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supervision-0.24.0

04 Oct 20:25
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Supervision 0.24.0 is here! We've added many new changes, including the F1 score, enhancements to LineZone, EasyOCR support, NCNN support, and the best Cookbook to date! You can also try out our annotators directly in the browser. Check out the release notes to find out more!

πŸ“’ Announcements

image-1

  • Supervision is celebrating Hacktoberfest! Whether you're a newcomer to open source or a veteran contributor, we welcome you to join us in improving supervision. You can grab any issue without an assigned contributor: Hacktoberfest Issues Board. We'll be adding many more issues next week! πŸŽ‰

  • We recently launched the Model Leaderboard. Come check how the latest models perform! It is also open-source, so you can contribute to it as well! πŸš€

Changelog

πŸš€ Added

  • Added F1 score as a new metric for detection and segmentation. The F1 score balances precision and recall, providing a single metric for model evaluation. #1521
import supervision as sv
from supervision.metrics import F1Score

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

f1_metric = F1Score()
f1_result = f1_metric.update(predictions, targets).compute()

print(f1_result)
print(f1_result.f1_50)
print(f1_result.small_objects.f1_50)

image-8-with-new

SAHI principle
Inference Slicer in action

Embedded workflow example

  • Enhanced LineZoneAnnotator, allowing the labels to align with the line, even when it's not horizontal. Also, you can now disable text background, and choose to draw labels off-center which minimizes overlaps for multiple LineZone labels. Thank you @jcruz-ferreyra! #854
import supervision as sv
import cv2

image = cv2.imread("<SOURCE_IMAGE_PATH>")

line_zone = sv.LineZone(
    start=sv.Point(0, 100),
    end=sv.Point(50, 200)
)
line_zone_annotator = sv.LineZoneAnnotator(
    text_orient_to_line=True,
    display_text_box=False,
    text_centered=False
)

annotated_frame = line_zone_annotator.annotate(
    frame=image.copy(), line_counter=line_zone
)

sv.plot_image(frame)
sheep_1_out_optim.mp4
  • Added per-class counting capabilities to LineZone and introduced LineZoneAnnotatorMulticlass for visualizing the counts per class. This feature allows tracking of individual classes crossing a line, enhancing the flexibility of use cases like traffic monitoring or crowd analysis. #1555
import supervision as sv
import cv2

image = cv2.imread("<SOURCE_IMAGE_PATH>")

line_zone = sv.LineZone(
    start=sv.Point(0, 100),
    end=sv.Point(50, 200)
)
line_zone_annotator = sv.LineZoneAnnotatorMulticlass()

frame = line_zone_annotator.annotate(
    frame=frame, line_zones=[line_zone]
)

sv.plot_image(frame)
street_out_optim.mp4
  • Added from_easyocr, allowing integration of OCR results into the supervision framework. EasyOCR is an open-source optical character recognition (OCR) library that can read text from images. Thank you @onuralpszr! #1515
import supervision as sv
import easyocr
import cv2

image = cv2.imread("<SOURCE_IMAGE_PATH>")

reader = easyocr.Reader(["en"])
result = reader.readtext("<SOURCE_IMAGE_PATH>", paragraph=True)
detections = sv.Detections.from_easyocr(result)

box_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)

annotated_image = image.copy()
annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections)
annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)

sv.plot_image(annotated_image)

EasyOCR example

  • Added oriented_box_iou_batch function to detection.utils. This function computes Intersection over Union (IoU) for oriented or rotated bounding boxes (OBB), making it easier to evaluate detections with non-axis-aligned boxes. Thank you @patel-zeel! #1502
import numpy as np

boxes_true = np.array([[[1, 0], [0, 1], [3, 4], [4, 3]]])
boxes_detection = np.array([[[1, 1], [2, 0], [4, 2], [3, 3]]])
ious = sv.oriented_box_iou_batch(boxes_true, boxes_detection)
print("IoU between true and detected boxes:", ious)

Note: the IoU is approximated as mask IoU.
Approximated OBB overlap

  • Extended PolygonZoneAnnotator to allow setting opacity when drawing zones, providing enhanced visualization by filling the zone with adjustable transparency. Thank you @grzegorz-roboflow! #1527

  • Added from_ncnn, a connector for the NCNN. It is a powerful object detection framework from Tencent, written from ground-up in C++, with no third party dependencies. Thank you @onuralpszr! #1524

import cv2
from ncnn.model_zoo import get_model
import supervision as sv

image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = get_model(
    "yolov8s",
    target_size=640,
    prob_threshold=0.5,
    nms_threshold=0.45,
    num_threads=4,
    use_gpu=True,
)
result = model(image)
detections = sv.Detections.from_ncnn(result)

