BLACK, thickness=2)

image = Image.open(<PATH_TO_IMAGE>).convert('RGB')
results = model.process(np.array(image))
key_points = sv.KeyPoints.from_mediapipe(results, resolution_wh=image.size)

annotated_image = edge_annotator.annotate(scene=image, key_points=key_points)

IMG_1777-result-refined-optimized.mp4

• sv.KeyPoints.from_detectron2 and sv.Detections.from_detectron2 extending support for Detectron2 models. (#1310, #1300)

• sv.RichLabelAnnotator allowing to draw unicode characters (e.g. from non-latin languages), as long as you provide a compatible font. (#1277)

rich-label-annotator-2.mp4

🌱 Changed

• sv.DetectionsDataset and sv.ClassificationDataset allowing to load the images into memory only when necessary (lazy loading). (#1326)

import roboflow
from roboflow import Roboflow
import supervision as sv

roboflow.login()
rf = Roboflow()

project = rf.workspace(<WORKSPACE_ID>).project(<PROJECT_ID>)
dataset = project.version(<PROJECT_VERSION>).download("coco")

ds_train = sv.DetectionDataset.from_coco(
    images_directory_path=f"{dataset.location}/train",
    annotations_path=f"{dataset.location}/train/_annotations.coco.json",
)

path, image, annotation = ds_train[0]
    # loads image on demand

for path, image, annotation in ds_train:
    # loads image on demand

• sv.Detections.from_lmm allowing to parse Florence-2 text result into sv.Detections object. (#1296)

• sv.DotAnnotator and sv.TriangleAnnotator allowing to add marker outlines. (#1294)

🛠️ Fixed

• sv.ColorAnnotator and sv.CropAnnotator buggy behaviours. (#1277, #1312)

🧑‍🍳 Cookbooks

This release, @onuralpszr added two new Cookbooks to our collection. Check them out to learn how to save Detections to a file and convert it back to Detections!

• Serialize detections to JSON (#975)
• Serialize detections to CSV (#975).

🏆 Contributors

@onuralpszr (Onuralp SEZER), @David-rn (David Redó), @jeslinpjames (Jeslin P James), @Bhavay-2001 (Bhavay Malhotra), @hardikdava (Hardik Dava), @kirilman, @dsaha21 (Dripto Saha), @cdragos (Dragos Catarahia), @mqasim41 (Muhammad Qasim), @SkalskiP (Piotr Skalski), @LinasKo (Linas Kondrackis)

Special thanks to @rolson24 (Raif Olson) for helping the community with ByteTrack!

supervision-0.21.0

📅 Timeline

The supervision-0.21.0 release is around the corner. Here is the timeline:

• 5 Jun 2024 08:00 PM CEST (UTC +2) / 5 Jun 2024 11:00 AM PDT (UTC -7) - merge develop into main - closing list supervision-0.21.0 features
• 6 Jun 2024 11:00 AM CEST (UTC +2) / 6 Jun 2024 02:00 AM PDT (UTC -7) - release supervision-0.21.0

🪵 Changelog

🚀 Added

• sv.Detections.with_nmm to perform non-maximum merging on the current set of object detections. (#500)

• sv.Detections.from_lmm allowing to parse Large Multimodal Model (LMM) text result into sv.Detections object. For now from_lmm supports only PaliGemma result parsing. (#1221)

import supervision as sv

paligemma_result = "<loc0256><loc0256><loc0768><loc0768> cat"
detections = sv.Detections.from_lmm(
    sv.LMM.PALIGEMMA,
    paligemma_result,
    resolution_wh=(1000, 1000),
    classes=['cat', 'dog']
)
detections.xyxy
# array([[250., 250., 750., 750.]])

detections.class_id
# array([0])

• sv.VertexLabelAnnotator allowing to annotate every vertex of a keypoint skeleton with custom text and color. (#1236)

import supervision as sv

image = ...
key_points = sv.KeyPoints(...)

