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CHANGELOG

0.24.0 Oct 4, 2024

  • Added F1 score as a new metric for detection and segmentation. #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)

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)
  • 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)
  • 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. #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)
  • Added oriented_box_iou_batch function to detection.utils. This function computes Intersection over Union (IoU) for oriented or rotated bounding boxes (OBB). #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)
  • Extended PolygonZoneAnnotator to allow setting opacity when drawing zones, providing enhanced visualization by filling the zone with adjustable transparency. #1527
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)

Removed

The frame_resolution_wh parameter in PolygonZone has been removed.

Removed

Supervision installation methods "headless" and "desktop" were removed, as they are no longer needed. pip install supervision[headless] will install the base library and harmlessly warn of non-existent extras.

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

  • Fixed 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

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

0.23.0 Aug 28, 2024

  • Added #930: IconAnnotator, a new annotator that allows drawing icons on each detection. Useful if you want to draw a specific icon for each class.
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
)

import supervision as sv
from inference import get_model

image = <SOURCE_IMAGE_PATH>

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

background_overlay_annotator = sv.BackgroundOverlayAnnotator()
annotated_frame = background_overlay_annotator.annotate(
    scene=image.copy(),
    detections=detections
)
  • Added #1386: Support for Transformers v5 functions in sv.Detections.from_transformers. This includes the DetrImageProcessor methods post_process_object_detection, post_process_panoptic_segmentation, post_process_semantic_segmentation, and post_process_instance_segmentation.
import torch
import supervision as sv
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection

processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

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_object_detection(
    outputs=outputs, target_sizes=target_size)[0]
detections = sv.Detections.from_transformers(
    transformers_results=results,
    id2label=model.config.id2label)

import supervision as sv
from segment_anything import (
    sam_model_registry,
    SamAutomaticMaskGenerator
)
sam_model_reg = sam_model_registry[MODEL_TYPE]
sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
mask_generator = SamAutomaticMaskGenerator(sam)
sam_result = mask_generator.generate(IMAGE)
detections = sv.Detections.from_sam(sam_result=sam_result)

import supervision as sv

for frame in sv.get_video_frames_generator(
    source_path=<SOURCE_VIDEO_PATH>,
    start=60,
    iterative_seek=True
):
    ...
  • Fix #1424: plot_image function now clearly indicates that the size is in inches.

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.

Removed

The triggering_position parameter in sv.PolygonZone was removed as of supervision-0.23.0. Use triggering_anchors instead.

Deprecated

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

Deprecated

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

0.22.0 Jul 12, 2024

Deprecated

Constructing DetectionDataset with parameter images as Dict[str, np.ndarray] is deprecated and will be removed in supervision-0.26.0. Please pass a list of paths List[str] instead.

Deprecated

The DetectionDataset.images property is deprecated and will be removed in supervision-0.26.0. Please loop over images with for path, image, annotation in dataset:, as that does not require loading all images into memory.

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

import supervision as sv
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
import cv2

image = cv2.imread(<SOURCE_IMAGE_PATH>)
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
predictor = DefaultPredictor(cfg)

result = predictor(image)
detections = sv.Detections.from_detectron2(result)

mask_annotator = sv.MaskAnnotator()
annotated_frame = mask_annotator.annotate(scene=image.copy(), detections=detections)
  • Added #1277: if you provide a font that supports symbols of a language, sv.RichLabelAnnotator will draw them on your images.
  • Various other annotators have been revised to ensure proper in-place functionality when used with numpy arrays. Additionally, we fixed a bug where sv.ColorAnnotator was filling boxes with solid color when used in-place.
import cv2
import supervision as sv
import

image = cv2.imread(<SOURCE_IMAGE_PATH>)

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

rich_label_annotator = sv.RichLabelAnnotator(font_path=<TTF_FONT_PATH>)
annotated_image = rich_label_annotator.annotate(scene=image.copy(), detections=detections)
  • Added #1227: Added support for loading Oriented Bounding Boxes dataset in YOLO format.
import supervision as sv

train_ds = sv.DetectionDataset.from_yolo(
    images_directory_path="/content/dataset/train/images",
    annotations_directory_path="/content/dataset/train/labels",
    data_yaml_path="/content/dataset/data.yaml",
    is_obb=True,
)

_, image, detections in train_ds[0]

obb_annotator = OrientedBoxAnnotator()
annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detections)

Removed

BoxAnnotator was removed, however BoundingBoxAnnotator has been renamed to BoxAnnotator. Use a combination of BoxAnnotator and LabelAnnotator to simulate old BoundingBox behavior.

Deprecated

The name BoundingBoxAnnotator has been deprecated and will be removed in supervision-0.26.0. It has been renamed to BoxAnnotator.

  • Added #975 📝 New Cookbooks: serialize detections into json and csv.

  • Added #1290: Mostly an internal change, our file utility function now support both str and pathlib paths.

