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

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[instance_segmentation_prediction, object_detection_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
}