🌱 Changed

  • Supervision now depends on opencv-python rather than opencv-python-headless. #1530

  • Fixed broken or outdated links in documentation and notebooks, improving navigation and ensuring accuracy of references. Thanks to @capjamesg for identifying these issues. #1523

  • Enabled and fixed Ruff rules for code formatting, including changes like avoiding unnecessary iterable allocations and using Optional for default mutable arguments. #1526

πŸ”§ Fixed

  • Updated the COCO 101 point Average Precision algorithm to correctly interpolate precision, providing a more precise calculation of average precision without averaging out intermediate values. #1500

  • Resolved miscellaneous issues highlighted when building documentation. This mostly includes whitespace adjustments and type inconsistencies. Updated documentation for clarity and fixed formatting issues. Added explicit version for mkdocstrings-python. #1549

  • Clarified documentation around the overlap_ratio_wh argument deprecation in InferenceSlicer. #1547

βœ… No deprecations this time!

❌ Removed

  • The frame_resolution_wh parameter in PolygonZone has been removed due to deprecation.
  • Supervision installation methods "headless" and "desktop" removed, as they are no longer needed. pip install supervision[headless] will install the base library and warn of non-existent extra.

πŸ† Contributors

@onuralpszr (Onuralp SEZER), @joaomarcoscrs (JoΓ£o Marcos Cardoso Ramos da Silva), @jcruz-ferreyra (Juan Cruz), @patel-zeel (Zeel B Patel), @grzegorz-roboflow (Grzegorz Klimaszewski), @Kadermiyanyedi (Kader Miyanyedi), @ediardo (Eddie Ramirez), @CharlesCNorton, @ethanwhite (Ethan...

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supervision-0.23.0

28 Aug 17:45
27c68f2
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πŸš€ Added

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:

πŸ“Š 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()

mini-benchmark

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

🌱 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)

πŸ› οΈ 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.

πŸ† 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

12 Jul 17:14
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πŸš€ Added

supervision cheatsheet

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
rich-label-annotator-2.mp4

🌱 Changed

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

florence-2-result

πŸ› οΈ Fixed

πŸ§‘β€πŸ³ 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!

πŸ† Contributors

@onuralpszr (Onuralp SEZER), @David-rn (David RedΓ³), @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

06 Jun 06:43
e50c761
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πŸ“… 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

πŸͺ΅ Changelog

πŸš€ Added

non-max-merging

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])
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
)

vertex-label-annotator-custom-example (1)

mask-to-rle (1)

🌱 Changed

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)

inference-slicer-segmentation-example

output

πŸ† Contributors

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

supervision-0.20.0

24 Apr 20:49
f7f40f0
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πŸš€ Added

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)

edge-annotator-example

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)

vertex-annotator-example

🌱 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.

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)

πŸ› οΈ Fixed

πŸ† Contributors

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

supervision-0.19.0

15 Mar 12:04
55f93a8
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πŸ§‘β€πŸ³ 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)

πŸš€ 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>})
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)

πŸ› οΈ Fixed

πŸ† 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

25 Jan 09:46
53f4cde
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πŸš€ 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
)

percentage-bar-annotator-example-purple

supervision-detection-smoothing.mp4
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
)

oriented-box-annotator

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), ...])

visualized_color_palette

🌱 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.

default-color-palette

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)

πŸ› οΈ 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

πŸ† Contributors

@onuralpszr (Onuralp SEZER), @HinePo (Rafael Levy), @xaristeidou (Christoforos Aristeidou), @revtheundead (Utku Γ–zbek), @paulguerrie (Paul Guerrie), @yeldarby (Brad Dwyer), @capjamesg (James Gallagher), @SkalskiP (Piotr Skalski)

supervision-0.17.1

08 Dec 14:21
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πŸš€ Added

  • Support for Python 3.12.

πŸ† Contributors

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

supervision-0.17.0

06 Dec 15:22
36ab9dc
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πŸš€ Added

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

🌱 Changed

mask_annotator_speed

πŸ› οΈ Fixed

πŸ† 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

19 Oct 08:26
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πŸš€ Added

supervision-0.16.0-annotators.mp4
>>> import supervision as sv

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

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

🌱 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)

πŸ› οΈ 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)

πŸ—‘οΈ 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)

πŸ† 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)