LABELS = [
    "nose", "left eye", "right eye", "left ear",
    "right ear", "left shoulder", "right shoulder", "left elbow",
    "right elbow", "left wrist", "right wrist", "left hip",
    "right hip", "left knee", "right knee", "left ankle",
    "right ankle"
]

COLORS = [
    "#FF6347", "#FF6347", "#FF6347", "#FF6347",
    "#FF6347", "#FF1493", "#00FF00", "#FF1493",
    "#00FF00", "#FF1493", "#00FF00", "#FFD700",
    "#00BFFF", "#FFD700", "#00BFFF", "#FFD700",
    "#00BFFF"
]
COLORS = [sv.Color.from_hex(color_hex=c) for c in COLORS]

vertex_label_annotator = sv.VertexLabelAnnotator(
    color=COLORS,
    text_color=sv.Color.BLACK,
    border_radius=5
)
annotated_frame = vertex_label_annotator.annotate(
    scene=image.copy(),
    key_points=key_points,
    labels=labels
)

• sv.KeyPoints.from_inference and sv.KeyPoints.from_yolo_nas allowing to create sv.KeyPoints from Inference and YOLO-NAS result. (#1147 and #1138)

• sv.mask_to_rle and sv.rle_to_mask allowing for easy conversion between mask and rle formats. (#1163)

🌱 Changed

• sv.InferenceSlicer allowing to select overlap filtering strategy (NONE, NON_MAX_SUPPRESSION and NON_MAX_MERGE). (#1236)

• sv.InferenceSlicer adding instance segmentation model support. (#1178)

import cv2
import numpy as np
import supervision as sv
from inference import get_model

model = get_model(model_id="yolov8x-seg-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)

def callback(image_slice: np.ndarray) -> sv.Detections:
    results = model.infer(image_slice)[0]
    return sv.Detections.from_inference(results)

slicer = sv.InferenceSlicer(callback = callback)
detections = slicer(image)

mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator()

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

• sv.LineZone making it 10-20 times faster, depending on the use case. (#1228)

• sv.DetectionDataset.from_coco and sv.DetectionDataset.as_coco adding support for run-length encoding (RLE) mask format. (#1163)

🏆 Contributors

@onuralpszr (Onuralp SEZER), @LinasKo (Linas Kondrackis), @rolson24 (Raif Olson), @mario-dg (Mario da Graca), @xaristeidou (Christoforos Aristeidou), @ManzarIMalik (Manzar Iqbal Malik), @tc360950 (Tomasz Cąkała), @emsko, @SkalskiP (Piotr Skalski)

supervision-0.20.0

🚀 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)

supervision-0.19.0

🧑‍🍳 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>})

traffic_csv_2.mp4

• 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
)

supervision-0.19.0-promo.mp4

🌱 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)

Warning

The track_buffer, track_thresh, and match_thresh parameters in sv.ByterTrack are deprecated and will be removed in supervision-0.23.0. Use lost_track_buffer, track_activation_threshold, and minimum_matching_threshold instead.

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

Warning

The triggering_position parameter in sv.PolygonZone is deprecated and will be removed in supervision-0.23.0. Use triggering_anchors instead.

• 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)

supervision-0.18.0

🚀 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)

supervision-detection-smoothing.mp4

• 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)

Warning

sv.ColorPalette.default() is deprecated and will be removed in supervision-0.21.0. Use sv.ColorPalette.DEFAULT instead.

• 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)

Warning

Detections.from_roboflow() is deprecated and will be removed in supervision-0.21.0. Use Detections.from_inference instead.

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)

supervision-0.18.0-promo-sample-2-result.mp4

🏆 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)

supervision-0.17.1

🚀 Added

• Support for Python 3.12.

🏆 Contributors

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

supervision-0.17.0

🚀 Added

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

walking-pixelate-corner-optimized.mp4

• 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
  ... )

walking-polygon-optimized.mp4

• 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)

supervision-0.16.0

🚀 Added

supervision-0.16.0-annotators.mp4

• 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)

supervision-0.15.0

🚀 Added

supervision-0.15.0.mp4

• 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)

traffic_analysis_result.mov

🌱 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)