  • Added #1340: Two new methods for converting between bounding box formats - xywh_to_xyxy and xcycwh_to_xyxy

Removed

from_roboflow method has been removed due to deprecation. Use from_inference instead.

Removed

Color.white() has been removed due to deprecation. Use color.WHITE instead.

Removed

Color.black() has been removed due to deprecation. Use color.BLACK instead.

Removed

Color.red() has been removed due to deprecation. Use color.RED instead.

Removed

Color.green() has been removed due to deprecation. Use color.GREEN instead.

Removed

Color.blue() has been removed due to deprecation. Use color.BLUE instead.

Removed

ColorPalette.default() has been removed due to deprecation. Use ColorPalette.DEFAULT instead.

Removed

FPSMonitor.__call__ has been removed due to deprecation. Use the attribute FPSMonitor.fps instead.

0.21.0 Jun 5, 2024

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(...)

edge_annotator = sv.EdgeAnnotator(
    color=sv.Color.GREEN,
    thickness=5
)
annotated_frame = edge_annotator.annotate(
    scene=image.copy(),
    key_points=key_points
)

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)

0.20.0 April 24, 2024

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)

  • Changed #1037: sv.LabelAnnotator by adding an additional corner_radius argument that allows for rounding the corners of the bounding box.

  • Changed #1109: sv.PolygonZone such that the frame_resolution_wh argument is no longer required to initialize sv.PolygonZone.

Deprecated

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)

0.19.0 March 15, 2024

  • Added #818: sv.CSVSink allowing for the straightforward saving of image, video, or stream inference results in a .csv file.
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>})
  • Added #819: sv.JSONSink allowing for the straightforward saving of image, video, or stream inference results in a .json file.
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
)

  • Changed #827: sv.ByteTrack.reset allowing users to clear trackers state, enabling the processing of multiple video files in sequence.

  • Changed #802: sv.LineZoneAnnotator allowing to hide in/out count using display_in_count and display_out_count properties.

  • Changed #787: sv.ByteTrack input arguments and docstrings updated to improve readability and ease of use.

Deprecated

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

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

Deprecated

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

  • Changed #875: 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.

  • Fixed #944: sv.DetectionsSmoother removing tracking_id from sv.Detections.

0.18.0 January 25, 2024

  • Added #720: sv.PercentageBarAnnotator allowing to annotate images and videos with percentage values representing confidence or other custom property.
>>> import supervision as sv

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

>>> percentage_bar_annotator = sv.PercentageBarAnnotator()
>>> annotated_frame = percentage_bar_annotator.annotate(
...     scene=image.copy(),
...     detections=detections
... )

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
)

>>> 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 #770: sv.Detections.from_ultralytics adding support for OBB (Oriented Bounding Boxes).

  • Changed #735: 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].

  • Changed #756: 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).

Deprecated

sv.ColorPalette.default() is deprecated and will be removed in supervision-0.22.0. Use sv.ColorPalette.DEFAULT instead.

Deprecated

Detections.from_roboflow() is deprecated and will be removed in supervision-0.22.0. Use Detections.from_inference instead.

  • Fixed #735: 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.

0.17.0 December 06, 2023

>>> import supervision as sv

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

>>> polygon_annotator = sv.PolygonAnnotator()
>>> annotated_frame = polygon_annotator.annotate(
...     scene=image.copy(),
...     detections=detections
... )
  • Added #476: sv.assets allowing download of video files that you can use in your demos.
>>> from supervision.assets import download_assets, VideoAssets
>>> download_assets(VideoAssets.VEHICLES)
"vehicles.mp4"

0.16.0 October 19, 2023

>>> import supervision as sv

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

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

  • Added #466: sv.HeatMapAnnotator allowing to annotate videos with heat maps.

  • Added #492: sv.DotAnnotator allowing to annotate images and videos with dots.

  • Added #449: sv.draw_image allowing to draw an image onto a given scene with specified opacity and dimensions.

  • Added #280: sv.FPSMonitor for monitoring frames per second (FPS) to benchmark latency.

  • Added #454: 🤗 Hugging Face Annotators space.

  • Changed #482: 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.

  • Changed #465: Annotator argument name from color_map: str to color_lookup: ColorLookup enum to increase type safety.

  • Changed #426: sv.MaskAnnotator allowing 2x faster annotation.

  • Fixed #477: Poetry env definition allowing proper local installation.

  • Fixed #430: sv.ByteTrack to return np.array([], dtype=int) when svDetections is empty.

Deprecated

sv.Detections.from_yolov8 and sv.Classifications.from_yolov8 as those are now replaced by sv.Detections.from_ultralytics and sv.Classifications.from_ultralytics.

