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

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, keypoint_detection_prediction, object_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
}