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Changelog
We write your reusable computer vision tools. ๐
Last updated about 1 month ago
BackgroundOverlayAnnotator annotates the background of your image! #1385https://github.com/user-attachments/assets/c1f3ce11-08c1-4648-9176-4e7920b91a8a
(video by Pexels)
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xyxysupervision[!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:
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()
IconAnnotator, which allows you to place icons on your images. #930https://github.com/user-attachments/assets/ff80acf5-67f2-4c20-a3fe-b63cac07ae31
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
)
from_sam, we've added support to from_ultralytics for loading the results if you ran it with Ultralytics. #1354import 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:
https://github.com/user-attachments/assets/6a98d651-2596-43e9-b485-ea6f0de4fffa
TriangleAnnotator and DotAnnotator contour color customization #1458VertexLabelAnnotator for keypoints now has text_color parameter #1409sv.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. #1386InferenceSlicer now features an overlap_ratio_wh parameter, making it easier to compute slice sizes when handling overlapping slices. #1434image_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)
plot_image now clearly states the size is in inches. #1424overlap_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.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.triggering_position parameter in sv.PolygonZone was removed as of supervision-0.23.0. Use triggering_anchors instead.@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)