Circle Visualization

Class: CircleVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.circle.v1.CircleVisualizationBlockV1

The CircleVisualization block draws a circle around detected objects in an image using Supervision's sv.CircleAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/circle_visualization@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
copy_image	bool	Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..	✅
color_palette	str	Select a color palette for the visualised elements..	✅
palette_size	int	Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..	✅
custom_colors	List[str]	Define a list of custom colors for bounding boxes in HEX format..	✅
color_axis	str	Choose how bounding box colors are assigned..	✅
thickness	int	Thickness of the lines in pixels..	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Circle Visualization in version v1.

• inputs: Image Contours, Line Counter, Stability AI Inpainting, Detections Consensus, Corner Visualization, Time in Zone, CSV Formatter, Google Gemini, Line Counter Visualization, Reference Path Visualization, Keypoint Detection Model, Model Monitoring Inference Aggregator, Florence-2 Model, YOLO-World Model, Circle Visualization, OCR Model, Llama 3.2 Vision, SIFT Comparison, Relative Static Crop, Dimension Collapse, Roboflow Dataset Upload, JSON Parser, PTZ Tracking (ONVIF).md), Dynamic Zone, Detections Classes Replacement, Image Convert Grayscale, Byte Tracker, Pixelate Visualization, Object Detection Model, Model Comparison Visualization, Trace Visualization, Detections Stitch, Path Deviation, Twilio SMS Notification, Velocity, Roboflow Dataset Upload, Bounding Rectangle, LMM, Depth Estimation, Label Visualization, Classification Label Visualization, Blur Visualization, OpenAI, Color Visualization, Moondream2, Bounding Box Visualization, Template Matching, Anthropic Claude, Ellipse Visualization, Detection Offset, Instance Segmentation Model, Polygon Zone Visualization, VLM as Classifier, Detections Transformation, Pixel Color Count, Object Detection Model, Roboflow Custom Metadata, Gaze Detection, Image Slicer, Image Slicer, VLM as Detector, Crop Visualization, Perspective Correction, Halo Visualization, Dot Visualization, Mask Visualization, Keypoint Visualization, Local File Sink, Detections Stabilizer, Absolute Static Crop, Stitch OCR Detections, Line Counter, Clip Comparison, Image Blur, OpenAI, VLM as Classifier, Path Deviation, Clip Comparison, Byte Tracker, Detections Merge, Identify Changes, Instance Segmentation Model, Overlap Filter, Triangle Visualization, Segment Anything 2 Model, Distance Measurement, Background Color Visualization, SIFT Comparison, Google Vision OCR, Camera Calibration, Florence-2 Model, Image Threshold, Single-Label Classification Model, Buffer, Image Preprocessing, OpenAI, CogVLM, Slack Notification, VLM as Detector, Keypoint Detection Model, Stability AI Image Generation, SIFT, Grid Visualization, Camera Focus, Stitch Images, Stability AI Outpainting, Identify Outliers, Size Measurement, Polygon Visualization, Multi-Label Classification Model, Webhook Sink, Dynamic Crop, Time in Zone, Email Notification, Byte Tracker, Detections Filter, LMM For Classification
• outputs: Image Contours, Stability AI Inpainting, Corner Visualization, Time in Zone, Google Gemini, Line Counter Visualization, Reference Path Visualization, Keypoint Detection Model, Florence-2 Model, YOLO-World Model, Circle Visualization, OCR Model, Llama 3.2 Vision, Relative Static Crop, Roboflow Dataset Upload, Multi-Label Classification Model, Image Convert Grayscale, Pixelate Visualization, Dominant Color, Object Detection Model, Model Comparison Visualization, Detections Stitch, Trace Visualization, LMM, Roboflow Dataset Upload, Label Visualization, Depth Estimation, Classification Label Visualization, Perception Encoder Embedding Model, Blur Visualization, OpenAI, Color Visualization, Moondream2, Bounding Box Visualization, Template Matching, Anthropic Claude, Pixel Color Count, Ellipse Visualization, Instance Segmentation Model, VLM as Classifier, Polygon Zone Visualization, Object Detection Model, Gaze Detection, Image Slicer, Image Slicer, VLM as Detector, Crop Visualization, Perspective Correction, Halo Visualization, Dot Visualization, QR Code Detection, Mask Visualization, Keypoint Visualization, Detections Stabilizer, SmolVLM2, Absolute Static Crop, Clip Comparison, Image Blur, OpenAI, Qwen2.5-VL, VLM as Classifier, Clip Comparison, Instance Segmentation Model, Triangle Visualization, Segment Anything 2 Model, Background Color Visualization, SIFT Comparison, CLIP Embedding Model, Florence-2 Model, Camera Calibration, Google Vision OCR, Image Threshold, Single-Label Classification Model, Buffer, Image Preprocessing, OpenAI, CogVLM, VLM as Detector, Keypoint Detection Model, Stability AI Image Generation, SIFT, Stitch Images, Camera Focus, Stability AI Outpainting, Polygon Visualization, Multi-Label Classification Model, Dynamic Crop, Barcode Detection, Byte Tracker, Single-Label Classification Model, LMM For Classification

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Circle Visualization in version v1 has.

Bindings

• input

  • image (image): The image to visualize on..
  • copy_image (boolean): Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..
  • predictions (Union[object_detection_prediction, instance_segmentation_prediction, keypoint_detection_prediction]): Model predictions to visualize..
  • color_palette (string): Select a color palette for the visualised elements..
  • palette_size (integer): Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..
  • custom_colors (list_of_values): Define a list of custom colors for bounding boxes in HEX format..
  • color_axis (string): Choose how bounding box colors are assigned..
  • thickness (integer): Thickness of the lines in pixels..

• output

  • image (image): Image in workflows.

Example JSON definition of step Circle Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/circle_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.object_detection_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "thickness": 2
}