0.15.0 October 5, 2023

>>> import supervision as sv

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

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

0.14.0 August 31, 2023

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

Deprecated

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

0.13.0 August 8, 2023

>>> 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

Deprecated

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

0.12.0 July 24, 2023

Python 3.7. Support Terminated

With the supervision-0.12.0 release, we are terminating official support for Python 3.7.

>>> 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.]
])

0.11.1 June 29, 2023

0.11.0 June 28, 2023

>>> import supervision as sv

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

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

>>> 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']

  • Added #162: additional start and end arguments to sv.get_video_frames_generator allowing to generate frames only for a selected part of the video.

  • Fix #157: incorrect loading of YOLO dataset class names from data.yaml.

0.10.0 June 14, 2023

>>> import supervision as sv

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

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

0.9.0 June 7, 2023

  • Added #118: ability to select sv.Detections by index, list of indexes or slice. Here is an example illustrating the new selection methods.
>>> import supervision as sv

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

  • Added #101: ability to extract masks from YOLOv8 result using sv.Detections.from_yolov8. Here is an example illustrating how to extract boolean masks from the result of the YOLOv8 model inference.

  • Added #122: ability to crop image using sv.crop. Here is an example showing how to get a separate crop for each detection in sv.Detections.

  • Added #120: ability to conveniently save multiple images into directory using sv.ImageSink. Here is an example showing how to save every tenth video frame as a separate image.

>>> 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 #106: 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.

0.8.0 May 17, 2023

  • Added #100: 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.
  • Added #100: ability to save datasets in YOLO format using DetectionDataset.as_yolo.
>>> 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("yolov5")

>>> ds = sv.DetectionDataset.from_yolo(
...     images_directory_path=f"{dataset.location}/train/images",
...     annotations_directory_path=f"{dataset.location}/train/labels",
...     data_yaml_path=f"{dataset.location}/data.yaml"
... )

>>> ds.classes
['dog', 'person']

>>> 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 #100: default value of approximation_percentage parameter from 0.75 to 0.0 in DetectionDataset.as_yolo and DetectionDataset.as_pascal_voc.

0.7.0 May 11, 2023

  • Added #91: Detections.from_yolo_nas to enable seamless integration with YOLO-NAS model.
  • Added #86: ability to load datasets in YOLO format using Dataset.from_yolo.
  • Added #84: Detections.merge to merge multiple Detections objects together.
  • Fixed #81: LineZoneAnnotator.annotate does not return annotated frame.
  • Changed #44: LineZoneAnnotator.annotate to allow for custom text for the in and out tags.

0.6.0 April 19, 2023

  • Added #71: initial Dataset support and ability to save Detections in Pascal VOC XML format.
  • Added #71: new mask_to_polygons, filter_polygons_by_area, polygon_to_xyxy and approximate_polygon utilities.
  • Added #72: ability to load Pascal VOC XML object detections dataset as Dataset.
  • Changed #70: order of Detections attributes to make it consistent with order of objects in __iter__ tuple.
  • Changed #71: generate_2d_mask to polygon_to_mask.

0.5.2 April 13, 2023

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

0.5.1 April 12, 2023

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

0.5.0 April 10, 2023

  • Added #58: Detections.mask to enable segmentation support.
  • Added #58: MaskAnnotator to allow easy Detections.mask annotation.
  • Added #58: Detections.from_sam to enable native Segment Anything Model (SAM) support.
  • Changed #58: Detections.area behaviour to work not only with boxes but also with masks.

0.4.0 April 5, 2023

  • Added #46: Detections.empty to allow easy creation of empty Detections objects.
  • Added #56: Detections.from_roboflow to allow easy creation of Detections objects from Roboflow API inference results.
  • Added #56: plot_images_grid to allow easy plotting of multiple images on single plot.
  • Added #56: initial support for Pascal VOC XML format with detections_to_voc_xml method.
  • Changed #56: show_frame_in_notebook refactored and renamed to plot_image.

0.3.2 March 23, 2023

  • Changed #50: Allow Detections.class_id to be None.

0.3.1 March 6, 2023

  • Fixed #41: PolygonZone throws an exception when the object touches the bottom edge of the image.
  • Fixed #42: Detections.wth_nms method throws an exception when Detections is empty.
  • Changed #36: Detections.wth_nms support class agnostic and non-class agnostic case.

0.3.0 March 6, 2023

  • Changed: Allow Detections.confidence to be None.
  • Added: Detections.from_transformers and Detections.from_detectron2 to enable seamless integration with Transformers and Detectron2 models.
  • Added: Detections.area to dynamically calculate bounding box area.
  • Added: Detections.wth_nms to filter out double detections with NMS. Initial - only class agnostic - implementation.

0.2.0 February 2, 2023

  • Added: Advanced Detections filtering with pandas-like API.
  • Added: Detections.from_yolov5 and Detections.from_yolov8 to enable seamless integration with YOLOv5 and YOLOv8 models.

0.1.0 January 19, 2023

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