Supervision: Computer Vision Toolkit by Roboflow

In computer vision projects, after a model detects objects, there is still much to do: drawing bounding boxes, filtering detections, counting objects, and tracking movement. All of this requires substantial and repetitive code if written from scratch.

What Is Supervision?

Supervision is an open-source Python library by Roboflow that simplifies computer vision workflows. Its key features include:

• Detections Object: Universal representation for detection results from various models
• Annotators: Various visual annotator types (bounding box, mask, label, trace, halo)
• Filtering: Filter detections by confidence, class, zone, and other criteria
• Polygon Zones: Define polygon zones for counting and area analysis
• Line Counters: Count objects crossing virtual lines
• Object Tracking: ByteTrack integration for cross-frame object tracking
• Video Processing: Utilities for frame-by-frame video processing
• FPS Monitor: Real-time performance monitoring

Installation

Basic Installation

pip install supervision

Installation with Model Dependencies

# With YOLO (Ultralytics)
pip install supervision ultralytics

With Detectron2
pip install supervision detectron2

With SAM (Segment Anything)
pip install supervision segment-anything

Verify Installation

import supervision as sv
print(f"Supervision version: {sv.version}")

Detections Object

Creating Detections from Various Models

import supervision as sv
import numpy as np

From Ultralytics YOLO
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
results = model("image.jpg")
detections = sv.Detections.fromultralytics(results[0])


print(f"Number of detections: {len(detections)}")
print(f"Bounding boxes: {detections.xyxy}")
print(f"Confidence: {detections.confidence}")
print(f"Class IDs: {detections.classid}")

From Detectron2

from detectron2.engine import DefaultPredictor
from detectron2.config import getcfg


cfg = getcfg()
cfg.mergefromfile("config.yaml")
predictor = DefaultPredictor(cfg)

outputs = predictor(image)
detections = sv.Detections.fromdetectron2(outputs)

from segmentanything import sammodelregistry, SamAutomaticMaskGenerator

sam = sammodelregistryvith.pth"">"vith"

maskgenerator = SamAutomaticMaskGenerator(sam)

masks = maskgenerator.generate(image)

detections = sv.Detections.fromsam(masks)

# Create detections manually
detections = sv.Detections(
    xyxy=np.array([
        [100, 200, 300, 400],
        [150, 250, 350, 450]
    ]),
    confidence=np.array([0.95, 0.87]),
    classid=np.array([0, 1])

)

import supervision as sv
import cv2

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

Bounding box annotator
bboxannotator = sv.BoxAnnotator(
    thickness=2,
    color=sv.ColorPalette.fromhex(["#FF0000", "#00FF00", "#0000FF"])

)

annotatedimage = bboxannotator.annotate(

    scene=image.copy(),
    detections=detections
)

cv2.imwrite("annotatedbbox.jpg", annotatedimage)